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| import torch | |
| import torch.nn as nn | |
| from constants import * | |
| """ | |
| Class for custom activation. | |
| """ | |
| class SymReLU(nn.Module): | |
| def __init__(self, inplace: bool = False): | |
| super().__init__() | |
| self.inplace = inplace | |
| def forward(self, input): | |
| return torch.min(torch.max(input, -torch.ones_like(input)), torch.ones_like(input)) | |
| def extra_repr(self) -> str: | |
| inplace_str = 'inplace=True' if self.inplace else '' | |
| return inplace_str | |
| """ | |
| Class implementing YOLO-Stamp architecture described in https://link.springer.com/article/10.1134/S1054661822040046. | |
| """ | |
| class YOLOStamp(nn.Module): | |
| def __init__( | |
| self, | |
| anchors=ANCHORS, | |
| in_channels=3, | |
| ): | |
| super().__init__() | |
| self.register_buffer('anchors', torch.tensor(anchors)) | |
| self.act = SymReLU() | |
| self.pool = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm1 = nn.BatchNorm2d(num_features=8) | |
| self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm2 = nn.BatchNorm2d(num_features=16) | |
| self.conv3 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm3 = nn.BatchNorm2d(num_features=16) | |
| self.conv4 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm4 = nn.BatchNorm2d(num_features=16) | |
| self.conv5 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm5 = nn.BatchNorm2d(num_features=16) | |
| self.conv6 = nn.Conv2d(in_channels=16, out_channels=24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm6 = nn.BatchNorm2d(num_features=24) | |
| self.conv7 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm7 = nn.BatchNorm2d(num_features=24) | |
| self.conv8 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm8 = nn.BatchNorm2d(num_features=48) | |
| self.conv9 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm9 = nn.BatchNorm2d(num_features=48) | |
| self.conv10 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm10 = nn.BatchNorm2d(num_features=48) | |
| self.conv11 = nn.Conv2d(in_channels=48, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) | |
| self.norm11 = nn.BatchNorm2d(num_features=64) | |
| self.conv12 = nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) | |
| self.norm12 = nn.BatchNorm2d(num_features=256) | |
| self.conv13 = nn.Conv2d(in_channels=256, out_channels=len(anchors) * 5, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0)) | |
| def forward(self, x, head=True): | |
| x = x.type(self.conv1.weight.dtype) | |
| x = self.act(self.pool(self.norm1(self.conv1(x)))) | |
| x = self.act(self.pool(self.norm2(self.conv2(x)))) | |
| x = self.act(self.pool(self.norm3(self.conv3(x)))) | |
| x = self.act(self.pool(self.norm4(self.conv4(x)))) | |
| x = self.act(self.pool(self.norm5(self.conv5(x)))) | |
| x = self.act(self.norm6(self.conv6(x))) | |
| x = self.act(self.norm7(self.conv7(x))) | |
| x = self.act(self.pool(self.norm8(self.conv8(x)))) | |
| x = self.act(self.norm9(self.conv9(x))) | |
| x = self.act(self.norm10(self.conv10(x))) | |
| x = self.act(self.norm11(self.conv11(x))) | |
| x = self.act(self.norm12(self.conv12(x))) | |
| x = self.conv13(x) | |
| nb, _, nh, nw= x.shape | |
| x = x.permute(0, 2, 3, 1).view(nb, nh, nw, self.anchors.shape[0], 5) | |
| return x | |
| class Encoder(torch.nn.Module): | |
| ''' | |
| Encoder Class | |
| Values: | |
| im_chan: the number of channels of the output image, a scalar | |
| hidden_dim: the inner dimension, a scalar | |
| ''' | |
| def __init__(self, im_chan=3, output_chan=Z_DIM, hidden_dim=ENC_HIDDEN_DIM): | |
| super(Encoder, self).__init__() | |
| self.z_dim = output_chan | |
| self.disc = torch.nn.Sequential( | |
| self.make_disc_block(im_chan, hidden_dim), | |
| self.make_disc_block(hidden_dim, hidden_dim * 2), | |
| self.make_disc_block(hidden_dim * 2, hidden_dim * 4), | |
| self.make_disc_block(hidden_dim * 4, hidden_dim * 8), | |
| self.make_disc_block(hidden_dim * 8, output_chan * 2, final_layer=True), | |
| ) | |
| def make_disc_block(self, input_channels, output_channels, kernel_size=4, stride=2, final_layer=False): | |
| ''' | |
| Function to return a sequence of operations corresponding to a encoder block of the VAE, | |
| corresponding to a convolution, a batchnorm (except for in the last layer), and an activation | |
| Parameters: | |
| input_channels: how many channels the input feature representation has | |
| output_channels: how many channels the output feature representation should have | |
| kernel_size: the size of each convolutional filter, equivalent to (kernel_size, kernel_size) | |
| stride: the stride of the convolution | |
| final_layer: whether we're on the final layer (affects activation and batchnorm) | |
| ''' | |
| if not final_layer: | |
| return torch.nn.Sequential( | |
| torch.nn.Conv2d(input_channels, output_channels, kernel_size, stride), | |
| torch.nn.BatchNorm2d(output_channels), | |
| torch.nn.LeakyReLU(0.2, inplace=True), | |
| ) | |
| else: | |
| return torch.nn.Sequential( | |
| torch.nn.Conv2d(input_channels, output_channels, kernel_size, stride), | |
| ) | |
| def forward(self, image): | |
| ''' | |
| Function for completing a forward pass of the Encoder: Given an image tensor, | |
| returns a 1-dimension tensor representing fake/real. | |
| Parameters: | |
| image: a flattened image tensor with dimension (im_dim) | |
| ''' | |
| disc_pred = self.disc(image) | |
| encoding = disc_pred.view(len(disc_pred), -1) | |
| # The stddev output is treated as the log of the variance of the normal | |
| # distribution by convention and for numerical stability | |
| return encoding[:, :self.z_dim], encoding[:, self.z_dim:].exp() |