import os import numpy as np import time import random import torch import torchvision.transforms as transforms import gradio as gr import matplotlib.pyplot as plt from models import get_model from dotmap import DotMap from PIL import Image #os.environ['TERM'] = 'linux' #os.environ['TERMINFO'] = '/etc/terminfo' # args args = DotMap() args.deploy = 'vanilla' args.arch = 'dino_small_patch16' args.resume = 'https://huggingface.co/hushell/pmf_dinosmall_lr1e-4/resolve/main/best_converted.pth' args.api_key = 'AIzaSyAFkOGnXhy-2ZB0imDvNNqf2rHb98vR_qY' args.cx = '06d75168141bc47f1' # model device = 'cpu' #torch.device("cuda" if torch.cuda.is_available() else "cpu") model = get_model(args) model.to(device) checkpoint = torch.hub.load_state_dict_from_url(args.resume, map_location='cpu') model.load_state_dict(checkpoint['model'], strict=True) # image transforms def test_transform(): def _convert_image_to_rgb(im): return im.convert('RGB') return transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), _convert_image_to_rgb, transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) preprocess = test_transform() @torch.no_grad() def denormalize(x, mean, std): # 3, H, W t = x.clone() t.mul_(std).add_(mean) return torch.clamp(t, 0, 1) # Google image search from google_images_search import GoogleImagesSearch class MyGIS(GoogleImagesSearch): def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): return # define search params # option for commonly used search param are shown below for easy reference. # For param marked with '##': # - Multiselect is currently not feasible. Choose ONE option only # - This param can also be omitted from _search_params if you do not wish to define any value _search_params = { 'q': '...', 'num': 10, 'fileType': 'png', #'jpg|gif|png', 'rights': 'cc_publicdomain', #'cc_publicdomain|cc_attribute|cc_sharealike|cc_noncommercial|cc_nonderived', #'safe': 'active|high|medium|off|safeUndefined', ## 'imgType': 'photo', #'clipart|face|lineart|stock|photo|animated|imgTypeUndefined', ## #'imgSize': 'huge|icon|large|medium|small|xlarge|xxlarge|imgSizeUndefined', ## #'imgDominantColor': 'black|blue|brown|gray|green|orange|pink|purple|red|teal|white|yellow|imgDominantColorUndefined', ## 'imgColorType': 'color', #'color|gray|mono|trans|imgColorTypeUndefined' ## } # Gradio UI def inference(query, labels, n_supp=10): ''' query: PIL image labels: list of class names ''' labels = labels.split(',') n_supp = int(n_supp) fig, axs = plt.subplots(len(labels), n_supp, figsize=(n_supp*4, len(labels)*4)) with torch.no_grad(): # query image query = preprocess(query).unsqueeze(0).unsqueeze(0).to(device) # (1, 1, 3, H, W) supp_x = [] supp_y = [] # search support images for idx, y in enumerate(labels): gis = GoogleImagesSearch(args.api_key, args.cx) _search_params['q'] = y _search_params['num'] = n_supp gis.search(search_params=_search_params, custom_image_name='my_image') gis._custom_image_name = 'my_image' for j, x in enumerate(gis.results()): x.download('./') x_im = Image.open(x.path) # vis axs[idx, j].imshow(x_im) axs[idx, j].set_title(f'{y}{j}:{x.path}') axs[idx, j].axis('off') x_im = preprocess(x_im) # (3, H, W) supp_x.append(x_im) supp_y.append(idx) print('Searching for support images is done.') supp_x = torch.stack(supp_x, dim=0).unsqueeze(0).to(device) # (1, n_supp*n_labels, 3, H, W) supp_y = torch.tensor(supp_y).long().unsqueeze(0).to(device) # (1, n_supp*n_labels) with torch.cuda.amp.autocast(True): output = model(supp_x, supp_y, query) # (1, 1, n_labels) probs = output.softmax(dim=-1).detach().cpu().numpy() return {k: float(v) for k, v in zip(labels, probs[0, 0])}, fig # DEBUG #query = Image.open('../labrador-puppy.jpg') ##labels = 'dog, cat' #labels = 'girl, boy' #output = inference(query, labels, n_supp=2) #print(output) gr.Interface(fn=inference, inputs=[ gr.inputs.Image(label="Image to classify", type="pil"), gr.inputs.Textbox(lines=1, label="Class hypotheses:", placeholder="Enter class names separated by ','",), gr.inputs.Slider(minimum=2, maximum=10, step=1, label="Number of support examples from Google") ], theme="grass", outputs=[ gr.outputs.Label(label="Predicted class probabilities"), gr.outputs.Image(type='plot', label="Support examples from Google image search"), ], description="PMF few-shot learning with Google image search").launch(debug=True)