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openvino/tests/layer_tests/tensorflow_tests/test_tf_Pooling.py
Ilya Churaev 0c9abf43a9 Updated copyright headers (#15124) 
* Updated copyright headers

* Revert "Fixed linker warnings in docs snippets on Windows (#15119)"

This reverts commit 372699ec49.
2023-01-16 11:02:17 +04:00

238 lines
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Python
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# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
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
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
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)
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