249 lines
11 KiB
Python
249 lines
11 KiB
Python
import random
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from time import time
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import torch
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import torchvision
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from torch.utils.data import Dataset
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from kornia import augmentation as augs
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from kornia import filters
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import torch.nn as nn
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import torch.nn.functional as F
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# Wrapper for a DLAS Dataset class that applies random augmentations from the BYOL paper to BOTH the 'lq' and 'hq'
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# inputs. These are then outputted as 'aug1' and 'aug2'.
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from tqdm import tqdm
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from data import create_dataset
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from models.archs.arch_util import PixelUnshuffle
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from utils.util import opt_get
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class RandomApply(nn.Module):
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def __init__(self, fn, p):
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super().__init__()
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self.fn = fn
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self.p = p
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def forward(self, x):
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if random.random() > self.p:
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return x
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return self.fn(x)
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class ByolDatasetWrapper(Dataset):
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def __init__(self, opt):
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super().__init__()
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self.wrapped_dataset = create_dataset(opt['dataset'])
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self.cropped_img_size = opt['crop_size']
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augmentations = [ \
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RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
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augs.RandomGrayscale(p=0.2),
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augs.RandomHorizontalFlip(),
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RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
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augs.RandomResizedCrop((self.cropped_img_size, self.cropped_img_size))]
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if opt['normalize']:
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# The paper calls for normalization. Recommend setting true if you want exactly like the paper.
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augmentations.append(augs.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225])))
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self.aug = nn.Sequential(*augmentations)
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def __getitem__(self, item):
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item = self.wrapped_dataset[item]
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item.update({'aug1': self.aug(item['hq']).squeeze(dim=0), 'aug2': self.aug(item['lq']).squeeze(dim=0)})
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return item
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def __len__(self):
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return len(self.wrapped_dataset)
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def no_batch_interpolate(i, size, mode):
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i = i.unsqueeze(0)
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i = F.interpolate(i, size=size, mode=mode)
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return i.squeeze(0)
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# Performs a 1-d translation of "other":
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# If other<ref, returns 0.
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# Else: return other-ref
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def snap(ref, other):
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if other < ref:
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return 0
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return other - ref
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# Pads a tensor with zeros so that it fits in a dxd square.
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def pad_to(im, d):
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if len(im.shape) == 3:
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pd = torch.zeros((im.shape[0],d,d))
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pd[:, :im.shape[1], :im.shape[2]] = im
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else:
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pd = torch.zeros((im.shape[0],im.shape[1],d,d), device=im.device)
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pd[:, :, :im.shape[2], :im.shape[3]] = im
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return pd
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# Variation of RandomResizedCrop, which picks a region of the image that the two augments must share. The augments
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# then propagate off random corners of the shared region, using the same scale.
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#
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# Operates in units of "multiple". The intent is that this multiple is equivalent to the compression multiple of the
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# latent space being used so that each structural unit corresponds to a latent unit.
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class RandomSharedRegionCrop(nn.Module):
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def __init__(self, multiple, jitter_range=0):
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super().__init__()
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self.multiple = multiple
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self.jitter_range = jitter_range # When specified, images are shifted an additional random([-j,j]) pixels where j=jitter_range
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def forward(self, i1, i2):
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assert i1.shape[-1] == i2.shape[-1]
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# Outline of the general algorithm:
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# 1. Assume the input is a square. Divide it by self.multiple to get working units.
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# 2. Pick a random width, height and top corner location for the first patch.
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# 3. Pick a random width, height and top corner location for the second patch.
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# Note: All dims from (2) and (3) must contain at least half of the image, guaranteeing overlap.
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# 6. Build patches from input images. Resize them appropriately. Apply translational jitter.
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# 7. Compute the metrics needed to extract overlapping regions from the resized patches: top, left,
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# original_height, original_width.
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# 8. Compute the "shared_view" from the above data.
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# Step 1
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c, d, _ = i1.shape
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assert d % self.multiple == 0 and d > (self.multiple*3)
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d = d // self.multiple
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# Step 2
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base_w = random.randint(d//2, d-1)
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base_l = random.randint(0, d-base_w)
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base_h = random.randint(base_w-1, base_w+1)
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base_t = random.randint(0, d-base_h)
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base_r, base_b = base_l+base_w, base_t+base_h
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# Step 3
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im2_w = random.randint(d//2, d-1)
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im2_l = random.randint(0, d-im2_w)
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im2_h = random.randint(im2_w-1, im2_w+1)
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im2_t = random.randint(0, d-im2_h)
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im2_r, im2_b = im2_l+im2_w, im2_t+im2_h
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# Step 6
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m = self.multiple
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jl, jt = random.randint(-self.jitter_range, self.jitter_range), random.randint(-self.jitter_range, self.jitter_range)
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jt = jt if base_t != 0 else abs(jt) # If the top of a patch is zero, a negative jitter will cause it to go negative.
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jt = jt if (base_t+base_h)*m != i1.shape[1] else 0 # Likewise, jitter shouldn't allow the patch to go over-bounds.
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jl = jl if base_l != 0 else abs(jl)
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jl = jl if (base_l+base_w)*m != i1.shape[1] else 0
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p1 = i1[:, base_t*m+jt:(base_t+base_h)*m+jt, base_l*m+jl:(base_l+base_w)*m+jl]
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p1_resized = no_batch_interpolate(p1, size=(d*m, d*m), mode="bilinear")
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jl, jt = random.randint(-self.jitter_range, self.jitter_range), random.randint(-self.jitter_range, self.jitter_range)
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jt = jt if im2_t != 0 else abs(jt)
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jt = jt if (im2_t+im2_h)*m != i2.shape[1] else 0
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jl = jl if im2_l != 0 else abs(jl)
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jl = jl if (im2_l+im2_w)*m != i2.shape[1] else 0
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p2 = i2[:, im2_t*m+jt:(im2_t+im2_h)*m+jt, im2_l*m+jl:(im2_l+im2_w)*m+jl]
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p2_resized = no_batch_interpolate(p2, size=(d*m, d*m), mode="bilinear")
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# Step 7
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i1_shared_t, i1_shared_l = snap(base_t, im2_t), snap(base_l, im2_l)
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i2_shared_t, i2_shared_l = snap(im2_t, base_t), snap(im2_l, base_l)
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ix_h = min(base_b, im2_b) - max(base_t, im2_t)
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ix_w = min(base_r, im2_r) - max(base_l, im2_l)
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recompute_package = torch.tensor([base_h, base_w, i1_shared_t, i1_shared_l, im2_h, im2_w, i2_shared_t, i2_shared_l, ix_h, ix_w], dtype=torch.long)
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# Step 8
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mask1 = torch.full((1, base_h*m, base_w*m), fill_value=.5)
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mask1[:, i1_shared_t*m:(i1_shared_t+ix_h)*m, i1_shared_l*m:(i1_shared_l+ix_w)*m] = 1
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masked1 = pad_to(p1 * mask1, d*m)
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mask2 = torch.full((1, im2_h*m, im2_w*m), fill_value=.5)
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mask2[:, i2_shared_t*m:(i2_shared_t+ix_h)*m, i2_shared_l*m:(i2_shared_l+ix_w)*m] = 1
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masked2 = pad_to(p2 * mask2, d*m)
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mask = torch.full((1, d*m, d*m), fill_value=.33)
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mask[:, base_t*m:(base_t+base_w)*m, base_l*m:(base_l+base_h)*m] += .33
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mask[:, im2_t*m:(im2_t+im2_w)*m, im2_l*m:(im2_l+im2_h)*m] += .33
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masked_dbg = i1 * mask
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return p1_resized, p2_resized, recompute_package, masked1, masked2, masked_dbg
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# Uses the recompute package returned from the above dataset to extract matched-size "similar regions" from two feature
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# maps.
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def reconstructed_shared_regions(fea1, fea2, recompute_package: torch.Tensor):
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package = recompute_package.cpu()
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res1 = []
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res2 = []
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pad_dim = torch.max(package[:, -2:]).item()
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# It'd be real nice if we could do this at the batch level, but I don't see a really good way to do that outside
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# of conforming the recompute_package across the entire batch.
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for b in range(package.shape[0]):
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f1_h, f1_w, f1s_t, f1s_l, f2_h, f2_w, f2s_t, f2s_l, s_h, s_w = tuple(package[b].tolist())
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# Resize the input features to match
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f1s = F.interpolate(fea1[b].unsqueeze(0), (f1_h, f1_w), mode="bilinear")
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f2s = F.interpolate(fea2[b].unsqueeze(0), (f2_h, f2_w), mode="bilinear")
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# Outputs must be padded so they can "get along" with each other.
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res1.append(pad_to(f1s[:, :, f1s_t:f1s_t+s_h, f1s_l:f1s_l+s_w], pad_dim))
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res2.append(pad_to(f2s[:, :, f2s_t:f2s_t+s_h, f2s_l:f2s_l+s_w], pad_dim))
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return torch.cat(res1, dim=0), torch.cat(res2, dim=0)
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# Follows the general template of BYOL dataset, with the following changes:
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# 1. Flip() is not applied.
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# 2. Instead of RandomResizedCrop, a custom Transform, RandomSharedRegionCrop is used.
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# 3. The dataset injects two integer tensors alongside the augmentations, which are used to index image regions shared
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# by the joint augmentations.
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# 4. The dataset injects an aug_shared_view for debugging purposes.
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class StructuredCropDatasetWrapper(Dataset):
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def __init__(self, opt):
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super().__init__()
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self.wrapped_dataset = create_dataset(opt['dataset'])
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augmentations = [RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
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augs.RandomGrayscale(p=0.2),
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RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1)]
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self.aug = nn.Sequential(*augmentations)
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self.rrc = RandomSharedRegionCrop(opt['latent_multiple'], opt_get(opt, ['jitter_range'], 0))
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def __getitem__(self, item):
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item = self.wrapped_dataset[item]
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a1 = self.aug(item['hq']).squeeze(dim=0)
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a2 = self.aug(item['lq']).squeeze(dim=0)
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a1, a2, sr_dim, m1, m2, db = self.rrc(a1, a2)
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item.update({'aug1': a1, 'aug2': a2, 'similar_region_dimensions': sr_dim,
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'masked1': m1, 'masked2': m2, 'aug_shared_view': db})
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return item
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def __len__(self):
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return len(self.wrapped_dataset)
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# For testing this dataset.
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if __name__ == '__main__':
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opt = {
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'dataset':
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{
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'mode': 'imagefolder',
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised'],
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'weights': [1],
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'target_size': 256,
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'force_multiple': 32,
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'scale': 1,
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'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
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'random_corruptions': ['noise-5', 'none'],
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'num_corrupts_per_image': 1,
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'corrupt_before_downsize': True,
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},
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'latent_multiple': 8,
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'jitter_range': 0,
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}
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ds = StructuredCropDatasetWrapper(opt)
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import os
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os.makedirs("debug", exist_ok=True)
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for i in tqdm(range(0, len(ds))):
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o = ds[random.randint(0, len(ds)-1)]
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#for k, v in o.items():
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# 'lq', 'hq', 'aug1', 'aug2',
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#if k in [ 'aug_shared_view', 'masked1', 'masked2']:
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#torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))
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rcpkg = o['similar_region_dimensions']
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pixun = PixelUnshuffle(8)
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pixsh = nn.PixelShuffle(8)
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rc1, rc2 = reconstructed_shared_regions(pixun(o['aug1'].unsqueeze(0)), pixun(o['aug2'].unsqueeze(0)), rcpkg)
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#torchvision.utils.save_image(pixsh(rc1), "debug/%i_rc1.png" % (i,))
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#torchvision.utils.save_image(pixsh(rc2), "debug/%i_rc2.png" % (i,))
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