211 lines
7.1 KiB
Python
211 lines
7.1 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 torch.nn.functional as F
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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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import utils
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from data.image_folder_dataset import ImageFolderDataset
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from models.resnet_with_checkpointing import resnet50
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from models.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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from utils import util
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from utils.kmeans import kmeans, kmeans_predict
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from utils.options import dict_to_nonedict
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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 key_value_difference(x, y):
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x = F.normalize(x, dim=-1, p=2)
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y = F.normalize(y, dim=-1, p=2)
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return 2 - 2 * (x * y).sum(dim=-1)
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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, target_size=224, shuffle=True):
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dataset_opt = dict_to_nonedict({
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'name': 'amalgam',
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#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\pn_coven\\cropped2'],
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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\\imageset_256_tiled_filtered_flattened'],
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#'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'],
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'paths': ['E:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'],
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'weights': [1],
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'target_size': target_size,
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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=shuffle)
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def _find_layer(net, layer_name):
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if type(layer_name) == str:
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modules = dict([*net.named_modules()])
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return modules.get(layer_name, None)
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elif type(layer_name) == int:
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children = [*net.children()]
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return children[layer_name]
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return None
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layer_hooked_value = None
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def _hook(_, __, output):
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global layer_hooked_value
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layer_hooked_value = output
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def register_hook(net, layer_name):
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layer = _find_layer(net, layer_name)
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assert layer is not None, f'hidden layer ({self.layer}) not found'
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layer.register_forward_hook(_hook)
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def get_latent_for_img(model, img):
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img_t = ToTensor()(Image.open(img)).to('cuda').unsqueeze(0)
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_, _, h, w = img_t.shape
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# Center crop img_t and resize to 224.
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d = min(h, w)
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dh, dw = (h-d)//2, (w-d)//2
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if dw != 0:
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img_t = img_t[:, :, :, dw:-dw]
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elif dh != 0:
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img_t = img_t[:, :, dh:-dh, :]
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img_t = img_t[:,:3,:,:]
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img_t = torch.nn.functional.interpolate(img_t, size=(224, 224), mode="area")
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model(img_t)
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latent = layer_hooked_value
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return latent
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def produce_latent_dict(model):
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batch_size = 32
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num_workers = 4
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dataloader = get_image_folder_dataloader(batch_size, num_workers)
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id = 0
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paths = []
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latents = []
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for batch in tqdm(dataloader):
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hq = batch['hq'].to('cuda')
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hq = F.interpolate(F.interpolate(hq, size=(16,16), mode='bilinear'), size=(224,244))
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model(hq)
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l = layer_hooked_value.cpu().split(1, dim=0)
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latents.extend(l)
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paths.extend(batch['HQ_path'])
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id += batch_size
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if id > 10000:
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print("Saving checkpoint..")
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torch.save((latents, paths), '../results_instance_resnet.pth')
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id = 0
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def find_similar_latents(model, compare_fn=structural_euc_dist):
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global layer_hooked_value
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img = 'D:\\dlas\\results\\bobz.png'
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#img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg'
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output_path = '../../../results/byol_resnet_similars'
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os.makedirs(output_path, exist_ok=True)
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imglatent = get_latent_for_img(model, img).squeeze().unsqueeze(0)
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_, c = imglatent.shape
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batch_size = 512
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num_workers = 8
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dataloader = get_image_folder_dataloader(batch_size, num_workers)
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id = 0
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output_batch = 1
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results = []
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result_paths = []
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for batch in tqdm(dataloader):
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hq = batch['hq'].to('cuda')
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model(hq)
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latent = layer_hooked_value.clone().squeeze()
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compared = compare_fn(imglatent.repeat(latent.shape[0], 1), latent)
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results.append(compared.cpu())
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result_paths.extend(batch['HQ_path'])
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id += batch_size
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if id > 10000:
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k = 200
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results = torch.cat(results, dim=0)
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vals, inds = torch.topk(results, k, largest=False)
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for i in inds:
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mag = int(results[i].item() * 1000)
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shutil.copy(result_paths[i], os.path.join(output_path, f'{mag:05}_{output_batch}_{i}.jpg'))
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results = []
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result_paths = []
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id = 0
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def build_kmeans():
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latents, _ = torch.load('../results_instance_resnet.pth')
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latents = torch.cat(latents, dim=0).squeeze().to('cuda')
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cluster_ids_x, cluster_centers = kmeans(latents, num_clusters=8, distance="euclidean", device=torch.device('cuda:0'))
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torch.save((cluster_ids_x, cluster_centers), '../k_means_instance_resnet.pth')
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def use_kmeans():
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output = "../results/k_means_instance_resnet/"
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_, centers = torch.load('../k_means_instance_resnet.pth')
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batch_size = 32
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num_workers = 1
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dataloader = get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=False)
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for i, batch in enumerate(tqdm(dataloader)):
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hq = batch['hq'].to('cuda')
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model(hq)
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l = layer_hooked_value.clone().squeeze()
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pred = kmeans_predict(l, centers, device=l.device)
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for b in range(pred.shape[0]):
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if pred[b] == 3:
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outpath = os.path.dirname(batch['HQ_path'][b]).replace('\\pn_coven\\cropped', '\\pn_coven\\modeling')
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os.makedirs(outpath, exist_ok=True)
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shutil.move(batch['HQ_path'][b], outpath)
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if __name__ == '__main__':
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pretrained_path = '../../../experiments/resnet_byol_diffframe_115k.pth'
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model = resnet50(pretrained=False).to('cuda')
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sd = torch.load(pretrained_path)
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resnet_sd = {}
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for k, v in sd.items():
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if 'target_encoder.net.' in k:
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resnet_sd[k.replace('target_encoder.net.', '')] = v
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model.load_state_dict(sd, strict=True)
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model.eval()
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register_hook(model, 'avgpool')
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with torch.no_grad():
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#find_similar_latents(model, structural_euc_dist)
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produce_latent_dict(model)
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#build_kmeans()
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#use_kmeans()
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