365 lines
12 KiB
Python
365 lines
12 KiB
Python
"""
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Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
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@author: tstandley
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Adapted by cadene
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Creates an Xception Model as defined in:
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Francois Chollet
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Xception: Deep Learning with Depthwise Separable Convolutions
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https://arxiv.org/pdf/1610.02357.pdf
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This weights ported from the Keras implementation. Achieves the following performance on the validation set:
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Loss:0.9173 Prec@1:78.892 Prec@5:94.292
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REMEMBER to set your image size to 3x299x299 for both test and validation
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normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
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std=[0.5, 0.5, 0.5])
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The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
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"""
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import torch.nn as nn
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import torch.nn.functional as F
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from utils.Stomach_Config import Model_Config
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from timm.layers import create_classifier
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from .ViT_Model import ViTBranch # 添加或替換這個導入
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class SeparableConv2d(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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kernel_size: int = 1,
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stride: int = 1,
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padding: int = 0,
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dilation: int = 1,
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device=None,
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dtype=None,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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self.conv1 = nn.Conv2d(
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in_channels,
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in_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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groups=in_channels,
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bias=False,
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**dd,
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)
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self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=False, **dd)
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def forward(self, x):
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x = self.conv1(x)
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x = self.pointwise(x)
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return x
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class Block(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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reps: int,
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strides: int = 1,
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start_with_relu: bool = True,
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grow_first: bool = True,
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device=None,
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dtype=None,
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):
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dd = {'device': device, 'dtype': dtype}
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super().__init__()
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if out_channels != in_channels or strides != 1:
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self.skip = nn.Conv2d(in_channels, out_channels, 1, stride=strides, bias=False, **dd)
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self.skipbn = nn.BatchNorm2d(out_channels, **dd)
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else:
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self.skip = None
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rep = []
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for i in range(reps):
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if grow_first:
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inc = in_channels if i == 0 else out_channels
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outc = out_channels
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else:
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inc = in_channels
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outc = in_channels if i < (reps - 1) else out_channels
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rep.append(nn.ReLU(inplace=True))
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rep.append(SeparableConv2d(inc, outc, 3, stride=1, padding=1, **dd))
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rep.append(nn.BatchNorm2d(outc, **dd))
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if not start_with_relu:
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rep = rep[1:]
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else:
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rep[0] = nn.ReLU(inplace=False)
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if strides != 1:
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rep.append(nn.MaxPool2d(3, strides, 1))
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self.rep = nn.Sequential(*rep)
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def forward(self, inp):
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x = self.rep(inp)
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if self.skip is not None:
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skip = self.skip(inp)
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skip = self.skipbn(skip)
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else:
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skip = inp
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x += skip
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return x
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class Xception(nn.Module):
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"""
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Xception optimized for the ImageNet dataset, as specified in
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https://arxiv.org/pdf/1610.02357.pdf
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"""
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def __init__(
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self,
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num_classes: int = 1000,
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in_chans: int = 3,
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drop_rate: float = 0.,
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global_pool: str = 'avg',
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device=None,
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dtype=None,
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):
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""" Constructor
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Args:
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num_classes: number of classes
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"""
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super().__init__()
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dd = {'device': device, 'dtype': dtype}
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self.drop_rate = drop_rate
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self.global_pool = global_pool
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self.num_classes = num_classes
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self.num_features = self.head_hidden_size = 2048
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self.conv1 = nn.Conv2d(in_chans, 32, 3, 2, 0, bias=False, **dd)
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self.bn1 = nn.BatchNorm2d(32, **dd)
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self.act1 = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(32, 64, 3, bias=False, **dd)
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self.bn2 = nn.BatchNorm2d(64, **dd)
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self.act2 = nn.ReLU(inplace=True)
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self.block1 = Block(64, 128, 2, 2, start_with_relu=False, **dd)
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self.block2 = Block(128, 256, 2, 2, **dd)
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self.block3 = Block(256, 728, 2, 2, **dd)
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self.block4 = Block(728, 728, 3, 1, **dd)
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self.block5 = Block(728, 728, 3, 1, **dd)
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self.block6 = Block(728, 728, 3, 1, **dd)
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self.block7 = Block(728, 728, 3, 1, **dd)
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self.block8 = Block(728, 728, 3, 1, **dd)
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self.block9 = Block(728, 728, 3, 1, **dd)
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self.block10 = Block(728, 728, 3, 1, **dd)
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self.block11 = Block(728, 728, 3, 1, **dd)
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self.block12 = Block(728, 1024, 2, 2, grow_first=False, **dd)
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self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1, **dd)
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self.bn3 = nn.BatchNorm2d(1536, **dd)
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self.act3 = nn.ReLU(inplace=True)
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self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1, **dd)
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self.bn4 = nn.BatchNorm2d(self.num_features, **dd)
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self.act4 = nn.ReLU(inplace=True)
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self.feature_info = [
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dict(num_chs=64, reduction=2, module='act2'),
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dict(num_chs=128, reduction=4, module='block2.rep.0'),
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dict(num_chs=256, reduction=8, module='block3.rep.0'),
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dict(num_chs=728, reduction=16, module='block12.rep.0'),
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dict(num_chs=2048, reduction=32, module='act4'),
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]
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self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool, **dd)
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self.hidden_layer = nn.Linear(2048, Model_Config["Linear Hidden Nodes"]) # 隱藏層，輸入大小取決於 Xception 的輸出大小
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self.output_layer = nn.Linear(Model_Config["Linear Hidden Nodes"], Model_Config["Output Linear Nodes"]) # 輸出層，依據分類數目設定
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# 激活函數與 dropout
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(Model_Config["Dropout Rate"])
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# #------- init weights --------
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def reset_classifier(self, num_classes: int, global_pool: str = 'avg'):
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self.num_classes = num_classes
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self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
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def forward_features(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.act1(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.act2(x)
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x = self.block1(x)
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x = self.block2(x)
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x = self.block3(x)
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x = self.block4(x)
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x = self.block5(x)
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x = self.block6(x)
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x = self.block7(x)
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x = self.block8(x)
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x = self.block9(x)
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x = self.block10(x)
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x = self.block11(x)
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x = self.block12(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x = self.act3(x)
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x = self.conv4(x)
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x = self.bn4(x)
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x = self.act4(x)
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return x
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def forward_head(self, x):
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x = self.global_pool(x)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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x = self.dropout(x) # Dropout
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x = self.hidden_layer(x)
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x = self.relu(x) # 隱藏層 + ReLU
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x = self.output_layer(x) # 輸出層
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return x
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# xception_vit.py
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class XceptionWithViT(nn.Module):
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"""
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Xception + ViT Branch (Middle Flow 後融合)
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"""
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def __init__(
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self,
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num_classes: int = 1000,
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in_chans: int = 3,
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drop_rate: float = 0.0,
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global_pool: str = 'avg',
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vit_patch_size: int = 4,
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vit_depth: int = 3,
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vit_heads: int = 8,
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device=None,
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dtype=None,
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):
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super().__init__()
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dd = {'device': device, 'dtype': dtype}
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self.drop_rate = drop_rate
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self.global_pool = global_pool
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self.num_features = 2048
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# === Entry Flow ===
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self.conv1 = nn.Conv2d(in_chans, 32, 3, 2, 0, bias=False, **dd)
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self.bn1 = nn.BatchNorm2d(32, **dd)
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self.act1 = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(32, 64, 3, bias=False, **dd)
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self.bn2 = nn.BatchNorm2d(64, **dd)
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self.act2 = nn.ReLU(inplace=True)
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self.block1 = Block(64, 128, 2, 2, start_with_relu=False, **dd)
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self.block2 = Block(128, 256, 2, 2, **dd)
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self.block3 = Block(256, 728, 2, 2, **dd)
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# === Middle Flow ===
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for i in range(4, 12):
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setattr(self, f'block{i}', Block(728, 728, 3, 1, **dd))
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# === ViT Branch ===
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self.vit_branch = ViTBranch(
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in_chs=728,
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embed_dim=728,
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patch_size=4,
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depth=3,
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num_heads=8,
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mlp_ratio=4.0,
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drop_rate=0.0,
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attn_drop_rate=0.0,
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device=device,
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dtype=dtype
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)
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# === Exit Flow ===
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self.block12 = Block(728, 1024, 2, 2, grow_first=False, **dd)
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self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1, **dd)
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self.bn3 = nn.BatchNorm2d(1536, **dd)
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self.act3 = nn.ReLU(inplace=True)
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self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1, **dd)
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self.bn4 = nn.BatchNorm2d(self.num_features, **dd)
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self.act4 = nn.ReLU(inplace=True)
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# === Classifier ===
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self.global_pool, _ = create_classifier(self.num_features, num_classes, pool_type=global_pool, **dd)
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self.hidden_layer = nn.Linear(2048, Model_Config["Linear Hidden Nodes"])
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self.output_layer = nn.Linear(Model_Config["Linear Hidden Nodes"], Model_Config["Output Linear Nodes"])
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(Model_Config["Dropout Rate"])
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# === Weight Init ===
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self._init_weights()
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def _init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, (nn.BatchNorm2d, nn.LayerNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def forward_features(self, x):
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# Entry
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x = self.act1(self.bn1(self.conv1(x)))
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x = self.act2(self.bn2(self.conv2(x)))
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x = self.block1(x)
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x = self.block2(x)
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x = self.block3(x)
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# Middle Flow
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for i in range(4, 12):
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x = getattr(self, f'block{i}')(x)
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# === ViT Fusion ===
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vit_out = self.vit_branch(x)
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x = x + vit_out # element-wise add
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# Exit Flow
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x = self.block12(x)
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x = self.act3(self.bn3(self.conv3(x)))
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x = self.act4(self.bn4(self.conv4(x)))
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return x
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def forward(self, x):
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x = self.forward_features(x)
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x = self.global_pool(x)
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x = self.dropout(x)
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x = self.relu(self.hidden_layer(x))
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x = self.output_layer(x)
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return x |