def test_average_pool_2d(self):
kernel_shape = [1, 2]
strides = [1, 2]
input_shape = [10, 10, 4, 4]
x = self._get_rnd_float32(shape=input_shape)
test_output = py_pool(x,
kernel_shape=kernel_shape,
strides=strides,
pooling_type="AVG",
include_indices=False)
node_def = helper.make_node(op_type="AveragePool",
inputs=["X"],
outputs=["Y"],
kernel_shape=kernel_shape,
strides=strides)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({"X": x})
np.testing.assert_almost_equal(output["Y"], test_output)
def test_max_pool_with_argmax_2d_dilations_ceil_pads(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest(
"ONNX version {} doesn't support dilations nor ceil mode.".
format(defs.onnx_opset_version()))
kernel_shape = [3, 3]
strides = [2, 2]
dilations = [3, 3]
pads = [1, 1, 2, 2]
ceil_mode = True
input_shape = [10, 3, 23, 23]
x = self._get_rnd_float32(shape=input_shape) - 2
node_def = helper.make_node("MaxPool", ["X"], ["Y", "Ind"],
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
pads=pads,
ceil_mode=ceil_mode)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None]),
helper.make_tensor_value_info("Ind", TensorProto.INT64,
[None, None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/max_pool_with_argmax_2d_dilations_ceil_pads'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
# run the model
tf_model_output = tf_model(X=x)
test_output, test_ind = py_pool(x,
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
padding=pads,
ceil_mode=ceil_mode,
pooling_type="MAX")
np.testing.assert_almost_equal(tf_model_output[0], test_output)
np.testing.assert_almost_equal(tf_model_output[1], test_ind)
def test_max_pool_2d_dilations_ceil_pads(self):
if legacy_opset_pre_ver(10):
raise unittest.SkipTest(
"ONNX version {} doesn't support dilations nor ceil mode.".
format(defs.onnx_opset_version()))
kernel_shape = [3, 3]
strides = [2, 2]
dilations = [3, 3]
pads = [1, 1, 2, 2]
ceil_mode = 1
input_shape = [10, 3, 23, 23]
x = self._get_rnd_float32(shape=input_shape)
test_output = py_pool(x,
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
padding=pads,
ceil_mode=ceil_mode,
pooling_type="MAX",
include_indices=False)
node_def = helper.make_node(op_type="MaxPool",
inputs=["X"],
outputs=["Y"],
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
pads=pads,
ceil_mode=ceil_mode)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
output = tf_rep.run({"X": x})
np.testing.assert_almost_equal(output["Y"], test_output)
def test_average_pool_2d(self):
kernel_shape = [1, 2]
strides = [1, 2]
input_shape = [10, 10, 4, 4]
x = self._get_rnd_float32(shape=input_shape)
test_output = py_pool(x,
kernel_shape=kernel_shape,
strides=strides,
pooling_type="AVG",
include_indices=False)
node_def = helper.make_node(op_type="AveragePool",
inputs=["X"],
outputs=["Y"],
kernel_shape=kernel_shape,
strides=strides)
graph_def = helper.make_graph(
[node_def],
name="test_unknown_shape",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT,
[None, None, None, None]),
],
outputs=[
helper.make_tensor_value_info("Y", TensorProto.FLOAT,
[None, None, None, None])
])
tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
# export to tf.saved_model
model_path = 'test_dynamic_shape/average_pool_2d'
tf_rep.export_graph(model_path)
# load the saved_model back
tf_model = tf.saved_model.load(model_path)
# run the model
tf_model_output = tf_model(X=x)
np.testing.assert_almost_equal(tf_model_output[0], test_output)
