138 lines
4.2 KiB
Python
Executable File
138 lines
4.2 KiB
Python
Executable File
import torch
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from torch.utils.data import DataLoader
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import torchvision
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from data import dataset
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import torch.nn as nn
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from ignite.utils import convert_tensor
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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def euclidean_dist(x, y):
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"""
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Compute euclidean distance between two tensors
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"""
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# x: B x N x D
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# y: B x M x D
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n = x.size(-2)
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m = y.size(-2)
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d = x.size(-1)
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if d != y.size(-1):
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raise Exception
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x = x.unsqueeze(2).expand(x.size(0), n, m, d) # B x N x M x D
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y = y.unsqueeze(1).expand(x.size(0), n, m, d)
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return torch.pow(x - y, 2).sum(-1)
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def evaluate(query, target, support):
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"""
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:param query: B x NK x D vector
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:param target: B x NK x 1 vector
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:param support: B x N x K x D vector
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:return:
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"""
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K = support.size(-2)
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prototypes = support.mean(-2) # B x N x D
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distance = euclidean_dist(query, prototypes) # B x NK x N
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print(distance.shape)
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indices = distance.argmin(-1) # B x NK
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print(indices, target)
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return torch.eq(target, indices).float().mean()
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class Flatten(nn.Module):
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def __init__(self):
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super(Flatten, self).__init__()
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def forward(self, x):
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return x.view(x.size(0), -1)
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# def make_extractor():
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# resnet50 = torchvision.models.resnet50(pretrained=True)
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# resnet50.to(torch.device("cuda"))
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# resnet50.fc = torch.nn.Identity()
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# resnet50.eval()
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#
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# def extract(images):
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# with torch.no_grad():
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# return resnet50(images)
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# return extract
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def make_extractor():
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model = resnet18()
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model.to(torch.device("cuda"))
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model.eval()
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def extract(images):
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with torch.no_grad():
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return model(images)
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return extract
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def resnet18(model_path="ResNet18Official.pth"):
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"""Constructs a ResNet-18 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model_w_fc = torchvision.models.resnet18(pretrained=False)
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seq = list(model_w_fc.children())[:-1]
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seq.append(Flatten())
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model = torch.nn.Sequential(*seq)
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# model.load_state_dict(torch.load(model_path), strict=False)
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model.load_state_dict(torch.load(model_path, map_location ='cpu'), strict=False)
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# model.load_state_dict(torch.load(model_path))
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model.eval()
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return model
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def test():
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data_transform = torchvision.transforms.Compose([
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torchvision.transforms.Resize([int(224*1.15), int(224*1.15)]),
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torchvision.transforms.CenterCrop(224),
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torchvision.transforms.ToTensor(),
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torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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origin_dataset = dataset.CARS("/data/few-shot/STANFORD-CARS/", transform=data_transform)
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#origin_dataset = dataset.ImprovedImageFolder("/data/few-shot/mini_imagenet_full_size/train", transform=data_transform)
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N = torch.randint(5, 10, (1,)).tolist()[0]
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K = torch.randint(1, 10, (1,)).tolist()[0]
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batch_size = 2
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episodic_dataset = dataset.EpisodicDataset(
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origin_dataset, # 抽取数据集
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N, # N
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K, # K
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100 # 任务数目
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)
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print(episodic_dataset)
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data_loader = DataLoader(episodic_dataset, batch_size=batch_size)
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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extractor = make_extractor()
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accs = []
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for item in data_loader:
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item = convert_tensor(item, device)
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# item["query"]: B x NK x 3 x W x H
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# item["support"]: B x NK x 3 x W x H
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# item["target"]: B x NK
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print(item["support"].shape, item["target"].shape)
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query_batch = extractor(item["query"].view([-1, *item["query"].shape[-3:]])).view(batch_size, N*K, -1)
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support_batch = extractor(item["support"].view([-1, *item["query"].shape[-3:]])).view(batch_size, N, K, -1)
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accs.append(evaluate(query_batch, item["target"], support_batch))
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print(torch.tensor(accs).mean().item())
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if __name__ == '__main__':
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setup_seed(100)
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test()
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