Spaces:
Running
on
A10G
Running
on
A10G
| """This script defines deep neural networks for Deep3DFaceRecon_pytorch | |
| """ | |
| import os | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from torch.nn import init | |
| import functools | |
| from torch.optim import lr_scheduler | |
| import torch | |
| from torch import Tensor | |
| import torch.nn as nn | |
| try: | |
| from torch.hub import load_state_dict_from_url | |
| except ImportError: | |
| from torch.utils.model_zoo import load_url as load_state_dict_from_url | |
| from typing import Type, Any, Callable, Union, List, Optional | |
| from .arcface_torch.backbones import get_model | |
| from kornia.geometry import warp_affine | |
| def resize_n_crop(image, M, dsize=112): | |
| # image: (b, c, h, w) | |
| # M : (b, 2, 3) | |
| return warp_affine(image, M, dsize=(dsize, dsize), align_corners=True) | |
| def filter_state_dict(state_dict, remove_name='fc'): | |
| new_state_dict = {} | |
| for key in state_dict: | |
| if remove_name in key: | |
| continue | |
| new_state_dict[key] = state_dict[key] | |
| return new_state_dict | |
| def get_scheduler(optimizer, opt): | |
| """Return a learning rate scheduler | |
| Parameters: | |
| optimizer -- the optimizer of the network | |
| opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions． | |
| opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine | |
| For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers. | |
| See https://pytorch.org/docs/stable/optim.html for more details. | |
| """ | |
| if opt.lr_policy == 'linear': | |
| def lambda_rule(epoch): | |
| lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs + 1) | |
| return lr_l | |
| scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule) | |
| elif opt.lr_policy == 'step': | |
| scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_epochs, gamma=0.2) | |
| elif opt.lr_policy == 'plateau': | |
| scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5) | |
| elif opt.lr_policy == 'cosine': | |
| scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0) | |
| else: | |
| return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy) | |
| return scheduler | |
| def define_net_recon(net_recon, use_last_fc=False, init_path=None): | |
| return ReconNetWrapper(net_recon, use_last_fc=use_last_fc, init_path=init_path) | |
| def define_net_recog(net_recog, pretrained_path=None): | |
| net = RecogNetWrapper(net_recog=net_recog, pretrained_path=pretrained_path) | |
| net.eval() | |
| return net | |
| class ReconNetWrapper(nn.Module): | |
| fc_dim=257 | |
| def __init__(self, net_recon, use_last_fc=False, init_path=None): | |
| super(ReconNetWrapper, self).__init__() | |
| self.use_last_fc = use_last_fc | |
| if net_recon not in func_dict: | |
| return NotImplementedError('network [%s] is not implemented', net_recon) | |
| func, last_dim = func_dict[net_recon] | |
| backbone = func(use_last_fc=use_last_fc, num_classes=self.fc_dim) | |
| if init_path and os.path.isfile(init_path): | |
| state_dict = filter_state_dict(torch.load(init_path, map_location='cpu')) | |
| backbone.load_state_dict(state_dict) | |
| print("loading init net_recon %s from %s" %(net_recon, init_path)) | |
| self.backbone = backbone | |
| if not use_last_fc: | |
| self.final_layers = nn.ModuleList([ | |
| conv1x1(last_dim, 80, bias=True), # id layer | |
| conv1x1(last_dim, 64, bias=True), # exp layer | |
| conv1x1(last_dim, 80, bias=True), # tex layer | |
| conv1x1(last_dim, 3, bias=True), # angle layer | |
| conv1x1(last_dim, 27, bias=True), # gamma layer | |
| conv1x1(last_dim, 2, bias=True), # tx, ty | |
| conv1x1(last_dim, 1, bias=True) # tz | |
| ]) | |
| for m in self.final_layers: | |
| nn.init.constant_(m.weight, 0.) | |
| nn.init.constant_(m.bias, 0.) | |
| def forward(self, x): | |
| x = self.backbone(x) | |
| if not self.use_last_fc: | |
| output = [] | |
| for layer in self.final_layers: | |
| output.append(layer(x)) | |
| x = torch.flatten(torch.cat(output, dim=1), 1) | |
| return x | |
| class RecogNetWrapper(nn.Module): | |
| def __init__(self, net_recog, pretrained_path=None, input_size=112): | |
| super(RecogNetWrapper, self).__init__() | |
| net = get_model(name=net_recog, fp16=False) | |
| if pretrained_path: | |
| state_dict = torch.load(pretrained_path, map_location='cpu') | |
| net.load_state_dict(state_dict) | |
| print("loading pretrained net_recog %s from %s" %(net_recog, pretrained_path)) | |
| for param in net.parameters(): | |
| param.requires_grad = False | |
| self.net = net | |
| self.preprocess = lambda x: 2 * x - 1 | |
| self.input_size=input_size | |
| def forward(self, image, M): | |
| image = self.preprocess(resize_n_crop(image, M, self.input_size)) | |
| id_feature = F.normalize(self.net(image), dim=-1, p=2) | |
| return id_feature | |
| # adapted from https://github.com/pytorch/vision/edit/master/torchvision/models/resnet.py | |
| __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, bias: bool = False) -> nn.Conv2d: | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=bias) | |
| class BasicBlock(nn.Module): | |
| expansion: int = 1 | |
| def __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(BasicBlock, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| if 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 != 1 | |
| self.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 = downsample | |
| self.stride = stride | |
| def forward(self, x: Tensor) -> Tensor: | |
| identity = x | |
| out = 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 += identity | |
| out = self.relu(out) | |
| return out | |
| class 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 = 4 | |
| def __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(Bottleneck, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| width = int(planes * (base_width / 64.)) * groups | |
| # Both self.conv2 and self.downsample layers downsample the input when stride != 1 | |
| self.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 = downsample | |
| self.stride = stride | |
| def forward(self, x: Tensor) -> Tensor: | |
| identity = x | |
| out = 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 += identity | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__( | |
| self, | |
| block: Type[Union[BasicBlock, Bottleneck]], | |
| layers: List[int], | |
| num_classes: int = 1000, | |
| zero_init_residual: bool = False, | |
| use_last_fc: 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(ResNet, self).__init__() | |
| if norm_layer is None: | |
| norm_layer = nn.BatchNorm2d | |
| self._norm_layer = norm_layer | |
| self.inplanes = 64 | |
| self.dilation = 1 | |
| if replace_stride_with_dilation is None: | |
| # each element in the tuple indicates if we should replace | |
| # the 2x2 stride with a dilated convolution instead | |
| replace_stride_with_dilation = [False, False, False] | |
| if len(replace_stride_with_dilation) != 3: | |
| raise ValueError("replace_stride_with_dilation should be None " | |
| "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) | |
| self.use_last_fc = use_last_fc | |
| self.groups = groups | |
| self.base_width = width_per_group | |
| self.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)) | |
| if self.use_last_fc: | |
| 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/1706.02677 | |
| if 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_layer | |
| downsample = None | |
| previous_dilation = self.dilation | |
| if dilate: | |
| self.dilation *= stride | |
| stride = 1 | |
| if 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.expansion | |
| for _ 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) | |
| if self.use_last_fc: | |
| x = torch.flatten(x, 1) | |
| x = self.fc(x) | |
| return x | |
| def 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 model | |
| def 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 ImageNet | |
| progress (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 ImageNet | |
| progress (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 ImageNet | |
| progress (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 ImageNet | |
| progress (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 ImageNet | |
| progress (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 ImageNet | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| """ | |
| kwargs['groups'] = 32 | |
| kwargs['width_per_group'] = 4 | |
| return _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 ImageNet | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| """ | |
| kwargs['groups'] = 32 | |
| kwargs['width_per_group'] = 8 | |
| return _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 channels | |
| which is twice larger in every block. The number of channels in outer 1x1 | |
| convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 | |
| channels, and in Wide ResNet-50-2 has 2048-1024-2048. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| """ | |
| kwargs['width_per_group'] = 64 * 2 | |
| return _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 channels | |
| which is twice larger in every block. The number of channels in outer 1x1 | |
| convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 | |
| channels, and in Wide ResNet-50-2 has 2048-1024-2048. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| progress (bool): If True, displays a progress bar of the download to stderr | |
| """ | |
| kwargs['width_per_group'] = 64 * 2 | |
| return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], | |
| pretrained, progress, **kwargs) | |
| func_dict = { | |
| 'resnet18': (resnet18, 512), | |
| 'resnet50': (resnet50, 2048) | |
| } | |