Added TF Deconvolution layer tests (#14906)
Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
This commit is contained in:
Georgy Krivoruchko
GitHub
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tests/layer_tests/tensorflow_tests/test_tf_Conv2DBackprop.py
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tests/layer_tests/tensorflow_tests/test_tf_Conv2DBackprop.py
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# Copyright (C) 2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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from common.tf_layer_test_class import CommonTFLayerTest
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import logging
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# Testing operation Conv2DBackpropInput
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# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv2DBackpropInput
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class TestConv2DBackprop(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_conv2dbackprop_placeholder_const_net(self, input_shape, input_filter, out_backprop, input_strides, input_padding, dilations, ir_version, use_new_frontend):
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"""
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TensorFlow net IR net
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Placeholder->Conv2DBackpropInput => Placeholder->Transpose->ConvolutionBackpropData->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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else:
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pytest.skip('Dilations != 1 isn\' supported on CPU by TensorFlow')
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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.constant(input_shape)
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tf_filter = tf.compat.v1.placeholder(tf.float32, input_filter, "Input")
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tf_backprop = tf.compat.v1.placeholder(tf.float32, out_backprop, "InputBack")
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tf.raw_ops.Conv2DBackpropInput(input_sizes = tf_input, filter = tf_filter, out_backprop = tf_backprop, strides = input_strides, padding = input_padding, 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=[1, 1, 1, 1], out_backprop=[1, 10, 10, 1], input_strides=[1, 1, 1, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 10, 10, 3], input_filter=[2, 2, 3, 3], out_backprop=[1, 10, 10, 3], input_strides=[1, 1, 1, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 10, 10, 3], input_filter=[2, 2, 3, 3], out_backprop=[1, 5, 5, 3], input_strides=[1, 2, 2, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 10, 10, 1], input_filter=[1, 1, 1, 1], out_backprop=[1, 10, 10, 1], input_strides=[1, 1, 1, 1], input_padding='VALID', dilations=None),
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dict(input_shape=[1, 10, 10, 3], input_filter=[2, 2, 3, 3], out_backprop=[1, 9, 9, 3], input_strides=[1, 1, 1, 1], input_padding='VALID', dilations=None),
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dict(input_shape=[1, 10, 10, 3], input_filter=[2, 2, 3, 3], out_backprop=[1, 5, 5, 3], input_strides=[1, 2, 2, 1], input_padding='VALID', dilations=None),
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pytest.param(
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dict(input_shape=[1, 56, 56, 3], input_filter=[2, 3, 3, 3], out_backprop=[1, 28, 28, 3], input_strides=[1, 2, 2, 1], input_padding='SAME', dilations=None),
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marks=pytest.mark.precommit_tf_fe),
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pytest.param(
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dict(input_shape=[1, 64, 48, 3], input_filter=[3, 2, 3, 3], out_backprop=[1, 31, 24, 3], input_strides=[1, 2, 2, 1], input_padding='VALID', dilations=None),
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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.nightly
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def test_conv2dbackprop_placeholder_const(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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self._test(*self.create_conv2dbackprop_placeholder_const_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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79
tests/layer_tests/tensorflow_tests/test_tf_Conv3DBackprop.py
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tests/layer_tests/tensorflow_tests/test_tf_Conv3DBackprop.py
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# Copyright (C) 2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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from common.tf_layer_test_class import CommonTFLayerTest
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import logging
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# Testing operation Conv3DBackpropInputV2
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# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv3DBackpropInputV2
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class TestConv3DBackprop(CommonTFLayerTest):
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# input_shape - should be an array, shape of input tensor in format [batch, depth, height, width, channels]
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# input_filter - should be an array, defines a filter
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# out_backprop - should be an array, shape of backprop
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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_conv3dbackprop_placeholder_const_net(self, input_shape, input_filter, out_backprop, input_strides, input_padding, dilations, ir_version, use_new_frontend):
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"""
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TensorFlow net IR net
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Placeholder->Conv3DBackpropInputV2 => Placeholder->Transpose->ConvolutionBackpropData->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, 1] #default value regarding Documentation
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else:
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pytest.skip('Dilations != 1 isn\' supported on CPU by TensorFlow')
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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.constant(input_shape)
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tf_filter = tf.compat.v1.placeholder(tf.float32, input_filter, "InputFilter")
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tf_backprop = tf.compat.v1.placeholder(tf.float32, out_backprop, "InputBackprop")
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tf.raw_ops.Conv3DBackpropInputV2(input_sizes = tf_input, filter = tf_filter, out_backprop = tf_backprop, strides = input_strides, padding = input_padding)
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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, 15, 15, 15, 1], input_filter=[1, 1, 1, 1, 1], out_backprop=[1, 15, 15, 15, 1], input_strides=[1, 1, 1, 1, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 15, 15, 15, 2], input_filter=[1, 1, 1, 2, 2], out_backprop=[1, 15, 15, 15, 2], input_strides=[1, 1, 1, 1, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 16, 16, 16, 3], input_filter=[2, 2, 2, 3, 3], out_backprop=[1, 8, 8, 8, 3], input_strides=[1, 2, 2, 2, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 10, 10, 20, 3], input_filter=[2, 2, 2, 3, 3], out_backprop=[1, 5, 5, 10, 3], input_strides=[1, 2, 2, 2, 1], input_padding='SAME', dilations=None),
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dict(input_shape=[1, 15, 15, 15, 1], input_filter=[1, 1, 1, 1, 1], out_backprop=[1, 15, 15, 15, 1], input_strides=[1, 1, 1, 1, 1], input_padding='VALID', dilations=None),
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dict(input_shape=[1, 15, 15, 15, 2], input_filter=[1, 1, 1, 2, 2], out_backprop=[1, 15, 15, 15, 2], input_strides=[1, 1, 1, 1, 1], input_padding='VALID', dilations=None),
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dict(input_shape=[1, 16, 16, 16, 3], input_filter=[2, 2, 2, 3, 3], out_backprop=[1, 8, 8, 8, 3], input_strides=[1, 2, 2, 2, 1], input_padding='VALID', dilations=None),
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dict(input_shape=[1, 10, 10, 20, 3], input_filter=[2, 2, 2, 3, 3], out_backprop=[1, 5, 5, 10, 3], input_strides=[1, 2, 2, 2, 1], input_padding='VALID', dilations=None),
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pytest.param(
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dict(input_shape=[1, 16, 20, 10, 3], input_filter=[3, 2, 4, 3, 3], out_backprop=[1, 8, 10, 5, 3], input_strides=[1, 2, 2, 2, 1], input_padding='SAME', dilations=None),
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marks=pytest.mark.precommit_tf_fe),
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pytest.param(
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dict(input_shape=[1, 16, 16, 16, 3], input_filter=[4, 2, 3, 3, 3], out_backprop=[1, 7, 8, 7, 3], input_strides=[1, 2, 2, 2, 1], input_padding='VALID', dilations=None),
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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.nightly
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def test_conv3dbackprop_placeholder_const(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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self._test(*self.create_conv3dbackprop_placeholder_const_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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