479 lines
18 KiB
Python
479 lines
18 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.functional as F
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from torch import nn, 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__ = ["Inception3", "InceptionOutputs", "_InceptionOutputs", "Inception_V3_Weights", "inception_v3"]
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InceptionOutputs = namedtuple("InceptionOutputs", ["logits", "aux_logits"])
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InceptionOutputs.__annotations__ = {"logits": Tensor, "aux_logits": Optional[Tensor]}
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# Script annotations failed with _GoogleNetOutputs = namedtuple ...
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# _InceptionOutputs set here for backwards compat
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_InceptionOutputs = InceptionOutputs
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class Inception3(nn.Module):
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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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inception_blocks: Optional[List[Callable[..., nn.Module]]] = None,
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init_weights: Optional[bool] = None,
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dropout: float = 0.5,
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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 inception_blocks is None:
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inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE, 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 inception_v3 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(inception_blocks) != 7:
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raise ValueError(f"length of inception_blocks should be 7 instead of {len(inception_blocks)}")
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conv_block = inception_blocks[0]
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inception_a = inception_blocks[1]
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inception_b = inception_blocks[2]
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inception_c = inception_blocks[3]
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inception_d = inception_blocks[4]
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inception_e = inception_blocks[5]
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inception_aux = inception_blocks[6]
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self.aux_logits = aux_logits
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self.transform_input = transform_input
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self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)
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self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
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self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
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self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
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self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
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self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.Mixed_5b = inception_a(192, pool_features=32)
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self.Mixed_5c = inception_a(256, pool_features=64)
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self.Mixed_5d = inception_a(288, pool_features=64)
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self.Mixed_6a = inception_b(288)
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self.Mixed_6b = inception_c(768, channels_7x7=128)
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self.Mixed_6c = inception_c(768, channels_7x7=160)
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self.Mixed_6d = inception_c(768, channels_7x7=160)
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self.Mixed_6e = inception_c(768, channels_7x7=192)
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self.AuxLogits: Optional[nn.Module] = None
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if aux_logits:
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self.AuxLogits = inception_aux(768, num_classes)
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self.Mixed_7a = inception_d(768)
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self.Mixed_7b = inception_e(1280)
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self.Mixed_7c = inception_e(2048)
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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(2048, 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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stddev = float(m.stddev) if hasattr(m, "stddev") else 0.1 # type: ignore
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torch.nn.init.trunc_normal_(m.weight, mean=0.0, std=stddev, 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]]:
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# N x 3 x 299 x 299
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x = self.Conv2d_1a_3x3(x)
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# N x 32 x 149 x 149
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x = self.Conv2d_2a_3x3(x)
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# N x 32 x 147 x 147
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x = self.Conv2d_2b_3x3(x)
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# N x 64 x 147 x 147
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x = self.maxpool1(x)
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# N x 64 x 73 x 73
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x = self.Conv2d_3b_1x1(x)
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# N x 80 x 73 x 73
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x = self.Conv2d_4a_3x3(x)
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# N x 192 x 71 x 71
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x = self.maxpool2(x)
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# N x 192 x 35 x 35
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x = self.Mixed_5b(x)
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# N x 256 x 35 x 35
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x = self.Mixed_5c(x)
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# N x 288 x 35 x 35
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x = self.Mixed_5d(x)
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# N x 288 x 35 x 35
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x = self.Mixed_6a(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6b(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6c(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6d(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6e(x)
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# N x 768 x 17 x 17
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aux: Optional[Tensor] = None
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if self.AuxLogits is not None:
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if self.training:
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aux = self.AuxLogits(x)
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# N x 768 x 17 x 17
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x = self.Mixed_7a(x)
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# N x 1280 x 8 x 8
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x = self.Mixed_7b(x)
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# N x 2048 x 8 x 8
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x = self.Mixed_7c(x)
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# N x 2048 x 8 x 8
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# Adaptive average pooling
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x = self.avgpool(x)
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# N x 2048 x 1 x 1
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x = self.dropout(x)
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# N x 2048 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 2048
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x = self.fc(x)
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# N x 1000 (num_classes)
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return x, aux
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@torch.jit.unused
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def eager_outputs(self, x: Tensor, aux: Optional[Tensor]) -> InceptionOutputs:
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if self.training and self.aux_logits:
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return InceptionOutputs(x, aux)
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else:
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return x # type: ignore[return-value]
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def forward(self, x: Tensor) -> InceptionOutputs:
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x = self._transform_input(x)
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x, aux = 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 Inception3 always returns Inception3 Tuple")
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return InceptionOutputs(x, aux)
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else:
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return self.eager_outputs(x, aux)
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class InceptionA(nn.Module):
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def __init__(
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self, in_channels: int, pool_features: int, 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.branch1x1 = conv_block(in_channels, 64, kernel_size=1)
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self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
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self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)
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self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
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self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
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self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)
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self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch1x1 = self.branch1x1(x)
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branch5x5 = self.branch5x5_1(x)
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branch5x5 = self.branch5x5_2(branch5x5)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
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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 InceptionB(nn.Module):
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def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> 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.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
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self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
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self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
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self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch3x3 = self.branch3x3(x)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
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outputs = [branch3x3, branch3x3dbl, branch_pool]
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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 InceptionC(nn.Module):
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def __init__(
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self, in_channels: int, channels_7x7: int, 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.branch1x1 = conv_block(in_channels, 192, kernel_size=1)
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c7 = channels_7x7
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self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
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self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
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self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
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self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
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self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))
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self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch1x1 = self.branch1x1(x)
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branch7x7 = self.branch7x7_1(x)
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branch7x7 = self.branch7x7_2(branch7x7)
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branch7x7 = self.branch7x7_3(branch7x7)
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branch7x7dbl = self.branch7x7dbl_1(x)
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branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
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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 InceptionD(nn.Module):
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def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> 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.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
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self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
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self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
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self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
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self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = self.branch3x3_2(branch3x3)
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branch7x7x3 = self.branch7x7x3_1(x)
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branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
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branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
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outputs = [branch3x3, branch7x7x3, branch_pool]
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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 InceptionE(nn.Module):
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def __init__(self, in_channels: int, conv_block: Optional[Callable[..., nn.Module]] = None) -> 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.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
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self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
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self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
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self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
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self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
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self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
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self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
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self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
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self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch1x1 = self.branch1x1(x)
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = [
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self.branch3x3_2a(branch3x3),
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self.branch3x3_2b(branch3x3),
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]
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branch3x3 = torch.cat(branch3x3, 1)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = [
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self.branch3x3dbl_3a(branch3x3dbl),
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self.branch3x3dbl_3b(branch3x3dbl),
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]
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branch3x3dbl = torch.cat(branch3x3dbl, 1)
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
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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, in_channels: int, num_classes: int, 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.conv0 = conv_block(in_channels, 128, kernel_size=1)
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self.conv1 = conv_block(128, 768, kernel_size=5)
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self.conv1.stddev = 0.01 # type: ignore[assignment]
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self.fc = nn.Linear(768, num_classes)
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self.fc.stddev = 0.001 # type: ignore[assignment]
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def forward(self, x: Tensor) -> Tensor:
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# N x 768 x 17 x 17
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x = F.avg_pool2d(x, kernel_size=5, stride=3)
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# N x 768 x 5 x 5
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x = self.conv0(x)
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# N x 128 x 5 x 5
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x = self.conv1(x)
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# N x 768 x 1 x 1
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# Adaptive average pooling
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x = F.adaptive_avg_pool2d(x, (1, 1))
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# N x 768 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 768
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x = self.fc(x)
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# N x 1000
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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 Inception_V3_Weights(WeightsEnum):
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|
IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth",
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|
transforms=partial(ImageClassification, crop_size=299, resize_size=342),
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meta={
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|
"num_params": 27161264,
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|
"min_size": (75, 75),
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|
"categories": _IMAGENET_CATEGORIES,
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|
"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,
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|
"_docs": """These weights are ported from the original paper.""",
|
|
},
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|
)
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|
DEFAULT = IMAGENET1K_V1
|
|
|
|
|
|
@register_model()
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@handle_legacy_interface(weights=("pretrained", Inception_V3_Weights.IMAGENET1K_V1))
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|
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 <http://arxiv.org/abs/1512.00567>`_.
|
|
|
|
.. 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
|
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py>`_
|
|
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
|