Merge remote-tracking branch 'origin/gan_lab' into gan_lab
This commit is contained in:
James Betker
commit
f13fdd43ed
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@ -71,8 +71,15 @@ class ImageCorruptor:
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# Large distortion blocks in part of an img, such as is used to mask out a face.
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pass
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elif 'lq_resampling' in aug:
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# Bicubic LR->HR
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pass
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# Random mode interpolation HR->LR->HR
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scale = 2
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if 'lq_resampling4x' == aug:
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scale = 4
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interpolation_modes = [cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_CUBIC, cv2.INTER_LINEAR, cv2.INTER_LANCZOS4]
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mode = rand_int % len(interpolation_modes)
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# Downsample first, then upsample using the random mode.
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img = cv2.resize(img, dsize=(img.shape[1]//scale, img.shape[0]//scale), interpolation=cv2.INTER_NEAREST)
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img = cv2.resize(img, dsize=(img.shape[1]*scale, img.shape[0]*scale), interpolation=mode)
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elif 'color_shift' in aug:
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# Color shift
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pass
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@ -13,6 +13,24 @@ import torchvision.transforms.functional as F
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from data.image_corruptor import ImageCorruptor
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# Selects the smallest dimension from the image and crops it randomly so the other dimension matches. The cropping
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# offset from center is chosen on a normal probability curve.
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def get_square_image(image):
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h, w, _ = image.shape
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if h == w:
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return image
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offset = max(min(np.random.normal(scale=.3), 1.0), -1.0)
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if h > w:
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diff = h - w
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center = diff // 2
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top = max(int(center + offset * (center - 2)), 0)
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return image[top:top + w, :, :]
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else:
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diff = w - h
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center = diff // 2
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left = max(int(center + offset * (center - 2)), 0)
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return image[:, left:left + h, :]
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class MultiScaleDataset(data.Dataset):
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def __init__(self, opt):
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super(MultiScaleDataset, self).__init__()
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@ -25,23 +43,6 @@ class MultiScaleDataset(data.Dataset):
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self.paths_hq, self.sizes_hq = util.get_image_paths(self.data_type, opt['paths'], [1 for _ in opt['paths']])
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self.corruptor = ImageCorruptor(opt)
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# Selects the smallest dimension from the image and crops it randomly so the other dimension matches. The cropping
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# offset from center is chosen on a normal probability curve.
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def get_square_image(self, image):
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h, w, _ = image.shape
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if h == w:
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return image
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offset = max(min(np.random.normal(scale=.3), 1.0), -1.0)
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if h > w:
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diff = h - w
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center = diff // 2
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top = int(center + offset * (center - 2))
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return image[top:top+w, :, :]
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else:
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diff = w - h
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center = diff // 2
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left = int(center + offset * (center - 2))
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return image[:, left:left+h, :]
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def recursively_extract_patches(self, input_img, result_list, depth):
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if depth >= self.num_scales:
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@ -62,7 +63,7 @@ class MultiScaleDataset(data.Dataset):
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loaded_img = util.read_img(None, full_path, None)
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img_full1 = util.channel_convert(loaded_img.shape[2], 'RGB', [loaded_img])[0]
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img_full2 = util.augment([img_full1], True, True)[0]
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img_full3 = self.get_square_image(img_full2)
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img_full3 = get_square_image(img_full2)
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# This error crops up from time to time. I suspect an issue with util.read_img.
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if img_full3.shape[0] == 0 or img_full3.shape[1] == 0:
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print("Error with image: %s. Loaded image shape: %s" % (full_path,str(loaded_img.shape)), str(img_full1.shape), str(img_full2.shape), str(img_full3.shape))
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@ -513,3 +513,93 @@ class RefDiscriminatorVgg128(nn.Module):
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out = self.output_linears(torch.cat([fea, ref_vector], dim=1))
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return out
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class PsnrApproximator(nn.Module):
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# input_img_factor = multiplier to support images over 128x128. Only certain factors are supported.
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def __init__(self, nf, input_img_factor=1):
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super(PsnrApproximator, self).__init__()
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# [64, 128, 128]
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self.fake_conv0_0 = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
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self.fake_conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
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self.fake_bn0_1 = nn.BatchNorm2d(nf, affine=True)
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# [64, 64, 64]
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self.fake_conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
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self.fake_bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
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self.fake_conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
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self.fake_bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
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# [128, 32, 32]
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self.fake_conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
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self.fake_bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
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self.fake_conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
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self.fake_bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
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# [64, 128, 128]
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self.real_conv0_0 = nn.Conv2d(3, nf, 3, 1, 1, bias=True)
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self.real_conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False)
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self.real_bn0_1 = nn.BatchNorm2d(nf, affine=True)
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# [64, 64, 64]
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self.real_conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False)
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self.real_bn1_0 = nn.BatchNorm2d(nf * 2, affine=True)
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self.real_conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False)
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self.real_bn1_1 = nn.BatchNorm2d(nf * 2, affine=True)
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# [128, 32, 32]
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self.real_conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False)
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self.real_bn2_0 = nn.BatchNorm2d(nf * 4, affine=True)
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self.real_conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False)
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self.real_bn2_1 = nn.BatchNorm2d(nf * 4, affine=True)
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# [512, 16, 16]
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self.conv3_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True)
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# [512, 8, 8]
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self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False)
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self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True)
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self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False)
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self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True)
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final_nf = nf * 8
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 1024)
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self.linear2 = nn.Linear(1024, 512)
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self.linear3 = nn.Linear(512, 128)
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self.linear4 = nn.Linear(128, 1)
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def compute_body1(self, real):
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fea = self.lrelu(self.real_conv0_0(real))
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fea = self.lrelu(self.real_bn0_1(self.real_conv0_1(fea)))
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fea = self.lrelu(self.real_bn1_0(self.real_conv1_0(fea)))
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fea = self.lrelu(self.real_bn1_1(self.real_conv1_1(fea)))
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fea = self.lrelu(self.real_bn2_0(self.real_conv2_0(fea)))
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fea = self.lrelu(self.real_bn2_1(self.real_conv2_1(fea)))
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return fea
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def compute_body2(self, fake):
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fea = self.lrelu(self.fake_conv0_0(fake))
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fea = self.lrelu(self.fake_bn0_1(self.fake_conv0_1(fea)))
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fea = self.lrelu(self.fake_bn1_0(self.fake_conv1_0(fea)))
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fea = self.lrelu(self.fake_bn1_1(self.fake_conv1_1(fea)))
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fea = self.lrelu(self.fake_bn2_0(self.fake_conv2_0(fea)))
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fea = self.lrelu(self.fake_bn2_1(self.fake_conv2_1(fea)))
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return fea
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def forward(self, real, fake):
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real_fea = checkpoint(self.compute_body1, real)
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fake_fea = checkpoint(self.compute_body2, fake)
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fea = torch.cat([real_fea, fake_fea], dim=1)
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fea = self.lrelu(self.bn3_0(self.conv3_0(fea)))
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fea = self.lrelu(self.bn3_1(self.conv3_1(fea)))
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fea = self.lrelu(self.bn4_0(self.conv4_0(fea)))
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fea = self.lrelu(self.bn4_1(self.conv4_1(fea)))
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fea = fea.contiguous().view(fea.size(0), -1)
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fea = self.lrelu(self.linear1(fea))
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fea = self.lrelu(self.linear2(fea))
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fea = self.lrelu(self.linear3(fea))
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out = self.linear4(fea)
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return out.squeeze()
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421
codes/models/archs/srg2_classic.py
Normal file
421
codes/models/archs/srg2_classic.py
Normal file
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@ -0,0 +1,421 @@
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import os
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import torch
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import torchvision
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from torch import nn
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import torch.nn.functional as F
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import functools
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from collections import OrderedDict
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from torch.nn import init
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from models.archs.arch_util import ConvBnLelu, ConvGnSilu
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from utils.util import checkpoint
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def initialize_weights(net_l, scale=1):
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if not isinstance(net_l, list):
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net_l = [net_l]
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for net in net_l:
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for m in net.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv3d):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale # for residual block
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, a=0, mode='fan_in')
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias.data, 0.0)
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class AttentionNorm(nn.Module):
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def __init__(self, group_size, accumulator_size=128):
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super(AttentionNorm, self).__init__()
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self.accumulator_desired_size = accumulator_size
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self.group_size = group_size
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# These are all tensors so that they get saved with the graph.
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self.accumulator = nn.Parameter(torch.zeros(accumulator_size, group_size), requires_grad=False)
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self.accumulator_index = nn.Parameter(torch.zeros(1, dtype=torch.long, device='cpu'), requires_grad=False)
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self.accumulator_filled = nn.Parameter(torch.zeros(1, dtype=torch.bool, device='cpu'), requires_grad=False)
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# Returns tensor of shape (group,) with a normalized mean across the accumulator in the range [0,1]. The intent
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# is to divide your inputs by this value.
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def compute_buffer_norm(self):
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if self.accumulator_filled:
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return torch.mean(self.accumulator, dim=0)
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else:
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return torch.ones(self.group_size, device=self.accumulator.device)
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def add_norm_to_buffer(self, x):
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flat = x.sum(dim=[0, 1, 2], keepdim=True)
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norm = flat / torch.mean(flat)
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# This often gets reset in GAN mode. We *never* want gradient accumulation in this parameter.
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self.accumulator.requires_grad = False
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self.accumulator[self.accumulator_index] = norm.detach()
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self.accumulator_index += 1
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if self.accumulator_index >= self.accumulator_desired_size:
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self.accumulator_index *= 0
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self.accumulator_filled |= True
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# Input into forward is an attention tensor of shape (batch,width,height,groups)
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def forward(self, x: torch.Tensor):
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assert len(x.shape) == 4
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# Push the accumulator to the right device on the first iteration.
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if self.accumulator.device != x.device:
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self.accumulator = self.accumulator.to(x.device)
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self.add_norm_to_buffer(x)
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norm = self.compute_buffer_norm()
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x = x / norm
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# Need to re-normalize x so that the groups dimension sum to 1, just like when it was fed in.
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groups_sum = x.sum(dim=3, keepdim=True)
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return x / groups_sum
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class BareConvSwitch(nn.Module):
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"""
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Initializes the ConvSwitch.
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initial_temperature: The initial softmax temperature of the attention mechanism. For training from scratch, this
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should be set to a high number, for example 30.
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attention_norm: If specified, the AttentionNorm layer applied immediately after Softmax.
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"""
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def __init__(
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self,
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initial_temperature=1,
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attention_norm=None
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):
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super(BareConvSwitch, self).__init__()
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self.softmax = nn.Softmax(dim=-1)
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self.temperature = initial_temperature
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self.attention_norm = attention_norm
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initialize_weights(self)
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def set_attention_temperature(self, temp):
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self.temperature = temp
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# SwitchedConv.forward takes these arguments;
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# conv_group: List of inputs (len=n) to the switch, each with shape (b,f,w,h)
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# conv_attention: Attention computation as an output from a conv layer, of shape (b,n,w,h). Before softmax
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# output_attention_weights: If True, post-softmax attention weights are returned.
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def forward(self, conv_group, conv_attention, output_attention_weights=False):
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# Stack up the conv_group input first and permute it to (batch, width, height, filter, groups)
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conv_outputs = torch.stack(conv_group, dim=0).permute(1, 3, 4, 2, 0)
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conv_attention = conv_attention.permute(0, 2, 3, 1)
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conv_attention = self.softmax(conv_attention / self.temperature)
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if self.attention_norm:
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conv_attention = self.attention_norm(conv_attention)
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# conv_outputs shape: (batch, width, height, filters, groups)
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# conv_attention shape: (batch, width, height, groups)
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# We want to format them so that we can matmul them together to produce:
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# desired shape: (batch, width, height, filters)
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# Note: conv_attention will generally be cast to float32 regardless of the input type, so cast conv_outputs to
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# float32 as well to match it.
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if self.training:
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# Doing it all in one op is substantially faster - better for training.
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attention_result = torch.einsum(
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"...ij,...j->...i", [conv_outputs.float(), conv_attention]
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)
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else:
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# eval_mode substantially reduces the GPU memory required to compute the attention result by performing the
|
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# attention multiplications one at a time. This is probably necessary for large images and attention breadths.
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attention_result = conv_outputs[:, :, :, :, 0] * conv_attention[:, :, :, 0].unsqueeze(dim=-1)
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for i in range(1, conv_attention.shape[-1]):
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attention_result += conv_outputs[:, :, :, :, i] * conv_attention[:, :, :, i].unsqueeze(dim=-1)
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|
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# Remember to shift the filters back into the expected slot.
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if output_attention_weights:
|
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return attention_result.permute(0, 3, 1, 2), conv_attention
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else:
|
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return attention_result.permute(0, 3, 1, 2)
|
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|
||||
|
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class MultiConvBlock(nn.Module):
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def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1.0, norm=False, weight_init_factor=1):
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assert depth >= 2
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super(MultiConvBlock, self).__init__()
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self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
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self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=norm, bias=False, weight_init_factor=weight_init_factor)] +
|
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[ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=norm, bias=False, weight_init_factor=weight_init_factor) for i in range(depth - 2)] +
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[ConvBnLelu(filters_mid, filters_out, kernel_size, activation=False, norm=False, bias=False, weight_init_factor=weight_init_factor)])
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self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
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self.bias = nn.Parameter(torch.zeros(1), requires_grad=False)
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def forward(self, x, noise=None):
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if noise is not None:
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noise = noise * self.noise_scale
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x = x + noise
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for m in self.bnconvs:
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x = m.forward(x)
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return x * self.scale + self.bias
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|
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|
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# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
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# Doubles the input filter count.
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class HalvingProcessingBlock(nn.Module):
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def __init__(self, filters):
|
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super(HalvingProcessingBlock, self).__init__()
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self.bnconv1 = ConvGnSilu(filters, filters * 2, stride=2, norm=False, bias=False)
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self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, norm=True, bias=False)
|
||||
|
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def forward(self, x):
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x = self.bnconv1(x)
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return self.bnconv2(x)
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|
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# Block that upsamples 2x and reduces incoming filters by 2x. It preserves structure by taking a passthrough feed
|
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# along with the feature representation.
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class ExpansionBlock(nn.Module):
|
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def __init__(self, filters_in, filters_out=None, block=ConvGnSilu):
|
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super(ExpansionBlock, self).__init__()
|
||||
if filters_out is None:
|
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filters_out = filters_in // 2
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self.decimate = block(filters_in, filters_out, kernel_size=1, bias=False, activation=False, norm=True)
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self.process_passthrough = block(filters_out, filters_out, kernel_size=3, bias=True, activation=False, norm=True)
|
||||
self.conjoin = block(filters_out*2, filters_out, kernel_size=3, bias=False, activation=True, norm=False)
|
||||
self.process = block(filters_out, filters_out, kernel_size=3, bias=False, activation=True, norm=True)
|
||||
|
||||
# input is the feature signal with shape (b, f, w, h)
|
||||
# passthrough is the structure signal with shape (b, f/2, w*2, h*2)
|
||||
# output is conjoined upsample with shape (b, f/2, w*2, h*2)
|
||||
def forward(self, input, passthrough):
|
||||
x = F.interpolate(input, scale_factor=2, mode="nearest")
|
||||
x = self.decimate(x)
|
||||
p = self.process_passthrough(passthrough)
|
||||
x = self.conjoin(torch.cat([x, p], dim=1))
|
||||
return self.process(x)
|
||||
|
||||
|
||||
# This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform
|
||||
# switching set.
|
||||
class ConvBasisMultiplexer(nn.Module):
|
||||
def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True):
|
||||
super(ConvBasisMultiplexer, self).__init__()
|
||||
self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True)
|
||||
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)])
|
||||
reduction_filters = base_filters * 2 ** reductions
|
||||
self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)]))
|
||||
self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
|
||||
|
||||
gap = base_filters - multiplexer_channels
|
||||
cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm.
|
||||
self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
|
||||
cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
|
||||
self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
|
||||
self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.filter_conv(x)
|
||||
reduction_identities = []
|
||||
for b in self.reduction_blocks:
|
||||
reduction_identities.append(x)
|
||||
x = b(x)
|
||||
x = self.processing_blocks(x)
|
||||
for i, b in enumerate(self.expansion_blocks):
|
||||
x = b(x, reduction_identities[-i - 1])
|
||||
|
||||
x = self.cbl1(x)
|
||||
x = self.cbl2(x)
|
||||
x = self.cbl3(x)
|
||||
return x
|
||||
|
||||
|
||||
class ConfigurableSwitchComputer(nn.Module):
|
||||
def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20,
|
||||
add_scalable_noise_to_transforms=False):
|
||||
super(ConfigurableSwitchComputer, self).__init__()
|
||||
|
||||
tc = transform_count
|
||||
self.multiplexer = multiplexer_net(tc)
|
||||
|
||||
self.pre_transform = pre_transform_block()
|
||||
self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
|
||||
self.add_noise = add_scalable_noise_to_transforms
|
||||
self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
|
||||
|
||||
# And the switch itself, including learned scalars
|
||||
self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count))
|
||||
self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
|
||||
self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
|
||||
# The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
|
||||
# depending on its needs.
|
||||
self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
|
||||
|
||||
def forward(self, x, output_attention_weights=True):
|
||||
identity = x
|
||||
if self.add_noise:
|
||||
rand_feature = torch.randn_like(x) * self.noise_scale
|
||||
x = x + rand_feature
|
||||
|
||||
x = self.pre_transform(x)
|
||||
xformed = [t.forward(x) for t in self.transforms]
|
||||
|
||||
m = self.multiplexer(identity)
|
||||
|
||||
outputs, attention = self.switch(xformed, m, True)
|
||||
outputs = identity + outputs * self.switch_scale
|
||||
outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale
|
||||
if output_attention_weights:
|
||||
return outputs, attention
|
||||
else:
|
||||
return outputs
|
||||
|
||||
def set_temperature(self, temp):
|
||||
self.switch.set_attention_temperature(temp)
|
||||
|
||||
|
||||
def compute_attention_specificity(att_weights, topk=3):
|
||||
att = att_weights.detach()
|
||||
vals, indices = torch.topk(att, topk, dim=-1)
|
||||
avg = torch.sum(vals, dim=-1)
|
||||
avg = avg.flatten().mean()
|
||||
return avg.item(), indices.flatten().detach()
|
||||
|
||||
|
||||
# Copied from torchvision.utils.save_image. Allows specifying pixel format.
|
||||
def save_image(tensor, fp, nrow=8, padding=2,
|
||||
normalize=False, range=None, scale_each=False, pad_value=0, format=None, pix_format=None):
|
||||
from PIL import Image
|
||||
grid = torchvision.utils.make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
|
||||
normalize=normalize, range=range, scale_each=scale_each)
|
||||
# Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
|
||||
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
|
||||
im = Image.fromarray(ndarr, mode=pix_format).convert('RGB')
|
||||
im.save(fp, format=format)
|
||||
|
||||
|
||||
def save_attention_to_image(folder, attention_out, attention_size, step, fname_part="map", l_mult=1.0):
|
||||
magnitude, indices = torch.topk(attention_out, 1, dim=-1)
|
||||
magnitude = magnitude.squeeze(3)
|
||||
indices = indices.squeeze(3)
|
||||
# indices is an integer tensor (b,w,h) where values are on the range [0,attention_size]
|
||||
# magnitude is a float tensor (b,w,h) [0,1] representing the magnitude of that attention.
|
||||
# Use HSV colorspace to show this. Hue is mapped to the indices, Lightness is mapped to intensity,
|
||||
# Saturation is left fixed.
|
||||
hue = indices.float() / attention_size
|
||||
saturation = torch.full_like(hue, .8)
|
||||
value = magnitude * l_mult
|
||||
hsv_img = torch.stack([hue, saturation, value], dim=1)
|
||||
|
||||
output_path=os.path.join(folder, "attention_maps", fname_part)
|
||||
os.makedirs(output_path, exist_ok=True)
|
||||
save_image(hsv_img, os.path.join(output_path, "attention_map_%i.png" % (step,)), pix_format="HSV")
|
||||
|
||||
|
||||
def save_attention_to_image_rgb(output_folder, attention_out, attention_size, file_prefix, step, cmap_discrete_name='viridis'):
|
||||
magnitude, indices = torch.topk(attention_out, 3, dim=-1)
|
||||
magnitude = magnitude.cpu()
|
||||
indices = indices.cpu()
|
||||
magnitude /= torch.max(torch.abs(torch.min(magnitude)), torch.abs(torch.max(magnitude)))
|
||||
colormap = cm.get_cmap(cmap_discrete_name, attention_size)
|
||||
colormap_mag = cm.get_cmap(cmap_discrete_name)
|
||||
os.makedirs(os.path.join(output_folder), exist_ok=True)
|
||||
for i in range(3):
|
||||
img = torch.tensor(colormap(indices[:,:,:,i].detach().numpy()))
|
||||
img = img.permute((0, 3, 1, 2))
|
||||
save_image(img, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "rgb_%i" % (i,))), pix_format="RGBA")
|
||||
|
||||
mag_image = torch.tensor(colormap_mag(magnitude[:,:,:,i].detach().numpy()))
|
||||
mag_image = mag_image.permute((0, 3, 1, 2))
|
||||
save_image(mag_image, os.path.join(output_folder, file_prefix + "_%i_%s.png" % (step, "mag_%i" % (i,))), pix_format="RGBA")
|
||||
|
||||
|
||||
class ConfigurableSwitchedResidualGenerator2(nn.Module):
|
||||
def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
|
||||
trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
|
||||
heightened_final_step=50000, upsample_factor=1,
|
||||
add_scalable_noise_to_transforms=False):
|
||||
super(ConfigurableSwitchedResidualGenerator2, self).__init__()
|
||||
switches = []
|
||||
self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
|
||||
self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
|
||||
self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
|
||||
self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
|
||||
self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
|
||||
for _ in range(switch_depth):
|
||||
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
|
||||
pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
|
||||
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
|
||||
switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||||
pre_transform_block=pretransform_fn, transform_block=transform_fn,
|
||||
transform_count=trans_counts, init_temp=initial_temp,
|
||||
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
|
||||
|
||||
self.switches = nn.ModuleList(switches)
|
||||
self.transformation_counts = trans_counts
|
||||
self.init_temperature = initial_temp
|
||||
self.final_temperature_step = final_temperature_step
|
||||
self.heightened_temp_min = heightened_temp_min
|
||||
self.heightened_final_step = heightened_final_step
|
||||
self.attentions = None
|
||||
self.upsample_factor = upsample_factor
|
||||
assert self.upsample_factor == 2 or self.upsample_factor == 4
|
||||
|
||||
def forward(self, x):
|
||||
x = self.initial_conv(x)
|
||||
|
||||
self.attentions = []
|
||||
for i, sw in enumerate(self.switches):
|
||||
x, att = checkpoint(sw, x)
|
||||
self.attentions.append(att)
|
||||
|
||||
x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
|
||||
if self.upsample_factor > 2:
|
||||
x = F.interpolate(x, scale_factor=2, mode="nearest")
|
||||
x = self.upconv2(x)
|
||||
x = self.final_conv(self.hr_conv(x))
|
||||
return x
|
||||
|
||||
def set_temperature(self, temp):
|
||||
[sw.set_temperature(temp) for sw in self.switches]
|
||||
|
||||
def update_for_step(self, step, experiments_path='.'):
|
||||
if self.attentions:
|
||||
temp = max(1,
|
||||
1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
|
||||
if temp == 1 and self.heightened_final_step and step > self.final_temperature_step and \
|
||||
self.heightened_final_step != 1:
|
||||
# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
|
||||
# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
|
||||
h_steps_total = self.heightened_final_step - self.final_temperature_step
|
||||
h_steps_current = min(step - self.final_temperature_step, h_steps_total)
|
||||
# The "gap" will represent the steps that need to be traveled as a linear function.
|
||||
h_gap = 1 / self.heightened_temp_min
|
||||
temp = h_gap * h_steps_current / h_steps_total
|
||||
# Invert temperature to represent reality on this side of the curve
|
||||
temp = 1 / temp
|
||||
self.set_temperature(temp)
|
||||
if step % 50 == 0:
|
||||
[save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts, step, "a%i" % (i+1,), l_mult=10) for i in range(len(self.attentions))]
|
||||
|
||||
def get_debug_values(self, step, net_name):
|
||||
temp = self.switches[0].switch.temperature
|
||||
mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
|
||||
means = [i[0] for i in mean_hists]
|
||||
hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
|
||||
val = {"switch_temperature": temp}
|
||||
for i in range(len(means)):
|
||||
val["switch_%i_specificity" % (i,)] = means[i]
|
||||
val["switch_%i_histogram" % (i,)] = hists[i]
|
||||
return val
|
||||
|
||||
|
||||
class Interpolate(nn.Module):
|
||||
def __init__(self, factor):
|
||||
super(Interpolate, self).__init__()
|
||||
self.factor = factor
|
||||
|
||||
def forward(self, x):
|
||||
return F.interpolate(x, scale_factor=self.factor)
|
||||
|
||||
|
|
@ -18,6 +18,7 @@ import models.archs.feature_arch as feature_arch
|
|||
import models.archs.panet.panet as panet
|
||||
import models.archs.rcan as rcan
|
||||
import models.archs.ChainedEmbeddingGen as chained
|
||||
from models.archs import srg2_classic
|
||||
from models.archs.teco_resgen import TecoGen
|
||||
|
||||
logger = logging.getLogger('base')
|
||||
|
|
@ -64,6 +65,15 @@ def define_G(opt, net_key='network_G', scale=None):
|
|||
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
|
||||
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
|
||||
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
|
||||
elif which_model == "srg2classic":
|
||||
netG = srg2_classic.ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'],
|
||||
switch_reductions=opt_net['switch_reductions'],
|
||||
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
|
||||
trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
|
||||
transformation_filters=opt_net['transformation_filters'],
|
||||
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
|
||||
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
|
||||
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
|
||||
elif which_model == 'spsr':
|
||||
netG = spsr.SPSRNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
|
||||
nb=opt_net['nb'], upscale=opt_net['scale'])
|
||||
|
|
@ -160,6 +170,8 @@ def define_D_net(opt_net, img_sz=None, wrap=False):
|
|||
netD = SRGAN_arch.CrossCompareDiscriminator(in_nc=opt_net['in_nc'], ref_channels=opt_net['ref_channels'] if 'ref_channels' in opt_net.keys() else 3, nf=opt_net['nf'], scale=opt_net['scale'])
|
||||
elif which_model == "discriminator_refvgg":
|
||||
netD = SRGAN_arch.RefDiscriminatorVgg128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
|
||||
elif which_model == "psnr_approximator":
|
||||
netD = SRGAN_arch.PsnrApproximator(nf=opt_net['nf'], input_img_factor=img_sz / 128)
|
||||
else:
|
||||
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
|
||||
return netD
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
import random
|
||||
|
||||
import torch.nn
|
||||
from torch.cuda.amp import autocast
|
||||
|
||||
|
|
@ -45,6 +47,12 @@ def create_injector(opt_inject, env):
|
|||
return ImageFftInjector(opt_inject, env)
|
||||
elif type == 'extract_indices':
|
||||
return IndicesExtractor(opt_inject, env)
|
||||
elif type == 'random_shift':
|
||||
return RandomShiftInjector(opt_inject, env)
|
||||
elif type == 'psnr':
|
||||
return PsnrInjector(opt_inject, env)
|
||||
elif type == 'batch_rotate':
|
||||
return BatchRotateInjector(opt_inject, env)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
|
|
@ -94,12 +102,13 @@ class DiscriminatorInjector(Injector):
|
|||
super(DiscriminatorInjector, self).__init__(opt, env)
|
||||
|
||||
def forward(self, state):
|
||||
d = self.env['discriminators'][self.opt['discriminator']]
|
||||
if isinstance(self.input, list):
|
||||
params = [state[i] for i in self.input]
|
||||
results = d(*params)
|
||||
else:
|
||||
results = d(state[self.input])
|
||||
with autocast(enabled=self.env['opt']['fp16']):
|
||||
d = self.env['discriminators'][self.opt['discriminator']]
|
||||
if isinstance(self.input, list):
|
||||
params = [state[i] for i in self.input]
|
||||
results = d(*params)
|
||||
else:
|
||||
results = d(state[self.input])
|
||||
new_state = {}
|
||||
if isinstance(self.output, list):
|
||||
# Only dereference tuples or lists, not tensors.
|
||||
|
|
@ -232,10 +241,25 @@ class MarginRemoval(Injector):
|
|||
def __init__(self, opt, env):
|
||||
super(MarginRemoval, self).__init__(opt, env)
|
||||
self.margin = opt['margin']
|
||||
self.random_shift_max = opt['random_shift_max'] if 'random_shift_max' in opt.keys() else 0
|
||||
|
||||
def forward(self, state):
|
||||
input = state[self.input]
|
||||
return {self.opt['out']: input[:, :, self.margin:-self.margin, self.margin:-self.margin]}
|
||||
if self.random_shift_max > 0:
|
||||
output = []
|
||||
# This is a really shitty way of doing this. If it works at all, I should reconsider using Resample2D, for example.
|
||||
for b in range(input.shape[0]):
|
||||
shiftleft = random.randint(-self.random_shift_max, self.random_shift_max)
|
||||
shifttop = random.randint(-self.random_shift_max, self.random_shift_max)
|
||||
output.append(input[b, :, self.margin+shiftleft:-(self.margin-shiftleft),
|
||||
self.margin+shifttop:-(self.margin-shifttop)])
|
||||
output = torch.stack(output, dim=0)
|
||||
else:
|
||||
output = input[:, :, self.margin:-self.margin,
|
||||
self.margin:-self.margin]
|
||||
|
||||
return {self.opt['out']: output}
|
||||
|
||||
|
||||
# Produces an injection which is composed of applying a single injector multiple times across a single dimension.
|
||||
class ForEachInjector(Injector):
|
||||
|
|
@ -254,7 +278,7 @@ class ForEachInjector(Injector):
|
|||
for i in range(inputs.shape[1]):
|
||||
st['_in'] = inputs[:, i]
|
||||
injs.append(self.injector(st)['_out'])
|
||||
return {self.output: torch.stack(injs, dim=1)}
|
||||
return {self.output: torch.stack(injs, dim=1)}
|
||||
|
||||
|
||||
class ConstantInjector(Injector):
|
||||
|
|
@ -316,3 +340,31 @@ class IndicesExtractor(Injector):
|
|||
results[o] = state[self.input][:, i]
|
||||
return results
|
||||
|
||||
|
||||
class RandomShiftInjector(Injector):
|
||||
def __init__(self, opt, env):
|
||||
super(RandomShiftInjector, self).__init__(opt, env)
|
||||
|
||||
def forward(self, state):
|
||||
img = state[self.input]
|
||||
return {self.output: img}
|
||||
|
||||
|
||||
class PsnrInjector(Injector):
|
||||
def __init__(self, opt, env):
|
||||
super(PsnrInjector, self).__init__(opt, env)
|
||||
|
||||
def forward(self, state):
|
||||
img1, img2 = state[self.input[0]], state[self.input[1]]
|
||||
mse = torch.mean((img1 - img2) ** 2, dim=[1,2,3])
|
||||
return {self.output: mse}
|
||||
|
||||
|
||||
class BatchRotateInjector(Injector):
|
||||
def __init__(self, opt, env):
|
||||
super(BatchRotateInjector, self).__init__(opt, env)
|
||||
|
||||
def forward(self, state):
|
||||
img = state[self.input]
|
||||
return {self.output: torch.roll(img, 1, 0)}
|
||||
|
||||
|
|
|
|||
|
|
@ -159,8 +159,6 @@ if __name__ == "__main__":
|
|||
if 'recurrent_hr_generator' in opt.keys():
|
||||
recurrent_gen = model.env['generators']['generator']
|
||||
model.env['generators']['generator'] = model.env['generators'][opt['recurrent_hr_generator']]
|
||||
else:
|
||||
model.env['generators']['generator'] = recurrent_gen
|
||||
|
||||
first_frame = False
|
||||
if recurrent_mode:
|
||||
|
|
|
|||
|
|
@ -278,7 +278,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_mi1_rrdb4x_10bl_bypass.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_mi1_srg2classic_4x.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
args = parser.parse_args()
|
||||
opt = option.parse(args.opt, is_train=True)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user