346 lines
12 KiB
Python
346 lines
12 KiB
Python
import warnings
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from collections import namedtuple
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from functools import partial
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from typing import Any, Callable, List, Optional, Tuple
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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 ..transforms._presets import ImageClassification
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from ..utils import _log_api_usage_once
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from ._api import register_model, Weights, WeightsEnum
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from ._meta import _IMAGENET_CATEGORIES
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from ._utils import _ovewrite_named_param, handle_legacy_interface
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__all__ = ["GoogLeNet", "GoogLeNetOutputs", "_GoogLeNetOutputs", "GoogLeNet_Weights", "googlenet"]
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GoogLeNetOutputs = namedtuple("GoogLeNetOutputs", ["logits", "aux_logits2", "aux_logits1"])
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GoogLeNetOutputs.__annotations__ = {"logits": Tensor, "aux_logits2": Optional[Tensor], "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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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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dropout: float = 0.2,
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dropout_aux: float = 0.7,
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) -> None:
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super().__init__()
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_log_api_usage_once(self)
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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(
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"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.",
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FutureWarning,
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)
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init_weights = True
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if len(blocks) != 3:
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raise ValueError(f"blocks length should be 3 instead of {len(blocks)}")
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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, dropout=dropout_aux)
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self.aux2 = inception_aux_block(528, num_classes, dropout=dropout_aux)
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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(p=dropout)
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self.fc = nn.Linear(1024, num_classes)
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if init_weights:
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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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torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=0.01, a=-2, b=2)
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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().__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), 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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dropout: float = 0.7,
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) -> None:
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super().__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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self.dropout = nn.Dropout(p=dropout)
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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 = self.dropout(x)
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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__(self, in_channels: int, out_channels: int, **kwargs: Any) -> None:
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super().__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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class GoogLeNet_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/googlenet-1378be20.pth",
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transforms=partial(ImageClassification, crop_size=224),
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meta={
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"num_params": 6624904,
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"min_size": (15, 15),
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"categories": _IMAGENET_CATEGORIES,
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#googlenet",
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 69.778,
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"acc@5": 89.530,
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}
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},
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"_ops": 1.498,
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"_file_size": 49.731,
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"_docs": """These weights are ported from the original paper.""",
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},
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)
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DEFAULT = IMAGENET1K_V1
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@register_model()
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@handle_legacy_interface(weights=("pretrained", GoogLeNet_Weights.IMAGENET1K_V1))
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def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
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"""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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weights (:class:`~torchvision.models.GoogLeNet_Weights`, optional): The
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pretrained weights for the model. See
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:class:`~torchvision.models.GoogLeNet_Weights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the
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download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.GoogLeNet``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/googlenet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.GoogLeNet_Weights
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:members:
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"""
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weights = GoogLeNet_Weights.verify(weights)
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original_aux_logits = kwargs.get("aux_logits", False)
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if weights is not None:
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if "transform_input" not in kwargs:
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_ovewrite_named_param(kwargs, "transform_input", True)
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_ovewrite_named_param(kwargs, "aux_logits", True)
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_ovewrite_named_param(kwargs, "init_weights", False)
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
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model = GoogLeNet(**kwargs)
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if weights is not None:
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model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
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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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else:
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warnings.warn(
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"auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
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)
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return model
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