upsample ( midlevel_features ) return outputĬlass AverageMeter ( object ): '''A handy class from the PyTorch ImageNet tutorial''' def _init_ ( self ): self. midlevel_resnet ( input ) # Upsample to get colors output = self. Upsample ( scale_factor = 2 ) ) def forward ( self, input ): # Pass input through ResNet-gray to extract features midlevel_features = self. Conv2d ( 32, 2, kernel_size = 3, stride = 1, padding = 1 ), nn. Conv2d ( 64, 32, kernel_size = 3, stride = 1, padding = 1 ), nn. Conv2d ( 64, 64, kernel_size = 3, stride = 1, padding = 1 ), nn. Conv2d ( MIDLEVEL_FEATURE_SIZE, 128, kernel_size = 3, stride = 1, padding = 1 ), nn. children ())) # Second half: Upsampling self. unsqueeze ( 1 )) # Extract midlevel features from ResNet-gray self. resnet18 ( num_classes = 365 ) # Change first conv layer to accept single-channel (grayscale) input resnet. _init_ () MIDLEVEL_FEATURE_SIZE = 128 # First half: ResNet resnet = models. Module ): def _init_ ( self, input_size = 128 ): super ( ColorizationNet, self ). There are other fancier ways of doing colorization with classification (see here), but we'll stick with regression for now as it's simple and works fairly well.Ĭlass ColorizationNet ( nn. We'll try to predict the color values of the input image directly (that is, we do regression). We'll make a helper function to do this conversion later on. This colorspace contains exactly the same information as RGB, but it will make it easier for us to separate out the lightness channel from the other two (which we call A and B). Rather than work with images in the RGB format, as people usually do, we will work with them in the LAB colorspace ( Lightness, A, and B). For simplicity, we will only work with images of size 256 x 256, so our inputs are of size 256 x 256 x 1 (the lightness channel) and our outputs are of size 256 x 256 x 2 (the other two channels). We aim to infer a full-colored image, which has 3 values per pixel (lightness, saturation, and hue), from a grayscale image, which has only 1 value per pixel (lightness only). what the image actually is) in colorization, although we are not yet sure what exacly makes these types of models perform so well.īefore explaining the model, we will first lay out our problem more precisely. This success may in part be due to their ability to capture and use semantic information (i.e. Recently, deep neural networks have shown remarkable success in automatic image colorization - going from grayscale to color with no additional human input. As a result, traditional models often relied on significant user input alongside a grayscale image. This problem is challenging because it is multimodal - a single grayscale image may correspond to many plausible colored images. In image colorization, our goal is to produce a colored image given a grayscale input image.
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