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 | annotator/uniformer/mmcv/ops/voxelize.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['dynamic_voxelize_forward', 'hard_voxelize_forward'])
class _Voxelization(Funct... | 133 | 5,286 |
ControlNet | annotator/uniformer/mmcv/ops/gather_points.py | .py | import torch
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['gather_points_forward', 'gather_points_backward'])
class GatherPoints(Function):
"""Gather points with given index."""
@staticmethod
def forward(ctx, features: torch.Tensor,
... | 58 | 1,607 |
ControlNet | annotator/uniformer/mmcv/ops/upfirdn2d.py | .py | # modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
# Augmentation (ADA)
# =========================================================... | 331 | 11,804 |
ControlNet | annotator/uniformer/mmcv/ops/ball_query.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['ball_query_forward'])
class BallQuery(Function):
"""Find nearby points in spherical space."""
@staticmethod
def forward(ctx, min_rad... | 56 | 1,695 |
ControlNet | annotator/uniformer/mmcv/ops/masked_conv.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['masked_im2... | 112 | 3,761 |
ControlNet | annotator/uniformer/mmcv/ops/psa_mask.py | .py | # Modified from https://github.com/hszhao/semseg/blob/master/lib/psa
from torch import nn
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext',
['psamask_forward', 'psamask_backward'])
cla... | 93 | 2,773 |
ControlNet | annotator/uniformer/mmcv/ops/fused_bias_leakyrelu.py | .py | # modified from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
# NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator
# Augmentation (ADA)
# ==========================================================... | 269 | 10,031 |
ControlNet | annotator/uniformer/mmcv/ops/roi_align.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from ..utils import deprecated_api_warning, ext_loader
ext_module = ext_loader.load_ext('_ext',
... | 224 | 8,519 |
ControlNet | annotator/uniformer/mmcv/ops/three_nn.py | .py | from typing import Tuple
import torch
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['three_nn_forward'])
class ThreeNN(Function):
"""Find the top-3 nearest neighbors of the target set from the source set.
Please refer to `Paper of PointNet++ <... | 52 | 1,515 |
ControlNet | annotator/uniformer/mmcv/ops/tin_shift.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
# Code reference from "Temporal Interlacing Network"
# https://github.com/deepcs233/TIN/blob/master/cuda_shift/rtc_wrap.py
# Hao Shao, Shengju Qian, Yu Liu
# shaoh19@mails.tsinghua.edu.cn, sjqian@cse.cuhk.edu.hk, yuliu@ee.cuhk.edu.hk
import torch
import torch.nn as nn
fr... | 69 | 2,141 |
ControlNet | annotator/uniformer/mmcv/ops/roipoint_pool3d.py | .py | from torch import nn as nn
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['roipoint_pool3d_forward'])
class RoIPointPool3d(nn.Module):
"""Encode the geometry-specific features of each 3D proposal.
Please refer to `Paper of PartA2 <https://arxiv.... | 78 | 2,990 |
ControlNet | annotator/uniformer/mmcv/ops/points_sampler.py | .py | from typing import List
import torch
from torch import nn as nn
from annotator.uniformer.mmcv.runner import force_fp32
from .furthest_point_sample import (furthest_point_sample,
furthest_point_sample_with_dist)
def calc_square_dist(point_feat_a, point_feat_b, norm=True):
"""C... | 178 | 6,063 |
ControlNet | annotator/uniformer/mmcv/ops/carafe.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from torch.nn.modules.module import Module
from ..cnn import UPSAMPLE_LAYERS, normal_init, xavier_init
from ..utils import ext_loader
ext_module = ext_loader.load_ext(... | 288 | 9,873 |
ControlNet | annotator/uniformer/mmcv/ops/point_sample.py | .py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa
from os import path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.onnx.operators import shape_as_tensor
def bilinear_grid_sample(im, g... | 337 | 12,291 |
ControlNet | annotator/uniformer/mmcv/ops/knn.py | .py | import torch
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['knn_forward'])
class KNN(Function):
r"""KNN (CUDA) based on heap data structure.
Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/
scene_seg/lib/pointops/src/knnq... | 78 | 2,599 |
ControlNet | annotator/uniformer/mmcv/ops/focal_loss.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', [
'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward',
... | 213 | 6,582 |
ControlNet | annotator/uniformer/mmcv/ops/saconv.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init
from annotator.uniformer.mmcv.ops.deform_conv import deform_conv2d
from annotator.uniformer.mmcv.utils import TORCH_VERSION, ... | 146 | 5,804 |
ControlNet | annotator/uniformer/mmcv/ops/correlation.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['correlation_forw... | 197 | 6,697 |
ControlNet | annotator/uniformer/mmcv/ops/scatter_points.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import nn
from torch.autograd import Function
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext',
['dynamic_point_to_voxel_forward', 'dynamic_point_to_voxel_backward'])
class _DynamicScatter(Function):
@staticm... | 136 | 5,201 |
ControlNet | annotator/uniformer/mmcv/video/io.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from collections import OrderedDict
import cv2
from cv2 import (CAP_PROP_FOURCC, CAP_PROP_FPS, CAP_PROP_FRAME_COUNT,
CAP_PROP_FRAME_HEIGHT, CAP_PROP_FRAME_WIDTH,
CAP_PROP_POS_FRAMES, VideoWriter_fourcc)
from annota... | 319 | 10,209 |
ControlNet | annotator/uniformer/mmcv/video/optflow.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import cv2
import numpy as np
from annotator.uniformer.mmcv.arraymisc import dequantize, quantize
from annotator.uniformer.mmcv.image import imread, imwrite
from annotator.uniformer.mmcv.utils import is_str
def flowread(flow_or_path, quantize=False, co... | 255 | 9,728 |
ControlNet | annotator/uniformer/mmcv/video/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .io import Cache, VideoReader, frames2video
from .optflow import (dequantize_flow, flow_from_bytes, flow_warp, flowread,
flowwrite, quantize_flow, sparse_flow_from_bytes)
from .processing import concat_video, convert_video, cut_video, resize_vid... | 12 | 570 |
ControlNet | annotator/uniformer/mmcv/video/processing.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import subprocess
import tempfile
from annotator.uniformer.mmcv.utils import requires_executable
@requires_executable('ffmpeg')
def convert_video(in_file,
out_file,
print_cmd=False,
p... | 161 | 5,291 |
ControlNet | annotator/uniformer/configs/_base_/default_runtime.py | .py | # yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True... | 15 | 321 |
ControlNet | annotator/uniformer/configs/_base_/schedules/schedule_20k.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=20000)
checkpoint_config = dict(by_epoch=False, inte... | 10 | 379 |
ControlNet | annotator/uniformer/configs/_base_/schedules/schedule_80k.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=80000)
checkpoint_config = dict(by_epoch=False, inte... | 10 | 379 |
ControlNet | annotator/uniformer/configs/_base_/schedules/schedule_160k.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, int... | 10 | 382 |
ControlNet | annotator/uniformer/configs/_base_/schedules/schedule_40k.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=40000)
checkpoint_config = dict(by_epoch=False, inte... | 10 | 379 |
ControlNet | annotator/uniformer/configs/_base_/datasets/pascal_context_59.py | .py | # dataset settings
dataset_type = 'PascalContextDataset59'
data_root = 'data/VOCdevkit/VOC2010/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (520, 520)
crop_size = (480, 480)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAn... | 61 | 2,024 |
ControlNet | annotator/uniformer/configs/_base_/datasets/pascal_voc12_aug.py | .py | _base_ = './pascal_voc12.py'
# dataset settings
data = dict(
train=dict(
ann_dir=['SegmentationClass', 'SegmentationClassAug'],
split=[
'ImageSets/Segmentation/train.txt',
'ImageSets/Segmentation/aug.txt'
]))
| 10 | 261 |
ControlNet | annotator/uniformer/configs/_base_/datasets/stare.py | .py | # dataset settings
dataset_type = 'STAREDataset'
data_root = 'data/STARE'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (605, 700)
crop_size = (128, 128)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(typ... | 60 | 1,917 |
ControlNet | annotator/uniformer/configs/_base_/datasets/pascal_context.py | .py | # dataset settings
dataset_type = 'PascalContextDataset'
data_root = 'data/VOCdevkit/VOC2010/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (520, 520)
crop_size = (480, 480)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnno... | 61 | 1,998 |
ControlNet | annotator/uniformer/configs/_base_/datasets/cityscapes_769x769.py | .py | _base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (769, 769)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
dict(... | 36 | 1,281 |
ControlNet | annotator/uniformer/configs/_base_/datasets/cityscapes.py | .py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize',... | 55 | 1,780 |
ControlNet | annotator/uniformer/configs/_base_/datasets/chase_db1.py | .py | # dataset settings
dataset_type = 'ChaseDB1Dataset'
data_root = 'data/CHASE_DB1'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (960, 999)
crop_size = (128, 128)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
d... | 60 | 1,924 |
ControlNet | annotator/uniformer/configs/_base_/datasets/ade20k.py | .py | # dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_labe... | 55 | 1,844 |
ControlNet | annotator/uniformer/configs/_base_/datasets/pascal_voc12.py | .py | # dataset settings
dataset_type = 'PascalVOCDataset'
data_root = 'data/VOCdevkit/VOC2012'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resi... | 58 | 1,930 |
ControlNet | annotator/uniformer/configs/_base_/datasets/drive.py | .py | # dataset settings
dataset_type = 'DRIVEDataset'
data_root = 'data/DRIVE'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (584, 565)
crop_size = (64, 64)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type=... | 60 | 1,915 |
ControlNet | annotator/uniformer/configs/_base_/datasets/hrf.py | .py | # dataset settings
dataset_type = 'HRFDataset'
data_root = 'data/HRF'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (2336, 3504)
crop_size = (256, 256)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type=... | 60 | 1,915 |
ControlNet | annotator/uniformer/configs/_base_/models/upernet_uniformer.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UniFormer',
embed_dim=[64, 128, 320, 512],
layers=[3, 4, 8, 3],
head_dim=64,
mlp_ratio=4.,
qkv_bias=True,
drop_ra... | 43 | 1,235 |
ControlNet | annotator/uniformer/configs/_base_/models/ann_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 47 | 1,346 |
ControlNet | annotator/uniformer/configs/_base_/models/dmnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 45 | 1,302 |
ControlNet | annotator/uniformer/configs/_base_/models/apcnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 45 | 1,302 |
ControlNet | annotator/uniformer/configs/_base_/models/pspnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 45 | 1,271 |
ControlNet | annotator/uniformer/configs/_base_/models/fast_scnn.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='FastSCNN',
downsample_dw_channels=(32, 48),
global_in_channels=64,
global_block_channels=(64, 96, 128),
global_block_strides=(2, 2,... | 58 | 1,761 |
ControlNet | annotator/uniformer/configs/_base_/models/fcn_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 46 | 1,285 |
ControlNet | annotator/uniformer/configs/_base_/models/fcn_unet_s5-d16.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | 52 | 1,512 |
ControlNet | annotator/uniformer/configs/_base_/models/deeplabv3plus_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 47 | 1,343 |
ControlNet | annotator/uniformer/configs/_base_/models/fpn_uniformer.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='UniFormer',
embed_dim=[64, 128, 320, 512],
layers=[3, 4, 8, 3],
head_dim=64,
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.,
at... | 36 | 977 |
ControlNet | annotator/uniformer/configs/_base_/models/encnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 49 | 1,435 |
ControlNet | annotator/uniformer/configs/_base_/models/upernet_r50.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | 45 | 1,301 |
ControlNet | annotator/uniformer/configs/_base_/models/ocrnet_hr18.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://msra/hrnetv2_w18',
backbone=dict(
type='HRNet',
norm_cfg=norm_cfg,
norm_eval=False,
extra=dict(
stage1=dict(
... | 69 | 2,196 |
ControlNet | annotator/uniformer/configs/_base_/models/pspnet_unet_s5-d16.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | 51 | 1,497 |
ControlNet | annotator/uniformer/configs/_base_/models/gcnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 47 | 1,326 |
ControlNet | annotator/uniformer/configs/_base_/models/emanet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 48 | 1,329 |
ControlNet | annotator/uniformer/configs/_base_/models/fpn_r50.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | 37 | 1,056 |
ControlNet | annotator/uniformer/configs/_base_/models/ccnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 45 | 1,258 |
ControlNet | annotator/uniformer/configs/_base_/models/deeplabv3_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 45 | 1,273 |
ControlNet | annotator/uniformer/configs/_base_/models/lraspp_m-v3-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='MobileNetV3',
arch='large',
out_indices=(1, 3, 16),
norm_cfg=norm_cfg),
decode_head=dict(
type='LRASPPHead',
in_channels=(1... | 26 | 766 |
ControlNet | annotator/uniformer/configs/_base_/models/cgnet.py | .py | # model settings
norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='CGNet',
norm_cfg=norm_cfg,
in_channels=3,
num_channels=(32, 64, 128),
num_blocks=(3, 21),
dilations=(2, 4),
reductions=... | 36 | 1,110 |
ControlNet | annotator/uniformer/configs/_base_/models/danet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 45 | 1,261 |
ControlNet | annotator/uniformer/configs/_base_/models/ocrnet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | 48 | 1,385 |
ControlNet | annotator/uniformer/configs/_base_/models/psanet_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 50 | 1,406 |
ControlNet | annotator/uniformer/configs/_base_/models/fcn_hr18.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://msra/hrnetv2_w18',
backbone=dict(
type='HRNet',
norm_cfg=norm_cfg,
norm_eval=False,
extra=dict(
stage1=dict(
num_modul... | 53 | 1,646 |
ControlNet | annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 47 | 1,315 |
ControlNet | annotator/uniformer/configs/_base_/models/dnl_r50-d8.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 47 | 1,316 |
ControlNet | annotator/uniformer/configs/_base_/models/pointrend_r50.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | 57 | 1,704 |
ControlNet | annotator/uniformer/configs/_base_/models/deeplabv3_unet_s5-d16.py | .py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | 51 | 1,499 |
ControlNet | annotator/uniformer/exp/upernet_global_small/test_config_w32.py | .py | _base_ = [
'../../configs/_base_/models/upernet_uniformer.py',
'../../configs/_base_/datasets/ade20k.py',
'../../configs/_base_/default_runtime.py',
'../../configs/_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='UniFormer',
embed_dim=[64, 128, 320, 512],
... | 39 | 1,339 |
ControlNet | annotator/uniformer/exp/upernet_global_small/test_config_g.py | .py | _base_ = [
'../../configs/_base_/models/upernet_uniformer.py',
'../../configs/_base_/datasets/ade20k.py',
'../../configs/_base_/default_runtime.py',
'../../configs/_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='UniFormer',
embed_dim=[64, 128, 320, 512],
... | 38 | 1,317 |
ControlNet | annotator/uniformer/exp/upernet_global_small/test_config_h32.py | .py | _base_ = [
'../../configs/_base_/models/upernet_uniformer.py',
'../../configs/_base_/datasets/ade20k.py',
'../../configs/_base_/default_runtime.py',
'../../configs/_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='UniFormer',
embed_dim=[64, 128, 320, 512],
... | 39 | 1,339 |
ControlNet | annotator/uniformer/exp/upernet_global_small/config.py | .py | _base_ = [
'../../configs/_base_/models/upernet_uniformer.py',
'../../configs/_base_/datasets/ade20k.py',
'../../configs/_base_/default_runtime.py',
'../../configs/_base_/schedules/schedule_160k.py'
]
model = dict(
backbone=dict(
type='UniFormer',
embed_dim=[64, 128, 320, 512],
... | 38 | 1,316 |
ControlNet | annotator/midas/__init__.py | .py | # Midas Depth Estimation
# From https://github.com/isl-org/MiDaS
# MIT LICENSE
import cv2
import numpy as np
import torch
from einops import rearrange
from .api import MiDaSInference
class MidasDetector:
def __init__(self):
self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
def __call__(se... | 43 | 1,480 |
ControlNet | annotator/midas/api.py | .py | # based on https://github.com/isl-org/MiDaS
import cv2
import os
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from .midas.dpt_depth import DPTDepthModel
from .midas.midas_net import MidasNet
from .midas.midas_net_custom import MidasNet_small
from .midas.transforms import Resize, Norma... | 170 | 5,229 |
ControlNet | annotator/openpose/util.py | .py | import math
import numpy as np
import matplotlib
import cv2
def padRightDownCorner(img, stride, padValue):
h = img.shape[0]
w = img.shape[1]
pad = 4 * [None]
pad[0] = 0 # up
pad[1] = 0 # left
pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
pad[3] = 0 if (w % stride == 0)... | 165 | 7,507 |
ControlNet | annotator/openpose/__init__.py | .py | # Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import torch
import numpy as np
from . import util
from .body import Body
from .hand import ... | 50 | 2,132 |
ControlNet | annotator/openpose/hand.py | .py | import cv2
import json
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from skimage.measure import label
from .model import handpose_model
from . import util
class Hand(object):
def __init__(self, model_pat... | 86 | 3,438 |
ControlNet | annotator/openpose/body.py | .py | import cv2
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from torchvision import transforms
from . import util
from .model import bodypose_model
class Body(object):
def __init__(self, model_path):
... | 220 | 10,994 |
TaskMatrix | visual_chatgpt.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# coding: utf-8
import os
import gradio as gr
import random
import torch
import cv2
import re
import uuid
from PIL import Image, ImageDraw, ImageOps, ImageFont
import math
import numpy as np
import argparse
import inspect
import tempfile
from tra... | 1,584 | 81,041 |
TaskMatrix | LowCodeLLM/src/app.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
from flask import Flask, request, send_from_directory
from flask_cors import CORS, cross_origin
from lowCodeLLM import lowCodeLLM
from flask.logging import default_handler
import logging
app = Flask('lowcode-llm', static_folder='', tem... | 67 | 2,493 |
TaskMatrix | LowCodeLLM/src/lowCodeLLM.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from planningLLM import planningLLM
from executingLLM import executingLLM
import json
class lowCodeLLM:
def __init__(self, PLLM_temperature=0.4, ELLM_temperature=0):
self.PLLM = planningLLM(PLLM_temperature)
self.ELLM = execu... | 47 | 2,021 |
TaskMatrix | LowCodeLLM/src/planningLLM.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import re
import json
from openAIWrapper import OpenAIWrapper
PLANNING_LLM_PREFIX = """Planning LLM is designed to provide a standard operating procedure so that an difficult task will be broken down into several steps, and the task will be easi... | 105 | 5,657 |
TaskMatrix | LowCodeLLM/src/openAIWrapper.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import openai
class OpenAIWrapper:
def __init__(self, temperature):
self.key = os.environ.get("OPENAIKEY")
openai.api_key = self.key
# Access the USE_AZURE environment variable
self.use_azure = os.e... | 64 | 2,180 |
TaskMatrix | LowCodeLLM/src/executingLLM.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from openAIWrapper import OpenAIWrapper
EXECUTING_LLM_PREFIX = """Executing LLM is designed to provide outstanding responses.
Executing LLM will be given a overall task as the background of the conversation between the Executing LLM and human.
W... | 42 | 1,988 |
TaskMatrix | LowCodeLLM/src/test/test_extend_workflow.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import sys
import os
import time
sys.path.append(os.getcwd())
def test_extend_workflow():
from lowCodeLLM import lowCodeLLM
cases = json.load(open("./test/testcases/extend_workflow_test_cases.json", "r"))
llm = lowCodeLLM... | 20 | 620 |
TaskMatrix | LowCodeLLM/src/test/test_get_workflow.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import sys
import os
sys.path.append(os.getcwd())
def test_get_workflow():
from lowCodeLLM import lowCodeLLM
cases = json.load(open("./test/testcases/get_workflow_test_cases.json", "r"))
llm = lowCodeLLM(0.5, 0)
for c... | 16 | 475 |
TaskMatrix | LowCodeLLM/src/test/test_execute.py | .py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import sys
import os
import time
sys.path.append(os.getcwd())
def test_extend_workflow():
from lowCodeLLM import lowCodeLLM
cases = json.load(open("./test/testcases/execute_test_cases.json", "r"))
llm = lowCodeLLM(0.5, 0)... | 22 | 692 |
ChatDev | run.py | .py | """CLI entry point for executing ChatDev_new workflows."""
import argparse
import json
from pathlib import Path
from typing import List, Union
from runtime.bootstrap.schema import ensure_schema_registry_populated
from check.check import load_config
from entity.graph_config import GraphConfig
from entity.messages impor... | 124 | 3,803 |
ChatDev | server_main.py | .py | import argparse
import logging
from pathlib import Path
from runtime.bootstrap.schema import ensure_schema_registry_populated
from server.app import app
ensure_schema_registry_populated()
# Directories containing the server's Python sources. When --reload is
# enabled, only these are watched so that agent-generate... | 169 | 5,046 |
ChatDev | functions/function_calling/web.py | .py | import os
def web_search(query: str, page: int = 1, language: str = "en", country: str = "us") -> str:
"""
Performs a web search based on the user-provided query with pagination.
Args:
query (str): The keyword(s) to search for.
page (int): The page number of the results to return. Default... | 173 | 6,565 |
ChatDev | functions/function_calling/user.py | .py | def call_user(instruction: str, _context: dict | None = None) -> str:
"""
If you think it's necessary to get input from the user, use this function to send the instruction to the user and get their response.
Args:
instruction: The instruction to send to the user.
"""
prompt = _context.get("... | 18 | 741 |
ChatDev | functions/function_calling/utils.py | .py | import time
from typing import Union
def wait(seconds: float):
"""
Wait for a specified number of seconds.
Args:
seconds: The number of seconds to wait.
"""
if isinstance(seconds, str): # Convert string to float if necessary
try:
if "." in seconds:
sec... | 32 | 822 |
ChatDev | functions/function_calling/uv_related.py | .py | """Utility tool to manage Python environments via uv."""
import os
import re
import subprocess
from pathlib import Path
from typing import Any, Dict, List, Mapping, Sequence
_SAFE_PACKAGE_RE = re.compile(r"^[A-Za-z0-9_.\-+=<>!\[\],@:/]+$")
_DEFAULT_TIMEOUT = float(os.getenv("LIB_INSTALL_TIMEOUT", "120"))
_OUTPUT_SNIP... | 314 | 10,561 |
ChatDev | functions/function_calling/file.py | .py | """File-related function tools for model-invoked file access."""
import fnmatch
import locale
import mimetypes
import os
import re
import shutil
from dataclasses import dataclass
from pathlib import Path
from typing import (
Annotated,
Any,
Dict,
Iterable,
List,
Literal,
Mapping,
Mutabl... | 1,101 | 37,221 |
ChatDev | functions/function_calling/video.py | .py | import shutil
import sys
from pathlib import Path
import ast
import subprocess
import tempfile
def _get_class_names(py_file: str) -> list[str]:
file_path = Path(py_file)
source = file_path.read_text(encoding="utf-8")
tree = ast.parse(source, filename=str(file_path))
return [node.name for node in ast.wa... | 85 | 2,618 |
ChatDev | functions/function_calling/deep_research.py | .py | """Deep research tools for search results and report management."""
import json
import re
from pathlib import Path
from typing import Annotated, Any, Dict, List, Optional, Tuple
from filelock import FileLock
from entity.messages import MessageBlock, MessageBlockType
from functions.function_calling.file import FileTo... | 646 | 22,180 |
ChatDev | functions/function_calling/code_executor.py | .py | def execute_code(code: str, time_out: int = 60) -> str:
"""
Execute code and return std outputs and std error.
Args:
code (str): Code to execute.
time_out (int): time out, in second.
Returns:
str: std output and std error
"""
import os
import sys
import subproce... | 67 | 2,078 |
ChatDev | functions/function_calling/weather.py | .py | def get_city_num(city: str) -> dict:
"""
Fetch the city code for a given city name.
Example response:
{
"city": "Beijing",
"city_num": "1010",
}
"""
return {
"city_num": 3701
}
def get_weather(city_num: int, unit: str = "celsius") -> dict:
"""
Fetch weath... | 35 | 788 |
ChatDev | functions/edge_processor/transformers.py | .py | from typing import Dict, Any, Tuple
import os
import re
import shutil
import signal
import subprocess
import time
from pathlib import Path
from functions.function_calling.file import FileToolContext
def uppercase_payload(data: str, _context: Dict[str, Any]) -> str:
"""Return an uppercase copy of the payload text."... | 131 | 4,573 |
ChatDev | functions/edge/conditions.py | .py | """Edge condition helpers used by workflow YAML definitions."""
import re
def contains_keyword(data: str) -> bool:
"""Check if data contains the keyword 'trigger'."""
return "trigger" in data.lower()
def length_greater_than_5(data: str) -> bool:
"""Check if data length is greater than 5."""
return le... | 49 | 1,535 |
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