File size: 1,507 Bytes
c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 c5a5fc6 7713d80 7874fd9 c5a5fc6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
class ModelColorization(nn.Module, PyTorchModelHubMixin):
def __init__(self):
super(ModelColorization, self).__init__()
self.encoder = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.ReLU(),
nn.BatchNorm2d(16),
nn.Flatten(),
nn.Linear(16*45*45, 4000),
)
self.decoder = nn.Sequential(
nn.Linear(4000, 16 * 45 * 45),
nn.ReLU(),
nn.Unflatten(1, (16, 45, 45)),
nn.ConvTranspose2d(16, 32, kernel_size=3, stride=2, padding=1, output_padding=1),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.ConvTranspose2d(32, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.ConvTranspose2d(64, 3, kernel_size=3, stride=2, padding=1, output_padding=1),
nn.Sigmoid()
)
def forward(self, x):
x = self.encoder(x)
x = self.decoder(x)
return x |