repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
ControlNet | gradio_hed2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.hed import HEDdetector
from cldm.model import create_model, load_state_dict
from cldm.... | 99 | 4,843 |
ControlNet | gradio_fake_scribble2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.hed import HEDdetector, nms
from cldm.model import create_model, load_state_dict
from ... | 103 | 5,077 |
ControlNet | gradio_scribble2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
model... | 93 | 4,509 |
ControlNet | gradio_pose2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.openpose import OpenposeDetector
from cldm.model import create_model, load_state_dict
... | 99 | 4,884 |
ControlNet | share.py | .py | import config
from cldm.hack import disable_verbosity, enable_sliced_attention
disable_verbosity()
if config.save_memory:
enable_sliced_attention()
| 9 | 155 |
ControlNet | tutorial_train.py | .py | from share import *
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from tutorial_dataset import MyDataset
from cldm.logger import ImageLogger
from cldm.model import create_model, load_state_dict
# Configs
resume_path = './models/control_sd15_ini.ckpt'
batch_size = 4
logger_freq = 300
learning... | 36 | 961 |
ControlNet | gradio_canny2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.canny import CannyDetector
from cldm.model import create_model, load_state_dict
from c... | 98 | 4,874 |
ControlNet | tutorial_dataset.py | .py | import json
import cv2
import numpy as np
from torch.utils.data import Dataset
class MyDataset(Dataset):
def __init__(self):
self.data = []
with open('./training/fill50k/prompt.json', 'rt') as f:
for line in f:
self.data.append(json.loads(line))
def __len__(self):... | 40 | 1,107 |
ControlNet | gradio_depth2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.midas import MidasDetector
from cldm.model import create_model, load_state_dict
from c... | 99 | 4,862 |
ControlNet | tutorial_train_sd21.py | .py | from share import *
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from tutorial_dataset import MyDataset
from cldm.logger import ImageLogger
from cldm.model import create_model, load_state_dict
# Configs
resume_path = './models/control_sd21_ini.ckpt'
batch_size = 4
logger_freq = 300
learning... | 36 | 961 |
ControlNet | gradio_hough2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.mlsd import MLSDdetector
from cldm.model import create_model, load_state_dict
from cld... | 101 | 5,310 |
ControlNet | tool_transfer_control.py | .py | path_sd15 = './models/v1-5-pruned.ckpt'
path_sd15_with_control = './models/control_sd15_openpose.pth'
path_input = './models/anything-v3-full.safetensors'
path_output = './models/control_any3_openpose.pth'
import os
assert os.path.exists(path_sd15), 'Input path_sd15 does not exists!'
assert os.path.exists(path_sd15... | 60 | 1,930 |
ControlNet | gradio_annotator.py | .py | import gradio as gr
from annotator.util import resize_image, HWC3
model_canny = None
def canny(img, res, l, h):
img = resize_image(HWC3(img), res)
global model_canny
if model_canny is None:
from annotator.canny import CannyDetector
model_canny = CannyDetector()
result = model_canny(... | 161 | 5,748 |
ControlNet | tool_add_control.py | .py | import sys
import os
assert len(sys.argv) == 3, 'Args are wrong.'
input_path = sys.argv[1]
output_path = sys.argv[2]
assert os.path.exists(input_path), 'Input model does not exist.'
assert not os.path.exists(output_path), 'Output filename already exists.'
assert os.path.exists(os.path.dirname(output_path)), 'Output ... | 51 | 1,382 |
ControlNet | gradio_normal2image.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.midas import MidasDetector
from cldm.model import create_model, load_state_dict
from c... | 100 | 5,051 |
ControlNet | gradio_scribble2image_interactive.py | .py | from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
model... | 103 | 5,333 |
ControlNet | ldm/util.py | .py | import importlib
import torch
from torch import optim
import numpy as np
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = list()
for bi in range(b):
... | 197 | 7,227 |
ControlNet | ldm/data/util.py | .py | import torch
from ldm.modules.midas.api import load_midas_transform
class AddMiDaS(object):
def __init__(self, model_type):
super().__init__()
self.transform = load_midas_transform(model_type)
def pt2np(self, x):
x = ((x + 1.0) * .5).detach().cpu().numpy()
return x
def n... | 24 | 629 |
ControlNet | ldm/modules/attention.py | .py | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE... | 342 | 11,806 |
ControlNet | ldm/modules/diffusionmodules/model.py | .py | # pytorch_diffusion + derived encoder decoder
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True... | 853 | 34,384 |
ControlNet | ldm/modules/diffusionmodules/util.py | .py | # adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/gu... | 270 | 9,868 |
ControlNet | ldm/modules/diffusionmodules/openaimodel.py | .py | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.module... | 787 | 30,364 |
ControlNet | ldm/modules/diffusionmodules/upscaling.py | .py | import torch
import torch.nn as nn
import numpy as np
from functools import partial
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ldm.util import default
class AbstractLowScaleModel(nn.Module):
# for concatenating a downsampled image to the latent representation
d... | 82 | 3,424 |
ControlNet | ldm/modules/midas/utils.py | .py | """Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
heig... | 190 | 4,582 |
ControlNet | ldm/modules/midas/api.py | .py | # based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
fr... | 171 | 5,338 |
ControlNet | ldm/modules/midas/midas/midas_net.py | .py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 77 | 2,709 |
ControlNet | ldm/modules/midas/midas/transforms.py | .py | import numpy as np
import cv2
import math
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
Args:
sample (dict): sample
size (tuple): image size
Returns:
tuple: new size
"""
shap... | 235 | 7,869 |
ControlNet | ldm/modules/midas/midas/midas_net_custom.py | .py | """MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
f... | 128 | 5,207 |
ControlNet | ldm/modules/midas/midas/base_model.py | .py | import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["mod... | 17 | 367 |
ControlNet | ldm/modules/midas/midas/dpt_depth.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
... | 110 | 3,154 |
ControlNet | ldm/modules/midas/midas/blocks.py | .py | import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ign... | 343 | 9,242 |
ControlNet | ldm/modules/midas/midas/vit.py | .py | import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class... | 492 | 14,625 |
ControlNet | ldm/modules/image_degradation/bsrgan.py | .py | # -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""
import numpy as np
import cv2
import tor... | 731 | 25,198 |
ControlNet | ldm/modules/image_degradation/utils_image.py | .py | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
'''
# ----------------------... | 916 | 29,024 |
ControlNet | ldm/modules/image_degradation/bsrgan_light.py | .py | # -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as ... | 652 | 22,341 |
ControlNet | ldm/modules/encoders/modules.py | .py | import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
import open_clip
from ldm.util import default, count_params
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
de... | 214 | 7,611 |
ControlNet | ldm/models/autoencoder.py | .py | import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.util import instantiate_from_config
from ldm.modul... | 220 | 8,560 |
ControlNet | ldm/models/diffusion/ddim.py | .py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__... | 336 | 17,304 |
ControlNet | ldm/models/diffusion/sampling_util.py | .py | import torch
import numpy as np
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
rais... | 22 | 753 |
ControlNet | ldm/models/diffusion/plms.py | .py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from ldm.models.diffusion.sampling_util import norm_thresholding
class PLMSSampler(object):
def __... | 245 | 12,927 |
ControlNet | ldm/models/diffusion/ddpm.py | .py | """
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | 1,798 | 84,659 |
ControlNet | ldm/models/diffusion/dpm_solver/dpm_solver.py | .py | import torch
import torch.nn.functional as F
import math
from tqdm import tqdm
class NoiseScheduleVP:
def __init__(
self,
schedule='discrete',
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.,
):
"""Cr... | 1,154 | 65,969 |
ControlNet | ldm/models/diffusion/dpm_solver/sampler.py | .py | """SAMPLING ONLY."""
import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
MODEL_TYPES = {
"eps": "noise",
"v": "v"
}
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone... | 87 | 2,990 |
ControlNet | cldm/logger.py | .py | import os
import numpy as np
import torch
import torchvision
from PIL import Image
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities.distributed import rank_zero_only
class ImageLogger(Callback):
def __init__(self, batch_frequency=2000, max_images=4, clamp=True, increase_log_steps... | 77 | 3,182 |
ControlNet | cldm/hack.py | .py | import torch
import einops
import ldm.modules.encoders.modules
import ldm.modules.attention
from transformers import logging
from ldm.modules.attention import default
def disable_verbosity():
logging.set_verbosity_error()
print('logging improved.')
return
def enable_sliced_attention():
ldm.modules... | 112 | 3,567 |
ControlNet | cldm/model.py | .py | import os
import torch
from omegaconf import OmegaConf
from ldm.util import instantiate_from_config
def get_state_dict(d):
return d.get('state_dict', d)
def load_state_dict(ckpt_path, location='cpu'):
_, extension = os.path.splitext(ckpt_path)
if extension.lower() == ".safetensors":
import safe... | 29 | 836 |
ControlNet | cldm/ddim_hacked.py | .py | """SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super().__init__... | 318 | 16,434 |
ControlNet | cldm/cldm.py | .py | import einops
import torch
import torch as th
import torch.nn as nn
from ldm.modules.diffusionmodules.util import (
conv_nd,
linear,
zero_module,
timestep_embedding,
)
from einops import rearrange, repeat
from torchvision.utils import make_grid
from ldm.modules.attention import SpatialTransformer
from... | 436 | 18,980 |
ControlNet | annotator/util.py | .py | import numpy as np
import cv2
import os
annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x... | 39 | 980 |
ControlNet | annotator/hed/__init__.py | .py | # This is an improved version and model of HED edge detection with Apache License, Version 2.0.
# Please use this implementation in your products
# This implementation may produce slightly different results from Saining Xie's official implementations,
# but it generates smoother edges and is more suitable for ControlNe... | 97 | 4,487 |
ControlNet | annotator/canny/__init__.py | .py | import cv2
class CannyDetector:
def __call__(self, img, low_threshold, high_threshold):
return cv2.Canny(img, low_threshold, high_threshold)
| 7 | 155 |
ControlNet | annotator/mlsd/utils.py | .py | '''
modified by lihaoweicv
pytorch version
'''
'''
M-LSD
Copyright 2021-present NAVER Corp.
Apache License v2.0
'''
import os
import numpy as np
import cv2
import torch
from torch.nn import functional as F
def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5):
'''
tpMap:
center: tpMap[1, ... | 581 | 24,049 |
ControlNet | annotator/mlsd/__init__.py | .py | # MLSD Line Detection
# From https://github.com/navervision/mlsd
# Apache-2.0 license
import cv2
import numpy as np
import torch
import os
from einops import rearrange
from .models.mbv2_mlsd_tiny import MobileV2_MLSD_Tiny
from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
from .utils import pred_lines
from anno... | 44 | 1,545 |
ControlNet | annotator/mlsd/models/mbv2_mlsd_large.py | .py | import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
class BlockTypeA(nn.Module):
def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
super(BlockTypeA, self).__init__()
self.conv1 = nn.Sequential(
... | 292 | 9,678 |
ControlNet | annotator/mlsd/models/mbv2_mlsd_tiny.py | .py | import os
import sys
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
class BlockTypeA(nn.Module):
def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale = True):
super(BlockTypeA, self).__init__()
self.conv1 = nn.Sequential(
... | 275 | 9,180 |
ControlNet | annotator/uniformer/__init__.py | .py | # Uniformer
# From https://github.com/Sense-X/UniFormer
# # Apache-2.0 license
import os
from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
from annotator.uniformer.mmseg.core.evaluation import get_palette
from annotator.util import annotator_ckpts_path
checkpoint_fil... | 28 | 1,150 |
ControlNet | annotator/uniformer/mmcv_custom/checkpoint.py | .py | # Copyright (c) Open-MMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from to... | 500 | 19,091 |
ControlNet | annotator/uniformer/mmcv_custom/__init__.py | .py | # -*- coding: utf-8 -*-
from .checkpoint import load_checkpoint
__all__ = ['load_checkpoint'] | 5 | 95 |
ControlNet | annotator/uniformer/mmseg/apis/inference.py | .py | import matplotlib.pyplot as plt
import annotator.uniformer.mmcv as mmcv
import torch
from annotator.uniformer.mmcv.parallel import collate, scatter
from annotator.uniformer.mmcv.runner import load_checkpoint
from annotator.uniformer.mmseg.datasets.pipelines import Compose
from annotator.uniformer.mmseg.models import b... | 137 | 4,729 |
ControlNet | annotator/uniformer/mmseg/apis/train.py | .py | import random
import warnings
import numpy as np
import torch
from annotator.uniformer.mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from annotator.uniformer.mmcv.runner import build_optimizer, build_runner
from annotator.uniformer.mmseg.core import DistEvalHook, EvalHook
from annotator.uniformer.mms... | 117 | 4,035 |
ControlNet | annotator/uniformer/mmseg/apis/__init__.py | .py | from .inference import inference_segmentor, init_segmentor, show_result_pyplot
from .test import multi_gpu_test, single_gpu_test
from .train import get_root_logger, set_random_seed, train_segmentor
__all__ = [
'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor',
'inference_segmentor', 'mu... | 10 | 381 |
ControlNet | annotator/uniformer/mmseg/apis/test.py | .py | import os.path as osp
import pickle
import shutil
import tempfile
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch
import torch.distributed as dist
from annotator.uniformer.mmcv.image import tensor2imgs
from annotator.uniformer.mmcv.runner import get_dist_info
def np2tmp(array, temp_file_name=... | 239 | 8,288 |
ControlNet | annotator/uniformer/mmseg/datasets/stare.py | .py | import os.path as osp
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class STAREDataset(CustomDataset):
"""STARE dataset.
In segmentation map annotation for STARE, 0 stands for background, which is
included in 2 categories. ``reduce_zero_label`` is fixed to F... | 28 | 761 |
ControlNet | annotator/uniformer/mmseg/datasets/builder.py | .py | import copy
import platform
import random
from functools import partial
import numpy as np
from annotator.uniformer.mmcv.parallel import collate
from annotator.uniformer.mmcv.runner import get_dist_info
from annotator.uniformer.mmcv.utils import Registry, build_from_cfg
from annotator.uniformer.mmcv.utils.parrots_wrap... | 170 | 5,951 |
ControlNet | annotator/uniformer/mmseg/datasets/pascal_context.py | .py | import os.path as osp
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class PascalContextDataset(CustomDataset):
"""PascalContext dataset.
In segmentation map annotation for PascalContext, 0 stands for background,
which is included in 60 categories. ``reduce_z... | 104 | 5,202 |
ControlNet | annotator/uniformer/mmseg/datasets/dataset_wrappers.py | .py | from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for image aspect rati... | 51 | 1,499 |
ControlNet | annotator/uniformer/mmseg/datasets/custom.py | .py | import os
import os.path as osp
from collections import OrderedDict
from functools import reduce
import annotator.uniformer.mmcv as mmcv
import numpy as np
from annotator.uniformer.mmcv.utils import print_log
from prettytable import PrettyTable
from torch.utils.data import Dataset
from annotator.uniformer.mmseg.core ... | 401 | 14,882 |
ControlNet | annotator/uniformer/mmseg/datasets/voc.py | .py | import os.path as osp
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class PascalVOCDataset(CustomDataset):
"""Pascal VOC dataset.
Args:
split (str): Split txt file for Pascal VOC.
"""
CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boa... | 30 | 1,130 |
ControlNet | annotator/uniformer/mmseg/datasets/__init__.py | .py | from .ade import ADE20KDataset
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .chase_db1 import ChaseDB1Dataset
from .cityscapes import CityscapesDataset
from .custom import CustomDataset
from .dataset_wrappers import ConcatDataset, RepeatDataset
from .drive import DRIVEDataset
from .hrf... | 20 | 798 |
ControlNet | annotator/uniformer/mmseg/datasets/ade.py | .py | from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class ADE20KDataset(CustomDataset):
"""ADE20K dataset.
In segmentation map annotation for ADE20K, 0 stands for background, which
is not included in 150 categories. ``reduce_zero_label`` is fixed to True.
The `... | 85 | 5,185 |
ControlNet | annotator/uniformer/mmseg/datasets/cityscapes.py | .py | import os.path as osp
import tempfile
import annotator.uniformer.mmcv as mmcv
import numpy as np
from annotator.uniformer.mmcv.utils import print_log
from PIL import Image
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class CityscapesDataset(CustomDataset):
"""Citys... | 218 | 8,494 |
ControlNet | annotator/uniformer/mmseg/datasets/chase_db1.py | .py | import os.path as osp
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class ChaseDB1Dataset(CustomDataset):
"""Chase_db1 dataset.
In segmentation map annotation for Chase_db1, 0 stands for background,
which is included in 2 categories. ``reduce_zero_label`` is... | 28 | 781 |
ControlNet | annotator/uniformer/mmseg/datasets/drive.py | .py | import os.path as osp
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class DRIVEDataset(CustomDataset):
"""DRIVE dataset.
In segmentation map annotation for DRIVE, 0 stands for background, which is
included in 2 categories. ``reduce_zero_label`` is fixed to F... | 28 | 771 |
ControlNet | annotator/uniformer/mmseg/datasets/hrf.py | .py | import os.path as osp
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class HRFDataset(CustomDataset):
"""HRF dataset.
In segmentation map annotation for HRF, 0 stands for background, which is
included in 2 categories. ``reduce_zero_label`` is fixed to False. ... | 28 | 747 |
ControlNet | annotator/uniformer/mmseg/datasets/pipelines/compose.py | .py | import collections
from annotator.uniformer.mmcv.utils import build_from_cfg
from ..builder import PIPELINES
@PIPELINES.register_module()
class Compose(object):
"""Compose multiple transforms sequentially.
Args:
transforms (Sequence[dict | callable]): Sequence of transform object or
con... | 52 | 1,484 |
ControlNet | annotator/uniformer/mmseg/datasets/pipelines/loading.py | .py | import os.path as osp
import annotator.uniformer.mmcv as mmcv
import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class LoadImageFromFile(object):
"""Load an image from file.
Required keys are "img_prefix" and "img_info" (a dict that must contain the
key "filename"). Added o... | 154 | 5,901 |
ControlNet | annotator/uniformer/mmseg/datasets/pipelines/__init__.py | .py | from .compose import Compose
from .formating import (Collect, ImageToTensor, ToDataContainer, ToTensor,
Transpose, to_tensor)
from .loading import LoadAnnotations, LoadImageFromFile
from .test_time_aug import MultiScaleFlipAug
from .transforms import (CLAHE, AdjustGamma, Normalize, Pad,
... | 17 | 813 |
ControlNet | annotator/uniformer/mmseg/datasets/pipelines/transforms.py | .py | import annotator.uniformer.mmcv as mmcv
import numpy as np
from annotator.uniformer.mmcv.utils import deprecated_api_warning, is_tuple_of
from numpy import random
from ..builder import PIPELINES
@PIPELINES.register_module()
class Resize(object):
"""Resize images & seg.
This transform resizes the input image... | 890 | 30,993 |
ControlNet | annotator/uniformer/mmseg/datasets/pipelines/test_time_aug.py | .py | import warnings
import annotator.uniformer.mmcv as mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug(object):
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-block::
... | 134 | 5,201 |
ControlNet | annotator/uniformer/mmseg/datasets/pipelines/formating.py | .py | from collections.abc import Sequence
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch
from annotator.uniformer.mmcv.parallel import DataContainer as DC
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported ty... | 289 | 9,276 |
ControlNet | annotator/uniformer/mmseg/core/seg/builder.py | .py | from annotator.uniformer.mmcv.utils import Registry, build_from_cfg
PIXEL_SAMPLERS = Registry('pixel sampler')
def build_pixel_sampler(cfg, **default_args):
"""Build pixel sampler for segmentation map."""
return build_from_cfg(cfg, PIXEL_SAMPLERS, default_args)
| 9 | 273 |
ControlNet | annotator/uniformer/mmseg/core/seg/__init__.py | .py | from .builder import build_pixel_sampler
from .sampler import BasePixelSampler, OHEMPixelSampler
__all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler']
| 5 | 172 |
ControlNet | annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py | .py | import torch
import torch.nn.functional as F
from ..builder import PIXEL_SAMPLERS
from .base_pixel_sampler import BasePixelSampler
@PIXEL_SAMPLERS.register_module()
class OHEMPixelSampler(BasePixelSampler):
"""Online Hard Example Mining Sampler for segmentation.
Args:
context (nn.Module): The contex... | 77 | 3,155 |
ControlNet | annotator/uniformer/mmseg/core/seg/sampler/base_pixel_sampler.py | .py | from abc import ABCMeta, abstractmethod
class BasePixelSampler(metaclass=ABCMeta):
"""Base class of pixel sampler."""
def __init__(self, **kwargs):
pass
@abstractmethod
def sample(self, seg_logit, seg_label):
"""Placeholder for sample function."""
| 13 | 284 |
ControlNet | annotator/uniformer/mmseg/core/seg/sampler/__init__.py | .py | from .base_pixel_sampler import BasePixelSampler
from .ohem_pixel_sampler import OHEMPixelSampler
__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
| 5 | 150 |
ControlNet | annotator/uniformer/mmseg/core/evaluation/__init__.py | .py | from .class_names import get_classes, get_palette
from .eval_hooks import DistEvalHook, EvalHook
from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
__all__ = [
'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
'eval_metrics', 'get_classes', 'get_palette'
]
| 9 | 301 |
ControlNet | annotator/uniformer/mmseg/core/evaluation/eval_hooks.py | .py | import os.path as osp
from annotator.uniformer.mmcv.runner import DistEvalHook as _DistEvalHook
from annotator.uniformer.mmcv.runner import EvalHook as _EvalHook
class EvalHook(_EvalHook):
"""Single GPU EvalHook, with efficient test support.
Args:
by_epoch (bool): Determine perform evaluation by epo... | 110 | 3,873 |
ControlNet | annotator/uniformer/mmseg/core/evaluation/metrics.py | .py | from collections import OrderedDict
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch
def f_score(precision, recall, beta=1):
"""calcuate the f-score value.
Args:
precision (float | torch.Tensor): The precision value.
recall (float | torch.Tensor): The recall value.
... | 327 | 13,079 |
ControlNet | annotator/uniformer/mmseg/core/evaluation/class_names.py | .py | import annotator.uniformer.mmcv as mmcv
def cityscapes_classes():
"""Cityscapes class names for external use."""
return [
'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
'person', 'rider', 'car', 'truck', 'bus', ... | 153 | 7,305 |
ControlNet | annotator/uniformer/mmseg/core/utils/misc.py | .py | def add_prefix(inputs, prefix):
"""Add prefix for dict.
Args:
inputs (dict): The input dict with str keys.
prefix (str): The prefix to add.
Returns:
dict: The dict with keys updated with ``prefix``.
"""
outputs = dict()
for name, value in inputs.items():
outpu... | 18 | 371 |
ControlNet | annotator/uniformer/mmseg/core/utils/__init__.py | .py | from .misc import add_prefix
__all__ = ['add_prefix']
| 4 | 55 |
ControlNet | annotator/uniformer/mmseg/utils/logger.py | .py | import logging
from annotator.uniformer.mmcv.utils import get_logger
def get_root_logger(log_file=None, log_level=logging.INFO):
"""Get the root logger.
The logger will be initialized if it has not been initialized. By default a
StreamHandler will be added. If `log_file` is specified, a FileHandler will... | 28 | 919 |
ControlNet | annotator/uniformer/mmseg/utils/collect_env.py | .py | from annotator.uniformer.mmcv.utils import collect_env as collect_base_env
from annotator.uniformer.mmcv.utils import get_git_hash
import annotator.uniformer.mmseg as mmseg
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMSegmentation... | 18 | 505 |
ControlNet | annotator/uniformer/mmseg/utils/__init__.py | .py | from .collect_env import collect_env
from .logger import get_root_logger
__all__ = ['get_root_logger', 'collect_env']
| 5 | 119 |
ControlNet | annotator/uniformer/mmseg/ops/__init__.py | .py | from .encoding import Encoding
from .wrappers import Upsample, resize
__all__ = ['Upsample', 'resize', 'Encoding']
| 5 | 116 |
ControlNet | annotator/uniformer/mmseg/ops/encoding.py | .py | import torch
from torch import nn
from torch.nn import functional as F
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Args:
channels: dimension of the ... | 75 | 2,788 |
ControlNet | annotator/uniformer/mmseg/ops/wrappers.py | .py | import warnings
import torch.nn as nn
import torch.nn.functional as F
def resize(input,
size=None,
scale_factor=None,
mode='nearest',
align_corners=None,
warning=True):
if warning:
if size is not None and align_corners:
input_h, input_w =... | 51 | 1,827 |
ControlNet | annotator/uniformer/mmseg/models/builder.py | .py | import warnings
from annotator.uniformer.mmcv.cnn import MODELS as MMCV_MODELS
from annotator.uniformer.mmcv.utils import Registry
MODELS = Registry('models', parent=MMCV_MODELS)
BACKBONES = MODELS
NECKS = MODELS
HEADS = MODELS
LOSSES = MODELS
SEGMENTORS = MODELS
def build_backbone(cfg):
"""Build backbone."""
... | 47 | 1,205 |
ControlNet | annotator/uniformer/mmseg/models/__init__.py | .py | from .backbones import * # noqa: F401,F403
from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone,
build_head, build_loss, build_segmentor)
from .decode_heads import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
from .necks import * # noqa: F401,F403
from .seg... | 13 | 489 |
ControlNet | annotator/uniformer/mmseg/models/decode_heads/enc_head.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, build_norm_layer
from annotator.uniformer.mmseg.ops import Encoding, resize
from ..builder import HEADS, build_loss
from .decode_head import BaseDecodeHead
class EncModule(nn.Module):
"""Encodi... | 188 | 6,784 |
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