92 lines
4.4 KiB
Python
92 lines
4.4 KiB
Python
# Copyright (C) 2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from common.tf_layer_test_class import CommonTFLayerTest
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# Testing operation Conv2D
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# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv2D
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class TestConv2D(CommonTFLayerTest):
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# input_shape - should be an array, shape of input tensor in format [batch, height, width, channels]
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# input_filter - should be an array, defines a filter
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# input_strides - should be an array, defines strides of a sliding window to use
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# input_padding - should be a string, defines padding algorithm
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# ir_version - common parameter
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# use_new_frontend - common parameter
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def create_conv2d_placeholder_const_net(self, input_shape, input_filter, input_strides, input_padding, dilations,
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ir_version, use_new_frontend):
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"""
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Tensorflow net IR net
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Placeholder->Conv2D => Placeholder->Transpose->Convolution->Transpose
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/ /
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Placeholder-/ Placeholder->Transpose-/
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"""
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import tensorflow as tf
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if dilations is None:
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dilations = [1, 1, 1, 1] # default value regarding Documentation
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# Batch Height Width Channel
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expl_paddings = [0, 0, 1, 1, 1, 1, 0, 0]
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if input_padding == 'EXPLICIT' and use_new_frontend == False:
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pytest.xfail(reason="100300")
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tf.compat.v1.reset_default_graph()
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# Create the graph and model
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with tf.compat.v1.Session() as sess:
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tf_input = tf.compat.v1.placeholder(tf.float32, input_shape, "InputShape")
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tf_filter = tf.compat.v1.placeholder(tf.float32, input_filter, "InputFilter")
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if input_padding != 'EXPLICIT':
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tf.raw_ops.Conv2D(input=tf_input, filter=tf_filter, strides=input_strides, padding=input_padding,
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dilations=dilations)
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else:
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tf.raw_ops.Conv2D(input=tf_input, filter=tf_filter, strides=input_strides, padding=input_padding,
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explicit_paddings=expl_paddings, dilations=dilations)
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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ref_net = None
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return tf_net, ref_net
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test_data = [
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dict(input_shape=[1, 10, 10, 1], input_filter=[3, 3, 1, 1], input_strides=[1, 1, 1, 1], dilations=None),
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dict(input_shape=[1, 10, 10, 2], input_filter=[3, 3, 2, 2], input_strides=[1, 1, 1, 1], dilations=None),
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dict(input_shape=[1, 10, 10, 2], input_filter=[3, 3, 2, 1], input_strides=[1, 1, 1, 1], dilations=None),
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dict(input_shape=[1, 10, 10, 3], input_filter=[2, 2, 3, 3], input_strides=[1, 1, 1, 1], dilations=None),
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dict(input_shape=[1, 16, 16, 3], input_filter=[2, 2, 3, 3], input_strides=[1, 2, 2, 1], dilations=None),
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dict(input_shape=[1, 10, 10, 4], input_filter=[2, 2, 4, 2], input_strides=[1, 1, 1, 1], dilations=None),
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dict(input_shape=[1, 16, 16, 3], input_filter=[2, 2, 3, 3], input_strides=[1, 2, 2, 1], dilations=[1, 2, 2, 1]),
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pytest.param(
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dict(input_shape=[1, 224, 224, 3], input_filter=[4, 4, 3, 2], input_strides=[1, 2, 2, 1],
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dilations=[1, 2, 2, 1]),
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marks=pytest.mark.precommit_tf_fe),
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# with four groups
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pytest.param(
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dict(input_shape=[2, 224, 224, 4], input_filter=[4, 4, 1, 12], input_strides=[1, 2, 2, 1],
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dilations=[1, 2, 2, 1]),
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marks=pytest.mark.precommit_tf_fe)
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]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("padding", ['EXPLICIT', 'SAME', 'VALID'])
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@pytest.mark.nightly
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def test_conv2d_placeholder_const(self, params, padding, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if ie_device == 'GPU':
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pytest.xfail('104862')
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self._test(*self.create_conv2d_placeholder_const_net(**params, input_padding=padding, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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ie_device, precision, ir_version, input_padding=padding, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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