+
+
+
+## Outline
+- [Dependencies](#dependencies)
+- [Testing with Trained Weights](#testing-with-trained-weights)
+- [Training](#training)
+- [Citation](#citation)
+- [Projects Using this Model/Method](#projects-using-this-modelmethod)
+- [Blogs Mentioning this Paper](#blogs-mentioning-this-paper)
+- [For Windows users](#-windows-users)
+
+## Dependencies
+ - [Tensorflow](https://www.tensorflow.org/) (1.12.0 <= version <=1.15.0)
+ - [opencv](https://opencv.org/) == 3.4.2
+ - [pillow](https://pillow.readthedocs.io/en/latest/index.html) == 6.2.0
+ - [scipy](https://www.scipy.org/) == 1.5.2
+ - [gizeh](https://github.com/Zulko/gizeh) == 0.1.11
+
+## Testing with Trained Weights
+### Model Preparation
+
+Download the models [here](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing):
+ - `pretrain_clean_line_drawings` (105 MB): for vectorization
+ - `pretrain_rough_sketches` (105 MB): for rough sketch simplification
+ - `pretrain_faces` (105 MB): for photograph to line drawing
+
+Then, place them in this file structure:
+```
+outputs/
+ snapshot/
+ pretrain_clean_line_drawings/
+ pretrain_rough_sketches/
+ pretrain_faces/
+```
+
+### Usage
+Choose the image in the `sample_inputs/` directory, and run one of the following commands for each task. The results will be under `outputs/sampling/`.
+
+``` python
+python3 test_vectorization.py --input muten.png
+
+python3 test_rough_sketch_simplification.py --input rocket.png
+
+python3 test_photograph_to_line.py --input 1390.png
+```
+
+**Note!!!** Our approach starts drawing from a randomly selected initial position, so it outputs different results in every testing trial (some might be fine and some might not be good enough). It is recommended to do several trials to select the visually best result. The number of outputs can be defined by the `--sample` argument:
+
+``` python
+python3 test_vectorization.py --input muten.png --sample 10
+
+python3 test_rough_sketch_simplification.py --input rocket.png --sample 10
+
+python3 test_photograph_to_line.py --input 1390.png --sample 10
+```
+
+**Reproducing Paper Figures:** our results (download from [here](https://drive.google.com/drive/folders/1-hi2cl8joZ6oMOp4yvk_hObJGAK6ELHB?usp=sharing)) are selected by doing a certain number of trials. Apparently, it is required to use the same initial drawing positions to reproduce our results.
+
+### Additional Tools
+
+#### a) Visualization
+
+Our vector output is stored in a `npz` package. Run the following command to obtain the rendered output and the drawing order. Results will be under the same directory of the `npz` file.
+``` python
+python3 tools/visualize_drawing.py --file path/to/the/result.npz
+```
+
+#### b) GIF Making
+
+To see the dynamic drawing procedure, run the following command to obtain the `gif`. Result will be under the same directory of the `npz` file.
+``` python
+python3 tools/gif_making.py --file path/to/the/result.npz
+```
+
+
+#### c) Conversion to SVG
+
+Our vector output in a `npz` package is stored as Eq.(1) in the main paper. Run the following command to convert it to the `svg` format. Result will be under the same directory of the `npz` file.
+
+``` python
+python3 tools/svg_conversion.py --file path/to/the/result.npz
+```
+ - The conversion is implemented in two modes (by setting the `--svg_type` argument):
+ - `single` (default): each stroke (a single segment) forms a path in the SVG file
+ - `cluster`: each continuous curve (with multiple strokes) forms a path in the SVG file
+
+**Important Notes**
+
+In SVG format, all the segments on a path share the same *stroke-width*. While in our stroke design, strokes on a common curve have different widths. Inside a stroke (a single segment), the thickness also changes linearly from an endpoint to another.
+Therefore, neither of the two conversion methods above generate visually the same results as the ones in our paper.
+*(Please mention this issue in your paper if you do qualitative comparisons with our results in SVG format.)*
+
+
+
| 
|
+| **[Geometrized cartoon line inbetweening](https://github.com/lisiyao21/AnimeInbet) (ICCV 2023)** | **[Stroke correspondence and inbetweening](https://github.com/MarkMoHR/JoSTC) (TOG 2024)** |
+| 
| 
|
+| **[Modelling complex vector drawings](https://github.com/Co-do/Stroke-Cloud) (ICLR 2024)** | **[Sketch-to-Image Generation](https://github.com/BlockDetail/Block-and-Detail) (UIST 2024)** |
+| 
| 
|
+
+## Blogs Mentioning this Paper
+
+- [The state of AI for hand-drawn animation inbetweening](https://yosefk.com/blog/the-state-of-ai-for-hand-drawn-animation-inbetweening.html)
+
+## 🪟 Windows users
+
+See [WINDOWS_INSTALL_GUIDE.md](WINDOWS_INSTALL_GUIDE.md) for a complete Windows installation guide and GUI usage.
+
diff --git a/README_CN.md b/README_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..76bed8fb250e9715b709a6f4ffe2d20f9cbfb4d4
--- /dev/null
+++ b/README_CN.md
@@ -0,0 +1,155 @@
+# General Virtual Sketching Framework for Vector Line Art - SIGGRAPH 2021
+
+[[论文]](https://esslab.jp/publications/HaoranSIGRAPH2021.pdf) | [[项目主页]](https://markmohr.github.io/virtual_sketching/)
+
+这份代码能用于实现：**线稿矢量化**、**粗糙草图简化**和**自然图像到矢量草图转换**。
+
+
+
+
+
+
+
+
+
+
+ General Virtual Sketching Framework for Vector Line Art
+
+
+
+
+ 1Sun Yat-sen University,
+ 2Waseda University,
+
+ 3Huawei Technologies Canada +
+ + 3Huawei Technologies Canada +
+
+ Accepted by ACM SIGGRAPH 2021
+
+
+
+
+
+
+
+
+
+
+Abstract
+
+ Vector line art plays an important role in graphic design, however, it is tedious to manually create.
+ We introduce a general framework to produce line drawings from a wide variety of images,
+ by learning a mapping from raster image space to vector image space.
+ Our approach is based on a recurrent neural network that draws the lines one by one.
+ A differentiable rasterization module allows for training with only supervised raster data.
+ We use a dynamic window around a virtual pen while drawing lines,
+ implemented with a proposed aligned cropping and differentiable pasting modules.
+ Furthermore, we develop a stroke regularization loss
+ that encourages the model to use fewer and longer strokes to simplify the resulting vector image.
+ Ablation studies and comparisons with existing methods corroborate the efficiency of our approach
+ which is able to generate visually better results in less computation time,
+ while generalizing better to a diversity of images and applications.
+
+
+
+
+
+
+
+
+Method
+ +
+
+Framework Overview
+ 
+
+
+
+ Our framework generates the parametrized strokes step by step in a recurrent manner.
+ It uses a dynamic window (dashed red boxes) around a virtual pen to draw the strokes,
+ and can both move and change the size of the window.
+ (a) Four main modules at each time step: aligned cropping, stroke generation, differentiable rendering and differentiable pasting.
+ (b) Architecture of the stroke generation module.
+ (c) Structural strokes predicted at each step;
+ movement only is illustrated by blue arrows during which no stroke is drawn on the canvas.
+
+
+
+
+
+ Overall Introduction +
+
+ (Or watch on Bilibili)
+
+ 👇
+
+ +
+
+
+
+
+
+
+
+
+
+Results
+
+ Our framework is applicable to a diversity of image types, such as clean line drawing images, rough sketches and photographs.
+
+
+
+
+ +
+
+
+ +
+
+
+ +
+ + + +
+Vectorization
+
 |
+ + |  |
+
+ +
Rough sketch simplification
+
 |
+  |
+ + |  |
+  |
+
+ +
Photograph to line drawing
+
 |
+  |
+ + |  |
+  |
+
+ +
+ More Results +
+
+ (Or watch on Bilibili)
+
+ 👇
+
+ +
+
+
+ + + + +
+
+
+
+
+
+Presentations
+
+
+
+ +
++ 3-5 minute presentation +
+
+ (Or watch on Bilibili)
+
+ 👇
+
+ +
+
+
+ + +
+ 👉 15-20 minute presentation:
+ [YouTube]
+ [Bilibili]
+
+
+
+ 👉 30-second fast forward:
+ [YouTube]
+ [Bilibili]
+
+
+
+
+
+
+
+
+
+
diff --git a/hyper_parameters.py b/hyper_parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..66a3fa9f938d0b35f09a84811cb0058cda94e6a6
--- /dev/null
+++ b/hyper_parameters.py
@@ -0,0 +1,341 @@
+import tensorflow as tf
+
+
+#############################################
+# Common parameters
+#############################################
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string(
+ 'dataset_dir',
+ 'datasets',
+ 'The directory of sketch data of the dataset.')
+tf.app.flags.DEFINE_string(
+ 'log_root',
+ 'outputs/log',
+ 'Directory to store tensorboard.')
+tf.app.flags.DEFINE_string(
+ 'log_img_root',
+ 'outputs/log_img',
+ 'Directory to store intermediate output images.')
+tf.app.flags.DEFINE_string(
+ 'snapshot_root',
+ 'outputs/snapshot',
+ 'Directory to store model checkpoints.')
+tf.app.flags.DEFINE_string(
+ 'neural_renderer_path',
+ 'outputs/snapshot/pretrain_neural_renderer/renderer_300000.tfmodel',
+ 'Path to the neural renderer model.')
+tf.app.flags.DEFINE_string(
+ 'perceptual_model_root',
+ 'outputs/snapshot/pretrain_perceptual_model',
+ 'Directory to store perceptual model.')
+tf.app.flags.DEFINE_string(
+ 'data',
+ '',
+ 'The dataset type.')
+
+
+def get_default_hparams_clean():
+ """Return default HParams for sketch-rnn."""
+ hparams = tf.contrib.training.HParams(
+ program_name='new_train_clean_line_drawings',
+ data_set='clean_line_drawings', # Our dataset.
+
+ input_channel=1,
+
+ num_steps=75040, # Total number of steps of training.
+ save_every=75000,
+ eval_every=5000,
+
+ max_seq_len=48,
+ batch_size=20,
+ gpus=[0, 1],
+ loop_per_gpu=1,
+
+ sn_loss_type='increasing', # ['decreasing', 'fixed', 'increasing']
+ stroke_num_loss_weight=0.02,
+ stroke_num_loss_weight_end=0.0,
+ increase_start_steps=25000,
+ decrease_stop_steps=40000,
+
+ perc_loss_layers=['ReLU1_2', 'ReLU2_2', 'ReLU3_3', 'ReLU5_1'],
+ perc_loss_fuse_type='add', # ['max', 'add', 'raw_add', 'weighted_sum']
+
+ init_cursor_on_undrawn_pixel=False,
+
+ early_pen_loss_type='move', # ['head', 'tail', 'move']
+ early_pen_loss_weight=0.1,
+ early_pen_length=7,
+
+ min_width=0.01,
+ min_window_size=32,
+ max_scaling=2.0,
+
+ encode_cursor_type='value',
+
+ image_size_small=128,
+ image_size_large=278,
+
+ cropping_type='v3', # ['v2', 'v3']
+ pasting_type='v3', # ['v2', 'v3']
+ pasting_diff=True,
+
+ concat_win_size=True,
+
+ encoder_type='conv13_c3',
+ # ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
+ # ['conv13_c3_attn']
+ # ['combine33', 'combine43', 'combine53', 'combineFC']
+ vary_thickness=False,
+
+ outside_loss_weight=10.0,
+ win_size_outside_loss_weight=10.0,
+
+ resize_method='AREA', # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
+
+ concat_cursor=True,
+
+ use_softargmax=True,
+ soft_beta=10, # value for the soft argmax
+
+ raster_loss_weight=1.0,
+
+ dec_rnn_size=256, # Size of decoder.
+ dec_model='hyper', # Decoder: lstm, layer_norm or hyper.
+ # z_size=128, # Size of latent vector z. Recommend 32, 64 or 128.
+ bin_gt=True,
+
+ stop_accu_grad=True,
+
+ random_cursor=True,
+ cursor_type='next',
+
+ raster_size=128,
+
+ pix_drop_kp=1.0, # Dropout keep rate
+ add_coordconv=True,
+ position_format='abs',
+ raster_loss_base_type='perceptual', # [l1, mse, perceptual]
+
+ grad_clip=1.0, # Gradient clipping. Recommend leaving at 1.0.
+
+ learning_rate=0.0001, # Learning rate.
+ decay_rate=0.9999, # Learning rate decay per minibatch.
+ decay_power=0.9,
+ min_learning_rate=0.000001, # Minimum learning rate.
+
+ use_recurrent_dropout=True, # Dropout with memory loss. Recommended
+ recurrent_dropout_prob=0.90, # Probability of recurrent dropout keep.
+ use_input_dropout=False, # Input dropout. Recommend leaving False.
+ input_dropout_prob=0.90, # Probability of input dropout keep.
+ use_output_dropout=False, # Output dropout. Recommend leaving False.
+ output_dropout_prob=0.90, # Probability of output dropout keep.
+
+ model_mode='train' # ['train', 'eval', 'sample']
+ )
+ return hparams
+
+
+def get_default_hparams_rough():
+ """Return default HParams for sketch-rnn."""
+ hparams = tf.contrib.training.HParams(
+ program_name='new_train_rough_sketches',
+ data_set='rough_sketches', # ['rough_sketches', 'faces']
+
+ input_channel=3,
+
+ num_steps=90040, # Total number of steps of training.
+ save_every=90000,
+ eval_every=5000,
+
+ max_seq_len=48,
+ batch_size=20,
+ gpus=[0, 1],
+ loop_per_gpu=1,
+
+ sn_loss_type='increasing', # ['decreasing', 'fixed', 'increasing']
+ stroke_num_loss_weight=0.1,
+ stroke_num_loss_weight_end=0.0,
+ increase_start_steps=25000,
+ decrease_stop_steps=40000,
+
+ photo_prob_type='one', # ['increasing', 'zero', 'one']
+ photo_prob_start_step=35000,
+
+ perc_loss_layers=['ReLU2_2', 'ReLU3_3', 'ReLU5_1'],
+ perc_loss_fuse_type='add', # ['max', 'add', 'raw_add', 'weighted_sum']
+
+ early_pen_loss_type='move', # ['head', 'tail', 'move']
+ early_pen_loss_weight=0.2,
+ early_pen_length=7,
+
+ min_width=0.01,
+ min_window_size=32,
+ max_scaling=2.0,
+
+ encode_cursor_type='value',
+
+ image_size_small=128,
+ image_size_large=278,
+
+ cropping_type='v3', # ['v2', 'v3']
+ pasting_type='v3', # ['v2', 'v3']
+ pasting_diff=True,
+
+ concat_win_size=True,
+
+ encoder_type='conv13_c3',
+ # ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
+ # ['conv13_c3_attn']
+ # ['combine33', 'combine43', 'combine53', 'combineFC']
+
+ outside_loss_weight=10.0,
+ win_size_outside_loss_weight=10.0,
+
+ resize_method='AREA', # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
+
+ concat_cursor=True,
+
+ use_softargmax=True,
+ soft_beta=10, # value for the soft argmax
+
+ raster_loss_weight=1.0,
+
+ dec_rnn_size=256, # Size of decoder.
+ dec_model='hyper', # Decoder: lstm, layer_norm or hyper.
+ # z_size=128, # Size of latent vector z. Recommend 32, 64 or 128.
+ bin_gt=True,
+
+ stop_accu_grad=True,
+
+ random_cursor=True,
+ cursor_type='next',
+
+ raster_size=128,
+
+ pix_drop_kp=1.0, # Dropout keep rate
+ add_coordconv=True,
+ position_format='abs',
+ raster_loss_base_type='perceptual', # [l1, mse, perceptual]
+
+ grad_clip=1.0, # Gradient clipping. Recommend leaving at 1.0.
+
+ learning_rate=0.0001, # Learning rate.
+ decay_rate=0.9999, # Learning rate decay per minibatch.
+ decay_power=0.9,
+ min_learning_rate=0.000001, # Minimum learning rate.
+
+ use_recurrent_dropout=True, # Dropout with memory loss. Recommended
+ recurrent_dropout_prob=0.90, # Probability of recurrent dropout keep.
+ use_input_dropout=False, # Input dropout. Recommend leaving False.
+ input_dropout_prob=0.90, # Probability of input dropout keep.
+ use_output_dropout=False, # Output dropout. Recommend leaving False.
+ output_dropout_prob=0.90, # Probability of output dropout keep.
+
+ model_mode='train' # ['train', 'eval', 'sample']
+ )
+ return hparams
+
+
+def get_default_hparams_normal():
+ """Return default HParams for sketch-rnn."""
+ hparams = tf.contrib.training.HParams(
+ program_name='new_train_faces',
+ data_set='faces', # ['rough_sketches', 'faces']
+
+ input_channel=3,
+
+ num_steps=90040, # Total number of steps of training.
+ save_every=90000,
+ eval_every=5000,
+
+ max_seq_len=48,
+ batch_size=20,
+ gpus=[0, 1],
+ loop_per_gpu=1,
+
+ sn_loss_type='fixed', # ['decreasing', 'fixed', 'increasing']
+ stroke_num_loss_weight=0.0,
+ stroke_num_loss_weight_end=0.0,
+ increase_start_steps=0,
+ decrease_stop_steps=40000,
+
+ photo_prob_type='interpolate', # ['increasing', 'zero', 'one', 'interpolate']
+ photo_prob_start_step=30000,
+ photo_prob_end_step=60000,
+
+ perc_loss_layers=['ReLU2_2', 'ReLU3_3', 'ReLU4_2', 'ReLU5_1'],
+ perc_loss_fuse_type='add', # ['max', 'add', 'raw_add', 'weighted_sum']
+
+ early_pen_loss_type='move', # ['head', 'tail', 'move']
+ early_pen_loss_weight=0.2,
+ early_pen_length=7,
+
+ min_width=0.01,
+ min_window_size=32,
+ max_scaling=2.0,
+
+ encode_cursor_type='value',
+
+ image_size_small=128,
+ image_size_large=256,
+
+ cropping_type='v3', # ['v2', 'v3']
+ pasting_type='v3', # ['v2', 'v3']
+ pasting_diff=True,
+
+ concat_win_size=True,
+
+ encoder_type='conv13_c3',
+ # ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
+ # ['conv13_c3_attn']
+ # ['combine33', 'combine43', 'combine53', 'combineFC']
+
+ outside_loss_weight=10.0,
+ win_size_outside_loss_weight=10.0,
+
+ resize_method='AREA', # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
+
+ concat_cursor=True,
+
+ use_softargmax=True,
+ soft_beta=10, # value for the soft argmax
+
+ raster_loss_weight=1.0,
+
+ dec_rnn_size=256, # Size of decoder.
+ dec_model='hyper', # Decoder: lstm, layer_norm or hyper.
+ # z_size=128, # Size of latent vector z. Recommend 32, 64 or 128.
+ bin_gt=True,
+
+ stop_accu_grad=True,
+
+ random_cursor=True,
+ cursor_type='next',
+
+ raster_size=128,
+
+ pix_drop_kp=1.0, # Dropout keep rate
+ add_coordconv=True,
+ position_format='abs',
+ raster_loss_base_type='perceptual', # [l1, mse, perceptual]
+
+ grad_clip=1.0, # Gradient clipping. Recommend leaving at 1.0.
+
+ learning_rate=0.0001, # Learning rate.
+ decay_rate=0.9999, # Learning rate decay per minibatch.
+ decay_power=0.9,
+ min_learning_rate=0.000001, # Minimum learning rate.
+
+ use_recurrent_dropout=True, # Dropout with memory loss. Recommended
+ recurrent_dropout_prob=0.90, # Probability of recurrent dropout keep.
+ use_input_dropout=False, # Input dropout. Recommend leaving False.
+ input_dropout_prob=0.90, # Probability of input dropout keep.
+ use_output_dropout=False, # Output dropout. Recommend leaving False.
+ output_dropout_prob=0.90, # Probability of output dropout keep.
+
+ model_mode='train' # ['train', 'eval', 'sample']
+ )
+ return hparams
diff --git a/launch_gui.bat b/launch_gui.bat
new file mode 100644
index 0000000000000000000000000000000000000000..d5ae9f67a2b71811a3c106bf1d6211d6e4e234bb
--- /dev/null
+++ b/launch_gui.bat
@@ -0,0 +1,21 @@
+@echo OFF
+
+REM === Cesta k instalaci Anacondy ===
+set "CONDAPATH=C:\ProgramData\anaconda3"
+
+REM === Název a cesta k prostředí ===
+set "ENVNAME=virtual_sketching"
+set "ENVPATH=%USERPROFILE%\.conda\envs\%ENVNAME%"
+
+REM === Aktivace prostředí ===
+call "%CONDAPATH%\Scripts\activate.bat" "%ENVPATH%"
+
+REM === Spuštění GUI ===
+python virtual_sketch_gui.py
+
+REM === Pozastavení po ukončení ===
+echo.
+pause
+
+REM === Deaktivace prostředí ===
+call conda deactivate
diff --git a/model_common_test.py b/model_common_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d78de71735806ed216167910246f31adf595fa7
--- /dev/null
+++ b/model_common_test.py
@@ -0,0 +1,604 @@
+import rnn
+import tensorflow as tf
+
+from subnet_tf_utils import generative_cnn_encoder, generative_cnn_encoder_deeper, generative_cnn_encoder_deeper13, \
+ generative_cnn_c3_encoder, generative_cnn_c3_encoder_deeper, generative_cnn_c3_encoder_deeper13, \
+ generative_cnn_c3_encoder_combine33, generative_cnn_c3_encoder_combine43, \
+ generative_cnn_c3_encoder_combine53, generative_cnn_c3_encoder_combineFC, \
+ generative_cnn_c3_encoder_deeper13_attn
+
+
+class DiffPastingV3(object):
+ def __init__(self, raster_size):
+ self.patch_canvas = tf.placeholder(dtype=tf.float32,
+ shape=(None, None, 1)) # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+ self.cursor_pos_a = tf.placeholder(dtype=tf.float32, shape=(2)) # (2), float32, in large size
+ self.image_size_a = tf.placeholder(dtype=tf.int32, shape=()) # ()
+ self.window_size_a = tf.placeholder(dtype=tf.float32, shape=()) # (), float32, with grad
+ self.raster_size_a = float(raster_size)
+
+ self.pasted_image = self.image_pasting_sampling_v3()
+ # (image_size, image_size, 1), [0.0-BG, 1.0-stroke]
+
+ def image_pasting_sampling_v3(self):
+ padding_size = tf.cast(tf.ceil(self.window_size_a / 2.0), tf.int32)
+
+ x1y1_a = self.cursor_pos_a - self.window_size_a / 2.0 # (2), float32
+ x2y2_a = self.cursor_pos_a + self.window_size_a / 2.0 # (2), float32
+
+ x1y1_a_floor = tf.floor(x1y1_a) # (2)
+ x2y2_a_ceil = tf.ceil(x2y2_a) # (2)
+
+ cursor_pos_b_oricoord = (x1y1_a_floor + x2y2_a_ceil) / 2.0 # (2)
+ cursor_pos_b = (cursor_pos_b_oricoord - x1y1_a) / self.window_size_a * self.raster_size_a # (2)
+ raster_size_b = (x2y2_a_ceil - x1y1_a_floor) # (x, y)
+ image_size_b = self.raster_size_a
+ window_size_b = self.raster_size_a * (raster_size_b / self.window_size_a) # (x, y)
+
+ cursor_b_x, cursor_b_y = tf.split(cursor_pos_b, 2, axis=-1) # (1)
+
+ y1_b = cursor_b_y - (window_size_b[1] - 1.) / 2.
+ x1_b = cursor_b_x - (window_size_b[0] - 1.) / 2.
+ y2_b = y1_b + (window_size_b[1] - 1.)
+ x2_b = x1_b + (window_size_b[0] - 1.)
+ boxes_b = tf.concat([y1_b, x1_b, y2_b, x2_b], axis=-1) # (4)
+ boxes_b = boxes_b / tf.cast(image_size_b - 1, tf.float32) # with grad to window_size_a
+
+ box_ind_b = tf.ones((1), dtype=tf.int32) # (1)
+ box_ind_b = tf.cumsum(box_ind_b) - 1
+
+ patch_canvas = tf.expand_dims(self.patch_canvas,
+ axis=0) # (1, raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+ boxes_b = tf.expand_dims(boxes_b, axis=0) # (1, 4)
+
+ valid_canvas = tf.image.crop_and_resize(patch_canvas, boxes_b, box_ind_b,
+ crop_size=[raster_size_b[1], raster_size_b[0]])
+ valid_canvas = valid_canvas[0] # (raster_size_b, raster_size_b, 1)
+
+ pad_up = tf.cast(x1y1_a_floor[1], tf.int32) + padding_size
+ pad_down = self.image_size_a + padding_size - tf.cast(x2y2_a_ceil[1], tf.int32)
+ pad_left = tf.cast(x1y1_a_floor[0], tf.int32) + padding_size
+ pad_right = self.image_size_a + padding_size - tf.cast(x2y2_a_ceil[0], tf.int32)
+
+ paddings = [[pad_up, pad_down],
+ [pad_left, pad_right],
+ [0, 0]]
+ pad_img = tf.pad(valid_canvas, paddings=paddings, mode='CONSTANT',
+ constant_values=0.0) # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+ pasted_image = pad_img[padding_size: padding_size + self.image_size_a,
+ padding_size: padding_size + self.image_size_a, :]
+ # (image_size, image_size, 1), [0.0-BG, 1.0-stroke]
+ return pasted_image
+
+
+class VirtualSketchingModel(object):
+ def __init__(self, hps, gpu_mode=True, reuse=False):
+ """Initializer for the model.
+
+ Args:
+ hps: a HParams object containing model hyperparameters
+ gpu_mode: a boolean that when True, uses GPU mode.
+ reuse: a boolean that when true, attemps to reuse variables.
+ """
+ self.hps = hps
+ assert hps.model_mode in ['train', 'eval', 'eval_sample', 'sample']
+ # with tf.variable_scope('SCC', reuse=reuse):
+ if not gpu_mode:
+ with tf.device('/cpu:0'):
+ print('Model using cpu.')
+ self.build_model()
+ else:
+ print('-' * 100)
+ print('model_mode:', hps.model_mode)
+ print('Model using gpu.')
+ self.build_model()
+
+ def build_model(self):
+ """Define model architecture."""
+ self.config_model()
+
+ initial_state = self.get_decoder_inputs()
+ self.initial_state = initial_state
+
+ ## use pred as the prev points
+ other_params, pen_ras, final_state = self.get_points_and_raster_image(self.image_size)
+ # other_params: (N * max_seq_len, 6)
+ # pen_ras: (N * max_seq_len, 2), after softmax
+
+ self.other_params = other_params # (N * max_seq_len, 6)
+ self.pen_ras = pen_ras # (N * max_seq_len, 2), after softmax
+ self.final_state = final_state
+
+ if not self.hps.use_softargmax:
+ pen_state_soft = pen_ras[:, 1:2] # (N * max_seq_len, 1)
+ else:
+ pen_state_soft = self.differentiable_argmax(pen_ras, self.hps.soft_beta) # (N * max_seq_len, 1)
+
+ pred_params = tf.concat([pen_state_soft, other_params], axis=1) # (N * max_seq_len, 7)
+ self.pred_params = tf.reshape(pred_params, shape=[-1, self.hps.max_seq_len, 7]) # (N, max_seq_len, 7)
+ # pred_params: (N, max_seq_len, 7)
+
+ def config_model(self):
+ if self.hps.model_mode == 'train':
+ self.global_step = tf.Variable(0, name='global_step', trainable=False)
+
+ if self.hps.dec_model == 'lstm':
+ dec_cell_fn = rnn.LSTMCell
+ elif self.hps.dec_model == 'layer_norm':
+ dec_cell_fn = rnn.LayerNormLSTMCell
+ elif self.hps.dec_model == 'hyper':
+ dec_cell_fn = rnn.HyperLSTMCell
+ else:
+ assert False, 'please choose a respectable cell'
+
+ use_recurrent_dropout = self.hps.use_recurrent_dropout
+ use_input_dropout = self.hps.use_input_dropout
+ use_output_dropout = self.hps.use_output_dropout
+
+ dec_cell = dec_cell_fn(
+ self.hps.dec_rnn_size,
+ use_recurrent_dropout=use_recurrent_dropout,
+ dropout_keep_prob=self.hps.recurrent_dropout_prob)
+
+ # dropout:
+ # print('Input dropout mode = %s.' % use_input_dropout)
+ # print('Output dropout mode = %s.' % use_output_dropout)
+ # print('Recurrent dropout mode = %s.' % use_recurrent_dropout)
+ if use_input_dropout:
+ print('Dropout to input w/ keep_prob = %4.4f.' % self.hps.input_dropout_prob)
+ dec_cell = tf.contrib.rnn.DropoutWrapper(
+ dec_cell, input_keep_prob=self.hps.input_dropout_prob)
+ if use_output_dropout:
+ print('Dropout to output w/ keep_prob = %4.4f.' % self.hps.output_dropout_prob)
+ dec_cell = tf.contrib.rnn.DropoutWrapper(
+ dec_cell, output_keep_prob=self.hps.output_dropout_prob)
+ self.dec_cell = dec_cell
+
+ self.input_photo = tf.placeholder(dtype=tf.float32,
+ shape=[self.hps.batch_size, None, None, self.hps.input_channel]) # [0.0-stroke, 1.0-BG]
+ self.init_cursor = tf.placeholder(
+ dtype=tf.float32,
+ shape=[self.hps.batch_size, 1, 2]) # (N, 1, 2), in size [0.0, 1.0)
+ self.init_width = tf.placeholder(
+ dtype=tf.float32,
+ shape=[self.hps.batch_size]) # (1), in [0.0, 1.0]
+ self.init_scaling = tf.placeholder(
+ dtype=tf.float32,
+ shape=[self.hps.batch_size]) # (N), in [0.0, 1.0]
+ self.init_window_size = tf.placeholder(
+ dtype=tf.float32,
+ shape=[self.hps.batch_size]) # (N)
+ self.image_size = tf.placeholder(dtype=tf.int32, shape=()) # ()
+
+ ###########################
+
+ def normalize_image_m1to1(self, in_img_0to1):
+ norm_img_m1to1 = tf.multiply(in_img_0to1, 2.0)
+ norm_img_m1to1 = tf.subtract(norm_img_m1to1, 1.0)
+ return norm_img_m1to1
+
+ def add_coords(self, input_tensor):
+ batch_size_tensor = tf.shape(input_tensor)[0] # get N size
+
+ xx_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32) # e.g. (N, raster_size)
+ xx_ones = tf.expand_dims(xx_ones, -1) # e.g. (N, raster_size, 1)
+ xx_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+ [batch_size_tensor, 1]) # e.g. (N, raster_size)
+ xx_range = tf.expand_dims(xx_range, 1) # e.g. (N, 1, raster_size)
+
+ xx_channel = tf.matmul(xx_ones, xx_range) # e.g. (N, raster_size, raster_size)
+ xx_channel = tf.expand_dims(xx_channel, -1) # e.g. (N, raster_size, raster_size, 1)
+
+ yy_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32) # e.g. (N, raster_size)
+ yy_ones = tf.expand_dims(yy_ones, 1) # e.g. (N, 1, raster_size)
+ yy_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+ [batch_size_tensor, 1]) # (N, raster_size)
+ yy_range = tf.expand_dims(yy_range, -1) # e.g. (N, raster_size, 1)
+
+ yy_channel = tf.matmul(yy_range, yy_ones) # e.g. (N, raster_size, raster_size)
+ yy_channel = tf.expand_dims(yy_channel, -1) # e.g. (N, raster_size, raster_size, 1)
+
+ xx_channel = tf.cast(xx_channel, 'float32') / (self.hps.raster_size - 1)
+ yy_channel = tf.cast(yy_channel, 'float32') / (self.hps.raster_size - 1)
+ # xx_channel = xx_channel * 2 - 1 # [-1, 1]
+ # yy_channel = yy_channel * 2 - 1
+
+ ret = tf.concat([
+ input_tensor,
+ xx_channel,
+ yy_channel,
+ ], axis=-1) # e.g. (N, raster_size, raster_size, 4)
+
+ return ret
+
+ def build_combined_encoder(self, patch_canvas, patch_photo, entire_canvas, entire_photo, cursor_pos,
+ image_size, window_size):
+ """
+ :param patch_canvas: (N, raster_size, raster_size, 1), [-1.0-stroke, 1.0-BG]
+ :param patch_photo: (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+ :param entire_canvas: (N, image_size, image_size, 1), [0.0-stroke, 1.0-BG]
+ :param entire_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+ :param cursor_pos: (N, 1, 2), in size [0.0, 1.0)
+ :param window_size: (N, 1, 1), float, in large size
+ :return:
+ """
+ if self.hps.resize_method == 'BILINEAR':
+ resize_method = tf.image.ResizeMethod.BILINEAR
+ elif self.hps.resize_method == 'NEAREST_NEIGHBOR':
+ resize_method = tf.image.ResizeMethod.NEAREST_NEIGHBOR
+ elif self.hps.resize_method == 'BICUBIC':
+ resize_method = tf.image.ResizeMethod.BICUBIC
+ elif self.hps.resize_method == 'AREA':
+ resize_method = tf.image.ResizeMethod.AREA
+ else:
+ raise Exception('unknown resize_method', self.hps.resize_method)
+
+ patch_photo = tf.stop_gradient(patch_photo)
+ patch_canvas = tf.stop_gradient(patch_canvas)
+ cursor_pos = tf.stop_gradient(cursor_pos)
+ window_size = tf.stop_gradient(window_size)
+
+ entire_photo_small = tf.stop_gradient(tf.image.resize_images(entire_photo,
+ (self.hps.raster_size, self.hps.raster_size),
+ method=resize_method))
+ entire_canvas_small = tf.stop_gradient(tf.image.resize_images(entire_canvas,
+ (self.hps.raster_size, self.hps.raster_size),
+ method=resize_method))
+ entire_photo_small = self.normalize_image_m1to1(entire_photo_small) # [-1.0-stroke, 1.0-BG]
+ entire_canvas_small = self.normalize_image_m1to1(entire_canvas_small) # [-1.0-stroke, 1.0-BG]
+
+ if self.hps.encode_cursor_type == 'value':
+ cursor_pos_norm = tf.expand_dims(cursor_pos, axis=1) # (N, 1, 1, 2)
+ cursor_pos_norm = tf.tile(cursor_pos_norm, [1, self.hps.raster_size, self.hps.raster_size, 1])
+ cursor_info = cursor_pos_norm
+ else:
+ raise Exception('Unknown encode_cursor_type', self.hps.encode_cursor_type)
+
+ batch_input_combined = tf.concat([patch_photo, patch_canvas, entire_photo_small, entire_canvas_small, cursor_info],
+ axis=-1) # [N, raster_size, raster_size, 6/10]
+ batch_input_local = tf.concat([patch_photo, patch_canvas], axis=-1) # [N, raster_size, raster_size, 2/4]
+ batch_input_global = tf.concat([entire_photo_small, entire_canvas_small, cursor_info],
+ axis=-1) # [N, raster_size, raster_size, 4/6]
+
+ if self.hps.model_mode == 'train':
+ is_training = True
+ dropout_keep_prob = self.hps.pix_drop_kp
+ else:
+ is_training = False
+ dropout_keep_prob = 1.0
+
+ if self.hps.add_coordconv:
+ batch_input_combined = self.add_coords(batch_input_combined) # (N, in_H, in_W, in_dim + 2)
+ batch_input_local = self.add_coords(batch_input_local) # (N, in_H, in_W, in_dim + 2)
+ batch_input_global = self.add_coords(batch_input_global) # (N, in_H, in_W, in_dim + 2)
+
+ if 'combine' in self.hps.encoder_type:
+ if self.hps.encoder_type == 'combine33':
+ image_embedding, _ = generative_cnn_c3_encoder_combine33(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'combine43':
+ image_embedding, _ = generative_cnn_c3_encoder_combine43(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'combine53':
+ image_embedding, _ = generative_cnn_c3_encoder_combine53(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'combineFC':
+ image_embedding, _ = generative_cnn_c3_encoder_combineFC(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 256)
+ else:
+ raise Exception('Unknown encoder_type', self.hps.encoder_type)
+ else:
+ with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+ if self.hps.encoder_type == 'conv10':
+ image_embedding, _ = generative_cnn_encoder(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv10_deep':
+ image_embedding, _ = generative_cnn_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob) # (N, 512)
+ elif self.hps.encoder_type == 'conv13':
+ image_embedding, _ = generative_cnn_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv10_c3':
+ image_embedding, _ = generative_cnn_c3_encoder(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv10_deep_c3':
+ image_embedding, _ = generative_cnn_c3_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob) # (N, 512)
+ elif self.hps.encoder_type == 'conv13_c3':
+ image_embedding, _ = generative_cnn_c3_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv13_c3_attn':
+ image_embedding, _ = generative_cnn_c3_encoder_deeper13_attn(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ else:
+ raise Exception('Unknown encoder_type', self.hps.encoder_type)
+ return image_embedding
+
+ def build_seq_decoder(self, dec_cell, actual_input_x, initial_state):
+ rnn_output, last_state = self.rnn_decoder(dec_cell, initial_state, actual_input_x)
+ rnn_output_flat = tf.reshape(rnn_output, [-1, self.hps.dec_rnn_size])
+
+ pen_n_out = 2
+ params_n_out = 6
+
+ with tf.variable_scope('DEC_RNN_out_pen', reuse=tf.AUTO_REUSE):
+ output_w_pen = tf.get_variable('output_w', [self.hps.dec_rnn_size, pen_n_out])
+ output_b_pen = tf.get_variable('output_b', [pen_n_out], initializer=tf.constant_initializer(0.0))
+ output_pen = tf.nn.xw_plus_b(rnn_output_flat, output_w_pen, output_b_pen) # (N, pen_n_out)
+
+ with tf.variable_scope('DEC_RNN_out_params', reuse=tf.AUTO_REUSE):
+ output_w_params = tf.get_variable('output_w', [self.hps.dec_rnn_size, params_n_out])
+ output_b_params = tf.get_variable('output_b', [params_n_out], initializer=tf.constant_initializer(0.0))
+ output_params = tf.nn.xw_plus_b(rnn_output_flat, output_w_params, output_b_params) # (N, params_n_out)
+
+ output = tf.concat([output_pen, output_params], axis=1) # (N, n_out)
+
+ return output, last_state
+
+ def get_mixture_coef(self, outputs):
+ z = outputs
+ z_pen_logits = z[:, 0:2] # (N, 2), pen states
+ z_other_params_logits = z[:, 2:] # (N, 6)
+
+ z_pen = tf.nn.softmax(z_pen_logits) # (N, 2)
+ if self.hps.position_format == 'abs':
+ x1y1 = tf.nn.sigmoid(z_other_params_logits[:, 0:2]) # (N, 2)
+ x2y2 = tf.tanh(z_other_params_logits[:, 2:4]) # (N, 2)
+ widths = tf.nn.sigmoid(z_other_params_logits[:, 4:5]) # (N, 1)
+ widths = tf.add(tf.multiply(widths, 1.0 - self.hps.min_width), self.hps.min_width)
+ scaling = tf.nn.sigmoid(z_other_params_logits[:, 5:6]) * self.hps.max_scaling # (N, 1), [0.0, max_scaling]
+ # scaling = tf.add(tf.multiply(scaling, (self.hps.max_scaling - self.hps.min_scaling) / self.hps.max_scaling),
+ # self.hps.min_scaling)
+ z_other_params = tf.concat([x1y1, x2y2, widths, scaling], axis=-1) # (N, 6)
+ else: # "rel"
+ raise Exception('Unknown position_format', self.hps.position_format)
+
+ r = [z_other_params, z_pen]
+ return r
+
+ ###########################
+
+ def get_decoder_inputs(self):
+ initial_state = self.dec_cell.zero_state(batch_size=self.hps.batch_size, dtype=tf.float32)
+ return initial_state
+
+ def rnn_decoder(self, dec_cell, initial_state, actual_input_x):
+ with tf.variable_scope("RNN_DEC", reuse=tf.AUTO_REUSE):
+ output, last_state = tf.nn.dynamic_rnn(
+ dec_cell,
+ actual_input_x,
+ initial_state=initial_state,
+ time_major=False,
+ swap_memory=True,
+ dtype=tf.float32)
+ return output, last_state
+
+ ###########################
+
+ def image_padding(self, ori_image, window_size, pad_value):
+ """
+ Pad with (bg)
+ :param ori_image:
+ :return:
+ """
+ paddings = [[0, 0],
+ [window_size // 2, window_size // 2],
+ [window_size // 2, window_size // 2],
+ [0, 0]]
+ pad_img = tf.pad(ori_image, paddings=paddings, mode='CONSTANT', constant_values=pad_value) # (N, H_p, W_p, k)
+ return pad_img
+
+ def image_cropping_fn(self, fn_inputs):
+ """
+ crop the patch
+ :return:
+ """
+ index_offset = self.hps.input_channel - 1
+ input_image = fn_inputs[:, :, 0:2 + index_offset] # (image_size, image_size, -), [0.0-BG, 1.0-stroke]
+ cursor_pos = fn_inputs[0, 0, 2 + index_offset:4 + index_offset] # (2), in [0.0, 1.0)
+ image_size = fn_inputs[0, 0, 4 + index_offset] # (), float32
+ window_size = tf.cast(fn_inputs[0, 0, 5 + index_offset], tf.int32) # ()
+
+ input_img_reshape = tf.expand_dims(input_image, axis=0)
+ pad_img = self.image_padding(input_img_reshape, window_size, pad_value=0.0)
+
+ cursor_pos = tf.cast(tf.round(tf.multiply(cursor_pos, image_size)), dtype=tf.int32)
+ x0, x1 = cursor_pos[0], cursor_pos[0] + window_size # ()
+ y0, y1 = cursor_pos[1], cursor_pos[1] + window_size # ()
+ patch_image = pad_img[:, y0:y1, x0:x1, :] # (1, window_size, window_size, 2/4)
+
+ # resize to raster_size
+ patch_image_scaled = tf.image.resize_images(patch_image, (self.hps.raster_size, self.hps.raster_size),
+ method=tf.image.ResizeMethod.AREA)
+ patch_image_scaled = tf.squeeze(patch_image_scaled, axis=0)
+ # patch_canvas_scaled: (raster_size, raster_size, 2/4), [0.0-BG, 1.0-stroke]
+
+ return patch_image_scaled
+
+ def image_cropping(self, cursor_position, input_img, image_size, window_sizes):
+ """
+ :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+ :param input_img: (N, image_size, image_size, 2/4), [0.0-BG, 1.0-stroke]
+ :param window_sizes: (N, 1, 1), float32, with grad
+ """
+ input_img_ = input_img
+ window_sizes_non_grad = tf.stop_gradient(tf.round(window_sizes)) # (N, 1, 1), no grad
+
+ cursor_position_ = tf.reshape(cursor_position, (-1, 1, 1, 2)) # (N, 1, 1, 2)
+ cursor_position_ = tf.tile(cursor_position_, [1, image_size, image_size, 1]) # (N, image_size, image_size, 2)
+
+ image_size_ = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1)) # (1, 1, 1, 1)
+ image_size_ = tf.tile(image_size_, [self.hps.batch_size, image_size, image_size, 1])
+
+ window_sizes_ = tf.reshape(window_sizes_non_grad, (-1, 1, 1, 1)) # (N, 1, 1, 1)
+ window_sizes_ = tf.tile(window_sizes_, [1, image_size, image_size, 1]) # (N, image_size, image_size, 1)
+
+ fn_inputs = tf.concat([input_img_, cursor_position_, image_size_, window_sizes_],
+ axis=-1) # (N, image_size, image_size, 2/4 + 4)
+ curr_patch_imgs = tf.map_fn(self.image_cropping_fn, fn_inputs, parallel_iterations=32) # (N, raster_size, raster_size, -)
+ return curr_patch_imgs
+
+ def image_cropping_v3(self, cursor_position, input_img, image_size, window_sizes):
+ """
+ :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+ :param input_img: (N, image_size, image_size, k), [0.0-BG, 1.0-stroke]
+ :param window_sizes: (N, 1, 1), float32, with grad
+ """
+ window_sizes_non_grad = tf.stop_gradient(window_sizes) # (N, 1, 1), no grad
+
+ cursor_pos = tf.multiply(cursor_position, tf.cast(image_size, tf.float32))
+ cursor_x, cursor_y = tf.split(cursor_pos, 2, axis=-1) # (N, 1, 1)
+
+ y1 = cursor_y - (window_sizes_non_grad - 1.0) / 2
+ x1 = cursor_x - (window_sizes_non_grad - 1.0) / 2
+ y2 = y1 + (window_sizes_non_grad - 1.0)
+ x2 = x1 + (window_sizes_non_grad - 1.0)
+ boxes = tf.concat([y1, x1, y2, x2], axis=-1) # (N, 1, 4)
+ boxes = tf.squeeze(boxes, axis=1) # (N, 4)
+ boxes = boxes / tf.cast(image_size - 1, tf.float32)
+
+ box_ind = tf.ones_like(cursor_x)[:, 0, 0] # (N)
+ box_ind = tf.cast(box_ind, dtype=tf.int32)
+ box_ind = tf.cumsum(box_ind) - 1
+
+ curr_patch_imgs = tf.image.crop_and_resize(input_img, boxes, box_ind,
+ crop_size=[self.hps.raster_size, self.hps.raster_size])
+ # (N, raster_size, raster_size, k), [0.0-BG, 1.0-stroke]
+ return curr_patch_imgs
+
+ def get_points_and_raster_image(self, image_size):
+ ## generate the other_params and pen_ras and raster image for raster loss
+ prev_state = self.initial_state # (N, dec_rnn_size * 3)
+
+ prev_width = self.init_width # (N)
+ prev_width = tf.expand_dims(tf.expand_dims(prev_width, axis=-1), axis=-1) # (N, 1, 1)
+
+ prev_scaling = self.init_scaling # (N)
+ prev_scaling = tf.reshape(prev_scaling, (-1, 1, 1)) # (N, 1, 1)
+
+ prev_window_size = self.init_window_size # (N)
+ prev_window_size = tf.reshape(prev_window_size, (-1, 1, 1)) # (N, 1, 1)
+
+ cursor_position_temp = self.init_cursor
+ self.cursor_position = cursor_position_temp # (N, 1, 2), in size [0.0, 1.0)
+ cursor_position_loop = self.cursor_position
+
+ other_params_list = []
+ pen_ras_list = []
+
+ curr_canvas_soft = tf.zeros_like(self.input_photo[:, :, :, 0]) # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+ curr_canvas_hard = tf.zeros_like(curr_canvas_soft) # [0.0-BG, 1.0-stroke]
+
+ #### sampling part - start ####
+ self.curr_canvas_hard = curr_canvas_hard
+
+ if self.hps.cropping_type == 'v3':
+ cropping_func = self.image_cropping_v3
+ # elif self.hps.cropping_type == 'v2':
+ # cropping_func = self.image_cropping
+ else:
+ raise Exception('Unknown cropping_type', self.hps.cropping_type)
+
+ for time_i in range(self.hps.max_seq_len):
+ cursor_position_non_grad = tf.stop_gradient(cursor_position_loop) # (N, 1, 2), in size [0.0, 1.0)
+
+ curr_window_size = tf.multiply(prev_scaling, tf.stop_gradient(prev_window_size)) # float, with grad
+ curr_window_size = tf.maximum(curr_window_size, tf.cast(self.hps.min_window_size, tf.float32))
+ curr_window_size = tf.minimum(curr_window_size, tf.cast(image_size, tf.float32))
+
+ ## patch-level encoding
+ # Here, we make the gradients from canvas_z to curr_canvas_hard be None to avoid recurrent gradient propagation.
+ curr_canvas_hard_non_grad = tf.stop_gradient(self.curr_canvas_hard)
+ curr_canvas_hard_non_grad = tf.expand_dims(curr_canvas_hard_non_grad, axis=-1)
+
+ # input_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+ crop_inputs = tf.concat([1.0 - self.input_photo, curr_canvas_hard_non_grad], axis=-1) # (N, H_p, W_p, 1+1)
+
+ cropped_outputs = cropping_func(cursor_position_non_grad, crop_inputs, image_size, curr_window_size)
+ index_offset = self.hps.input_channel - 1
+ curr_patch_inputs = cropped_outputs[:, :, :, 0:1 + index_offset] # [0.0-BG, 1.0-stroke]
+ curr_patch_canvas_hard_non_grad = cropped_outputs[:, :, :, 1 + index_offset:2 + index_offset]
+ # (N, raster_size, raster_size, 1/3), [0.0-BG, 1.0-stroke]
+
+ curr_patch_inputs = 1.0 - curr_patch_inputs # [0.0-stroke, 1.0-BG]
+ curr_patch_inputs = self.normalize_image_m1to1(curr_patch_inputs)
+ # (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+
+ # Normalizing image
+ curr_patch_canvas_hard_non_grad = 1.0 - curr_patch_canvas_hard_non_grad # [0.0-stroke, 1.0-BG]
+ curr_patch_canvas_hard_non_grad = self.normalize_image_m1to1(curr_patch_canvas_hard_non_grad) # [-1.0-stroke, 1.0-BG]
+
+ ## image-level encoding
+ combined_z = self.build_combined_encoder(
+ curr_patch_canvas_hard_non_grad,
+ curr_patch_inputs,
+ 1.0 - curr_canvas_hard_non_grad,
+ self.input_photo,
+ cursor_position_non_grad,
+ image_size,
+ curr_window_size) # (N, z_size)
+ combined_z = tf.expand_dims(combined_z, axis=1) # (N, 1, z_size)
+
+ curr_window_size_top_side_norm_non_grad = \
+ tf.stop_gradient(curr_window_size / tf.cast(image_size, tf.float32))
+ curr_window_size_bottom_side_norm_non_grad = \
+ tf.stop_gradient(curr_window_size / tf.cast(self.hps.min_window_size, tf.float32))
+ if not self.hps.concat_win_size:
+ combined_z = tf.concat([tf.stop_gradient(prev_width), combined_z], 2) # (N, 1, 2+z_size)
+ else:
+ combined_z = tf.concat([tf.stop_gradient(prev_width),
+ curr_window_size_top_side_norm_non_grad,
+ curr_window_size_bottom_side_norm_non_grad,
+ combined_z],
+ 2) # (N, 1, 2+z_size)
+
+ if self.hps.concat_cursor:
+ prev_input_x = tf.concat([cursor_position_non_grad, combined_z], 2) # (N, 1, 2+2+z_size)
+ else:
+ prev_input_x = combined_z # (N, 1, 2+z_size)
+
+ h_output, next_state = self.build_seq_decoder(self.dec_cell, prev_input_x, prev_state)
+ # h_output: (N * 1, n_out), next_state: (N, dec_rnn_size * 3)
+ [o_other_params, o_pen_ras] = self.get_mixture_coef(h_output)
+ # o_other_params: (N * 1, 6)
+ # o_pen_ras: (N * 1, 2), after softmax
+
+ o_other_params = tf.reshape(o_other_params, [-1, 1, 6]) # (N, 1, 6)
+ o_pen_ras_raw = tf.reshape(o_pen_ras, [-1, 1, 2]) # (N, 1, 2)
+
+ other_params_list.append(o_other_params)
+ pen_ras_list.append(o_pen_ras_raw)
+
+ #### sampling part - end ####
+
+ prev_state = next_state
+
+ other_params_ = tf.reshape(tf.concat(other_params_list, axis=1), [-1, 6]) # (N * max_seq_len, 6)
+ pen_ras_ = tf.reshape(tf.concat(pen_ras_list, axis=1), [-1, 2]) # (N * max_seq_len, 2)
+
+ return other_params_, pen_ras_, prev_state
+
+ def differentiable_argmax(self, input_pen, soft_beta):
+ """
+ Differentiable argmax trick.
+ :param input_pen: (N, n_class)
+ :return: pen_state: (N, 1)
+ """
+ def sign_onehot(x):
+ """
+ :param x: (N, n_class)
+ :return: (N, n_class)
+ """
+ y = tf.sign(tf.reduce_max(x, axis=-1, keepdims=True) - x)
+ y = (y - 1) * (-1)
+ return y
+
+ def softargmax(x, beta=1e2):
+ """
+ :param x: (N, n_class)
+ :param beta: 1e10 is the best. 1e2 is acceptable.
+ :return: (N)
+ """
+ x_range = tf.cumsum(tf.ones_like(x), axis=1) # (N, 2)
+ return tf.reduce_sum(tf.nn.softmax(x * beta) * x_range, axis=1) - 1
+
+ ## Better to use softargmax(beta=1e2). The sign_onehot's gradient is close to zero.
+ # pen_onehot = sign_onehot(input_pen) # one-hot form, (N * max_seq_len, 2)
+ # pen_state = pen_onehot[:, 1:2] # (N * max_seq_len, 1)
+ pen_state = softargmax(input_pen, soft_beta)
+ pen_state = tf.expand_dims(pen_state, axis=1) # (N * max_seq_len, 1)
+ return pen_state
diff --git a/model_common_train.py b/model_common_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7c22b33f45a9cbd7e69c878866cb2fb6dd81f7c
--- /dev/null
+++ b/model_common_train.py
@@ -0,0 +1,1193 @@
+import rnn
+import tensorflow as tf
+
+from subnet_tf_utils import generative_cnn_encoder, generative_cnn_encoder_deeper, generative_cnn_encoder_deeper13, \
+ generative_cnn_c3_encoder, generative_cnn_c3_encoder_deeper, generative_cnn_c3_encoder_deeper13, \
+ generative_cnn_c3_encoder_combine33, generative_cnn_c3_encoder_combine43, \
+ generative_cnn_c3_encoder_combine53, generative_cnn_c3_encoder_combineFC, \
+ generative_cnn_c3_encoder_deeper13_attn
+from rasterization_utils.NeuralRenderer import NeuralRasterizorStep
+from vgg_utils.VGG16 import vgg_net_slim
+
+
+class VirtualSketchingModel(object):
+ def __init__(self, hps, gpu_mode=True, reuse=False):
+ """Initializer for the model.
+
+ Args:
+ hps: a HParams object containing model hyperparameters
+ gpu_mode: a boolean that when True, uses GPU mode.
+ reuse: a boolean that when true, attemps to reuse variables.
+ """
+ self.hps = hps
+ assert hps.model_mode in ['train', 'eval', 'eval_sample', 'sample']
+ # with tf.variable_scope('SCC', reuse=reuse):
+ if not gpu_mode:
+ with tf.device('/cpu:0'):
+ print('Model using cpu.')
+ self.build_model()
+ else:
+ print('-' * 100)
+ print('model_mode:', hps.model_mode)
+ print('Model using gpu.')
+ self.build_model()
+
+ def build_model(self):
+ """Define model architecture."""
+ self.config_model()
+
+ initial_state = self.get_decoder_inputs()
+ self.initial_state = initial_state
+ self.initial_state_list = tf.split(self.initial_state, self.total_loop, axis=0)
+
+ total_loss_list = []
+ ras_loss_list = []
+ perc_relu_raw_list = []
+ perc_relu_norm_list = []
+ sn_loss_list = []
+ cursor_outside_loss_list = []
+ win_size_outside_loss_list = []
+ early_state_loss_list = []
+
+ tower_grads = []
+
+ pred_raster_imgs_list = []
+ pred_raster_imgs_rgb_list = []
+
+ for t_i in range(self.total_loop):
+ gpu_idx = t_i // self.hps.loop_per_gpu
+ gpu_i = self.hps.gpus[gpu_idx]
+ print(self.hps.model_mode, 'model, gpu:', gpu_i, ', loop:', t_i % self.hps.loop_per_gpu)
+ with tf.device('/gpu:%d' % gpu_i):
+ with tf.name_scope('GPU_%d' % gpu_i) as scope:
+ if t_i > 0:
+ tf.get_variable_scope().reuse_variables()
+ else:
+ total_loss_list.clear()
+ ras_loss_list.clear()
+ perc_relu_raw_list.clear()
+ perc_relu_norm_list.clear()
+ sn_loss_list.clear()
+ cursor_outside_loss_list.clear()
+ win_size_outside_loss_list.clear()
+ early_state_loss_list.clear()
+ tower_grads.clear()
+ pred_raster_imgs_list.clear()
+ pred_raster_imgs_rgb_list.clear()
+
+ split_input_photo = self.input_photo_list[t_i]
+ split_image_size = self.image_size[t_i]
+ split_init_cursor = self.init_cursor_list[t_i]
+ split_initial_state = self.initial_state_list[t_i]
+ if self.hps.input_channel == 1:
+ split_target_sketch = split_input_photo
+ else:
+ split_target_sketch = self.target_sketch_list[t_i]
+
+ ## use pred as the prev points
+ other_params, pen_ras, final_state, pred_raster_images, pred_raster_images_rgb, \
+ pos_before_max_min, win_size_before_max_min \
+ = self.get_points_and_raster_image(split_initial_state, split_init_cursor, split_input_photo,
+ split_image_size)
+ # other_params: (N * max_seq_len, 6)
+ # pen_ras: (N * max_seq_len, 2), after softmax
+ # pos_before_max_min: (N, max_seq_len, 2), in image_size
+ # win_size_before_max_min: (N, max_seq_len, 1), in image_size
+
+ pred_raster_imgs = 1.0 - pred_raster_images # (N, image_size, image_size), [0.0-stroke, 1.0-BG]
+ pred_raster_imgs_rgb = 1.0 - pred_raster_images_rgb # (N, image_size, image_size, 3)
+ pred_raster_imgs_list.append(pred_raster_imgs)
+ pred_raster_imgs_rgb_list.append(pred_raster_imgs_rgb)
+
+ if not self.hps.use_softargmax:
+ pen_state_soft = pen_ras[:, 1:2] # (N * max_seq_len, 1)
+ else:
+ pen_state_soft = self.differentiable_argmax(pen_ras, self.hps.soft_beta) # (N * max_seq_len, 1)
+
+ pred_params = tf.concat([pen_state_soft, other_params], axis=1) # (N * max_seq_len, 7)
+ pred_params = tf.reshape(pred_params, shape=[-1, self.hps.max_seq_len, 7]) # (N, max_seq_len, 7)
+ # pred_params: (N, max_seq_len, 7)
+
+ if self.hps.model_mode == 'train' or self.hps.model_mode == 'eval':
+ raster_cost, sn_cost, cursor_outside_cost, winsize_outside_cost, \
+ early_pen_states_cost, \
+ perc_relu_loss_raw, perc_relu_loss_norm = \
+ self.build_losses(split_target_sketch, pred_raster_imgs, pred_params,
+ pos_before_max_min, win_size_before_max_min,
+ split_image_size)
+ # perc_relu_loss_raw, perc_relu_loss_norm: (n_layers)
+
+ ras_loss_list.append(raster_cost)
+ perc_relu_raw_list.append(perc_relu_loss_raw)
+ perc_relu_norm_list.append(perc_relu_loss_norm)
+ sn_loss_list.append(sn_cost)
+ cursor_outside_loss_list.append(cursor_outside_cost)
+ win_size_outside_loss_list.append(winsize_outside_cost)
+ early_state_loss_list.append(early_pen_states_cost)
+
+ if self.hps.model_mode == 'train':
+ total_cost_split, grads_and_vars_split = self.build_training_op_split(
+ raster_cost, sn_cost, cursor_outside_cost, winsize_outside_cost,
+ early_pen_states_cost)
+ total_loss_list.append(total_cost_split)
+ tower_grads.append(grads_and_vars_split)
+
+ self.raster_cost = tf.reduce_mean(tf.stack(ras_loss_list, axis=0))
+ self.perc_relu_losses_raw = tf.reduce_mean(tf.stack(perc_relu_raw_list, axis=0), axis=0) # (n_layers)
+ self.perc_relu_losses_norm = tf.reduce_mean(tf.stack(perc_relu_norm_list, axis=0), axis=0) # (n_layers)
+ self.stroke_num_cost = tf.reduce_mean(tf.stack(sn_loss_list, axis=0))
+ self.pos_outside_cost = tf.reduce_mean(tf.stack(cursor_outside_loss_list, axis=0))
+ self.win_size_outside_cost = tf.reduce_mean(tf.stack(win_size_outside_loss_list, axis=0))
+ self.early_pen_states_cost = tf.reduce_mean(tf.stack(early_state_loss_list, axis=0))
+ self.cost = tf.reduce_mean(tf.stack(total_loss_list, axis=0))
+
+ self.pred_raster_imgs = tf.concat(pred_raster_imgs_list, axis=0) # (N, image_size, image_size), [0.0-stroke, 1.0-BG]
+ self.pred_raster_imgs_rgb = tf.concat(pred_raster_imgs_rgb_list, axis=0) # (N, image_size, image_size, 3)
+
+ if self.hps.model_mode == 'train':
+ self.build_training_op(tower_grads)
+
+ def config_model(self):
+ if self.hps.model_mode == 'train':
+ self.global_step = tf.Variable(0, name='global_step', trainable=False)
+
+ if self.hps.dec_model == 'lstm':
+ dec_cell_fn = rnn.LSTMCell
+ elif self.hps.dec_model == 'layer_norm':
+ dec_cell_fn = rnn.LayerNormLSTMCell
+ elif self.hps.dec_model == 'hyper':
+ dec_cell_fn = rnn.HyperLSTMCell
+ else:
+ assert False, 'please choose a respectable cell'
+
+ use_recurrent_dropout = self.hps.use_recurrent_dropout
+ use_input_dropout = self.hps.use_input_dropout
+ use_output_dropout = self.hps.use_output_dropout
+
+ dec_cell = dec_cell_fn(
+ self.hps.dec_rnn_size,
+ use_recurrent_dropout=use_recurrent_dropout,
+ dropout_keep_prob=self.hps.recurrent_dropout_prob)
+
+ # dropout:
+ # print('Input dropout mode = %s.' % use_input_dropout)
+ # print('Output dropout mode = %s.' % use_output_dropout)
+ # print('Recurrent dropout mode = %s.' % use_recurrent_dropout)
+ if use_input_dropout:
+ print('Dropout to input w/ keep_prob = %4.4f.' % self.hps.input_dropout_prob)
+ dec_cell = tf.contrib.rnn.DropoutWrapper(
+ dec_cell, input_keep_prob=self.hps.input_dropout_prob)
+ if use_output_dropout:
+ print('Dropout to output w/ keep_prob = %4.4f.' % self.hps.output_dropout_prob)
+ dec_cell = tf.contrib.rnn.DropoutWrapper(
+ dec_cell, output_keep_prob=self.hps.output_dropout_prob)
+ self.dec_cell = dec_cell
+
+ self.total_loop = len(self.hps.gpus) * self.hps.loop_per_gpu
+
+ self.init_cursor = tf.placeholder(
+ dtype=tf.float32,
+ shape=[self.hps.batch_size, 1, 2]) # (N, 1, 2), in size [0.0, 1.0)
+ self.init_width = tf.placeholder(
+ dtype=tf.float32,
+ shape=[1]) # (1), in [0.0, 1.0]
+ self.image_size = tf.placeholder(dtype=tf.int32, shape=(self.total_loop)) # ()
+
+ self.init_cursor_list = tf.split(self.init_cursor, self.total_loop, axis=0)
+ self.input_photo_list = []
+ for loop_i in range(self.total_loop):
+ input_photo_i = tf.placeholder(dtype=tf.float32, shape=[None, None, None, self.hps.input_channel]) # [0.0-stroke, 1.0-BG]
+ self.input_photo_list.append(input_photo_i)
+
+ if self.hps.input_channel == 3:
+ self.target_sketch_list = []
+ for loop_i in range(self.total_loop):
+ target_sketch_i = tf.placeholder(dtype=tf.float32, shape=[None, None, None, 1]) # [0.0-stroke, 1.0-BG]
+ self.target_sketch_list.append(target_sketch_i)
+
+ if self.hps.model_mode == 'train' or self.hps.model_mode == 'eval':
+ self.stroke_num_loss_weight = tf.Variable(0.0, trainable=False)
+ self.early_pen_loss_start_idx = tf.Variable(0, dtype=tf.int32, trainable=False)
+ self.early_pen_loss_end_idx = tf.Variable(0, dtype=tf.int32, trainable=False)
+
+ if self.hps.model_mode == 'train':
+ self.perc_loss_mean_list = []
+ for loop_i in range(len(self.hps.perc_loss_layers)):
+ relu_loss_mean = tf.Variable(0.0, trainable=False)
+ self.perc_loss_mean_list.append(relu_loss_mean)
+ self.last_step_num = tf.Variable(0.0, trainable=False)
+
+ with tf.variable_scope('train_op', reuse=tf.AUTO_REUSE):
+ self.lr = tf.Variable(self.hps.learning_rate, trainable=False)
+ self.optimizer = tf.train.AdamOptimizer(self.lr)
+
+ ###########################
+
+ def normalize_image_m1to1(self, in_img_0to1):
+ norm_img_m1to1 = tf.multiply(in_img_0to1, 2.0)
+ norm_img_m1to1 = tf.subtract(norm_img_m1to1, 1.0)
+ return norm_img_m1to1
+
+ def add_coords(self, input_tensor):
+ batch_size_tensor = tf.shape(input_tensor)[0] # get N size
+
+ xx_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32) # e.g. (N, raster_size)
+ xx_ones = tf.expand_dims(xx_ones, -1) # e.g. (N, raster_size, 1)
+ xx_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+ [batch_size_tensor, 1]) # e.g. (N, raster_size)
+ xx_range = tf.expand_dims(xx_range, 1) # e.g. (N, 1, raster_size)
+
+ xx_channel = tf.matmul(xx_ones, xx_range) # e.g. (N, raster_size, raster_size)
+ xx_channel = tf.expand_dims(xx_channel, -1) # e.g. (N, raster_size, raster_size, 1)
+
+ yy_ones = tf.ones([batch_size_tensor, self.hps.raster_size], dtype=tf.int32) # e.g. (N, raster_size)
+ yy_ones = tf.expand_dims(yy_ones, 1) # e.g. (N, 1, raster_size)
+ yy_range = tf.tile(tf.expand_dims(tf.range(self.hps.raster_size), 0),
+ [batch_size_tensor, 1]) # (N, raster_size)
+ yy_range = tf.expand_dims(yy_range, -1) # e.g. (N, raster_size, 1)
+
+ yy_channel = tf.matmul(yy_range, yy_ones) # e.g. (N, raster_size, raster_size)
+ yy_channel = tf.expand_dims(yy_channel, -1) # e.g. (N, raster_size, raster_size, 1)
+
+ xx_channel = tf.cast(xx_channel, 'float32') / (self.hps.raster_size - 1)
+ yy_channel = tf.cast(yy_channel, 'float32') / (self.hps.raster_size - 1)
+ # xx_channel = xx_channel * 2 - 1 # [-1, 1]
+ # yy_channel = yy_channel * 2 - 1
+
+ ret = tf.concat([
+ input_tensor,
+ xx_channel,
+ yy_channel,
+ ], axis=-1) # e.g. (N, raster_size, raster_size, 4)
+
+ return ret
+
+ def build_combined_encoder(self, patch_canvas, patch_photo, entire_canvas, entire_photo, cursor_pos,
+ image_size, window_size):
+ """
+ :param patch_canvas: (N, raster_size, raster_size, 1), [-1.0-stroke, 1.0-BG]
+ :param patch_photo: (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+ :param entire_canvas: (N, image_size, image_size, 1), [0.0-stroke, 1.0-BG]
+ :param entire_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+ :param cursor_pos: (N, 1, 2), in size [0.0, 1.0)
+ :param window_size: (N, 1, 1), float, in large size
+ :return:
+ """
+ if self.hps.resize_method == 'BILINEAR':
+ resize_method = tf.image.ResizeMethod.BILINEAR
+ elif self.hps.resize_method == 'NEAREST_NEIGHBOR':
+ resize_method = tf.image.ResizeMethod.NEAREST_NEIGHBOR
+ elif self.hps.resize_method == 'BICUBIC':
+ resize_method = tf.image.ResizeMethod.BICUBIC
+ elif self.hps.resize_method == 'AREA':
+ resize_method = tf.image.ResizeMethod.AREA
+ else:
+ raise Exception('unknown resize_method', self.hps.resize_method)
+
+ patch_photo = tf.stop_gradient(patch_photo)
+ patch_canvas = tf.stop_gradient(patch_canvas)
+ cursor_pos = tf.stop_gradient(cursor_pos)
+ window_size = tf.stop_gradient(window_size)
+
+ entire_photo_small = tf.stop_gradient(tf.image.resize_images(entire_photo,
+ (self.hps.raster_size, self.hps.raster_size),
+ method=resize_method))
+ entire_canvas_small = tf.stop_gradient(tf.image.resize_images(entire_canvas,
+ (self.hps.raster_size, self.hps.raster_size),
+ method=resize_method))
+ entire_photo_small = self.normalize_image_m1to1(entire_photo_small) # [-1.0-stroke, 1.0-BG]
+ entire_canvas_small = self.normalize_image_m1to1(entire_canvas_small) # [-1.0-stroke, 1.0-BG]
+
+ if self.hps.encode_cursor_type == 'value':
+ cursor_pos_norm = tf.expand_dims(cursor_pos, axis=1) # (N, 1, 1, 2)
+ cursor_pos_norm = tf.tile(cursor_pos_norm, [1, self.hps.raster_size, self.hps.raster_size, 1])
+ cursor_info = cursor_pos_norm
+ else:
+ raise Exception('Unknown encode_cursor_type', self.hps.encode_cursor_type)
+
+ batch_input_combined = tf.concat([patch_photo, patch_canvas, entire_photo_small, entire_canvas_small, cursor_info],
+ axis=-1) # [N, raster_size, raster_size, 6/10]
+ batch_input_local = tf.concat([patch_photo, patch_canvas], axis=-1) # [N, raster_size, raster_size, 2/4]
+ batch_input_global = tf.concat([entire_photo_small, entire_canvas_small, cursor_info],
+ axis=-1) # [N, raster_size, raster_size, 4/6]
+
+ if self.hps.model_mode == 'train':
+ is_training = True
+ dropout_keep_prob = self.hps.pix_drop_kp
+ else:
+ is_training = False
+ dropout_keep_prob = 1.0
+
+ if self.hps.add_coordconv:
+ batch_input_combined = self.add_coords(batch_input_combined) # (N, in_H, in_W, in_dim + 2)
+ batch_input_local = self.add_coords(batch_input_local) # (N, in_H, in_W, in_dim + 2)
+ batch_input_global = self.add_coords(batch_input_global) # (N, in_H, in_W, in_dim + 2)
+
+ if 'combine' in self.hps.encoder_type:
+ if self.hps.encoder_type == 'combine33':
+ image_embedding, _ = generative_cnn_c3_encoder_combine33(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'combine43':
+ image_embedding, _ = generative_cnn_c3_encoder_combine43(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'combine53':
+ image_embedding, _ = generative_cnn_c3_encoder_combine53(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'combineFC':
+ image_embedding, _ = generative_cnn_c3_encoder_combineFC(batch_input_local, batch_input_global,
+ is_training, dropout_keep_prob) # (N, 256)
+ else:
+ raise Exception('Unknown encoder_type', self.hps.encoder_type)
+ else:
+ with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+ if self.hps.encoder_type == 'conv10':
+ image_embedding, _ = generative_cnn_encoder(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv10_deep':
+ image_embedding, _ = generative_cnn_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob) # (N, 512)
+ elif self.hps.encoder_type == 'conv13':
+ image_embedding, _ = generative_cnn_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv10_c3':
+ image_embedding, _ = generative_cnn_c3_encoder(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv10_deep_c3':
+ image_embedding, _ = generative_cnn_c3_encoder_deeper(batch_input_combined, is_training, dropout_keep_prob) # (N, 512)
+ elif self.hps.encoder_type == 'conv13_c3':
+ image_embedding, _ = generative_cnn_c3_encoder_deeper13(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ elif self.hps.encoder_type == 'conv13_c3_attn':
+ image_embedding, _ = generative_cnn_c3_encoder_deeper13_attn(batch_input_combined, is_training, dropout_keep_prob) # (N, 128)
+ else:
+ raise Exception('Unknown encoder_type', self.hps.encoder_type)
+ return image_embedding
+
+ def build_seq_decoder(self, dec_cell, actual_input_x, initial_state):
+ rnn_output, last_state = self.rnn_decoder(dec_cell, initial_state, actual_input_x)
+ rnn_output_flat = tf.reshape(rnn_output, [-1, self.hps.dec_rnn_size])
+
+ pen_n_out = 2
+ params_n_out = 6
+
+ with tf.variable_scope('DEC_RNN_out_pen', reuse=tf.AUTO_REUSE):
+ output_w_pen = tf.get_variable('output_w', [self.hps.dec_rnn_size, pen_n_out])
+ output_b_pen = tf.get_variable('output_b', [pen_n_out], initializer=tf.constant_initializer(0.0))
+ output_pen = tf.nn.xw_plus_b(rnn_output_flat, output_w_pen, output_b_pen) # (N, pen_n_out)
+
+ with tf.variable_scope('DEC_RNN_out_params', reuse=tf.AUTO_REUSE):
+ output_w_params = tf.get_variable('output_w', [self.hps.dec_rnn_size, params_n_out])
+ output_b_params = tf.get_variable('output_b', [params_n_out], initializer=tf.constant_initializer(0.0))
+ output_params = tf.nn.xw_plus_b(rnn_output_flat, output_w_params, output_b_params) # (N, params_n_out)
+
+ output = tf.concat([output_pen, output_params], axis=1) # (N, n_out)
+
+ return output, last_state
+
+ def get_mixture_coef(self, outputs):
+ z = outputs
+ z_pen_logits = z[:, 0:2] # (N, 2), pen states
+ z_other_params_logits = z[:, 2:] # (N, 6)
+
+ z_pen = tf.nn.softmax(z_pen_logits) # (N, 2)
+ if self.hps.position_format == 'abs':
+ x1y1 = tf.nn.sigmoid(z_other_params_logits[:, 0:2]) # (N, 2)
+ x2y2 = tf.tanh(z_other_params_logits[:, 2:4]) # (N, 2)
+ widths = tf.nn.sigmoid(z_other_params_logits[:, 4:5]) # (N, 1)
+ widths = tf.add(tf.multiply(widths, 1.0 - self.hps.min_width), self.hps.min_width)
+ scaling = tf.nn.sigmoid(z_other_params_logits[:, 5:6]) * self.hps.max_scaling # (N, 1), [0.0, max_scaling]
+ # scaling = tf.add(tf.multiply(scaling, (self.hps.max_scaling - self.hps.min_scaling) / self.hps.max_scaling),
+ # self.hps.min_scaling)
+ z_other_params = tf.concat([x1y1, x2y2, widths, scaling], axis=-1) # (N, 6)
+ else: # "rel"
+ raise Exception('Unknown position_format', self.hps.position_format)
+
+ r = [z_other_params, z_pen]
+ return r
+
+ ###########################
+
+ def get_decoder_inputs(self):
+ initial_state = self.dec_cell.zero_state(batch_size=self.hps.batch_size, dtype=tf.float32)
+ return initial_state
+
+ def rnn_decoder(self, dec_cell, initial_state, actual_input_x):
+ with tf.variable_scope("RNN_DEC", reuse=tf.AUTO_REUSE):
+ output, last_state = tf.nn.dynamic_rnn(
+ dec_cell,
+ actual_input_x,
+ initial_state=initial_state,
+ time_major=False,
+ swap_memory=True,
+ dtype=tf.float32)
+ return output, last_state
+
+ ###########################
+
+ def image_padding(self, ori_image, window_size, pad_value):
+ """
+ Pad with (bg)
+ :param ori_image:
+ :return:
+ """
+ paddings = [[0, 0],
+ [window_size // 2, window_size // 2],
+ [window_size // 2, window_size // 2],
+ [0, 0]]
+ pad_img = tf.pad(ori_image, paddings=paddings, mode='CONSTANT', constant_values=pad_value) # (N, H_p, W_p, k)
+ return pad_img
+
+ def image_cropping_fn(self, fn_inputs):
+ """
+ crop the patch
+ :return:
+ """
+ index_offset = self.hps.input_channel - 1
+ input_image = fn_inputs[:, :, 0:2 + index_offset] # (image_size, image_size, 2), [0.0-BG, 1.0-stroke]
+ cursor_pos = fn_inputs[0, 0, 2 + index_offset:4 + index_offset] # (2), in [0.0, 1.0)
+ image_size = fn_inputs[0, 0, 4 + index_offset] # (), float32
+ window_size = tf.cast(fn_inputs[0, 0, 5 + index_offset], tf.int32) # ()
+
+ input_img_reshape = tf.expand_dims(input_image, axis=0)
+ pad_img = self.image_padding(input_img_reshape, window_size, pad_value=0.0)
+
+ cursor_pos = tf.cast(tf.round(tf.multiply(cursor_pos, image_size)), dtype=tf.int32)
+ x0, x1 = cursor_pos[0], cursor_pos[0] + window_size # ()
+ y0, y1 = cursor_pos[1], cursor_pos[1] + window_size # ()
+ patch_image = pad_img[:, y0:y1, x0:x1, :] # (1, window_size, window_size, 2/4)
+
+ # resize to raster_size
+ patch_image_scaled = tf.image.resize_images(patch_image, (self.hps.raster_size, self.hps.raster_size),
+ method=tf.image.ResizeMethod.AREA)
+ patch_image_scaled = tf.squeeze(patch_image_scaled, axis=0)
+ # patch_canvas_scaled: (raster_size, raster_size, 2/4), [0.0-BG, 1.0-stroke]
+
+ return patch_image_scaled
+
+ def image_cropping(self, cursor_position, input_img, image_size, window_sizes):
+ """
+ :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+ :param input_img: (N, image_size, image_size, 2/4), [0.0-BG, 1.0-stroke]
+ :param window_sizes: (N, 1, 1), float32, with grad
+ """
+ input_img_ = input_img
+ window_sizes_non_grad = tf.stop_gradient(tf.round(window_sizes)) # (N, 1, 1), no grad
+
+ cursor_position_ = tf.reshape(cursor_position, (-1, 1, 1, 2)) # (N, 1, 1, 2)
+ cursor_position_ = tf.tile(cursor_position_, [1, image_size, image_size, 1]) # (N, image_size, image_size, 2)
+
+ image_size_ = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1)) # (1, 1, 1, 1)
+ image_size_ = tf.tile(image_size_, [self.hps.batch_size // self.total_loop, image_size, image_size, 1])
+
+ window_sizes_ = tf.reshape(window_sizes_non_grad, (-1, 1, 1, 1)) # (N, 1, 1, 1)
+ window_sizes_ = tf.tile(window_sizes_, [1, image_size, image_size, 1]) # (N, image_size, image_size, 1)
+
+ fn_inputs = tf.concat([input_img_, cursor_position_, image_size_, window_sizes_],
+ axis=-1) # (N, image_size, image_size, 2/4 + 4)
+ curr_patch_imgs = tf.map_fn(self.image_cropping_fn, fn_inputs, parallel_iterations=32) # (N, raster_size, raster_size, -)
+ return curr_patch_imgs
+
+ def image_cropping_v3(self, cursor_position, input_img, image_size, window_sizes):
+ """
+ :param cursor_position: (N, 1, 2), float type, in size [0.0, 1.0)
+ :param input_img: (N, image_size, image_size, k), [0.0-BG, 1.0-stroke]
+ :param window_sizes: (N, 1, 1), float32, with grad
+ """
+ window_sizes_non_grad = tf.stop_gradient(window_sizes) # (N, 1, 1), no grad
+
+ cursor_pos = tf.multiply(cursor_position, tf.cast(image_size, tf.float32))
+ cursor_x, cursor_y = tf.split(cursor_pos, 2, axis=-1) # (N, 1, 1)
+
+ y1 = cursor_y - (window_sizes_non_grad - 1.0) / 2
+ x1 = cursor_x - (window_sizes_non_grad - 1.0) / 2
+ y2 = y1 + (window_sizes_non_grad - 1.0)
+ x2 = x1 + (window_sizes_non_grad - 1.0)
+ boxes = tf.concat([y1, x1, y2, x2], axis=-1) # (N, 1, 4)
+ boxes = tf.squeeze(boxes, axis=1) # (N, 4)
+ boxes = boxes / tf.cast(image_size - 1, tf.float32)
+
+ box_ind = tf.ones_like(cursor_x)[:, 0, 0] # (N)
+ box_ind = tf.cast(box_ind, dtype=tf.int32)
+ box_ind = tf.cumsum(box_ind) - 1
+
+ curr_patch_imgs = tf.image.crop_and_resize(input_img, boxes, box_ind,
+ crop_size=[self.hps.raster_size, self.hps.raster_size])
+ # (N, raster_size, raster_size, k), [0.0-BG, 1.0-stroke]
+ return curr_patch_imgs
+
+ def get_pixel_value(self, img, x, y):
+ """
+ Utility function to get pixel value for coordinate vectors x and y from a 4D tensor image.
+
+ Input
+ -----
+ - img: tensor of shape (B, H, W, C)
+ - x: flattened tensor of shape (B, H', W')
+ - y: flattened tensor of shape (B, H', W')
+
+ Returns
+ -------
+ - output: tensor of shape (B, H', W', C)
+ """
+ shape = tf.shape(x)
+ batch_size = shape[0]
+ height = shape[1]
+ width = shape[2]
+
+ batch_idx = tf.range(0, batch_size)
+ batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))
+ b = tf.tile(batch_idx, (1, height, width))
+
+ indices = tf.stack([b, y, x], 3)
+
+ return tf.gather_nd(img, indices)
+
+ def image_pasting_nondiff_single(self, fn_inputs):
+ patch_image = fn_inputs[:, :, 0:1] # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+ cursor_pos = fn_inputs[0, 0, 1:3] # (2), in large size
+ image_size = tf.cast(fn_inputs[0, 0, 3], tf.int32) # ()
+ window_size = tf.cast(fn_inputs[0, 0, 4], tf.int32) # ()
+
+ patch_image_scaled = tf.expand_dims(patch_image, axis=0) # (1, raster_size, raster_size, 1)
+ patch_image_scaled = tf.image.resize_images(patch_image_scaled, (window_size, window_size),
+ method=tf.image.ResizeMethod.BILINEAR)
+ patch_image_scaled = tf.squeeze(patch_image_scaled, axis=0)
+ # patch_canvas_scaled: (window_size, window_size, 1)
+
+ cursor_pos = tf.cast(tf.round(cursor_pos), dtype=tf.int32) # (2)
+ cursor_x, cursor_y = cursor_pos[0], cursor_pos[1]
+
+ pad_up = cursor_y
+ pad_down = image_size - cursor_y
+ pad_left = cursor_x
+ pad_right = image_size - cursor_x
+
+ paddings = [[pad_up, pad_down],
+ [pad_left, pad_right],
+ [0, 0]]
+ pad_img = tf.pad(patch_image_scaled, paddings=paddings, mode='CONSTANT',
+ constant_values=0.0) # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+ crop_start = window_size // 2
+ pasted_image = pad_img[crop_start: crop_start + image_size, crop_start: crop_start + image_size, :]
+ return pasted_image
+
+ def image_pasting_diff_single(self, fn_inputs):
+ patch_canvas = fn_inputs[:, :, 0:1] # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+ cursor_pos = fn_inputs[0, 0, 1:3] # (2), in large size
+ image_size = tf.cast(fn_inputs[0, 0, 3], tf.int32) # ()
+ window_size = tf.cast(fn_inputs[0, 0, 4], tf.int32) # ()
+ cursor_x, cursor_y = cursor_pos[0], cursor_pos[1]
+
+ patch_canvas_scaled = tf.expand_dims(patch_canvas, axis=0) # (1, raster_size, raster_size, 1)
+ patch_canvas_scaled = tf.image.resize_images(patch_canvas_scaled, (window_size, window_size),
+ method=tf.image.ResizeMethod.BILINEAR)
+ # patch_canvas_scaled: (1, window_size, window_size, 1)
+
+ valid_canvas = self.image_pasting_diff_batch(patch_canvas_scaled,
+ tf.expand_dims(tf.expand_dims(cursor_pos, axis=0), axis=0),
+ window_size)
+ valid_canvas = tf.squeeze(valid_canvas, axis=0)
+ # (window_size + 1, window_size + 1, 1)
+
+ pad_up = tf.cast(tf.floor(cursor_y), tf.int32)
+ pad_down = image_size - 1 - tf.cast(tf.floor(cursor_y), tf.int32)
+ pad_left = tf.cast(tf.floor(cursor_x), tf.int32)
+ pad_right = image_size - 1 - tf.cast(tf.floor(cursor_x), tf.int32)
+
+ paddings = [[pad_up, pad_down],
+ [pad_left, pad_right],
+ [0, 0]]
+ pad_img = tf.pad(valid_canvas, paddings=paddings, mode='CONSTANT',
+ constant_values=0.0) # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+ crop_start = window_size // 2
+ pasted_image = pad_img[crop_start: crop_start + image_size, crop_start: crop_start + image_size, :]
+ return pasted_image
+
+ def image_pasting_diff_single_v3(self, fn_inputs):
+ patch_canvas = fn_inputs[:, :, 0:1] # (raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+ cursor_pos_a = fn_inputs[0, 0, 1:3] # (2), float32, in large size
+ image_size_a = tf.cast(fn_inputs[0, 0, 3], tf.int32) # ()
+ window_size_a = fn_inputs[0, 0, 4] # (), float32, with grad
+ raster_size_a = float(self.hps.raster_size)
+
+ padding_size = tf.cast(tf.ceil(window_size_a / 2.0), tf.int32)
+
+ x1y1_a = cursor_pos_a - window_size_a / 2.0 # (2), float32
+ x2y2_a = cursor_pos_a + window_size_a / 2.0 # (2), float32
+
+ x1y1_a_floor = tf.floor(x1y1_a) # (2)
+ x2y2_a_ceil = tf.ceil(x2y2_a) # (2)
+
+ cursor_pos_b_oricoord = (x1y1_a_floor + x2y2_a_ceil) / 2.0 # (2)
+ cursor_pos_b = (cursor_pos_b_oricoord - x1y1_a) / window_size_a * raster_size_a # (2)
+ raster_size_b = (x2y2_a_ceil - x1y1_a_floor) # (x, y)
+ image_size_b = raster_size_a
+ window_size_b = raster_size_a * (raster_size_b / window_size_a) # (x, y)
+
+ cursor_b_x, cursor_b_y = tf.split(cursor_pos_b, 2, axis=-1) # (1)
+
+ y1_b = cursor_b_y - (window_size_b[1] - 1.) / 2.
+ x1_b = cursor_b_x - (window_size_b[0] - 1.) / 2.
+ y2_b = y1_b + (window_size_b[1] - 1.)
+ x2_b = x1_b + (window_size_b[0] - 1.)
+ boxes_b = tf.concat([y1_b, x1_b, y2_b, x2_b], axis=-1) # (4)
+ boxes_b = boxes_b / tf.cast(image_size_b - 1, tf.float32) # with grad to window_size_a
+
+ box_ind_b = tf.ones((1), dtype=tf.int32) # (1)
+ box_ind_b = tf.cumsum(box_ind_b) - 1
+
+ patch_canvas = tf.expand_dims(patch_canvas, axis=0) # (1, raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+ boxes_b = tf.expand_dims(boxes_b, axis=0) # (1, 4)
+
+ valid_canvas = tf.image.crop_and_resize(patch_canvas, boxes_b, box_ind_b,
+ crop_size=[raster_size_b[1], raster_size_b[0]])
+ valid_canvas = valid_canvas[0] # (raster_size_b, raster_size_b, 1)
+
+ pad_up = tf.cast(x1y1_a_floor[1], tf.int32) + padding_size
+ pad_down = image_size_a + padding_size - tf.cast(x2y2_a_ceil[1], tf.int32)
+ pad_left = tf.cast(x1y1_a_floor[0], tf.int32) + padding_size
+ pad_right = image_size_a + padding_size - tf.cast(x2y2_a_ceil[0], tf.int32)
+
+ paddings = [[pad_up, pad_down],
+ [pad_left, pad_right],
+ [0, 0]]
+ pad_img = tf.pad(valid_canvas, paddings=paddings, mode='CONSTANT',
+ constant_values=0.0) # (H_p, W_p, 1), [0.0-BG, 1.0-stroke]
+
+ pasted_image = pad_img[padding_size: padding_size + image_size_a, padding_size: padding_size + image_size_a, :]
+ return pasted_image
+
+ def image_pasting_diff_batch(self, patch_image, cursor_position, window_size):
+ """
+ :param patch_img: (N, window_size, window_size, 1), [0.0-BG, 1.0-stroke]
+ :param cursor_position: (N, 1, 2), in large size
+ :return:
+ """
+ paddings1 = [[0, 0],
+ [1, 1],
+ [1, 1],
+ [0, 0]]
+ patch_image_pad1 = tf.pad(patch_image, paddings=paddings1, mode='CONSTANT',
+ constant_values=0.0) # (N, window_size+2, window_size+2, 1), [0.0-BG, 1.0-stroke]
+
+ cursor_x, cursor_y = cursor_position[:, :, 0:1], cursor_position[:, :, 1:2] # (N, 1, 1)
+ cursor_x_f, cursor_y_f = tf.floor(cursor_x), tf.floor(cursor_y)
+ patch_x, patch_y = 1.0 - (cursor_x - cursor_x_f), 1.0 - (cursor_y - cursor_y_f) # (N, 1, 1)
+
+ x_ones = tf.ones_like(patch_x, dtype=tf.float32) # (N, 1, 1)
+ x_ones = tf.tile(x_ones, [1, 1, window_size]) # (N, 1, window_size)
+ patch_x = tf.concat([patch_x, x_ones], axis=-1) # (N, 1, window_size + 1)
+ patch_x = tf.tile(patch_x, [1, window_size + 1, 1]) # (N, window_size + 1, window_size + 1)
+ patch_x = tf.cumsum(patch_x, axis=-1) # (N, window_size + 1, window_size + 1)
+ patch_x0 = tf.cast(tf.floor(patch_x), tf.int32) # (N, window_size + 1, window_size + 1)
+ patch_x1 = patch_x0 + 1 # (N, window_size + 1, window_size + 1)
+
+ y_ones = tf.ones_like(patch_y, dtype=tf.float32) # (N, 1, 1)
+ y_ones = tf.tile(y_ones, [1, window_size, 1]) # (N, window_size, 1)
+ patch_y = tf.concat([patch_y, y_ones], axis=1) # (N, window_size + 1, 1)
+ patch_y = tf.tile(patch_y, [1, 1, window_size + 1]) # (N, window_size + 1, window_size + 1)
+ patch_y = tf.cumsum(patch_y, axis=1) # (N, window_size + 1, window_size + 1)
+ patch_y0 = tf.cast(tf.floor(patch_y), tf.int32) # (N, window_size + 1, window_size + 1)
+ patch_y1 = patch_y0 + 1 # (N, window_size + 1, window_size + 1)
+
+ # get pixel value at corner coords
+ valid_canvas_patch_a = self.get_pixel_value(patch_image_pad1, patch_x0, patch_y0)
+ valid_canvas_patch_b = self.get_pixel_value(patch_image_pad1, patch_x0, patch_y1)
+ valid_canvas_patch_c = self.get_pixel_value(patch_image_pad1, patch_x1, patch_y0)
+ valid_canvas_patch_d = self.get_pixel_value(patch_image_pad1, patch_x1, patch_y1)
+ # (N, window_size + 1, window_size + 1, 1)
+
+ patch_x0 = tf.cast(patch_x0, tf.float32)
+ patch_x1 = tf.cast(patch_x1, tf.float32)
+ patch_y0 = tf.cast(patch_y0, tf.float32)
+ patch_y1 = tf.cast(patch_y1, tf.float32)
+
+ # calculate deltas
+ wa = (patch_x1 - patch_x) * (patch_y1 - patch_y)
+ wb = (patch_x1 - patch_x) * (patch_y - patch_y0)
+ wc = (patch_x - patch_x0) * (patch_y1 - patch_y)
+ wd = (patch_x - patch_x0) * (patch_y - patch_y0)
+ # (N, window_size + 1, window_size + 1)
+
+ # add dimension for addition
+ wa = tf.expand_dims(wa, axis=3)
+ wb = tf.expand_dims(wb, axis=3)
+ wc = tf.expand_dims(wc, axis=3)
+ wd = tf.expand_dims(wd, axis=3)
+ # (N, window_size + 1, window_size + 1, 1)
+
+ # compute output
+ valid_canvas_patch_ = tf.add_n([wa * valid_canvas_patch_a,
+ wb * valid_canvas_patch_b,
+ wc * valid_canvas_patch_c,
+ wd * valid_canvas_patch_d]) # (N, window_size + 1, window_size + 1, 1)
+ return valid_canvas_patch_
+
+ def image_pasting(self, cursor_position_norm, patch_img, image_size, window_sizes, is_differentiable=False):
+ """
+ paste the patch_img to padded size based on cursor_position
+ :param cursor_position_norm: (N, 1, 2), float type, in size [0.0, 1.0)
+ :param patch_img: (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+ :param window_sizes: (N, 1, 1), float32, with grad
+ :return:
+ """
+ cursor_position = tf.multiply(cursor_position_norm, tf.cast(image_size, tf.float32)) # in large size
+ window_sizes_r = tf.round(window_sizes) # (N, 1, 1), no grad
+
+ patch_img_ = tf.expand_dims(patch_img, axis=-1) # (N, raster_size, raster_size, 1)
+ cursor_position_step = tf.reshape(cursor_position, (-1, 1, 1, 2)) # (N, 1, 1, 2)
+ cursor_position_step = tf.tile(cursor_position_step, [1, self.hps.raster_size, self.hps.raster_size,
+ 1]) # (N, raster_size, raster_size, 2)
+ image_size_tile = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1)) # (N, 1, 1, 1)
+ image_size_tile = tf.tile(image_size_tile, [self.hps.batch_size // self.total_loop, self.hps.raster_size,
+ self.hps.raster_size, 1])
+ window_sizes_tile = tf.reshape(window_sizes_r, (-1, 1, 1, 1)) # (N, 1, 1, 1)
+ window_sizes_tile = tf.tile(window_sizes_tile, [1, self.hps.raster_size, self.hps.raster_size, 1])
+
+ pasting_inputs = tf.concat([patch_img_, cursor_position_step, image_size_tile, window_sizes_tile],
+ axis=-1) # (N, raster_size, raster_size, 5)
+
+ if is_differentiable:
+ curr_paste_imgs = tf.map_fn(self.image_pasting_diff_single, pasting_inputs,
+ parallel_iterations=32) # (N, image_size, image_size, 1)
+ else:
+ curr_paste_imgs = tf.map_fn(self.image_pasting_nondiff_single, pasting_inputs,
+ parallel_iterations=32) # (N, image_size, image_size, 1)
+ curr_paste_imgs = tf.squeeze(curr_paste_imgs, axis=-1) # (N, image_size, image_size)
+ return curr_paste_imgs
+
+ def image_pasting_v3(self, cursor_position_norm, patch_img, image_size, window_sizes, is_differentiable=False):
+ """
+ paste the patch_img to padded size based on cursor_position
+ :param cursor_position_norm: (N, 1, 2), float type, in size [0.0, 1.0)
+ :param patch_img: (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+ :param window_sizes: (N, 1, 1), float32, with grad
+ :return:
+ """
+ cursor_position = tf.multiply(cursor_position_norm, tf.cast(image_size, tf.float32)) # in large size
+
+ if is_differentiable:
+ patch_img_ = tf.expand_dims(patch_img, axis=-1) # (N, raster_size, raster_size, 1)
+ cursor_position_step = tf.reshape(cursor_position, (-1, 1, 1, 2)) # (N, 1, 1, 2)
+ cursor_position_step = tf.tile(cursor_position_step, [1, self.hps.raster_size, self.hps.raster_size,
+ 1]) # (N, raster_size, raster_size, 2)
+ image_size_tile = tf.reshape(tf.cast(image_size, tf.float32), (1, 1, 1, 1)) # (N, 1, 1, 1)
+ image_size_tile = tf.tile(image_size_tile, [self.hps.batch_size // self.total_loop, self.hps.raster_size,
+ self.hps.raster_size, 1])
+ window_sizes_tile = tf.reshape(window_sizes, (-1, 1, 1, 1)) # (N, 1, 1, 1)
+ window_sizes_tile = tf.tile(window_sizes_tile, [1, self.hps.raster_size, self.hps.raster_size, 1])
+
+ pasting_inputs = tf.concat([patch_img_, cursor_position_step, image_size_tile, window_sizes_tile],
+ axis=-1) # (N, raster_size, raster_size, 5)
+ curr_paste_imgs = tf.map_fn(self.image_pasting_diff_single_v3, pasting_inputs,
+ parallel_iterations=32) # (N, image_size, image_size, 1)
+ else:
+ raise Exception('Unfinished...')
+ curr_paste_imgs = tf.squeeze(curr_paste_imgs, axis=-1) # (N, image_size, image_size)
+ return curr_paste_imgs
+
+ def get_points_and_raster_image(self, initial_state, init_cursor, input_photo, image_size):
+ ## generate the other_params and pen_ras and raster image for raster loss
+ prev_state = initial_state # (N, dec_rnn_size * 3)
+
+ prev_width = self.init_width # (1)
+ prev_width = tf.expand_dims(tf.expand_dims(prev_width, axis=0), axis=0) # (1, 1, 1)
+ prev_width = tf.tile(prev_width, [self.hps.batch_size // self.total_loop, 1, 1]) # (N, 1, 1)
+
+ prev_scaling = tf.ones((self.hps.batch_size // self.total_loop, 1, 1)) # (N, 1, 1)
+ prev_window_size = tf.ones((self.hps.batch_size // self.total_loop, 1, 1),
+ dtype=tf.float32) * float(self.hps.raster_size) # (N, 1, 1)
+
+ cursor_position_temp = init_cursor
+ self.cursor_position = cursor_position_temp # (N, 1, 2), in size [0.0, 1.0)
+ cursor_position_loop = self.cursor_position
+
+ other_params_list = []
+ pen_ras_list = []
+
+ pos_before_max_min_list = []
+ win_size_before_max_min_list = []
+
+ curr_canvas_soft = tf.zeros_like(input_photo[:, :, :, 0]) # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+ curr_canvas_soft_rgb = tf.tile(tf.zeros_like(input_photo[:, :, :, 0:1]), [1, 1, 1, 3]) # (N, image_size, image_size, 3), [0.0-BG, 1.0-stroke]
+ curr_canvas_hard = tf.zeros_like(curr_canvas_soft) # [0.0-BG, 1.0-stroke]
+
+ #### sampling part - start ####
+ self.curr_canvas_hard = curr_canvas_hard
+
+ rasterizor_st = NeuralRasterizorStep(
+ raster_size=self.hps.raster_size,
+ position_format=self.hps.position_format)
+
+ if self.hps.cropping_type == 'v3':
+ cropping_func = self.image_cropping_v3
+ # elif self.hps.cropping_type == 'v2':
+ # cropping_func = self.image_cropping
+ else:
+ raise Exception('Unknown cropping_type', self.hps.cropping_type)
+
+ if self.hps.pasting_type == 'v3':
+ pasting_func = self.image_pasting_v3
+ # elif self.hps.pasting_type == 'v2':
+ # pasting_func = self.image_pasting
+ else:
+ raise Exception('Unknown pasting_type', self.hps.pasting_type)
+
+ for time_i in range(self.hps.max_seq_len):
+ cursor_position_non_grad = tf.stop_gradient(cursor_position_loop) # (N, 1, 2), in size [0.0, 1.0)
+
+ curr_window_size = tf.multiply(prev_scaling, tf.stop_gradient(prev_window_size)) # float, with grad
+ curr_window_size = tf.maximum(curr_window_size, tf.cast(self.hps.min_window_size, tf.float32))
+ curr_window_size = tf.minimum(curr_window_size, tf.cast(image_size, tf.float32))
+
+ ## patch-level encoding
+ # Here, we make the gradients from canvas_z to curr_canvas_hard be None to avoid recurrent gradient propagation.
+ curr_canvas_hard_non_grad = tf.stop_gradient(self.curr_canvas_hard)
+ curr_canvas_hard_non_grad = tf.expand_dims(curr_canvas_hard_non_grad, axis=-1)
+
+ # input_photo: (N, image_size, image_size, 1/3), [0.0-stroke, 1.0-BG]
+ crop_inputs = tf.concat([1.0 - input_photo, curr_canvas_hard_non_grad], axis=-1) # (N, H_p, W_p, 1/3+1)
+
+ cropped_outputs = cropping_func(cursor_position_non_grad, crop_inputs, image_size, curr_window_size)
+ index_offset = self.hps.input_channel - 1
+ curr_patch_inputs = cropped_outputs[:, :, :, 0:1 + index_offset] # [0.0-BG, 1.0-stroke]
+ curr_patch_canvas_hard_non_grad = cropped_outputs[:, :, :, 1 + index_offset:2 + index_offset]
+ # (N, raster_size, raster_size, 1), [0.0-BG, 1.0-stroke]
+
+ curr_patch_inputs = 1.0 - curr_patch_inputs # [0.0-stroke, 1.0-BG]
+ curr_patch_inputs = self.normalize_image_m1to1(curr_patch_inputs)
+ # (N, raster_size, raster_size, 1/3), [-1.0-stroke, 1.0-BG]
+
+ # Normalizing image
+ curr_patch_canvas_hard_non_grad = 1.0 - curr_patch_canvas_hard_non_grad # [0.0-stroke, 1.0-BG]
+ curr_patch_canvas_hard_non_grad = self.normalize_image_m1to1(curr_patch_canvas_hard_non_grad) # [-1.0-stroke, 1.0-BG]
+
+ ## image-level encoding
+ combined_z = self.build_combined_encoder(
+ curr_patch_canvas_hard_non_grad,
+ curr_patch_inputs,
+ 1.0 - curr_canvas_hard_non_grad,
+ input_photo,
+ cursor_position_non_grad,
+ image_size,
+ curr_window_size) # (N, z_size)
+ combined_z = tf.expand_dims(combined_z, axis=1) # (N, 1, z_size)
+
+ curr_window_size_top_side_norm_non_grad = \
+ tf.stop_gradient(curr_window_size / tf.cast(image_size, tf.float32))
+ curr_window_size_bottom_side_norm_non_grad = \
+ tf.stop_gradient(curr_window_size / tf.cast(self.hps.min_window_size, tf.float32))
+ if not self.hps.concat_win_size:
+ combined_z = tf.concat([tf.stop_gradient(prev_width), combined_z], 2) # (N, 1, 2+z_size)
+ else:
+ combined_z = tf.concat([tf.stop_gradient(prev_width),
+ curr_window_size_top_side_norm_non_grad,
+ curr_window_size_bottom_side_norm_non_grad,
+ combined_z],
+ 2) # (N, 1, 2+z_size)
+
+ if self.hps.concat_cursor:
+ prev_input_x = tf.concat([cursor_position_non_grad, combined_z], 2) # (N, 1, 2+2+z_size)
+ else:
+ prev_input_x = combined_z # (N, 1, 2+z_size)
+
+ h_output, next_state = self.build_seq_decoder(self.dec_cell, prev_input_x, prev_state)
+ # h_output: (N * 1, n_out), next_state: (N, dec_rnn_size * 3)
+ [o_other_params, o_pen_ras] = self.get_mixture_coef(h_output)
+ # o_other_params: (N * 1, 6)
+ # o_pen_ras: (N * 1, 2), after softmax
+
+ o_other_params = tf.reshape(o_other_params, [-1, 1, 6]) # (N, 1, 6)
+ o_pen_ras_raw = tf.reshape(o_pen_ras, [-1, 1, 2]) # (N, 1, 2)
+
+ other_params_list.append(o_other_params)
+ pen_ras_list.append(o_pen_ras_raw)
+
+ #### sampling part - end ####
+
+ if self.hps.model_mode == 'train' or self.hps.model_mode == 'eval' or self.hps.model_mode == 'eval_sample':
+ # use renderer here to convert the strokes to image
+ curr_other_params = tf.squeeze(o_other_params, axis=1) # (N, 6), (x1, y1)=[0.0, 1.0], (x2, y2)=[-1.0, 1.0]
+ x1y1, x2y2, width2, scaling = curr_other_params[:, 0:2], curr_other_params[:, 2:4],\
+ curr_other_params[:, 4:5], curr_other_params[:, 5:6]
+ x0y0 = tf.zeros_like(x2y2) # (N, 2), [-1.0, 1.0]
+ x0y0 = tf.div(tf.add(x0y0, 1.0), 2.0) # (N, 2), [0.0, 1.0]
+ x2y2 = tf.div(tf.add(x2y2, 1.0), 2.0) # (N, 2), [0.0, 1.0]
+ widths = tf.concat([tf.squeeze(prev_width, axis=1), width2], axis=1) # (N, 2)
+ curr_other_params = tf.concat([x0y0, x1y1, x2y2, widths], axis=-1) # (N, 8), (x0, y0)&(x2, y2)=[0.0, 1.0]
+ curr_stroke_image = rasterizor_st.raster_func_stroke_abs(curr_other_params)
+ # (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+
+ curr_stroke_image_large = pasting_func(cursor_position_loop, curr_stroke_image,
+ image_size, curr_window_size,
+ is_differentiable=self.hps.pasting_diff)
+ # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+
+ ## soft
+ if not self.hps.use_softargmax:
+ curr_state_soft = o_pen_ras[:, 1:2] # (N, 1)
+ else:
+ curr_state_soft = self.differentiable_argmax(o_pen_ras, self.hps.soft_beta) # (N, 1)
+
+ curr_state_soft = tf.expand_dims(curr_state_soft, axis=1) # (N, 1, 1)
+
+ filter_curr_stroke_image_soft = tf.multiply(tf.subtract(1.0, curr_state_soft), curr_stroke_image_large)
+ # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+ curr_canvas_soft = tf.add(curr_canvas_soft, filter_curr_stroke_image_soft) # [0.0-BG, 1.0-stroke]
+
+ ## hard
+ curr_state_hard = tf.expand_dims(tf.cast(tf.argmax(o_pen_ras_raw, axis=-1), dtype=tf.float32),
+ axis=-1) # (N, 1, 1)
+ filter_curr_stroke_image_hard = tf.multiply(tf.subtract(1.0, curr_state_hard), curr_stroke_image_large)
+ # (N, image_size, image_size), [0.0-BG, 1.0-stroke]
+ self.curr_canvas_hard = tf.add(self.curr_canvas_hard, filter_curr_stroke_image_hard) # [0.0-BG, 1.0-stroke]
+ self.curr_canvas_hard = tf.clip_by_value(self.curr_canvas_hard, 0.0, 1.0) # [0.0-BG, 1.0-stroke]
+
+ next_width = o_other_params[:, :, 4:5]
+ next_scaling = o_other_params[:, :, 5:6]
+ next_window_size = tf.multiply(next_scaling, tf.stop_gradient(curr_window_size)) # float, with grad
+ window_size_before_max_min = next_window_size # (N, 1, 1), large-level
+ win_size_before_max_min_list.append(window_size_before_max_min)
+ next_window_size = tf.maximum(next_window_size, tf.cast(self.hps.min_window_size, tf.float32))
+ next_window_size = tf.minimum(next_window_size, tf.cast(image_size, tf.float32))
+
+ prev_state = next_state
+ prev_width = next_width * curr_window_size / next_window_size # (N, 1, 1)
+ prev_scaling = next_scaling # (N, 1, 1))
+ prev_window_size = curr_window_size
+
+ # update the cursor position
+ new_cursor_offsets = tf.multiply(o_other_params[:, :, 2:4],
+ tf.divide(curr_window_size, 2.0)) # (N, 1, 2), window-level
+ new_cursor_offset_next = new_cursor_offsets
+ new_cursor_offset_next = tf.concat([new_cursor_offset_next[:, :, 1:2], new_cursor_offset_next[:, :, 0:1]], axis=-1)
+
+ cursor_position_loop_large = tf.multiply(cursor_position_loop, tf.cast(image_size, tf.float32))
+
+ if self.hps.stop_accu_grad:
+ stroke_position_next = tf.stop_gradient(cursor_position_loop_large) + new_cursor_offset_next # (N, 1, 2), large-level
+ else:
+ stroke_position_next = cursor_position_loop_large + new_cursor_offset_next # (N, 1, 2), large-level
+
+ stroke_position_before_max_min = stroke_position_next # (N, 1, 2), large-level
+ pos_before_max_min_list.append(stroke_position_before_max_min)
+
+ if self.hps.cursor_type == 'next':
+ cursor_position_loop_large = stroke_position_next # (N, 1, 2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_position_loop_large = tf.maximum(cursor_position_loop_large, 0.0)
+ cursor_position_loop_large = tf.minimum(cursor_position_loop_large, tf.cast(image_size - 1, tf.float32))
+ cursor_position_loop = tf.div(cursor_position_loop_large, tf.cast(image_size, tf.float32))
+
+ curr_canvas_soft = tf.clip_by_value(curr_canvas_soft, 0.0, 1.0) # (N, raster_size, raster_size), [0.0-BG, 1.0-stroke]
+
+ other_params_ = tf.reshape(tf.concat(other_params_list, axis=1), [-1, 6]) # (N * max_seq_len, 6)
+ pen_ras_ = tf.reshape(tf.concat(pen_ras_list, axis=1), [-1, 2]) # (N * max_seq_len, 2)
+ pos_before_max_min_ = tf.concat(pos_before_max_min_list, axis=1) # (N, max_seq_len, 2)
+ win_size_before_max_min_ = tf.concat(win_size_before_max_min_list, axis=1) # (N, max_seq_len, 1)
+
+ return other_params_, pen_ras_, prev_state, curr_canvas_soft, curr_canvas_soft_rgb, \
+ pos_before_max_min_, win_size_before_max_min_
+
+ def differentiable_argmax(self, input_pen, soft_beta):
+ """
+ Differentiable argmax trick.
+ :param input_pen: (N, n_class)
+ :return: pen_state: (N, 1)
+ """
+ def sign_onehot(x):
+ """
+ :param x: (N, n_class)
+ :return: (N, n_class)
+ """
+ y = tf.sign(tf.reduce_max(x, axis=-1, keepdims=True) - x)
+ y = (y - 1) * (-1)
+ return y
+
+ def softargmax(x, beta=1e2):
+ """
+ :param x: (N, n_class)
+ :param beta: 1e10 is the best. 1e2 is acceptable.
+ :return: (N)
+ """
+ x_range = tf.cumsum(tf.ones_like(x), axis=1) # (N, 2)
+ return tf.reduce_sum(tf.nn.softmax(x * beta) * x_range, axis=1) - 1
+
+ ## Better to use softargmax(beta=1e2). The sign_onehot's gradient is close to zero.
+ # pen_onehot = sign_onehot(input_pen) # one-hot form, (N * max_seq_len, 2)
+ # pen_state = pen_onehot[:, 1:2] # (N * max_seq_len, 1)
+ pen_state = softargmax(input_pen, soft_beta)
+ pen_state = tf.expand_dims(pen_state, axis=1) # (N * max_seq_len, 1)
+ return pen_state
+
+ def build_losses(self, target_sketch, pred_raster_imgs, pred_params,
+ pos_before_max_min, win_size_before_max_min, image_size):
+ def get_raster_loss(pred_imgs, gt_imgs, loss_type):
+ perc_layer_losses_raw = []
+ perc_layer_losses_weighted = []
+ perc_layer_losses_norm = []
+
+ if loss_type == 'l1':
+ ras_cost = tf.reduce_mean(tf.abs(tf.subtract(gt_imgs, pred_imgs))) # ()
+ elif loss_type == 'l1_small':
+ gt_imgs_small = tf.image.resize_images(tf.expand_dims(gt_imgs, axis=3), (32, 32))
+ pred_imgs_small = tf.image.resize_images(tf.expand_dims(pred_imgs, axis=3), (32, 32))
+ ras_cost = tf.reduce_mean(tf.abs(tf.subtract(gt_imgs_small, pred_imgs_small))) # ()
+ elif loss_type == 'mse':
+ ras_cost = tf.reduce_mean(tf.pow(tf.subtract(gt_imgs, pred_imgs), 2)) # ()
+ elif loss_type == 'perceptual':
+ return_map_pred = vgg_net_slim(pred_imgs, image_size)
+ return_map_gt = vgg_net_slim(gt_imgs, image_size)
+ perc_loss_type = 'l1' # [l1, mse]
+ weighted_map = {'ReLU1_1': 100.0, 'ReLU1_2': 100.0,
+ 'ReLU2_1': 100.0, 'ReLU2_2': 100.0,
+ 'ReLU3_1': 10.0, 'ReLU3_2': 10.0, 'ReLU3_3': 10.0,
+ 'ReLU4_1': 1.0, 'ReLU4_2': 1.0, 'ReLU4_3': 1.0,
+ 'ReLU5_1': 1.0, 'ReLU5_2': 1.0, 'ReLU5_3': 1.0}
+
+ for perc_layer in self.hps.perc_loss_layers:
+ if perc_loss_type == 'l1':
+ perc_layer_loss = tf.reduce_mean(tf.abs(tf.subtract(return_map_pred[perc_layer],
+ return_map_gt[perc_layer]))) # ()
+ elif perc_loss_type == 'mse':
+ perc_layer_loss = tf.reduce_mean(tf.pow(tf.subtract(return_map_pred[perc_layer],
+ return_map_gt[perc_layer]), 2)) # ()
+ else:
+ raise NameError('Unknown perceptual loss type:', perc_loss_type)
+ perc_layer_losses_raw.append(perc_layer_loss)
+
+ assert perc_layer in weighted_map
+ perc_layer_losses_weighted.append(perc_layer_loss * weighted_map[perc_layer])
+
+ for loop_i in range(len(self.hps.perc_loss_layers)):
+ perc_relu_loss_raw = perc_layer_losses_raw[loop_i] # ()
+
+ if self.hps.model_mode == 'train':
+ curr_relu_mean = (self.perc_loss_mean_list[loop_i] * self.last_step_num + perc_relu_loss_raw) / (self.last_step_num + 1.0)
+ relu_cost_norm = perc_relu_loss_raw / curr_relu_mean
+ else:
+ relu_cost_norm = perc_relu_loss_raw
+ perc_layer_losses_norm.append(relu_cost_norm)
+
+ perc_layer_losses_raw = tf.stack(perc_layer_losses_raw, axis=0)
+ perc_layer_losses_norm = tf.stack(perc_layer_losses_norm, axis=0)
+
+ if self.hps.perc_loss_fuse_type == 'max':
+ ras_cost = tf.reduce_max(perc_layer_losses_norm)
+ elif self.hps.perc_loss_fuse_type == 'add':
+ ras_cost = tf.reduce_mean(perc_layer_losses_norm)
+ elif self.hps.perc_loss_fuse_type == 'raw_add':
+ ras_cost = tf.reduce_mean(perc_layer_losses_raw)
+ elif self.hps.perc_loss_fuse_type == 'weighted_sum':
+ ras_cost = tf.reduce_mean(perc_layer_losses_weighted)
+ else:
+ raise NameError('Unknown perc_loss_fuse_type:', self.hps.perc_loss_fuse_type)
+
+ elif loss_type == 'triplet':
+ raise Exception('Solution for triplet loss is coming soon.')
+ else:
+ raise NameError('Unknown loss type:', loss_type)
+
+ if loss_type != 'perceptual':
+ for perc_layer_i in self.hps.perc_loss_layers:
+ perc_layer_losses_raw.append(tf.constant(0.0))
+ perc_layer_losses_norm.append(tf.constant(0.0))
+
+ perc_layer_losses_raw = tf.stack(perc_layer_losses_raw, axis=0)
+ perc_layer_losses_norm = tf.stack(perc_layer_losses_norm, axis=0)
+
+ return ras_cost, perc_layer_losses_raw, perc_layer_losses_norm
+
+ gt_raster_images = tf.squeeze(target_sketch, axis=3) # (N, raster_h, raster_w), [0.0-stroke, 1.0-BG]
+ raster_cost, perc_relu_losses_raw, perc_relu_losses_norm = \
+ get_raster_loss(pred_raster_imgs, gt_raster_images, loss_type=self.hps.raster_loss_base_type)
+
+ def get_stroke_num_loss(input_strokes):
+ ending_state = input_strokes[:, :, 0] # (N, seq_len)
+ stroke_num_loss_pre = tf.reduce_mean(ending_state) # larger is better, [0.0, 1.0]
+ stroke_num_loss = 1.0 - stroke_num_loss_pre # lower is better, [0.0, 1.0]
+ return stroke_num_loss
+
+ stroke_num_cost = get_stroke_num_loss(pred_params) # lower is better
+
+ def get_pos_outside_loss(pos_before_max_min_):
+ pos_after_max_min = tf.maximum(pos_before_max_min_, 0.0)
+ pos_after_max_min = tf.minimum(pos_after_max_min, tf.cast(image_size - 1, tf.float32)) # (N, max_seq_len, 2)
+ pos_outside_loss = tf.reduce_mean(tf.abs(pos_before_max_min_ - pos_after_max_min))
+ return pos_outside_loss
+
+ pos_outside_cost = get_pos_outside_loss(pos_before_max_min) # lower is better
+
+ def get_win_size_outside_loss(win_size_before_max_min_, min_window_size):
+ win_size_outside_top_loss = tf.divide(
+ tf.maximum(win_size_before_max_min_ - tf.cast(image_size, tf.float32), 0.0),
+ tf.cast(image_size, tf.float32)) # (N, max_seq_len, 1)
+ win_size_outside_bottom_loss = tf.divide(
+ tf.maximum(tf.cast(min_window_size, tf.float32) - win_size_before_max_min_, 0.0),
+ tf.cast(min_window_size, tf.float32)) # (N, max_seq_len, 1)
+ win_size_outside_loss = tf.reduce_mean(win_size_outside_top_loss + win_size_outside_bottom_loss)
+ return win_size_outside_loss
+
+ win_size_outside_cost = get_win_size_outside_loss(win_size_before_max_min, self.hps.min_window_size) # lower is better
+
+ def get_early_pen_states_loss(input_strokes, curr_start, curr_end):
+ # input_strokes: (N, max_seq_len, 7)
+ pred_early_pen_states = input_strokes[:, curr_start:curr_end, 0] # (N, curr_early_len)
+ pred_early_pen_states_min = tf.reduce_min(pred_early_pen_states, axis=1) # (N), should not be 1
+ early_pen_states_loss = tf.reduce_mean(pred_early_pen_states_min) # lower is better
+ return early_pen_states_loss
+
+ early_pen_states_cost = get_early_pen_states_loss(pred_params,
+ self.early_pen_loss_start_idx, self.early_pen_loss_end_idx)
+
+ return raster_cost, stroke_num_cost, pos_outside_cost, win_size_outside_cost, \
+ early_pen_states_cost, \
+ perc_relu_losses_raw, perc_relu_losses_norm
+
+ def build_training_op_split(self, raster_cost, sn_cost, cursor_outside_cost, win_size_outside_cost,
+ early_pen_states_cost):
+ total_cost = self.hps.raster_loss_weight * raster_cost + \
+ self.hps.early_pen_loss_weight * early_pen_states_cost + \
+ self.stroke_num_loss_weight * sn_cost + \
+ self.hps.outside_loss_weight * cursor_outside_cost + \
+ self.hps.win_size_outside_loss_weight * win_size_outside_cost
+
+ tvars = [var for var in tf.trainable_variables()
+ if 'raster_unit' not in var.op.name and 'VGG16' not in var.op.name]
+ gvs = self.optimizer.compute_gradients(total_cost, var_list=tvars)
+ return total_cost, gvs
+
+ def build_training_op(self, grad_list):
+ with tf.variable_scope('train_op', reuse=tf.AUTO_REUSE):
+ gvs = self.average_gradients(grad_list)
+ g = self.hps.grad_clip
+
+ for grad, var in gvs:
+ print('>>', var.op.name)
+ if grad is None:
+ print(' >> None value')
+
+ capped_gvs = [(tf.clip_by_value(grad, -g, g), var) for grad, var in gvs]
+
+ self.train_op = self.optimizer.apply_gradients(
+ capped_gvs, global_step=self.global_step, name='train_step')
+
+ def average_gradients(self, grads_list):
+ """
+ Compute the average gradients.
+ :param grads_list: list(of length N_GPU) of list(grad, var)
+ :return:
+ """
+ avg_grads = []
+ for grad_and_vars in zip(*grads_list):
+ grads = []
+ for g, _ in grad_and_vars:
+ expanded_g = tf.expand_dims(g, 0)
+ grads.append(expanded_g)
+ grad = tf.concat(grads, axis=0)
+ grad = tf.reduce_mean(grad, axis=0)
+
+ v = grad_and_vars[0][1]
+ grad_and_var = (grad, v)
+ avg_grads.append(grad_and_var)
+
+ return avg_grads
\ No newline at end of file
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/datasets/CelebAMask-faces.zip b/outputs/snapshot/virtual_sketching_SIG2021/datasets/CelebAMask-faces.zip
new file mode 100644
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+++ b/outputs/snapshot/virtual_sketching_SIG2021/datasets/CelebAMask-faces.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/datasets/QuickDraw-clean.zip b/outputs/snapshot/virtual_sketching_SIG2021/datasets/QuickDraw-clean.zip
new file mode 100644
index 0000000000000000000000000000000000000000..a13f2a19c3b3f77fdbc5b78eff1153b2121eeef2
--- /dev/null
+++ b/outputs/snapshot/virtual_sketching_SIG2021/datasets/QuickDraw-clean.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/datasets/QuickDraw-rough.zip b/outputs/snapshot/virtual_sketching_SIG2021/datasets/QuickDraw-rough.zip
new file mode 100644
index 0000000000000000000000000000000000000000..46d713ea8ecbcffebe0b98530e0060a0a2e73048
--- /dev/null
+++ b/outputs/snapshot/virtual_sketching_SIG2021/datasets/QuickDraw-rough.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_clean_line_drawings.zip b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_clean_line_drawings.zip
new file mode 100644
index 0000000000000000000000000000000000000000..6a544a989cde05341c066d8bce2fae4e10449ef2
--- /dev/null
+++ b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_clean_line_drawings.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 110155277
diff --git a/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_clean_line_drawings_reg=1.0.zip b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_clean_line_drawings_reg=1.0.zip
new file mode 100644
index 0000000000000000000000000000000000000000..3730140e04095888a739243e5413a9349fc900d8
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+++ b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_clean_line_drawings_reg=1.0.zip
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+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_faces.zip b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_faces.zip
new file mode 100644
index 0000000000000000000000000000000000000000..db664edce56545aa4c97e7e41260ea6f84fd4fa2
--- /dev/null
+++ b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_faces.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_rough_sketches.zip b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_rough_sketches.zip
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--- /dev/null
+++ b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_testing/pretrain_rough_sketches.zip
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+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_training/pretrain_neural_renderer.zip b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_training/pretrain_neural_renderer.zip
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+version https://git-lfs.github.com/spec/v1
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diff --git a/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_training/pretrain_perceptual_model.zip b/outputs/snapshot/virtual_sketching_SIG2021/pretrained_models/used_for_training/pretrain_perceptual_model.zip
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+version https://git-lfs.github.com/spec/v1
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+size 725584042
diff --git a/rasterization_utils/NeuralRenderer.py b/rasterization_utils/NeuralRenderer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca9aedc253d4c53621f49f958ac005954f18792c
--- /dev/null
+++ b/rasterization_utils/NeuralRenderer.py
@@ -0,0 +1,171 @@
+import tensorflow as tf
+
+
+class RasterUnit(object):
+ def __init__(self,
+ raster_size,
+ input_params, # (N, 10)
+ reuse=False):
+ self.raster_size = raster_size
+ self.input_params = input_params
+
+ with tf.variable_scope("raster_unit", reuse=reuse):
+ self.build_unit()
+
+ def build_unit(self):
+ x = self.input_params # (N, 10)
+ x = self.fully_connected(x, 10, 512, scope='fc1') # (N, 512)
+ x = tf.nn.relu(x)
+ x = self.fully_connected(x, 512, 1024, scope='fc2') # (N, 1024)
+ x = tf.nn.relu(x)
+ x = self.fully_connected(x, 1024, 2048, scope='fc3') # (N, 2048)
+ x = tf.nn.relu(x)
+ x = self.fully_connected(x, 2048, 4096, scope='fc4') # (N, 4096)
+ x = tf.nn.relu(x)
+ x = tf.reshape(x, (-1, 16, 16, 16)) # (N, 16, 16, 16)
+ x = tf.transpose(x, (0, 2, 3, 1))
+
+ x = self.conv2d(x, 32, 3, 1, scope='conv1') # (N, 16, 16, 32)
+ x = tf.nn.relu(x)
+ x = self.conv2d(x, 32, 3, 1, scope='conv2') # (N, 16, 16, 32)
+ x = self.pixel_shuffle(x, upscale_factor=2) # (N, 32, 32, 8)
+
+ x = self.conv2d(x, 16, 3, 1, scope='conv3') # (N, 32, 32, 16)
+ x = tf.nn.relu(x)
+ x = self.conv2d(x, 16, 3, 1, scope='conv4') # (N, 32, 32, 16)
+ x = self.pixel_shuffle(x, upscale_factor=2) # (N, 64, 64, 4)
+
+ x = self.conv2d(x, 8, 3, 1, scope='conv5') # (N, 64, 64, 8)
+ x = tf.nn.relu(x)
+ x = self.conv2d(x, 4, 3, 1, scope='conv6') # (N, 64, 64, 4)
+ x = self.pixel_shuffle(x, upscale_factor=2) # (N, 128, 128, 1)
+ x = tf.sigmoid(x)
+
+ # (N, 128, 128), [0.0-stroke, 1.0-BG]
+ self.stroke_image = 1.0 - tf.reshape(x, (-1, self.raster_size, self.raster_size))
+
+ def conv2d(self, input_tensor, out_channels, kernel_size, stride, scope, reuse=False):
+ with tf.variable_scope(scope, reuse=reuse):
+ output_tensor = tf.layers.conv2d(input_tensor, out_channels, kernel_size=kernel_size,
+ strides=(stride, stride),
+ padding="same", kernel_initializer=tf.keras.initializers.he_normal())
+ return output_tensor
+
+ def fully_connected(self, input_tensor, in_dim, out_dim, scope, reuse=False):
+ with tf.variable_scope(scope, reuse=reuse):
+ weight = tf.get_variable("weight", [in_dim, out_dim], dtype=tf.float32,
+ initializer=tf.random_normal_initializer())
+ bias = tf.get_variable("bias", [out_dim], dtype=tf.float32,
+ initializer=tf.random_normal_initializer())
+ output_tensor = tf.matmul(input_tensor, weight) + bias
+ return output_tensor
+
+ def pixel_shuffle(self, input_tensor, upscale_factor):
+ params_shape = input_tensor.get_shape()
+ n, h, w, c = params_shape
+ input_tensor_proc = tf.reshape(input_tensor, (n, h, w, c // 4, 4))
+ input_tensor_proc = tf.transpose(input_tensor_proc, (0, 1, 2, 4, 3))
+ input_tensor_proc = tf.reshape(input_tensor_proc, (n, h, w, -1))
+ output_tensor = tf.depth_to_space(input_tensor_proc, block_size=upscale_factor)
+ return output_tensor
+
+
+class NeuralRasterizor(object):
+ def __init__(self,
+ raster_size,
+ seq_len,
+ position_format='abs',
+ raster_padding=10,
+ strokes_format=3):
+ self.raster_size = raster_size
+ self.seq_len = seq_len
+ self.position_format = position_format
+ self.raster_padding = raster_padding
+ self.strokes_format = strokes_format
+
+ assert position_format in ['abs', 'rel']
+
+ def raster_func_abs(self, input_data, raster_seq_len=None):
+ """
+ x and y in absolute position.
+ :param input_data: (N, seq_len, 10): [x0, y0, x1, y1, x2, y2, r0, r2, w0, w2]. All in [0.0, 1.0]
+ :return:
+ """
+ seq_len = raster_seq_len if raster_seq_len is not None else self.seq_len
+
+ raster_params = tf.transpose(input_data, [1, 0, 2]) # (seq_len, N, 10)
+
+ seq_stroke_images = tf.map_fn(self.stroke_drawer_with_raster_unit, raster_params,
+ parallel_iterations=32) # (seq_len, N, raster_size, raster_size)
+ seq_stroke_images = tf.transpose(seq_stroke_images, [1, 2, 3, 0])
+ # (N, raster_size, raster_size, seq_len), [0.0-stroke, 1.0-BG]
+
+ filter_seq_stroke_images = 1.0 - seq_stroke_images
+ # (N, raster_size, raster_size, seq_len), [0.0-BG, 1.0-stroke]
+
+ # stacking
+ stroke_images_unclip = tf.reduce_sum(filter_seq_stroke_images, axis=-1) # (N, raster_size, raster_size)
+ stroke_images = tf.clip_by_value(stroke_images_unclip, 0.0, 1.0) # [0.0-BG, 1.0-stroke]
+ return stroke_images, stroke_images_unclip, seq_stroke_images
+
+ def stroke_drawer_with_raster_unit(self, params_batch):
+ """
+ Convert two points into a raster stroke image with RasterUnit.
+ :param params_batch: (N, 10)
+ :return: (N, raster_size, raster_size)
+ """
+ raster_unit = RasterUnit(
+ raster_size=self.raster_size,
+ input_params=params_batch,
+ reuse=tf.AUTO_REUSE
+ )
+ stroke_image = raster_unit.stroke_image # (N, raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ return stroke_image
+
+
+class NeuralRasterizorStep(object):
+ def __init__(self,
+ raster_size,
+ position_format='abs'):
+ self.raster_size = raster_size
+ self.position_format = position_format
+
+ assert position_format in ['abs', 'rel']
+
+ def raster_func_stroke_abs(self, input_data):
+ """
+ x and y in absolute position.
+ :param input_data: (N, 8): [x0, y0, x1, y1, x2, y2, r0, r2]. All in [0.0, 1.0]
+ :return:
+ """
+ w_in = tf.ones(shape=(input_data.shape[0], 2), dtype=tf.float32)
+ raster_params = tf.concat([input_data, w_in], axis=-1) # (N, 10)
+ stroke_image = self.stroke_drawer_with_raster_unit(raster_params) # (N, raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ stroke_image = 1.0 - stroke_image # [0.0-BG, 1.0-stroke]
+
+ return stroke_image
+
+ def mask_ending_state(self, input_states):
+ """
+ Mask the ending state to be 1
+ :param input_states: (N, seq_len, 1) in offset manner
+ :param seq_len:
+ :return:
+ """
+ ending_state_accu = tf.cumsum(input_states, axis=1) # (N, seq_len, 1)
+ ending_state_clip = tf.clip_by_value(ending_state_accu, 0.0, 1.0) # (N, seq_len, 1)
+ return ending_state_clip
+
+ def stroke_drawer_with_raster_unit(self, params_batch):
+ """
+ Convert two points into a raster stroke image with RasterUnit.
+ :param params_batch: (N, 10)
+ :return: (N, raster_size, raster_size)
+ """
+ raster_unit = RasterUnit(
+ raster_size=self.raster_size,
+ input_params=params_batch,
+ reuse=tf.AUTO_REUSE
+ )
+ stroke_image = raster_unit.stroke_image # (N, raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ return stroke_image
diff --git a/rasterization_utils/RealRenderer.py b/rasterization_utils/RealRenderer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a6e58df2869ae6a2b768938426434884a1b1209
--- /dev/null
+++ b/rasterization_utils/RealRenderer.py
@@ -0,0 +1,177 @@
+import numpy as np
+import gizeh
+
+
+class GizehRasterizor(object):
+ def __init__(self):
+ self.name = 'GizehRasterizor'
+
+ def get_line_array_v2(self, image_size, seq_strokes, stroke_width, is_bin=True):
+ """
+ :param p1: (x, y)
+ :param p2: (x, y)
+ :return: line_arr: (image_size, image_size), {0, 1}, 0 for BG and 1 for strokes
+ """
+ surface = gizeh.Surface(width=image_size, height=image_size) # in pixels
+ shape_list = []
+ for seq_i in range(len(seq_strokes) - 1):
+ p1, p2 = seq_strokes[seq_i, :2], seq_strokes[seq_i + 1, :2]
+ pen_state = seq_strokes[seq_i, 2]
+
+ if pen_state == 0.0:
+ line = gizeh.polyline(points=[p1, p2], stroke_width=stroke_width, stroke=(1, 1, 1), fill=(0, 0, 0))
+ shape_list.append(line)
+
+ group = gizeh.Group(shape_list)
+ group.draw(surface)
+
+ # Now export the surface
+ line_arr = surface.get_npimage()[:, :, 0] # returns a (width x height x 3) numpy array
+
+ if is_bin:
+ line_arr[line_arr <= 128] = 0
+ line_arr[line_arr != 0] = 1 # (image_size, image_size)
+ else:
+ line_arr = np.array(line_arr, dtype=np.float32) / 255.0
+
+ return line_arr
+
+ def get_line_array(self, p1, p2, image_size, stroke_width, is_bin=True):
+ """
+ :param p1: (x, y)
+ :param p2: (x, y)
+ :return: line_arr: (image_size, image_size), {0, 1}, 0 for BG and 1 for strokes
+ """
+ surface = gizeh.Surface(width=image_size, height=image_size) # in pixels
+ line = gizeh.polyline(points=[p1, p2], stroke_width=stroke_width, stroke=(1, 1, 1), fill=(0, 0, 0))
+ line.draw(surface)
+
+ # Now export the surface
+ line_arr = surface.get_npimage()[:, :, 0] # returns a (width x height x 3) numpy array
+
+ if is_bin:
+ line_arr[line_arr <= 128] = 0
+ line_arr[line_arr != 0] = 1 # (image_size, image_size)
+ else:
+ line_arr = np.array(line_arr, dtype=np.float32) / 255.0
+
+ return line_arr
+
+ def load_sketch_images_on_the_fly_v2(self, image_size, norm_strokes3, stroke_width, is_bin=True):
+ """
+ :param norm_strokes3: list (N_sketches,), each with (N_points, 3)
+ :return: list (N_sketches,), each with (raster_size, raster_size), 0-BG and 1-strokes
+ """
+ assert type(norm_strokes3) is list
+ sketch_imgs_list = []
+ for stroke_i in range(len(norm_strokes3)):
+ seq_strokes3 = norm_strokes3[stroke_i] # (N_points, 3)
+ sketch_img = self.get_line_array_v2(image_size, seq_strokes3, stroke_width=stroke_width, is_bin=is_bin)
+ sketch_img = np.clip(sketch_img, 0.0, 1.0) # (image_size, image_size), 0 for BG and 1 for strokes
+ sketch_imgs_list.append(sketch_img)
+
+ return sketch_imgs_list
+
+ def load_sketch_images_on_the_fly(self, image_size, norm_strokes3, stroke_width, is_bin=True):
+ """
+ :param norm_strokes3: list (N_sketches,), each with (N_points, 3)
+ :return: list (N_sketches,), each with (raster_size, raster_size), 0-BG and 1-strokes
+ """
+ assert type(norm_strokes3) is list
+ sketch_imgs_list = []
+ for stroke_i in range(len(norm_strokes3)):
+ seq_strokes3 = norm_strokes3[stroke_i] # (N_points, 3)
+ seq_len = len(seq_strokes3)
+ stroke_imgs_list = []
+
+ for seq_i in range(seq_len - 1):
+ stroke_img = self.get_line_array(seq_strokes3[seq_i, :2], seq_strokes3[seq_i + 1, :2], image_size,
+ stroke_width=stroke_width, is_bin=is_bin)
+ pen_state = seq_strokes3[seq_i, 2]
+ stroke_img = stroke_img.astype(np.float32) * (1. - pen_state)
+ stroke_imgs_list.append(stroke_img)
+
+ stroke_imgs_list = np.stack(stroke_imgs_list,
+ axis=-1) # (image_size, image_size, seq_len-1), 0 for BG and 1 for strokes
+ stroke_imgs_list = np.sum(stroke_imgs_list, axis=-1)
+ stroke_imgs_list = np.clip(stroke_imgs_list, 0.0, 1.0) # (image_size, image_size), 0 for BG and 1 for strokes
+ sketch_imgs_list.append(stroke_imgs_list)
+
+ return sketch_imgs_list
+
+ def normalize_coordinate_np(self, sx, sy, image_size, raster_padding=10.0):
+ """
+ Convert offset to normalized absolute points. The numpy version as in NeuralRasterizor.
+ :param sx: (N, seq_len)
+ :param sy: (N, seq_len)
+ :return:
+ """
+ seq_len = sx.shape[1]
+
+ # transfer to abs points
+ abs_x = np.cumsum(sx, axis=1) # (N, seq_len)
+ abs_y = np.cumsum(sy, axis=1)
+
+ min_x = np.min(abs_x, axis=1, keepdims=True) # (N, 1)
+ max_x = np.max(abs_x, axis=1, keepdims=True)
+ min_y = np.min(abs_y, axis=1, keepdims=True)
+ max_y = np.max(abs_y, axis=1, keepdims=True)
+
+ # transform to positive coordinate
+ abs_x = np.subtract(abs_x, np.tile(min_x, [1, seq_len])) # (N, seq_len)
+ abs_y = np.subtract(abs_y, np.tile(min_y, [1, seq_len]))
+
+ # scaling to [0.0, raster_size - 2 * padding - 1]
+ bbox_w = np.squeeze(np.subtract(max_x, min_x), axis=-1) # (N)
+ bbox_h = np.squeeze(np.subtract(max_y, min_y), axis=-1)
+
+ unpad_raster_size = (image_size - 1.0) - 2.0 * raster_padding
+ scaling = np.divide(unpad_raster_size, np.maximum(bbox_w, bbox_h)) # (N)
+ scaling_tile = np.tile(np.expand_dims(scaling, axis=-1), [1, seq_len]) # (N, seq_len)
+ abs_x = np.multiply(abs_x, scaling_tile) # (N, seq_len)
+ abs_y = np.multiply(abs_y, scaling_tile)
+
+ # add padding
+ abs_x = np.add(abs_x, raster_padding) # (N, seq_len)
+ abs_y = np.add(abs_y, raster_padding)
+
+ # transform to the middle
+ trans_x = np.divide(np.subtract(unpad_raster_size, np.multiply(bbox_w, scaling)), 2.0) # (N)
+ trans_y = np.divide(np.subtract(unpad_raster_size, np.multiply(bbox_h, scaling)), 2.0)
+ trans_x = np.tile(np.expand_dims(trans_x, axis=-1), [1, seq_len]) # (N, seq_len)
+ trans_y = np.tile(np.expand_dims(trans_y, axis=-1), [1, seq_len]) # (N, seq_len)
+ abs_x = np.add(abs_x, trans_x) # (N, seq_len)
+ abs_y = np.add(abs_y, trans_y)
+
+ return abs_x, abs_y
+
+ def normalize_strokes_np(self, strokes_list, image_size):
+ """
+
+ :param strokes_list: list (N_sketches,), each with (N_points, 3)
+ :return:
+ """
+ assert type(strokes_list) is list
+
+ rst_list = []
+ for i in range(len(strokes_list)):
+ strokes_data = strokes_list[i] # (N_points, 3)
+ norm_x, norm_y = self.normalize_coordinate_np(np.expand_dims(strokes_data[:, 0], axis=0),
+ np.expand_dims(strokes_data[:, 1], axis=0),
+ image_size) # (1, N_points)
+ norm_strokes_data = np.stack([norm_x[0], norm_y[0], strokes_data[:, 2]], axis=-1) # (N_points, 3)
+ rst_list.append(norm_strokes_data)
+ return rst_list
+
+ def raster_func(self, input_data, image_size, stroke_width, is_bin=True, version='v2'):
+ """
+ :param input_data: (N_sketches,), each with (N_points, 3)
+ :return: raster_image_array: list (N_sketches,), each with (raster_size, raster_size), 0-BG and 1-strokes
+ """
+ norm_test_strokes3 = self.normalize_strokes_np(input_data, image_size)
+ if version == 'v1':
+ raster_image_array = self.load_sketch_images_on_the_fly(image_size, norm_test_strokes3, stroke_width, is_bin=is_bin)
+ else:
+ raster_image_array = self.load_sketch_images_on_the_fly_v2(image_size, norm_test_strokes3, stroke_width, is_bin=is_bin)
+
+ return raster_image_array
diff --git a/rnn.py b/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..533b0aa25e0be7424f74c766a228859a30754d27
--- /dev/null
+++ b/rnn.py
@@ -0,0 +1,499 @@
+# Copyright 2019 The Magenta Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""SketchRNN RNN definition."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+
+def orthogonal(shape):
+ """Orthogonal initilaizer."""
+ flat_shape = (shape[0], np.prod(shape[1:]))
+ a = np.random.normal(0.0, 1.0, flat_shape)
+ u, _, v = np.linalg.svd(a, full_matrices=False)
+ q = u if u.shape == flat_shape else v
+ return q.reshape(shape)
+
+
+def orthogonal_initializer(scale=1.0):
+ """Orthogonal initializer."""
+
+ def _initializer(shape, dtype=tf.float32,
+ partition_info=None): # pylint: disable=unused-argument
+ return tf.constant(orthogonal(shape) * scale, dtype)
+
+ return _initializer
+
+
+def lstm_ortho_initializer(scale=1.0):
+ """LSTM orthogonal initializer."""
+
+ def _initializer(shape, dtype=tf.float32,
+ partition_info=None): # pylint: disable=unused-argument
+ size_x = shape[0]
+ size_h = shape[1] // 4 # assumes lstm.
+ t = np.zeros(shape)
+ t[:, :size_h] = orthogonal([size_x, size_h]) * scale
+ t[:, size_h:size_h * 2] = orthogonal([size_x, size_h]) * scale
+ t[:, size_h * 2:size_h * 3] = orthogonal([size_x, size_h]) * scale
+ t[:, size_h * 3:] = orthogonal([size_x, size_h]) * scale
+ return tf.constant(t, dtype)
+
+ return _initializer
+
+
+class LSTMCell(tf.contrib.rnn.RNNCell):
+ """Vanilla LSTM cell.
+
+ Uses ortho initializer, and also recurrent dropout without memory loss
+ (https://arxiv.org/abs/1603.05118)
+ """
+
+ def __init__(self,
+ num_units,
+ forget_bias=1.0,
+ use_recurrent_dropout=False,
+ dropout_keep_prob=0.9):
+ self.num_units = num_units
+ self.forget_bias = forget_bias
+ self.use_recurrent_dropout = use_recurrent_dropout
+ self.dropout_keep_prob = dropout_keep_prob
+
+ @property
+ def state_size(self):
+ return 2 * self.num_units
+
+ @property
+ def output_size(self):
+ return self.num_units
+
+ def get_output(self, state):
+ unused_c, h = tf.split(state, 2, 1)
+ return h
+
+ def __call__(self, x, state, scope=None):
+ with tf.variable_scope(scope or type(self).__name__):
+ c, h = tf.split(state, 2, 1)
+
+ x_size = x.get_shape().as_list()[1]
+
+ w_init = None # uniform
+
+ h_init = lstm_ortho_initializer(1.0)
+
+ # Keep W_xh and W_hh separate here as well to use different init methods.
+ w_xh = tf.get_variable(
+ 'W_xh', [x_size, 4 * self.num_units], initializer=w_init)
+ w_hh = tf.get_variable(
+ 'W_hh', [self.num_units, 4 * self.num_units], initializer=h_init)
+ bias = tf.get_variable(
+ 'bias', [4 * self.num_units],
+ initializer=tf.constant_initializer(0.0))
+
+ concat = tf.concat([x, h], 1)
+ w_full = tf.concat([w_xh, w_hh], 0)
+ hidden = tf.matmul(concat, w_full) + bias
+
+ i, j, f, o = tf.split(hidden, 4, 1)
+
+ if self.use_recurrent_dropout:
+ g = tf.nn.dropout(tf.tanh(j), self.dropout_keep_prob)
+ else:
+ g = tf.tanh(j)
+
+ new_c = c * tf.sigmoid(f + self.forget_bias) + tf.sigmoid(i) * g
+ new_h = tf.tanh(new_c) * tf.sigmoid(o)
+
+ return new_h, tf.concat([new_c, new_h], 1) # fuk tuples.
+
+
+def layer_norm_all(h,
+ batch_size,
+ base,
+ num_units,
+ scope='layer_norm',
+ reuse=False,
+ gamma_start=1.0,
+ epsilon=1e-3,
+ use_bias=True):
+ """Layer Norm (faster version, but not using defun)."""
+ # Performs layer norm on multiple base at once (ie, i, g, j, o for lstm)
+ # Reshapes h in to perform layer norm in parallel
+ h_reshape = tf.reshape(h, [batch_size, base, num_units])
+ mean = tf.reduce_mean(h_reshape, [2], keep_dims=True)
+ var = tf.reduce_mean(tf.square(h_reshape - mean), [2], keep_dims=True)
+ epsilon = tf.constant(epsilon)
+ rstd = tf.rsqrt(var + epsilon)
+ h_reshape = (h_reshape - mean) * rstd
+ # reshape back to original
+ h = tf.reshape(h_reshape, [batch_size, base * num_units])
+ with tf.variable_scope(scope):
+ if reuse:
+ tf.get_variable_scope().reuse_variables()
+ gamma = tf.get_variable(
+ 'ln_gamma', [4 * num_units],
+ initializer=tf.constant_initializer(gamma_start))
+ if use_bias:
+ beta = tf.get_variable(
+ 'ln_beta', [4 * num_units], initializer=tf.constant_initializer(0.0))
+ if use_bias:
+ return gamma * h + beta
+ return gamma * h
+
+
+def layer_norm(x,
+ num_units,
+ scope='layer_norm',
+ reuse=False,
+ gamma_start=1.0,
+ epsilon=1e-3,
+ use_bias=True):
+ """Calculate layer norm."""
+ axes = [1]
+ mean = tf.reduce_mean(x, axes, keep_dims=True)
+ x_shifted = x - mean
+ var = tf.reduce_mean(tf.square(x_shifted), axes, keep_dims=True)
+ inv_std = tf.rsqrt(var + epsilon)
+ with tf.variable_scope(scope):
+ if reuse:
+ tf.get_variable_scope().reuse_variables()
+ gamma = tf.get_variable(
+ 'ln_gamma', [num_units],
+ initializer=tf.constant_initializer(gamma_start))
+ if use_bias:
+ beta = tf.get_variable(
+ 'ln_beta', [num_units], initializer=tf.constant_initializer(0.0))
+ output = gamma * (x_shifted) * inv_std
+ if use_bias:
+ output += beta
+ return output
+
+
+def raw_layer_norm(x, epsilon=1e-3):
+ axes = [1]
+ mean = tf.reduce_mean(x, axes, keep_dims=True)
+ std = tf.sqrt(
+ tf.reduce_mean(tf.square(x - mean), axes, keep_dims=True) + epsilon)
+ output = (x - mean) / (std)
+ return output
+
+
+def super_linear(x,
+ output_size,
+ scope=None,
+ reuse=False,
+ init_w='ortho',
+ weight_start=0.0,
+ use_bias=True,
+ bias_start=0.0,
+ input_size=None):
+ """Performs linear operation. Uses ortho init defined earlier."""
+ shape = x.get_shape().as_list()
+ with tf.variable_scope(scope or 'linear'):
+ if reuse:
+ tf.get_variable_scope().reuse_variables()
+
+ w_init = None # uniform
+ if input_size is None:
+ x_size = shape[1]
+ else:
+ x_size = input_size
+ if init_w == 'zeros':
+ w_init = tf.constant_initializer(0.0)
+ elif init_w == 'constant':
+ w_init = tf.constant_initializer(weight_start)
+ elif init_w == 'gaussian':
+ w_init = tf.random_normal_initializer(stddev=weight_start)
+ elif init_w == 'ortho':
+ w_init = lstm_ortho_initializer(1.0)
+
+ w = tf.get_variable(
+ 'super_linear_w', [x_size, output_size], tf.float32, initializer=w_init)
+ if use_bias:
+ b = tf.get_variable(
+ 'super_linear_b', [output_size],
+ tf.float32,
+ initializer=tf.constant_initializer(bias_start))
+ return tf.matmul(x, w) + b
+ return tf.matmul(x, w)
+
+
+class LayerNormLSTMCell(tf.contrib.rnn.RNNCell):
+ """Layer-Norm, with Ortho Init. and Recurrent Dropout without Memory Loss.
+
+ https://arxiv.org/abs/1607.06450 - Layer Norm
+ https://arxiv.org/abs/1603.05118 - Recurrent Dropout without Memory Loss
+ """
+
+ def __init__(self,
+ num_units,
+ forget_bias=1.0,
+ use_recurrent_dropout=False,
+ dropout_keep_prob=0.90):
+ """Initialize the Layer Norm LSTM cell.
+
+ Args:
+ num_units: int, The number of units in the LSTM cell.
+ forget_bias: float, The bias added to forget gates (default 1.0).
+ use_recurrent_dropout: Whether to use Recurrent Dropout (default False)
+ dropout_keep_prob: float, dropout keep probability (default 0.90)
+ """
+ self.num_units = num_units
+ self.forget_bias = forget_bias
+ self.use_recurrent_dropout = use_recurrent_dropout
+ self.dropout_keep_prob = dropout_keep_prob
+
+ @property
+ def input_size(self):
+ return self.num_units
+
+ @property
+ def output_size(self):
+ return self.num_units
+
+ @property
+ def state_size(self):
+ return 2 * self.num_units
+
+ def get_output(self, state):
+ h, unused_c = tf.split(state, 2, 1)
+ return h
+
+ def __call__(self, x, state, timestep=0, scope=None):
+ with tf.variable_scope(scope or type(self).__name__):
+ h, c = tf.split(state, 2, 1)
+
+ h_size = self.num_units
+ x_size = x.get_shape().as_list()[1]
+ batch_size = x.get_shape().as_list()[0]
+
+ w_init = None # uniform
+
+ h_init = lstm_ortho_initializer(1.0)
+
+ w_xh = tf.get_variable(
+ 'W_xh', [x_size, 4 * self.num_units], initializer=w_init)
+ w_hh = tf.get_variable(
+ 'W_hh', [self.num_units, 4 * self.num_units], initializer=h_init)
+
+ concat = tf.concat([x, h], 1) # concat for speed.
+ w_full = tf.concat([w_xh, w_hh], 0)
+ concat = tf.matmul(concat, w_full) # + bias # live life without garbage.
+
+ # i = input_gate, j = new_input, f = forget_gate, o = output_gate
+ concat = layer_norm_all(concat, batch_size, 4, h_size, 'ln_all')
+ i, j, f, o = tf.split(concat, 4, 1)
+
+ if self.use_recurrent_dropout:
+ g = tf.nn.dropout(tf.tanh(j), self.dropout_keep_prob)
+ else:
+ g = tf.tanh(j)
+
+ new_c = c * tf.sigmoid(f + self.forget_bias) + tf.sigmoid(i) * g
+ new_h = tf.tanh(layer_norm(new_c, h_size, 'ln_c')) * tf.sigmoid(o)
+
+ return new_h, tf.concat([new_h, new_c], 1)
+
+
+class HyperLSTMCell(tf.contrib.rnn.RNNCell):
+ """HyperLSTM with Ortho Init, Layer Norm, Recurrent Dropout, no Memory Loss.
+
+ https://arxiv.org/abs/1609.09106
+ http://blog.otoro.net/2016/09/28/hyper-networks/
+ """
+
+ def __init__(self,
+ num_units,
+ forget_bias=1.0,
+ use_recurrent_dropout=False,
+ dropout_keep_prob=0.90,
+ use_layer_norm=True,
+ hyper_num_units=256,
+ hyper_embedding_size=32,
+ hyper_use_recurrent_dropout=False):
+ """Initialize the Layer Norm HyperLSTM cell.
+
+ Args:
+ num_units: int, The number of units in the LSTM cell.
+ forget_bias: float, The bias added to forget gates (default 1.0).
+ use_recurrent_dropout: Whether to use Recurrent Dropout (default False)
+ dropout_keep_prob: float, dropout keep probability (default 0.90)
+ use_layer_norm: boolean. (default True)
+ Controls whether we use LayerNorm layers in main LSTM & HyperLSTM cell.
+ hyper_num_units: int, number of units in HyperLSTM cell.
+ (default is 128, recommend experimenting with 256 for larger tasks)
+ hyper_embedding_size: int, size of signals emitted from HyperLSTM cell.
+ (default is 16, recommend trying larger values for large datasets)
+ hyper_use_recurrent_dropout: boolean. (default False)
+ Controls whether HyperLSTM cell also uses recurrent dropout.
+ Recommend turning this on only if hyper_num_units becomes large (>= 512)
+ """
+ self.num_units = num_units
+ self.forget_bias = forget_bias
+ self.use_recurrent_dropout = use_recurrent_dropout
+ self.dropout_keep_prob = dropout_keep_prob
+ self.use_layer_norm = use_layer_norm
+ self.hyper_num_units = hyper_num_units
+ self.hyper_embedding_size = hyper_embedding_size
+ self.hyper_use_recurrent_dropout = hyper_use_recurrent_dropout
+
+ self.total_num_units = self.num_units + self.hyper_num_units
+
+ if self.use_layer_norm:
+ cell_fn = LayerNormLSTMCell
+ else:
+ cell_fn = LSTMCell
+ self.hyper_cell = cell_fn(
+ hyper_num_units,
+ use_recurrent_dropout=hyper_use_recurrent_dropout,
+ dropout_keep_prob=dropout_keep_prob)
+
+ @property
+ def input_size(self):
+ return self._input_size
+
+ @property
+ def output_size(self):
+ return self.num_units
+
+ @property
+ def state_size(self):
+ return 2 * self.total_num_units
+
+ def get_output(self, state):
+ total_h, unused_total_c = tf.split(state, 2, 1)
+ h = total_h[:, 0:self.num_units]
+ return h
+
+ def hyper_norm(self, layer, scope='hyper', use_bias=True):
+ num_units = self.num_units
+ embedding_size = self.hyper_embedding_size
+ # recurrent batch norm init trick (https://arxiv.org/abs/1603.09025).
+ init_gamma = 0.10 # cooijmans' da man.
+ with tf.variable_scope(scope):
+ zw = super_linear(
+ self.hyper_output,
+ embedding_size,
+ init_w='constant',
+ weight_start=0.00,
+ use_bias=True,
+ bias_start=1.0,
+ scope='zw')
+ alpha = super_linear(
+ zw,
+ num_units,
+ init_w='constant',
+ weight_start=init_gamma / embedding_size,
+ use_bias=False,
+ scope='alpha')
+ result = tf.multiply(alpha, layer)
+ if use_bias:
+ zb = super_linear(
+ self.hyper_output,
+ embedding_size,
+ init_w='gaussian',
+ weight_start=0.01,
+ use_bias=False,
+ bias_start=0.0,
+ scope='zb')
+ beta = super_linear(
+ zb,
+ num_units,
+ init_w='constant',
+ weight_start=0.00,
+ use_bias=False,
+ scope='beta')
+ result += beta
+ return result
+
+ def __call__(self, x, state, timestep=0, scope=None):
+ with tf.variable_scope(scope or type(self).__name__):
+ total_h, total_c = tf.split(state, 2, 1)
+ h = total_h[:, 0:self.num_units]
+ c = total_c[:, 0:self.num_units]
+ self.hyper_state = tf.concat(
+ [total_h[:, self.num_units:], total_c[:, self.num_units:]], 1)
+
+ batch_size = x.get_shape().as_list()[0]
+ x_size = x.get_shape().as_list()[1]
+ self._input_size = x_size
+
+ w_init = None # uniform
+
+ h_init = lstm_ortho_initializer(1.0)
+
+ w_xh = tf.get_variable(
+ 'W_xh', [x_size, 4 * self.num_units], initializer=w_init)
+ w_hh = tf.get_variable(
+ 'W_hh', [self.num_units, 4 * self.num_units], initializer=h_init)
+ bias = tf.get_variable(
+ 'bias', [4 * self.num_units],
+ initializer=tf.constant_initializer(0.0))
+
+ # concatenate the input and hidden states for hyperlstm input
+ hyper_input = tf.concat([x, h], 1)
+ hyper_output, hyper_new_state = self.hyper_cell(hyper_input,
+ self.hyper_state)
+ self.hyper_output = hyper_output
+ self.hyper_state = hyper_new_state
+
+ xh = tf.matmul(x, w_xh)
+ hh = tf.matmul(h, w_hh)
+
+ # split Wxh contributions
+ ix, jx, fx, ox = tf.split(xh, 4, 1)
+ ix = self.hyper_norm(ix, 'hyper_ix', use_bias=False)
+ jx = self.hyper_norm(jx, 'hyper_jx', use_bias=False)
+ fx = self.hyper_norm(fx, 'hyper_fx', use_bias=False)
+ ox = self.hyper_norm(ox, 'hyper_ox', use_bias=False)
+
+ # split Whh contributions
+ ih, jh, fh, oh = tf.split(hh, 4, 1)
+ ih = self.hyper_norm(ih, 'hyper_ih', use_bias=True)
+ jh = self.hyper_norm(jh, 'hyper_jh', use_bias=True)
+ fh = self.hyper_norm(fh, 'hyper_fh', use_bias=True)
+ oh = self.hyper_norm(oh, 'hyper_oh', use_bias=True)
+
+ # split bias
+ ib, jb, fb, ob = tf.split(bias, 4, 0) # bias is to be broadcasted.
+
+ # i = input_gate, j = new_input, f = forget_gate, o = output_gate
+ i = ix + ih + ib
+ j = jx + jh + jb
+ f = fx + fh + fb
+ o = ox + oh + ob
+
+ if self.use_layer_norm:
+ concat = tf.concat([i, j, f, o], 1)
+ concat = layer_norm_all(concat, batch_size, 4, self.num_units, 'ln_all')
+ i, j, f, o = tf.split(concat, 4, 1)
+
+ if self.use_recurrent_dropout:
+ g = tf.nn.dropout(tf.tanh(j), self.dropout_keep_prob)
+ else:
+ g = tf.tanh(j)
+
+ new_c = c * tf.sigmoid(f + self.forget_bias) + tf.sigmoid(i) * g
+ new_h = tf.tanh(layer_norm(new_c, self.num_units, 'ln_c')) * tf.sigmoid(o)
+
+ hyper_h, hyper_c = tf.split(hyper_new_state, 2, 1)
+ new_total_h = tf.concat([new_h, hyper_h], 1)
+ new_total_c = tf.concat([new_c, hyper_c], 1)
+ new_total_state = tf.concat([new_total_h, new_total_c], 1)
+ return new_h, new_total_state
diff --git a/sample_inputs/clean_line_drawings/banana_tree.png b/sample_inputs/clean_line_drawings/banana_tree.png
new file mode 100644
index 0000000000000000000000000000000000000000..a607bf0efceb5382d1cb987c2f99b5df8b9de91f
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/banana_tree.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:651eb4a8de6f194a866863d6eb3a0a29f81fae08d933024db0d8ff08f215e448
+size 40544
diff --git a/sample_inputs/clean_line_drawings/dracolion.png b/sample_inputs/clean_line_drawings/dracolion.png
new file mode 100644
index 0000000000000000000000000000000000000000..0b0d4f42f93746a1fc593ce3b0e29f5cb78e1157
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/dracolion.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d48ae1e1de031a1f3b6f0328f49854a7a1725fde6a1fa2c9563a9bd32c667a53
+size 21065
diff --git a/sample_inputs/clean_line_drawings/duck.png b/sample_inputs/clean_line_drawings/duck.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae242d9b5599da8e75cf8142ad3c5e66825533aa
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/duck.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 29925
diff --git a/sample_inputs/clean_line_drawings/elephant.png b/sample_inputs/clean_line_drawings/elephant.png
new file mode 100644
index 0000000000000000000000000000000000000000..f83dc648860f4a80cc7a8d993b7b440d203739ed
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/elephant.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d0ecbf4d5ca181787165be9ebc21ab7a0e46592f3a7dae8fb80eccf7c1963a9
+size 36866
diff --git a/sample_inputs/clean_line_drawings/hippo.png b/sample_inputs/clean_line_drawings/hippo.png
new file mode 100644
index 0000000000000000000000000000000000000000..93e9412decd08440053f961626916543c6fcf86a
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/hippo.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:5777c7a8c23ec58fbaf57b9d1f393627ee8b2762e38e0ae95f013d63c5bd1258
+size 32497
diff --git a/sample_inputs/clean_line_drawings/kitten.png b/sample_inputs/clean_line_drawings/kitten.png
new file mode 100644
index 0000000000000000000000000000000000000000..1109356a762fb23de56519bbac817e7c930fa550
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/kitten.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:ca74946799c7eab10c379339bb90927de62e3da9b99bcdddeb3558eab9a38849
+size 11784
diff --git a/sample_inputs/clean_line_drawings/mouse.png b/sample_inputs/clean_line_drawings/mouse.png
new file mode 100644
index 0000000000000000000000000000000000000000..17c69c203a6631c9183a15402bd02be0089915b6
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/mouse.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:8dd80a3c7ad6f603e396cdfda758b90ca2aab170eeae9a20f008c1b528b96379
+size 17674
diff --git a/sample_inputs/clean_line_drawings/muten.png b/sample_inputs/clean_line_drawings/muten.png
new file mode 100644
index 0000000000000000000000000000000000000000..8389254427916bd9260ac4c0a601a3492ad85aac
--- /dev/null
+++ b/sample_inputs/clean_line_drawings/muten.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c26c1459aeabfcaeb3870ddb862eedf136e98b83f369dc7c8c45c6fd95176e53
+size 18667
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diff --git a/subnet_tf_utils.py b/subnet_tf_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c4726aac4c02e40ccde4b032f592fe042e0b3e4
--- /dev/null
+++ b/subnet_tf_utils.py
@@ -0,0 +1,989 @@
+import tensorflow as tf
+
+
+def get_initializer(init_method):
+ if init_method == 'xavier_normal':
+ initializer = tf.glorot_normal_initializer()
+ elif init_method == 'xavier_uniform':
+ initializer = tf.glorot_uniform_initializer()
+ elif init_method == 'he_normal':
+ initializer = tf.keras.initializers.he_normal()
+ elif init_method == 'he_uniform':
+ initializer = tf.keras.initializers.he_uniform()
+ elif init_method == 'lecun_normal':
+ initializer = tf.keras.initializers.lecun_normal()
+ elif init_method == 'lecun_uniform':
+ initializer = tf.keras.initializers.lecun_uniform()
+ else:
+ raise Exception('Unknown initializer:', init_method)
+ return initializer
+
+
+def lrelu(x, leak=0.2, name="lrelu", alt_relu_impl=False):
+ with tf.variable_scope(name) as scope:
+ if alt_relu_impl:
+ f1 = 0.5 * (1 + leak)
+ f2 = 0.5 * (1 - leak)
+ return f1 * x + f2 * abs(x)
+ else:
+ return tf.maximum(x, leak * x)
+
+
+def batchnorm(input, name='batch_norm', init_method=None):
+ if init_method is not None:
+ initializer = get_initializer(init_method)
+ else:
+ initializer = tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32)
+
+ with tf.variable_scope(name):
+ # this block looks like it has 3 inputs on the graph unless we do this
+ input = tf.identity(input)
+
+ channels = input.get_shape()[3]
+ offset = tf.get_variable("offset", [channels], dtype=tf.float32, initializer=tf.zeros_initializer())
+ scale = tf.get_variable("scale", [channels], dtype=tf.float32,
+ initializer=initializer)
+ mean, variance = tf.nn.moments(input, axes=[0, 1, 2], keep_dims=False)
+ variance_epsilon = 1e-5
+ normalized = tf.nn.batch_normalization(input, mean, variance, offset, scale, variance_epsilon=variance_epsilon)
+ return normalized
+
+
+def layernorm(input, name='layer_norm', init_method=None):
+ if init_method is not None:
+ initializer = get_initializer(init_method)
+ else:
+ initializer = tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32)
+
+ with tf.variable_scope(name):
+ n_neurons = input.get_shape()[3]
+ offset = tf.get_variable("offset", [n_neurons], dtype=tf.float32, initializer=tf.zeros_initializer())
+ scale = tf.get_variable("scale", [n_neurons], dtype=tf.float32,
+ initializer=initializer)
+ offset = tf.reshape(offset, [1, 1, -1])
+ scale = tf.reshape(scale, [1, 1, -1])
+ mean, variance = tf.nn.moments(input, axes=[1, 2, 3], keep_dims=True)
+ variance_epsilon = 1e-5
+ normalized = tf.nn.batch_normalization(input, mean, variance, offset, scale, variance_epsilon=variance_epsilon)
+ return normalized
+
+
+def instance_norm(input, name="instance_norm", init_method=None):
+ if init_method is not None:
+ initializer = get_initializer(init_method)
+ else:
+ initializer = tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32)
+
+ with tf.variable_scope(name):
+ depth = input.get_shape()[3]
+ scale = tf.get_variable("scale", [depth], initializer=initializer)
+ offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0))
+ mean, variance = tf.nn.moments(input, axes=[1, 2], keep_dims=True)
+ epsilon = 1e-5
+ inv = tf.rsqrt(variance + epsilon)
+ normalized = (input - mean) * inv
+ return scale * normalized + offset
+
+
+def linear1d(inputlin, inputdim, outputdim, name="linear1d", init_method=None):
+ if init_method is not None:
+ initializer = get_initializer(init_method)
+ else:
+ initializer = None
+
+ with tf.variable_scope(name) as scope:
+ weight = tf.get_variable("weight", [inputdim, outputdim], initializer=initializer)
+ bias = tf.get_variable("bias", [outputdim], dtype=tf.float32, initializer=tf.constant_initializer(0.0))
+ return tf.matmul(inputlin, weight) + bias
+
+
+def general_conv2d(inputconv, output_dim=64, filter_height=4, filter_width=4, stride_height=2, stride_width=2,
+ stddev=0.02, padding="SAME", name="conv2d", do_norm=True, norm_type='instance_norm', do_relu=True,
+ relufactor=0, is_training=True, init_method=None):
+ if init_method is not None:
+ initializer = get_initializer(init_method)
+ else:
+ initializer = tf.truncated_normal_initializer(stddev=stddev)
+
+ with tf.variable_scope(name) as scope:
+ conv = tf.contrib.layers.conv2d(inputconv, output_dim, [filter_width, filter_height],
+ [stride_width, stride_height], padding, activation_fn=None,
+ weights_initializer=initializer,
+ biases_initializer=tf.constant_initializer(0.0))
+ if do_norm:
+ if norm_type == 'instance_norm':
+ conv = instance_norm(conv, init_method=init_method)
+ # conv = tf.contrib.layers.instance_norm(conv, epsilon=1e-05, center=True, scale=True,
+ # scope='instance_norm')
+ elif norm_type == 'batch_norm':
+ # conv = batchnorm(conv, init_method=init_method)
+ conv = tf.contrib.layers.batch_norm(conv, decay=0.9, is_training=is_training, updates_collections=None,
+ epsilon=1e-5, center=True, scale=True, scope="batch_norm")
+ elif norm_type == 'layer_norm':
+ # conv = layernorm(conv, init_method=init_method)
+ conv = tf.contrib.layers.layer_norm(conv, center=True, scale=True, scope='layer_norm')
+
+ if do_relu:
+ if relufactor == 0:
+ conv = tf.nn.relu(conv, "relu")
+ else:
+ conv = lrelu(conv, relufactor, "lrelu")
+
+ return conv
+
+
+def generative_cnn_c3_encoder(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 256 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 256 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_deeper(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 512]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 512 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 512 * 4 * 4, 512, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_combine33(local_inputs, global_inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ local_x = local_inputs
+ global_x = global_inputs
+
+ with tf.variable_scope('Local_Encoder', reuse=tf.AUTO_REUSE):
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ with tf.variable_scope('Global_Encoder', reuse=tf.AUTO_REUSE):
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ tensor_x = tf.concat([local_x, global_x], axis=-1)
+
+ with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 512 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_combine43(local_inputs, global_inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ local_x = local_inputs
+ global_x = global_inputs
+
+ with tf.variable_scope('Local_Encoder', reuse=tf.AUTO_REUSE):
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ with tf.variable_scope('Global_Encoder', reuse=tf.AUTO_REUSE):
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ tensor_x = tf.concat([local_x, global_x], axis=-1)
+
+ with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 512 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_combine53(local_inputs, global_inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ local_x = local_inputs
+ global_x = global_inputs
+
+ with tf.variable_scope('Local_Encoder', reuse=tf.AUTO_REUSE):
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ with tf.variable_scope('Global_Encoder', reuse=tf.AUTO_REUSE):
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ tensor_x = tf.concat([local_x, global_x], axis=-1)
+
+ with tf.variable_scope('Combined_Encoder', reuse=tf.AUTO_REUSE):
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+ tensor_x = tf.reshape(tensor_x, (-1, 1024 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 1024 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_combineFC(local_inputs, global_inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ local_x = local_inputs
+ global_x = global_inputs
+
+ with tf.variable_scope('Local_Encoder', reuse=tf.AUTO_REUSE):
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ local_x = tf.reshape(local_x, (-1, 512 * 4 * 4))
+ local_x = linear1d(local_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ local_x = tf.nn.dropout(local_x, drop_keep_prob)
+
+ with tf.variable_scope('Global_Encoder', reuse=tf.AUTO_REUSE):
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ global_x = tf.reshape(global_x, (-1, 512 * 4 * 4))
+ global_x = linear1d(global_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ global_x = tf.nn.dropout(global_x, drop_keep_prob)
+
+ tensor_x_sp = None
+ tensor_x = tf.concat([local_x, global_x], axis=-1)
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_combineFC_jointAttn(local_inputs, global_inputs, is_training=True, drop_keep_prob=0.5,
+ init_method=None, combine_manner='attn'):
+ local_x = local_inputs
+ global_x = global_inputs
+
+ with tf.variable_scope('Local_Encoder', reuse=tf.AUTO_REUSE):
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ share_x = local_x
+
+ with tf.variable_scope('Attn_branch', reuse=tf.AUTO_REUSE):
+ attn_x = general_conv2d(share_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ attn_x = general_conv2d(attn_x, 32, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ attn_x = general_conv2d(attn_x, 1, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ attn_map = tf.nn.sigmoid(attn_x) # (N, H/8, W/8, 1), [0.0, 1.0]
+
+ if combine_manner == 'attn':
+ attn_feat = attn_map * share_x + share_x
+ else:
+ raise Exception('Unknown combine_manner', combine_manner)
+
+ local_x = general_conv2d(attn_feat, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ local_x = tf.reshape(local_x, (-1, 512 * 4 * 4))
+ local_x = linear1d(local_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ local_x = tf.nn.dropout(local_x, drop_keep_prob)
+
+ with tf.variable_scope('Global_Encoder', reuse=tf.AUTO_REUSE):
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ global_x = tf.reshape(global_x, (-1, 512 * 4 * 4))
+ global_x = linear1d(global_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ global_x = tf.nn.dropout(global_x, drop_keep_prob)
+
+ tensor_x_sp = None
+ tensor_x = tf.concat([local_x, global_x], axis=-1)
+ return tensor_x, tensor_x_sp, attn_map
+
+
+def generative_cnn_c3_encoder_combineFC_sepAttn(local_inputs, global_inputs, is_training=True, drop_keep_prob=0.5,
+ init_method=None, combine_manner='attn'):
+ local_x = local_inputs
+ global_x = global_inputs
+
+ with tf.variable_scope('Attn_branch', reuse=tf.AUTO_REUSE):
+ attn_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ attn_x = general_conv2d(attn_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ attn_x = general_conv2d(attn_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ attn_x = general_conv2d(attn_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ attn_x = general_conv2d(attn_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ attn_x = general_conv2d(attn_x, 32, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ attn_x = general_conv2d(attn_x, 1, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+ attn_map = tf.nn.sigmoid(attn_x) # (N, H/8, W/8, 1), [0.0, 1.0]
+
+ with tf.variable_scope('Local_Encoder', reuse=tf.AUTO_REUSE):
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ local_x = general_conv2d(local_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ if combine_manner == 'attn':
+ attn_feat = attn_map * local_x + local_x
+ elif combine_manner == 'channel':
+ attn_feat = tf.concat([local_x, attn_map], axis=-1)
+ else:
+ raise Exception('Unknown combine_manner', combine_manner)
+
+ local_x = general_conv2d(attn_feat, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ local_x = general_conv2d(local_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ local_x = tf.reshape(local_x, (-1, 512 * 4 * 4))
+ local_x = linear1d(local_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ local_x = tf.nn.dropout(local_x, drop_keep_prob)
+
+ with tf.variable_scope('Global_Encoder', reuse=tf.AUTO_REUSE):
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ global_x = general_conv2d(global_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ global_x = general_conv2d(global_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+
+ global_x = tf.reshape(global_x, (-1, 512 * 4 * 4))
+ global_x = linear1d(global_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ global_x = tf.nn.dropout(global_x, drop_keep_prob)
+
+ tensor_x_sp = None
+ tensor_x = tf.concat([local_x, global_x], axis=-1)
+ return tensor_x, tensor_x_sp, attn_map
+
+
+def generative_cnn_c3_encoder_deeper13(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 512 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_c3_encoder_deeper13_attn(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_1_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = self_attention(tensor_x, 64)
+
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_3_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_4_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3,
+ is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, filter_height=3, filter_width=3, stride_height=1, stride_width=1,
+ is_training=is_training, name="CNN_ENC_5_3", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 512 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 512 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_encoder(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_1_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 128, is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_3_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 256 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 256 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_encoder_deeper(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, is_training=is_training, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_1_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, is_training=is_training, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 128, is_training=is_training, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_3_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, is_training=is_training, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, is_training=is_training, name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 512, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 512]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 512 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 512 * 4 * 4, 512, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def generative_cnn_encoder_deeper13(inputs, is_training=True, drop_keep_prob=0.5, init_method=None):
+ tensor_x = inputs
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, is_training=is_training,
+ name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 32, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_1_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 64, is_training=is_training,
+ name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_2_2", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 128, is_training=is_training,
+ name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_3_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_3_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, is_training=is_training,
+ name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_4_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_4_3", init_method=init_method)
+
+ tensor_x = general_conv2d(tensor_x, 256, is_training=is_training,
+ name="CNN_ENC_5", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_5_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 256, stride_height=1, stride_width=1, is_training=is_training,
+ name="CNN_ENC_5_3", init_method=init_method)
+ tensor_x_sp = tensor_x # [N, h, w, 256]
+
+ tensor_x = tf.reshape(tensor_x, (-1, 256 * 4 * 4))
+ tensor_x = linear1d(tensor_x, 256 * 4 * 4, 128, name='CNN_ENC_FC', init_method=init_method)
+
+ if is_training:
+ tensor_x = tf.nn.dropout(tensor_x, drop_keep_prob)
+
+ return tensor_x, tensor_x_sp
+
+
+def max_pooling(x) :
+ return tf.layers.max_pooling2d(x, pool_size=2, strides=2, padding='SAME')
+
+
+def hw_flatten(x) :
+ return tf.reshape(x, shape=[x.shape[0], -1, x.shape[-1]])
+
+
+def self_attention(x, in_channel, name='self_attention'):
+ with tf.variable_scope(name) as scope:
+ f = general_conv2d(x, in_channel // 8, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ do_norm=False, do_relu=False, name='f_conv') # (N, h, w, c')
+ f = max_pooling(f) # (N, h', w', c')
+ g = general_conv2d(x, in_channel // 8, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ do_norm=False, do_relu=False, name='g_conv') # (N, h, w, c')
+ h = general_conv2d(x, in_channel, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ do_norm=False, do_relu=False, name='h_conv') # (N, h, w, c)
+ h = max_pooling(h) # (N, h', w', c)
+
+ # M = h * w, M' = h' * w'
+ s = tf.matmul(hw_flatten(g), hw_flatten(f), transpose_b=True) # (N, M, M')
+ beta = tf.nn.softmax(s) # attention map
+
+ o = tf.matmul(beta, hw_flatten(h)) # (N, M, c)
+ gamma = tf.get_variable("gamma", [1], initializer=tf.constant_initializer(0.0))
+
+ o = tf.reshape(o, shape=x.shape) # (N, h, w, c)
+ o = general_conv2d(o, in_channel, filter_height=1, filter_width=1, stride_height=1, stride_width=1,
+ do_norm=False, do_relu=False, name='attn_conv')
+
+ x = gamma * o + x
+
+ return x
+
+
+def global_avg_pooling(x):
+ gap = tf.reduce_mean(x, axis=[1, 2])
+ return gap
+
+
+def cnn_discriminator_wgan_gp(discrim_inputs, discrim_targets, init_method=None):
+ tensor_x = tf.concat([discrim_inputs, discrim_targets], axis=3) # (N, H, W, 3 + 1)
+
+ with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) as scope:
+ tensor_x = general_conv2d(tensor_x, 32, filter_height=3, filter_width=3,
+ is_training=True, name="CNN_ENC_1", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 64, filter_height=3, filter_width=3,
+ is_training=True, name="CNN_ENC_2", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3,
+ is_training=True, name="CNN_ENC_3", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 128, filter_height=3, filter_width=3,
+ is_training=True, name="CNN_ENC_4", init_method=init_method)
+ tensor_x = general_conv2d(tensor_x, 1, filter_height=3, filter_width=3,
+ is_training=True, name="CNN_ENC_5", init_method=init_method)
+ # (N, H/32, W/32, 1)
+
+ d_out = global_avg_pooling(tensor_x) # (N, 1)
+
+ return d_out
diff --git a/test_photograph_to_line.py b/test_photograph_to_line.py
new file mode 100644
index 0000000000000000000000000000000000000000..751d243d3565095e246a9092a94cd026a0de4d8e
--- /dev/null
+++ b/test_photograph_to_line.py
@@ -0,0 +1,260 @@
+import numpy as np
+import os
+import tensorflow as tf
+from six.moves import range
+from PIL import Image
+import argparse
+
+import hyper_parameters as hparams
+from model_common_test import DiffPastingV3, VirtualSketchingModel
+from utils import reset_graph, load_checkpoint, update_hyperparams, draw, \
+ save_seq_data, image_pasting_v3_testing, draw_strokes
+from dataset_utils import load_dataset_testing
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def sample(sess, model, input_photos, init_cursor, image_size, init_len, seq_len, state_dependent,
+ pasting_func):
+ """Samples a sequence from a pre-trained model."""
+ select_times = 1
+ cursor_pos = np.squeeze(init_cursor, axis=0) # (select_times, 1, 2)
+ curr_canvas = np.zeros(dtype=np.float32,
+ shape=(select_times, image_size, image_size)) # [0.0-BG, 1.0-stroke]
+
+ initial_state = sess.run(model.initial_state)
+ prev_state = initial_state
+ prev_width = np.stack([model.hps.min_width for _ in range(select_times)], axis=0)
+ prev_scaling = np.ones((select_times), dtype=np.float32) # (N)
+ prev_window_size = np.ones((select_times), dtype=np.float32) * model.hps.raster_size # (N)
+
+ params_list = [[] for _ in range(select_times)]
+ state_raw_list = [[] for _ in range(select_times)]
+ state_soft_list = [[] for _ in range(select_times)]
+ window_size_list = [[] for _ in range(select_times)]
+
+ input_photos_tiles = np.tile(input_photos, (select_times, 1, 1, 1))
+
+ for i in range(seq_len):
+ if not state_dependent and i % init_len == 0:
+ prev_state = initial_state
+
+ curr_window_size = prev_scaling * prev_window_size # (N)
+ curr_window_size = np.maximum(curr_window_size, model.hps.min_window_size)
+ curr_window_size = np.minimum(curr_window_size, image_size)
+
+ feed = {
+ model.initial_state: prev_state,
+ model.input_photo: input_photos_tiles,
+ model.curr_canvas_hard: curr_canvas.copy(),
+ model.cursor_position: cursor_pos,
+ model.image_size: image_size,
+ model.init_width: prev_width,
+ model.init_scaling: prev_scaling,
+ model.init_window_size: prev_window_size,
+ }
+
+ o_other_params_list, o_pen_list, o_pred_params_list, next_state_list = \
+ sess.run([model.other_params, model.pen_ras, model.pred_params, model.final_state], feed_dict=feed)
+ # o_other_params: (N, 6), o_pen: (N, 2), pred_params: (N, 1, 7), next_state: (N, 1024)
+ # o_other_params: [tanh*2, sigmoid*2, tanh*2, sigmoid*2]
+
+ idx_eos_list = np.argmax(o_pen_list, axis=1) # (N)
+
+ for output_i in range(idx_eos_list.shape[0]):
+ idx_eos = idx_eos_list[output_i]
+
+ eos = [0, 0]
+ eos[idx_eos] = 1
+
+ other_params = o_other_params_list[output_i].tolist() # (6)
+ params_list[output_i].append([eos[1]] + other_params)
+ state_raw_list[output_i].append(o_pen_list[output_i][1])
+ state_soft_list[output_i].append(o_pred_params_list[output_i, 0, 0])
+ window_size_list[output_i].append(curr_window_size[output_i])
+
+ # draw the stroke and add to the canvas
+ x1y1, x2y2, width2 = o_other_params_list[output_i, 0:2], o_other_params_list[output_i, 2:4], \
+ o_other_params_list[output_i, 4]
+ x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
+ x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
+ x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
+ widths = np.stack([prev_width[output_i], width2], axis=0) # (2)
+ o_other_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1).tolist() # (8)
+
+ if idx_eos == 0:
+ f = o_other_params_proc + [1.0, 1.0]
+ pred_stroke_img = draw(f) # (raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ pred_stroke_img_large = image_pasting_v3_testing(1.0 - pred_stroke_img, cursor_pos[output_i, 0],
+ image_size,
+ curr_window_size[output_i],
+ pasting_func, sess) # [0.0-BG, 1.0-stroke]
+ curr_canvas[output_i] += pred_stroke_img_large # [0.0-BG, 1.0-stroke]
+ curr_canvas = np.clip(curr_canvas, 0.0, 1.0)
+
+ next_width = o_other_params_list[:, 4] # (N)
+ next_scaling = o_other_params_list[:, 5]
+ next_window_size = next_scaling * curr_window_size # (N)
+ next_window_size = np.maximum(next_window_size, model.hps.min_window_size)
+ next_window_size = np.minimum(next_window_size, image_size)
+
+ prev_state = next_state_list
+ prev_width = next_width * curr_window_size / next_window_size # (N,)
+ prev_scaling = next_scaling # (N)
+ prev_window_size = curr_window_size
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = o_other_params_list[:, 2:4] * (np.expand_dims(curr_window_size, axis=-1) / 2.0) # (N, 2), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[:, 1:2], new_cursor_offset_next[:, 0:1]], axis=-1)
+
+ cursor_pos_large = cursor_pos * float(image_size)
+ stroke_position_next = cursor_pos_large[:, 0, :] + new_cursor_offset_next # (N, 2), large-level
+
+ if model.hps.cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (N, 2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (N, 2), large-level
+ cursor_pos_large = np.expand_dims(cursor_pos_large, axis=1) # (N, 1, 2)
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ return params_list, state_raw_list, state_soft_list, curr_canvas, window_size_list
+
+
+def main_testing(test_image_base_dir, test_dataset, test_image_name,
+ sampling_base_dir, model_base_dir, model_name,
+ sampling_num,
+ draw_seq=False, draw_order=False,
+ state_dependent=True, longer_infer_len=-1):
+ model_params_default = hparams.get_default_hparams_normal()
+ model_params = update_hyperparams(model_params_default, model_base_dir, model_name, infer_dataset=test_dataset)
+
+ [test_set, eval_hps_model, sample_hps_model] = \
+ load_dataset_testing(test_image_base_dir, test_dataset, test_image_name, model_params)
+
+ test_image_raw_name = test_image_name[:test_image_name.find('.')]
+ model_dir = os.path.join(model_base_dir, model_name)
+
+ reset_graph()
+ sampling_model = VirtualSketchingModel(sample_hps_model)
+
+ # differentiable pasting graph
+ paste_v3_func = DiffPastingV3(sample_hps_model.raster_size)
+
+ tfconfig = tf.ConfigProto()
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ # loads the weights from checkpoint into our model
+ snapshot_step = load_checkpoint(sess, model_dir, gen_model_pretrain=True)
+ print('snapshot_step', snapshot_step)
+ sampling_dir = os.path.join(sampling_base_dir, test_dataset + '__' + model_name)
+ os.makedirs(sampling_dir, exist_ok=True)
+
+ if longer_infer_len == -1:
+ tmp_max_len = eval_hps_model.max_seq_len
+ else:
+ tmp_max_len = longer_infer_len
+
+ for sampling_i in range(sampling_num):
+ input_photos, init_cursors, test_image_size = test_set.get_test_image()
+ # input_photos: (1, image_size, image_size, 3), [0-stroke, 1-BG]
+ # init_cursors: (N, 1, 2), in size [0.0, 1.0)
+
+ print()
+ print(test_image_name, ', image_size:', test_image_size, ', sampling_i:', sampling_i)
+ print('Processing ...')
+
+ if init_cursors.ndim == 3:
+ init_cursors = np.expand_dims(init_cursors, axis=0)
+
+ input_photos = input_photos[0:1, :, :, :]
+
+ ori_img = (input_photos.copy()[0] * 255.0).astype(np.uint8)
+ ori_img_png = Image.fromarray(ori_img, 'RGB')
+ ori_img_png.save(os.path.join(sampling_dir, test_image_raw_name + '_input.png'), 'PNG')
+
+ # decoding for sampling
+ strokes_raw_out_list, states_raw_out_list, states_soft_out_list, pred_imgs_out, window_size_out_list = sample(
+ sess, sampling_model, input_photos, init_cursors, test_image_size,
+ eval_hps_model.max_seq_len, tmp_max_len, state_dependent, paste_v3_func)
+ # pred_imgs_out: (N, H, W), [0.0-BG, 1.0-stroke]
+
+ output_i = 0
+ strokes_raw_out = np.stack(strokes_raw_out_list[output_i], axis=0)
+ states_raw_out = states_raw_out_list[output_i]
+ states_soft_out = states_soft_out_list[output_i]
+ window_size_out = window_size_out_list[output_i]
+
+ round_new_lengths = [tmp_max_len]
+ multi_cursors = [init_cursors[0, output_i, 0, :]]
+
+ print('strokes_raw_out', strokes_raw_out.shape)
+
+ clean_states_soft_out = np.array(states_soft_out) # (N)
+
+ flag_list = strokes_raw_out[:, 0].astype(np.int32) # (N)
+ drawing_len = len(flag_list) - np.sum(flag_list)
+ assert drawing_len >= 0
+
+ # print(' flag raw\t soft\t x1\t\t y1\t\t x2\t\t y2\t\t r2\t\t s2')
+ for i in range(strokes_raw_out.shape[0]):
+ flag, x1, y1, x2, y2, r2, s2 = strokes_raw_out[i]
+ win_size = window_size_out[i]
+ out_format = '#%d: %d | %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f'
+ out_values = (i, flag, states_raw_out[i], clean_states_soft_out[i], x1, y1, x2, y2, r2, s2)
+ out_log = out_format % out_values
+ # print(out_log)
+
+ print('Saving results ...')
+ save_seq_data(sampling_dir, test_image_raw_name + '_' + str(sampling_i),
+ strokes_raw_out, init_cursors[0, output_i, 0, :],
+ test_image_size, tmp_max_len, eval_hps_model.min_width)
+
+ draw_strokes(strokes_raw_out, sampling_dir, test_image_raw_name + '_' + str(sampling_i) + '_pred.png',
+ ori_img, test_image_size,
+ multi_cursors, round_new_lengths, eval_hps_model.min_width, eval_hps_model.cursor_type,
+ sample_hps_model.raster_size, sample_hps_model.min_window_size,
+ sess,
+ pasting_func=paste_v3_func,
+ save_seq=draw_seq, draw_order=draw_order)
+
+
+def main(model_name, test_image_name, sampling_num):
+ test_dataset = 'faces'
+ test_image_base_dir = 'sample_inputs'
+
+ sampling_base_dir = 'outputs/sampling'
+ model_base_dir = 'outputs/snapshot'
+
+ state_dependent = False
+ longer_infer_len = 100
+
+ draw_seq = False
+ draw_color_order = True
+
+ # set numpy output to something sensible
+ np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
+
+ main_testing(test_image_base_dir, test_dataset, test_image_name,
+ sampling_base_dir, model_base_dir, model_name, sampling_num,
+ draw_seq=draw_seq, draw_order=draw_color_order,
+ state_dependent=state_dependent, longer_infer_len=longer_infer_len)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input', '-i', type=str, default='', help="The test image name.")
+ parser.add_argument('--model', '-m', type=str, default='pretrain_faces', help="The trained model.")
+ parser.add_argument('--sample', '-s', type=int, default=1, help="The number of outputs.")
+ args = parser.parse_args()
+
+ assert args.input != ''
+ assert args.sample > 0
+
+ main(args.model, args.input, args.sample)
diff --git a/test_rough_sketch_simplification.py b/test_rough_sketch_simplification.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebe7f2ebcecfd2a207a8d41b948fd5c6bb62709e
--- /dev/null
+++ b/test_rough_sketch_simplification.py
@@ -0,0 +1,361 @@
+import numpy as np
+import os
+import tensorflow as tf
+from six.moves import range
+from PIL import Image
+import argparse
+
+import hyper_parameters as hparams
+from model_common_test import DiffPastingV3, VirtualSketchingModel
+from utils import reset_graph, load_checkpoint, update_hyperparams, draw, \
+ save_seq_data, image_pasting_v3_testing, draw_strokes
+from dataset_utils import load_dataset_testing
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def move_cursor_to_undrawn(current_pos_list, input_image_, patch_size,
+ move_min_dist, move_max_dist, trial_times=20):
+ """
+ :param current_pos_list: (select_times, 1, 2), [0.0, 1.0)
+ :param input_image_: (1, image_size, image_size, 3), [0-stroke, 1-BG]
+ :return: new_cursor_pos: (select_times, 1, 2), [0.0, 1.0)
+ """
+
+ def crop_patch(image, center, image_size, crop_size):
+ x0 = center[0] - crop_size // 2
+ x1 = x0 + crop_size
+ y0 = center[1] - crop_size // 2
+ y1 = y0 + crop_size
+ x0 = max(0, min(x0, image_size))
+ y0 = max(0, min(y0, image_size))
+ x1 = max(0, min(x1, image_size))
+ y1 = max(0, min(y1, image_size))
+ patch = image[y0:y1, x0:x1]
+ return patch
+
+ def isvalid_cursor(input_img, cursor, raster_size, image_size):
+ # input_img: (image_size, image_size, 3), [0.0-BG, 1.0-stroke]
+ cursor_large = cursor * float(image_size)
+ cursor_large = np.round(cursor_large).astype(np.int32)
+ input_crop_patch = crop_patch(input_img, cursor_large, image_size, raster_size)
+ if np.sum(input_crop_patch) > 0.0:
+ return True
+ else:
+ return False
+
+ def randomly_move_cursor(cursor_position, img_size, min_dist_p, max_dist_p):
+ # cursor_position: (2), [0.0, 1.0)
+ cursor_pos_large = cursor_position * img_size
+ min_dist = int(min_dist_p / 2.0 * img_size)
+ max_dist = int(max_dist_p / 2.0 * img_size)
+ rand_cursor_offset = np.random.randint(min_dist, max_dist, size=cursor_pos_large.shape)
+ rand_cursor_offset_sign = np.random.randint(0, 1 + 1, size=cursor_pos_large.shape)
+ rand_cursor_offset_sign[rand_cursor_offset_sign == 0] = -1
+ rand_cursor_offset = rand_cursor_offset * rand_cursor_offset_sign
+
+ new_cursor_pos_large = cursor_pos_large + rand_cursor_offset
+ new_cursor_pos_large = np.minimum(np.maximum(new_cursor_pos_large, 0), img_size - 1) # (2), large-level
+ new_cursor_pos = new_cursor_pos_large.astype(np.float32) / float(img_size)
+ return new_cursor_pos
+
+ input_image = 1.0 - input_image_[0] # (image_size, image_size, 3), [0-BG, 1-stroke]
+ img_size = input_image.shape[0]
+
+ new_cursor_pos = []
+ for cursor_i in range(current_pos_list.shape[0]):
+ curr_cursor = current_pos_list[cursor_i][0]
+
+ for trial_i in range(trial_times):
+ new_cursor = randomly_move_cursor(curr_cursor, img_size, move_min_dist, move_max_dist) # (2), [0.0, 1.0)
+
+ if isvalid_cursor(input_image, new_cursor, patch_size, img_size) or trial_i == trial_times - 1:
+ new_cursor_pos.append(new_cursor)
+ break
+
+ assert len(new_cursor_pos) == current_pos_list.shape[0]
+ new_cursor_pos = np.expand_dims(np.stack(new_cursor_pos, axis=0), axis=1) # (select_times, 1, 2), [0.0, 1.0)
+ return new_cursor_pos
+
+
+def sample(sess, model, input_photos, init_cursor, image_size, init_len, seq_lens,
+ state_dependent, pasting_func, round_stop_state_num,
+ min_dist_p, max_dist_p):
+ """Samples a sequence from a pre-trained model."""
+ select_times = 1
+ curr_canvas = np.zeros(dtype=np.float32,
+ shape=(select_times, image_size, image_size)) # [0.0-BG, 1.0-stroke]
+
+ initial_state = sess.run(model.initial_state)
+
+ params_list = [[] for _ in range(select_times)]
+ state_raw_list = [[] for _ in range(select_times)]
+ state_soft_list = [[] for _ in range(select_times)]
+ window_size_list = [[] for _ in range(select_times)]
+
+ round_cursor_list = []
+ round_length_real_list = []
+
+ input_photos_tiles = np.tile(input_photos, (select_times, 1, 1, 1))
+
+ for cursor_i, seq_len in enumerate(seq_lens):
+ if cursor_i == 0:
+ cursor_pos = np.squeeze(init_cursor, axis=0) # (select_times, 1, 2)
+ else:
+ cursor_pos = move_cursor_to_undrawn(cursor_pos, input_photos, model.hps.raster_size,
+ min_dist_p, max_dist_p) # (select_times, 1, 2)
+ round_cursor_list.append(cursor_pos)
+
+ prev_state = initial_state
+ prev_width = np.stack([model.hps.min_width for _ in range(select_times)], axis=0)
+ prev_scaling = np.ones((select_times), dtype=np.float32) # (N)
+ prev_window_size = np.ones((select_times), dtype=np.float32) * model.hps.raster_size # (N)
+
+ continuous_one_state_num = 0
+
+ for i in range(seq_len):
+ if not state_dependent and i % init_len == 0:
+ prev_state = initial_state
+
+ curr_window_size = prev_scaling * prev_window_size # (N)
+ curr_window_size = np.maximum(curr_window_size, model.hps.min_window_size)
+ curr_window_size = np.minimum(curr_window_size, image_size)
+
+ feed = {
+ model.initial_state: prev_state,
+ model.input_photo: input_photos_tiles,
+ model.curr_canvas_hard: curr_canvas.copy(),
+ model.cursor_position: cursor_pos,
+ model.image_size: image_size,
+ model.init_width: prev_width,
+ model.init_scaling: prev_scaling,
+ model.init_window_size: prev_window_size,
+ }
+
+ o_other_params_list, o_pen_list, o_pred_params_list, next_state_list = \
+ sess.run([model.other_params, model.pen_ras, model.pred_params, model.final_state], feed_dict=feed)
+ # o_other_params: (N, 6), o_pen: (N, 2), pred_params: (N, 1, 7), next_state: (N, 1024)
+ # o_other_params: [tanh*2, sigmoid*2, tanh*2, sigmoid*2]
+
+ idx_eos_list = np.argmax(o_pen_list, axis=1) # (N)
+
+ output_i = 0
+ idx_eos = idx_eos_list[output_i]
+
+ eos = [0, 0]
+ eos[idx_eos] = 1
+
+ other_params = o_other_params_list[output_i].tolist() # (6)
+ params_list[output_i].append([eos[1]] + other_params)
+ state_raw_list[output_i].append(o_pen_list[output_i][1])
+ state_soft_list[output_i].append(o_pred_params_list[output_i, 0, 0])
+ window_size_list[output_i].append(curr_window_size[output_i])
+
+ # draw the stroke and add to the canvas
+ x1y1, x2y2, width2 = o_other_params_list[output_i, 0:2], o_other_params_list[output_i, 2:4], \
+ o_other_params_list[output_i, 4]
+ x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
+ x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
+ x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
+ widths = np.stack([prev_width[output_i], width2], axis=0) # (2)
+ o_other_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1).tolist() # (8)
+
+ if idx_eos == 0:
+ f = o_other_params_proc + [1.0, 1.0]
+ pred_stroke_img = draw(f) # (raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ pred_stroke_img_large = image_pasting_v3_testing(1.0 - pred_stroke_img,
+ cursor_pos[output_i, 0],
+ image_size,
+ curr_window_size[output_i],
+ pasting_func, sess) # [0.0-BG, 1.0-stroke]
+ curr_canvas[output_i] += pred_stroke_img_large # [0.0-BG, 1.0-stroke]
+
+ continuous_one_state_num = 0
+ else:
+ continuous_one_state_num += 1
+
+ curr_canvas = np.clip(curr_canvas, 0.0, 1.0)
+
+ next_width = o_other_params_list[:, 4] # (N)
+ next_scaling = o_other_params_list[:, 5]
+ next_window_size = next_scaling * curr_window_size # (N)
+ next_window_size = np.maximum(next_window_size, model.hps.min_window_size)
+ next_window_size = np.minimum(next_window_size, image_size)
+
+ prev_state = next_state_list
+ prev_width = next_width * curr_window_size / next_window_size # (N,)
+ prev_scaling = next_scaling # (N)
+ prev_window_size = curr_window_size
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = o_other_params_list[:, 2:4] * (
+ np.expand_dims(curr_window_size, axis=-1) / 2.0) # (N, 2), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[:, 1:2], new_cursor_offset_next[:, 0:1]],
+ axis=-1)
+
+ cursor_pos_large = cursor_pos * float(image_size)
+ stroke_position_next = cursor_pos_large[:, 0, :] + new_cursor_offset_next # (N, 2), large-level
+
+ if model.hps.cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (N, 2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0),
+ float(image_size - 1)) # (N, 2), large-level
+ cursor_pos_large = np.expand_dims(cursor_pos_large, axis=1) # (N, 1, 2)
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ if continuous_one_state_num >= round_stop_state_num or i == seq_len - 1:
+ round_length_real_list.append(i + 1)
+ break
+
+ return params_list, state_raw_list, state_soft_list, curr_canvas, window_size_list, \
+ round_cursor_list, round_length_real_list
+
+
+def main_testing(test_image_base_dir, test_dataset, test_image_name,
+ sampling_base_dir, model_base_dir, model_name,
+ sampling_num,
+ min_dist_p, max_dist_p,
+ longer_infer_lens, round_stop_state_num,
+ draw_seq=False, draw_order=False,
+ state_dependent=True):
+ model_params_default = hparams.get_default_hparams_rough()
+ model_params = update_hyperparams(model_params_default, model_base_dir, model_name, infer_dataset=test_dataset)
+
+ [test_set, eval_hps_model, sample_hps_model] = \
+ load_dataset_testing(test_image_base_dir, test_dataset, test_image_name, model_params)
+
+ test_image_raw_name = test_image_name[:test_image_name.find('.')]
+ model_dir = os.path.join(model_base_dir, model_name)
+
+ reset_graph()
+ sampling_model = VirtualSketchingModel(sample_hps_model)
+
+ # differentiable pasting graph
+ paste_v3_func = DiffPastingV3(sample_hps_model.raster_size)
+
+ tfconfig = tf.ConfigProto()
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ # loads the weights from checkpoint into our model
+ snapshot_step = load_checkpoint(sess, model_dir, gen_model_pretrain=True)
+ print('snapshot_step', snapshot_step)
+ sampling_dir = os.path.join(sampling_base_dir, test_dataset + '__' + model_name)
+ os.makedirs(sampling_dir, exist_ok=True)
+
+ for sampling_i in range(sampling_num):
+ input_photos, init_cursors, test_image_size = test_set.get_test_image()
+ # input_photos: (1, image_size, image_size, 3), [0-stroke, 1-BG]
+ # init_cursors: (N, 1, 2), in size [0.0, 1.0)
+
+ print()
+ print(test_image_name, ', image_size:', test_image_size, ', sampling_i:', sampling_i)
+ print('Processing ...')
+
+ if init_cursors.ndim == 3:
+ init_cursors = np.expand_dims(init_cursors, axis=0)
+
+ input_photos = input_photos[0:1, :, :, :]
+
+ ori_img = (input_photos.copy()[0] * 255.0).astype(np.uint8)
+ ori_img_png = Image.fromarray(ori_img, 'RGB')
+ ori_img_png.save(os.path.join(sampling_dir, test_image_raw_name + '_input.png'), 'PNG')
+
+ # decoding for sampling
+ strokes_raw_out_list, states_raw_out_list, states_soft_out_list, pred_imgs_out, \
+ window_size_out_list, round_new_cursors, round_new_lengths = sample(
+ sess, sampling_model, input_photos, init_cursors, test_image_size,
+ eval_hps_model.max_seq_len, longer_infer_lens, state_dependent, paste_v3_func,
+ round_stop_state_num, min_dist_p, max_dist_p)
+ # pred_imgs_out: (N, H, W), [0.0-BG, 1.0-stroke]
+
+ print('## round_lengths:', len(round_new_lengths), ':', round_new_lengths)
+
+ output_i = 0
+ strokes_raw_out = np.stack(strokes_raw_out_list[output_i], axis=0)
+ states_raw_out = states_raw_out_list[output_i]
+ states_soft_out = states_soft_out_list[output_i]
+ window_size_out = window_size_out_list[output_i]
+
+ multi_cursors = [init_cursors[0, output_i, 0]]
+ for c_i in range(len(round_new_cursors)):
+ best_cursor = round_new_cursors[c_i][output_i, 0] # (2)
+ multi_cursors.append(best_cursor)
+ assert len(multi_cursors) == len(round_new_lengths)
+
+ print('strokes_raw_out', strokes_raw_out.shape)
+
+ clean_states_soft_out = np.array(states_soft_out) # (N)
+
+ flag_list = strokes_raw_out[:, 0].astype(np.int32) # (N)
+ drawing_len = len(flag_list) - np.sum(flag_list)
+ assert drawing_len >= 0
+
+ # print(' flag raw\t soft\t x1\t\t y1\t\t x2\t\t y2\t\t r2\t\t s2')
+ for i in range(strokes_raw_out.shape[0]):
+ flag, x1, y1, x2, y2, r2, s2 = strokes_raw_out[i]
+ win_size = window_size_out[i]
+ out_format = '#%d: %d | %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f'
+ out_values = (i, flag, states_raw_out[i], clean_states_soft_out[i], x1, y1, x2, y2, r2, s2)
+ out_log = out_format % out_values
+ # print(out_log)
+
+ print('Saving results ...')
+ save_seq_data(sampling_dir, test_image_raw_name + '_' + str(sampling_i),
+ strokes_raw_out, multi_cursors,
+ test_image_size, round_new_lengths, eval_hps_model.min_width)
+
+ draw_strokes(strokes_raw_out, sampling_dir, test_image_raw_name + '_' + str(sampling_i) + '_pred.png',
+ ori_img, test_image_size,
+ multi_cursors, round_new_lengths, eval_hps_model.min_width, eval_hps_model.cursor_type,
+ sample_hps_model.raster_size, sample_hps_model.min_window_size,
+ sess,
+ pasting_func=paste_v3_func,
+ save_seq=draw_seq, draw_order=draw_order)
+
+
+def main(model_name, test_image_name, sampling_num):
+ test_dataset = 'rough_sketches'
+ test_image_base_dir = 'sample_inputs'
+
+ sampling_base_dir = 'outputs/sampling'
+ model_base_dir = 'outputs/snapshot'
+
+ state_dependent = False
+ longer_infer_lens = [128 for _ in range(10)]
+ round_stop_state_num = 12
+ min_dist_p = 0.3
+ max_dist_p = 0.9
+
+ draw_seq = False
+ draw_color_order = True
+
+ # set numpy output to something sensible
+ np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
+
+ main_testing(test_image_base_dir, test_dataset, test_image_name,
+ sampling_base_dir, model_base_dir, model_name, sampling_num,
+ min_dist_p=min_dist_p, max_dist_p=max_dist_p,
+ draw_seq=draw_seq, draw_order=draw_color_order,
+ state_dependent=state_dependent, longer_infer_lens=longer_infer_lens,
+ round_stop_state_num=round_stop_state_num)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input', '-i', type=str, default='', help="The test image name.")
+ parser.add_argument('--model', '-m', type=str, default='pretrain_rough_sketches', help="The trained model.")
+ parser.add_argument('--sample', '-s', type=int, default=1, help="The number of outputs.")
+ args = parser.parse_args()
+
+ assert args.input != ''
+ assert args.sample > 0
+
+ main(args.model, args.input, args.sample)
diff --git a/test_vectorization.py b/test_vectorization.py
new file mode 100644
index 0000000000000000000000000000000000000000..31260f57057a39f331b71ec6ce6435822acbfdf1
--- /dev/null
+++ b/test_vectorization.py
@@ -0,0 +1,463 @@
+import numpy as np
+import random
+import os
+import tensorflow as tf
+from six.moves import range
+from PIL import Image
+import time
+import argparse
+
+import hyper_parameters as hparams
+from model_common_test import DiffPastingV3, VirtualSketchingModel
+from utils import reset_graph, load_checkpoint, update_hyperparams, draw, \
+ save_seq_data, image_pasting_v3_testing, draw_strokes
+from dataset_utils import load_dataset_testing
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def move_cursor_to_undrawn(current_canvas_list, input_image_, last_min_acc_list, grid_patch_size=128,
+ stroke_acc_threshold=0.95, stroke_num_threshold=5, continuous_min_acc_threshold=2):
+ """
+ :param current_canvas_list: (select_times, image_size, image_size), [0.0-BG, 1.0-stroke]
+ :param input_image_: (1, image_size, image_size), [0-stroke, 1-BG]
+ :return: new_cursor_pos: (select_times, 1, 2), [0.0, 1.0)
+ """
+ def split_images(in_img, image_size, grid_size):
+ if image_size % grid_size == 0:
+ paddings_ = 0
+ else:
+ paddings_ = grid_size - image_size % grid_size
+ paddings = [[0, paddings_],
+ [0, paddings_]]
+ image_pad = np.pad(in_img, paddings, mode='constant', constant_values=0.0) # (H_p, W_p), [0.0-BG, 1.0-stroke]
+
+ assert image_pad.shape[0] % grid_size == 0
+
+ split_num = image_pad.shape[0] // grid_size
+
+ images_h = np.hsplit(image_pad, split_num)
+ image_patches = []
+ for image_h in images_h:
+ images_v = np.vsplit(image_h, split_num)
+ image_patches += images_v
+ image_patches = np.array(image_patches, dtype=np.float32)
+ return image_patches, split_num
+
+ def line_drawing_rounding(line_drawing):
+ line_drawing_r = np.copy(line_drawing) # [0.0-BG, 1.0-stroke]
+ line_drawing_r[line_drawing_r != 0.0] = 1.0
+ return line_drawing_r
+
+ def cal_undrawn_pixels(in_canvas, in_sketch):
+ in_canvas_round = line_drawing_rounding(in_canvas).astype(np.int32) # (N, H, W), [0.0-BG, 1.0-stroke]
+ in_sketch_round = line_drawing_rounding(in_sketch).astype(np.int32)
+
+ intersection = np.bitwise_and(in_canvas_round, in_sketch_round)
+
+ intersection_sum = np.sum(intersection, axis=(1, 2))
+ gt_sum = np.sum(in_sketch_round, axis=(1, 2)) # (N)
+
+ undrawn_num = gt_sum - intersection_sum
+ return undrawn_num
+
+ def cal_stroke_acc(in_canvas, in_sketch):
+ in_canvas_round = line_drawing_rounding(in_canvas).astype(np.int32) # (N, H, W), [0.0-BG, 1.0-stroke]
+ in_sketch_round = line_drawing_rounding(in_sketch).astype(np.int32)
+
+ intersection = np.bitwise_and(in_canvas_round, in_sketch_round)
+
+ intersection_sum = np.sum(intersection, axis=(1, 2)).astype(np.float32)
+ gt_sum = np.sum(in_sketch_round, axis=(1, 2)).astype(np.float32) # (N)
+ undrawn_num = gt_sum - intersection_sum # (N)
+
+ stroke_acc = intersection_sum / gt_sum # (N)
+ stroke_acc[gt_sum == 0.0] = 1.0
+ stroke_acc[undrawn_num <= stroke_num_threshold] = 1.0
+ return stroke_acc
+
+ def get_cursor(patch_idx, img_size, grid_size, split_num):
+ y_pos = patch_idx % split_num
+ x_pos = patch_idx // split_num
+
+ y_top = y_pos * grid_size + grid_size // 4
+ y_bottom = y_top + grid_size // 2
+ x_left = x_pos * grid_size + grid_size // 4
+ x_right = x_left + grid_size // 2
+
+ cursor_y = random.randint(y_top, y_bottom)
+ cursor_x = random.randint(x_left, x_right)
+
+ cursor_y = max(0, min(cursor_y, img_size - 1))
+ cursor_x = max(0, min(cursor_x, img_size - 1)) # (2), in large size
+ center = np.array([cursor_x, cursor_y], dtype=np.float32)
+
+ return center / float(img_size) # (2), in size [0.0, 1.0)
+
+ input_image = 1.0 - input_image_[0] # (image_size, image_size), [0-BG, 1-stroke]
+ img_size = input_image.shape[0]
+
+ input_image_patches, split_number = split_images(input_image, img_size, grid_patch_size) # (N, grid_size, grid_size)
+
+ new_cursor_pos = []
+ last_min_acc_list_new = [item for item in last_min_acc_list]
+ for canvas_i in range(current_canvas_list.shape[0]):
+ curr_canvas = current_canvas_list[canvas_i] # (image_size, image_size), [0.0-BG, 1.0-stroke]
+
+ curr_canvas_patches, _ = split_images(curr_canvas, img_size, grid_patch_size) # (N, grid_size, grid_size)
+
+ # 1. detect ending flag by stroke accuracy
+ stroke_accuracy = cal_stroke_acc(curr_canvas_patches, input_image_patches)
+ min_acc_idx = np.argmin(stroke_accuracy)
+ min_acc= stroke_accuracy[min_acc_idx]
+ # print('min_acc_idx', min_acc_idx, ' | ', 'min_acc', min_acc)
+
+ if min_acc >= stroke_acc_threshold: # end of drawing
+ return None, None
+
+ # 2. detect undrawn pixels
+ undrawn_pixel_num = cal_undrawn_pixels(curr_canvas_patches, input_image_patches)
+ # undrawn_pixel_num_dis = np.reshape(undrawn_pixel_num, (split_number, split_number)).T
+ # print('undrawn_pixel_num_dis')
+ # print(undrawn_pixel_num_dis)
+
+ max_undrawn_idx = np.argmax(undrawn_pixel_num)
+ # max_undrawn = undrawn_pixel_num[max_undrawn_idx]
+ # print('max_undrawn_idx', max_undrawn_idx, ' | ', 'max_undrawn', max_undrawn)
+
+ # 3. select a random position
+ last_min_acc_idx, last_min_acc_times = last_min_acc_list[canvas_i]
+ if last_min_acc_times >= continuous_min_acc_threshold and last_min_acc_idx == min_acc_idx:
+ selected_patch_idx = last_min_acc_idx
+ new_min_acc_times = 1
+ else:
+ selected_patch_idx = max_undrawn_idx
+
+ if min_acc_idx == last_min_acc_idx:
+ new_min_acc_times = last_min_acc_times + 1
+ else:
+ new_min_acc_times = 1
+
+ new_min_acc_idx = min_acc_idx
+ last_min_acc_list_new[canvas_i] = (new_min_acc_idx, new_min_acc_times)
+ # print('selected_patch_idx', selected_patch_idx)
+
+ # 4. get cursor according to the selected_patch_idx
+ rand_cursor = get_cursor(selected_patch_idx, img_size, grid_patch_size, split_number) # (2), in size [0.0, 1.0)
+ new_cursor_pos.append(rand_cursor)
+
+ assert len(new_cursor_pos) == current_canvas_list.shape[0]
+ new_cursor_pos = np.expand_dims(np.stack(new_cursor_pos, axis=0), axis=1) # (select_times, 1, 2), [0.0, 1.0)
+ return new_cursor_pos, last_min_acc_list_new
+
+
+def sample(sess, model, input_photos, init_cursor, image_size, init_len, seq_lens, state_dependent,
+ pasting_func, round_stop_state_num, stroke_acc_threshold):
+ """Samples a sequence from a pre-trained model."""
+ select_times = 1
+ curr_canvas = np.zeros(dtype=np.float32,
+ shape=(select_times, image_size, image_size)) # [0.0-BG, 1.0-stroke]
+
+ initial_state = sess.run(model.initial_state)
+ prev_width = np.stack([model.hps.min_width for _ in range(select_times)], axis=0)
+
+ params_list = [[] for _ in range(select_times)]
+ state_raw_list = [[] for _ in range(select_times)]
+ state_soft_list = [[] for _ in range(select_times)]
+ window_size_list = [[] for _ in range(select_times)]
+
+ last_min_stroke_acc_list = [(-1, 0) for _ in range(select_times)]
+
+ round_cursor_list = []
+ round_length_real_list = []
+
+ input_photos_tiles = np.tile(input_photos, (select_times, 1, 1))
+
+ for cursor_i, seq_len in enumerate(seq_lens):
+ # print('\n')
+ # print('@@ Round', cursor_i + 1)
+ if cursor_i == 0:
+ cursor_pos = np.squeeze(init_cursor, axis=0) # (select_times, 1, 2)
+ else:
+ cursor_pos, last_min_stroke_acc_list_updated = \
+ move_cursor_to_undrawn(curr_canvas, input_photos, last_min_stroke_acc_list,
+ grid_patch_size=model.hps.raster_size,
+ stroke_acc_threshold=stroke_acc_threshold) # (select_times, 1, 2)
+ if cursor_pos is not None:
+ round_cursor_list.append(cursor_pos)
+ last_min_stroke_acc_list = last_min_stroke_acc_list_updated
+ else:
+ break
+
+ prev_state = initial_state
+ if not model.hps.init_cursor_on_undrawn_pixel:
+ prev_width = np.stack([model.hps.min_width for _ in range(select_times)], axis=0)
+ prev_scaling = np.ones((select_times), dtype=np.float32) # (N)
+ prev_window_size = np.ones((select_times), dtype=np.float32) * model.hps.raster_size # (N)
+
+ continuous_one_state_num = 0
+
+ for i in range(seq_len):
+ if not state_dependent and i % init_len == 0:
+ prev_state = initial_state
+
+ curr_window_size = prev_scaling * prev_window_size # (N)
+ curr_window_size = np.maximum(curr_window_size, model.hps.min_window_size)
+ curr_window_size = np.minimum(curr_window_size, image_size)
+
+ feed = {
+ model.initial_state: prev_state,
+ model.input_photo: np.expand_dims(input_photos_tiles, axis=-1),
+ model.curr_canvas_hard: curr_canvas.copy(),
+ model.cursor_position: cursor_pos,
+ model.image_size: image_size,
+ model.init_width: prev_width,
+ model.init_scaling: prev_scaling,
+ model.init_window_size: prev_window_size,
+ }
+
+ o_other_params_list, o_pen_list, o_pred_params_list, next_state_list = \
+ sess.run([model.other_params, model.pen_ras, model.pred_params, model.final_state], feed_dict=feed)
+ # o_other_params: (N, 6), o_pen: (N, 2), pred_params: (N, 1, 7), next_state: (N, 1024)
+ # o_other_params: [tanh*2, sigmoid*2, tanh*2, sigmoid*2]
+
+ idx_eos_list = np.argmax(o_pen_list, axis=1) # (N)
+
+ output_i = 0
+ idx_eos = idx_eos_list[output_i]
+
+ eos = [0, 0]
+ eos[idx_eos] = 1
+
+ other_params = o_other_params_list[output_i].tolist() # (6)
+ params_list[output_i].append([eos[1]] + other_params)
+ state_raw_list[output_i].append(o_pen_list[output_i][1])
+ state_soft_list[output_i].append(o_pred_params_list[output_i, 0, 0])
+ window_size_list[output_i].append(curr_window_size[output_i])
+
+ # draw the stroke and add to the canvas
+ x1y1, x2y2, width2 = o_other_params_list[output_i, 0:2], o_other_params_list[output_i, 2:4], \
+ o_other_params_list[output_i, 4]
+ x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
+ x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
+ x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
+ widths = np.stack([prev_width[output_i], width2], axis=0) # (2)
+ o_other_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1).tolist() # (8)
+
+ if idx_eos == 0:
+ f = o_other_params_proc + [1.0, 1.0]
+ pred_stroke_img = draw(f) # (raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ pred_stroke_img_large = image_pasting_v3_testing(1.0 - pred_stroke_img, cursor_pos[output_i, 0],
+ image_size,
+ curr_window_size[output_i],
+ pasting_func, sess) # [0.0-BG, 1.0-stroke]
+ curr_canvas[output_i] += pred_stroke_img_large # [0.0-BG, 1.0-stroke]
+
+ continuous_one_state_num = 0
+ else:
+ continuous_one_state_num += 1
+
+ curr_canvas = np.clip(curr_canvas, 0.0, 1.0)
+
+ next_width = o_other_params_list[:, 4] # (N)
+ next_scaling = o_other_params_list[:, 5]
+ next_window_size = next_scaling * curr_window_size # (N)
+ next_window_size = np.maximum(next_window_size, model.hps.min_window_size)
+ next_window_size = np.minimum(next_window_size, image_size)
+
+ prev_state = next_state_list
+ prev_width = next_width * curr_window_size / next_window_size # (N,)
+ prev_scaling = next_scaling # (N)
+ prev_window_size = curr_window_size
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = o_other_params_list[:, 2:4] * (np.expand_dims(curr_window_size, axis=-1) / 2.0) # (N, 2), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[:, 1:2], new_cursor_offset_next[:, 0:1]], axis=-1)
+
+ cursor_pos_large = cursor_pos * float(image_size)
+ stroke_position_next = cursor_pos_large[:, 0, :] + new_cursor_offset_next # (N, 2), large-level
+
+ if model.hps.cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (N, 2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (N, 2), large-level
+ cursor_pos_large = np.expand_dims(cursor_pos_large, axis=1) # (N, 1, 2)
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ if continuous_one_state_num >= round_stop_state_num or i == seq_len - 1:
+ round_length_real_list.append(i + 1)
+ break
+
+ return params_list, state_raw_list, state_soft_list, curr_canvas, window_size_list, \
+ round_cursor_list, round_length_real_list
+
+
+def main_testing(test_image_base_dir, test_dataset, test_image_name,
+ sampling_base_dir, model_base_dir, model_name,
+ sampling_num,
+ longer_infer_lens,
+ round_stop_state_num, stroke_acc_threshold,
+ draw_seq=False, draw_order=False,
+ state_dependent=True):
+ model_params_default = hparams.get_default_hparams_clean()
+ model_params = update_hyperparams(model_params_default, model_base_dir, model_name, infer_dataset=test_dataset)
+
+ [test_set, eval_hps_model, sample_hps_model] \
+ = load_dataset_testing(test_image_base_dir, test_dataset, test_image_name, model_params)
+
+ test_image_raw_name = test_image_name[:test_image_name.find('.')]
+ model_dir = os.path.join(model_base_dir, model_name)
+
+ reset_graph()
+ sampling_model = VirtualSketchingModel(sample_hps_model)
+
+ # differentiable pasting graph
+ paste_v3_func = DiffPastingV3(sample_hps_model.raster_size)
+
+ tfconfig = tf.ConfigProto()
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ # loads the weights from checkpoint into our model
+ snapshot_step = load_checkpoint(sess, model_dir, gen_model_pretrain=True)
+ print('snapshot_step', snapshot_step)
+ sampling_dir = os.path.join(sampling_base_dir, test_dataset + '__' + model_name)
+ os.makedirs(sampling_dir, exist_ok=True)
+
+ stroke_number_list = []
+ compute_time_list = []
+
+ for sampling_i in range(sampling_num):
+ start_time_point = time.time()
+ input_photos, init_cursors, test_image_size = test_set.get_test_image()
+ # input_photos: (1, image_size, image_size), [0-stroke, 1-BG]
+ # init_cursors: (1, 1, 2), in size [0.0, 1.0)
+
+ print()
+ print(test_image_name, ', image_size:', test_image_size, ', sampling_i:', sampling_i)
+ print('Processing ...')
+
+ if init_cursors.ndim == 3:
+ init_cursors = np.expand_dims(init_cursors, axis=0)
+
+ input_photos = input_photos[0:1, :, :]
+ ori_img = (input_photos.copy()[0] * 255.0).astype(np.uint8)
+ ori_img = np.stack([ori_img for _ in range(3)], axis=2)
+ ori_img_png = Image.fromarray(ori_img, 'RGB')
+ ori_img_png.save(os.path.join(sampling_dir, test_image_raw_name + '_input.png'), 'PNG')
+
+ data_loading_time_point = time.time()
+
+ # decoding for sampling
+ strokes_raw_out_list, states_raw_out_list, states_soft_out_list, pred_imgs_out, \
+ window_size_out_list, round_new_cursors, round_new_lengths = sample(
+ sess, sampling_model, input_photos, init_cursors, test_image_size,
+ eval_hps_model.max_seq_len, longer_infer_lens, state_dependent,
+ paste_v3_func, round_stop_state_num, stroke_acc_threshold)
+ # pred_imgs_out: [0.0-BG, 1.0-stroke]
+
+ print('## round_lengths:', len(round_new_lengths), ':', round_new_lengths)
+
+ sampling_time_point = time.time()
+
+ data_loading_time = data_loading_time_point - start_time_point
+ sampling_time_total = sampling_time_point - start_time_point
+ sampling_time_wo_data_loading = sampling_time_point - data_loading_time_point
+ compute_time_list.append(sampling_time_total)
+ # print(' >>> data_loading_time', data_loading_time)
+ print(' >>> sampling_time_total', sampling_time_total)
+ # print(' >>> sampling_time_wo_data_loading', sampling_time_wo_data_loading)
+
+ best_result_idx = 0
+ strokes_raw_out = np.stack(strokes_raw_out_list[best_result_idx], axis=0)
+ states_raw_out = states_raw_out_list[best_result_idx]
+ states_soft_out = states_soft_out_list[best_result_idx]
+ window_size_out = window_size_out_list[best_result_idx]
+
+ multi_cursors = [init_cursors[0, best_result_idx, 0]]
+ for c_i in range(len(round_new_cursors)):
+ best_cursor = round_new_cursors[c_i][best_result_idx, 0] # (2)
+ multi_cursors.append(best_cursor)
+ assert len(multi_cursors) == len(round_new_lengths)
+
+ print('strokes_raw_out', strokes_raw_out.shape)
+ stroke_number_list.append(strokes_raw_out.shape[0])
+
+ clean_states_soft_out = np.array(states_soft_out) # (N)
+
+ flag_list = strokes_raw_out[:, 0].astype(np.int32) # (N)
+ drawing_len = len(flag_list) - np.sum(flag_list)
+ assert drawing_len >= 0
+
+ # print(' flag raw\t soft\t x1\t\t y1\t\t x2\t\t y2\t\t r2\t\t s2')
+ for i in range(strokes_raw_out.shape[0]):
+ flag, x1, y1, x2, y2, r2, s2 = strokes_raw_out[i]
+ win_size = window_size_out[i]
+ out_format = '#%d: %d | %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f, %.4f'
+ out_values = (i, flag, states_raw_out[i], clean_states_soft_out[i], x1, y1, x2, y2, r2, s2)
+ out_log = out_format % out_values
+ # print(out_log)
+
+ print('Saving results ...')
+ save_seq_data(sampling_dir, test_image_raw_name + '_' + str(sampling_i),
+ strokes_raw_out, multi_cursors,
+ test_image_size, round_new_lengths, eval_hps_model.min_width)
+
+ draw_strokes(strokes_raw_out, sampling_dir, test_image_raw_name + '_' + str(sampling_i) + '_pred.png',
+ ori_img, test_image_size,
+ multi_cursors, round_new_lengths, eval_hps_model.min_width, eval_hps_model.cursor_type,
+ sample_hps_model.raster_size, sample_hps_model.min_window_size,
+ sess,
+ pasting_func=paste_v3_func,
+ save_seq=draw_seq, draw_order=draw_order)
+
+ average_stroke_number = np.mean(stroke_number_list)
+ average_compute_time = np.mean(compute_time_list)
+ print()
+ print('@@@ Total summary:')
+ print(' >>> average_stroke_number', average_stroke_number)
+ print(' >>> average_compute_time', average_compute_time)
+
+
+def main(model_name, test_image_name, sampling_num):
+ test_dataset = 'clean_line_drawings'
+ test_image_base_dir = 'sample_inputs'
+
+ sampling_base_dir = 'outputs/sampling'
+ model_base_dir = 'outputs/snapshot'
+
+ state_dependent = False
+ longer_infer_lens = [500 for _ in range(10)]
+ round_stop_state_num = 12
+ stroke_acc_threshold = 0.95
+
+ draw_seq = False
+ draw_color_order = True
+
+ # set numpy output to something sensible
+ np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
+
+ main_testing(test_image_base_dir, test_dataset, test_image_name,
+ sampling_base_dir, model_base_dir, model_name, sampling_num,
+ draw_seq=draw_seq, draw_order=draw_color_order,
+ state_dependent=state_dependent, longer_infer_lens=longer_infer_lens,
+ round_stop_state_num=round_stop_state_num, stroke_acc_threshold=stroke_acc_threshold)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input', '-i', type=str, default='', help="The test image name.")
+ parser.add_argument('--model', '-m', type=str, default='pretrain_clean_line_drawings', help="The trained model.")
+ parser.add_argument('--sample', '-s', type=int, default=1, help="The number of outputs.")
+ args = parser.parse_args()
+
+ assert args.input != ''
+ assert args.sample > 0
+
+ main(args.model, args.input, args.sample)
diff --git a/tools/gif_making.py b/tools/gif_making.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1cf77f9333895dacf58bb669879bd20645f924a
--- /dev/null
+++ b/tools/gif_making.py
@@ -0,0 +1,209 @@
+import os
+import sys
+import argparse
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+sys.path.append('./')
+from utils import draw, image_pasting_v3_testing
+from model_common_test import DiffPastingV3
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def add_scaling_visualization(canvas_images, cursor, window_size, image_size):
+ """
+ :param canvas_images: (N, H, W, 3)
+ :param cursor:
+ :param window_size:
+ :param image_size:
+ :return:
+ """
+ cursor_pos = cursor * float(image_size)
+ cursor_x, cursor_y = int(round(cursor_pos[0])), int(round(cursor_pos[1])) # in large size
+
+ vis_color = [255, 0, 0]
+ cursor_width = 3
+ box_width = 2
+
+ canvas_imgs = 255 - np.round(canvas_images * 255.0).astype(np.uint8)
+
+ # add cursor visualization
+ canvas_imgs[:, cursor_y - cursor_width: cursor_y + cursor_width, cursor_x - cursor_width: cursor_x + cursor_width, :] = vis_color
+
+ # add box visualization
+ up = max(0, cursor_y - window_size // 2)
+ down = min(image_size, cursor_y + window_size // 2)
+ left = max(0, cursor_x - window_size // 2)
+ right = min(image_size, cursor_x + window_size // 2)
+ # up = cursor_y - window_size // 2
+ # down = cursor_y + window_size // 2
+ # left = cursor_x - window_size // 2
+ # right = cursor_x + window_size // 2
+
+ if up > 0:
+ canvas_imgs[:, up: up + box_width, left: right, :] = vis_color
+ if down < image_size:
+ canvas_imgs[:, down - box_width: down, left: right, :] = vis_color
+ if left > 0:
+ canvas_imgs[:, up: down, left: left + box_width, :] = vis_color
+ if right < image_size:
+ canvas_imgs[:, up: down, right - box_width: right, :] = vis_color
+ return canvas_imgs
+
+
+def make_gif(sess, pasting_func, data, init_cursor, image_size, infer_lengths, init_width,
+ save_base,
+ cursor_type='next', min_window_size=32, raster_size=128, add_box=True):
+ """
+ :param data: (N_strokes, 9): flag, x0, y0, x1, y1, x2, y2, r0, r2
+ :return:
+ """
+ canvas = np.zeros((image_size, image_size), dtype=np.float32) # [0.0-BG, 1.0-stroke]
+ gif_frames = []
+
+ cursor_idx = 0
+
+ if init_cursor.ndim == 1:
+ init_cursor = [init_cursor]
+
+ for round_idx in range(len(infer_lengths)):
+ print('Making progress', round_idx + 1, '/', len(infer_lengths))
+ round_length = infer_lengths[round_idx]
+
+ cursor_pos = init_cursor[cursor_idx] # (2)
+ cursor_idx += 1
+
+ prev_width = init_width
+ prev_scaling = 1.0
+ prev_window_size = float(raster_size) # (1)
+
+ for round_inner_i in range(round_length):
+ stroke_idx = np.sum(infer_lengths[:round_idx]).astype(np.int32) + round_inner_i
+
+ curr_window_size_raw = prev_scaling * prev_window_size
+ curr_window_size_raw = np.maximum(curr_window_size_raw, min_window_size)
+ curr_window_size_raw = np.minimum(curr_window_size_raw, image_size)
+ curr_window_size = int(round(curr_window_size_raw)) # ()
+
+ pen_state = data[stroke_idx, 0]
+ stroke_params = data[stroke_idx, 1:] # (8)
+
+ x1y1, x2y2, width2, scaling2 = stroke_params[0:2], stroke_params[2:4], stroke_params[4], stroke_params[5]
+ x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
+ x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
+ x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
+ widths = np.stack([prev_width, width2], axis=0) # (2)
+ stroke_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1) # (8)
+
+ next_width = stroke_params[4]
+ next_scaling = stroke_params[5]
+ next_window_size = next_scaling * curr_window_size_raw
+ next_window_size = np.maximum(next_window_size, min_window_size)
+ next_window_size = np.minimum(next_window_size, image_size)
+
+ prev_width = next_width * curr_window_size_raw / next_window_size
+ prev_scaling = next_scaling
+ prev_window_size = curr_window_size_raw
+
+ f = stroke_params_proc.tolist() # (8)
+ f += [1.0, 1.0]
+ gt_stroke_img = draw(f) # (H, W), [0.0-stroke, 1.0-BG]
+
+ gt_stroke_img_large = image_pasting_v3_testing(1.0 - gt_stroke_img, cursor_pos,
+ image_size,
+ curr_window_size_raw,
+ pasting_func, sess) # [0.0-BG, 1.0-stroke]
+
+ if pen_state == 0:
+ canvas += gt_stroke_img_large # [0.0-BG, 1.0-stroke]
+
+ canvas_rgb = np.stack([np.clip(canvas, 0.0, 1.0) for _ in range(3)], axis=-1)
+
+ if add_box:
+ vis_inputs = np.expand_dims(canvas_rgb, axis=0)
+ vis_outputs = add_scaling_visualization(vis_inputs, cursor_pos, curr_window_size, image_size)
+ canvas_vis = vis_outputs[0]
+ else:
+ canvas_vis = canvas_rgb
+
+ canvas_vis_png = Image.fromarray(canvas_vis, 'RGB')
+ gif_frames.append(canvas_vis_png)
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = stroke_params[2:4] * (float(curr_window_size_raw) / 2.0) # (1, 6), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[1:2], new_cursor_offset_next[0:1]], axis=-1)
+
+ cursor_pos_large = cursor_pos * float(image_size)
+
+ stroke_position_next = cursor_pos_large + new_cursor_offset_next # (2), large-level
+
+ if cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (2), large-level
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ print('Saving to GIF ...')
+ save_path = os.path.join(save_base, 'dynamic.gif')
+ first_frame = gif_frames[0]
+ first_frame.save(save_path, save_all=True, append_images=gif_frames, loop=0, duration=0.01)
+
+
+def gif_making(npz_path):
+ assert npz_path != ''
+
+ min_window_size = 32
+ raster_size = 128
+
+ split_idx = npz_path.rfind('/')
+ if split_idx == -1:
+ file_base = './'
+ file_name = npz_path[:-4]
+ else:
+ file_base = npz_path[:npz_path.rfind('/')]
+ file_name = npz_path[npz_path.rfind('/') + 1: -4]
+
+ gif_base = os.path.join(file_base, file_name)
+ os.makedirs(gif_base, exist_ok=True)
+
+ # differentiable pasting graph
+ paste_v3_func = DiffPastingV3(raster_size)
+
+ tfconfig = tf.ConfigProto()
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ data = np.load(npz_path, encoding='latin1', allow_pickle=True)
+ strokes_data = data['strokes_data']
+ init_cursors = data['init_cursors']
+ image_size = data['image_size']
+ round_length = data['round_length']
+ init_width = data['init_width']
+
+ if round_length.ndim == 0:
+ round_lengths = [round_length]
+ else:
+ round_lengths = round_length
+
+ # print('round_lengths', round_lengths)
+
+ make_gif(sess, paste_v3_func,
+ strokes_data, init_cursors, image_size, round_lengths, init_width,
+ gif_base,
+ min_window_size=min_window_size, raster_size=raster_size)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--file', '-f', type=str, default='', help="define a npz path")
+ args = parser.parse_args()
+
+ gif_making(args.file)
diff --git a/tools/svg_conversion.py b/tools/svg_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddeea7a14ece96a70419181897d78ee5ecd2aa0e
--- /dev/null
+++ b/tools/svg_conversion.py
@@ -0,0 +1,242 @@
+import os
+import argparse
+import numpy as np
+from xml.dom import minidom
+
+
+def write_svg_1(path_list, img_size, save_path):
+ ''' A long curve consisting of several strokes forms a path. '''
+ impl = minidom.getDOMImplementation()
+
+ doc = impl.createDocument(None, None, None)
+
+ rootElement = doc.createElement('svg')
+ rootElement.setAttribute('xmlns', 'http://www.w3.org/2000/svg')
+ rootElement.setAttribute('height', str(img_size))
+ rootElement.setAttribute('width', str(img_size))
+
+ path_num = len(path_list)
+ for path_i in range(path_num):
+ path_items = path_list[path_i]
+
+ assert len(path_items) > 0
+ if len(path_items) == 1:
+ continue
+
+ childElement = doc.createElement('path')
+ childElement.setAttribute('id', 'curve_' + str(path_i))
+ childElement.setAttribute('stroke', '#000000')
+ childElement.setAttribute('stroke-width', '3.5')
+ childElement.setAttribute('stroke-linejoin', 'round')
+ childElement.setAttribute('stroke-linecap', 'round')
+ childElement.setAttribute('fill', 'none')
+
+ command_str = ''
+ for stroke_i, stroke_item in enumerate(path_items):
+ if stroke_i == 0:
+ command_str += 'M '
+ stroke_position = stroke_item[0]
+ command_str += str(stroke_position[0]) + ', ' + str(stroke_position[1]) + ' '
+ else:
+ command_str += 'Q '
+ ctrl_position, stroke_position, stroke_width = stroke_item[0], stroke_item[1], stroke_item[2]
+
+ ctrl_position_0 = last_position[0] + (stroke_position[0] - last_position[0]) * ctrl_position[1]
+ ctrl_position_1 = last_position[1] + (stroke_position[1] - last_position[1]) * ctrl_position[0]
+
+ command_str += str(ctrl_position_0) + ', ' + str(ctrl_position_1) + ', ' + \
+ str(stroke_position[0]) + ', ' + str(stroke_position[1]) + ' '
+
+ last_position = stroke_position
+
+ childElement.setAttribute('d', command_str)
+ rootElement.appendChild(childElement)
+
+ doc.appendChild(rootElement)
+
+ f = open(save_path, 'w')
+ doc.writexml(f, addindent=' ', newl='\n')
+ f.close()
+
+
+def write_svg_2(path_list, img_size, save_path):
+ ''' A single stroke forms a path. '''
+ impl = minidom.getDOMImplementation()
+
+ doc = impl.createDocument(None, None, None)
+
+ rootElement = doc.createElement('svg')
+ rootElement.setAttribute('xmlns', 'http://www.w3.org/2000/svg')
+ rootElement.setAttribute('height', str(img_size))
+ rootElement.setAttribute('width', str(img_size))
+
+ path_num = len(path_list)
+ for path_i in range(path_num):
+ path_items = path_list[path_i]
+
+ assert len(path_items) > 0
+ if len(path_items) == 1:
+ continue
+
+ for stroke_i, stroke_item in enumerate(path_items):
+ if stroke_i == 0:
+ last_position = stroke_item[0]
+ else:
+ childElement = doc.createElement('path')
+ childElement.setAttribute('id', 'curve_' + str(path_i))
+ childElement.setAttribute('stroke', '#000000')
+ childElement.setAttribute('stroke-linejoin', 'round')
+ childElement.setAttribute('stroke-linecap', 'round')
+ childElement.setAttribute('fill', 'none')
+
+ command_str = 'M ' + str(last_position[0]) + ', ' + str(last_position[1]) + ' '
+ command_str += 'Q '
+
+ ctrl_position, stroke_position, stroke_width = stroke_item[0], stroke_item[1], stroke_item[2]
+
+ ctrl_position_0 = last_position[0] + (stroke_position[0] - last_position[0]) * ctrl_position[1]
+ ctrl_position_1 = last_position[1] + (stroke_position[1] - last_position[1]) * ctrl_position[0]
+
+ command_str += str(ctrl_position_0) + ', ' + str(ctrl_position_1) + ', ' + \
+ str(stroke_position[0]) + ', ' + str(stroke_position[1]) + ' '
+
+ last_position = stroke_position
+
+ childElement.setAttribute('d', command_str)
+ childElement.setAttribute('stroke-width', str(stroke_width * img_size / 1.66))
+ rootElement.appendChild(childElement)
+
+ doc.appendChild(rootElement)
+
+ f = open(save_path, 'w')
+ doc.writexml(f, addindent=' ', newl='\n')
+ f.close()
+
+
+def convert_strokes_to_svg(data, init_cursor, image_size, infer_lengths, init_width, save_path, svg_type,
+ cursor_type='next', min_window_size=32, raster_size=128):
+ """
+ :param data: (N_strokes, 7): flag, x_c, y_c, dx, dy, r, ds
+ :return:
+ """
+ cursor_idx = 0
+
+ absolute_strokes = []
+ absolute_strokes_path = []
+
+ if init_cursor.ndim == 1:
+ init_cursor = [init_cursor]
+
+ for round_idx in range(len(infer_lengths)):
+ round_length = infer_lengths[round_idx]
+
+ cursor_pos = init_cursor[cursor_idx] # (2)
+ cursor_idx += 1
+
+ cursor_pos_large = cursor_pos * float(image_size)
+
+ if len(absolute_strokes_path) > 0:
+ absolute_strokes.append(absolute_strokes_path)
+ absolute_strokes_path = [[cursor_pos_large]]
+
+ prev_width = init_width
+ prev_scaling = 1.0
+ prev_window_size = float(raster_size) # (1)
+
+ for round_inner_i in range(round_length):
+ stroke_idx = np.sum(infer_lengths[:round_idx]).astype(np.int32) + round_inner_i
+
+ curr_window_size_raw = prev_scaling * prev_window_size
+ curr_window_size_raw = np.maximum(curr_window_size_raw, min_window_size)
+ curr_window_size_raw = np.minimum(curr_window_size_raw, image_size)
+ # curr_window_size = int(round(curr_window_size_raw)) # ()
+
+ stroke_params = data[stroke_idx, 1:] # (6)
+ pen_state = data[stroke_idx, 0]
+
+ next_width = stroke_params[4]
+ next_scaling = stroke_params[5]
+
+ next_width_abs = next_width * curr_window_size_raw / float(image_size)
+
+ prev_scaling = next_scaling
+ prev_window_size = curr_window_size_raw
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = stroke_params[2:4] * (float(curr_window_size_raw) / 2.0) # (1, 6), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[1:2], new_cursor_offset_next[0:1]], axis=-1)
+ cursor_pos_large = cursor_pos * float(image_size)
+ stroke_position_next = cursor_pos_large + new_cursor_offset_next # (2), large-level
+
+ if pen_state == 0:
+ absolute_strokes_path.append([stroke_params[0:2], stroke_position_next, next_width_abs])
+ else:
+ absolute_strokes.append(absolute_strokes_path)
+ absolute_strokes_path = [[stroke_position_next]]
+
+ if cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (2), large-level
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ absolute_strokes.append(absolute_strokes_path)
+
+ if svg_type == 'cluster':
+ write_svg_1(absolute_strokes, image_size, save_path)
+ elif svg_type == 'single':
+ write_svg_2(absolute_strokes, image_size, save_path)
+ else:
+ raise Exception('Unknown svg_type', svg_type)
+
+
+def data_convert_to_absolute(npz_path, svg_type):
+ assert npz_path != ''
+ assert svg_type in ['single', 'cluster']
+
+ min_window_size = 32
+ raster_size = 128
+
+ split_idx = npz_path.rfind('/')
+ if split_idx == -1:
+ file_base = './'
+ file_name = npz_path[:-4]
+ else:
+ file_base = npz_path[:npz_path.rfind('/')]
+ file_name = npz_path[npz_path.rfind('/') + 1: -4]
+
+ svg_data_base = os.path.join(file_base, file_name)
+ os.makedirs(svg_data_base, exist_ok=True)
+
+ data = np.load(npz_path, encoding='latin1', allow_pickle=True)
+ strokes_data = data['strokes_data']
+ init_cursors = data['init_cursors']
+ image_size = data['image_size']
+ round_length = data['round_length']
+ init_width = data['init_width']
+
+ if round_length.ndim == 0:
+ round_lengths = [round_length]
+ else:
+ round_lengths = round_length
+
+ save_path = os.path.join(svg_data_base, str(svg_type) + '.svg')
+
+ convert_strokes_to_svg(strokes_data, init_cursors, image_size, round_lengths, init_width,
+ min_window_size=min_window_size, raster_size=raster_size, save_path=save_path,
+ svg_type=svg_type)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--file', '-f', type=str, default='', help="define a npz path")
+ parser.add_argument('--svg_type', '-st', type=str, choices=['single', 'cluster'], default='single',
+ help="svg type")
+ args = parser.parse_args()
+
+ data_convert_to_absolute(args.file, args.svg_type)
diff --git a/tools/visualize_drawing.py b/tools/visualize_drawing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cf0add4b574e20b83583edfe4f72365b724c4a7
--- /dev/null
+++ b/tools/visualize_drawing.py
@@ -0,0 +1,223 @@
+import os
+import sys
+import argparse
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+sys.path.append('./')
+from utils import get_colors, draw, image_pasting_v3_testing
+from model_common_test import DiffPastingV3
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def display_strokes_final(sess, pasting_func, data, init_cursor, image_size, infer_lengths, init_width,
+ save_base,
+ cursor_type='next', min_window_size=32, raster_size=128):
+ """
+ :param data: (N_strokes, 9): flag, x0, y0, x1, y1, x2, y2, r0, r2
+ :return:
+ """
+ canvas = np.zeros((image_size, image_size), dtype=np.float32) # [0.0-BG, 1.0-stroke]
+ drawn_region = np.zeros_like(canvas)
+ overlap_region = np.zeros_like(canvas)
+ canvas_color_with_overlap = np.zeros((image_size, image_size, 3), dtype=np.float32)
+ canvas_color_wo_overlap = np.zeros((image_size, image_size, 3), dtype=np.float32)
+ canvas_color_with_moving = np.zeros((image_size, image_size, 3), dtype=np.float32)
+
+ cursor_idx = 0
+
+ if init_cursor.ndim == 1:
+ init_cursor = [init_cursor]
+
+ stroke_count = len(data)
+ color_rgb_set = get_colors(stroke_count) # list of (3,) in [0, 255]
+ color_idx = 0
+
+ valid_stroke_count = stroke_count - np.sum(data[:, 0]).astype(np.int32) + len(init_cursor)
+ valid_color_rgb_set = get_colors(valid_stroke_count) # list of (3,) in [0, 255]
+ valid_color_idx = -1
+
+ # print('Drawn stroke number', valid_stroke_count)
+ # print(' flag x1\t\t y1\t\t x2\t\t y2\t\t r2\t\t s2')
+
+ for round_idx in range(len(infer_lengths)):
+ round_length = infer_lengths[round_idx]
+
+ cursor_pos = init_cursor[cursor_idx] # (2)
+ cursor_idx += 1
+
+ prev_width = init_width
+ prev_scaling = 1.0
+ prev_window_size = float(raster_size) # (1)
+
+ for round_inner_i in range(round_length):
+ stroke_idx = np.sum(infer_lengths[:round_idx]).astype(np.int32) + round_inner_i
+
+ curr_window_size_raw = prev_scaling * prev_window_size
+ curr_window_size_raw = np.maximum(curr_window_size_raw, min_window_size)
+ curr_window_size_raw = np.minimum(curr_window_size_raw, image_size)
+
+ pen_state = data[stroke_idx, 0]
+ stroke_params = data[stroke_idx, 1:] # (8)
+
+ x1y1, x2y2, width2, scaling2 = stroke_params[0:2], stroke_params[2:4], stroke_params[4], stroke_params[5]
+ x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
+ x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
+ x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
+ widths = np.stack([prev_width, width2], axis=0) # (2)
+ stroke_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1) # (8)
+
+ next_width = stroke_params[4]
+ next_scaling = stroke_params[5]
+ next_window_size = next_scaling * curr_window_size_raw
+ next_window_size = np.maximum(next_window_size, min_window_size)
+ next_window_size = np.minimum(next_window_size, image_size)
+
+ prev_width = next_width * curr_window_size_raw / next_window_size
+ prev_scaling = next_scaling
+ prev_window_size = curr_window_size_raw
+
+ f = stroke_params_proc.tolist() # (8)
+ f += [1.0, 1.0]
+ gt_stroke_img = draw(f) # (H, W), [0.0-stroke, 1.0-BG]
+
+ gt_stroke_img_large = image_pasting_v3_testing(1.0 - gt_stroke_img, cursor_pos,
+ image_size,
+ curr_window_size_raw,
+ pasting_func, sess) # [0.0-BG, 1.0-stroke]
+
+ is_overlap = False
+
+ if pen_state == 0:
+ canvas += gt_stroke_img_large # [0.0-BG, 1.0-stroke]
+
+ curr_drawn_stroke_region = np.zeros_like(gt_stroke_img_large)
+ curr_drawn_stroke_region[gt_stroke_img_large > 0.5] = 1
+ intersection = drawn_region * curr_drawn_stroke_region
+ # regard stroke with >50% overlap area as overlaped stroke
+ if np.sum(intersection) / np.sum(curr_drawn_stroke_region) > 0.5:
+ # enlarge the stroke a bit for better visualization
+ overlap_region[gt_stroke_img_large > 0] += 1
+ is_overlap = True
+
+ drawn_region[gt_stroke_img_large > 0.5] = 1
+
+ color_rgb = color_rgb_set[color_idx] # (3) in [0, 255]
+ color_idx += 1
+
+ color_rgb = np.reshape(color_rgb, (1, 1, 3)).astype(np.float32)
+ color_stroke = np.expand_dims(gt_stroke_img_large, axis=-1) * (1.0 - color_rgb / 255.0)
+ canvas_color_with_moving = canvas_color_with_moving * np.expand_dims((1.0 - gt_stroke_img_large),
+ axis=-1) + color_stroke # (H, W, 3)
+
+ if pen_state == 0:
+ valid_color_idx += 1
+
+ if pen_state == 0:
+ valid_color_rgb = valid_color_rgb_set[valid_color_idx] # (3) in [0, 255]
+ # valid_color_idx += 1
+
+ valid_color_rgb = np.reshape(valid_color_rgb, (1, 1, 3)).astype(np.float32)
+ valid_color_stroke = np.expand_dims(gt_stroke_img_large, axis=-1) * (1.0 - valid_color_rgb / 255.0)
+ canvas_color_with_overlap = canvas_color_with_overlap * np.expand_dims((1.0 - gt_stroke_img_large),
+ axis=-1) + valid_color_stroke # (H, W, 3)
+ if not is_overlap:
+ canvas_color_wo_overlap = canvas_color_wo_overlap * np.expand_dims((1.0 - gt_stroke_img_large),
+ axis=-1) + valid_color_stroke # (H, W, 3)
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = stroke_params[2:4] * (float(curr_window_size_raw) / 2.0) # (1, 6), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[1:2], new_cursor_offset_next[0:1]], axis=-1)
+
+ cursor_pos_large = cursor_pos * float(image_size)
+
+ stroke_position_next = cursor_pos_large + new_cursor_offset_next # (2), large-level
+
+ if cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (2), large-level
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ canvas_rgb = np.stack([np.clip(canvas, 0.0, 1.0) for _ in range(3)], axis=-1)
+ canvas_black = 255 - np.round(canvas_rgb * 255.0).astype(np.uint8)
+ canvas_color_with_overlap = 255 - np.round(canvas_color_with_overlap * 255.0).astype(np.uint8)
+ canvas_color_wo_overlap = 255 - np.round(canvas_color_wo_overlap * 255.0).astype(np.uint8)
+ canvas_color_with_moving = 255 - np.round(canvas_color_with_moving * 255.0).astype(np.uint8)
+
+ canvas_black_png = Image.fromarray(canvas_black, 'RGB')
+ canvas_black_save_path = os.path.join(save_base, 'output_rendered.png')
+ canvas_black_png.save(canvas_black_save_path, 'PNG')
+
+ canvas_color_png = Image.fromarray(canvas_color_with_overlap, 'RGB')
+ canvas_color_save_path = os.path.join(save_base, 'output_order_with_overlap.png')
+ canvas_color_png.save(canvas_color_save_path, 'PNG')
+
+ canvas_color_wo_png = Image.fromarray(canvas_color_wo_overlap, 'RGB')
+ canvas_color_wo_save_path = os.path.join(save_base, 'output_order_wo_overlap.png')
+ canvas_color_wo_png.save(canvas_color_wo_save_path, 'PNG')
+
+ canvas_color_m_png = Image.fromarray(canvas_color_with_moving, 'RGB')
+ canvas_color_m_save_path = os.path.join(save_base, 'output_order_with_moving.png')
+ canvas_color_m_png.save(canvas_color_m_save_path, 'PNG')
+
+
+def visualize_drawing(npz_path):
+ assert npz_path != ''
+
+ min_window_size = 32
+ raster_size = 128
+
+ split_idx = npz_path.rfind('/')
+ if split_idx == -1:
+ file_base = './'
+ file_name = npz_path[:-4]
+ else:
+ file_base = npz_path[:npz_path.rfind('/')]
+ file_name = npz_path[npz_path.rfind('/') + 1: -4]
+
+ regenerate_base = os.path.join(file_base, file_name)
+ os.makedirs(regenerate_base, exist_ok=True)
+
+ # differentiable pasting graph
+ paste_v3_func = DiffPastingV3(raster_size)
+
+ tfconfig = tf.ConfigProto()
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ data = np.load(npz_path, encoding='latin1', allow_pickle=True)
+ strokes_data = data['strokes_data']
+ init_cursors = data['init_cursors']
+ image_size = data['image_size']
+ round_length = data['round_length']
+ init_width = data['init_width']
+
+ if round_length.ndim == 0:
+ round_lengths = [round_length]
+ else:
+ round_lengths = round_length
+
+ # print('round_lengths', round_lengths)
+
+ print('Processing ...')
+ display_strokes_final(sess, paste_v3_func,
+ strokes_data, init_cursors, image_size, round_lengths, init_width,
+ regenerate_base,
+ min_window_size=min_window_size, raster_size=raster_size)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--file', '-f', type=str, default='', help="define a npz path")
+ args = parser.parse_args()
+
+ visualize_drawing(args.file)
diff --git a/train_rough_photograph.py b/train_rough_photograph.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b59b94dba436ae0b3dc3feca036ca6f61129864
--- /dev/null
+++ b/train_rough_photograph.py
@@ -0,0 +1,342 @@
+import json
+import os
+import time
+import numpy as np
+import six
+import tensorflow as tf
+from PIL import Image
+import argparse
+
+import model_common_train as sketch_image_model
+from hyper_parameters import FLAGS, get_default_hparams_rough, get_default_hparams_normal
+from utils import create_summary, save_model, reset_graph, load_checkpoint
+from dataset_utils import load_dataset_training
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+
+def should_save_log_img(step_):
+ if step_ % 500 == 0:
+ return True
+ else:
+ return False
+
+
+def save_log_images(sess, model, data_set, save_root, step_num, curr_photo_prob, interpolate_type, save_num=10):
+ res_gap = (model.hps.image_size_large - model.hps.image_size_small) // (save_num - 1)
+ log_img_resolutions = []
+ for ii in range(save_num - 1):
+ log_img_resolutions.append(model.hps.image_size_small + ii * res_gap)
+ log_img_resolutions.append(model.hps.image_size_large)
+
+ for res_i in range(len(log_img_resolutions)):
+ resolution = log_img_resolutions[res_i]
+
+ sub_save_root = os.path.join(save_root, 'res_' + str(resolution))
+ os.makedirs(sub_save_root, exist_ok=True)
+
+ input_photos, target_sketches, init_cursors, image_size_rand = \
+ data_set.get_batch_from_memory(memory_idx=res_i,
+ fixed_image_size=resolution,
+ random_cursor=model.hps.random_cursor,
+ photo_prob=curr_photo_prob,
+ interpolate_type=interpolate_type)
+ # input_photos: (N, image_size, image_size, 3), [0-stroke, 1-BG]
+ # target_sketches: (N, image_size, image_size), [0-stroke, 1-BG]
+ # init_cursors: (N, 1, 2), in size [0.0, 1.0)
+
+ input_photo_val = input_photos
+
+ init_cursor_input = [init_cursors for _ in range(model.total_loop)]
+ init_cursor_input = np.concatenate(init_cursor_input, axis=0)
+ image_size_input = [image_size_rand for _ in range(model.total_loop)]
+ image_size_input = np.stack(image_size_input, axis=0)
+
+ feed = {
+ model.init_cursor: init_cursor_input,
+ model.image_size: image_size_input,
+ model.init_width: [model.hps.min_width],
+ }
+ for loop_i in range(model.total_loop):
+ feed[model.input_photo_list[loop_i]] = input_photo_val
+
+ raster_images_pred, raster_images_pred_rgb = sess.run([model.pred_raster_imgs, model.pred_raster_imgs_rgb],
+ feed) # (N, image_size, image_size), [0.0-stroke, 1.0-BG]
+ raster_images_pred = (np.array(raster_images_pred[0]) * 255.0).astype(np.uint8)
+ input_photo = (np.array(input_photo_val[0, :, :, :]) * 255.0).astype(np.uint8)
+ target_sketch = (np.array(target_sketches[0]) * 255.0).astype(np.uint8)
+ raster_images_pred_rgb = (np.array(raster_images_pred_rgb[0]) * 255.0).astype(np.uint8)
+
+ pred_save_path = os.path.join(sub_save_root, str(step_num) + '.png')
+ input_save_path = os.path.join(sub_save_root, 'input.png')
+ target_save_path = os.path.join(sub_save_root, 'gt.png')
+
+ pred_rgb_save_root = os.path.join(sub_save_root, 'rgb')
+ os.makedirs(pred_rgb_save_root, exist_ok=True)
+ pred_rgb_save_path = os.path.join(pred_rgb_save_root, str(step_num) + '.png')
+
+ raster_images_pred = Image.fromarray(raster_images_pred, 'L')
+ raster_images_pred.save(pred_save_path, 'PNG')
+ input_photo = Image.fromarray(input_photo, 'RGB')
+ input_photo.save(input_save_path, 'PNG')
+ target_sketch = Image.fromarray(target_sketch, 'L')
+ target_sketch.save(target_save_path, 'PNG')
+ raster_images_pred_rgb = Image.fromarray(raster_images_pred_rgb, 'RGB')
+ raster_images_pred_rgb.save(pred_rgb_save_path, 'PNG')
+
+
+def train(sess, train_model, eval_sample_model, train_set, valid_set, sub_log_root, sub_snapshot_root, sub_log_img_root):
+ # Setup summary writer.
+ summary_writer = tf.summary.FileWriter(sub_log_root)
+
+ print('-' * 100)
+
+ # Calculate trainable params.
+ t_vars = tf.trainable_variables()
+ count_t_vars = 0
+ for var in t_vars:
+ num_param = np.prod(var.get_shape().as_list())
+ count_t_vars += num_param
+ print('%s | shape: %s | num_param: %i' % (var.name, str(var.get_shape()), num_param))
+ print('Total trainable variables %i.' % count_t_vars)
+ print('-' * 100)
+
+ # main train loop
+
+ hps = train_model.hps
+ start = time.time()
+
+ # create saver
+ snapshot_save_vars = [var for var in tf.global_variables()
+ if 'raster_unit' not in var.op.name and 'VGG16' not in var.op.name]
+ saver = tf.train.Saver(var_list=snapshot_save_vars, max_to_keep=20)
+
+ start_step = 1
+ print('start_step', start_step)
+
+ mean_perc_relu_losses = [0.0 for _ in range(len(hps.perc_loss_layers))]
+
+ for _ in range(start_step, hps.num_steps + 1):
+ step = sess.run(train_model.global_step) # start from 0
+
+ count_step = min(step, hps.num_steps)
+ curr_learning_rate = ((hps.learning_rate - hps.min_learning_rate) *
+ (1 - count_step / hps.num_steps) ** hps.decay_power + hps.min_learning_rate)
+
+ if hps.sn_loss_type == 'decreasing':
+ assert hps.decrease_stop_steps <= hps.num_steps
+ assert hps.stroke_num_loss_weight_end <= hps.stroke_num_loss_weight
+ curr_sn_k = (hps.stroke_num_loss_weight - hps.stroke_num_loss_weight_end) / float(hps.decrease_stop_steps)
+ curr_stroke_num_loss_weight = hps.stroke_num_loss_weight - count_step * curr_sn_k
+ curr_stroke_num_loss_weight = max(curr_stroke_num_loss_weight, hps.stroke_num_loss_weight_end)
+ elif hps.sn_loss_type == 'fixed':
+ curr_stroke_num_loss_weight = hps.stroke_num_loss_weight
+ elif hps.sn_loss_type == 'increasing':
+ curr_sn_k = hps.stroke_num_loss_weight / float(hps.num_steps - hps.increase_start_steps)
+ curr_stroke_num_loss_weight = max(count_step - hps.increase_start_steps, 0) * curr_sn_k
+ else:
+ raise Exception('Unknown sn_loss_type', hps.sn_loss_type)
+
+ if hps.early_pen_loss_type == 'head':
+ curr_early_pen_k = (hps.max_seq_len - hps.early_pen_length) / float(hps.num_steps)
+ curr_early_pen_loss_len = count_step * curr_early_pen_k + hps.early_pen_length
+
+ curr_early_pen_loss_start = 1
+ curr_early_pen_loss_end = curr_early_pen_loss_len
+ elif hps.early_pen_loss_type == 'tail':
+ curr_early_pen_k = (hps.max_seq_len // 2 - 1) / float(hps.num_steps)
+ curr_early_pen_loss_len = count_step * curr_early_pen_k + hps.max_seq_len // 2
+
+ curr_early_pen_loss_end = hps.max_seq_len
+ curr_early_pen_loss_start = curr_early_pen_loss_end - curr_early_pen_loss_len
+ elif hps.early_pen_loss_type == 'move':
+ curr_early_pen_k = (hps.max_seq_len // 2 - 1) / float(hps.num_steps)
+ curr_early_pen_loss_len = count_step * curr_early_pen_k + hps.max_seq_len // 2
+
+ curr_early_pen_loss_start = hps.max_seq_len - curr_early_pen_loss_len
+ curr_early_pen_loss_end = curr_early_pen_loss_start + hps.max_seq_len // 2
+ else:
+ raise Exception('Unknown early_pen_loss_type', hps.early_pen_loss_type)
+ curr_early_pen_loss_start = int(round(curr_early_pen_loss_start))
+ curr_early_pen_loss_end = int(round(curr_early_pen_loss_end))
+
+ if hps.photo_prob_type == 'increasing' or hps.photo_prob_type == 'interpolate':
+ assert hps.photo_prob_end_step >= hps.photo_prob_start_step
+ curr_photo_prob_k = 1.0 / float(hps.photo_prob_end_step - hps.photo_prob_start_step)
+ curr_photo_prob = (count_step - hps.photo_prob_start_step) * curr_photo_prob_k
+ curr_photo_prob = max(0.0, curr_photo_prob)
+ curr_photo_prob = min(1.0, curr_photo_prob)
+ interpolate_type = 'prob' if hps.photo_prob_type == 'increasing' else 'image'
+ elif hps.photo_prob_type == 'zero':
+ curr_photo_prob = 0.0
+ interpolate_type = 'prob'
+ elif hps.photo_prob_type == 'one':
+ curr_photo_prob = 1.0
+ interpolate_type = 'prob'
+ else:
+ raise Exception('Unknown photo_prob_type', hps.photo_prob_type)
+
+ input_photos, target_sketches, init_cursors, image_sizes = \
+ train_set.get_batch_multi_res(loop_num=train_model.total_loop,
+ random_cursor=hps.random_cursor,
+ photo_prob=curr_photo_prob,
+ interpolate_type=interpolate_type)
+ # input_photos: list of (N, image_size, image_size, 3), [0-stroke, 1-BG]
+ # target_sketches: list of (N, image_size, image_size), [0-stroke, 1-BG]
+ # init_cursors: list of (N, 1, 2), in size [0.0, 1.0)
+
+ init_cursors_input = np.concatenate(init_cursors, axis=0)
+ image_size_input = np.stack(image_sizes, axis=0)
+
+ feed = {
+ train_model.init_cursor: init_cursors_input,
+ train_model.image_size: image_size_input,
+ train_model.init_width: [hps.min_width],
+
+ train_model.lr: curr_learning_rate,
+ train_model.stroke_num_loss_weight: curr_stroke_num_loss_weight,
+ train_model.early_pen_loss_start_idx: curr_early_pen_loss_start,
+ train_model.early_pen_loss_end_idx: curr_early_pen_loss_end,
+
+ train_model.last_step_num: float(step),
+ }
+ for layer_i in range(len(hps.perc_loss_layers)):
+ feed[train_model.perc_loss_mean_list[layer_i]] = mean_perc_relu_losses[layer_i]
+
+ for loop_i in range(train_model.total_loop):
+ input_photo_val = input_photos[loop_i]
+ target_sketch_val = target_sketches[loop_i]
+ feed[train_model.input_photo_list[loop_i]] = input_photo_val
+ feed[train_model.target_sketch_list[loop_i]] = np.expand_dims(target_sketch_val, axis=-1)
+
+ (train_cost, raster_cost, perc_relu_costs_raw, perc_relu_costs_norm,
+ stroke_num_cost, early_pen_states_cost,
+ pos_outside_cost, win_size_outside_cost,
+ train_step) = sess.run([
+ train_model.cost, train_model.raster_cost,
+ train_model.perc_relu_losses_raw, train_model.perc_relu_losses_norm,
+ train_model.stroke_num_cost,
+ train_model.early_pen_states_cost,
+ train_model.pos_outside_cost, train_model.win_size_outside_cost,
+ train_model.global_step
+ ], feed)
+
+ ## update mean_raster_loss
+ for layer_i in range(len(hps.perc_loss_layers)):
+ perc_relu_cost_raw = perc_relu_costs_raw[layer_i]
+ mean_perc_relu_loss = mean_perc_relu_losses[layer_i]
+ mean_perc_relu_loss = (mean_perc_relu_loss * step + perc_relu_cost_raw) / float(step + 1)
+ mean_perc_relu_losses[layer_i] = mean_perc_relu_loss
+
+ _ = sess.run(train_model.train_op, feed)
+
+ if step % 20 == 0 and step > 0:
+ end = time.time()
+ time_taken = end - start
+
+ train_summary_map = {
+ 'Train_Cost': train_cost,
+ 'Train_raster_Cost': raster_cost,
+ 'Train_stroke_num_Cost': stroke_num_cost,
+ 'Train_early_pen_states_cost': early_pen_states_cost,
+ 'Train_pos_outside_Cost': pos_outside_cost,
+ 'Train_win_size_outside_Cost': win_size_outside_cost,
+ 'Learning_Rate': curr_learning_rate,
+ 'Time_Taken_Train': time_taken
+ }
+ for layer_i in range(len(hps.perc_loss_layers)):
+ layer_name = hps.perc_loss_layers[layer_i]
+ train_summary_map['Train_raster_Cost_' + layer_name] = perc_relu_costs_raw[layer_i]
+
+ create_summary(summary_writer, train_summary_map, train_step)
+
+ output_format = ('step: %d, lr: %.6f, '
+ 'snw: %.3f, '
+ 'cost: %.4f, '
+ 'ras: %.4f, stroke_num: %.4f, early_pen: %.4f, '
+ 'pos_outside: %.4f, win_outside: %.4f, '
+ 'train_time_taken: %.1f')
+ output_values = (step, curr_learning_rate,
+ curr_stroke_num_loss_weight,
+ train_cost,
+ raster_cost, stroke_num_cost, early_pen_states_cost,
+ pos_outside_cost, win_size_outside_cost,
+ time_taken)
+ output_log = output_format % output_values
+ # print(output_log)
+ tf.logging.info(output_log)
+ start = time.time()
+
+ if should_save_log_img(step) and step > 0:
+ save_log_images(sess, eval_sample_model, valid_set, sub_log_img_root, step, curr_photo_prob, interpolate_type)
+
+ if step % hps.save_every == 0 and step > 0:
+ save_model(sess, saver, sub_snapshot_root, step)
+
+
+def trainer(model_params):
+ np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
+
+ print('Hyperparams:')
+ for key, val in six.iteritems(model_params.values()):
+ print('%s = %s' % (key, str(val)))
+ print('Loading data files.')
+ print('-' * 100)
+
+ datasets = load_dataset_training(FLAGS.dataset_dir, model_params)
+
+ sub_snapshot_root = os.path.join(FLAGS.snapshot_root, model_params.program_name)
+ sub_log_root = os.path.join(FLAGS.log_root, model_params.program_name)
+ sub_log_img_root = os.path.join(FLAGS.log_img_root, model_params.program_name)
+
+ train_set = datasets[0]
+ valid_set = datasets[1]
+ train_model_params = datasets[2]
+ eval_sample_model_params = datasets[3]
+
+ eval_sample_model_params.loop_per_gpu = 1
+ eval_sample_model_params.batch_size = len(eval_sample_model_params.gpus) * eval_sample_model_params.loop_per_gpu
+
+ reset_graph()
+ train_model = sketch_image_model.VirtualSketchingModel(train_model_params)
+ eval_sample_model = sketch_image_model.VirtualSketchingModel(eval_sample_model_params, reuse=True)
+
+ tfconfig = tf.ConfigProto(allow_soft_placement=True)
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ load_checkpoint(sess, FLAGS.neural_renderer_path, ras_only=True)
+ if train_model_params.raster_loss_base_type == 'perceptual':
+ load_checkpoint(sess, FLAGS.perceptual_model_root, perceptual_only=True)
+
+ # Write config file to json file.
+ os.makedirs(sub_log_root, exist_ok=True)
+ os.makedirs(sub_log_img_root, exist_ok=True)
+ os.makedirs(sub_snapshot_root, exist_ok=True)
+ with tf.gfile.Open(os.path.join(sub_snapshot_root, 'model_config.json'), 'w') as f:
+ json.dump(train_model_params.values(), f, indent=True)
+
+ train(sess, train_model, eval_sample_model, train_set, valid_set,
+ sub_log_root, sub_snapshot_root, sub_log_img_root)
+
+
+def main(dataset_type):
+ if dataset_type == 'rough':
+ model_params = get_default_hparams_rough()
+ elif dataset_type == 'face':
+ model_params = get_default_hparams_normal()
+ else:
+ raise Exception('Unknown dataset_type:', dataset_type)
+
+ trainer(model_params)
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--data', '-d', type=str, default='rough', choices=['rough', 'face'], help="The dataset type.")
+ args = parser.parse_args()
+
+ main(args.data)
diff --git a/train_vectorization.py b/train_vectorization.py
new file mode 100644
index 0000000000000000000000000000000000000000..9242f813fcaf02f9968ec4f3e02a62ebc4bf6b95
--- /dev/null
+++ b/train_vectorization.py
@@ -0,0 +1,313 @@
+import json
+import os
+import time
+import numpy as np
+import six
+import tensorflow as tf
+from PIL import Image
+
+import model_common_train as sketch_vector_model
+from hyper_parameters import FLAGS, get_default_hparams_clean
+from utils import create_summary, save_model, reset_graph, load_checkpoint
+from dataset_utils import load_dataset_training
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+
+def should_save_log_img(step_):
+ if step_ % 500 == 0:
+ return True
+ else:
+ return False
+
+
+def save_log_images(sess, model, data_set, save_root, step_num, save_num=10):
+ res_gap = (model.hps.image_size_large - model.hps.image_size_small) // (save_num - 1)
+ log_img_resolutions = []
+ for ii in range(save_num - 1):
+ log_img_resolutions.append(model.hps.image_size_small + ii * res_gap)
+ log_img_resolutions.append(model.hps.image_size_large)
+
+ for res_i in range(len(log_img_resolutions)):
+ resolution = log_img_resolutions[res_i]
+
+ sub_save_root = os.path.join(save_root, 'res_' + str(resolution))
+ os.makedirs(sub_save_root, exist_ok=True)
+
+ input_photos, target_sketches, init_cursors, image_size_rand = \
+ data_set.get_batch_from_memory(memory_idx=res_i, vary_thickness=model.hps.vary_thickness,
+ fixed_image_size=resolution,
+ random_cursor=model.hps.random_cursor,
+ init_cursor_on_undrawn_pixel=model.hps.init_cursor_on_undrawn_pixel)
+ # input_photos: (N, image_size, image_size), [0-stroke, 1-BG]
+ # target_sketches: (N, image_size, image_size), [0-stroke, 1-BG]
+ # init_cursors: (N, 1, 2), in size [0.0, 1.0)
+
+ if input_photos is not None:
+ input_photo_val = np.expand_dims(input_photos, axis=-1)
+ else:
+ input_photo_val = np.expand_dims(target_sketches, axis=-1)
+
+ init_cursor_input = [init_cursors for _ in range(model.total_loop)]
+ init_cursor_input = np.concatenate(init_cursor_input, axis=0)
+ image_size_input = [image_size_rand for _ in range(model.total_loop)]
+ image_size_input = np.stack(image_size_input, axis=0)
+
+ feed = {
+ model.init_cursor: init_cursor_input,
+ model.image_size: image_size_input,
+ model.init_width: [model.hps.min_width],
+ }
+ for loop_i in range(model.total_loop):
+ feed[model.input_photo_list[loop_i]] = input_photo_val
+
+ raster_images_pred, raster_images_pred_rgb = sess.run([model.pred_raster_imgs, model.pred_raster_imgs_rgb],
+ feed) # (N, image_size, image_size), [0.0-stroke, 1.0-BG]
+ raster_images_pred = (np.array(raster_images_pred[0]) * 255.0).astype(np.uint8)
+ input_sketch = (np.array(target_sketches[0]) * 255.0).astype(np.uint8)
+ raster_images_pred_rgb = (np.array(raster_images_pred_rgb[0]) * 255.0).astype(np.uint8)
+
+ pred_save_path = os.path.join(sub_save_root, str(step_num) + '.png')
+ target_save_path = os.path.join(sub_save_root, 'gt.png')
+
+ pred_rgb_save_root = os.path.join(sub_save_root, 'rgb')
+ os.makedirs(pred_rgb_save_root, exist_ok=True)
+ pred_rgb_save_path = os.path.join(pred_rgb_save_root, str(step_num) + '.png')
+
+ raster_images_pred = Image.fromarray(raster_images_pred, 'L')
+ raster_images_pred.save(pred_save_path, 'PNG')
+ input_sketch = Image.fromarray(input_sketch, 'L')
+ input_sketch.save(target_save_path, 'PNG')
+ raster_images_pred_rgb = Image.fromarray(raster_images_pred_rgb, 'RGB')
+ raster_images_pred_rgb.save(pred_rgb_save_path, 'PNG')
+
+
+def train(sess, train_model, eval_sample_model, train_set, val_set, sub_log_root, sub_snapshot_root, sub_log_img_root):
+ # Setup summary writer.
+ summary_writer = tf.summary.FileWriter(sub_log_root)
+
+ print('-' * 100)
+
+ # Calculate trainable params.
+ t_vars = tf.trainable_variables()
+ count_t_vars = 0
+ for var in t_vars:
+ num_param = np.prod(var.get_shape().as_list())
+ count_t_vars += num_param
+ print('%s | shape: %s | num_param: %i' % (var.name, str(var.get_shape()), num_param))
+ print('Total trainable variables %i.' % count_t_vars)
+ print('-' * 100)
+
+ # main train loop
+
+ hps = train_model.hps
+ start = time.time()
+
+ # create saver
+ snapshot_save_vars = [var for var in tf.global_variables()
+ if 'raster_unit' not in var.op.name and 'VGG16' not in var.op.name]
+ saver = tf.train.Saver(var_list=snapshot_save_vars, max_to_keep=20)
+
+ start_step = 1
+ print('start_step', start_step)
+
+ mean_perc_relu_losses = [0.0 for _ in range(len(hps.perc_loss_layers))]
+
+ for _ in range(start_step, hps.num_steps + 1):
+ step = sess.run(train_model.global_step) # start from 0
+
+ count_step = min(step, hps.num_steps)
+ curr_learning_rate = ((hps.learning_rate - hps.min_learning_rate) *
+ (1 - count_step / hps.num_steps) ** hps.decay_power + hps.min_learning_rate)
+
+ if hps.sn_loss_type == 'decreasing':
+ assert hps.decrease_stop_steps <= hps.num_steps
+ assert hps.stroke_num_loss_weight_end <= hps.stroke_num_loss_weight
+ curr_sn_k = (hps.stroke_num_loss_weight - hps.stroke_num_loss_weight_end) / float(hps.decrease_stop_steps)
+ curr_stroke_num_loss_weight = hps.stroke_num_loss_weight - count_step * curr_sn_k
+ curr_stroke_num_loss_weight = max(curr_stroke_num_loss_weight, hps.stroke_num_loss_weight_end)
+ elif hps.sn_loss_type == 'fixed':
+ curr_stroke_num_loss_weight = hps.stroke_num_loss_weight
+ elif hps.sn_loss_type == 'increasing':
+ curr_sn_k = hps.stroke_num_loss_weight / float(hps.num_steps - hps.increase_start_steps)
+ curr_stroke_num_loss_weight = max(count_step - hps.increase_start_steps, 0) * curr_sn_k
+ else:
+ raise Exception('Unknown sn_loss_type', hps.sn_loss_type)
+
+ if hps.early_pen_loss_type == 'head':
+ curr_early_pen_k = (hps.max_seq_len - hps.early_pen_length) / float(hps.num_steps)
+ curr_early_pen_loss_len = count_step * curr_early_pen_k + hps.early_pen_length
+
+ curr_early_pen_loss_start = 1
+ curr_early_pen_loss_end = curr_early_pen_loss_len
+ elif hps.early_pen_loss_type == 'tail':
+ curr_early_pen_k = (hps.max_seq_len // 2 - 1) / float(hps.num_steps)
+ curr_early_pen_loss_len = count_step * curr_early_pen_k + hps.max_seq_len // 2
+
+ curr_early_pen_loss_end = hps.max_seq_len
+ curr_early_pen_loss_start = curr_early_pen_loss_end - curr_early_pen_loss_len
+ elif hps.early_pen_loss_type == 'move':
+ curr_early_pen_k = (hps.max_seq_len // 2 - 1) / float(hps.num_steps)
+ curr_early_pen_loss_len = count_step * curr_early_pen_k + hps.max_seq_len // 2
+
+ curr_early_pen_loss_start = hps.max_seq_len - curr_early_pen_loss_len
+ curr_early_pen_loss_end = curr_early_pen_loss_start + hps.max_seq_len // 2
+ else:
+ raise Exception('Unknown early_pen_loss_type', hps.early_pen_loss_type)
+ curr_early_pen_loss_start = int(round(curr_early_pen_loss_start))
+ curr_early_pen_loss_end = int(round(curr_early_pen_loss_end))
+
+ input_photos, target_sketches, init_cursors, image_sizes = \
+ train_set.get_batch_multi_res(loop_num=train_model.total_loop, vary_thickness=hps.vary_thickness,
+ random_cursor=hps.random_cursor,
+ init_cursor_on_undrawn_pixel=hps.init_cursor_on_undrawn_pixel)
+ # input_photos: list of (N, image_size, image_size), [0-stroke, 1-BG]
+ # target_sketches: list of (N, image_size, image_size), [0-stroke, 1-BG]
+ # init_cursors: list of (N, 1, 2), in size [0.0, 1.0)
+
+ init_cursors_input = np.concatenate(init_cursors, axis=0)
+ image_size_input = np.stack(image_sizes, axis=0)
+
+ feed = {
+ train_model.init_cursor: init_cursors_input,
+ train_model.image_size: image_size_input,
+ train_model.init_width: [hps.min_width],
+
+ train_model.lr: curr_learning_rate,
+ train_model.stroke_num_loss_weight: curr_stroke_num_loss_weight,
+ train_model.early_pen_loss_start_idx: curr_early_pen_loss_start,
+ train_model.early_pen_loss_end_idx: curr_early_pen_loss_end,
+
+ train_model.last_step_num: float(step),
+ }
+ for layer_i in range(len(hps.perc_loss_layers)):
+ feed[train_model.perc_loss_mean_list[layer_i]] = mean_perc_relu_losses[layer_i]
+
+ for loop_i in range(train_model.total_loop):
+ if input_photos is not None:
+ input_photo_val = np.expand_dims(input_photos[loop_i], axis=-1)
+ else:
+ input_photo_val = np.expand_dims(target_sketches[loop_i], axis=-1)
+ feed[train_model.input_photo_list[loop_i]] = input_photo_val
+
+ (train_cost, raster_cost, perc_relu_costs_raw, perc_relu_costs_norm,
+ stroke_num_cost, early_pen_states_cost,
+ pos_outside_cost, win_size_outside_cost,
+ train_step) = sess.run([
+ train_model.cost, train_model.raster_cost,
+ train_model.perc_relu_losses_raw, train_model.perc_relu_losses_norm,
+ train_model.stroke_num_cost,
+ train_model.early_pen_states_cost,
+ train_model.pos_outside_cost, train_model.win_size_outside_cost,
+ train_model.global_step
+ ], feed)
+
+ ## update mean_raster_loss
+ for layer_i in range(len(hps.perc_loss_layers)):
+ perc_relu_cost_raw = perc_relu_costs_raw[layer_i]
+ mean_perc_relu_loss = mean_perc_relu_losses[layer_i]
+ mean_perc_relu_loss = (mean_perc_relu_loss * step + perc_relu_cost_raw) / float(step + 1)
+ mean_perc_relu_losses[layer_i] = mean_perc_relu_loss
+
+ _ = sess.run(train_model.train_op, feed)
+
+ if step % 20 == 0 and step > 0:
+ end = time.time()
+ time_taken = end - start
+
+ train_summary_map = {
+ 'Train_Cost': train_cost,
+ 'Train_raster_Cost': raster_cost,
+ 'Train_stroke_num_Cost': stroke_num_cost,
+ 'Train_early_pen_states_cost': early_pen_states_cost,
+ 'Train_pos_outside_Cost': pos_outside_cost,
+ 'Train_win_size_outside_Cost': win_size_outside_cost,
+ 'Learning_Rate': curr_learning_rate,
+ 'Time_Taken_Train': time_taken
+ }
+ for layer_i in range(len(hps.perc_loss_layers)):
+ layer_name = hps.perc_loss_layers[layer_i]
+ train_summary_map['Train_raster_Cost_' + layer_name] = perc_relu_costs_raw[layer_i]
+
+ create_summary(summary_writer, train_summary_map, train_step)
+
+ output_format = ('step: %d, lr: %.6f, '
+ 'snw: %.3f, '
+ 'cost: %.4f, '
+ 'ras: %.4f, stroke_num: %.4f, early_pen: %.4f, '
+ 'pos_outside: %.4f, win_outside: %.4f, '
+ 'train_time_taken: %.1f')
+ output_values = (step, curr_learning_rate,
+ curr_stroke_num_loss_weight,
+ train_cost,
+ raster_cost, stroke_num_cost, early_pen_states_cost,
+ pos_outside_cost, win_size_outside_cost,
+ time_taken)
+ output_log = output_format % output_values
+ # print(output_log)
+ tf.logging.info(output_log)
+ start = time.time()
+
+ if should_save_log_img(step) and step > 0:
+ save_log_images(sess, eval_sample_model, val_set, sub_log_img_root, step)
+
+ if step % hps.save_every == 0 and step > 0:
+ save_model(sess, saver, sub_snapshot_root, step)
+
+
+def trainer(model_params):
+ np.set_printoptions(precision=8, edgeitems=6, linewidth=200, suppress=True)
+
+ print('Hyperparams:')
+ for key, val in six.iteritems(model_params.values()):
+ print('%s = %s' % (key, str(val)))
+ print('Loading data files.')
+ print('-' * 100)
+
+ datasets = load_dataset_training(FLAGS.dataset_dir, model_params)
+
+ sub_snapshot_root = os.path.join(FLAGS.snapshot_root, model_params.program_name)
+ sub_log_root = os.path.join(FLAGS.log_root, model_params.program_name)
+ sub_log_img_root = os.path.join(FLAGS.log_img_root, model_params.program_name)
+
+ train_set = datasets[0]
+ val_set = datasets[1]
+ train_model_params = datasets[2]
+ eval_sample_model_params = datasets[3]
+
+ eval_sample_model_params.loop_per_gpu = 1
+ eval_sample_model_params.batch_size = len(eval_sample_model_params.gpus) * eval_sample_model_params.loop_per_gpu
+
+ reset_graph()
+ train_model = sketch_vector_model.VirtualSketchingModel(train_model_params)
+ eval_sample_model = sketch_vector_model.VirtualSketchingModel(eval_sample_model_params, reuse=True)
+
+ tfconfig = tf.ConfigProto(allow_soft_placement=True)
+ tfconfig.gpu_options.allow_growth = True
+ sess = tf.InteractiveSession(config=tfconfig)
+ sess.run(tf.global_variables_initializer())
+
+ load_checkpoint(sess, FLAGS.neural_renderer_path, ras_only=True)
+ if train_model_params.raster_loss_base_type == 'perceptual':
+ load_checkpoint(sess, FLAGS.perceptual_model_root, perceptual_only=True)
+
+ # Write config file to json file.
+ os.makedirs(sub_log_root, exist_ok=True)
+ os.makedirs(sub_log_img_root, exist_ok=True)
+ os.makedirs(sub_snapshot_root, exist_ok=True)
+ with tf.gfile.Open(os.path.join(sub_snapshot_root, 'model_config.json'), 'w') as f:
+ json.dump(train_model_params.values(), f, indent=True)
+
+ train(sess, train_model, eval_sample_model, train_set, val_set,
+ sub_log_root, sub_snapshot_root, sub_log_img_root)
+
+
+def main():
+ model_params = get_default_hparams_clean()
+ trainer(model_params)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0d294ec6eade4037aeba3a560813bbb59c30283
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,390 @@
+import os
+import cv2
+import json
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import matplotlib.pyplot as plt
+
+
+#############################################
+# Tensorflow utils
+#############################################
+
+def reset_graph():
+ """Closes the current default session and resets the graph."""
+ sess = tf.get_default_session()
+ if sess:
+ sess.close()
+ tf.reset_default_graph()
+
+
+def load_checkpoint(sess, checkpoint_path, ras_only=False, perceptual_only=False, gen_model_pretrain=False,
+ train_entire=False):
+ if ras_only:
+ load_var = {var.op.name: var for var in tf.global_variables() if 'raster_unit' in var.op.name}
+ elif perceptual_only:
+ load_var = {var.op.name: var for var in tf.global_variables() if 'VGG16' in var.op.name}
+ elif train_entire:
+ load_var = {var.op.name: var for var in tf.global_variables()
+ if 'discriminator' not in var.op.name
+ and 'raster_unit' not in var.op.name
+ and 'VGG16' not in var.op.name
+ and 'beta1' not in var.op.name
+ and 'beta2' not in var.op.name
+ and 'global_step' not in var.op.name
+ and 'Entire' not in var.op.name
+ }
+ else:
+ if gen_model_pretrain:
+ load_var = {var.op.name: var for var in tf.global_variables()
+ if 'discriminator' not in var.op.name
+ and 'raster_unit' not in var.op.name
+ and 'VGG16' not in var.op.name
+ and 'beta1' not in var.op.name
+ and 'beta2' not in var.op.name
+ # and 'global_step' not in var.op.name
+ }
+ else:
+ load_var = tf.global_variables()
+
+ restorer = tf.train.Saver(load_var)
+ if not ras_only:
+ ckpt = tf.train.get_checkpoint_state(checkpoint_path)
+ model_checkpoint_path = ckpt.model_checkpoint_path
+ else:
+ model_checkpoint_path = checkpoint_path
+ print('Loading model %s' % model_checkpoint_path)
+ restorer.restore(sess, model_checkpoint_path)
+
+ snapshot_step = model_checkpoint_path[model_checkpoint_path.rfind('-') + 1:]
+ return snapshot_step
+
+
+def create_summary(summary_writer, summ_map, step):
+ for summ_key in summ_map:
+ summ_value = summ_map[summ_key]
+ summ = tf.summary.Summary()
+ summ.value.add(tag=summ_key, simple_value=float(summ_value))
+ summary_writer.add_summary(summ, step)
+ summary_writer.flush()
+
+
+def save_model(sess, saver, model_save_path, global_step):
+ checkpoint_path = os.path.join(model_save_path, 'p2s')
+ print('saving model %s.' % checkpoint_path)
+ print('global_step %i.' % global_step)
+ saver.save(sess, checkpoint_path, global_step=global_step)
+
+
+#############################################
+# Utils for basic image processing
+#############################################
+
+
+def normal(x, width):
+ return (int)(x * (width - 1) + 0.5)
+
+
+def draw(f, width=128):
+ x0, y0, x1, y1, x2, y2, z0, z2, w0, w2 = f
+ x1 = x0 + (x2 - x0) * x1
+ y1 = y0 + (y2 - y0) * y1
+ x0 = normal(x0, width * 2)
+ x1 = normal(x1, width * 2)
+ x2 = normal(x2, width * 2)
+ y0 = normal(y0, width * 2)
+ y1 = normal(y1, width * 2)
+ y2 = normal(y2, width * 2)
+ z0 = (int)(1 + z0 * width // 2)
+ z2 = (int)(1 + z2 * width // 2)
+ canvas = np.zeros([width * 2, width * 2]).astype('float32')
+ tmp = 1. / 100
+ for i in range(100):
+ t = i * tmp
+ x = (int)((1-t) * (1-t) * x0 + 2 * t * (1-t) * x1 + t * t * x2)
+ y = (int)((1-t) * (1-t) * y0 + 2 * t * (1-t) * y1 + t * t * y2)
+ z = (int)((1-t) * z0 + t * z2)
+ w = (1-t) * w0 + t * w2
+ cv2.circle(canvas, (y, x), z, w, -1)
+ return 1 - cv2.resize(canvas, dsize=(width, width))
+
+
+def rgb_trans(split_num, break_values):
+ slice_per_split = split_num // 8
+ break_values_head, break_values_tail = break_values[:-1], break_values[1:]
+
+ results = []
+
+ for split_i in range(8):
+ break_value_head = break_values_head[split_i]
+ break_value_tail = break_values_tail[split_i]
+
+ slice_gap = float(break_value_tail - break_value_head) / float(slice_per_split)
+ for slice_i in range(slice_per_split):
+ slice_val = break_value_head + slice_gap * slice_i
+ slice_val = int(round(slice_val))
+ results.append(slice_val)
+
+ return results
+
+
+def get_colors(color_num):
+ split_num = (color_num // 8 + 1) * 8
+
+ r_break_values = [0, 0, 0, 0, 128, 255, 255, 255, 128]
+ g_break_values = [0, 0, 128, 255, 255, 255, 128, 0, 0]
+ b_break_values = [128, 255, 255, 255, 128, 0, 0, 0, 0]
+
+ r_rst_list = rgb_trans(split_num, r_break_values)
+ g_rst_list = rgb_trans(split_num, g_break_values)
+ b_rst_list = rgb_trans(split_num, b_break_values)
+
+ assert len(r_rst_list) == len(g_rst_list)
+ assert len(b_rst_list) == len(g_rst_list)
+
+ rgb_color_list = [(r_rst_list[i], g_rst_list[i], b_rst_list[i]) for i in range(len(r_rst_list))]
+ return rgb_color_list
+
+
+#############################################
+# Utils for testing
+#############################################
+
+def save_seq_data(save_root, save_filename, strokes_data, init_cursors, image_size, round_length, init_width):
+ seq_save_root = os.path.join(save_root, 'seq_data')
+ os.makedirs(seq_save_root, exist_ok=True)
+ save_npz_path = os.path.join(seq_save_root, save_filename + '.npz')
+ np.savez(save_npz_path, strokes_data=strokes_data, init_cursors=init_cursors,
+ image_size=image_size, round_length=round_length, init_width=init_width)
+
+
+def image_pasting_v3_testing(patch_image, cursor, image_size, window_size_f, pasting_func, sess):
+ """
+ :param patch_image: (raster_size, raster_size), [0.0-BG, 1.0-stroke]
+ :param cursor: (2), in size [0.0, 1.0)
+ :param window_size_f: (), float32, [0.0, image_size)
+ :return: (image_size, image_size), [0.0-BG, 1.0-stroke]
+ """
+ cursor_pos = cursor * float(image_size)
+ pasted_image = sess.run(pasting_func.pasted_image,
+ feed_dict={pasting_func.patch_canvas: np.expand_dims(patch_image, axis=-1),
+ pasting_func.cursor_pos_a: cursor_pos,
+ pasting_func.image_size_a: image_size,
+ pasting_func.window_size_a: window_size_f})
+ # (image_size, image_size, 1), [0.0-BG, 1.0-stroke]
+ pasted_image = pasted_image[:, :, 0]
+ return pasted_image
+
+
+def draw_strokes(data, save_root, save_filename, input_img, image_size, init_cursor, infer_lengths, init_width,
+ cursor_type, raster_size, min_window_size,
+ sess,
+ pasting_func=None,
+ save_seq=False, draw_order=False):
+ """
+ :param data: (N_strokes, 9): flag, x1, y1, x2, y2, r2, s2
+ :return:
+ """
+ canvas = np.zeros((image_size, image_size), dtype=np.float32) # [0.0-BG, 1.0-stroke]
+ canvas_color = np.zeros((image_size, image_size, 3), dtype=np.float32)
+ canvas_color_with_moving = np.zeros((image_size, image_size, 3), dtype=np.float32)
+ frames = []
+
+ cursor_idx = 0
+
+ stroke_count = len(data)
+ color_rgb_set = get_colors(stroke_count) # list of (3,) in [0, 255]
+ color_idx = 0
+
+ for round_idx in range(len(infer_lengths)):
+ round_length = infer_lengths[round_idx]
+
+ cursor_pos = init_cursor[cursor_idx] # (2)
+ cursor_idx += 1
+
+ prev_width = init_width
+ prev_scaling = 1.0
+ prev_window_size = raster_size # (1)
+
+ for round_inner_i in range(round_length):
+ stroke_idx = np.sum(infer_lengths[:round_idx]).astype(np.int32) + round_inner_i
+
+ curr_window_size = prev_scaling * prev_window_size
+ curr_window_size = np.maximum(curr_window_size, min_window_size)
+ curr_window_size = np.minimum(curr_window_size, image_size)
+
+ pen_state = data[stroke_idx, 0]
+ stroke_params = data[stroke_idx, 1:] # (8)
+
+ x1y1, x2y2, width2, scaling2 = stroke_params[0:2], stroke_params[2:4], stroke_params[4], stroke_params[5]
+ x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
+ x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
+ x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
+ widths = np.stack([prev_width, width2], axis=0) # (2)
+ stroke_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1) # (8)
+
+ next_width = stroke_params[4]
+ next_scaling = stroke_params[5]
+ next_window_size = next_scaling * curr_window_size
+ next_window_size = np.maximum(next_window_size, min_window_size)
+ next_window_size = np.minimum(next_window_size, image_size)
+
+ prev_width = next_width * curr_window_size / next_window_size
+ prev_scaling = next_scaling
+ prev_window_size = curr_window_size
+
+ f = stroke_params_proc.tolist() # (8)
+ f += [1.0, 1.0]
+ gt_stroke_img = draw(f) # (raster_size, raster_size), [0.0-stroke, 1.0-BG]
+ gt_stroke_img_large = image_pasting_v3_testing(1.0 - gt_stroke_img, cursor_pos, image_size,
+ curr_window_size,
+ pasting_func, sess) # [0.0-BG, 1.0-stroke]
+
+ if pen_state == 0:
+ canvas += gt_stroke_img_large # [0.0-BG, 1.0-stroke]
+
+ if draw_order:
+ color_rgb = color_rgb_set[color_idx] # (3) in [0, 255]
+ color_idx += 1
+
+ color_rgb = np.reshape(color_rgb, (1, 1, 3)).astype(np.float32)
+ color_stroke = np.expand_dims(gt_stroke_img_large, axis=-1) * (1.0 - color_rgb / 255.0)
+ canvas_color_with_moving = canvas_color_with_moving * np.expand_dims((1.0 - gt_stroke_img_large),
+ axis=-1) + color_stroke # (H, W, 3)
+
+ if pen_state == 0:
+ canvas_color = canvas_color * np.expand_dims((1.0 - gt_stroke_img_large),
+ axis=-1) + color_stroke # (H, W, 3)
+
+ # update cursor_pos based on hps.cursor_type
+ new_cursor_offsets = stroke_params[2:4] * (curr_window_size / 2.0) # (1, 6), patch-level
+ new_cursor_offset_next = new_cursor_offsets
+
+ # important!!!
+ new_cursor_offset_next = np.concatenate([new_cursor_offset_next[1:2], new_cursor_offset_next[0:1]], axis=-1)
+
+ cursor_pos_large = cursor_pos * float(image_size)
+
+ stroke_position_next = cursor_pos_large + new_cursor_offset_next # (2), large-level
+
+ if cursor_type == 'next':
+ cursor_pos_large = stroke_position_next # (2), large-level
+ else:
+ raise Exception('Unknown cursor_type')
+
+ cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (2), large-level
+ cursor_pos = cursor_pos_large / float(image_size)
+
+ frames.append(canvas.copy())
+
+ canvas = np.clip(canvas, 0.0, 1.0)
+ canvas = np.round((1.0 - canvas) * 255.0).astype(np.uint8) # [0-stroke, 255-BG]
+
+ os.makedirs(save_root, exist_ok=True)
+ save_path = os.path.join(save_root, save_filename)
+ canvas_img = Image.fromarray(canvas, 'L')
+ canvas_img.save(save_path, 'PNG')
+
+ if save_seq:
+ seq_save_root = os.path.join(save_root, 'seq', save_filename[:-4])
+ os.makedirs(seq_save_root, exist_ok=True)
+ for len_i in range(len(frames)):
+ frame = frames[len_i]
+ frame = np.round((1.0 - frame) * 255.0).astype(np.uint8)
+ save_path = os.path.join(seq_save_root, str(len_i) + '.png')
+ frame_img = Image.fromarray(frame, 'L')
+ frame_img.save(save_path, 'PNG')
+
+ if draw_order:
+ order_save_root = os.path.join(save_root, 'order')
+ order_comp_save_root = os.path.join(save_root, 'order-compare')
+ os.makedirs(order_save_root, exist_ok=True)
+ os.makedirs(order_comp_save_root, exist_ok=True)
+
+ canvas_color = 255 - np.round(canvas_color * 255.0).astype(np.uint8)
+ canvas_color_img = Image.fromarray(canvas_color, 'RGB')
+ save_path = os.path.join(order_save_root, save_filename)
+ canvas_color_img.save(save_path, 'PNG')
+
+ canvas_color_with_moving = 255 - np.round(canvas_color_with_moving * 255.0).astype(np.uint8)
+
+ # comparsions
+ rows = 2
+ cols = 3
+ plt.figure(figsize=(5 * cols, 5 * rows))
+
+ plt.subplot(rows, cols, 1)
+ plt.title('Input', fontsize=12)
+ # plt.axis('off')
+ input_rgb = input_img
+ plt.imshow(input_rgb)
+
+ # plt.subplot(rows, cols, 2)
+ # plt.title('GT', fontsize=12)
+ # # plt.axis('off')
+ # gt_rgb = np.stack([gt_img for _ in range(3)], axis=2)
+ # plt.imshow(gt_rgb)
+
+ plt.subplot(rows, cols, 2)
+ plt.title('Sketch', fontsize=12)
+ # plt.axis('off')
+ canvas_rgb = np.stack([canvas for _ in range(3)], axis=2)
+ plt.imshow(canvas_rgb)
+
+ plt.subplot(rows, cols, 4)
+ plt.title('Sketch Order', fontsize=12)
+ # plt.axis('off')
+ plt.imshow(canvas_color)
+
+ plt.subplot(rows, cols, 5)
+ plt.title('Sketch Order with moving', fontsize=12)
+ # plt.axis('off')
+ plt.imshow(canvas_color_with_moving)
+
+ plt.subplot(rows, cols, 6)
+ plt.title('Order', fontsize=12)
+ plt.axis('off')
+
+ img_h = 5
+ img_w = 10
+ color_array = np.zeros([len(color_rgb_set) * img_h, img_w, 3], dtype=np.uint8)
+ for i in range(len(color_rgb_set)):
+ color_array[i * img_h: i * img_h + img_h, :, :] = color_rgb_set[i]
+
+ plt.imshow(color_array)
+
+ comp_save_path = os.path.join(order_comp_save_root, save_filename)
+ plt.savefig(comp_save_path)
+ plt.close()
+ # plt.show()
+
+
+def update_hyperparams(model_params, model_base_dir, model_name, infer_dataset):
+ with tf.gfile.Open(os.path.join(model_base_dir, model_name, 'model_config.json'), 'r') as f:
+ data = json.load(f)
+
+ ignored_keys = ['image_size_small', 'image_size_large', 'z_size', 'raster_perc_loss_layer', 'raster_loss_wk',
+ 'decreasing_sn', 'raster_loss_weight']
+ for name in model_params._hparam_types.keys():
+ if name not in data and name not in ignored_keys:
+ raise Exception(name, 'not in model_config.json')
+
+ assert data['resize_method'] == 'AREA'
+ data['data_set'] = infer_dataset
+ fix_list = ['use_input_dropout', 'use_output_dropout', 'use_recurrent_dropout']
+ for fix in fix_list:
+ data[fix] = (data[fix] == 1)
+
+ pop_keys = ['gpus', 'image_size', 'resolution_type', 'loop_per_gpu', 'stroke_num_loss_weight_end',
+ 'perc_loss_fuse_type',
+ 'early_pen_length', 'early_pen_loss_type', 'early_pen_loss_weight',
+ 'increase_start_steps', 'perc_loss_layers', 'sn_loss_type', 'photo_prob_end_step',
+ 'sup_weight', 'gan_weight', 'base_raster_loss_base_type']
+ for pop_key in pop_keys:
+ if pop_key in data.keys():
+ data.pop(pop_key)
+
+ model_params.parse_json(json.dumps(data))
+
+ return model_params
diff --git a/vgg_utils/VGG16.py b/vgg_utils/VGG16.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb0d76b9b25601fe6f31be9f2b91d567459a06d7
--- /dev/null
+++ b/vgg_utils/VGG16.py
@@ -0,0 +1,128 @@
+import tensorflow as tf
+
+
+def vgg_net(x, n_classes, img_size, reuse, is_train=True, dropout_rate=0.5):
+ # Define a scope for reusing the variables
+ with tf.variable_scope('VGG16', reuse=reuse):
+ x = tf.reshape(x, [-1, img_size, img_size, 1])
+
+ x = tf.layers.conv2d(inputs=x, filters=64, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=64, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#1', x.shape)
+
+ x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#2', x.shape)
+
+ x = tf.layers.conv2d(inputs=x, filters=256, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=256, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=256, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#3', x.shape)
+
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#4', x.shape)
+
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#5', x.shape)
+
+ x_shape = x.get_shape().as_list()
+ nodes = x_shape[1] * x_shape[2] * x_shape[3]
+ x = tf.reshape(x, [-1, nodes])
+
+ x = tf.layers.dense(x, 4096, activation=tf.nn.relu)
+ if is_train:
+ x = tf.layers.dropout(x, dropout_rate)
+
+ x = tf.layers.dense(x, 4096, activation=tf.nn.relu)
+ if is_train:
+ x = tf.layers.dropout(x, dropout_rate)
+
+ out = tf.layers.dense(x, n_classes)
+ print(out)
+
+ return out
+
+
+def vgg_net_slim(x, img_size):
+ return_map = {}
+ # Define a scope for reusing the variables
+ with tf.variable_scope('VGG16', reuse=tf.AUTO_REUSE):
+ x = tf.reshape(x, [-1, img_size, img_size, 1])
+
+ x = tf.layers.conv2d(inputs=x, filters=64, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU1_1'] = x
+ x = tf.layers.conv2d(inputs=x, filters=64, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU1_2'] = x
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#1', x.shape) #1 (?, 64, 64, 64)
+
+ x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU2_1'] = x
+ x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU2_2'] = x
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#2', x.shape) #2 (?, 32, 32, 128)
+
+ x = tf.layers.conv2d(inputs=x, filters=256, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU3_1'] = x
+ x = tf.layers.conv2d(inputs=x, filters=256, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU3_2'] = x
+ x = tf.layers.conv2d(inputs=x, filters=256, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU3_3'] = x
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#3', x.shape) #3 (?, 16, 16, 256)
+
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU4_1'] = x
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU4_2'] = x
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU4_3'] = x
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#4', x.shape) #4 (?, 8, 8, 512)
+
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU5_1'] = x
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU5_2'] = x
+ x = tf.layers.conv2d(inputs=x, filters=512, kernel_size=[3, 3], strides=1,
+ padding='SAME', activation=tf.nn.relu)
+ return_map['ReLU5_3'] = x
+ x = tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
+ print('#5', x.shape) #5 (?, 4, 4, 512)
+
+ return return_map
diff --git a/virtual_sketch_gui.py b/virtual_sketch_gui.py
new file mode 100644
index 0000000000000000000000000000000000000000..5204b7e9eb950b8d1b2cc82905a452d6442348a8
--- /dev/null
+++ b/virtual_sketch_gui.py
@@ -0,0 +1,117 @@
+import tkinter as tk
+from tkinter import filedialog, messagebox
+import subprocess
+import os
+import threading
+import glob
+import sys
+import shutil
+
+# ==== Nastavení cesty ke skriptům ====
+MODEL_OPTIONS = {
+ "Rough → Clean Sketch": "test_rough_sketch_simplification.py",
+ "Photo → Line Drawing": "test_photograph_to_line.py",
+ "Clean Sketch → Vector": "test_vectorization.py",
+}
+
+SVG_CONVERTER = os.path.join("tools", "svg_conversion.py")
+
+class VirtualSketchApp:
+ def __init__(self, root):
+ self.root = root
+ self.root.title("Virtual Sketching GUI")
+ self.input_file = None
+ self.model_script = tk.StringVar(value=list(MODEL_OPTIONS.values())[0])
+
+ self.build_ui()
+
+ def build_ui(self):
+ tk.Label(self.root, text="1. Vyber vstupní obrázek:").pack(anchor="w")
+ tk.Button(self.root, text="Vybrat obrázek", command=self.choose_file).pack(fill="x")
+
+ self.file_label = tk.Label(self.root, text="Žádný soubor nevybrán", fg="gray")
+ self.file_label.pack(anchor="w")
+
+ tk.Label(self.root, text="2. Zvol model:").pack(anchor="w")
+ for name, script in MODEL_OPTIONS.items():
+ tk.Radiobutton(self.root, text=name, variable=self.model_script, value=script).pack(anchor="w")
+
+ tk.Button(self.root, text="3. Spustit zpracování", command=self.run_processing).pack(pady=10, fill="x")
+
+ self.status = tk.Label(self.root, text="Připraven", fg="green")
+ self.status.pack(anchor="w")
+
+ def choose_file(self):
+ path = filedialog.askopenfilename(filetypes=[
+ ("Obrázkové soubory", "*.png *.jpg *.jpeg *.bmp *.gif *.tif *.tiff"),
+ ("PNG", "*.png"),
+ ("JPEG", "*.jpg;*.jpeg"),
+ ("BMP", "*.bmp"),
+ ("GIF", "*.gif"),
+ ("TIFF", "*.tif;*.tiff")
+ ])
+ if path:
+ self.input_file = path
+ self.file_label.config(text=os.path.basename(path), fg="black")
+
+ def run_processing(self):
+ if not self.input_file:
+ messagebox.showerror("Chyba", "Nejprve vyber obrázek.")
+ return
+
+ script = self.model_script.get()
+ cmd = [sys.executable, script, "--input", self.input_file]
+
+ def task():
+ self.status.config(text="Zpracovávám...", fg="blue")
+ try:
+ subprocess.run(cmd, check=True)
+ self.status.config(text="✅ Hotovo", fg="green")
+ self.move_outputs_to_sketches()
+ self.run_svg_conversion()
+ except subprocess.CalledProcessError:
+ self.status.config(text="❌ Chyba při běhu skriptu", fg="red")
+
+ threading.Thread(target=task).start()
+
+ def move_outputs_to_sketches(self):
+ if not self.input_file:
+ return
+
+ input_dir = os.path.dirname(self.input_file)
+ input_base = os.path.splitext(os.path.basename(self.input_file))[0]
+ sketches_dir = os.path.join(input_dir, "sketches")
+ os.makedirs(sketches_dir, exist_ok=True)
+
+ for ext in ["_0.npz", "_0_pred.png", "_input.png", "_0.svg"]:
+ candidate = os.path.join(input_dir, f"{input_base}{ext}")
+ if os.path.isfile(candidate):
+ shutil.move(candidate, os.path.join(sketches_dir, os.path.basename(candidate)))
+
+ def run_svg_conversion(self):
+ if not self.input_file:
+ return
+
+ input_dir = os.path.dirname(self.input_file)
+ input_base = os.path.splitext(os.path.basename(self.input_file))[0]
+ npz_file = os.path.join(input_dir, f"{input_base}_0.npz")
+ sketches_dir = os.path.join(input_dir, "sketches")
+ npz_file_in_sketches = os.path.join(sketches_dir, f"{input_base}_0.npz")
+
+ if not os.path.isfile(npz_file_in_sketches):
+ print("⚠️ .npz soubor nebyl nalezen pro SVG konverzi.")
+ return
+
+ cmd = [sys.executable, SVG_CONVERTER, "--file", npz_file_in_sketches, "--svg_type", "single"]
+ try:
+ subprocess.run(cmd, check=True)
+ svg_path = os.path.join(sketches_dir, f"{input_base}_0.svg")
+ if os.path.isfile(svg_path):
+ print(f"✅ SVG vytvořeno: {svg_path}")
+ except subprocess.CalledProcessError:
+ print("⚠️ Chyba při SVG konverzi")
+
+if __name__ == "__main__":
+ root = tk.Tk()
+ app = VirtualSketchApp(root)
+ root.mainloop()
BibTeX
+
+@article{mo2021virtualsketching,
+ title = {General Virtual Sketching Framework for Vector Line Art},
+ author = {Mo, Haoran and Simo-Serra, Edgar and Gao, Chengying and Zou, Changqing and Wang, Ruomei},
+ journal = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2021)},
+ year = {2021},
+ volume = {40},
+ number = {4},
+ pages = {51:1--51:14}
+}
+
+
+ +
Related Work
+
+
+
+ Mikhail Bessmeltsev and Justin Solomon.
+ Vectorization of Line Drawings via PolyVector Fields. SIGGRAPH 2019.
+ [Paper]
+ [Code]
+
+
+
+
+
+
+ +
+