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)