54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
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import os
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import torch
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from torch.autograd import Variable as V
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from torch import nn
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from torchvision import transforms as T
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from Normalize import Normalize, TfNormalize
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from torch.utils.data import DataLoader
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from torch_nets import (
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tf_inception_v3,
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tf_resnet_v2_50,
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tf_resnet_v2_101,
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)
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from dataset import CNNDataset
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batch_size = 10
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adv_dir = './advimages/model_pit_b_224-method'
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os.environ["CUDA_VISIBLE_DEVICES"] = '5'
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def get_model(net_name, model_dir):
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model_path = os.path.join(model_dir, net_name + '.npy')
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if net_name == 'tf2torch_inception_v3':
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net = tf_inception_v3
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elif net_name == 'tf2torch_resnet_v2_50':
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net = tf_resnet_v2_50
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elif net_name == 'tf2torch_resnet_v2_101':
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net = tf_resnet_v2_101
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else:
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print('Wrong model name!')
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model = nn.Sequential(
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TfNormalize('tensorflow'),
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net.KitModel(model_path).eval().cuda(),)
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return model
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def verify(model_name, path):
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model = get_model(model_name, path)
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dataset = CNNDataset("inc-v3", adv_dir)
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data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0)
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sum = 0
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for batch_idx, batch_data in enumerate(data_loader):
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batch_x = batch_data[0].cuda()
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batch_y = batch_data[1].cuda()
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batch_name = batch_data[2]
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with torch.no_grad():
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sum += (model(batch_x)[0].argmax(1) != batch_y+1).detach().sum().cpu()
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print(model_name + ' acu = {:.2%}'.format(sum / 1000.0))
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def main():
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model_names = ['tf2torch_inception_v3','tf2torch_resnet_v2_50','tf2torch_resnet_v2_101']
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models_path = './models/'
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for model_name in model_names:
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verify(model_name, models_path)
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print("===================================================")
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if __name__ == '__main__':
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main()
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