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on
Zero
Running
on
Zero
| import os | |
| import clip | |
| import json | |
| import argparse | |
| import ruamel.yaml as yaml | |
| from PIL import Image | |
| import torch | |
| import torchvision.transforms as transforms | |
| from tqdm import tqdm | |
| from albef.utils import * | |
| from executor import AlbefExecutor | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--input_path", type=str, help="Path to input JSON file") | |
| parser.add_argument("--image_root", type=str, help="Path to directory containing images") | |
| parser.add_argument("--albef_path", type=str, default=None, help="Path to ALBEF model/config/etc. if the goal is to use ALBEF") | |
| parser.add_argument("--albef_itc", action="store_true", help="Use ITC output of ALBEF") | |
| parser.add_argument("--clip_model", type=str, help="CLIP model to use") | |
| parser.add_argument("--gpu", type=int, default=-1, help="Which gpu to use") | |
| parser.add_argument("--batch_size", type=int, default=32, help="Batch size for running CLIP") | |
| args = parser.parse_args() | |
| if args.albef_path is not None: | |
| executor = AlbefExecutor(checkpoint_path = os.path.join(args.albef_path, "checkpoint.pth"), config_path = os.path.join(args.albef_path, "config.yaml"), device = "cpu" if args.gpu < 0 else "cuda:"+str(args.gpu)) | |
| model = executor.models[0] | |
| preprocess = executor.preprocesses[0] | |
| model = model.eval() | |
| else: | |
| model, preprocess = clip.load(args.clip_model, jit=False, device="cuda:"+str(args.gpu)) | |
| preprocess.transforms[0] == transforms.Resize((model.visual.input_resolution, model.visual.input_resolution), transforms.InterpolationMode.BICUBIC) | |
| model = model.eval() | |
| input_file = open(args.input_path) | |
| data = json.load(input_file) | |
| input_file.close() | |
| correct = 0 | |
| for i in tqdm(range(0, len(data), args.batch_size)): | |
| batch_images = [] | |
| batch_text = [] | |
| for datum in data[i:min(i+args.batch_size, len(data))]: | |
| img = Image.open(os.path.join(args.image_root, datum["image_filename"])).convert('RGB') | |
| batch_images.append(preprocess(img)) | |
| if "text2" in datum: | |
| if args.albef_path is None: | |
| datum["text1"] = "a photo of "+datum["text1"] | |
| datum["text2"] = "a photo of "+datum["text2"] | |
| batch_text.append(datum["text1"]) | |
| batch_text.append(datum["text2"]) | |
| else: | |
| img2 = Image.open(os.path.join(args.image_root, datum["image_filename2"])).convert('RGB') | |
| batch_images.append(preprocess(img2)) | |
| batch_text.append(datum["text1"]) | |
| batch_images = torch.stack(batch_images).to("cuda:"+str(args.gpu)) | |
| if args.albef_path is None: | |
| batch_text = clip.tokenize(batch_text).to("cuda:"+str(args.gpu)) | |
| else: | |
| modified_text = [pre_caption(txt, executor.max_words) for txt in batch_text] | |
| batch_text = executor.tokenizer(modified_text, padding='longest', return_tensors="pt") | |
| for key in batch_text: | |
| batch_text[key] = batch_text[key].to(batch_images.device) | |
| with torch.no_grad(): | |
| if args.albef_path is None: | |
| logits_per_image, logits_per_text = model(batch_images, batch_text) | |
| else: | |
| if not args.albef_itc: | |
| if batch_images.shape[0]*2 == batch_text.input_ids.shape[0]: | |
| batch_images = batch_images.unsqueeze(1).repeat(1, 2, 1, 1, 1).view(batch_images.shape[0]*2, batch_images.shape[1], batch_images.shape[2], batch_images.shape[3]) | |
| else: | |
| assert batch_images.shape[0] ==2*batch_text.input_ids.shape[0] | |
| batch_text.input_ids = batch_text.input_ids.unsqueeze(1).repeat(1, 2, 1).view(batch_images.shape[0], -1) | |
| batch_text.attention_mask = batch_text.attention_mask.unsqueeze(1).repeat(1, 2, 1).view(batch_images.shape[0], -1) | |
| image_embeds = model.visual_encoder(batch_images) | |
| image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(batch_images.device) | |
| output = model.text_encoder( | |
| batch_text.input_ids, | |
| attention_mask = batch_text.attention_mask, | |
| encoder_hidden_states = image_embeds, | |
| encoder_attention_mask = image_atts, | |
| return_dict = True, | |
| ) | |
| vl_embeddings = output.last_hidden_state[:,0,:] | |
| vl_output = model.itm_head(vl_embeddings) | |
| logits_per_image = vl_output[:,1:2].view(-1, 2) | |
| else: | |
| image_embeds = model.visual_encoder(batch_images) | |
| image_feat = torch.nn.functional.normalize(model.vision_proj(image_embeds[:,0,:]),dim=-1) | |
| text_output = model.text_encoder(batch_text.input_ids, attention_mask = batch_text.attention_mask, | |
| return_dict = True, mode = 'text') | |
| text_embeds = text_output.last_hidden_state | |
| text_feat = torch.nn.functional.normalize(model.text_proj(text_embeds[:,0,:]),dim=-1) | |
| sim = image_feat@text_feat.t()/model.temp | |
| logits_per_image = sim | |
| if args.albef_path is None or args.albef_itc: | |
| if logits_per_image.shape[0]*2 == logits_per_image.shape[1]: | |
| for j in range(logits_per_image.shape[0]): | |
| correct += 1 if logits_per_image[j,2*j].item() > logits_per_image[j,2*j+1].item() else 0 | |
| else: | |
| assert logits_per_image.shape[0] == 2*logits_per_image.shape[1] | |
| for j in range(logits_per_image.shape[1]): | |
| correct += 1 if logits_per_image[2*j,j].item() > logits_per_image[2*j+1,j].item() else 0 | |
| else: | |
| correct += (logits_per_image[:,0] > logits_per_image[:,1]).long().sum().item() | |
| print("Accuracy:", correct/len(data)) | |