Layer test tflite (#17410)
* tfl argvalue test * tfl add_n test * tfl batch to space and space to batch * tfl batch to space and space to batch * tfl broadcast * tfl avgpool * tfl concat * tfl conv2d and depthtospace * tfl depthwise_conv2d * tfl broadcast_args * tfl fill * tfl floordiv * tfl fully_connected * tfl fully_connected * tfl gather * tfl gather_nd * tfl l2_normalization * tfl matrix_diag * tfl maxpool2d * tfl mirror_pad * tfl one_hot * tfl pack * tfl pad * tfl range * tfl relu * tfl reshape * tfl resize_bilinear * tfl resize * tfl reverse * tfl tfft2d * tfl scatter_nd * tfl rfft2d fix * tfl segment_sum * tfl select * tfl select_v2 * tfl slice * tfl space_to_depth * tfl split * tfl split_v * tfl strided_slice * tfl tile * tfl topk_v2 * tfl transpose * tfl transpose_conv * tfl fix conv tests * tfl unique + refactoring * tfl unpack * refactoring * tfl review - reformat params * tfl review - fixes * tfl review - fixes * tfl review - xfails * tfl review - remove redundant tickets * tfl review - xfail for pad
This commit is contained in:
Ruslan Nugmanov
GitHub
parent
8fed094168
commit
aa932d341a
@ -5,7 +5,7 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorflow as tf
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from tensorflow.lite.tools import flatbuffer_utils as utils
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from common.layer_test_class import CommonLayerTest
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from common.utils.tflite_utils import get_tflite_results, get_tensors_from_graph
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from common.utils.tflite_utils import get_tflite_results, get_tensors_from_graph, data_generators
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class TFLiteLayerTest(CommonLayerTest):
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@ -14,6 +14,11 @@ class TFLiteLayerTest(CommonLayerTest):
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outputs = None
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allowed_ops = None
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def _prepare_input(self, inputs_dict, generator=None):
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if generator is None:
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return super()._prepare_input(inputs_dict)
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return data_generators[generator](inputs_dict)
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def make_model(self, params):
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raise RuntimeError("This is TensorFlow Lite base layer test class, "
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"please implement make_model function for the specific test")
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@ -1,7 +1,9 @@
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import itertools
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import os
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import tensorflow as tf
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import numpy as np
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import tensorflow as tf
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from common.utils.tf_utils import summarize_graph, transpose_nhwc_to_nchw
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@ -23,10 +25,17 @@ def make_boolean_array(inputs_dict):
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return inputs_dict
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def make_int32_positive_array(inputs_dict):
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for input in inputs_dict.keys():
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inputs_dict[input] = np.random.randint(1, 10, inputs_dict[input]).astype(np.int32)
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return inputs_dict
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data_generators = {
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'positive': make_positive_array,
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'short_range': short_range,
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'boolean': make_boolean_array,
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'int32_positive': make_int32_positive_array,
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}
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@ -41,7 +50,7 @@ additional_test_params = [
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[
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{'axis': None},
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{'axis': -1}
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],
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],
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[
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{'activation': None},
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{'activation': tf.nn.relu},
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@ -51,7 +60,7 @@ additional_test_params = [
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# {'activation': tf.math.tanh},
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# {'activation': lambda x, name: tf.identity(tf.experimental.numpy.signbit(x), name=name)},
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{'activation': lambda x, name: tf.math.minimum(tf.math.maximum(-1., x), 1., name=name)}
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]
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]
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]
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@ -119,3 +128,10 @@ def get_tensors_from_graph(graph, ops: list):
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return tensors
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def parametrize_tests(lhs, rhs):
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test_data = list(itertools.product(lhs, rhs))
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for i, (parameters, shapes) in enumerate(test_data):
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parameters.update(shapes)
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test_data[i] = parameters.copy()
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return test_data
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46
tests/layer_tests/tensorflow_lite_tests/test_tfl_AddN.py
Normal file
46
tests/layer_tests/tensorflow_lite_tests/test_tfl_AddN.py
Normal file
@ -0,0 +1,46 @@
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import pytest
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import tensorflow as tf
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from common.tflite_layer_test_class import TFLiteLayerTest
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from common.utils.tflite_utils import parametrize_tests
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num_inputs = [
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{'num_inputs': 2},
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{'num_inputs': 4},
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{'num_inputs': 5},
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]
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test_params = [
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{'shape': [2, 10, 10, 3]},
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{'shape': [2, 10]}
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]
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test_data = parametrize_tests(num_inputs, test_params)
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class TestTFLiteAddNLayerTest(TFLiteLayerTest):
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inputs = []
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outputs = ["AddN"]
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allowed_ops = ['ADD_N']
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'shape', 'num_inputs'})) == 2, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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self.inputs = []
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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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inputs = []
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for j in range(params['num_inputs']):
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name = f"Input_{j}"
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self.inputs.append(name)
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placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=name)
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inputs.append(placeholder)
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tf.math.add_n(inputs, name=self.outputs[0])
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net = sess.graph_def
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return net
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_add_n(self, params, ie_device, precision, temp_dir):
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self._test(ie_device, precision, temp_dir, params)
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41
tests/layer_tests/tensorflow_lite_tests/test_tfl_ArgValue.py
Normal file
41
tests/layer_tests/tensorflow_lite_tests/test_tfl_ArgValue.py
Normal file
@ -0,0 +1,41 @@
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import pytest
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import tensorflow as tf
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from common.tflite_layer_test_class import TFLiteLayerTest
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from common.utils.tflite_utils import additional_test_params
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from common.utils.tflite_utils import parametrize_tests
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test_ops = [
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{'op_name': 'ARG_MAX', 'op_func': tf.math.argmax},
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{'op_name': 'ARG_MIN', 'op_func': tf.math.argmin},
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]
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test_params = [
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{'shape': [2, 10, 10, 3]},
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{'shape': [2, 10]}
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]
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test_data = parametrize_tests(test_ops, test_params)
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test_data = parametrize_tests(test_data, additional_test_params[0])
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class TestTFLiteArgValueLayerTest(TFLiteLayerTest):
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inputs = ["Input"]
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outputs = ["ArgValue"]
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape', 'axis'})) == 4, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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self.allowed_ops = [params['op_name']]
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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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place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=self.inputs[0])
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params['op_func'](place_holder, axis=params['axis'], name=self.outputs[0])
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net = sess.graph_def
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return net
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_arg_value(self, params, ie_device, precision, temp_dir):
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self._test(ie_device, precision, temp_dir, params)
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@ -0,0 +1,32 @@
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import pytest
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import tensorflow as tf
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from common.tflite_layer_test_class import TFLiteLayerTest
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test_params = [
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{'shape': [8, 10, 10, 3], 'block_shape': [2, 2], 'crops': [[0, 2], [0, 0]]},
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{'shape': [24, 10, 10, 1], 'block_shape': [2, 12], 'crops': [[2, 0], [0, 2]]}
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]
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class TestTFLiteBatchToSpaceNDLayerTest(TFLiteLayerTest):
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inputs = ["Input"]
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outputs = ["BatchToSpaceND"]
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allowed_ops = ['BATCH_TO_SPACE_ND']
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'shape', 'block_shape', 'crops'})) == 3, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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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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place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=self.inputs[0])
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tf.batch_to_space(place_holder, params['block_shape'], params['crops'],
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name=self.outputs[0])
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net = sess.graph_def
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return net
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@pytest.mark.parametrize("params", test_params)
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@pytest.mark.nightly
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def test_batch_to_space_nd(self, params, ie_device, precision, temp_dir):
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self._test(ie_device, precision, temp_dir, params)
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@ -1,14 +1,13 @@
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import itertools
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import pytest
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import tensorflow as tf
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from common.tflite_layer_test_class import TFLiteLayerTest
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from tensorflow_lite_tests.test_tfl_Unary import data_generators
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from common.utils.tflite_utils import parametrize_tests
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test_ops = [
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{'op_name': 'EQUAL', 'op_func': tf.math.equal},
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{'op_name': 'FLOOR_MOD', 'op_func': tf.math.floormod},
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{'op_name': 'FLOOR_MOD', 'op_func': tf.math.floormod, 'kwargs_to_prepare_input': 'positive'},
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{'op_name': 'FLOOR_DIV', 'op_func': tf.math.floordiv, 'kwargs_to_prepare_input': 'positive'},
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{'op_name': 'GREATER', 'op_func': tf.math.greater},
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{'op_name': 'GREATER_EQUAL', 'op_func': tf.math.greater_equal},
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{'op_name': 'LESS', 'op_func': tf.math.less},
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@ -27,21 +26,13 @@ test_params = [
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{'shape': [2, 10]}
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]
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test_data = list(itertools.product(test_ops, test_params))
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for i, (parameters, shapes) in enumerate(test_data):
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parameters.update(shapes)
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test_data[i] = parameters.copy()
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test_data = parametrize_tests(test_ops, test_params)
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class TestTFLiteBinaryLayerTest(TFLiteLayerTest):
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inputs = ["Input_0", "Input_1"]
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outputs = ["BinaryOperation"]
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def _prepare_input(self, inputs_dict, generator=None):
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if generator is None:
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return super()._prepare_input(inputs_dict)
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return data_generators[generator](inputs_dict)
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape'})) == 3, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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@ -49,10 +40,10 @@ class TestTFLiteBinaryLayerTest(TFLiteLayerTest):
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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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place_holder0 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=TestTFLiteBinaryLayerTest.inputs[0])
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name=self.inputs[0])
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place_holder1 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=TestTFLiteBinaryLayerTest.inputs[1])
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params['op_func'](place_holder0, place_holder1, name=TestTFLiteBinaryLayerTest.outputs[0])
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name=self.inputs[1])
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params['op_func'](place_holder0, place_holder1, name=self.outputs[0])
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net = sess.graph_def
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return net
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@ -1,10 +1,9 @@
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import itertools
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import tensorflow as tf
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import pytest
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import tensorflow as tf
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from common.tflite_layer_test_class import TFLiteLayerTest
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from common.utils.tflite_utils import data_generators, additional_test_params, activation_helper
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from common.utils.tflite_utils import additional_test_params, activation_helper
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from common.utils.tflite_utils import parametrize_tests
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test_ops = [
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{'op_name': 'ADD', 'op_func': tf.math.add},
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@ -18,41 +17,26 @@ test_params = [
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{'shape': [2, 10]}
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]
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test_data = list(itertools.product(test_ops, test_params))
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for i, (parameters, shapes) in enumerate(test_data):
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parameters.update(shapes)
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test_data[i] = parameters.copy()
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test_data = list(itertools.product(test_data, additional_test_params[1]))
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for i, (parameters, additional_test_params[1]) in enumerate(test_data):
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parameters.update(additional_test_params[1])
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test_data[i] = parameters.copy()
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test_data = parametrize_tests(test_ops, test_params)
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test_data = parametrize_tests(test_data, additional_test_params[1])
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class TestTFLiteBinaryWithActivationLayerTest(TFLiteLayerTest):
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inputs = ["Input_0", "Input_1"]
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outputs = ["BinaryOperation"]
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def _prepare_input(self, inputs_dict, generator=None):
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if generator is None:
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return super()._prepare_input(inputs_dict)
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return data_generators[generator](inputs_dict)
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape', 'activation'})) == 4, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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self.allowed_ops = [params['op_name']]
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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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in0 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=TestTFLiteBinaryWithActivationLayerTest.inputs[0])
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in1 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=TestTFLiteBinaryWithActivationLayerTest.inputs[1])
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bin_op_name = TestTFLiteBinaryWithActivationLayerTest.outputs[0] if not params['activation'] else \
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TestTFLiteBinaryWithActivationLayerTest.outputs[0] + "/op"
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in0 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'], name=self.inputs[0])
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in1 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'], name=self.inputs[1])
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bin_op_name = self.outputs[0] if not params['activation'] else \
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self.outputs[0] + "/op"
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op = params['op_func'](in0, in1, name=bin_op_name)
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op = activation_helper(op, params['activation'], TestTFLiteBinaryWithActivationLayerTest.outputs[0])
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op = activation_helper(op, params['activation'], self.outputs[0])
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net = sess.graph_def
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return net
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@ -0,0 +1,42 @@
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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.tflite_layer_test_class import TFLiteLayerTest
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test_params = [
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{'shape': [1, 5], 'broadcast_shape': [5, 5]},
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{'shape': [1], 'broadcast_shape': [7]}
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]
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class TestTFLiteBroadcastArgsLayerTest(TFLiteLayerTest):
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inputs = ["Input", "Input1"]
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outputs = ["BroadcastArgsOP"]
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allowed_ops = ['BROADCAST_ARGS']
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def _prepare_input(self, inputs_dict, generator=None):
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inputs_dict["Input"] = np.array(self.shape).astype(np.int32)
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inputs_dict["Input1"] = np.array(self.broadcast_shape).astype(np.int32)
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return inputs_dict
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'shape', 'broadcast_shape'})) == 2, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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tf.compat.v1.reset_default_graph()
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self.shape = params['shape']
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self.broadcast_shape = params['broadcast_shape']
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with tf.compat.v1.Session() as sess:
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s0 = tf.compat.v1.placeholder(params.get('dtype', tf.int32), [len(self.shape)],
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name=self.inputs[0])
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s1 = tf.compat.v1.placeholder(params.get('dtype', tf.int32), [len(self.shape)],
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name=self.inputs[1])
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tf.raw_ops.BroadcastArgs(s0=s0, s1=s1, name=self.outputs[0])
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net = sess.graph_def
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return net
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@pytest.mark.parametrize("params", test_params)
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@pytest.mark.nightly
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def test_broadcast_args(self, params, ie_device, precision, temp_dir):
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self._test(ie_device, precision, temp_dir, params)
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@ -0,0 +1,33 @@
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import pytest
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import tensorflow as tf
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from common.tflite_layer_test_class import TFLiteLayerTest
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test_params = [
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{'shape': [1, 1, 1, 1, 40], 'broadcast_shape': [1, 1, 56, 56, 40]},
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{'shape': [1, 1, 1, 1, 10], 'broadcast_shape': [1, 1, 10, 10, 10]}
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]
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class TestTFLiteBroadcastToLayerTest(TFLiteLayerTest):
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inputs = ["Input"]
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outputs = ["BroadcastToOP"]
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allowed_ops = ['BROADCAST_TO']
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def make_model(self, params):
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assert len(set(params.keys()).intersection({'shape', 'broadcast_shape'})) == 2, \
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'Unexpected parameters for test: ' + ','.join(params.keys())
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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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place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
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name=self.inputs[0])
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shape_of = tf.reshape(place_holder, params['shape'])
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tf.broadcast_to(input=shape_of, shape=params['broadcast_shape'],
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name=self.outputs[0])
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net = sess.graph_def
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return net
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@pytest.mark.parametrize("params", test_params)
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@pytest.mark.nightly
|
||||
def test_broadcast_to(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
39
tests/layer_tests/tensorflow_lite_tests/test_tfl_Concat.py
Normal file
39
tests/layer_tests/tensorflow_lite_tests/test_tfl_Concat.py
Normal file
@ -0,0 +1,39 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shapes': [[1, 3], [1, 7]], 'axis': -1},
|
||||
{'shapes': [[1, 3, 10], [1, 3, 56], [1, 3, 2]], 'axis': -1},
|
||||
{'shapes': [[1, 3, 10, 17], [1, 3, 10, 1], [1, 3, 10, 5], [1, 3, 10, 3]], 'axis': -1},
|
||||
{'shapes': [[2, 7], [2, 8]], 'axis': 1},
|
||||
{'shapes': [[1, 3, 10], [1, 2, 10], [1, 5, 10]], 'axis': 1},
|
||||
{'shapes': [[1, 3, 10, 17], [1, 10, 10, 17], [1, 12, 10, 17], [1, 1, 10, 17]], 'axis': 1},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteConcatTest(TFLiteLayerTest):
|
||||
outputs = ['Concat']
|
||||
allowed_ops = ['CONCATENATION']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shapes', 'axis'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholders = []
|
||||
self.inputs = []
|
||||
for j, placeholder_shape in enumerate(params['shapes']):
|
||||
concat_input = f'concat_input_{j}'
|
||||
self.inputs.append(concat_input)
|
||||
placeholders.append(tf.compat.v1.placeholder(params.get('dtype', tf.float32), placeholder_shape,
|
||||
name=concat_input))
|
||||
tf.concat(placeholders, params['axis'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_concat(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
43
tests/layer_tests/tensorflow_lite_tests/test_tfl_Conv2D.py
Normal file
43
tests/layer_tests/tensorflow_lite_tests/test_tfl_Conv2D.py
Normal file
@ -0,0 +1,43 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [32, 3, 4, 4], 'strides': 2, 'padding': 'SAME', 'data_format': 'NHWC',
|
||||
'dilations': [1, 1, 1, 1]},
|
||||
{'shape': [1, 22, 22, 9], 'ksize': [32, 3, 3, 3], 'strides': (2, 1), 'padding': 'SAME', 'data_format': 'NHWC',
|
||||
'dilations': [1, 2, 2, 1]},
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [1, 3, 4, 4], 'strides': 2, 'padding': 'VALID', 'data_format': 'NHWC',
|
||||
'dilations': [1, 1, 1, 1]},
|
||||
{'shape': [1, 22, 22, 3], 'ksize': [1, 3, 3, 3], 'strides': (3, 4), 'padding': 'VALID', 'data_format': 'NHWC',
|
||||
'dilations': [1, 2, 2, 1]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteConv2DLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Conv2D"]
|
||||
allowed_ops = ['CONV_2D']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'ksize', 'strides',
|
||||
'padding', 'data_format', 'dilations'})) == 6, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
weights = tf.constant(np.random.randint(-1, 1, params['ksize']), dtype=tf.float32)
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.nn.conv2d(place_holder, weights, params['strides'], params['padding'], params['data_format'],
|
||||
params['dilations'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_conv2d(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,31 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [8, 10, 10, 16], 'block_size': 2, 'data_format': 'NHWC'},
|
||||
{'shape': [24, 10, 10, 50], 'block_size': 5, 'data_format': 'NHWC'},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteDepthToSpaceLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["DepthToSpace"]
|
||||
allowed_ops = ['DEPTH_TO_SPACE']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'block_size', 'data_format'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.nn.depth_to_space(place_holder, params['block_size'], params['data_format'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_depth_to_space(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,39 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [3, 3, 8, 2], 'strides': [1, 2, 2, 1], 'padding': 'SAME', 'data_format': 'NHWC',
|
||||
'dilations': [1, 1]},
|
||||
{'shape': [1, 22, 22, 9], 'ksize': [3, 3, 9, 1], 'strides': [1, 1, 1, 1], 'padding': 'SAME', 'data_format': 'NHWC',
|
||||
'dilations': [1, 1]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteDepthwiseConv2DLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["DepthwiseConv2D"]
|
||||
allowed_ops = ['DEPTHWISE_CONV_2D']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'ksize', 'strides',
|
||||
'padding', 'data_format', 'dilations'})) == 6, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
weights = tf.constant(np.random.randint(-1, 1, params['ksize']), dtype=tf.float32)
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.nn.depthwise_conv2d(place_holder, weights, params['strides'], params['padding'], params['data_format'],
|
||||
params['dilations'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_depthwise_conv2d(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
32
tests/layer_tests/tensorflow_lite_tests/test_tfl_Fill.py
Normal file
32
tests/layer_tests/tensorflow_lite_tests/test_tfl_Fill.py
Normal file
@ -0,0 +1,32 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2, 3], 'value': 1, 'kwargs_to_prepare_input': 'int32_positive'},
|
||||
{'shape': [2, 3, 3, 4], 'value': -1, 'kwargs_to_prepare_input': 'int32_positive'},
|
||||
{'shape': [1], 'value': 0, 'kwargs_to_prepare_input': 'int32_positive'}
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteBroadcastToLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Fill"]
|
||||
allowed_ops = ['FILL']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'value'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.int32), [len(params['shape'])],
|
||||
name=self.inputs[0])
|
||||
tf.fill(placeholder, params['value'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_fill(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,35 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape_x': [40, 37], 'shape_y': [37, 37], 'transpose_a': False, 'transpose_b': True},
|
||||
{'shape_x': [5, 5], 'shape_y': [4, 5], 'transpose_a': False, 'transpose_b': True},
|
||||
{'shape_x': [1, 5, 5], 'shape_y': [4, 5], 'transpose_a': False, 'transpose_b': True},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteFullyConnectedLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input_x", "Input_y"]
|
||||
outputs = ["FullyConnected"]
|
||||
allowed_ops = ['FULLY_CONNECTED']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape_x', 'shape_y',
|
||||
'transpose_a', 'transpose_b'})) == 4, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
x = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape_x'], name=self.inputs[0])
|
||||
y = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape_y'], name=self.inputs[1])
|
||||
|
||||
tf.matmul(x, y, transpose_a=params['transpose_a'], transpose_b=params['transpose_b'],
|
||||
name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_fully_connected(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
38
tests/layer_tests/tensorflow_lite_tests/test_tfl_Gather.py
Normal file
38
tests/layer_tests/tensorflow_lite_tests/test_tfl_Gather.py
Normal file
@ -0,0 +1,38 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'shape': [1, 10], 'indices_shape': [1, 5]},
|
||||
{'shape': [2, 3, 5, 5], 'indices_shape': [4, 5]},
|
||||
{'shape': [1, 5, 5], 'indices_shape': [2, 4, 5]},
|
||||
{'shape': [1, 5, 5], 'indices_shape': [2, 4, 5, 6]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteGatherLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input_x"]
|
||||
outputs = ["Gather"]
|
||||
allowed_ops = ['GATHER']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'indices_shape', })) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.float32),
|
||||
params['shape'], name=self.inputs[0])
|
||||
constant = tf.constant(np.random.randint(0, 1, size=params['indices_shape']))
|
||||
|
||||
tf.gather(placeholder, constant, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_gather(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
50
tests/layer_tests/tensorflow_lite_tests/test_tfl_GatherND.py
Normal file
50
tests/layer_tests/tensorflow_lite_tests/test_tfl_GatherND.py
Normal file
@ -0,0 +1,50 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [5, 1], 'indices_shape': [1, 1], 'batch_dims': 0},
|
||||
|
||||
{'shape': [5, 5], 'indices_shape': [2, 1], 'batch_dims': 0},
|
||||
{'shape': [5, 5], 'indices_shape': [2, 2], 'batch_dims': 0},
|
||||
#
|
||||
{'shape': [3, 5, 10], 'indices_shape': [3, 2], 'batch_dims': 1},
|
||||
{'shape': [5, 5, 10], 'indices_shape': [2, 3], 'batch_dims': 0},
|
||||
{'shape': [4, 5, 10], 'indices_shape': [2, 10, 1], 'batch_dims': 2},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteGatherLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input_x"]
|
||||
outputs = ["GatherND"]
|
||||
allowed_ops = ['GATHER_ND']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'indices_shape', 'batch_dims'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
self.outputs = ["GatherND"]
|
||||
self.allowed_ops = ["GATHER_ND"]
|
||||
if params['batch_dims'] > 0:
|
||||
output_name = "GatherND"
|
||||
self.outputs = [f"GatherND/Reshape_{params['batch_dims'] + 2}"]
|
||||
if params['batch_dims'] > 1:
|
||||
self.allowed_ops.append('RESHAPE')
|
||||
else:
|
||||
output_name = self.outputs[0]
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.float32),
|
||||
params['shape'], name=self.inputs[0])
|
||||
constant = tf.constant(np.random.randint(0, params['shape'][0] - 1, params['indices_shape']))
|
||||
|
||||
tf.gather_nd(placeholder, constant, name=output_name, batch_dims=params['batch_dims'])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_gather_nd(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,37 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
from common.utils.tflite_utils import additional_test_params
|
||||
from common.utils.tflite_utils import parametrize_tests
|
||||
|
||||
test_params = [
|
||||
{'shape': [1]},
|
||||
{'shape': [1, 22]},
|
||||
{'shape': [1, 1, 8]},
|
||||
{'shape': [1, 22, 22, 8]},
|
||||
]
|
||||
|
||||
test_data = parametrize_tests(test_params, additional_test_params[0])
|
||||
|
||||
|
||||
class TestTFLiteExpandDimsLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["L2_Normalization"]
|
||||
allowed_ops = ['L2_NORMALIZATION']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape'})) == 1, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.math.l2_normalize(place_holder, axis=params['axis'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.nightly
|
||||
def test_l2_normalization(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,34 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [3]},
|
||||
{'shape': [1, 22]},
|
||||
{'shape': [1, 1, 8]},
|
||||
{'shape': [1, 22, 22, 8]},
|
||||
{'shape': [1, 22, 22, 8, 3]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteMatrixDiagLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["MatrixDiag"]
|
||||
allowed_ops = ['MATRIX_DIAG']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape'})) == 1, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.linalg.diag(place_holder, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_matrix_diag(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,39 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2, 3], 'padding_matrix': [[1, 1], [2, 1]], 'mode': 'REFLECT'},
|
||||
{'shape': [2, 3], 'padding_matrix': [[1, 1], [1, 1]], 'mode': 'REFLECT'},
|
||||
{'shape': [2, 3], 'padding_matrix': [[1, 1], [2, 1]], 'mode': 'SYMMETRIC'},
|
||||
|
||||
{'shape': [3], 'padding_matrix': [[0, 2]], 'mode': 'SYMMETRIC'},
|
||||
{'shape': [3], 'padding_matrix': [[0, 2]], 'mode': 'REFLECT'},
|
||||
|
||||
{'shape': [3, 2, 4, 5], 'padding_matrix': [[1, 1], [2, 2], [1, 1], [1, 1]], 'mode': 'SYMMETRIC'},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteMirrorPadLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["MirrorPad"]
|
||||
allowed_ops = ['MIRROR_PAD']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'padding_matrix', 'mode'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
padding_matrix = tf.constant(np.array(params["padding_matrix"]))
|
||||
tf.pad(tensor=place_holder, paddings=padding_matrix, mode=params['mode'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_mirror_pad(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
38
tests/layer_tests/tensorflow_lite_tests/test_tfl_OneHot.py
Normal file
38
tests/layer_tests/tensorflow_lite_tests/test_tfl_OneHot.py
Normal file
@ -0,0 +1,38 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [3], 'axis': 0, 'kwargs_to_prepare_input': 'int32_positive'},
|
||||
{'shape': [4, 4], 'axis': 1, 'kwargs_to_prepare_input': 'int32_positive'},
|
||||
{'shape': [1, 5, 3], 'axis': 0, 'kwargs_to_prepare_input': 'int32_positive'},
|
||||
{'shape': [5, 1, 2, 4], 'axis': 1, 'kwargs_to_prepare_input': 'int32_positive'},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteOneHotLayerTest(TFLiteLayerTest):
|
||||
inputs = ['Indices', 'Depth', 'OnValue', 'OffValue']
|
||||
outputs = ["OneHot"]
|
||||
allowed_ops = ['ONE_HOT']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'axis'})) == 1, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
indices = tf.compat.v1.placeholder(dtype=tf.int32, name=self.inputs[0], shape=params["shape"])
|
||||
depth = tf.compat.v1.placeholder(dtype=tf.int32, name=self.inputs[1], shape=())
|
||||
|
||||
on_value = tf.compat.v1.placeholder(dtype=tf.int32, name=self.inputs[2], shape=())
|
||||
off_value = tf.compat.v1.placeholder(dtype=tf.int32, name=self.inputs[3], shape=())
|
||||
|
||||
tf.one_hot(indices=indices, depth=depth, on_value=on_value, off_value=off_value,
|
||||
axis=params['axis'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_one_hot(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
40
tests/layer_tests/tensorflow_lite_tests/test_tfl_Pack.py
Normal file
40
tests/layer_tests/tensorflow_lite_tests/test_tfl_Pack.py
Normal file
@ -0,0 +1,40 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [3], 'num_tensors': 2},
|
||||
{'shape': [1, 22], 'num_tensors': 10},
|
||||
{'shape': [1, 1, 8], 'num_tensors': 5},
|
||||
{'shape': [1, 22, 22, 8], 'num_tensors': 5},
|
||||
{'shape': [1, 22, 22, 8, 3], 'num_tensors': 7},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLitePackLayerTest(TFLiteLayerTest):
|
||||
inputs = []
|
||||
outputs = ["Pack"]
|
||||
allowed_ops = ['PACK']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'num_tensors'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
tensors = []
|
||||
self.inputs = []
|
||||
for j in range(params['num_tensors']):
|
||||
name = f'Input_{j}'
|
||||
self.inputs.append(name)
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=name)
|
||||
tensors.append(place_holder)
|
||||
tf.stack(tensors, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_pack(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
43
tests/layer_tests/tensorflow_lite_tests/test_tfl_Pad.py
Normal file
43
tests/layer_tests/tensorflow_lite_tests/test_tfl_Pad.py
Normal file
@ -0,0 +1,43 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [1, 1, 2, 1, 1], 'paddings': [[0, 0], [0, 1], [2, 3], [0, 0], [1, 0]]},
|
||||
{'shape': [2, 1, 1, 1, 1], 'paddings': [[0, 1], [0, 0], [0, 0], [2, 3], [1, 0]]},
|
||||
|
||||
{'shape': [1, 1, 2, 1], 'paddings': [[0, 0], [0, 1], [2, 3], [0, 0]]},
|
||||
{'shape': [1, 1, 2, 1], 'paddings': [[0, 0], [0, 1], [2, 3], [0, 0]]},
|
||||
|
||||
{'shape': [1, 2], 'paddings': [[0, 1], [2, 1]]},
|
||||
{'shape': [1, 2], 'paddings': [[2, 3], [0, 1]]},
|
||||
|
||||
{'shape': [1], 'paddings': [[1, 2]]},
|
||||
|
||||
]
|
||||
|
||||
|
||||
class TestTFLitePadLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input", 'Paddings']
|
||||
outputs = ["Pad"]
|
||||
allowed_ops = ['PAD']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'paddings'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
shape = [len(params["paddings"]), 2]
|
||||
paddings = tf.compat.v1.placeholder(dtype=tf.int32, name=self.inputs[1], shape=shape)
|
||||
tf.pad(tensor=place_holder, paddings=paddings, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_pad(self, params, ie_device, precision, temp_dir):
|
||||
pytest.xfail("CVS-110828")
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
44
tests/layer_tests/tensorflow_lite_tests/test_tfl_Pool.py
Normal file
44
tests/layer_tests/tensorflow_lite_tests/test_tfl_Pool.py
Normal file
@ -0,0 +1,44 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
from common.utils.tflite_utils import parametrize_tests
|
||||
|
||||
test_ops = [
|
||||
{'op_name': 'AVERAGE_POOL_2D', 'op_func': tf.nn.avg_pool2d},
|
||||
{'op_name': 'MAX_POOL_2D', 'op_func': tf.nn.max_pool2d},
|
||||
]
|
||||
|
||||
test_params = [
|
||||
# TFLite doesn't support avgpool with 'NCHW' format
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [3, 3], 'strides': 2, 'padding': 'SAME', 'data_format': 'NHWC'},
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [3, 3], 'strides': (2, 1), 'padding': 'SAME', 'data_format': 'NHWC'},
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [3, 3], 'strides': 2, 'padding': 'VALID', 'data_format': 'NHWC'},
|
||||
{'shape': [1, 22, 22, 8], 'ksize': [3, 3], 'strides': (3, 4), 'padding': 'VALID', 'data_format': 'NHWC'},
|
||||
]
|
||||
|
||||
test_data = parametrize_tests(test_ops, test_params)
|
||||
|
||||
|
||||
class TestTFLitePoolLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Pool"]
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape', 'ksize', 'strides',
|
||||
'padding', 'data_format'})) == 7, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
self.allowed_ops = [params['op_name']]
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
params['op_func'](place_holder, params['ksize'], params['strides'],
|
||||
params['padding'], params['data_format'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.nightly
|
||||
def test_pool(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
34
tests/layer_tests/tensorflow_lite_tests/test_tfl_RFFT2D.py
Normal file
34
tests/layer_tests/tensorflow_lite_tests/test_tfl_RFFT2D.py
Normal file
@ -0,0 +1,34 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [96, 1, 512], 'fft_length': [1, 512], 'reshape_to': [96, 257]},
|
||||
{'shape': [5, 1, 16], 'fft_length': [1, 16], 'reshape_to': [5, 9]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteRFFT2DLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ['RFFT2D_1']
|
||||
allowed_ops = ['COMPLEX_ABS', 'RESHAPE', 'RFFT2D']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'fft_length', 'reshape_to'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
rfft_2d = tf.signal.rfft2d(placeholder, fft_length=params["fft_length"])
|
||||
reshape = tf.reshape(rfft_2d, params['reshape_to'])
|
||||
out = tf.raw_ops.ComplexAbs(x=reshape, Tout=tf.float32, name='RFFT2D')
|
||||
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_rfft2d(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
52
tests/layer_tests/tensorflow_lite_tests/test_tfl_Range.py
Normal file
52
tests/layer_tests/tensorflow_lite_tests/test_tfl_Range.py
Normal file
@ -0,0 +1,52 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'dtype': np.float32, 'negative_delta': False},
|
||||
{'dtype': np.int32, 'negative_delta': True},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteRangeLayerTest(TFLiteLayerTest):
|
||||
inputs = ['Start', 'Limit', 'Delta']
|
||||
outputs = ["Range"]
|
||||
allowed_ops = ['RANGE']
|
||||
|
||||
def _prepare_input(self, inputs_dict, generator=None):
|
||||
inputs_data = {}
|
||||
if self.negative_delta:
|
||||
inputs_data['Start'] = np.random.randint(1, 10, []).astype(self.dtype)
|
||||
inputs_data['Limit'] = np.random.randint(-10, 0, []).astype(self.dtype)
|
||||
inputs_data['Delta'] = np.random.randint(-5, -1, []).astype(self.dtype)
|
||||
else:
|
||||
inputs_data['Start'] = np.random.randint(1, 10, []).astype(self.dtype)
|
||||
inputs_data['Limit'] = np.random.randint(10, 30, []).astype(self.dtype)
|
||||
inputs_data['Delta'] = np.random.randint(1, 5, []).astype(self.dtype)
|
||||
|
||||
return inputs_data
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'dtype', 'negative_delta'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
self.dtype = params['dtype']
|
||||
self.negative_delta = params['negative_delta']
|
||||
|
||||
start = tf.compat.v1.placeholder(self.dtype, [], self.inputs[0])
|
||||
limit = tf.compat.v1.placeholder(self.dtype, [], self.inputs[1])
|
||||
delta = tf.compat.v1.placeholder(self.dtype, [], self.inputs[2])
|
||||
|
||||
tf.range(start, limit, delta, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_range(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -1,10 +1,9 @@
|
||||
import itertools
|
||||
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
from common.utils.tflite_utils import data_generators, additional_test_params
|
||||
from common.utils.tflite_utils import additional_test_params
|
||||
from common.utils.tflite_utils import parametrize_tests
|
||||
|
||||
test_ops = [
|
||||
{'op_name': 'MEAN', 'op_func': tf.math.reduce_mean},
|
||||
@ -21,28 +20,14 @@ test_params = [
|
||||
{'shape': [2, 10]}
|
||||
]
|
||||
|
||||
|
||||
test_data = list(itertools.product(test_ops, test_params))
|
||||
for i, (parameters, shapes) in enumerate(test_data):
|
||||
parameters.update(shapes)
|
||||
test_data[i] = parameters.copy()
|
||||
|
||||
|
||||
test_data = list(itertools.product(test_data, additional_test_params[0]))
|
||||
for i, (parameters, additional_test_params[0]) in enumerate(test_data):
|
||||
parameters.update(additional_test_params[0])
|
||||
test_data[i] = parameters.copy()
|
||||
test_data = parametrize_tests(test_ops, test_params)
|
||||
test_data = parametrize_tests(test_data, additional_test_params[0])
|
||||
|
||||
|
||||
class TestTFLiteReduceLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["ReduceOperation"]
|
||||
|
||||
def _prepare_input(self, inputs_dict, generator=None):
|
||||
if generator is None:
|
||||
return super()._prepare_input(inputs_dict)
|
||||
return data_generators[generator](inputs_dict)
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape', 'axis'})) == 4, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
@ -50,8 +35,8 @@ class TestTFLiteReduceLayerTest(TFLiteLayerTest):
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=TestTFLiteReduceLayerTest.inputs[0])
|
||||
params['op_func'](place_holder, axis=params['axis'], name=TestTFLiteReduceLayerTest.outputs[0])
|
||||
name=self.inputs[0])
|
||||
params['op_func'](place_holder, axis=params['axis'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
|
||||
34
tests/layer_tests/tensorflow_lite_tests/test_tfl_Reshape.py
Normal file
34
tests/layer_tests/tensorflow_lite_tests/test_tfl_Reshape.py
Normal file
@ -0,0 +1,34 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2, 6], 'out_shape': [2, 3, 2]},
|
||||
{'shape': [2, 4, 6], 'out_shape': [2, -1]},
|
||||
{'shape': [1], 'out_shape': []},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteReshapeLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Reshape"]
|
||||
allowed_ops = ['RESHAPE']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'out_shape'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
out_shape = tf.constant(params['out_shape'], dtype=tf.int32)
|
||||
|
||||
tf.reshape(place_holder, out_shape, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_reshape(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
50
tests/layer_tests/tensorflow_lite_tests/test_tfl_Resize.py
Normal file
50
tests/layer_tests/tensorflow_lite_tests/test_tfl_Resize.py
Normal file
@ -0,0 +1,50 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
from common.utils.tflite_utils import parametrize_tests
|
||||
|
||||
test_ops = [
|
||||
{'op_name': ['RESIZE_BILINEAR'], 'op_func': tf.compat.v1.image.resize_bilinear},
|
||||
{'op_name': ['RESIZE_NEAREST_NEIGHBOR'], 'op_func': tf.compat.v1.image.resize_nearest_neighbor},
|
||||
]
|
||||
|
||||
test_params = [
|
||||
{'shape': [1, 3, 4, 3], 'size': [1, 1], 'align_corners': True, 'half_pixel_centers': False},
|
||||
{'shape': [1, 3, 4, 3], 'size': [1, 1], 'align_corners': False, 'half_pixel_centers': False},
|
||||
{'shape': [1, 3, 4, 3], 'size': [1, 1], 'align_corners': False, 'half_pixel_centers': True}, # accuracy failure
|
||||
|
||||
{'shape': [1, 3, 4, 3], 'size': [10, 10], 'align_corners': True, 'half_pixel_centers': False},
|
||||
{'shape': [1, 3, 4, 3], 'size': [10, 10], 'align_corners': False, 'half_pixel_centers': False},
|
||||
{'shape': [1, 3, 4, 3], 'size': [10, 10], 'align_corners': False, 'half_pixel_centers': True}, # accuracy failure
|
||||
]
|
||||
|
||||
test_data = parametrize_tests(test_ops, test_params)
|
||||
|
||||
|
||||
class TestTFLiteResizeLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Resize"]
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape', 'size', 'align_corners',
|
||||
'half_pixel_centers'})) == 6, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
self.allowed_ops = params['op_name']
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get("dtype", tf.float32), params["shape"],
|
||||
name=self.inputs[0])
|
||||
params['op_func'](placeholder, size=params['size'],
|
||||
align_corners=params['align_corners'],
|
||||
half_pixel_centers=params['half_pixel_centers'],
|
||||
name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.nightly
|
||||
def test_resize_resize(self, params, ie_device, precision, temp_dir):
|
||||
if not params['align_corners'] and params['half_pixel_centers'] and params['op_name'] == ['RESIZE_NEAREST_NEIGHBOR']:
|
||||
pytest.xfail("CVS-110473")
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,43 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [1], 'axis': [-1]},
|
||||
{'shape': [1], 'axis': [0]},
|
||||
{'shape': [2, 6], 'axis': [-1, -2]},
|
||||
{'shape': [2, 6], 'axis': [1]},
|
||||
{'shape': [2, 4, 6], 'axis': [0, -2]},
|
||||
{'shape': [2, 4, 6], 'axis': [2]},
|
||||
{'shape': [2, 4, 6, 8], 'axis': [0, 3, -3, 2]},
|
||||
{'shape': [2, 4, 6, 8], 'axis': [-3]},
|
||||
{'shape': [2, 3, 1, 2, 2], 'axis': [0, 3, -3, 1, -1]},
|
||||
{'shape': [2, 3, 1, 2, 2], 'axis': [4]},
|
||||
{'shape': [2, 1, 1, 1, 2, 3, 2, 2], 'axis': [-1]},
|
||||
{'shape': [2, 1, 1, 1, 2, 3, 2, 2], 'axis': [0, 1, 2, 3, 4, 5, 6, 7]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteReverseV2LayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["ReverseV2"]
|
||||
allowed_ops = ['REVERSE_V2']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'axis'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.reverse(place_holder, params['axis'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_reverse_v2(self, params, ie_device, precision, temp_dir):
|
||||
if len(params['axis']) > 1:
|
||||
pytest.xfail('CVS-109932')
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,74 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'indices_shape': [4, 1], 'indices_value': [4, 3, 1, 7], 'updates_dtype': np.int32, 'updates_shape': [4],
|
||||
'shape_shape': [1], 'shape_value': [8]},
|
||||
{'indices_shape': [4, 1], 'indices_value': [4, 3, 1, 7], 'updates_dtype': np.int64, 'updates_shape': [4],
|
||||
'shape_shape': [1], 'shape_value': [8]},
|
||||
{'indices_shape': [4, 1], 'indices_value': [4, 3, 1, 7], 'updates_dtype': np.float32, 'updates_shape': [4],
|
||||
'shape_shape': [1], 'shape_value': [8]},
|
||||
{'indices_shape': [4, 1], 'indices_value': [4, 3, 1, 7], 'updates_dtype': np.bool, 'updates_shape': [4],
|
||||
'shape_shape': [1], 'shape_value': [8]},
|
||||
|
||||
{'indices_shape': [4, 2], 'indices_value': [[0, 0], [1, 0], [0, 2], [1, 2]], 'updates_dtype': np.int32,
|
||||
'updates_shape': [4, 5], 'shape_shape': [3], 'shape_value': [2, 3, 5]},
|
||||
{'indices_shape': [4, 2], 'indices_value': [[0, 0], [1, 0], [0, 2], [1, 2]], 'updates_dtype': np.int64,
|
||||
'updates_shape': [4, 5], 'shape_shape': [3], 'shape_value': [2, 3, 5]},
|
||||
{'indices_shape': [4, 2], 'indices_value': [[0, 0], [1, 0], [0, 2], [1, 2]], 'updates_dtype': np.float32,
|
||||
'updates_shape': [4, 5], 'shape_shape': [3], 'shape_value': [2, 3, 5]},
|
||||
{'indices_shape': [4, 2], 'indices_value': [[0, 0], [1, 0], [0, 2], [1, 2]], 'updates_dtype': np.bool,
|
||||
'updates_shape': [4, 5], 'shape_shape': [3], 'shape_value': [2, 3, 5]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteScatterNDLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Indices", "Updates", "Shape"]
|
||||
outputs = ["ScatterND"]
|
||||
allowed_ops = ['SCATTER_ND']
|
||||
|
||||
def _prepare_input(self, inputs_dict, generator=None):
|
||||
inputs_dict['Indices'] = np.array(self.indices_value, dtype=np.int32).reshape(self.indices_shape)
|
||||
|
||||
if self.updates_dtype in [tf.int32, tf.int64]:
|
||||
inputs_dict['Updates'] = np.random.randint(-100, 100 + 1, self.updates_shape)
|
||||
if self.updates_dtype == tf.float32:
|
||||
inputs_dict['Updates'] = (100 - (-100) * np.random.random_sample(self.updates_shape) + (-100))
|
||||
if self.updates_dtype == tf.bool:
|
||||
inputs_dict['Updates'] = np.random.choice([True, False], size=self.updates_shape)
|
||||
|
||||
inputs_dict['Updates'] = inputs_dict['Updates'].astype(self.updates_dtype)
|
||||
inputs_dict['Shape'] = np.array(self.shape_value, dtype=np.int32)
|
||||
|
||||
return inputs_dict
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'indices_shape', 'indices_value',
|
||||
'updates_dtype', 'updates_shape', 'shape_shape',
|
||||
'shape_value'})) == 6, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
self.indices_value = params['indices_value']
|
||||
self.indices_shape = params['indices_shape']
|
||||
self.updates_dtype = params['updates_dtype']
|
||||
self.updates_shape = params['updates_shape']
|
||||
self.shape_value = params['shape_value']
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
indices = tf.compat.v1.placeholder(tf.int32, self.indices_shape, name=self.inputs[0])
|
||||
updates = tf.compat.v1.placeholder(self.updates_dtype, self.updates_shape, name=self.inputs[1])
|
||||
shape = tf.compat.v1.placeholder(tf.int32, params['shape_shape'], name=self.inputs[2])
|
||||
|
||||
tf.scatter_nd(indices, updates, shape, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_scatter_nd(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,44 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [4], 'segment_ids': [0, 0, 1, 1]},
|
||||
{'shape': [4], 'segment_ids': [0, 1, 2, 2]},
|
||||
{'shape': [4], 'segment_ids': [0, 1, 2, 3]},
|
||||
{'shape': [4], 'segment_ids': [0, 0, 0, 0]},
|
||||
|
||||
{'shape': [4, 4], 'segment_ids': [0, 0, 1, 1]},
|
||||
{'shape': [4, 4], 'segment_ids': [0, 1, 2, 2]},
|
||||
{'shape': [4, 4], 'segment_ids': [0, 1, 2, 3]},
|
||||
{'shape': [4, 4], 'segment_ids': [0, 0, 0, 0]},
|
||||
|
||||
{'shape': [4, 3, 2], 'segment_ids': [0, 0, 0, 0]},
|
||||
{'shape': [4, 3, 2], 'segment_ids': [0, 0, 1, 1]},
|
||||
{'shape': [4, 3, 2], 'segment_ids': [0, 1, 2, 2]},
|
||||
{'shape': [4, 3, 2], 'segment_ids': [0, 1, 2, 3]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSegmentSumLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["SegmentSum"]
|
||||
allowed_ops = ['SEGMENT_SUM']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'segment_ids'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
constant = tf.constant(params['segment_ids'], dtype=tf.int32)
|
||||
tf.math.segment_sum(place_holder, constant, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_segment_sum(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
35
tests/layer_tests/tensorflow_lite_tests/test_tfl_Select.py
Normal file
35
tests/layer_tests/tensorflow_lite_tests/test_tfl_Select.py
Normal file
@ -0,0 +1,35 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2, 3, 1, 2, 2], 'condition': [True, False]},
|
||||
{'shape': [4, 3, 1, 2], 'condition': [False, False, False, True]},
|
||||
{'shape': [2, 3], 'condition': [[True, True, True], [False, False, False]]},
|
||||
{'shape': [2], 'condition': [False, True]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSelectLayerTest(TFLiteLayerTest):
|
||||
inputs = ["X", "Y"]
|
||||
outputs = ["Select"]
|
||||
allowed_ops = ['SELECT']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'condition'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
cond = tf.constant(params['condition'], dtype=tf.bool)
|
||||
x = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
y = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[1])
|
||||
|
||||
tf.raw_ops.Select(condition=cond, x=x, y=y, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_select(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
36
tests/layer_tests/tensorflow_lite_tests/test_tfl_SelectV2.py
Normal file
36
tests/layer_tests/tensorflow_lite_tests/test_tfl_SelectV2.py
Normal file
@ -0,0 +1,36 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2, 3, 1, 2, 2], 'condition': [True, False]},
|
||||
{'shape': [4, 3, 1, 2], 'condition': [False, True]},
|
||||
{'shape': [3, 3, 3, 3], 'condition': [True, True, False]},
|
||||
{'shape': [3, 3, 3], 'condition': [True, True, False]},
|
||||
{'shape': [2, 3], 'condition': [True, False, True]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSelectLayerTest(TFLiteLayerTest):
|
||||
inputs = ["X", "Y"]
|
||||
outputs = ["Select"]
|
||||
allowed_ops = ['SELECT_V2']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'condition'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
cond = tf.constant(params['condition'], dtype=tf.bool)
|
||||
x = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
y = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[1])
|
||||
|
||||
tf.raw_ops.SelectV2(condition=cond, t=x, e=y, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_select_v2(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
57
tests/layer_tests/tensorflow_lite_tests/test_tfl_Slice.py
Normal file
57
tests/layer_tests/tensorflow_lite_tests/test_tfl_Slice.py
Normal file
@ -0,0 +1,57 @@
|
||||
import string
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
# OV doesn't support string format
|
||||
# {'shape': [12, 2, 2, 5], 'begin': [0, 0, 0, 0], 'size': [8, 2, 2, 3], 'dtype': tf.string},
|
||||
{'shape': [12, 2, 2, 5], 'begin': [1, 0, 1, 0], 'size': [11, 2, 1, 5], 'dtype': np.float32},
|
||||
{'shape': [1, 4, 4, 4], 'begin': [0, 1, 0, 0], 'size': [1, -1, 1, 1], 'dtype': np.float32},
|
||||
{'shape': [6, 2, 2, 2, 5], 'begin': [0, 0, 0, 0, 0], 'size': [4, 2, 2, 2, 3], 'dtype': np.int32},
|
||||
{'shape': [2, 3], 'begin': [1, 0], 'size': [-1, -1], 'dtype': np.int64},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSliceLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input", "Begin", "Size"]
|
||||
outputs = ["Slice"]
|
||||
allowed_ops = ['SLICE']
|
||||
|
||||
def _prepare_input(self, inputs_dict, generator=None):
|
||||
if self.input_dtype == tf.string:
|
||||
letters = list(string.ascii_uppercase)
|
||||
inputs_dict["Input"] = np.random.choice(letters, size=self.shape).astype(self.input_dtype)
|
||||
else:
|
||||
inputs_dict["Input"] = np.random.randint(-1, 2, self.shape).astype(self.input_dtype)
|
||||
|
||||
inputs_dict["Begin"] = np.array(self.begin).astype(np.int32)
|
||||
inputs_dict['Size'] = np.array(self.size).astype(np.int32)
|
||||
|
||||
return inputs_dict
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'begin', 'size', 'dtype'})) == 4, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
self.input_dtype = params['dtype']
|
||||
self.shape = params['shape']
|
||||
self.begin = params['begin']
|
||||
self.size = params['size']
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params['dtype'], params['shape'], self.inputs[0])
|
||||
begin = tf.compat.v1.placeholder(tf.int32, len(params['shape']), name=self.inputs[1])
|
||||
size = tf.compat.v1.placeholder(tf.int32, len(params['shape']), name=self.inputs[2])
|
||||
|
||||
tf.slice(placeholder, begin, size, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_slice(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,32 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [8, 10, 10, 3], 'block_shape': [2, 2], 'paddings': [[0, 2], [0, 0]]},
|
||||
{'shape': [24, 10, 10, 1], 'block_shape': [6, 6], 'paddings': [[2, 0], [0, 2]]}
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSpaceToBatchNDLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["SpaceToBatchOP"]
|
||||
allowed_ops = ['SPACE_TO_BATCH_ND']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'block_shape', 'paddings'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.space_to_batch(place_holder, params['block_shape'], params['paddings'],
|
||||
name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_space_to_batch_nd(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,31 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [8, 10, 10, 16], 'block_size': 2, 'data_format': 'NHWC'},
|
||||
{'shape': [24, 10, 10, 50], 'block_size': 5, 'data_format': 'NHWC'},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSpaceToDepthLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["SpaceToDepth"]
|
||||
allowed_ops = ['SPACE_TO_DEPTH']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'block_size', 'data_format'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.nn.space_to_depth(place_holder, params['block_size'], params['data_format'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_space_to_depth(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
32
tests/layer_tests/tensorflow_lite_tests/test_tfl_Split.py
Normal file
32
tests/layer_tests/tensorflow_lite_tests/test_tfl_Split.py
Normal file
@ -0,0 +1,32 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [6], 'num_or_size_splits': 2, 'axis': 0},
|
||||
{'shape': [2, 1, 6], 'num_or_size_splits': 3, 'axis': 2},
|
||||
{'shape': [4, 3, 2, 7], 'num_or_size_splits': 4, 'axis': -4},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSplitLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Split"]
|
||||
allowed_ops = ['SPLIT']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'num_or_size_splits',
|
||||
'axis'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
tf.split(placeholder, params["num_or_size_splits"], params["axis"], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_split(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
39
tests/layer_tests/tensorflow_lite_tests/test_tfl_SplitV.py
Normal file
39
tests/layer_tests/tensorflow_lite_tests/test_tfl_SplitV.py
Normal file
@ -0,0 +1,39 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [3], 'num_or_size_splits': [0, 2, 0, 1], 'axis': 0},
|
||||
{'shape': [2, 3, 9], 'num_or_size_splits': [1, 2, 3, -1, 1], 'axis': 2},
|
||||
{'shape': [3, 9, 5, 4], 'num_or_size_splits': [1, 2, 0, -1, 2, 0], 'axis': -3},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteSplitLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["SplitV"]
|
||||
allowed_ops = ['SPLIT_V']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'num_or_size_splits',
|
||||
'axis'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
size_splits = tf.constant(params['num_or_size_splits'], dtype=tf.int32)
|
||||
axis = tf.constant(params['axis'], dtype=tf.int32)
|
||||
num_split = len(params['num_or_size_splits'])
|
||||
|
||||
tf.raw_ops.SplitV(value=placeholder, size_splits=size_splits, axis=axis,
|
||||
num_split=num_split, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_split_v(self, params, ie_device, precision, temp_dir):
|
||||
if 0 in params['num_or_size_splits']:
|
||||
pytest.skip("CVS-110040")
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,69 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [12, 2, 2, 5], 'dtype': np.int32, 'strides': [2, 1, 3, 1], 'begin': [0, 0, 0, 0], 'end': [12, 2, 2, 5],
|
||||
'begin_mask': None, 'end_mask': None, 'shrink_axis_mask': 4},
|
||||
{'shape': [12, 2, 2, 5], 'dtype': np.float32, 'strides': None, 'begin': [0, 0, 0, 0], 'end': [12, 2, 2, 5],
|
||||
'begin_mask': None, 'end_mask': None, 'shrink_axis_mask': None},
|
||||
{'shape': [12, 2, 2, 5], 'dtype': np.float32, 'strides': None, 'begin': [1, 0, 1, 0], 'end': [8, 2, 2, 3],
|
||||
'begin_mask': 8, 'end_mask': 3, 'shrink_axis_mask': 4},
|
||||
{'shape': [12, 2, 2, 5], 'dtype': np.int64, 'strides': [1], 'begin': [0], 'end': [1],
|
||||
'begin_mask': 8, 'end_mask': 3, 'shrink_axis_mask': None},
|
||||
{'shape': [12, 2, 2, 5], 'dtype': np.bool, 'strides': [1], 'begin': [0], 'end': [1],
|
||||
'begin_mask': 8, 'end_mask': 3, 'shrink_axis_mask': None},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteStridedSliceLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input", "Begin", "End"]
|
||||
outputs = ["StridedSlice"]
|
||||
allowed_ops = ['STRIDED_SLICE']
|
||||
|
||||
def _prepare_input(self, inputs_dict, generator=None):
|
||||
if self.input_dtype == np.bool:
|
||||
inputs_dict['Input'] = np.random.choice([True, False], size=inputs_dict['Input'])
|
||||
else:
|
||||
inputs_dict['Input'] = np.random.randint(-255, 255, inputs_dict['Input']).astype(self.input_dtype)
|
||||
inputs_dict['Begin'] = np.array(self.begin).astype(np.int32)
|
||||
inputs_dict['End'] = np.array(self.end).astype(np.int32)
|
||||
if self.strides:
|
||||
inputs_dict["Strides"] = np.array(self.strides).astype(np.int32)
|
||||
|
||||
return inputs_dict
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'dtype', 'strides', 'begin',
|
||||
'end', 'begin_mask', 'end_mask', 'shrink_axis_mask'})) == 8, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
self.input_dtype = params['dtype']
|
||||
self.begin = params['begin']
|
||||
self.end = params['end']
|
||||
self.strides = params['strides']
|
||||
if "Strides" in self.inputs:
|
||||
self.inputs.pop(-1)
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params['dtype'], params['shape'], self.inputs[0])
|
||||
begin = tf.compat.v1.placeholder(tf.int32, [len(params['begin'])], self.inputs[1])
|
||||
end = tf.compat.v1.placeholder(tf.int32, [len(params['end'])], self.inputs[2])
|
||||
strides = None
|
||||
if params['strides']:
|
||||
name = "Strides"
|
||||
self.inputs.append(name)
|
||||
strides = tf.compat.v1.placeholder(tf.int32, [len(params['end'])], name)
|
||||
|
||||
tf.strided_slice(placeholder, begin, end, strides,
|
||||
begin_mask=params['begin_mask'], end_mask=params['end_mask'],
|
||||
shrink_axis_mask=params['shrink_axis_mask'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_strided_slice(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
32
tests/layer_tests/tensorflow_lite_tests/test_tfl_Tile.py
Normal file
32
tests/layer_tests/tensorflow_lite_tests/test_tfl_Tile.py
Normal file
@ -0,0 +1,32 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2], 'multiples': [2]},
|
||||
{'shape': [2, 3], 'multiples': [1, 2]},
|
||||
{'shape': [2, 3, 1], 'multiples': [5, 1, 3]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteTileLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Tile"]
|
||||
allowed_ops = ['TILE']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'multiples'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
tf.tile(placeholder, params['multiples'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_tile(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
33
tests/layer_tests/tensorflow_lite_tests/test_tfl_TopKV2.py
Normal file
33
tests/layer_tests/tensorflow_lite_tests/test_tfl_TopKV2.py
Normal file
@ -0,0 +1,33 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2], 'k': 2, 'sorted': True},
|
||||
{'shape': [2, 3], 'k': 1, 'sorted': False},
|
||||
{'shape': [2, 3, 5], 'k': 2, 'sorted': True},
|
||||
{'shape': [2, 3, 5, 10], 'k': 9, 'sorted': False},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteTopKV2LayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["TopKV2"]
|
||||
allowed_ops = ['TOPK_V2']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'k', 'sorted'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
tf.raw_ops.TopKV2(input=placeholder, k=params['k'], sorted=params['sorted'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_topk_v2(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,32 @@
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
test_params = [
|
||||
{'shape': [2, 3], 'perm': [1, 0], 'conjugate': False},
|
||||
{'shape': [2, 3, 5], 'perm': [2, 0, 1], 'conjugate': True},
|
||||
{'shape': [2, 3, 5, 10], 'perm': [1, 2, 3, 0], 'conjugate': False},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteTransposeLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Transpose"]
|
||||
allowed_ops = ['TRANSPOSE']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'perm', 'conjugate'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(tf.float32, params['shape'], self.inputs[0])
|
||||
tf.transpose(placeholder, params['perm'], params['conjugate'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_transpose(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -0,0 +1,45 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'shape': [1, 3, 4, 1], 'ksize': [1, 1, 1, 1], 'output_shape': [1, 3, 4, 1], 'strides': [1, 1, 1, 1],
|
||||
'padding': 'SAME', 'data_format': 'NHWC', 'dilations': [1, 1, 1, 1]},
|
||||
{'shape': [1, 4, 4, 1], 'ksize': [1, 1, 1, 1], 'output_shape': [1, 4, 4, 1], 'strides': [1, 1], 'padding': 'SAME',
|
||||
'data_format': 'NHWC', 'dilations': [1, 2, 2, 1]},
|
||||
#
|
||||
{'shape': [1, 22, 22, 3], 'ksize': [1, 1, 6, 3], 'output_shape': [1, 22, 22, 6], 'strides': [1, 1],
|
||||
'padding': 'VALID', 'data_format': 'NHWC', 'dilations': [1, 1, 1, 1]},
|
||||
{'shape': [1, 22, 22, 3], 'ksize': [3, 3, 3, 3], 'output_shape': [1, 24, 24, 3], 'strides': [1, 1],
|
||||
'padding': 'VALID', 'data_format': 'NHWC', 'dilations': [1, 1, 1, 1]},
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteTransposeConvLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["TransposeConv"]
|
||||
allowed_ops = ['TRANSPOSE_CONV']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'ksize', 'strides',
|
||||
'padding', 'data_format', 'dilations', 'output_shape'})) == 7, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
filter_input = tf.constant(np.random.randint(-1, 1, size=(params['ksize'])), dtype=tf.float32)
|
||||
tf.nn.conv2d_transpose(placeholder, filter_input, params['output_shape'], params["strides"],
|
||||
params["padding"],
|
||||
params["data_format"], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_transpose_conv(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
@ -1,17 +1,16 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
import itertools
|
||||
from functools import partial
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
from common.utils.tflite_utils import data_generators
|
||||
from common.utils.tflite_utils import parametrize_tests
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
|
||||
test_ops = [
|
||||
{'op_name': 'ABS', 'op_func': tf.math.abs},
|
||||
{'op_name': 'CAST', 'op_func': partial(tf.cast, dtype=tf.int32)},
|
||||
@ -27,6 +26,7 @@ test_ops = [
|
||||
{'op_name': 'LOGISTIC', 'op_func': tf.math.sigmoid},
|
||||
{'op_name': 'NEG', 'op_func': tf.math.negative},
|
||||
{'op_name': 'RELU6', 'op_func': tf.nn.relu6},
|
||||
{'op_name': 'RELU', 'op_func': tf.nn.relu},
|
||||
{'op_name': 'ROUND', 'op_func': tf.math.round},
|
||||
{'op_name': 'RSQRT', 'op_func': tf.math.rsqrt, 'kwargs_to_prepare_input': 'positive'},
|
||||
{'op_name': 'SIN', 'op_func': tf.math.sin},
|
||||
@ -51,21 +51,13 @@ test_params = [
|
||||
{'shape': [2, 10]}
|
||||
]
|
||||
|
||||
test_data = list(itertools.product(test_ops, test_params))
|
||||
for i, (parameters, shapes) in enumerate(test_data):
|
||||
parameters.update(shapes)
|
||||
test_data[i] = parameters.copy()
|
||||
test_data = parametrize_tests(test_ops, test_params)
|
||||
|
||||
|
||||
class TestTFLiteUnaryLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["UnaryOperation"]
|
||||
|
||||
def _prepare_input(self, inputs_dict, generator=None):
|
||||
if generator is None:
|
||||
return super()._prepare_input(inputs_dict)
|
||||
return data_generators[generator](inputs_dict)
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'op_name', 'op_func', 'shape'})) == 3, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
@ -73,8 +65,8 @@ class TestTFLiteUnaryLayerTest(TFLiteLayerTest):
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=TestTFLiteUnaryLayerTest.inputs[0])
|
||||
params['op_func'](place_holder, name=TestTFLiteUnaryLayerTest.outputs[0])
|
||||
name=self.inputs[0])
|
||||
params['op_func'](place_holder, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
|
||||
37
tests/layer_tests/tensorflow_lite_tests/test_tfl_Unique.py
Normal file
37
tests/layer_tests/tensorflow_lite_tests/test_tfl_Unique.py
Normal file
@ -0,0 +1,37 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'shape': [15]},
|
||||
{'shape': [1]}
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteUniqueLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Unqiue"]
|
||||
allowed_ops = ['UNIQUE']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape'})) == 1, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
placeholder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.unique(placeholder, name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_unique(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
41
tests/layer_tests/tensorflow_lite_tests/test_tfl_Unpack.py
Normal file
41
tests/layer_tests/tensorflow_lite_tests/test_tfl_Unpack.py
Normal file
@ -0,0 +1,41 @@
|
||||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import tensorflow as tf
|
||||
|
||||
from common.tflite_layer_test_class import TFLiteLayerTest
|
||||
|
||||
np.random.seed(42)
|
||||
|
||||
test_params = [
|
||||
{'shape': [3, 4, 3], 'axis': 0},
|
||||
{'shape': [3, 4, 3], 'axis': -1},
|
||||
{'shape': [3, 4, 3, 5], 'axis': -2},
|
||||
{'shape': [3, 4, 3, 5], 'axis': 2},
|
||||
{'shape': [1], 'axis': -1},
|
||||
{'shape': [1], 'axis': 0}
|
||||
]
|
||||
|
||||
|
||||
class TestTFLiteUnpackLayerTest(TFLiteLayerTest):
|
||||
inputs = ["Input"]
|
||||
outputs = ["Unpack"]
|
||||
allowed_ops = ['UNPACK']
|
||||
|
||||
def make_model(self, params):
|
||||
assert len(set(params.keys()).intersection({'shape', 'axis'})) == 2, \
|
||||
'Unexpected parameters for test: ' + ','.join(params.keys())
|
||||
tf.compat.v1.reset_default_graph()
|
||||
with tf.compat.v1.Session() as sess:
|
||||
place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
|
||||
name=self.inputs[0])
|
||||
tf.unstack(place_holder, axis=params['axis'], name=self.outputs[0])
|
||||
net = sess.graph_def
|
||||
return net
|
||||
|
||||
@pytest.mark.parametrize("params", test_params)
|
||||
@pytest.mark.nightly
|
||||
def test_unpack(self, params, ie_device, precision, temp_dir):
|
||||
self._test(ie_device, precision, temp_dir, params)
|
||||
Loading…
Reference in New Issue
Block a user