import os import shutil import torch import torch.nn as nn import torch.nn.functional as F import torchvision from PIL import Image from torch.utils.data import DataLoader from torchvision.transforms import ToTensor, Resize from tqdm import tqdm import numpy as np import utils from data.image_folder_dataset import ImageFolderDataset from models.resnet_with_checkpointing import resnet50 from models.spinenet_arch import SpineNet # Computes the structural euclidean distance between [x,y]. "Structural" here means the [h,w] dimensions are preserved # and the distance is computed across the channel dimension. from utils import util from utils.kmeans import kmeans, kmeans_predict from utils.options import dict_to_nonedict def structural_euc_dist(x, y): diff = torch.square(x - y) sum = torch.sum(diff, dim=-1) return torch.sqrt(sum) def cosine_similarity(x, y): x = norm(x) y = norm(y) return -nn.CosineSimilarity()(x, y) # probably better to just use this class to perform the calc. Just left this here to remind myself. def key_value_difference(x, y): x = F.normalize(x, dim=-1, p=2) y = F.normalize(y, dim=-1, p=2) return 2 - 2 * (x * y).sum(dim=-1) def norm(x): sh = x.shape sh_r = tuple([sh[i] if i != len(sh)-1 else 1 for i in range(len(sh))]) return (x - torch.mean(x, dim=-1).reshape(sh_r)) / torch.std(x, dim=-1).reshape(sh_r) def im_norm(x): 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 def get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=True): dataset_opt = dict_to_nonedict({ 'name': 'amalgam', 'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\pn_coven\\cropped2'], #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new'], #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'], #'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test'], 'weights': [1], 'target_size': target_size, 'force_multiple': 32, 'scale': 1 }) dataset = ImageFolderDataset(dataset_opt) return DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle) def _find_layer(net, layer_name): if type(layer_name) == str: modules = dict([*net.named_modules()]) return modules.get(layer_name, None) elif type(layer_name) == int: children = [*net.children()] return children[layer_name] return None layer_hooked_value = None def _hook(_, __, output): global layer_hooked_value layer_hooked_value = output def register_hook(net, layer_name): layer = _find_layer(net, layer_name) assert layer is not None, f'hidden layer ({self.layer}) not found' layer.register_forward_hook(_hook) def get_latent_for_img(model, img): img_t = ToTensor()(Image.open(img)).to('cuda').unsqueeze(0) _, _, h, w = img_t.shape # Center crop img_t and resize to 224. d = min(h, w) dh, dw = (h-d)//2, (w-d)//2 if dw != 0: img_t = img_t[:, :, :, dw:-dw] elif dh != 0: img_t = img_t[:, :, dh:-dh, :] img_t = torch.nn.functional.interpolate(img_t, size=(224, 224), mode="area") model(img_t) latent = layer_hooked_value return latent def produce_latent_dict(model): batch_size = 32 num_workers = 4 dataloader = get_image_folder_dataloader(batch_size, num_workers) id = 0 paths = [] latents = [] for batch in tqdm(dataloader): hq = batch['hq'].to('cuda') model(hq) l = layer_hooked_value.cpu().split(1, dim=0) latents.extend(l) paths.extend(batch['HQ_path']) id += batch_size if id > 10000: print("Saving checkpoint..") torch.save((latents, paths), '../results_instance_resnet.pth') id = 0 def find_similar_latents(model, compare_fn=structural_euc_dist): global layer_hooked_value img = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\1024_test\\80692045.jpg.jpg' #img = 'F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\nicky_xx.jpg' output_path = '../../../results/byol_resnet_similars' os.makedirs(output_path, exist_ok=True) imglatent = get_latent_for_img(model, img).squeeze().unsqueeze(0) _, c = imglatent.shape batch_size = 128 num_workers = 8 dataloader = get_image_folder_dataloader(batch_size, num_workers) id = 0 output_batch = 1 results = [] result_paths = [] for batch in tqdm(dataloader): hq = batch['hq'].to('cuda') model(hq) latent = layer_hooked_value.clone().squeeze() compared = compare_fn(imglatent.repeat(latent.shape[0], 1), latent) results.append(compared.cpu()) result_paths.extend(batch['HQ_path']) id += batch_size if id > 10000: k = 500 results = torch.cat(results, dim=0) vals, inds = torch.topk(results, k, largest=False) for i in inds: mag = int(results[i].item() * 1000) shutil.copy(result_paths[i], os.path.join(output_path, f'{mag:05}_{output_batch}_{i}.jpg')) results = [] result_paths = [] id = 0 def build_kmeans(): latents, _ = torch.load('../results_instance_resnet.pth') latents = torch.cat(latents, dim=0).squeeze().to('cuda') cluster_ids_x, cluster_centers = kmeans(latents, num_clusters=8, distance="euclidean", device=torch.device('cuda:0')) torch.save((cluster_ids_x, cluster_centers), '../k_means_instance_resnet.pth') def use_kmeans(): output = "../results/k_means_instance_resnet/" _, centers = torch.load('../k_means_instance_resnet.pth') batch_size = 32 num_workers = 1 dataloader = get_image_folder_dataloader(batch_size, num_workers, target_size=224, shuffle=False) for i, batch in enumerate(tqdm(dataloader)): hq = batch['hq'].to('cuda') model(hq) l = layer_hooked_value.clone().squeeze() pred = kmeans_predict(l, centers, device=l.device) for b in range(pred.shape[0]): if pred[b] == 3: outpath = os.path.dirname(batch['HQ_path'][b]).replace('\\pn_coven\\cropped', '\\pn_coven\\modeling') os.makedirs(outpath, exist_ok=True) shutil.move(batch['HQ_path'][b], outpath) if __name__ == '__main__': pretrained_path = '../../../experiments/resnet_byol_diffframe_115k.pth' model = resnet50(pretrained=False).to('cuda') sd = torch.load(pretrained_path) resnet_sd = {} for k, v in sd.items(): if 'target_encoder.net.' in k: resnet_sd[k.replace('target_encoder.net.', '')] = v model.load_state_dict(sd, strict=True) model.eval() register_hook(model, 'avgpool') with torch.no_grad(): #find_similar_latents(model, structural_euc_dist) #produce_latent_dict(model) #build_kmeans() use_kmeans()