repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/libs/agno/agno/cloud/aws/s3/__init__.py | libs/agno/agno/cloud/aws/s3/__init__.py | from agno.cloud.aws.s3.bucket import S3Bucket
from agno.cloud.aws.s3.object import S3Object
| python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/libs/agno/agno/agent/__init__.py | libs/agno/agno/agent/__init__.py | from agno.agent.agent import (
Agent,
AgentSession,
Function,
Message,
Toolkit,
)
from agno.agent.remote import RemoteAgent
from agno.run.agent import (
MemoryUpdateCompletedEvent,
MemoryUpdateStartedEvent,
ReasoningCompletedEvent,
ReasoningStartedEvent,
ReasoningStepEvent,
R... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/libs/agno/agno/agent/remote.py | libs/agno/agno/agent/remote.py | import json
import time
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Literal, Optional, Sequence, Tuple, Union, overload
from pydantic import BaseModel
from agno.media import Audio, File, Image, Video
from agno.models.base import Model
from agno.models.mes... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/libs/agno/agno/agent/agent.py | libs/agno/agno/agent/agent.py | from __future__ import annotations
import asyncio
import time
import warnings
from asyncio import CancelledError, Task, create_task
from collections import ChainMap, deque
from concurrent.futures import Future
from dataclasses import dataclass
from inspect import iscoroutinefunction
from os import getenv
from textwrap... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | true |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_fake_scribble2image.py | gradio_fake_scribble2image.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tutorial_train_sd21.py | tutorial_train_sd21.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_hough2image.py | gradio_hough2image.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_canny2image.py | gradio_canny2image.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_normal2image.py | gradio_normal2image.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tool_transfer_control.py | tool_transfer_control.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_scribble2image.py | gradio_scribble2image.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tool_add_control_sd21.py | tool_add_control_sd21.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tutorial_dataset.py | tutorial_dataset.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):... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_seg2image.py | gradio_seg2image.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.uniformer import UniformerDetector
from cldm.model import create_model, load_state_dic... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_depth2image.py | gradio_depth2image.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_hed2image.py | gradio_hed2image.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.... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tool_add_control.py | tool_add_control.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_pose2image.py | gradio_pose2image.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
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/config.py | config.py | save_memory = False
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/share.py | share.py | import config
from cldm.hack import disable_verbosity, enable_sliced_attention
disable_verbosity()
if config.save_memory:
enable_sliced_attention()
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_scribble2image_interactive.py | gradio_scribble2image_interactive.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tutorial_dataset_test.py | tutorial_dataset_test.py | from tutorial_dataset import MyDataset
dataset = MyDataset()
print(len(dataset))
item = dataset[1234]
jpg = item['jpg']
txt = item['txt']
hint = item['hint']
print(txt)
print(jpg.shape)
print(hint.shape)
| python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/tutorial_train.py | tutorial_train.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/gradio_annotator.py | gradio_annotator.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(... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/util.py | annotator/util.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/mlsd/utils.py | annotator/mlsd/utils.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, ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/mlsd/__init__.py | annotator/mlsd/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/mlsd/models/mbv2_mlsd_tiny.py | annotator/mlsd/models/mbv2_mlsd_tiny.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(
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/mlsd/models/mbv2_mlsd_large.py | annotator/mlsd/models/mbv2_mlsd_large.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(
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/hed/__init__.py | annotator/hed/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/api.py | annotator/midas/api.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/utils.py | annotator/midas/utils.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/__init__.py | annotator/midas/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/midas_net_custom.py | annotator/midas/midas/midas_net_custom.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/base_model.py | annotator/midas/midas/base_model.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/dpt_depth.py | annotator/midas/midas/dpt_depth.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(
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/blocks.py | annotator/midas/midas/blocks.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/midas_net.py | annotator/midas/midas/midas_net.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/vit.py | annotator/midas/midas/vit.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/__init__.py | annotator/midas/midas/__init__.py | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false | |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/midas/midas/transforms.py | annotator/midas/midas/transforms.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/openpose/hand.py | annotator/openpose/hand.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/openpose/util.py | annotator/openpose/util.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)... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/openpose/model.py | annotator/openpose/model.py | import torch
from collections import OrderedDict
import torch
import torch.nn as nn
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if 'pool' in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
paddi... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/openpose/__init__.py | annotator/openpose/__init__.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/openpose/body.py | annotator/openpose/body.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):
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/__init__.py | annotator/uniformer/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/exp/upernet_global_small/test_config_w32.py | annotator/uniformer/exp/upernet_global_small/test_config_w32.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],
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/exp/upernet_global_small/test_config_h32.py | annotator/uniformer/exp/upernet_global_small/test_config_h32.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],
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/exp/upernet_global_small/config.py | annotator/uniformer/exp/upernet_global_small/config.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],
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/exp/upernet_global_small/test_config_g.py | annotator/uniformer/exp/upernet_global_small/test_config_g.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],
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/hrf.py | annotator/uniformer/mmseg/datasets/hrf.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. ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/drive.py | annotator/uniformer/mmseg/datasets/drive.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/dataset_wrappers.py | annotator/uniformer/mmseg/datasets/dataset_wrappers.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/voc.py | annotator/uniformer/mmseg/datasets/voc.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/stare.py | annotator/uniformer/mmseg/datasets/stare.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/chase_db1.py | annotator/uniformer/mmseg/datasets/chase_db1.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/cityscapes.py | annotator/uniformer/mmseg/datasets/cityscapes.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/__init__.py | annotator/uniformer/mmseg/datasets/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pascal_context.py | annotator/uniformer/mmseg/datasets/pascal_context.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/builder.py | annotator/uniformer/mmseg/datasets/builder.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/custom.py | annotator/uniformer/mmseg/datasets/custom.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 ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/ade.py | annotator/uniformer/mmseg/datasets/ade.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 `... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pipelines/compose.py | annotator/uniformer/mmseg/datasets/pipelines/compose.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pipelines/formating.py | annotator/uniformer/mmseg/datasets/pipelines/formating.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pipelines/loading.py | annotator/uniformer/mmseg/datasets/pipelines/loading.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pipelines/__init__.py | annotator/uniformer/mmseg/datasets/pipelines/__init__.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,
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pipelines/test_time_aug.py | annotator/uniformer/mmseg/datasets/pipelines/test_time_aug.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::
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/datasets/pipelines/transforms.py | annotator/uniformer/mmseg/datasets/pipelines/transforms.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/__init__.py | annotator/uniformer/mmseg/models/__init__.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/builder.py | annotator/uniformer/mmseg/models/builder.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."""
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/losses/accuracy.py | annotator/uniformer/mmseg/models/losses/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
target (torch.Tensor): The target of each prediction, shape (N, , ...)
topk (... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py | annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import get_class_weight, weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
class_weight=None,
reduction='mean',
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/losses/utils.py | annotator/uniformer/mmseg/models/losses/utils.py | import functools
import annotator.uniformer.mmcv as mmcv
import numpy as np
import torch.nn.functional as F
def get_class_weight(class_weight):
"""Get class weight for loss function.
Args:
class_weight (list[float] | str | None): If class_weight is a str,
take it as a file name and read ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/losses/dice_loss.py | annotator/uniformer/mmseg/models/losses/dice_loss.py | """Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
segmentron/solver/loss.py (Apache-2.0 License)"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import get_class_weight, weighted_loss
@weighted_loss
def dice_loss(pred,
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/losses/__init__.py | annotator/uniformer/mmseg/models/losses/__init__.py | from .accuracy import Accuracy, accuracy
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
from .dice_loss import DiceLoss
from .lovasz_loss import LovaszLoss
from .utils import reduce_loss, weight_reduce_loss, weighted_loss
__a... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/losses/lovasz_loss.py | annotator/uniformer/mmseg/models/losses/lovasz_loss.py | """Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
import annotator.uniformer.mmcv as mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..b... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/dnl_head.py | annotator/uniformer/mmseg/models/decode_heads/dnl_head.py | import torch
from annotator.uniformer.mmcv.cnn import NonLocal2d
from torch import nn
from ..builder import HEADS
from .fcn_head import FCNHead
class DisentangledNonLocal2d(NonLocal2d):
"""Disentangled Non-Local Blocks.
Args:
temperature (float): Temperature to adjust attention. Default: 0.05
""... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/dm_head.py | annotator/uniformer/mmseg/models/decode_heads/dm_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, build_activation_layer, build_norm_layer
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class DCM(nn.Module):
"""Dynamic Convolutional Module used in DMNet.
Args:
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/da_head.py | annotator/uniformer/mmseg/models/decode_heads/da_head.py | import torch
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule, Scale
from torch import nn
from annotator.uniformer.mmseg.core import add_prefix
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class PA... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/ocr_head.py | annotator/uniformer/mmseg/models/decode_heads/ocr_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .cascade_decode_head import BaseCascadeDecodeHead
clas... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/enc_head.py | annotator/uniformer/mmseg/models/decode_heads/enc_head.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... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/point_head.py | annotator/uniformer/mmseg/models/decode_heads/point_head.py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, normal_init
from annotator.uniformer.mmcv.ops import point_sample
from annotator.uniformer.mmseg.models... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/uper_head.py | annotator/uniformer/mmseg/models/decode_heads/uper_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
from .psp_head import PPM
@HEADS.register_module()
class UPerHead(BaseDecodeHead):
"""Unified Perceptual Pars... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/ann_head.py | annotator/uniformer/mmseg/models/decode_heads/ann_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from ..utils import SelfAttentionBlock as _SelfAttentionBlock
from .decode_head import BaseDecodeHead
class PPMConcat(nn.ModuleList):
"""Pyramid Pooling Module that only concat the features of each ... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/nl_head.py | annotator/uniformer/mmseg/models/decode_heads/nl_head.py | import torch
from annotator.uniformer.mmcv.cnn import NonLocal2d
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class NLHead(FCNHead):
"""Non-local Neural Networks.
This head is the implementation of `NLNet
<https://arxiv.org/abs/1711.07971>`_.
Args:
redu... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/ema_head.py | annotator/uniformer/mmseg/models/decode_heads/ema_head.py | import math
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from .decode_head import BaseDecodeHead
def reduce_mean(tensor):
"""Reduce mean when distributed training."""
if not... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/fpn_head.py | annotator/uniformer/mmseg/models/decode_heads/fpn_head.py | import numpy as np
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
@HEADS.register_module()
class FPNHead(BaseDecodeHead):
"""Panoptic Feature Pyramid Networks.
This... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/psa_head.py | annotator/uniformer/mmseg/models/decode_heads/psa_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
try:
from annotator.uniformer.mmcv.ops import PSAMask
except ModuleNotFoundErr... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/aspp_head.py | annotator/uniformer/mmseg/models/decode_heads/aspp_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ASPPModule(nn.ModuleList):
"""Atrous Spatial Pyramid Pooling (ASPP) Module.
Args:
dilation... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/sep_fcn_head.py | annotator/uniformer/mmseg/models/decode_heads/sep_fcn_head.py | from annotator.uniformer.mmcv.cnn import DepthwiseSeparableConvModule
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class DepthwiseSeparableFCNHead(FCNHead):
"""Depthwise-Separable Fully Convolutional Network for Semantic
Segmentation.
This head is implemented accord... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/lraspp_head.py | annotator/uniformer/mmseg/models/decode_heads/lraspp_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv import is_tuple_of
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
@HEADS.register_module()
class LRASPPHead(BaseDecodeHead):
"... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/gc_head.py | annotator/uniformer/mmseg/models/decode_heads/gc_head.py | import torch
from annotator.uniformer.mmcv.cnn import ContextBlock
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class GCHead(FCNHead):
"""GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
This head is the implementation of `GCNet
<https://arxiv.... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/psp_head.py | annotator/uniformer/mmseg/models/decode_heads/psp_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class PPM(nn.ModuleList):
"""Pooling Pyramid Module used in PSPNet.
Args:
pool_scales (tuple[int... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/sep_aspp_head.py | annotator/uniformer/mmseg/models/decode_heads/sep_aspp_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .aspp_head import ASPPHead, ASPPModule
class DepthwiseSeparableASPPModule(ASPPModule):
"""Atrous Spatial Pyramid P... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/apc_head.py | annotator/uniformer/mmseg/models/decode_heads/apc_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from annotator.uniformer.mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ACM(nn.Module):
"""Adaptive Context Module used in APCNet.
Args:
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/cascade_decode_head.py | annotator/uniformer/mmseg/models/decode_heads/cascade_decode_head.py | from abc import ABCMeta, abstractmethod
from .decode_head import BaseDecodeHead
class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta):
"""Base class for cascade decode head used in
:class:`CascadeEncoderDecoder."""
def __init__(self, *args, **kwargs):
super(BaseCascadeDecodeHead, self).... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/fcn_head.py | annotator/uniformer/mmseg/models/decode_heads/fcn_head.py | import torch
import torch.nn as nn
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import HEADS
from .decode_head import BaseDecodeHead
@HEADS.register_module()
class FCNHead(BaseDecodeHead):
"""Fully Convolution Networks for Semantic Segmentation.
This head is implemented of `FCNNet <htt... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/__init__.py | annotator/uniformer/mmseg/models/decode_heads/__init__.py | from .ann_head import ANNHead
from .apc_head import APCHead
from .aspp_head import ASPPHead
from .cc_head import CCHead
from .da_head import DAHead
from .dm_head import DMHead
from .dnl_head import DNLHead
from .ema_head import EMAHead
from .enc_head import EncHead
from .fcn_head import FCNHead
from .fpn_head import FP... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
lllyasviel/ControlNet | https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/annotator/uniformer/mmseg/models/decode_heads/cc_head.py | annotator/uniformer/mmseg/models/decode_heads/cc_head.py | import torch
from ..builder import HEADS
from .fcn_head import FCNHead
try:
from annotator.uniformer.mmcv.ops import CrissCrossAttention
except ModuleNotFoundError:
CrissCrossAttention = None
@HEADS.register_module()
class CCHead(FCNHead):
"""CCNet: Criss-Cross Attention for Semantic Segmentation.
... | python | Apache-2.0 | ed85cd1e25a5ed592f7d8178495b4483de0331bf | 2026-01-04T14:42:42.940079Z | false |
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