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[TF FE] Stabilize layer tests for Keras GRU layer on all platforms (#…
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…27543)

**Details:** Stabilize layer tests for Keras GRU layer on all platforms

**Ticket:** 156967

---------

Signed-off-by: Kazantsev, Roman <[email protected]>
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@rkazants
rkazants authored Nov 14, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/job_tensorflow_layer_tests.yml
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Expand Up @@ -30,7 +30,7 @@ env:
jobs:
TensorFlow_Layer_Tests:
name: TensorFlow Layer Tests
timeout-minutes: 30
timeout-minutes: 45
runs-on: ${{ inputs.runner }}
container: ${{ fromJSON(inputs.container) }}
defaults:
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144 changes: 35 additions & 109 deletions tests/layer_tests/tensorflow2_keras_tests/test_tf2_keras_gru.py
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@@ -1,135 +1,61 @@
# Copyright (C) 2022-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pytest
import tensorflow as tf

from common.tf2_layer_test_class import CommonTF2LayerTest

rng = np.random.default_rng(233534)


class TestKerasGru(CommonTF2LayerTest):
def create_keras_gru_net(self, input_names, input_shapes, input_type, units, activation,
recurrent_activation,
use_bias, dropouts, flags, ir_version):
"""
create TensorFlow 2 model with Keras GRU operation
"""
def _prepare_input(self, inputs_info):
assert 'x' in inputs_info, "Test error: inputs_info must contain `x`"
x_shape = inputs_info['x']
inputs_data = {}
inputs_data['x'] = rng.uniform(-2.0, 2.0, x_shape).astype(self.input_type)
return inputs_data

def create_keras_gru_net(self, input_shapes, input_type, units,
activation, recurrent_activation,
dropouts, use_bias, flag1, flag2):
self.input_type = input_type
tf.keras.backend.clear_session() # For easy reset of notebook state
x1 = tf.keras.Input(shape=input_shapes[0][1:], name=input_names[0])
x1 = tf.keras.Input(shape=input_shapes[0][1:], dtype=input_type, name='x')
dropout, recurrent_dropout = dropouts
go_backwards, reset_after = flags
go_backwards, reset_after = flag1, flag2
y = tf.keras.layers.GRU(units=units, activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias, dropout=dropout,
recurrent_dropout=recurrent_dropout,
return_sequences=False, return_state=False,
go_backwards=go_backwards, reset_after=reset_after)(x1)
tf2_net = tf.keras.Model(inputs=[x1], outputs=[y])

# TODO: add reference IR net. Now it is omitted since inference is more
# important and needs to be checked in the first
ref_net = None

return tf2_net, ref_net

test_data_simple = [
dict(input_names=["x"], input_shapes=[[2, 2, 3]], input_type=tf.float32, units=1,
activation='tanh', recurrent_activation='sigmoid', dropouts=(.0, .3), use_bias=True,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[1, 2, 3]], input_type=tf.float32, units=4,
activation='relu', recurrent_activation='sigmoid', dropouts=(.2, .4), use_bias=True,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[3, 2, 3]], input_type=tf.float32, units=2,
activation='elu', recurrent_activation='tanh', dropouts=(.3, .5), use_bias=True,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[2, 3, 4]], input_type=tf.float32, units=1,
activation='elu', recurrent_activation='softmax', dropouts=(.0, .5), use_bias=True,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[1, 3, 4]], input_type=tf.float32, units=3,
activation='linear', recurrent_activation='sigmoid', dropouts=(.4, .6),
flags=(False, False), use_bias=True)
]

@pytest.mark.parametrize("params", test_data_simple)
@pytest.mark.nightly
@pytest.mark.precommit
def test_keras_gru_with_bias_float32(self, params, ie_device, precision, temp_dir, ir_version,
use_legacy_frontend):
self._test(*self.create_keras_gru_net(**params, ir_version=ir_version),
ie_device, precision, temp_dir=temp_dir, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend, **params)

test_data_without_bias = [
dict(input_names=["x"], input_shapes=[[2, 2, 7]], input_type=tf.float32, units=1,
activation='tanh', recurrent_activation='sigmoid', dropouts=(.0, .3), use_bias=False,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[3, 8, 3]], input_type=tf.float32, units=4,
activation='relu', recurrent_activation='sigmoid', dropouts=(.7, .4), use_bias=False,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[4, 2, 2]], input_type=tf.float32, units=2,
activation='elu', recurrent_activation='tanh', dropouts=(.0, .5), use_bias=False,
flags=(False, False))
]

@pytest.mark.parametrize("params", test_data_without_bias)
@pytest.mark.nightly
@pytest.mark.precommit
def test_keras_gru_without_bias_float32(self, params, ie_device, precision, temp_dir,
ir_version, use_legacy_frontend):
self._test(*self.create_keras_gru_net(**params, ir_version=ir_version),
ie_device, precision, temp_dir=temp_dir, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend, **params)

test_data_different_flags = [
dict(input_names=["x"], input_shapes=[[2, 3, 2]], input_type=tf.float32, units=1,
activation='elu', recurrent_activation='sigmoid', dropouts=(.0, .3), use_bias=True,
flags=(True, False)),
dict(input_names=["x"], input_shapes=[[4, 8, 3]], input_type=tf.float32, dropouts=(.1, .3),
units=3, activation='relu', use_bias=False, recurrent_activation='tanh',
flags=(False, True)),
dict(input_names=["x"], input_shapes=[[4, 2, 7]], input_type=tf.float32, units=5,
activation='relu', recurrent_activation='tanh', dropouts=(.2, .6),
use_bias=True, flags=(False, False)),
dict(input_names=["x"], input_shapes=[[4, 16, 2]], input_type=tf.float32, units=5,
activation='relu', recurrent_activation='tanh', dropouts=(.2, .6),
use_bias=True, flags=(False, True)),
dict(input_names=["x"], input_shapes=[[4, 8, 7]], input_type=tf.float32, units=5,
activation='elu', recurrent_activation='sigmoid', dropouts=(.2, .6),
use_bias=True, flags=(True, True)),
]

@pytest.mark.parametrize("params", test_data_different_flags)
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.xfail(reason="sporadic inference mismatch")
def test_keras_gru_flags_float32(self, params, ie_device, precision, temp_dir, ir_version,
use_legacy_frontend):
self._test(*self.create_keras_gru_net(**params, ir_version=ir_version),
ie_device, precision, temp_dir=temp_dir, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend, **params)

test_data_zero_recurrent_dropout = [
dict(input_names=["x"], input_shapes=[[8, 2, 3]], input_type=tf.float32, units=3,
activation='elu', recurrent_activation='tanh', dropouts=(.7, .0), use_bias=True,
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[4, 8, 5]], input_type=tf.float32, dropouts=(.6, .0),
units=2, activation='elu', use_bias=True, recurrent_activation='tanh',
flags=(False, False)),
dict(input_names=["x"], input_shapes=[[4, 3, 1]], input_type=tf.float32, units=8,
activation='elu', recurrent_activation='tanh', dropouts=(.5, .0),
use_bias=True, flags=(True, False)),
dict(input_names=["x"], input_shapes=[[3, 4, 2]], input_type=tf.float32, units=3,
activation='elu', recurrent_activation='tanh', dropouts=(.7, .0), use_bias=True,
flags=(True, False)),
]

@pytest.mark.parametrize("params", test_data_zero_recurrent_dropout)
@pytest.mark.parametrize('input_shapes', [[[2, 3, 4]]])
@pytest.mark.parametrize('input_type', [np.float32, np.float64])
@pytest.mark.parametrize('units', [1, 2, 3])
@pytest.mark.parametrize('activation', ['tanh', 'relu', 'elu', 'linear'])
@pytest.mark.parametrize('recurrent_activation', ['sigmoid', 'tanh', 'softmax'])
@pytest.mark.parametrize('dropouts', [(.0, .0), (.0, .3), (.2, .4), ])
@pytest.mark.parametrize('use_bias', [True, False])
@pytest.mark.parametrize('flag1', [True, False])
@pytest.mark.parametrize('flag2', [True, False])
@pytest.mark.nightly
@pytest.mark.precommit
@pytest.mark.xfail(reason="50176")
def test_keras_gru_flags_zero_recurrent_dropout_float32(self, params, ie_device, precision,
temp_dir, ir_version,
use_legacy_frontend):
self._test(*self.create_keras_gru_net(**params, ir_version=ir_version),
def test_keras_gru(self, input_shapes, input_type, units,
activation, recurrent_activation,
dropouts, use_bias, flag1, flag2,
ie_device, precision, temp_dir, ir_version,
use_legacy_frontend):
params = {}
params['input_shapes'] = input_shapes
self._test(*self.create_keras_gru_net(input_shapes, input_type, units,
activation, recurrent_activation,
dropouts, use_bias, flag1, flag2),
ie_device, precision, temp_dir=temp_dir, ir_version=ir_version,
use_legacy_frontend=use_legacy_frontend, **params)

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