65 lines
2.7 KiB
Python
Executable File
65 lines
2.7 KiB
Python
Executable File
# coding=utf-8
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import os
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import torch
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import numpy as np
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from PIL import Image
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from torchvision import transforms
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import argparse
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from models.modeling import VisionTransformer, CONFIGS
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset", choices=["CUB_200_2011", "emptyJudge5", "emptyJudge4"], default="emptyJudge5", help="Which dataset.")
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parser.add_argument("--img_size", default=448, type=int, help="Resolution size")
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parser.add_argument('--split', type=str, default='overlap', help="Split method") # non-overlap
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parser.add_argument('--slide_step', type=int, default=12, help="Slide step for overlap split")
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parser.add_argument('--smoothing_value', type=float, default=0.0, help="Label smoothing value\n")
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parser.add_argument("--pretrained_model", type=str, default="output/emptyjudge5_checkpoint.bin", help="load pretrained model")
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args = parser.parse_args()
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args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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args.nprocs = torch.cuda.device_count()
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# Prepare Model
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config = CONFIGS["ViT-B_16"]
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config.split = args.split
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config.slide_step = args.slide_step
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cls_dict = {}
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num_classes = 0
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if args.dataset == "emptyJudge5":
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num_classes = 5
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cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
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elif args.dataset == "emptyJudge4":
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num_classes = 4
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cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "stack"}
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elif args.dataset == "emptyJudge3":
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num_classes = 3
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cls_dict = {0: "noemp", 1: "yesemp", 2: "hard"}
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elif args.dataset == "emptyJudge2":
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num_classes = 2
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cls_dict = {0: "noemp", 1: "yesemp"}
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model = VisionTransformer(config, args.img_size, zero_head=True, num_classes=num_classes, smoothing_value=args.smoothing_value)
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if args.pretrained_model is not None:
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pretrained_model = torch.load(args.pretrained_model, map_location=torch.device('cpu'))['model']
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model.load_state_dict(pretrained_model)
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model.to(args.device)
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model.eval()
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# test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
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# transforms.CenterCrop((448, 448)),
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# transforms.ToTensor(),
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# transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
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test_transform = transforms.Compose([transforms.Resize((448, 448), Image.BILINEAR),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
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img = Image.open("img.jpg")
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x = test_transform(img)
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part_logits = model(x.unsqueeze(0))
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probs = torch.nn.Softmax(dim=-1)(part_logits)
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top5 = torch.argsort(probs, dim=-1, descending=True)
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print("Prediction Label\n")
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for idx in top5[0, :5]:
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print(f'{probs[0, idx.item()]:.5f} : {cls_dict[idx.item()]}', end='\n')
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