# Copyright (C) 2018-2023 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import platform import pytest from common.layer_test_class import check_ir_version from common.tf_layer_test_class import CommonTFLayerTest from unit_tests.utils.graph import build_graph class TestPooling(CommonTFLayerTest): def create_pooling_net(self, kernel_size, strides, pads, in_shape, out_shape, method, ir_version, use_new_frontend): """ Tensorflow net IR net Input->Pooling => Input->Pooling (AvgPool, MaxPool) """ import tensorflow as tf tf.compat.v1.reset_default_graph() # Create the graph and model with tf.compat.v1.Session() as sess: pads_begin, pads_end, padding = pads # 4D tensors if len(in_shape) == 4: input_shape = [in_shape[0], in_shape[2], in_shape[3], in_shape[1]] input = tf.compat.v1.placeholder(tf.float32, input_shape, 'Input') stride = [1, strides[0], strides[1], 1] kernel = [1, kernel_size[0], kernel_size[1], 1] if method == 'max': tf.raw_ops.MaxPool(input=input, ksize=kernel, strides=stride, padding=padding, name='Operation') elif method == 'avg': tf.raw_ops.AvgPool(value=input, ksize=kernel, strides=stride, padding=padding, name='Operation') # 5D tensors elif len(in_shape) == 5: input_shape = [in_shape[0], in_shape[2], in_shape[3], in_shape[4], in_shape[1]] input = tf.compat.v1.placeholder(tf.float32, input_shape, 'Input') stride = [1, strides[0], strides[1], strides[2], 1] kernel = [1, kernel_size[0], kernel_size[1], kernel_size[2], 1] if method == 'max': tf.raw_ops.MaxPool3D(input=input, ksize=kernel, strides=stride, padding=padding, name='Operation') elif method == 'avg': tf.raw_ops.AvgPool3D(input=input, ksize=kernel, strides=stride, padding=padding, name='Operation') tf.compat.v1.global_variables_initializer() tf_net = sess.graph_def ref_net = None return tf_net, ref_net test_data_4D = [] for method in ['max', 'avg']: test_data_4D.extend([dict(kernel_size=[1, 1], strides=[1, 1], pads=[[0, 0], [0, 0], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 224, 224], method=method), pytest.param( dict(kernel_size=[2, 2], strides=[2, 2], pads=[[0, 0], [0, 0], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112], method=method), marks=pytest.mark.precommit_tf_fe), dict(kernel_size=[2, 4], strides=[2, 4], pads=[[0, 0], [0, 0], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 56], method=method), dict(kernel_size=[4, 2], strides=[4, 2], pads=[[0, 0], [0, 0], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 56, 112], method=method), dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 1], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 75], method=method), dict(kernel_size=[3, 2], strides=[3, 2], pads=[[0, 0], [1, 0], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 75, 112], method=method), dict(kernel_size=[3, 3], strides=[2, 2], pads=[[0, 0], [1, 1], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112], method=method), dict(kernel_size=[3, 2], strides=[2, 2], pads=[[0, 0], [1, 0], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112], method=method), dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 1], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 75], method=method), pytest.param( dict(kernel_size=[111, 111], strides=[111, 111], pads=[[54, 54], [55, 55], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 3, 3], method=method), marks=pytest.mark.precommit_tf_fe), dict(kernel_size=[111, 113], strides=[111, 113], pads=[[54, 1], [55, 1], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 3, 2], method=method), dict(kernel_size=[113, 113], strides=[113, 113], pads=[[1, 1], [1, 1], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 2], method=method), dict(kernel_size=[113, 113], strides=[111, 111], pads=[[55, 55], [56, 56], 'SAME'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 3, 3], method=method)]) test_data_4D.extend( [dict(kernel_size=[1, 1], strides=[1, 1], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 224, 224], method=method), dict(kernel_size=[2, 2], strides=[2, 2], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112], method=method), pytest.param( dict(kernel_size=[2, 4], strides=[2, 4], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 56], method=method), marks=pytest.mark.precommit_tf_fe), dict(kernel_size=[4, 2], strides=[4, 2], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 56, 112], method=method), dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 74], method=method), dict(kernel_size=[3, 2], strides=[3, 2], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 74, 112], method=method), dict(kernel_size=[3, 3], strides=[2, 2], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 111, 111], method=method), dict(kernel_size=[3, 2], strides=[2, 2], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 111, 112], method=method), dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 74], method=method), dict(kernel_size=[111, 111], strides=[111, 111], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 2], method=method), dict(kernel_size=[111, 113], strides=[111, 113], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 1], method=method), dict(kernel_size=[113, 113], strides=[113, 113], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 1, 1], method=method), dict(kernel_size=[113, 113], strides=[111, 111], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 2], method=method), dict(kernel_size=[224, 224], strides=[1, 1], pads=[[0, 0], [0, 0], 'VALID'], in_shape=[1, 3, 224, 224], out_shape=[1, 3, 1, 1], method=method)]) @pytest.mark.parametrize("params", test_data_4D) @pytest.mark.nightly @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64', reason='Ticket - 122716') def test_pool_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend, use_old_api): self._test(*self.create_pooling_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend), ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend, use_old_api=use_old_api) test_data_5D = [] for method in ['max', 'avg']: test_data_5D.extend( [dict(kernel_size=[1, 1, 1], strides=[1, 1, 1], pads=[[0, 0, 0], [0, 0, 0], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 224, 224, 224], method=method), pytest.param( dict(kernel_size=[2, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method), marks=pytest.mark.precommit_tf_fe), dict(kernel_size=[2, 2, 4], strides=[2, 2, 4], pads=[[0, 0, 0], [0, 0, 0], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 56], method=method), dict(kernel_size=[4, 2, 2], strides=[4, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 56, 112, 112], method=method), dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 1], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 75], method=method), dict(kernel_size=[3, 2, 2], strides=[3, 2, 2], pads=[[0, 0, 0], [1, 0, 0], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 75, 112, 112], method=method), dict(kernel_size=[3, 3, 3], strides=[2, 2, 2], pads=[[0, 0, 0], [1, 1, 1], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method), dict(kernel_size=[3, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [1, 0, 0], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method), dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 1], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 75], method=method), dict(kernel_size=[111, 111, 111], strides=[111, 111, 111], pads=[[54, 54, 54], [55, 55, 55], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 3, 3, 3], method=method), dict(kernel_size=[111, 111, 113], strides=[111, 111, 113], pads=[[54, 54, 1], [55, 55, 1], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 3, 3, 2], method=method), dict(kernel_size=[113, 113, 113], strides=[113, 113, 113], pads=[[1, 1, 1], [1, 1, 1], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 2], method=method), dict(kernel_size=[113, 113, 113], strides=[111, 111, 111], pads=[[55, 55, 55], [56, 56, 56], 'SAME'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 3, 3, 3], method=method)]) test_data_5D.extend( [dict(kernel_size=[1, 1, 1], strides=[1, 1, 1], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 224, 224, 224], method=method), pytest.param( dict(kernel_size=[2, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method), marks=pytest.mark.precommit_tf_fe), dict(kernel_size=[2, 2, 4], strides=[2, 2, 4], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 56], method=method), dict(kernel_size=[4, 2, 2], strides=[4, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 56, 112, 112], method=method), dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 74], method=method), dict(kernel_size=[3, 2, 2], strides=[3, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 74, 112, 112], method=method), dict(kernel_size=[3, 3, 3], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 111, 111, 111], method=method), dict(kernel_size=[3, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 111, 112, 112], method=method), dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 74], method=method), dict(kernel_size=[111, 111, 111], strides=[111, 111, 111], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 2], method=method), dict(kernel_size=[111, 111, 113], strides=[111, 111, 113], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 1], method=method), dict(kernel_size=[113, 113, 113], strides=[113, 113, 113], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 1, 1, 1], method=method), dict(kernel_size=[113, 113, 113], strides=[111, 111, 111], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 2], method=method), dict(kernel_size=[224, 224, 224], strides=[1, 1, 1], pads=[[0, 0, 0], [0, 0, 0], 'VALID'], in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 1, 1, 1], method=method)]) @pytest.mark.parametrize("params", test_data_5D) @pytest.mark.nightly @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64', reason='Ticket - 122716') def test_pool_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend, use_old_api): if ie_device == 'GPU': pytest.skip("5D tensors is not supported on GPU") self._test(*self.create_pooling_net(**params, ir_version=ir_version, use_new_frontend=use_new_frontend), ie_device, precision, ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend, use_old_api=use_old_api)