206 lines
8.0 KiB
Python
206 lines
8.0 KiB
Python
import os
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import shutil
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import torch
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import torch.nn as nn
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import torchvision
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from PIL import Image
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from torch.utils.data import DataLoader
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from torchvision.transforms import ToTensor, Resize
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from tqdm import tqdm
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import numpy as np
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from data.image_folder_dataset import ImageFolderDataset
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from models.archs.spinenet_arch import SpineNet
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# Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved
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# and the distance is computed across the channel dimension.
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def structural_euc_dist(x, y):
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diff = torch.square(x - y)
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sum = torch.sum(diff, dim=-1)
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return torch.sqrt(sum)
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def cosine_similarity(x, y):
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x = norm(x)
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y = norm(y)
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return -nn.CosineSimilarity()(x, y) # probably better to just use this class to perform the calc. Just left this here to remind myself.
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def norm(x):
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sh = x.shape
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sh_r = tuple([sh[i] if i != len(sh)-1 else 1 for i in range(len(sh))])
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return (x - torch.mean(x, dim=-1).reshape(sh_r)) / torch.std(x, dim=-1).reshape(sh_r)
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def im_norm(x):
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return (((x - torch.mean(x, dim=(2,3)).reshape(-1,1,1,1)) / torch.std(x, dim=(2,3)).reshape(-1,1,1,1)) * .5) + .5
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def get_image_folder_dataloader(batch_size, num_workers):
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dataset_opt = {
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'name': 'amalgam',
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#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'],
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'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
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'weights': [1],
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'target_size': 512,
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'force_multiple': 32,
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'scale': 1
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}
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dataset = ImageFolderDataset(dataset_opt)
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return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True)
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def create_latent_database(model):
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batch_size = 8
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num_workers = 1
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output_path = '../../results/byol_spinenet_latents/'
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os.makedirs(output_path, exist_ok=True)
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dataloader = get_image_folder_dataloader(batch_size, num_workers)
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id = 0
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dict_count = 1
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latent_dict = {}
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all_paths = []
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for batch in tqdm(dataloader):
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hq = batch['hq'].to('cuda:1')
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latent = model(hq)[1] # BYOL trainer only trains the '4' output, which is indexed at [1]. Confusing.
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for b in range(latent.shape[0]):
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im_path = batch['HQ_path'][b]
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all_paths.append(im_path)
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latent_dict[id] = latent[b].detach().cpu()
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if (id+1) % 1000 == 0:
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print("Saving checkpoint..")
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torch.save(latent_dict, os.path.join(output_path, "latent_dict_%i.pth" % (dict_count,)))
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latent_dict = {}
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torch.save(all_paths, os.path.join(output_path, "all_paths.pth"))
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dict_count += 1
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id += 1
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def _get_mins_from_latent_dictionary(latent, hq_img_repo, ld_file_name, batch_size):
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_, c, h, w = latent.shape
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lat_dict = torch.load(os.path.join(hq_img_repo, ld_file_name))
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comparables = torch.stack(list(lat_dict.values()), dim=0).permute(0,2,3,1)
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cbl_shape = comparables.shape[:3]
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assert cbl_shape[1] == 32
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comparables = comparables.reshape(-1, c)
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clat = latent.reshape(1,-1,h*w).permute(2,0,1)
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cpbl_chunked = torch.chunk(comparables, len(comparables) // batch_size)
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assert len(comparables) % batch_size == 0 # The reconstruction logic doesn't work if this is not the case.
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mins = []
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min_offsets = []
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for cpbl_chunk in tqdm(cpbl_chunked):
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cpbl_chunk = cpbl_chunk.to('cuda:1')
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dist = structural_euc_dist(clat, cpbl_chunk.unsqueeze(0))
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_min = torch.min(dist, dim=-1)
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mins.append(_min[0])
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min_offsets.append(_min[1])
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mins = torch.min(torch.stack(mins, dim=-1), dim=-1)
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# There's some way to do this in torch, I just can't figure it out..
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for i in range(len(mins[1])):
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mins[1][i] = mins[1][i] * batch_size + min_offsets[mins[1][i]][i]
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return mins[0].cpu(), mins[1].cpu(), len(comparables)
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def find_similar_latents(model):
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img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\adrianna_xx.jpg'
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#img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg'
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hq_img_repo = '../../results/byol_spinenet_latents'
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output_path = '../../results/byol_spinenet_similars'
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batch_size = 1024
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num_maps = 8
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os.makedirs(output_path, exist_ok=True)
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img_bank_paths = torch.load(os.path.join(hq_img_repo, "all_paths.pth"))
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img_t = ToTensor()(Image.open(img)).to('cuda:1').unsqueeze(0)
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_, _, h, w = img_t.shape
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img_t = img_t[:, :, :128*(h//128), :128*(w//128)]
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latent = model(img_t)[1]
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_, c, h, w = latent.shape
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mins, min_offsets = [], []
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total_latents = -1
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for d_id in range(1,num_maps+1):
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mn, of, tl = _get_mins_from_latent_dictionary(latent, hq_img_repo, "latent_dict_%i.pth" % (d_id), batch_size)
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if total_latents != -1:
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assert total_latents == tl
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else:
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total_latents = tl
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mins.append(mn)
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min_offsets.append(of)
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mins = torch.min(torch.stack(mins, dim=-1), dim=-1)
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# There's some way to do this in torch, I just can't figure it out..
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for i in range(len(mins[1])):
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mins[1][i] = mins[1][i] * total_latents + min_offsets[mins[1][i]][i]
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min_ids = mins[1]
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print("Constructing image map..")
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doc_out = '''
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<html><body><img id="imgmap" src="source.png" usemap="#map">
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<map name="map">%s</map><br>
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<button onclick="if(imgmap.src.includes('output.png')){imgmap.src='source.png';}else{imgmap.src='output.png';}">Swap Images</button>
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</body></html>
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'''
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img_map_areas = []
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img_out = torch.zeros((1,3,h*16,w*16))
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for i, ind in enumerate(tqdm(min_ids)):
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u = np.unravel_index(ind.item(), (num_maps*total_latents//(32*32),32,32))
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h_, w_ = np.unravel_index(i, (h, w))
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img = ToTensor()(Resize((512, 512))(Image.open(img_bank_paths[u[0]])))
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t = 16 * u[1]
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l = 16 * u[2]
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patch = img[:, t:t+16, l:l+16]
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img_out[:,:,h_*16:h_*16+16,w_*16:w_*16+16] = patch
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# Also save the image with a masked map
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mask = torch.full_like(img, fill_value=.3)
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mask[:, t:t+16, l:l+16] = 1
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masked_img = img * mask
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masked_src_img_output_file = os.path.join(output_path, "%i_%i__%i.png" % (t, l, u[0]))
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torchvision.utils.save_image(masked_img, masked_src_img_output_file)
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# Update the image map areas.
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img_map_areas.append('<area shape="rect" coords="%i,%i,%i,%i" href="%s">' % (w_*16,h_*16,w_*16+16,h_*16+16,masked_src_img_output_file))
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torchvision.utils.save_image(img_out, os.path.join(output_path, "output.png"))
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torchvision.utils.save_image(img_t, os.path.join(output_path, "source.png"))
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doc_out = doc_out % ('\n'.join(img_map_areas))
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with open(os.path.join(output_path, 'map.html'), 'w') as f:
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print(doc_out, file=f)
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def explore_latent_results(model):
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batch_size = 16
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num_workers = 1
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output_path = '../../results/byol_spinenet_explore_latents/'
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os.makedirs(output_path, exist_ok=True)
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dataloader = get_image_folder_dataloader(batch_size, num_workers)
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id = 0
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for batch in tqdm(dataloader):
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hq = batch['hq'].to('cuda:1')
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latent = model(hq)[1] # BYOL trainer only trains the '4' output, which is indexed at [1]. Confusing.
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# This operation works by computing the distance of every structural index from the center and using that
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# as a "heatmap".
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b, c, h, w = latent.shape
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center = latent[:, :, h//2, w//2].unsqueeze(-1).unsqueeze(-1)
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centers = center.repeat(1, 1, h, w)
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dist = cosine_similarity(latent, centers).unsqueeze(1)
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dist = im_norm(dist)
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torchvision.utils.save_image(dist, os.path.join(output_path, "%i.png" % id))
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id += 1
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if __name__ == '__main__':
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pretrained_path = '../../experiments/spinenet49_imgset_byol.pth'
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model = SpineNet('49', in_channels=3, use_input_norm=True).to('cuda:1')
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model.load_state_dict(torch.load(pretrained_path), strict=True)
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model.eval()
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with torch.no_grad():
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find_similar_latents(model) |