af6ed211d6
* [TF FE] Support TF2 Object detection models For support of OOB conversion of OD models (Faster RCNN, SSD models) several fixes were done for Select, BroadcastArgs, Slice, and Concat operations. Implement tests for each case Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> * Switch off Transpose Sinking that breaks some model conversion * Apply code-review feedback: copyright and extra commented out code * Mention that for concat this is workaround Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
64 lines
2.6 KiB
Python
64 lines
2.6 KiB
Python
# Copyright (C) 2018-2023 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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import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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class TestBroadcastArgs(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 's0' in inputs_info, "Test error: inputs_info must contain `s0`"
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assert 's1' in inputs_info, "Test error: inputs_info must contain `s1`"
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s0_shape = inputs_info['s0']
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s1_shape = inputs_info['s1']
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inputs_data = {}
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inputs_data['s0'] = np.random.randint(1, 6, s0_shape)
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inputs_data['s1'] = np.random.randint(1, 6, s1_shape)
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# compute mask where we need to change dimension size in s1
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# so that s1 will be broadcastable to s0
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non_one_mask = inputs_data['s0'] != 1
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diff_size = len(inputs_data['s1']) - len(inputs_data['s0'])
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if diff_size > 0:
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# pad False elements to non_one_mask to the begin
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pad = np.full([diff_size], False, dtype=bool)
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non_one_mask = np.concatenate((pad, non_one_mask), axis=0)
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else:
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# cut extra mask elements
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diff_size = abs(diff_size)
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non_one_mask = non_one_mask[diff_size:]
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update_inds = np.argwhere(non_one_mask)
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inputs_data['s1'][update_inds] = 1
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print("inputs_data = ", inputs_data)
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return inputs_data
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def create_broadcast_args_net(self, s0_shape, s1_shape, input_type):
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tf.compat.v1.reset_default_graph()
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with tf.compat.v1.Session() as sess:
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s0 = tf.compat.v1.placeholder(input_type, s0_shape, 's0')
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s1 = tf.compat.v1.placeholder(input_type, s1_shape, 's1')
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tf.raw_ops.BroadcastArgs(s0=s0, s1=s1)
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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_basic = [
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dict(s0_shape=[6], s1_shape=[6], input_type=tf.int32),
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dict(s0_shape=[2], s1_shape=[5], input_type=tf.int64),
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dict(s0_shape=[7], s1_shape=[1], input_type=tf.int32),
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_broadcast_args_basic(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
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use_old_api):
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self._test(*self.create_broadcast_args_net(**params),
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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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