274 lines
10 KiB
Python
274 lines
10 KiB
Python
import pdb
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import torch
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import torch.nn as nn
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import torch.nn.init as init
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from model.resnet_pre import resnet18, conv1x1, BasicBlock, load_state_dict_from_url, model_urls
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class MLP(nn.Module):
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def __init__(self, input_dim=256, output_dim=1):
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super(MLP, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.fc1 = nn.Linear(self.input_dim, 128) # 32
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self.fc2 = nn.Linear(128, 64)
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self.fc3 = nn.Linear(64, 32)
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self.fc4 = nn.Linear(32, 16)
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self.fc5 = nn.Linear(16, self.output_dim)
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self.relu = nn.ReLU()
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self.sigmoid = nn.Sigmoid()
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self.dropout = nn.Dropout(0.5)
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self.bn1 = nn.BatchNorm1d(128)
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self.bn2 = nn.BatchNorm1d(64)
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self.bn3 = nn.BatchNorm1d(32)
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self.bn4 = nn.BatchNorm1d(16)
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for m in self.modules():
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if isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, x):
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x = self.fc1(x)
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x = self.relu(self.bn1(x))
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x = self.fc2(x)
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x = self.relu(self.bn2(x))
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x = self.fc3(x)
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x = self.relu(self.bn3(x))
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x = self.fc4(x)
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x = self.relu(self.bn4(x))
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x = self.sigmoid(self.fc5(x))
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return x
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class Net2(nn.Module): # 该网络部署有风险，dnn推理有障碍
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def __init__(self, input_dim=960, output_dim=1):
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super(Net2, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)
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self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=2, padding=1)
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# self.conv3 = nn.Conv1d(32, 64, kernel_size=3, stride=2, padding=1)
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# self.conv4 = nn.Conv1d(64, 64, kernel_size=5, stride=2, padding=1)
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self.maxPool1 = nn.MaxPool1d(kernel_size=3, stride=2)
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self.conv5 = nn.Conv1d(32, 64, kernel_size=5, stride=2, padding=1)
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self.maxPool2 = nn.MaxPool1d(kernel_size=3, stride=2)
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self.avgPool = nn.AdaptiveAvgPool1d(1)
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self.MaxPool = nn.AdaptiveMaxPool1d(1)
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self.relu = nn.ReLU()
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self.sigmoid = nn.Sigmoid()
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self.dropout = nn.Dropout(0.5)
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self.flatten = nn.Flatten()
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# self.conv6 = nn.Conv1d(128, 128, kernel_size=5, stride=2, padding=1)
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self.fc1 = nn.Linear(960, 128)
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self.fc21 = nn.Linear(960, 32)
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self.fc22 = nn.Linear(32, 128)
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self.fc3 = nn.Linear(128, 1)
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self.bn1 = nn.BatchNorm1d(16)
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self.bn2 = nn.BatchNorm1d(32)
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self.bn3 = nn.BatchNorm1d(64)
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self.bn4 = nn.BatchNorm1d(128)
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for m in self.modules():
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if isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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def forward(self, x):
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x = self.conv1(x) # 16
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x = self.relu(x)
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x = self.conv2(x) # 32
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x = self.relu(x)
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# x = self.conv3(x)
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# x = self.relu(x)
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# x = self.conv4(x) # 64
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# x = self.relu(x)
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# x = self.maxPool1(x)
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x = self.conv5(x)
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x = self.relu(x)
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# x = self.conv6(x)
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# x = self.relu(x)
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# x = self.maxPool2(x)
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# x = self.MaxPool(x)
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x = x.view(x.size(0), -1)
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x = self.dropout(x)
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x = self.flatten(x)
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# pdb.set_trace()
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x1 = self.fc1(x)
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x2 = self.fc22(self.fc21(x))
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x = self.fc3(x1 + x2)
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x = self.sigmoid(x)
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return x
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class Net3(nn.Module): # 目前较合适的网络结构，相较于Net2，Net3的输出结果更加准确
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def __init__(self, pretrained=True, progress=True, num_classes=1, scale=0.75):
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super(Net3, self).__init__()
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self.resnet18 = resnet18(pretrained=pretrained, progress=progress)
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# Remove the last three layers (layer3, layer4, avgpool, fc)
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# self.resnet18.layer3 = nn.Identity()
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# self.resnet18.layer4 = nn.Identity()
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self.resnet18.avgpool = nn.Identity()
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self.resnet18.fc = nn.Identity()
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self.flatten = nn.Flatten()
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# Calculate the output size after layer2
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# Assuming input size is 224x224, layer2 will have output size of 56x56
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# So, the flattened size will be 128 * scale * 56 * 56
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self.flattened_size = int(128 * (56 * 56) * scale * scale)
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# Add new layers for classification
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self.classifier = nn.Sequential(
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nn.AdaptiveAvgPool2d((1, 1)),
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nn.Flatten(),
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nn.Linear(384, num_classes), # layer1, layer2 in_features=96 # layer1 in_features=48 #layer3 in_features=192
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# nn.ReLU(),
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nn.Dropout(0.6),
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# nn.Linear(256, num_classes),
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nn.Sigmoid()
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)
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def forward(self, x):
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x = self.resnet18.layer1(x)
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x = self.resnet18.layer2(x)
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x = self.resnet18.layer3(x)
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x = self.resnet18.layer4(x)
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# Debugging: Print the shape of the tensor before flattening
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# print("Shape before flattening:", x.shape)
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# Ensure the tensor is flattened correctly
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# x = x.view(x.size(0), -1)
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x = self.classifier(x)
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return x
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class ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1, zero_init_residual=False,
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groups=1, width_per_group=64, replace_stride_with_dilation=None,
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norm_layer=None, scale=0.75):
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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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# each element in the tuple indicates if we should replace
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# the 2x2 stride with a dilated convolution instead
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = norm_layer(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, int(64 * scale), layers[0])
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self.layer2 = self._make_layer(block, int(128 * scale), layers[1], stride=2,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block, int(256 * scale), layers[2], stride=2,
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dilate=replace_stride_with_dilation[1])
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self.layer4 = self._make_layer(block, int(512 * scale), layers[3], stride=2,
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dilate=replace_stride_with_dilation[2])
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(int(512 * block.expansion * scale), num_classes)
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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.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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self.sigmoid = nn.Sigmoid()
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups=self.groups,
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base_width=self.base_width, dilation=self.dilation,
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norm_layer=norm_layer))
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return nn.Sequential(*layers)
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def _forward_impl(self, x):
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# See note [TorchScript super()]
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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x = self.sigmoid(x)
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return x
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def forward(self, x):
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return self._forward_impl(x)
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def Net4(arch, pretrained, progress, **kwargs):
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
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if pretrained:
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state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
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src_state_dict = state_dict
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target_state_dict = model.state_dict()
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skip_keys = []
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# skip mismatch size tensors in case of pretraining
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for k in src_state_dict.keys():
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if k not in target_state_dict:
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continue
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if src_state_dict[k].size() != target_state_dict[k].size():
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skip_keys.append(k)
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for k in skip_keys:
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del src_state_dict[k]
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missing_keys, unexpected_keys = model.load_state_dict(src_state_dict, strict=False)
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return model
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if __name__ == '__main__':
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'''
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net2 = Net2()
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input_tensor = torch.randn(10, 1, 64)
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# 前向传播
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output_tensor = net2(input_tensor)
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# pdb.set_trace()
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print("输入张量形状:", input_tensor.shape)
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print("输出张量形状:", output_tensor.shape)
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'''
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# model = Net3(pretrained=True, num_classes=1) # 预训练从resnet中间结果获取数据训练模型
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model = Net4('resnet18', True, True)
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input_tensor = torch.randn(1, 3, 224, 244) # Adjust batch size to 10
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output = model(input_tensor)
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print(output.shape) # Should be [10, 2] |