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