def test_convert_weight(self):
handlers = [weights_converter.WeightHandler() for i in range(5)]
not_processed_return = weights_converter.WeightHandlerReturn(
processed=False, matched_source=False, matched_target=False)
processed_return = weights_converter.WeightHandlerReturn(
processed=True, matched_source=True, matched_target=False)
for i in range(0, len(handlers) - 1):
handlers[i].then(handlers[i + 1])
handlers[i]._process_weights = lambda a, b: not_processed_return
handlers[-1]._process_weights = lambda a, b: processed_return
result = handlers[0].convert_weight(None, None)
self.assertEqual(result, processed_return)
def test(self):
in_channels = 3
kernel_size = 3
out_channels = 5
inp = np.ones((1, in_channels, 15, 15), dtype=np.float32)
model_tensorflow = tf.keras.layers.Conv2D(out_channels,
kernel_size,
use_bias=False,
data_format='channels_first')
model_tensorflow(inp)
model_pytorch = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size,
bias=False)
model_pytorch.weight.uniform_()
converter = weights_converter.PytorchToTensorflowHandlers.ConvolutionHandler(
)
res = converter(('', model_pytorch.state_dict()['weight']),
model_tensorflow.weights[0])
np.testing.assert_allclose(model_tensorflow(inp).numpy(),
model_pytorch(torch.tensor(inp)).numpy(),
rtol=1e-5)
expected_res = weights_converter.WeightHandlerReturn(
processed=True, matched_source=True, matched_target=True)
self.assertEqual(res, expected_res)
def test(self):
inp = np.array([[1, 2, 3]], dtype=np.float32)
model_tensorflow = tf.keras.layers.BatchNormalization(momentum=0.1,
epsilon=0.001)
model_tensorflow.trainable = False
model_tensorflow(inp)
model_pytorch = torch.nn.BatchNorm1d(3, momentum=0.1, eps=0.001)
model_pytorch.train(False)
model_pytorch.weight.uniform_()
converter = weights_converter.PytorchToTensorflowHandlers.SameShapeHandler(
)
res_weight = converter(('', model_pytorch.state_dict()['weight']),
model_tensorflow.weights[0])
res_bias = converter(('', model_pytorch.state_dict()['bias']),
model_tensorflow.weights[1])
np.testing.assert_allclose(model_tensorflow(inp).numpy(),
model_pytorch(torch.tensor(inp)).numpy(),
rtol=1e-5)
expected_res = weights_converter.WeightHandlerReturn(
processed=True, matched_source=True, matched_target=True)
self.assertEqual(res_weight, expected_res)
self.assertEqual(res_bias, expected_res)
def test(self):
model_pytorch = torch.nn.BatchNorm1d(3, momentum=0.1, eps=0.001)
converter = weights_converter.PytorchToTensorflowHandlers.BatchNormalizationSkipExtraHandler(
)
res = converter(list(model_pytorch.state_dict().items())[-1], None)
expected_res = weights_converter.WeightHandlerReturn(
processed=True, matched_source=True, matched_target=False)
self.assertEqual(res, expected_res)