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import torch.nn as nn
import torch.nn.functional as F
def conv_block(in_channels, out_channels, pool=False):
layers = [
nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
padding=1
),
nn.BatchNorm2d(out_channels),
nn.ReLU()
]
if pool:
layers.append(
nn.MaxPool2d(4)
)
return nn.Sequential(*layers)
class resnetModel_128(nn.Module):
def __init__(self):
super().__init__()
self.model_name = 'resnetModel_128'
self.conv_1 = conv_block(1, 64)
self.res_1 = nn.Sequential(
conv_block(64, 64),
conv_block(64, 64)
)
self.conv_2 = conv_block(64, 256, pool=True)
self.res_2 = nn.Sequential(
conv_block(256, 256),
conv_block(256, 256)
)
self.conv_3 = conv_block(256, 512, pool=True)
self.res_3 = nn.Sequential(
conv_block(512, 512),
conv_block(512, 512)
)
self.conv_4 = conv_block(512, 1024, pool=True)
self.res_4 = nn.Sequential(
conv_block(1024, 1024),
conv_block(1024, 1024)
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(2*2*1024, 2048),
nn.Dropout(0.5),
nn.ReLU(),
nn.Linear(2048, 1024),
nn.Dropout(0.5),
nn.ReLU(),
nn.Linear(1024, 2)
)
def forward(self, x):
x = self.conv_1(x)
x = self.res_1(x) + x
x = self.conv_2(x)
x = self.res_2(x) + x
x = self.conv_3(x)
x = self.res_3(x) + x
x = self.conv_4(x)
x = self.res_4(x) + x
x = self.classifier(x)
x = F.softmax(x, dim=1)
return x |