FHardtanhTest, LayerReLUTest, FReLUTest, LayerELUTest, FPELUTest, ]: for i in range(10): model = act_type() model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) input_var = torch.FloatTensor(input_np) torch.onnx.export(model, input_var, "_tmpnet.onnx", verbose=True, input_names=['test_in'], output_names=['test_out']) onnx_model = onnx.load('_tmpnet.onnx') k_model = onnx_to_keras(onnx_model, ['test_in']) os.unlink('_tmpnet.onnx') error = check_torch_keras_error(model, k_model, input_np) print('Error:', error) if max_error < error: max_error = error print('Max error: {0}'.format(max_error))
model = LayerTest(kernel_size=kernel_size, padding=padding, stride=stride) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) input_var = Variable(torch.FloatTensor(input_np)) torch.onnx.export(model, input_var, "_tmpnet.onnx", verbose=True, input_names=['test_in'], output_names=['test_out']) onnx_model = onnx.load('_tmpnet.onnx') k_model = onnx_to_keras(onnx_model, ['test_in'], change_ordering=change_ordering) error = check_torch_keras_error( model, k_model, input_np, change_ordering=change_ordering) print('Error:', error) if max_error < error: max_error = error print('Max error: {0}'.format(max_error))
for stride in [1, 2, 3]: for bias in [True, False]: # for dilation in [1, 2, 3]: for dilation in [3,]: for groups in [1, 3]: # ValueError: strides > 1 not supported in conjunction with dilation_rate > 1 if stride > 1 and dilation > 1: continue model = LayerTest( 3, groups, kernel_size=kernel_size, padding=padding, stride=stride, bias=bias, dilation=dilation, groups=groups) model.eval() input_np = np.random.uniform(0, 1, (1, 3, 224, 224)) input_var = Variable(torch.FloatTensor(input_np)) torch.onnx.export(model, input_var, "_tmpnet.onnx", verbose=True, input_names=['test_in'], output_names=['test_out']) onnx_model = onnx.load('_tmpnet.onnx') k_model = onnx_to_keras(onnx_model, ['test_in']) error = check_torch_keras_error(model, k_model, input_np, epsilon=1e-2) print('Error:', error) if max_error < error: max_error = error print('Max error: {0}'.format(max_error))