317 lines
11 KiB
Python
317 lines
11 KiB
Python
import warnings
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from collections import namedtuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from .utils import load_state_dict_from_url
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from typing import Optional, Tuple, List, Callable, Any
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__all__ = ['GoogLeNet', 'googlenet', "GoogLeNetOutputs", "_GoogLeNetOutputs"]
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model_urls = {
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# GoogLeNet ported from TensorFlow
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'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
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}
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GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
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GoogLeNetOutputs.__annotations__ = {'logits': Tensor, 'aux_logits2': Optional[Tensor],
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'aux_logits1': Optional[Tensor]}
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# Script annotations failed with _GoogleNetOutputs = namedtuple ...
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# _GoogLeNetOutputs set here for backwards compat
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_GoogLeNetOutputs = GoogLeNetOutputs
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def googlenet(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> "GoogLeNet":
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r"""GoogLeNet (Inception v1) model architecture from
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`"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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aux_logits (bool): If True, adds two auxiliary branches that can improve training.
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Default: *False* when pretrained is True otherwise *True*
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transform_input (bool): If True, preprocesses the input according to the method with which it
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was trained on ImageNet. Default: *False*
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"""
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if pretrained:
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if 'transform_input' not in kwargs:
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kwargs['transform_input'] = True
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if 'aux_logits' not in kwargs:
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kwargs['aux_logits'] = False
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if kwargs['aux_logits']:
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warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, '
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'so make sure to train them')
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original_aux_logits = kwargs['aux_logits']
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kwargs['aux_logits'] = True
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kwargs['init_weights'] = False
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model = GoogLeNet(**kwargs)
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state_dict = load_state_dict_from_url(model_urls['googlenet'],
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progress=progress)
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model.load_state_dict(state_dict)
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if not original_aux_logits:
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model.aux_logits = False
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model.aux1 = None # type: ignore[assignment]
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model.aux2 = None # type: ignore[assignment]
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return model
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return GoogLeNet(**kwargs)
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class GoogLeNet(nn.Module):
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__constants__ = ['aux_logits', 'transform_input']
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def __init__(
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self,
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num_classes: int = 1000,
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aux_logits: bool = True,
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transform_input: bool = False,
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init_weights: Optional[bool] = None,
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blocks: Optional[List[Callable[..., nn.Module]]] = None
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) -> None:
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super(GoogLeNet, self).__init__()
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if blocks is None:
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blocks = [BasicConv2d, Inception, InceptionAux]
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if init_weights is None:
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warnings.warn('The default weight initialization of GoogleNet will be changed in future releases of '
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'torchvision. If you wish to keep the old behavior (which leads to long initialization times'
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' due to scipy/scipy#11299), please set init_weights=True.', FutureWarning)
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init_weights = True
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assert len(blocks) == 3
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conv_block = blocks[0]
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inception_block = blocks[1]
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inception_aux_block = blocks[2]
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self.aux_logits = aux_logits
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self.transform_input = transform_input
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self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)
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self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.conv2 = conv_block(64, 64, kernel_size=1)
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self.conv3 = conv_block(64, 192, kernel_size=3, padding=1)
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self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)
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self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)
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self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
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self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)
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self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)
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self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)
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self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)
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self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128)
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self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
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self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128)
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self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128)
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if aux_logits:
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self.aux1 = inception_aux_block(512, num_classes)
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self.aux2 = inception_aux_block(528, num_classes)
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else:
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self.aux1 = None # type: ignore[assignment]
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self.aux2 = None # type: ignore[assignment]
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.dropout = nn.Dropout(0.2)
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self.fc = nn.Linear(1024, num_classes)
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if init_weights:
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self._initialize_weights()
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def _initialize_weights(self) -> None:
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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import scipy.stats as stats
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X = stats.truncnorm(-2, 2, scale=0.01)
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values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)
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values = values.view(m.weight.size())
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with torch.no_grad():
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m.weight.copy_(values)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def _transform_input(self, x: Tensor) -> Tensor:
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if self.transform_input:
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x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
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x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
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x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
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x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
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return x
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def _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
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# N x 3 x 224 x 224
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x = self.conv1(x)
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# N x 64 x 112 x 112
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x = self.maxpool1(x)
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# N x 64 x 56 x 56
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x = self.conv2(x)
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# N x 64 x 56 x 56
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x = self.conv3(x)
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# N x 192 x 56 x 56
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x = self.maxpool2(x)
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# N x 192 x 28 x 28
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x = self.inception3a(x)
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# N x 256 x 28 x 28
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x = self.inception3b(x)
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# N x 480 x 28 x 28
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x = self.maxpool3(x)
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# N x 480 x 14 x 14
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x = self.inception4a(x)
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# N x 512 x 14 x 14
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aux1: Optional[Tensor] = None
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if self.aux1 is not None:
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if self.training:
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aux1 = self.aux1(x)
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x = self.inception4b(x)
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# N x 512 x 14 x 14
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x = self.inception4c(x)
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# N x 512 x 14 x 14
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x = self.inception4d(x)
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# N x 528 x 14 x 14
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aux2: Optional[Tensor] = None
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if self.aux2 is not None:
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if self.training:
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aux2 = self.aux2(x)
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x = self.inception4e(x)
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# N x 832 x 14 x 14
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x = self.maxpool4(x)
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# N x 832 x 7 x 7
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x = self.inception5a(x)
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# N x 832 x 7 x 7
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x = self.inception5b(x)
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# N x 1024 x 7 x 7
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x = self.avgpool(x)
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# N x 1024 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 1024
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x = self.dropout(x)
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x = self.fc(x)
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# N x 1000 (num_classes)
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return x, aux2, aux1
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@torch.jit.unused
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def eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> GoogLeNetOutputs:
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if self.training and self.aux_logits:
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return _GoogLeNetOutputs(x, aux2, aux1)
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else:
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return x # type: ignore[return-value]
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def forward(self, x: Tensor) -> GoogLeNetOutputs:
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x = self._transform_input(x)
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x, aux1, aux2 = self._forward(x)
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aux_defined = self.training and self.aux_logits
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if torch.jit.is_scripting():
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if not aux_defined:
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warnings.warn("Scripted GoogleNet always returns GoogleNetOutputs Tuple")
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return GoogLeNetOutputs(x, aux2, aux1)
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else:
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return self.eager_outputs(x, aux2, aux1)
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class Inception(nn.Module):
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def __init__(
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self,
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in_channels: int,
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ch1x1: int,
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ch3x3red: int,
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ch3x3: int,
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ch5x5red: int,
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ch5x5: int,
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pool_proj: int,
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conv_block: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(Inception, self).__init__()
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if conv_block is None:
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conv_block = BasicConv2d
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self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)
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self.branch2 = nn.Sequential(
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conv_block(in_channels, ch3x3red, kernel_size=1),
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conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1)
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)
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self.branch3 = nn.Sequential(
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conv_block(in_channels, ch5x5red, kernel_size=1),
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# Here, kernel_size=3 instead of kernel_size=5 is a known bug.
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# Please see https://github.com/pytorch/vision/issues/906 for details.
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conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1)
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)
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self.branch4 = nn.Sequential(
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
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conv_block(in_channels, pool_proj, kernel_size=1)
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)
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch1 = self.branch1(x)
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branch2 = self.branch2(x)
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branch3 = self.branch3(x)
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branch4 = self.branch4(x)
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outputs = [branch1, branch2, branch3, branch4]
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return outputs
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def forward(self, x: Tensor) -> Tensor:
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outputs = self._forward(x)
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return torch.cat(outputs, 1)
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class InceptionAux(nn.Module):
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def __init__(
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self,
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in_channels: int,
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num_classes: int,
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conv_block: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(InceptionAux, self).__init__()
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if conv_block is None:
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conv_block = BasicConv2d
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self.conv = conv_block(in_channels, 128, kernel_size=1)
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self.fc1 = nn.Linear(2048, 1024)
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self.fc2 = nn.Linear(1024, num_classes)
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def forward(self, x: Tensor) -> Tensor:
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# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
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x = F.adaptive_avg_pool2d(x, (4, 4))
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# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
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x = self.conv(x)
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# N x 128 x 4 x 4
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x = torch.flatten(x, 1)
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# N x 2048
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x = F.relu(self.fc1(x), inplace=True)
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# N x 1024
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x = F.dropout(x, 0.7, training=self.training)
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# N x 1024
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x = self.fc2(x)
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# N x 1000 (num_classes)
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return x
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class BasicConv2d(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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**kwargs: Any
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) -> None:
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super(BasicConv2d, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
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self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
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def forward(self, x: Tensor) -> Tensor:
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x = self.conv(x)
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x = self.bn(x)
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return F.relu(x, inplace=True)
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