def test_squeezenet(self):
# SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and
# <0.5MB model size
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
sqnet_v1_0 = SqueezeNet(version=1.1)
self.exportTest(toC(sqnet_v1_0), toC(x))
# SqueezeNet 1.1 has 2.4x less computation and slightly fewer params
# than SqueezeNet 1.0, without sacrificing accuracy.
x = Variable(torch.randn(BATCH_SIZE, 3, 224, 224).fill_(1.0))
sqnet_v1_1 = SqueezeNet(version=1.1)
self.exportTest(toC(sqnet_v1_1), toC(x))
def test_squeezenet(self):
sqnet_v1_1 = SqueezeNet(version=1.1)
state_dict = model_zoo.load_url(model_urls['squeezenet1_1'])
# state_dict = model_zoo.load_url(model_urls['squeezenet1_0'])
self.run_model_test(sqnet_v1_1,
train=False,
batch_size=BATCH_SIZE,
state_dict=state_dict)
