重命名 pt2tf 为 pt2pb
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zhutian
Gitee
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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import tensorflow as tf
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import unittest
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from onnx_tf.backend import onnx_graph_to_tensorflow_rep
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from onnx_tf.common.legacy import legacy_opset_pre_ver
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from onnx import defs
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from onnx import helper
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from onnx import TensorProto
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# Run the following test in graph mode
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tf.compat.v1.disable_eager_execution()
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class TestDynamicShape(unittest.TestCase):
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""" Tests for dynamic shape support
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"""
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def _get_rnd_float32(self, low=-1.0, high=1.0, shape=None):
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output = np.random.uniform(low, high, shape)
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if shape is None:
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return np.float32(output)
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else:
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return output.astype(np.float32)
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def _get_rnd_int(self, low, high=None, shape=None, dtype=np.int32):
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return np.random.randint(low, high, size=shape, dtype=dtype)
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def test_arg_max(self):
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if legacy_opset_pre_ver(12):
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raise unittest.SkipTest(
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"ONNX version {} doesn't support select_last_index attribute for ArgMax that depends on shape.".format(
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defs.onnx_opset_version()))
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axis = 1
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node_def = helper.make_node("ArgMax",
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inputs=['X'],
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outputs=['Y'],
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axis=axis,
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keepdims=0,
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select_last_index=1)
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
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],
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outputs=[
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
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])
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x = np.array([[ 1, 2, 3, 5, 3, 4, 5, 1 ], [ 2, 9, 3, 5, 9, 4, 5, 1 ]])
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tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
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output = tf_rep.run({"X": x})
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expected_output = np.argmax(np.flip(x, axis), axis=axis)
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expected_output = x.shape[axis] - expected_output - 1
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np.testing.assert_almost_equal(output['Y'], expected_output)
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def test_arg_min(self):
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if legacy_opset_pre_ver(12):
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raise unittest.SkipTest(
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"ONNX version {} doesn't support select_last_index attribute for ArgMin that depends on shape.".format(
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defs.onnx_opset_version()))
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axis = 1
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node_def = helper.make_node("ArgMin",
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inputs=['X'],
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outputs=['Y'],
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axis=axis,
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keepdims=0,
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select_last_index=1)
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
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],
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outputs=[
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
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])
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x = np.array([[ 1, 2, 3, 5, 3, 4, 5, 1 ], [ 2, 7, 3, 5, 2, 4, 5, 6 ]])
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tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
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output = tf_rep.run({"X": x})
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expected_output = np.argmin(np.flip(x, axis), axis=axis)
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expected_output = x.shape[axis] - expected_output - 1
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np.testing.assert_almost_equal(output['Y'], expected_output)
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def _batch_normalization(self, x, mean, variance, bias, scale,
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variance_epsilon):
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inv = np.reciprocal(np.sqrt(variance + variance_epsilon))
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if scale is not None:
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inv *= scale
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return x * inv + (bias - mean * inv if bias is not None else -mean * inv)
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def test_batch_normalization(self):
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if legacy_opset_pre_ver(6):
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raise unittest.SkipTest("Backend doesn't support consumed flag")
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node_def = helper.make_node("BatchNormalization",
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["X", "scale", "bias", "mean", "var"], ["Y"],
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epsilon=0.001)
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None]),
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helper.make_tensor_value_info("scale", TensorProto.FLOAT, [None]),
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helper.make_tensor_value_info("bias", TensorProto.FLOAT, [None]),
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helper.make_tensor_value_info("mean", TensorProto.FLOAT, [None]),
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helper.make_tensor_value_info("var", TensorProto.FLOAT, [None])
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],
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outputs=[
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
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])
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x_shape = [3, 5, 4, 2]
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param_shape = [5]
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_param_shape = [1, 5, 1, 1]
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x = self._get_rnd_float32(0, 1, shape=x_shape)
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m = self._get_rnd_float32(0, 1, shape=param_shape)
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_m = m.reshape(_param_shape)
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v = self._get_rnd_float32(0, 1, shape=param_shape)
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_v = v.reshape(_param_shape)
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scale = self._get_rnd_float32(0, 1, shape=param_shape)
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_scale = scale.reshape(_param_shape)
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bias = self._get_rnd_float32(0, 1, shape=param_shape)
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_bias = bias.reshape(_param_shape)
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golden = self._batch_normalization(x, _m, _v, _bias, _scale, 0.001)
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tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
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output = tf_rep.run({"X": x, "scale": scale, "bias": bias, "mean": m, "var": v})
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np.testing.assert_almost_equal(output["Y"], golden, decimal=5)
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def test_conv_transpose(self):
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# test dynamic batch size on transpose of 2d convolution
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pads = [1, 1, 1, 1]
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x_shape = [1, 3, 4, 6]
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x = self._get_rnd_float32(shape=x_shape)
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weight_shape = [3, 5, 2, 2]
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weights = self._get_rnd_float32(shape=weight_shape)
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node_def = helper.make_node("ConvTranspose", ["X", "weights"], ["Y"],
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pads=pads)
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, 3, 4, 6]),
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helper.make_tensor_value_info("weights", TensorProto.FLOAT, weight_shape)
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],
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outputs=[
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helper.make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
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])
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tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
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output = tf_rep.run({"X": x, "weights": weights})
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padh_left = weight_shape[2] - 1 - pads[0]
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padh_right = weight_shape[2] - 1 - pads[1]
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padw_left = weight_shape[3] - 1 - pads[2]
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padw_right = weight_shape[3] - 1 - pads[3]
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kh = weight_shape[2]
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kw = weight_shape[3]
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outh = x_shape[2] + padh_right + padh_right - (kh - 1)
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outw = x_shape[3] + padw_right + padw_right - (kw - 1)
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out_shape = [x_shape[0], weight_shape[1], outh, outw]
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test_output = np.zeros(out_shape)
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for b in range(0, x_shape[0]):
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for m in range(0, weight_shape[1]):
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for c in range(0, x_shape[1]):
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for h in range(0, outh):
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for w in range(0, outw):
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for k1 in range(h, h + kh):
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for k2 in range(w, w + kw):
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if (k1 - padh_left >= 0 and k2 - padw_left >= 0):
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test_output[b][m][h][w] += x[b][c][k1 - padh_left][
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k2 - padw_left] * weights[c][m][kh + h - 1 -
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k1][kw + w - 1 - k2]
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np.testing.assert_almost_equal(output["Y"], test_output, decimal=5)
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def test_slice(self):
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# test case 1 with normal inputs
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axes = [0, 1, 2]
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starts = [0, 0, 0]
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ends = [2, 2, 2]
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if legacy_opset_pre_ver(10):
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node_def = helper.make_node("Slice", ["X"], ["S"],
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axes=axes,
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starts=starts,
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ends=ends)
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT,
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[None, None, None])
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],
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outputs=[
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helper.make_tensor_value_info("S", TensorProto.FLOAT,
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[None, None, None])
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])
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else:
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node_def = helper.make_node("Slice",
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["X", "starts", "ends", "axes"],
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["S"])
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT,
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[None, None, None]),
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helper.make_tensor_value_info("starts", TensorProto.INT32,
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[None]),
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helper.make_tensor_value_info("ends", TensorProto.INT32,
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[None]),
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helper.make_tensor_value_info("axes", TensorProto.INT32,
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[None]),
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],
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outputs=[
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helper.make_tensor_value_info("S", TensorProto.FLOAT,
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[None, None, None])
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])
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tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
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if legacy_opset_pre_ver(10):
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x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
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output = tf_rep.run({"X": x})
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np.testing.assert_almost_equal(output["S"], x[0:2, 0:2, 0:2])
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else:
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x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
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output = tf_rep.run({"X": x, "starts": starts, "ends": ends, "axes": axes})
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np.testing.assert_almost_equal(output["S"], x[0:2, 0:2, 0:2])
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# test case 2 with negative, out-of-bound and default inputs
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axes = [0, 2]
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starts = [0, -7]
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ends = [-8, 20]
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steps = [1, 1]
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if legacy_opset_pre_ver(10):
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node_def = helper.make_node("Slice", ["X"], ["S"],
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axes=axes,
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starts=starts,
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ends=ends)
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT,
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[None, None, None])
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],
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outputs=[
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helper.make_tensor_value_info("S", TensorProto.FLOAT,
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[None, None, None])
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])
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else:
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node_def = helper.make_node("Slice",
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["X", "starts", "ends", "axes", "steps"],
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["S"])
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graph_def = helper.make_graph(
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[node_def],
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name="test_unknown_shape",
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inputs=[
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helper.make_tensor_value_info("X", TensorProto.FLOAT,
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[None, None, None]),
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helper.make_tensor_value_info("starts", TensorProto.INT32,
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[None]),
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helper.make_tensor_value_info("ends", TensorProto.INT32,
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[None]),
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helper.make_tensor_value_info("axes", TensorProto.INT32,
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[None]),
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helper.make_tensor_value_info("steps", TensorProto.INT32,
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[None]),
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],
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outputs=[
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helper.make_tensor_value_info("S", TensorProto.FLOAT,
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[None, None, None])
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])
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tf_rep = onnx_graph_to_tensorflow_rep(graph_def)
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if legacy_opset_pre_ver(10):
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x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
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output = tf_rep.run({"X": x})
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np.testing.assert_almost_equal(output["S"], x[0:-8, :, -7:20])
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else:
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x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
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output = tf_rep.run({"X": x, "starts": starts, "ends": ends, "axes": axes, "steps": steps})
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np.testing.assert_almost_equal(output["S"], x[0:-8, :, -7:20])
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# test case 3 with non-default steps
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axes = [0, 1, 2]
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starts = [0, 0, 0]
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ends = [2, 2, 2]
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steps = [2, -2, -1]
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if not legacy_opset_pre_ver(10):
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x = self._get_rnd_float32(shape=[1000]).reshape([10, 10, 10])
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output = tf_rep.run({"X": x, "starts": starts, "ends": ends, "axes": axes, "steps": steps})
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np.testing.assert_almost_equal(output["S"], x[0:2:2, 0:2:-2, 0:2:-1])
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if __name__ == '__main__':
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unittest.main()
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