56f2bc2f12
* Add support api 2.0 * Upd imports * Update onnx_tests * Upd tenserflow_tests * Fix test_split_concat.py * Fixed comparing
266 lines
16 KiB
Python
266 lines
16 KiB
Python
# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from common.layer_test_class import check_ir_version
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from common.tf_layer_test_class import CommonTFLayerTest
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from unit_tests.utils.graph import build_graph
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class TestPooling(CommonTFLayerTest):
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def create_pooling_net(self, kernel_size, strides, pads, in_shape, out_shape, method,
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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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Input->Pooling => Input->Pooling
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"""
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#
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# Create Tensorflow model
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#
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import tensorflow as tf
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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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pads_begin, pads_end, padding = pads
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# 4D tensors
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if len(in_shape) == 4:
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input_shape = [in_shape[0], in_shape[2], in_shape[3], in_shape[1]]
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input = tf.compat.v1.placeholder(tf.float32, input_shape, 'Input')
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stride = [1, strides[0], strides[1], 1]
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kernel = [1, kernel_size[0], kernel_size[1], 1]
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if method == 'max':
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tf.nn.max_pool2d(input=input, ksize=kernel, strides=stride, padding=padding,
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name='Operation')
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elif method == 'avg':
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tf.nn.avg_pool2d(input=input, ksize=kernel, strides=stride, padding=padding,
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name='Operation')
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# 5D tensors
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elif len(in_shape) == 5:
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input_shape = [in_shape[0], in_shape[2], in_shape[3], in_shape[4], in_shape[1]]
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input = tf.compat.v1.placeholder(tf.float32, input_shape, 'Input')
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stride = [1, strides[0], strides[1], strides[2], 1]
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kernel = [1, kernel_size[0], kernel_size[1], kernel_size[2], 1]
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if method == 'max':
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tf.nn.max_pool3d(input, kernel, stride, padding,
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name='Operation') # , data_format='NCHW')
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elif method == 'avg':
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tf.nn.avg_pool3d(input, kernel, stride, padding,
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name='Operation') # , data_format='NCHW')
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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ref_net = None
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if check_ir_version(10, None, ir_version) and not use_new_frontend:
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nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': in_shape, 'kind': 'data'},
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'pooling': {'kernel': kernel_size, 'pads_begin': pads_begin, 'pads_end': pads_end,
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'strides': strides, 'kind': 'op', 'type': None},
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'pooling_data': {'shape': out_shape, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'},
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'pooling_indicies_data': {'kind': 'data', 'shape': out_shape}
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}
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if method == 'avg':
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nodes_attributes['pooling']['type'] = 'AvgPool'
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elif method == 'max':
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nodes_attributes['pooling']['type'] = 'MaxPool'
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edges = [('input', 'input_data'),
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('input_data', 'pooling'),
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('pooling', 'pooling_data', {'out': 0}),
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('pooling_data', 'result')]
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if method == 'max':
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edges.append(('pooling', 'pooling_indicies_data', {'out': 1}))
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ref_net = build_graph(nodes_attributes,
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edges=edges,
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nodes_with_edges_only=True)
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return tf_net, ref_net
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test_data_4D = []
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for method in ['max', 'avg']:
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test_data_4D.extend([dict(kernel_size=[1, 1], strides=[1, 1], pads=[[0, 0], [0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 224, 224],
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method=method),
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dict(kernel_size=[2, 2], strides=[2, 2], pads=[[0, 0], [0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112],
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method=method),
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dict(kernel_size=[2, 4], strides=[2, 4], pads=[[0, 0], [0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 56],
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method=method),
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dict(kernel_size=[4, 2], strides=[4, 2], pads=[[0, 0], [0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 56, 112],
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method=method),
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dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 1], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 75],
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method=method),
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dict(kernel_size=[3, 2], strides=[3, 2], pads=[[0, 0], [1, 0], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 75, 112],
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method=method),
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dict(kernel_size=[3, 3], strides=[2, 2], pads=[[0, 0], [1, 1], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112],
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method=method),
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dict(kernel_size=[3, 2], strides=[2, 2], pads=[[0, 0], [1, 0], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112],
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method=method),
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dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 1], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 75],
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method=method),
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dict(kernel_size=[111, 111], strides=[111, 111],
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pads=[[54, 54], [55, 55], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 3, 3], method=method),
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dict(kernel_size=[111, 113], strides=[111, 113],
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pads=[[54, 1], [55, 1], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 3, 2], method=method),
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dict(kernel_size=[113, 113], strides=[113, 113],
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pads=[[1, 1], [1, 1], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 2], method=method),
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dict(kernel_size=[113, 113], strides=[111, 111],
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pads=[[55, 55], [56, 56], 'SAME'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 3, 3],
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method=method)])
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test_data_4D.extend(
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[dict(kernel_size=[1, 1], strides=[1, 1], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 224, 224], method=method),
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dict(kernel_size=[2, 2], strides=[2, 2], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 112], method=method),
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dict(kernel_size=[2, 4], strides=[2, 4], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 56], method=method),
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dict(kernel_size=[4, 2], strides=[4, 2], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 56, 112], method=method),
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dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 74], method=method),
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dict(kernel_size=[3, 2], strides=[3, 2], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 74, 112], method=method),
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dict(kernel_size=[3, 3], strides=[2, 2], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 111, 111], method=method),
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dict(kernel_size=[3, 2], strides=[2, 2], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 111, 112], method=method),
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dict(kernel_size=[2, 3], strides=[2, 3], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 112, 74], method=method),
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dict(kernel_size=[111, 111], strides=[111, 111], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 2], method=method),
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dict(kernel_size=[111, 113], strides=[111, 113], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 1], method=method),
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dict(kernel_size=[113, 113], strides=[113, 113], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 1, 1], method=method),
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dict(kernel_size=[113, 113], strides=[111, 111], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 2, 2], method=method),
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dict(kernel_size=[224, 224], strides=[1, 1], pads=[[0, 0], [0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224], out_shape=[1, 3, 1, 1], method=method)])
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@pytest.mark.parametrize("params", test_data_4D)
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@pytest.mark.nightly
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def test_pool_4D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
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api_2):
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self._test(*self.create_pooling_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, api_2=api_2)
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test_data_5D = []
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for method in ['max', 'avg']:
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test_data_5D.extend(
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[dict(kernel_size=[1, 1, 1], strides=[1, 1, 1], pads=[[0, 0, 0], [0, 0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 224, 224, 224], method=method),
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dict(kernel_size=[2, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method),
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dict(kernel_size=[2, 2, 4], strides=[2, 2, 4], pads=[[0, 0, 0], [0, 0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 56], method=method),
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dict(kernel_size=[4, 2, 2], strides=[4, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 56, 112, 112], method=method),
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dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 1], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 75], method=method),
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dict(kernel_size=[3, 2, 2], strides=[3, 2, 2], pads=[[0, 0, 0], [1, 0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 75, 112, 112], method=method),
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dict(kernel_size=[3, 3, 3], strides=[2, 2, 2], pads=[[0, 0, 0], [1, 1, 1], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method),
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dict(kernel_size=[3, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [1, 0, 0], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method),
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dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 1], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 75], method=method),
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dict(kernel_size=[111, 111, 111], strides=[111, 111, 111],
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pads=[[54, 54, 54], [55, 55, 55], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 3, 3, 3], method=method),
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dict(kernel_size=[111, 111, 113], strides=[111, 111, 113],
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pads=[[54, 54, 1], [55, 55, 1], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 3, 3, 2], method=method),
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dict(kernel_size=[113, 113, 113], strides=[113, 113, 113],
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pads=[[1, 1, 1], [1, 1, 1], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 2], method=method),
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dict(kernel_size=[113, 113, 113], strides=[111, 111, 111],
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pads=[[55, 55, 55], [56, 56, 56], 'SAME'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 3, 3, 3], method=method)])
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test_data_5D.extend(
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[dict(kernel_size=[1, 1, 1], strides=[1, 1, 1], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 224, 224, 224], method=method),
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dict(kernel_size=[2, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 112], method=method),
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dict(kernel_size=[2, 2, 4], strides=[2, 2, 4], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 56], method=method),
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dict(kernel_size=[4, 2, 2], strides=[4, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 56, 112, 112], method=method),
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dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 74], method=method),
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dict(kernel_size=[3, 2, 2], strides=[3, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 74, 112, 112], method=method),
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dict(kernel_size=[3, 3, 3], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 111, 111, 111], method=method),
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dict(kernel_size=[3, 2, 2], strides=[2, 2, 2], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 111, 112, 112], method=method),
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dict(kernel_size=[2, 2, 3], strides=[2, 2, 3], pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 112, 112, 74], method=method),
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dict(kernel_size=[111, 111, 111], strides=[111, 111, 111],
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pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 2], method=method),
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dict(kernel_size=[111, 111, 113], strides=[111, 111, 113],
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pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 1], method=method),
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dict(kernel_size=[113, 113, 113], strides=[113, 113, 113],
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pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 1, 1, 1], method=method),
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dict(kernel_size=[113, 113, 113], strides=[111, 111, 111],
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pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 2, 2, 2], method=method),
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dict(kernel_size=[224, 224, 224], strides=[1, 1, 1],
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pads=[[0, 0, 0], [0, 0, 0], 'VALID'],
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in_shape=[1, 3, 224, 224, 224], out_shape=[1, 3, 1, 1, 1], method=method)])
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@pytest.mark.parametrize("params", test_data_5D)
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@pytest.mark.nightly
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def test_pool_5D(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
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api_2):
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if ie_device == 'GPU':
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pytest.skip("5D tensors is not supported on GPU")
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self._test(*self.create_pooling_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, api_2=api_2)
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