RESNET官方代码（pytorch）

RESNET官方代码（pytorch）

vision/resnet.py at main · pytorch/vision · GitHub

主要由三部分组成：

一、主程序

二、附属代码1

三、附属代码2

一、主程序

from typing import Type, Any, Callable, Union, List, Optionalimport torch
import torch.nn as nn
from torch import Tensorfrom .._internally_replaced_utils import load_state_dict_from_url
from ..utils import _log_api_usage_once__all__ = ["ResNet","resnet18","resnet34","resnet50","resnet101","resnet152","resnext50_32x4d","resnext101_32x8d","wide_resnet50_2","wide_resnet101_2",
]model_urls = {"resnet18": "https://download.pytorch.org/models/resnet18-f37072fd.pth","resnet34": "https://download.pytorch.org/models/resnet34-b627a593.pth","resnet50": "https://download.pytorch.org/models/resnet50-0676ba61.pth","resnet101": "https://download.pytorch.org/models/resnet101-63fe2227.pth","resnet152": "https://download.pytorch.org/models/resnet152-394f9c45.pth","resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth","resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth","wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth","wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth",
}def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:"""3x3 convolution with padding"""return nn.Conv2d(in_planes,out_planes,kernel_size=3,stride=stride,padding=dilation,groups=groups,bias=False,dilation=dilation,)def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:"""1x1 convolution"""return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)class BasicBlock(nn.Module):expansion: int = 1def __init__(self,inplanes: int,planes: int,stride: int = 1,downsample: Optional[nn.Module] = None,groups: int = 1,base_width: int = 64,dilation: int = 1,norm_layer: Optional[Callable[..., nn.Module]] = None,) -> None:super().__init__()if norm_layer is None:norm_layer = nn.BatchNorm2dif groups != 1 or base_width != 64:raise ValueError("BasicBlock only supports groups=1 and base_width=64")if dilation > 1:raise NotImplementedError("Dilation > 1 not supported in BasicBlock")# Both self.conv1 and self.downsample layers downsample the input when stride != 1self.conv1 = conv3x3(inplanes, planes, stride)self.bn1 = norm_layer(planes)self.relu = nn.ReLU(inplace=True)self.conv2 = conv3x3(planes, planes)self.bn2 = norm_layer(planes)self.downsample = downsampleself.stride = stridedef forward(self, x: Tensor) -> Tensor:identity = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)if self.downsample is not None:identity = self.downsample(x)out += identityout = self.relu(out)return outclass Bottleneck(nn.Module):# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)# while original implementation places the stride at the first 1x1 convolution(self.conv1)# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.# This variant is also known as ResNet V1.5 and improves accuracy according to# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.expansion: int = 4def __init__(self,inplanes: int,planes: int,stride: int = 1,downsample: Optional[nn.Module] = None,groups: int = 1,base_width: int = 64,dilation: int = 1,norm_layer: Optional[Callable[..., nn.Module]] = None,) -> None:super().__init__()if norm_layer is None:norm_layer = nn.BatchNorm2dwidth = int(planes * (base_width / 64.0)) * groups# Both self.conv2 and self.downsample layers downsample the input when stride != 1self.conv1 = conv1x1(inplanes, width)self.bn1 = norm_layer(width)self.conv2 = conv3x3(width, width, stride, groups, dilation)self.bn2 = norm_layer(width)self.conv3 = conv1x1(width, planes * self.expansion)self.bn3 = norm_layer(planes * self.expansion)self.relu = nn.ReLU(inplace=True)self.downsample = downsampleself.stride = stridedef forward(self, x: Tensor) -> Tensor:identity = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)if self.downsample is not None:identity = self.downsample(x)out += identityout = self.relu(out)return outclass ResNet(nn.Module):def __init__(self,block: Type[Union[BasicBlock, Bottleneck]],layers: List[int],num_classes: int = 1000,zero_init_residual: bool = False,groups: int = 1,width_per_group: int = 64,replace_stride_with_dilation: Optional[List[bool]] = None,norm_layer: Optional[Callable[..., nn.Module]] = None,) -> None:super().__init__()_log_api_usage_once(self)if norm_layer is None:norm_layer = nn.BatchNorm2dself._norm_layer = norm_layerself.inplanes = 64self.dilation = 1if replace_stride_with_dilation is None:# each element in the tuple indicates if we should replace# the 2x2 stride with a dilated convolution insteadreplace_stride_with_dilation = [False, False, False]if len(replace_stride_with_dilation) != 3:raise ValueError("replace_stride_with_dilation should be None "f"or a 3-element tuple, got {replace_stride_with_dilation}")self.groups = groupsself.base_width = width_per_groupself.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)self.bn1 = norm_layer(self.inplanes)self.relu = nn.ReLU(inplace=True)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, layers[0])self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(512 * block.expansion, num_classes)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)# Zero-initialize the last BN in each residual branch,# so that the residual branch starts with zeros, and each residual block behaves like an identity.# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677if zero_init_residual:for m in self.modules():if isinstance(m, Bottleneck):nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]elif isinstance(m, BasicBlock):nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]def _make_layer(self,block: Type[Union[BasicBlock, Bottleneck]],planes: int,blocks: int,stride: int = 1,dilate: bool = False,) -> nn.Sequential:norm_layer = self._norm_layerdownsample = Noneprevious_dilation = self.dilationif dilate:self.dilation *= stridestride = 1if stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride),norm_layer(planes * block.expansion),)layers = []layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer))self.inplanes = planes * block.expansionfor _ in range(1, blocks):layers.append(block(self.inplanes,planes,groups=self.groups,base_width=self.base_width,dilation=self.dilation,norm_layer=norm_layer,))return nn.Sequential(*layers)def _forward_impl(self, x: Tensor) -> Tensor:# See note [TorchScript super()]x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.avgpool(x)x = torch.flatten(x, 1)x = self.fc(x)return xdef forward(self, x: Tensor) -> Tensor:return self._forward_impl(x)def _resnet(arch: str,block: Type[Union[BasicBlock, Bottleneck]],layers: List[int],pretrained: bool,progress: bool,**kwargs: Any,
) -> ResNet:model = ResNet(block, layers, **kwargs)if pretrained:state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)model.load_state_dict(state_dict)return modeldef resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNet-18 model from`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNet-34 model from`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""return _resnet("resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNet-50 model from`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNet-101 model from`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""return _resnet("resnet101", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNet-152 model from`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""return _resnet("resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs)def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNeXt-50 32x4d model from`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""kwargs["groups"] = 32kwargs["width_per_group"] = 4return _resnet("resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""ResNeXt-101 32x8d model from`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""kwargs["groups"] = 32kwargs["width_per_group"] = 8return _resnet("resnext101_32x8d", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""Wide ResNet-50-2 model from`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.The model is the same as ResNet except for the bottleneck number of channelswhich is twice larger in every block. The number of channels in outer 1x1convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048channels, and in Wide ResNet-50-2 has 2048-1024-2048.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""kwargs["width_per_group"] = 64 * 2return _resnet("wide_resnet50_2", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:r"""Wide ResNet-101-2 model from`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.The model is the same as ResNet except for the bottleneck number of channelswhich is twice larger in every block. The number of channels in outer 1x1convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048channels, and in Wide ResNet-50-2 has 2048-1024-2048.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""kwargs["width_per_group"] = 64 * 2return _resnet("wide_resnet101_2", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)

二、附属代码1：  _internally_replaced_utils

import importlib.machinery
import osfrom torch.hub import _get_torch_home_HOME = os.path.join(_get_torch_home(), "datasets", "vision")
_USE_SHARDED_DATASETS = Falsedef _download_file_from_remote_location(fpath: str, url: str) -> None:passdef _is_remote_location_available() -> bool:return Falsetry:from torch.hub import load_state_dict_from_url  # noqa: 401
except ImportError:from torch.utils.model_zoo import load_url as load_state_dict_from_url  # noqa: 401def _get_extension_path(lib_name):lib_dir = os.path.dirname(__file__)if os.name == "nt":# Register the main torchvision library location on the default DLL pathimport ctypesimport syskernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True)with_load_library_flags = hasattr(kernel32, "AddDllDirectory")prev_error_mode = kernel32.SetErrorMode(0x0001)if with_load_library_flags:kernel32.AddDllDirectory.restype = ctypes.c_void_pif sys.version_info >= (3, 8):os.add_dll_directory(lib_dir)elif with_load_library_flags:res = kernel32.AddDllDirectory(lib_dir)if res is None:err = ctypes.WinError(ctypes.get_last_error())err.strerror += f' Error adding "{lib_dir}" to the DLL directories.'raise errkernel32.SetErrorMode(prev_error_mode)loader_details = (importlib.machinery.ExtensionFileLoader, importlib.machinery.EXTENSION_SUFFIXES)extfinder = importlib.machinery.FileFinder(lib_dir, loader_details)ext_specs = extfinder.find_spec(lib_name)if ext_specs is None:raise ImportErrorreturn ext_specs.origin

三、附属代码2：  utils

import math
import pathlib
import warnings
from typing import Union, Optional, List, Tuple, BinaryIO, no_type_checkimport numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont, ImageColor__all__ = ["make_grid", "save_image", "draw_bounding_boxes", "draw_segmentation_masks", "draw_keypoints"]@torch.no_grad()
def make_grid(tensor: Union[torch.Tensor, List[torch.Tensor]],nrow: int = 8,padding: int = 2,normalize: bool = False,value_range: Optional[Tuple[int, int]] = None,scale_each: bool = False,pad_value: int = 0,**kwargs,
) -> torch.Tensor:"""Make a grid of images.Args:tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)or a list of images all of the same size.nrow (int, optional): Number of images displayed in each row of the grid.The final grid size is ``(B / nrow, nrow)``. Default: ``8``.padding (int, optional): amount of padding. Default: ``2``.normalize (bool, optional): If True, shift the image to the range (0, 1),by the min and max values specified by ``value_range``. Default: ``False``.value_range (tuple, optional): tuple (min, max) where min and max are numbers,then these numbers are used to normalize the image. By default, min and maxare computed from the tensor.scale_each (bool, optional): If ``True``, scale each image in the batch ofimages separately rather than the (min, max) over all images. Default: ``False``.pad_value (float, optional): Value for the padded pixels. Default: ``0``.Returns:grid (Tensor): the tensor containing grid of images."""if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):raise TypeError(f"tensor or list of tensors expected, got {type(tensor)}")if "range" in kwargs.keys():warning = "range will be deprecated, please use value_range instead."warnings.warn(warning)value_range = kwargs["range"]# if list of tensors, convert to a 4D mini-batch Tensorif isinstance(tensor, list):tensor = torch.stack(tensor, dim=0)if tensor.dim() == 2:  # single image H x Wtensor = tensor.unsqueeze(0)if tensor.dim() == 3:  # single imageif tensor.size(0) == 1:  # if single-channel, convert to 3-channeltensor = torch.cat((tensor, tensor, tensor), 0)tensor = tensor.unsqueeze(0)if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel imagestensor = torch.cat((tensor, tensor, tensor), 1)if normalize is True:tensor = tensor.clone()  # avoid modifying tensor in-placeif value_range is not None:assert isinstance(value_range, tuple), "value_range has to be a tuple (min, max) if specified. min and max are numbers"def norm_ip(img, low, high):img.clamp_(min=low, max=high)img.sub_(low).div_(max(high - low, 1e-5))def norm_range(t, value_range):if value_range is not None:norm_ip(t, value_range[0], value_range[1])else:norm_ip(t, float(t.min()), float(t.max()))if scale_each is True:for t in tensor:  # loop over mini-batch dimensionnorm_range(t, value_range)else:norm_range(tensor, value_range)if tensor.size(0) == 1:return tensor.squeeze(0)# make the mini-batch of images into a gridnmaps = tensor.size(0)xmaps = min(nrow, nmaps)ymaps = int(math.ceil(float(nmaps) / xmaps))height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)num_channels = tensor.size(1)grid = tensor.new_full((num_channels, height * ymaps + padding, width * xmaps + padding), pad_value)k = 0for y in range(ymaps):for x in range(xmaps):if k >= nmaps:break# Tensor.copy_() is a valid method but seems to be missing from the stubs# https://pytorch.org/docs/stable/tensors.html#torch.Tensor.copy_grid.narrow(1, y * height + padding, height - padding).narrow(  # type: ignore[attr-defined]2, x * width + padding, width - padding).copy_(tensor[k])k = k + 1return grid@torch.no_grad()
def save_image(tensor: Union[torch.Tensor, List[torch.Tensor]],fp: Union[str, pathlib.Path, BinaryIO],format: Optional[str] = None,**kwargs,
) -> None:"""Save a given Tensor into an image file.Args:tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,saves the tensor as a grid of images by calling ``make_grid``.fp (string or file object): A filename or a file objectformat(Optional):  If omitted, the format to use is determined from the filename extension.If a file object was used instead of a filename, this parameter should always be used.**kwargs: Other arguments are documented in ``make_grid``."""grid = make_grid(tensor, **kwargs)# Add 0.5 after unnormalizing to [0, 255] to round to nearest integerndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()im = Image.fromarray(ndarr)im.save(fp, format=format)@torch.no_grad()
def draw_bounding_boxes(image: torch.Tensor,boxes: torch.Tensor,labels: Optional[List[str]] = None,colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None,fill: Optional[bool] = False,width: int = 1,font: Optional[str] = None,font_size: int = 10,
) -> torch.Tensor:"""Draws bounding boxes on given image.The values of the input image should be uint8 between 0 and 255.If fill is True, Resulting Tensor should be saved as PNG image.Args:image (Tensor): Tensor of shape (C x H x W) and dtype uint8.boxes (Tensor): Tensor of size (N, 4) containing bounding boxes in (xmin, ymin, xmax, ymax) format. Note thatthe boxes are absolute coordinates with respect to the image. In other words: `0 <= xmin < xmax < W` and`0 <= ymin < ymax < H`.labels (List[str]): List containing the labels of bounding boxes.colors (color or list of colors, optional): List containing the colorsof the boxes or single color for all boxes. The color can be represented asPIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.fill (bool): If `True` fills the bounding box with specified color.width (int): Width of bounding box.font (str): A filename containing a TrueType font. If the file is not found in this filename, the loader mayalso search in other directories, such as the `fonts/` directory on Windows or `/Library/Fonts/`,`/System/Library/Fonts/` and `~/Library/Fonts/` on macOS.font_size (int): The requested font size in points.Returns:img (Tensor[C, H, W]): Image Tensor of dtype uint8 with bounding boxes plotted."""if not isinstance(image, torch.Tensor):raise TypeError(f"Tensor expected, got {type(image)}")elif image.dtype != torch.uint8:raise ValueError(f"Tensor uint8 expected, got {image.dtype}")elif image.dim() != 3:raise ValueError("Pass individual images, not batches")elif image.size(0) not in {1, 3}:raise ValueError("Only grayscale and RGB images are supported")if image.size(0) == 1:image = torch.tile(image, (3, 1, 1))ndarr = image.permute(1, 2, 0).numpy()img_to_draw = Image.fromarray(ndarr)img_boxes = boxes.to(torch.int64).tolist()if fill:draw = ImageDraw.Draw(img_to_draw, "RGBA")else:draw = ImageDraw.Draw(img_to_draw)txt_font = ImageFont.load_default() if font is None else ImageFont.truetype(font=font, size=font_size)for i, bbox in enumerate(img_boxes):if colors is None:color = Noneelif isinstance(colors, list):color = colors[i]else:color = colorsif fill:if color is None:fill_color = (255, 255, 255, 100)elif isinstance(color, str):# This will automatically raise Error if rgb cannot be parsed.fill_color = ImageColor.getrgb(color) + (100,)elif isinstance(color, tuple):fill_color = color + (100,)draw.rectangle(bbox, width=width, outline=color, fill=fill_color)else:draw.rectangle(bbox, width=width, outline=color)if labels is not None:margin = width + 1draw.text((bbox[0] + margin, bbox[1] + margin), labels[i], fill=color, font=txt_font)return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)@torch.no_grad()
def draw_segmentation_masks(image: torch.Tensor,masks: torch.Tensor,alpha: float = 0.8,colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None,
) -> torch.Tensor:"""Draws segmentation masks on given RGB image.The values of the input image should be uint8 between 0 and 255.Args:image (Tensor): Tensor of shape (3, H, W) and dtype uint8.masks (Tensor): Tensor of shape (num_masks, H, W) or (H, W) and dtype bool.alpha (float): Float number between 0 and 1 denoting the transparency of the masks.0 means full transparency, 1 means no transparency.colors (color or list of colors, optional): List containing the colorsof the masks or single color for all masks. The color can be represented asPIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.By default, random colors are generated for each mask.Returns:img (Tensor[C, H, W]): Image Tensor, with segmentation masks drawn on top."""if not isinstance(image, torch.Tensor):raise TypeError(f"The image must be a tensor, got {type(image)}")elif image.dtype != torch.uint8:raise ValueError(f"The image dtype must be uint8, got {image.dtype}")elif image.dim() != 3:raise ValueError("Pass individual images, not batches")elif image.size()[0] != 3:raise ValueError("Pass an RGB image. Other Image formats are not supported")if masks.ndim == 2:masks = masks[None, :, :]if masks.ndim != 3:raise ValueError("masks must be of shape (H, W) or (batch_size, H, W)")if masks.dtype != torch.bool:raise ValueError(f"The masks must be of dtype bool. Got {masks.dtype}")if masks.shape[-2:] != image.shape[-2:]:raise ValueError("The image and the masks must have the same height and width")num_masks = masks.size()[0]if colors is not None and num_masks > len(colors):raise ValueError(f"There are more masks ({num_masks}) than colors ({len(colors)})")if colors is None:colors = _generate_color_palette(num_masks)if not isinstance(colors, list):colors = [colors]if not isinstance(colors[0], (tuple, str)):raise ValueError("colors must be a tuple or a string, or a list thereof")if isinstance(colors[0], tuple) and len(colors[0]) != 3:raise ValueError("It seems that you passed a tuple of colors instead of a list of colors")out_dtype = torch.uint8colors_ = []for color in colors:if isinstance(color, str):color = ImageColor.getrgb(color)colors_.append(torch.tensor(color, dtype=out_dtype))img_to_draw = image.detach().clone()# TODO: There might be a way to vectorize thisfor mask, color in zip(masks, colors_):img_to_draw[:, mask] = color[:, None]out = image * (1 - alpha) + img_to_draw * alphareturn out.to(out_dtype)@torch.no_grad()
def draw_keypoints(image: torch.Tensor,keypoints: torch.Tensor,connectivity: Optional[List[Tuple[int, int]]] = None,colors: Optional[Union[str, Tuple[int, int, int]]] = None,radius: int = 2,width: int = 3,
) -> torch.Tensor:"""Draws Keypoints on given RGB image.The values of the input image should be uint8 between 0 and 255.Args:image (Tensor): Tensor of shape (3, H, W) and dtype uint8.keypoints (Tensor): Tensor of shape (num_instances, K, 2) the K keypoints location for each of the N instances,in the format [x, y].connectivity (List[Tuple[int, int]]]): A List of tuple where,each tuple contains pair of keypoints to be connected.colors (str, Tuple): The color can be represented asPIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.radius (int): Integer denoting radius of keypoint.width (int): Integer denoting width of line connecting keypoints.Returns:img (Tensor[C, H, W]): Image Tensor of dtype uint8 with keypoints drawn."""if not isinstance(image, torch.Tensor):raise TypeError(f"The image must be a tensor, got {type(image)}")elif image.dtype != torch.uint8:raise ValueError(f"The image dtype must be uint8, got {image.dtype}")elif image.dim() != 3:raise ValueError("Pass individual images, not batches")elif image.size()[0] != 3:raise ValueError("Pass an RGB image. Other Image formats are not supported")if keypoints.ndim != 3:raise ValueError("keypoints must be of shape (num_instances, K, 2)")ndarr = image.permute(1, 2, 0).numpy()img_to_draw = Image.fromarray(ndarr)draw = ImageDraw.Draw(img_to_draw)img_kpts = keypoints.to(torch.int64).tolist()for kpt_id, kpt_inst in enumerate(img_kpts):for inst_id, kpt in enumerate(kpt_inst):x1 = kpt[0] - radiusx2 = kpt[0] + radiusy1 = kpt[1] - radiusy2 = kpt[1] + radiusdraw.ellipse([x1, y1, x2, y2], fill=colors, outline=None, width=0)if connectivity:for connection in connectivity:start_pt_x = kpt_inst[connection[0]][0]start_pt_y = kpt_inst[connection[0]][1]end_pt_x = kpt_inst[connection[1]][0]end_pt_y = kpt_inst[connection[1]][1]draw.line(((start_pt_x, start_pt_y), (end_pt_x, end_pt_y)),width=width,)return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)def _generate_color_palette(num_masks: int):palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])return [tuple((i * palette) % 255) for i in range(num_masks)]@no_type_check
def _log_api_usage_once(obj: str) -> None:  # type: ignoreif torch.jit.is_scripting() or torch.jit.is_tracing():return# NOTE: obj can be an object as well, but mocking it here to be# only a string to appease torchscriptif isinstance(obj, str):torch._C._log_api_usage_once(obj)else:torch._C._log_api_usage_once(f"{obj.__module__}.{obj.__class__.__name__}")