import warnings from collections import namedtuple from functools import partial from typing import Any, Callable, List, Optional, Tuple import torch import torch.nn.functional as F from torch import nn, Tensor from ..transforms._presets import ImageClassification from ..utils import _log_api_usage_once from ._api import register_model, Weights, WeightsEnum from ._meta import _IMAGENET_CATEGORIES from ._utils import _ovewrite_named_param, handle_legacy_interface __all__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "Inception_V3_Weights", "inception_v3"] InceptionOutputs = namedtuple("InceptionOutputs", ["logits", "aux_logits"]) InceptionOutputs.__annotations__ = {"logits": Tensor, "aux_logits": Optional[Tensor]} # Script annotations failed with _GoogleNetOutputs = namedtuple ... # _InceptionOutputs set here for backwards compat _InceptionOutputs = InceptionOutputs class Inception3(nn.Module): def __init__( self, num_classes: int = 1000, aux_logits: bool = True, transform_input: bool = False, inception_blocks: Optional[List[Callable[..., nn.Module]]] = None, init_weights: Optional[bool] = None, dropout: float = 0.5, ) -> None: super().__init__() _log_api_usage_once(self) if inception_blocks is None: inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE, InceptionAux] if init_weights is None: warnings.warn( "The default weight initialization of inception_v3 will be changed in future releases of " "torchvision. If you wish to keep the old behavior (which leads to long initialization times" " due to scipy/scipy#11299), please set init_weights=True.", FutureWarning, ) init_weights = True if len(inception_blocks) != 7: raise ValueError(f"length of inception_blocks should be 7 instead of {len(inception_blocks)}") conv_block = inception_blocks[0] inception_a = inception_blocks[1] inception_b = inception_blocks[2] inception_c = inception_blocks[3] inception_d = inception_blocks[4] inception_e = inception_blocks[5] inception_aux = inception_blocks[6] self.aux_logits = aux_logits self.transform_input = transform_input self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2) self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3) self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1) self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2) self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1) self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3) self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2) self.Mixed_5b = inception_a(192, pool_features=32) self.Mixed_5c = inception_a(256, pool_features=64) self.Mixed_5d = inception_a(288, pool_features=64) self.Mixed_6a = inception_b(288) self.Mixed_6b = inception_c(768, channels_7x7=128) self.Mixed_6c = inception_c(768, channels_7x7=160) self.Mixed_6d = inception_c(768, channels_7x7=160) self.Mixed_6e = inception_c(768, channels_7x7=192) self.AuxLogits: Optional[nn.Module] = None if aux_logits: self.AuxLogits = inception_aux(768, num_classes) self.Mixed_7a = inception_d(768) self.Mixed_7b = inception_e(1280) self.Mixed_7c = inception_e(2048) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.dropout = nn.Dropout(p=dropout) self.fc = nn.Linear(2048, num_classes) if init_weights: for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): stddev = float(m.stddev) if hasattr(m, "stddev") else 0.1 # type: ignore torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=stddev, a=-2, b=2) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def _transform_input(self, x: Tensor) -> Tensor: if self.transform_input: x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 x = torch.cat((x_ch0, x_ch1, x_ch2), 1) return x def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor]]: # N x 3 x 299 x 299 x = self.Conv2d_1a_3x3(x) # N x 32 x 149 x 149 x = self.Conv2d_2a_3x3(x) # N x 32 x 147 x 147 x = self.Conv2d_2b_3x3(x) # N x 64 x 147 x 147 x = self.maxpool1(x) # N x 64 x 73 x 73 x = self.Conv2d_3b_1x1(x) # N x 80 x 73 x 73 x = self.Conv2d_4a_3x3(x) # N x 192 x 71 x 71 x = self.maxpool2(x) # N x 192 x 35 x 35 x = self.Mixed_5b(x) # N x 256 x 35 x 35 x = self.Mixed_5c(x) # N x 288 x 35 x 35 x = self.Mixed_5d(x) # N x 288 x 35 x 35 x = self.Mixed_6a(x) # N x 768 x 17 x 17 x = self.Mixed_6b(x) # N x 768 x 17 x 17 x = self.Mixed_6c(x) # N x 768 x 17 x 17 x = self.Mixed_6d(x) # N x 768 x 17 x 17 x = self.Mixed_6e(x) # N x 768 x 17 x 17 aux: Optional[Tensor] = None if self.AuxLogits is not None: if self.training: aux = self.AuxLogits(x) # N x 768 x 17 x 17 x = self.Mixed_7a(x) # N x 1280 x 8 x 8 x = self.Mixed_7b(x) # N x 2048 x 8 x 8 x = self.Mixed_7c(x) # N x 2048 x 8 x 8 # Adaptive average pooling x = self.avgpool(x) # N x 2048 x 1 x 1 x = self.dropout(x) # N x 2048 x 1 x 1 x = torch.flatten(x, 1) # N x 2048 x = self.fc(x) # N x 1000 (num_classes) return x, aux @torch.jit.unused def eager_outputs(self, x: Tensor, aux: Optional[Tensor]) -> InceptionOutputs: if self.training and self.aux_logits: return InceptionOutputs(x, aux) else: return x # type: ignore[return-value] def forward(self, x: Tensor) -> InceptionOutputs: x = self._transform_input(x) x, aux = self._forward(x) aux_defined = self.training and self.aux_logits if torch.jit.is_scripting(): if not aux_defined: warnings.warn("Scripted Inception3 always returns Inception3 Tuple") return InceptionOutputs(x, aux) else: return self.eager_outputs(x, aux) class InceptionA(nn.Module): def __init__( self, in_channels: int, pool_features: int, conv_block: Optional[Callable[..., nn.Module]] = None ) -> None: super().__init__() if conv_block is None: conv_block = BasicConv2d self.branch1x1 = conv_block(in_channels, 64, kernel_size=1) self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1) self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2) self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1) self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1) def _forward(self, x: Tensor) -> List[Tensor]: branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] return outputs def forward(self, x: Tensor) -> Tensor: outputs = self._forward(x) return torch.cat(outputs, 1) class InceptionB(nn.Module): def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None: super().__init__() if conv_block is None: conv_block = BasicConv2d self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2) self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2) def _forward(self, x: Tensor) -> List[Tensor]: branch3x3 = self.branch3x3(x) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) outputs = [branch3x3, branch3x3dbl, branch_pool] return outputs def forward(self, x: Tensor) -> Tensor: outputs = self._forward(x) return torch.cat(outputs, 1) class InceptionC(nn.Module): def __init__( self, in_channels: int, channels_7x7: int, conv_block: Optional[Callable[..., nn.Module]] = None ) -> None: super().__init__() if conv_block is None: conv_block = BasicConv2d self.branch1x1 = conv_block(in_channels, 192, kernel_size=1) c7 = channels_7x7 self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1) self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1) self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3)) self.branch_pool = conv_block(in_channels, 192, kernel_size=1) def _forward(self, x: Tensor) -> List[Tensor]: branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] return outputs def forward(self, x: Tensor) -> Tensor: outputs = self._forward(x) return torch.cat(outputs, 1) class InceptionD(nn.Module): def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None: super().__init__() if conv_block is None: conv_block = BasicConv2d self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1) self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2) self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1) self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3)) self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0)) self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2) def _forward(self, x: Tensor) -> List[Tensor]: branch3x3 = self.branch3x3_1(x) branch3x3 = self.branch3x3_2(branch3x3) branch7x7x3 = self.branch7x7x3_1(x) branch7x7x3 = self.branch7x7x3_2(branch7x7x3) branch7x7x3 = self.branch7x7x3_3(branch7x7x3) branch7x7x3 = self.branch7x7x3_4(branch7x7x3) branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) outputs = [branch3x3, branch7x7x3, branch_pool] return outputs def forward(self, x: Tensor) -> Tensor: outputs = self._forward(x) return torch.cat(outputs, 1) class InceptionE(nn.Module): def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> None: super().__init__() if conv_block is None: conv_block = BasicConv2d self.branch1x1 = conv_block(in_channels, 320, kernel_size=1) self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1) self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1) self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1) self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) self.branch_pool = conv_block(in_channels, 192, kernel_size=1) def _forward(self, x: Tensor) -> List[Tensor]: branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = torch.cat(branch3x3, 1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = torch.cat(branch3x3dbl, 1) branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) branch_pool = self.branch_pool(branch_pool) outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] return outputs def forward(self, x: Tensor) -> Tensor: outputs = self._forward(x) return torch.cat(outputs, 1) class InceptionAux(nn.Module): def __init__( self, in_channels: int, num_classes: int, conv_block: Optional[Callable[..., nn.Module]] = None ) -> None: super().__init__() if conv_block is None: conv_block = BasicConv2d self.conv0 = conv_block(in_channels, 128, kernel_size=1) self.conv1 = conv_block(128, 768, kernel_size=5) self.conv1.stddev = 0.01 # type: ignore[assignment] self.fc = nn.Linear(768, num_classes) self.fc.stddev = 0.001 # type: ignore[assignment] def forward(self, x: Tensor) -> Tensor: # N x 768 x 17 x 17 x = F.avg_pool2d(x, kernel_size=5, stride=3) # N x 768 x 5 x 5 x = self.conv0(x) # N x 128 x 5 x 5 x = self.conv1(x) # N x 768 x 1 x 1 # Adaptive average pooling x = F.adaptive_avg_pool2d(x, (1, 1)) # N x 768 x 1 x 1 x = torch.flatten(x, 1) # N x 768 x = self.fc(x) # N x 1000 return x class BasicConv2d(nn.Module): def __init__(self, in_channels: int, out_channels: int, **kwargs: Any) -> None: super().__init__() self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) self.bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x: Tensor) -> Tensor: x = self.conv(x) x = self.bn(x) return F.relu(x, inplace=True) class Inception_V3_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth", transforms=partial(ImageClassification, crop_size=299, resize_size=342), meta={ "num_params": 27161264, "min_size": (75, 75), "categories": _IMAGENET_CATEGORIES, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#inception-v3", "_metrics": { "ImageNet-1K": { "acc@1": 77.294, "acc@5": 93.450, } }, "_ops": 5.713, "_file_size": 103.903, "_docs": """These weights are ported from the original paper.""", }, ) DEFAULT = IMAGENET1K_V1 @register_model() @handle_legacy_interface(weights=("pretrained", Inception_V3_Weights.IMAGENET1K_V1)) def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bool = True, **kwargs: Any) -> Inception3: """ Inception v3 model architecture from `Rethinking the Inception Architecture for Computer Vision `_. .. note:: **Important**: In contrast to the other models the inception_v3 expects tensors with a size of N x 3 x 299 x 299, so ensure your images are sized accordingly. Args: weights (:class:`~torchvision.models.Inception_V3_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.Inception_V3_Weights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. **kwargs: parameters passed to the ``torchvision.models.Inception3`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.Inception_V3_Weights :members: """ weights = Inception_V3_Weights.verify(weights) original_aux_logits = kwargs.get("aux_logits", True) if weights is not None: if "transform_input" not in kwargs: _ovewrite_named_param(kwargs, "transform_input", True) _ovewrite_named_param(kwargs, "aux_logits", True) _ovewrite_named_param(kwargs, "init_weights", False) _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) model = Inception3(**kwargs) if weights is not None: model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) if not original_aux_logits: model.aux_logits = False model.AuxLogits = None return model