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Philip Meixner
249
model.py
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249
model.py
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import torch
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class SimpleCNN(torch.nn.Module):
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def __init__(self,
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in_channels: int = 1,
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out_channels: int = 64,
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hidden_channels: int = 3,
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kernel_size: int = 3,
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dropout_rate: float = 0.01):
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super().__init__()
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layers = []
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for i in range(hidden_channels):
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layers.append(torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2))
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layers.append(torch.nn.ReLU())
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layers.append(torch.nn.BatchNorm2d(num_features=out_channels))
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layers.append(torch.nn.Dropout2d(p=dropout_rate))
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layers.append(torch.nn.MaxPool2d(kernel_size=2, stride=2))
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layers.append(
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torch.nn.ConvTranspose2d(in_channels=out_channels, out_channels=out_channels, kernel_size=2, stride=2))
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in_channels = out_channels
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self.hidden_layers = torch.nn.Sequential(*layers)
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self.out_layer = torch.nn.Conv2d(in_channels, 1, kernel_size=kernel_size, padding=kernel_size // 2)
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self.ac_out = torch.nn.Sigmoid()
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def forward(self, x):
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normalized_x = x.float() / 255
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img = self.hidden_layers(normalized_x)
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output = self.out_layer(img)
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scaled_output = self.ac_out(output) * 255
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return scaled_output
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# ---------- Past Models ----------
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# Researched alot about possible CNNs - this is Vanilla UNet Architecture,
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# turns out it doesn't generalize well since its to complex
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class Unet(torch.nn.Module):
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def __init__(self, input_channels: int, padding='same', kernel_size: int = 3):
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super().__init__()
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# Encoder
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self.max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
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self.ac = torch.nn.ReLU()
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# input size = 1
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# input shape torch.Tensor[3, 1, 64, 64]
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print(input_channels)
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self.conv_1 = torch.nn.Conv2d(in_channels=1,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_1_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_1 = torch.nn.BatchNorm2d(input_channels)
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# apply conv layer 2 times, ReLu after every application, max pooling after every 2 applications of conv layer
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input_channels *= 2
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# input size = 128
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self.conv_2 = torch.nn.Conv2d(in_channels=input_channels // 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_2_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_2 = torch.nn.BatchNorm2d(input_channels)
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input_channels *= 2
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# input size = 256
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self.conv_3 = torch.nn.Conv2d(in_channels=input_channels // 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_3_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_3 = torch.nn.BatchNorm2d(input_channels)
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input_channels *= 2
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# Bridge
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# input size = 512
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self.conv_4 = torch.nn.Conv2d(in_channels=input_channels // 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_4_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_4 = torch.nn.BatchNorm2d(input_channels)
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# Decoder
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self.up_samp = torch.nn.UpsamplingBilinear2d(scale_factor=2)
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# input size = 256, kernel size = 3 - 1
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input_channels //= 2
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self.conv_5 = torch.nn.Conv2d(in_channels=input_channels * 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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# concatenate in forward function (tensor after last application of conv 3 merged with current one(up sampled))
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# input size = 512
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self.conv_6 = torch.nn.Conv2d(in_channels=input_channels * 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_6_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_5 = torch.nn.BatchNorm2d(input_channels)
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# apply conv_6 2 times
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# up sampling
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# input size = 128, kernel size = 3 - 1
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input_channels //= 2
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self.conv_7 = torch.nn.Conv2d(in_channels=input_channels * 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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# concatenate in forward function (tensor after conv 2 with current one)
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# input size = 128, kernel size = 3
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self.conv_8 = torch.nn.Conv2d(in_channels=input_channels * 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_8_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_6 = torch.nn.BatchNorm2d(input_channels)
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# up sampling
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# input size = 64, kernel size = 3 - 1
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input_channels //= 2
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self.conv_9 = torch.nn.Conv2d(in_channels=input_channels * 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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# concatenate in forward function (tensor after conv1 with current one)
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self.conv_10 = torch.nn.Conv2d(in_channels=input_channels * 2,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.conv_10_1 = torch.nn.Conv2d(in_channels=input_channels,
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out_channels=input_channels,
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kernel_size=kernel_size,
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padding=padding)
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self.batch_norm_7 = torch.nn.BatchNorm2d(input_channels)
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for m in self.modules():
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if isinstance(m, torch.nn.Conv2d):
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torch.nn.init.xavier_uniform_(m.weight)
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self.out = torch.nn.Conv2d(64, 1, kernel_size=1)
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self.ac_out = torch.nn.Sigmoid()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# Encoder
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x = x.float() / 255
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img = self.ac(self.conv_1(x))
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img = self.batch_norm_1(img)
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conv1 = img = self.ac(self.conv_1_1(img))
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img = self.batch_norm_1(img)
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img = self.max_pool(img)
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img = self.ac(self.conv_2(img))
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img = self.batch_norm_2(img)
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conv2 = img = self.ac(self.conv_2_1(img))
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img = self.batch_norm_2(img)
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img = self.max_pool(img)
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img = self.ac(self.conv_3(img))
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img = self.batch_norm_3(img)
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conv3 = img = self.ac(self.conv_3_1(img))
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img = self.batch_norm_3(img)
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img = self.max_pool(img)
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# Bridge
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img = self.ac(self.conv_4(img))
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img = self.batch_norm_4(img)
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img = torch.nn.Dropout(p=0.001)(img)
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img = self.ac(self.conv_4_1(img))
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img = self.batch_norm_4(img)
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img = torch.nn.Dropout(p=0.001)(img)
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# Decoder
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img = self.up_samp(img)
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img = self.ac(self.conv_5(img))
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img = self.batch_norm_5(img)
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img = torch.cat([conv3, img], dim=1)
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img = self.ac(self.conv_6(img))
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img = self.batch_norm_5(img)
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img = self.ac(self.conv_6_1(img))
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img = self.batch_norm_5(img)
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img = self.up_samp(img)
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img = self.ac(self.conv_7(img))
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img = self.batch_norm_6(img)
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img = torch.concatenate([conv2, img], dim=1)
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img = self.ac(self.conv_8(img))
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img = self.batch_norm_6(img)
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img = self.ac(self.conv_8_1(img))
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img = self.batch_norm_6(img)
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img = self.up_samp(img)
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img = self.ac(self.conv_9(img))
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img = self.batch_norm_7(img)
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img = torch.cat([conv1, img], dim=1)
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img = self.ac(self.conv_10(img))
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img = self.batch_norm_7(img)
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img = self.ac(self.conv_10_1(img))
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img = self.batch_norm_7(img)
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# Output
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output = self.ac_out(self.out(img))
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return output
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# Testing around with basic idea of UNet above but decreasing model complexity - did not work to well either
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class Model(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.in_chn = torch.nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, padding=1)
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self.bn2d_64 = torch.nn.BatchNorm2d(num_features=64)
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self.ac = torch.nn.ReLU()
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self.max_pool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
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self.conv_64_128 = torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
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self.bn2d_128 = torch.nn.BatchNorm2d(num_features=128)
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self.conv_128_256 = torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
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self.bn2d_256 = torch.nn.BatchNorm2d(num_features=256)
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self.conv_256_512 = torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1)
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self.bn2d_512 = torch.nn.BatchNorm2d(num_features=512)
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# concat
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self.conv_256_128 = torch.nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, padding=1)
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self.conv_128_64 = torch.nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, padding=1)
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self.out_chn = torch.nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1)
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self.ac_out = torch.nn.Sigmoid()
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def forward(self, x):
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img = self.bn2d_64(self.ac(self.in_chn(x)))
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state_1 = img = self.bn2d_128(self.ac(self.conv_64_128(img)))
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img = torch.nn.Dropout(p=0.08)(img)
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img = self.bn2d_256(self.ac(self.conv_128_256(img)))
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img = torch.nn.Dropout(p=0.01)(img)
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img = self.bn2d_256(self.ac(self.conv_128_256(img)))
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img = torch.nn.Dropout(p=0.01)(img)
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img = self.bn2d_128(self.ac(self.conv_256_128(img)))
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img = torch.nn.Dropout(p=0.1)(img)
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img = torch.concat([state_1, img], dim=1)
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img = self.bn2d_128(self.ac(self.conv_256_128(img)))
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img = torch.nn.Dropout(p=0.01)(img)
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img = self.bn2d_64(self.ac(self.conv_128_64(img)))
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img = torch.nn.Dropout(p=0.08)(img)
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img = self.out_chn(img)
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return self.ac_out(img)
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