import os import requests from requests.adapters import HTTPAdapter import torch from torch import nn from torch.nn import functional as F from .utils.download import download_url_to_file class BasicConv2d(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super().__init__() self.conv = nn.Conv2d( in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False ) # verify bias false self.bn = nn.BatchNorm2d( out_planes, eps=0.001, # value found in tensorflow momentum=0.1, # default pytorch value affine=True ) self.relu = nn.ReLU(inplace=False) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Block35(nn.Module): def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.branch2 = nn.Sequential( BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1) ) self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Block17(nn.Module): def __init__(self, scale=1.0): super().__init__() self.scale = scale self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(896, 128, kernel_size=1, stride=1), BasicConv2d(128, 128, kernel_size=(1,7), stride=1, padding=(0,3)), BasicConv2d(128, 128, kernel_size=(7,1), stride=1, padding=(3,0)) ) self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Block8(nn.Module): def __init__(self, scale=1.0, noReLU=False): super().__init__() self.scale = scale self.noReLU = noReLU self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(1792, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=(1,3), stride=1, padding=(0,1)), BasicConv2d(192, 192, kernel_size=(3,1), stride=1, padding=(1,0)) ) self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1) if not self.noReLU: self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x if not self.noReLU: out = self.relu(out) return out class Mixed_6a(nn.Module): def __init__(self): super().__init__() self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2) self.branch1 = nn.Sequential( BasicConv2d(256, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1), BasicConv2d(192, 256, kernel_size=3, stride=2) ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Mixed_7a(nn.Module): def __init__(self): super().__init__() self.branch0 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2) ) self.branch1 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=2) ) self.branch2 = nn.Sequential( BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), BasicConv2d(256, 256, kernel_size=3, stride=2) ) self.branch3 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class InceptionResnetV1(nn.Module): """Inception Resnet V1 model with optional loading of pretrained weights. Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface datasets. Pretrained state_dicts are automatically downloaded on model instantiation if requested and cached in the torch cache. Subsequent instantiations use the cache rather than redownloading. Keyword Arguments: pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'. (default: {None}) classify {bool} -- Whether the model should output classification probabilities or feature embeddings. (default: {False}) num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not equal to that used for the pretrained model, the final linear layer will be randomly initialized. (default: {None}) dropout_prob {float} -- Dropout probability. (default: {0.6}) """ def __init__(self, pretrained=None, classify=False, num_classes=None, dropout_prob=0.6, device=None): super().__init__() # Set simple attributes self.pretrained = pretrained self.classify = classify self.num_classes = num_classes if pretrained == 'vggface2': tmp_classes = 8631 elif pretrained == 'casia-webface': tmp_classes = 10575 elif pretrained is None and self.classify and self.num_classes is None: raise Exception('If "pretrained" is not specified and "classify" is True, "num_classes" must be specified') # Define layers self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) self.maxpool_3a = nn.MaxPool2d(3, stride=2) self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2) self.repeat_1 = nn.Sequential( Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), ) self.mixed_6a = Mixed_6a() self.repeat_2 = nn.Sequential( Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), ) self.mixed_7a = Mixed_7a() self.repeat_3 = nn.Sequential( Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), ) self.block8 = Block8(noReLU=True) self.avgpool_1a = nn.AdaptiveAvgPool2d(1) self.dropout = nn.Dropout(dropout_prob) self.last_linear = nn.Linear(1792, 512, bias=False) self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True) if pretrained is not None: self.logits = nn.Linear(512, tmp_classes) load_weights(self, pretrained) if self.classify and self.num_classes is not None: self.logits = nn.Linear(512, self.num_classes) self.device = torch.device('cpu') if device is not None: self.device = device self.to(device) def forward(self, x): """Calculate embeddings or logits given a batch of input image tensors. Arguments: x {torch.tensor} -- Batch of image tensors representing faces. Returns: torch.tensor -- Batch of embedding vectors or multinomial logits. """ x = self.conv2d_1a(x) x = self.conv2d_2a(x) x = self.conv2d_2b(x) x = self.maxpool_3a(x) x = self.conv2d_3b(x) x = self.conv2d_4a(x) x = self.conv2d_4b(x) x = self.repeat_1(x) x = self.mixed_6a(x) x = self.repeat_2(x) x = self.mixed_7a(x) x = self.repeat_3(x) x = self.block8(x) x = self.avgpool_1a(x) x = self.dropout(x) x = self.last_linear(x.view(x.shape[0], -1)) x = self.last_bn(x) if self.classify: x = self.logits(x) else: x = F.normalize(x, p=2, dim=1) return x def load_weights(mdl, name): """Download pretrained state_dict and load into model. Arguments: mdl {torch.nn.Module} -- Pytorch model. name {str} -- Name of dataset that was used to generate pretrained state_dict. Raises: ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'. """ if name == 'vggface2': path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt' elif name == 'casia-webface': path = 'https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt' else: raise ValueError('Pretrained models only exist for "vggface2" and "casia-webface"') model_dir = os.path.join(get_torch_home(), 'checkpoints') os.makedirs(model_dir, exist_ok=True) cached_file = os.path.join(model_dir, os.path.basename(path)) if not os.path.exists(cached_file): download_url_to_file(path, cached_file) state_dict = torch.load(cached_file) mdl.load_state_dict(state_dict) def get_torch_home(): torch_home = os.path.expanduser( os.getenv( 'TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch') ) ) return torch_home