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Source code for torchvision.models.googlenet

import warnings
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from .._internally_replaced_utils import load_state_dict_from_url
from typing import Optional, Tuple, List, Callable, Any

__all__ = ['GoogLeNet', 'googlenet', "GoogLeNetOutputs", "_GoogLeNetOutputs"]

model_urls = {
    # GoogLeNet ported from TensorFlow
    'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}

GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
GoogLeNetOutputs.__annotations__ = {'logits': Tensor, 'aux_logits2': Optional[Tensor],
                                    'aux_logits1': Optional[Tensor]}

# Script annotations failed with _GoogleNetOutputs = namedtuple ...
# _GoogLeNetOutputs set here for backwards compat
_GoogLeNetOutputs = GoogLeNetOutputs


[docs]def googlenet(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> "GoogLeNet": r"""GoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_. The required minimum input size of the model is 15x15. 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 aux_logits (bool): If True, adds two auxiliary branches that can improve training. Default: *False* when pretrained is True otherwise *True* transform_input (bool): If True, preprocesses the input according to the method with which it was trained on ImageNet. Default: *False* """ if pretrained: if 'transform_input' not in kwargs: kwargs['transform_input'] = True if 'aux_logits' not in kwargs: kwargs['aux_logits'] = False if kwargs['aux_logits']: warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, ' 'so make sure to train them') original_aux_logits = kwargs['aux_logits'] kwargs['aux_logits'] = True kwargs['init_weights'] = False model = GoogLeNet(**kwargs) state_dict = load_state_dict_from_url(model_urls['googlenet'], progress=progress) model.load_state_dict(state_dict) if not original_aux_logits: model.aux_logits = False model.aux1 = None # type: ignore[assignment] model.aux2 = None # type: ignore[assignment] return model return GoogLeNet(**kwargs)
class GoogLeNet(nn.Module): __constants__ = ['aux_logits', 'transform_input'] def __init__( self, num_classes: int = 1000, aux_logits: bool = True, transform_input: bool = False, init_weights: Optional[bool] = None, blocks: Optional[List[Callable[..., nn.Module]]] = None ) -> None: super(GoogLeNet, self).__init__() if blocks is None: blocks = [BasicConv2d, Inception, InceptionAux] if init_weights is None: warnings.warn('The default weight initialization of GoogleNet 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 assert len(blocks) == 3 conv_block = blocks[0] inception_block = blocks[1] inception_aux_block = blocks[2] self.aux_logits = aux_logits self.transform_input = transform_input self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3) self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.conv2 = conv_block(64, 64, kernel_size=1) self.conv3 = conv_block(64, 192, kernel_size=3, padding=1) self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32) self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64) self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True) self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64) self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64) self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64) self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64) self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128) self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128) self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128) if aux_logits: self.aux1 = inception_aux_block(512, num_classes) self.aux2 = inception_aux_block(528, num_classes) else: self.aux1 = None # type: ignore[assignment] self.aux2 = None # type: ignore[assignment] self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.dropout = nn.Dropout(0.2) self.fc = nn.Linear(1024, num_classes) if init_weights: self._initialize_weights() def _initialize_weights(self) -> None: for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=0.01, 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], Optional[Tensor]]: # N x 3 x 224 x 224 x = self.conv1(x) # N x 64 x 112 x 112 x = self.maxpool1(x) # N x 64 x 56 x 56 x = self.conv2(x) # N x 64 x 56 x 56 x = self.conv3(x) # N x 192 x 56 x 56 x = self.maxpool2(x) # N x 192 x 28 x 28 x = self.inception3a(x) # N x 256 x 28 x 28 x = self.inception3b(x) # N x 480 x 28 x 28 x = self.maxpool3(x) # N x 480 x 14 x 14 x = self.inception4a(x) # N x 512 x 14 x 14 aux1: Optional[Tensor] = None if self.aux1 is not None: if self.training: aux1 = self.aux1(x) x = self.inception4b(x) # N x 512 x 14 x 14 x = self.inception4c(x) # N x 512 x 14 x 14 x = self.inception4d(x) # N x 528 x 14 x 14 aux2: Optional[Tensor] = None if self.aux2 is not None: if self.training: aux2 = self.aux2(x) x = self.inception4e(x) # N x 832 x 14 x 14 x = self.maxpool4(x) # N x 832 x 7 x 7 x = self.inception5a(x) # N x 832 x 7 x 7 x = self.inception5b(x) # N x 1024 x 7 x 7 x = self.avgpool(x) # N x 1024 x 1 x 1 x = torch.flatten(x, 1) # N x 1024 x = self.dropout(x) x = self.fc(x) # N x 1000 (num_classes) return x, aux2, aux1 @torch.jit.unused def eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> GoogLeNetOutputs: if self.training and self.aux_logits: return _GoogLeNetOutputs(x, aux2, aux1) else: return x # type: ignore[return-value] def forward(self, x: Tensor) -> GoogLeNetOutputs: x = self._transform_input(x) x, aux1, aux2 = self._forward(x) aux_defined = self.training and self.aux_logits if torch.jit.is_scripting(): if not aux_defined: warnings.warn("Scripted GoogleNet always returns GoogleNetOutputs Tuple") return GoogLeNetOutputs(x, aux2, aux1) else: return self.eager_outputs(x, aux2, aux1) class Inception(nn.Module): def __init__( self, in_channels: int, ch1x1: int, ch3x3red: int, ch3x3: int, ch5x5red: int, ch5x5: int, pool_proj: int, conv_block: Optional[Callable[..., nn.Module]] = None ) -> None: super(Inception, self).__init__() if conv_block is None: conv_block = BasicConv2d self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1) self.branch2 = nn.Sequential( conv_block(in_channels, ch3x3red, kernel_size=1), conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1) ) self.branch3 = nn.Sequential( conv_block(in_channels, ch5x5red, kernel_size=1), # Here, kernel_size=3 instead of kernel_size=5 is a known bug. # Please see https://github.com/pytorch/vision/issues/906 for details. conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1) ) self.branch4 = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True), conv_block(in_channels, pool_proj, kernel_size=1) ) def _forward(self, x: Tensor) -> List[Tensor]: branch1 = self.branch1(x) branch2 = self.branch2(x) branch3 = self.branch3(x) branch4 = self.branch4(x) outputs = [branch1, branch2, branch3, branch4] 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(InceptionAux, self).__init__() if conv_block is None: conv_block = BasicConv2d self.conv = conv_block(in_channels, 128, kernel_size=1) self.fc1 = nn.Linear(2048, 1024) self.fc2 = nn.Linear(1024, num_classes) def forward(self, x: Tensor) -> Tensor: # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14 x = F.adaptive_avg_pool2d(x, (4, 4)) # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4 x = self.conv(x) # N x 128 x 4 x 4 x = torch.flatten(x, 1) # N x 2048 x = F.relu(self.fc1(x), inplace=True) # N x 1024 x = F.dropout(x, 0.7, training=self.training) # N x 1024 x = self.fc2(x) # N x 1000 (num_classes) return x class BasicConv2d(nn.Module): def __init__( self, in_channels: int, out_channels: int, **kwargs: Any ) -> None: super(BasicConv2d, self).__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)

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