Spaces:
Sleeping
Sleeping
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from PIL import Image | |
| import random | |
| import cv2 | |
| import io | |
| from ssl_models.simclr2 import get_simclr2_model | |
| from ssl_models.barlow_twins import get_barlow_twins_model | |
| from ssl_models.simsiam import get_simsiam | |
| from ssl_models.dino import get_dino_model_without_loss, get_dino_model_with_loss | |
| def get_ssl_model(network, variant): | |
| if network == 'simclrv2': | |
| if variant == '1x': | |
| ssl_model = get_simclr2_model('r50_1x_sk0_ema.pth').eval() | |
| else: | |
| ssl_model = get_simclr2_model('r50_2x_sk0_ema.pth').eval() | |
| elif network == 'barlow_twins': | |
| ssl_model = get_barlow_twins_model().eval() | |
| elif network == 'simsiam': | |
| ssl_model = get_simsiam().eval() | |
| elif network == 'dino': | |
| ssl_model = get_dino_model_without_loss().eval() | |
| elif network == 'dino+loss': | |
| ssl_model, dino_score = get_dino_model_with_loss() | |
| ssl_model = ssl_model.eval() | |
| return ssl_model | |
| def overlay_heatmap(img, heatmap, denormalize = False): | |
| loaded_img = img.squeeze(0).cpu().numpy().transpose((1, 2, 0)) | |
| if denormalize: | |
| mean = np.array([0.485, 0.456, 0.406]) | |
| std = np.array([0.229, 0.224, 0.225]) | |
| loaded_img = std * loaded_img + mean | |
| loaded_img = (loaded_img.clip(0, 1) * 255).astype(np.uint8) | |
| cam = heatmap / heatmap.max() | |
| cam = cv2.resize(cam, (224, 224)) | |
| cam = np.uint8(255 * cam) | |
| cam = cv2.applyColorMap(cam, cv2.COLORMAP_JET) # jet: blue --> red | |
| cam = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB) | |
| added_image = cv2.addWeighted(cam, 0.5, loaded_img, 0.5, 0) | |
| return added_image | |
| def viz_map(img_path, heatmap): | |
| "For pixel invariance" | |
| img = np.array(Image.open(img_path).resize((224,224))) if isinstance(img_path, str) else np.array(img_path.resize((224,224))) | |
| width, height, _ = img.shape | |
| cam = heatmap.detach().cpu().numpy() | |
| cam = cam / cam.max() | |
| cam = cv2.resize(cam, (height, width)) | |
| heatmap = np.uint8(255 * cam) | |
| heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) | |
| heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) | |
| added_image = cv2.addWeighted(heatmap, 0.5, img, 0.7, 0) | |
| return added_image | |
| def show_image(x, squeeze = True, denormalize = False): | |
| if squeeze: | |
| x = x.squeeze(0) | |
| x = x.cpu().numpy().transpose((1, 2, 0)) | |
| if denormalize: | |
| mean = np.array([0.485, 0.456, 0.406]) | |
| std = np.array([0.229, 0.224, 0.225]) | |
| x = std * x + mean | |
| return x.clip(0, 1) | |
| def deprocess(inp, to_numpy = True, to_PIL = False, denormalize = False): | |
| if to_numpy: | |
| inp = inp.detach().cpu().numpy() | |
| inp = inp.squeeze(0).transpose((1, 2, 0)) | |
| if denormalize: | |
| mean = np.array([0.485, 0.456, 0.406]) | |
| std = np.array([0.229, 0.224, 0.225]) | |
| inp = std * inp + mean | |
| inp = (inp.clip(0, 1) * 255).astype(np.uint8) | |
| if to_PIL: | |
| return Image.fromarray(inp) | |
| return inp | |
| def fig2img(fig): | |
| """Convert a Matplotlib figure to a PIL Image and return it""" | |
| buf = io.BytesIO() | |
| fig.savefig(buf, bbox_inches='tight', pad_inches=0) | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |