def main():
n_classes = 1000
model = PeleeNet(n_classes=n_classes, pretrained=False)
model.eval()
input_data = misc.imread('../../../data/cat.jpg')
# 按照imagenet的图像格式预处理
input_data = imagenet_utils.imagenet_preprocess(input_data)
# x = Variable(torch.randn(1, 3, 224, 224))
x = Variable(torch.FloatTensor(torch.from_numpy(input_data)))
y = Variable(torch.LongTensor(np.ones(1, dtype=np.int)))
# print(x.shape)
start = time.time()
pred = model(x)
end = time.time()
print("PeleeNet forward time:", end - start)
imagenet_utils.get_imagenet_label(pred)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
if __name__ == '__main__':
image_height, image_width, image_channel = (224, 224, 3)
input = misc.imread('../../../data/cat.jpg')
# 按照imagenet的图像格式预处理
input = imagenet_utils.imagenet_preprocess(input)
n_classes = 1000
model = MobileNetV2(n_classes=n_classes)
model.eval()
# 训练模型为gpu模型
# model.load_state_dict(torch.load(os.path.expanduser('~/Data/mobilenetv2.pth.tar'), map_location=lambda storage, loc: storage))
# x = Variable(torch.randn(1, image_channel, image_height, image_width))
x = Variable(torch.FloatTensor(torch.from_numpy(input)))
y = Variable(torch.LongTensor(np.ones(1, dtype=np.int)))
# print(x.shape)
start = time.time()
pred = model(x)
end = time.time()
print("MobileNetV2 forward time:", end-start)
"""
x = self.features(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.classifier(x)
return x
if __name__ == '__main__':
n_classes = 1000
model = AlexNet(n_classes=n_classes)
model.eval()
model_pretrain_filename = os.path.expanduser(
'~/.torch/models/alexnet-owt-4df8aa71.pth')
if os.path.exists(model_pretrain_filename):
model.load_state_dict(torch.load(model_pretrain_filename))
input_data = misc.imread('../../../data/cat.jpg')
# 按照imagenet的图像格式预处理
input_data = imagenet_utils.imagenet_preprocess(input_data)
# x = Variable(torch.randn(1, 3, 224, 224))
x = Variable(torch.FloatTensor(torch.from_numpy(input_data)))
y = Variable(torch.LongTensor(np.ones(1, dtype=np.int)))
# print(x.shape)
start = time.time()
pred = model(x)
end = time.time()
print("AlexNet forward time:", end - start)
imagenet_utils.get_imagenet_label(pred)
def xception(pretrained=False, **kwargs):
"""
Construct Xception.
"""
model = Xception(**kwargs)
if pretrained:
# model.load_state_dict(model_zoo.load_url(model_urls['xception']))
model.init_weights(pretrained=True)
return model
if __name__ == '__main__':
n_classes = 1000
model = xception(n_classes=n_classes, pretrained=True)
model.eval()
input_data = misc.imread('../../../data/cat.jpg')
# 按照imagenet的图像格式预处理
input_data = imagenet_utils.imagenet_preprocess(input_data,
height=299,
width=299)
x = Variable(torch.FloatTensor(torch.from_numpy(input_data)))
y = Variable(torch.LongTensor(np.ones(1, dtype=np.int)))
start = time.time()
pred = model(x)
end = time.time()
print("MobileNet forward time:", end - start)
imagenet_utils.get_imagenet_label(pred)