forked from mrq/DL-Art-School
154 lines
7.8 KiB
Python
154 lines
7.8 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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from utils.fdpl_util import extract_patches_2d, dct_2d
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from utils.colors import rgb2ycbcr
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class CharbonnierLoss(nn.Module):
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"""Charbonnier Loss (L1)"""
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def __init__(self, eps=1e-6):
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super(CharbonnierLoss, self).__init__()
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self.eps = eps
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def forward(self, x, y):
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diff = x - y
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loss = torch.sum(torch.sqrt(diff * diff + self.eps))
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return loss
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class ZeroSpreadLoss(nn.Module):
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def __init__(self):
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super(ZeroSpreadLoss, self).__init__()
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def forward(self, x, _):
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return 2 * torch.nn.functional.sigmoid(1 / torch.abs(torch.mean(x))) - 1
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# Define GAN loss: [vanilla | lsgan]
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class GANLoss(nn.Module):
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def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0):
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super(GANLoss, self).__init__()
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self.gan_type = gan_type.lower()
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self.real_label_val = real_label_val
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self.fake_label_val = fake_label_val
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if self.gan_type in ['gan', 'ragan', 'pixgan', 'pixgan_fea', 'crossgan', 'crossgan_lrref']:
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self.loss = nn.BCEWithLogitsLoss()
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elif self.gan_type == 'lsgan':
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self.loss = nn.MSELoss()
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elif self.gan_type == 'max_spread':
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self.loss = ZeroSpreadLoss()
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else:
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raise NotImplementedError('GAN type [{:s}] is not found'.format(self.gan_type))
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def get_target_label(self, input, target_is_real):
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if target_is_real:
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return torch.empty_like(input).fill_(self.real_label_val)
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else:
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return torch.empty_like(input).fill_(self.fake_label_val)
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def forward(self, input, target_is_real):
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if self.gan_type in ['pixgan', 'pixgan_fea', 'crossgan', 'crossgan_lrref'] and not isinstance(target_is_real, bool):
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target_label = target_is_real
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else:
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target_label = self.get_target_label(input, target_is_real)
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loss = self.loss(input.float(), target_label.float())
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return loss
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# Frequency Domain Perceptual Loss, from https://github.com/sdv4/FDPL
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# Utilizes pre-computed perceptual_weights. To generate these from your dataset, see scripts/compute_fdpl_perceptual_weights.py
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# In practice, per the paper, these precomputed weights can generally be used across broad image classes (e.g. all photographs).
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class FDPLLoss(nn.Module):
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"""
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Loss function taking the MSE between the 2D DCT coefficients
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of predicted and target images or an image channel.
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DCT coefficients are computed for each 8x8 block of the image
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Important note about this loss: Since it operates in the frequency domain, precision is highly important.
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It works on FP64 numbers and will fail if you attempt to use it with AMP. Recommend you split this loss
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off from the rest of amp.scale_loss().
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"""
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def __init__(self, dataset_diff_means_file, device):
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"""
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dataset_diff_means (torch.tensor): Pre-computed frequency-domain mean differences between LR and HR images.
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"""
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# These values are derived from the JPEG standard.
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qt_Y = torch.tensor([[16, 11, 10, 16, 24, 40, 51, 61],
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[12, 12, 14, 19, 26, 58, 60, 55],
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[14, 13, 16, 24, 40, 57, 69, 56],
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[14, 17, 22, 29, 51, 87, 80, 62],
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[18, 22, 37, 56, 68, 109, 103, 77],
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[24, 35, 55, 64, 81, 104, 113, 92],
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[49, 64, 78, 87, 103, 121, 120, 101],
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[72, 92, 95, 98, 112, 100, 103, 99]],
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dtype=torch.double,
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device=device,
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requires_grad=False)
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qt_C = torch.tensor([[17, 18, 24, 47, 99, 99, 99, 99],
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[18, 21, 26, 66, 99, 99, 99, 99],
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[24, 26, 56, 99, 99, 99, 99, 99],
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[47, 66, 99, 99, 99, 99, 99, 99],
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[99, 99, 99, 99, 99, 99, 99, 99],
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[99, 99, 99, 99, 99, 99, 99, 99],
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[99, 99, 99, 99, 99, 99, 99, 99],
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[99, 99, 99, 99, 99, 99, 99, 99]],
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dtype=torch.double,
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device=device,
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requires_grad=False)
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"""
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Reasoning behind this perceptual weight matrix: JPEG gives as a model of frequencies that are important
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for human perception. In that model, lower frequencies are more important than higher frequencies. Because
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of this, the higher frequencies are the first to go during compression. As compression increases, the affect
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spreads to the lower frequencies, which degrades perceptual quality. But when the lower frequencies are
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preserved, JPEG does an excellent job of compression without a noticeable loss of quality.
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In super resolution, we already have the low frequencies. In fact that is really all we have in the low
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resolution images.
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As evidenced by the diff_means matrix above, what is lost in the SR images is the mid-range frequencies;
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those across and towards the centre of the diagonal. We can bias our model to recover these frequencies
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by having our loss function prioritize these coefficients, with priority determined by the magnitude of
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relative change between the low-res and high-res images. But we can take this further and into a true
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preceptual loss by further prioritizing DCT coefficients by the importance that has been assigned to them
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by the JPEG quantization table. That is how the table below is created.
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The problem is that we don't know if the JPEG model is optimal. So there is room for qualitative evaluation
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of the quantization table values. We can further our perspective weights deleting each in turn for a small
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set of images and evaluating the resulting change in percieved quality. I can do this on my own to start and
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if it works, I can do a small user study to determine this.
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"""
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diff_means = torch.tensor(torch.load(dataset_diff_means_file), device=device)
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perceptual_weights = torch.stack([(torch.ones_like(qt_Y, device=device) / qt_Y),
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(torch.ones_like(qt_C, device=device) / qt_C),
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(torch.ones_like(qt_C, device=device) / qt_C)])
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perceptual_weights = perceptual_weights * diff_means
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self.perceptual_weights = perceptual_weights / torch.mean(perceptual_weights)
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super(FDPLLoss, self).__init__()
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def forward(self, predictions, targets):
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"""
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Args:
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predictions (torch.tensor): output of an image transformation model.
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shape: batch_size x 3 x H x W
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targets (torch.tensor): ground truth images corresponding to outputs
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shape: batch_size x 3 x H x W
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criterion (torch.nn.MSELoss): object used to calculate MSE
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Returns:
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loss (float): MSE between predicted and ground truth 2D DCT coefficients
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"""
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# transition to fp64 and then convert to YCC color space.
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predictions = rgb2ycbcr(predictions.double())
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targets = rgb2ycbcr(targets.double())
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# get DCT coefficients of ground truth patches
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patches = extract_patches_2d(img=targets, patch_shape=(8, 8), batch_first=True)
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ground_truth_dct = dct_2d(patches, norm='ortho')
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# get DCT coefficients of transformed images
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patches = extract_patches_2d(img=predictions, patch_shape=(8, 8), batch_first=True)
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outputs_dct = dct_2d(patches, norm='ortho')
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loss = torch.sum(((outputs_dct - ground_truth_dct).pow(2)) * self.perceptual_weights)
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return loss |