import torch import numpy as np import torch.nn as nn import torchvision.transforms as transforms import matplotlib import matplotlib.pyplot as plt from PIL import Image import cv2 import gradio as gr device = torch.device("cuda" if torch.cuda.is_available() else "cpu") from data_transforms import normal_transforms, no_shift_transforms, ig_transforms, modify_transforms from utils import overlay_heatmap, viz_map, show_image, deprocess, get_ssl_model, fig2img from methods import occlusion, occlusion_context_agnositc, pairwise_occlusion from methods import get_difference from methods import create_mixed_images, averaged_transforms, sailency, smooth_grad from methods import get_sample_dataset, pixel_invariance, get_gradcam, get_interactioncam matplotlib.use('Agg') def load_model(model_name): global network, ssl_model, denorm if model_name == "simclrv2 (1X)": variant = '1x' network = 'simclrv2' denorm = False elif model_name == "simclrv2 (2X)": variant = '2x' network = 'simclrv2' denorm = False elif model_name == "Barlow Twins": network = 'barlow_twins' variant = None denorm = True ssl_model = get_ssl_model(network, variant) if network != 'simclrv2': global normal_transforms, no_shift_transforms, ig_transforms normal_transforms, no_shift_transforms, ig_transforms = modify_transforms(normal_transforms, no_shift_transforms, ig_transforms) return "Loaded Model Successfully" def load_or_augment_images(img1_input, img2_input, use_aug): global img_main, img1, img2 img_main = img1_input.convert('RGB') if use_aug: img1 = normal_transforms['pure'](img_main).unsqueeze(0).to(device) img2 = normal_transforms['aug'](img_main).unsqueeze(0).to(device) else: img1 = normal_transforms['pure'](img_main).unsqueeze(0).to(device) img2 = img2_input.convert('RGB') img2 = normal_transforms['pure'](img2).unsqueeze(0).to(device) similarity = "Similarity: {:.3f}".format(nn.CosineSimilarity(dim=-1)(ssl_model(img1), ssl_model(img2)).item()) fig, axs = plt.subplots(1, 2, figsize=(10,10)) np.vectorize(lambda ax:ax.axis('off'))(axs) axs[0].imshow(show_image(img1, denormalize = denorm)) axs[1].imshow(show_image(img2, denormalize = denorm)) plt.subplots_adjust(wspace=0.1, hspace = 0) pil_output = fig2img(fig) return pil_output, similarity def run_occlusion(w_size, stride): heatmap1, heatmap2 = occlusion(img1, img2, ssl_model, w_size = 64, stride = 8, batch_size = 32) heatmap1_ca, heatmap2_ca = occlusion_context_agnositc(img1, img2, ssl_model, w_size = 64, stride = 8, batch_size = 32) heatmap1_po, heatmap2_po = pairwise_occlusion(img1, img2, ssl_model, batch_size = 32, erase_scale = (0.1, 0.3), erase_ratio = (1, 1.5), num_erases = 100) added_image1 = overlay_heatmap(img1, heatmap1, denormalize = denorm) added_image2 = overlay_heatmap(img2, heatmap2, denormalize = denorm) added_image1_ca = overlay_heatmap(img1, heatmap1_ca, denormalize = denorm) added_image2_ca = overlay_heatmap(img2, heatmap2_ca, denormalize = denorm) fig, axs = plt.subplots(2, 4, figsize=(20,10)) np.vectorize(lambda ax:ax.axis('off'))(axs) axs[0, 0].imshow(show_image(img1, denormalize = denorm)) axs[0, 1].imshow(added_image1) axs[0, 1].set_title("Conditional Occlusion") axs[0, 2].imshow(added_image1_ca) axs[0, 2].set_title("CA Cond. Occlusion") axs[0, 3].imshow((deprocess(img1, denormalize = denorm) * heatmap1_po[:,:,None]).astype('uint8')) axs[0, 3].set_title("Pairwise Occlusion") axs[1, 0].imshow(show_image(img2, denormalize = denorm)) axs[1, 1].imshow(added_image2) axs[1, 2].imshow(added_image2_ca) axs[1, 3].imshow((deprocess(img2, denormalize = denorm) * heatmap2_po[:,:,None]).astype('uint8')) plt.subplots_adjust(wspace=0, hspace = 0.01) pil_output = fig2img(fig) return pil_output def get_model_difference(later): imagenet_images, ssl_images = get_difference(ssl_model = ssl_model, baseline = 'imagenet', image = img2, lr = 1e4, l2_weight = 0.1, alpha_weight = 1e-7, alpha_power = 6, tv_weight = 1e-8, init_scale = 0.1, network = network) fig, axs = plt.subplots(3, 3, figsize=(10,10)) np.vectorize(lambda ax:ax.axis('off'))(axs) for aa, (in_img, ssl_img) in enumerate(zip(imagenet_images, ssl_images)): axs[aa,0].imshow(deprocess(img2, denormalize = denorm)) axs[aa,1].imshow(deprocess(in_img)) axs[aa,2].imshow(deprocess(ssl_img)) axs[0,0].set_title("Original Image") axs[0,1].set_title("Synthesized (cls)") axs[0,2].set_title("Synthesized (contastive)") plt.subplots_adjust(wspace=0.01, hspace = 0.01) pil_output = fig2img(fig) return pil_output def get_avg_trasforms(transform_type, add_noise, blur_output, guided): mixed_images = create_mixed_images(transform_type = transform_type, ig_transforms = ig_transforms, step = 0.1, img_path = img_main, add_noise = add_noise) # vanilla gradients (for comparison purposes) sailency1_van, sailency2_van = sailency(guided = guided, ssl_model = ssl_model, img1 = mixed_images[0], img2 = mixed_images[-1], blur_output = blur_output) # smooth gradients (for comparison purposes) sailency1_s, sailency2_s = smooth_grad(guided = guided, ssl_model = ssl_model, img1 = mixed_images[0], img2 = mixed_images[-1], blur_output = blur_output, steps = 50) # integrated transform sailency1, sailency2 = averaged_transforms(guided = guided, ssl_model = ssl_model, mixed_images = mixed_images, blur_output = blur_output) fig, axs = plt.subplots(2, 4, figsize=(20,10)) np.vectorize(lambda ax:ax.axis('off'))(axs) axs[0,0].imshow(show_image(mixed_images[0], denormalize = denorm)) axs[0,1].imshow(show_image(sailency1_van.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet) axs[0,1].imshow(show_image(mixed_images[0], denormalize = denorm), alpha=0.5) axs[0,1].set_title("Vanilla Gradients") axs[0,2].imshow(show_image(sailency1_s.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet) axs[0,2].imshow(show_image(mixed_images[0], denormalize = denorm), alpha=0.5) axs[0,2].set_title("Smooth Gradients") axs[0,3].imshow(show_image(sailency1.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet) axs[0,3].imshow(show_image(mixed_images[0], denormalize = denorm), alpha=0.5) axs[0,3].set_title("Integrated Transform") axs[1,0].imshow(show_image(mixed_images[-1], denormalize = denorm)) axs[1,1].imshow(show_image(sailency2_van.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet) axs[1,1].imshow(show_image(mixed_images[-1], denormalize = denorm), alpha=0.5) axs[1,2].imshow(show_image(sailency2_s.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet) axs[1,2].imshow(show_image(mixed_images[-1], denormalize = denorm), alpha=0.5) axs[1,3].imshow(show_image(sailency2.detach(), squeeze = False).squeeze(), cmap = plt.cm.jet) axs[1,3].imshow(show_image(mixed_images[-1], denormalize = denorm), alpha=0.5) plt.subplots_adjust(wspace=0.02, hspace = 0.02) pil_output = fig2img(fig) return pil_output def get_cams(): gradcam1, gradcam2 = get_gradcam(ssl_model, img1, img2) intcam1_mean, intcam2_mean = get_interactioncam(ssl_model, img1, img2, reduction = 'mean') intcam1_maxmax, intcam2_maxmax = get_interactioncam(ssl_model, img1, img2, reduction = 'max', grad_interact = True) intcam1_attnmax, intcam2_attnmax = get_interactioncam(ssl_model, img1, img2, reduction = 'attn', grad_interact = True) fig, axs = plt.subplots(2, 5, figsize=(20,8)) np.vectorize(lambda ax:ax.axis('off'))(axs) axs[0,0].imshow(show_image(img1[0], squeeze = False, denormalize = denorm)) axs[0,1].imshow(overlay_heatmap(img1, gradcam1, denormalize = denorm)) axs[0,1].set_title("Grad-CAM") axs[0,2].imshow(overlay_heatmap(img1, intcam1_mean, denormalize = denorm)) axs[0,2].set_title("IntCAM Mean") axs[0,3].imshow(overlay_heatmap(img1, intcam1_maxmax, denormalize = denorm)) axs[0,3].set_title("IntCAM Max + IntGradMax") axs[0,4].imshow(overlay_heatmap(img1, intcam1_attnmax, denormalize = denorm)) axs[0,4].set_title("IntCAM Attn + IntGradMax") axs[1,0].imshow(show_image(img2[0], squeeze = False, denormalize = denorm)) axs[1,1].imshow(overlay_heatmap(img2, gradcam2, denormalize = denorm)) axs[1,2].imshow(overlay_heatmap(img2, intcam2_mean, denormalize = denorm)) axs[1,3].imshow(overlay_heatmap(img2, intcam2_maxmax, denormalize = denorm)) axs[1,4].imshow(overlay_heatmap(img2, intcam2_attnmax, denormalize = denorm)) plt.subplots_adjust(wspace=0.01, hspace = 0.01) pil_output = fig2img(fig) return pil_output def get_pixel_invariance(): data_samples1, data_samples2, data_labels, labels_invariance = get_sample_dataset(img_path = img_main, num_augments = 1000, batch_size = 32, no_shift_transforms = no_shift_transforms, ssl_model = ssl_model, n_components = 10) inv_heatmap = pixel_invariance(data_samples1 = data_samples1, data_samples2 = data_samples2, data_labels = data_labels, labels_invariance = labels_invariance, resize_transform = transforms.Resize, size = 64, epochs = 1000, learning_rate = 0.1, l1_weight = 0.2, zero_small_values = True, blur_output = True, nmf_weight = 0) inv_heatmap_nmf = pixel_invariance(data_samples1 = data_samples1, data_samples2 = data_samples2, data_labels = data_labels, labels_invariance = labels_invariance, resize_transform = transforms.Resize, size = 64, epochs = 100, learning_rate = 0.1, l1_weight = 0.2, zero_small_values = True, blur_output = True, nmf_weight = 1) fig, axs = plt.subplots(1, 2, figsize=(10,5)) np.vectorize(lambda ax:ax.axis('off'))(axs) axs[0].imshow(viz_map(img_main, inv_heatmap)) axs[0].set_title("Heatmap w/o NMF") axs[1].imshow(viz_map(img_main, inv_heatmap_nmf)) axs[1].set_title("Heatmap w/ NMF") plt.subplots_adjust(wspace=0.01, hspace = 0.01) pil_output = fig2img(fig) return pil_output xai = gr.Blocks() with xai: gr.Markdown("

Methods for Explaining Contrastive Learning, CVPR 2023 Submission

") gr.Markdown("The interface is simplified as much as possible with only necessary options to select for each method. Please use our Google Colab demo for more flexibility.") with gr.Row(): model_name = gr.Dropdown(["simclrv2 (1X)", "simclrv2 (2X)", "Barlow Twins"], label="Choose Model and press \"Load Model\"") load_model_button = gr.Button("Load Model") status_or_similarity = gr.inputs.Textbox(label = "Status") with gr.Row(): gr.Markdown("You can either load two images or load a single image and augment it to get the second image (in that case please check the \"Use Augmentations\" button). After that, please press on \"Show Images\"") img1 = gr.Image(type='pil', label = "First Image") img2 = gr.Image(type='pil', label = "Second Image") with gr.Row(): use_aug = gr.Checkbox(value = False, label = "Use Augmentations") load_images_button = gr.Button("Show Images") gr.Markdown("Choose a method from the different tabs. You may leave the default options as they are and press on \"Run\" ") with gr.Row(): with gr.Column(): with gr.Tabs(): with gr.TabItem("Interaction-CAM"): cams_button = gr.Button("Get Heatmaps") with gr.TabItem("Perturbation Methods"): w_size = gr.Number(value = 64, label = "Occlusion Window Size", precision = 0) stride = gr.Number(value = 8, label = "Occlusion Stride", precision = 0) occlusion_button = gr.Button("Get Heatmap") with gr.TabItem("Averaged Transforms"): transform_type = gr.inputs.Radio(label="Data Augment", choices=['color_jitter', 'blur', 'grayscale', 'solarize', 'combine'], default="combine") add_noise = gr.Checkbox(value = True, label = "Add Noise") blur_output = gr.Checkbox(value = True, label = "Blur Output") guided = gr.Checkbox(value = True, label = "Guided Backprop") avgtransform_button = gr.Button("Get Saliency") with gr.TabItem("Pixel Invariance"): gr.Markdown("Note: Invariance map will be obtained for the first image") pixel_invariance_button = gr.Button("Get Invariance Map") with gr.TabItem("Image Synthesization"): baseline = gr.inputs.Radio(label="Compare With", choices=["Supervised Classification"], default="Supervised Classification") modeldiff_button = gr.Button("Compare") with gr.Column(): output_image = gr.Image(type='pil', show_label = False) load_model_button.click(load_model, inputs = model_name, outputs = status_or_similarity) load_images_button.click(load_or_augment_images, inputs = [img1, img2, use_aug], outputs = [output_image, status_or_similarity]) occlusion_button.click(run_occlusion, inputs=[w_size,stride], outputs=output_image) modeldiff_button.click(get_model_difference, inputs = baseline, outputs = output_image) avgtransform_button.click(get_avg_trasforms, inputs = [transform_type, add_noise, blur_output, guided], outputs = output_image) cams_button.click(get_cams, inputs = [], outputs = output_image) pixel_invariance_button.click(get_pixel_invariance, inputs = [], outputs = output_image) xai.launch()