Added support of shapes and types from original FW in ov.convert_model() (#20009)
* Added support of shapes and types from paddle, torch and tf. * Removed changes from requirements. * Corrected test. * Moved helper methods to utils. * Separated tests by frameworks. * Removed changes from complex_params test.
This commit is contained in:
Anastasiia Pnevskaia
GitHub
parent
c3565e3eac
commit
2bbfe7b44d
|
|
@ -101,3 +101,35 @@ class TestMoConvertPaddle(CommonMOConvertTest):
|
|||
if mo_params is not None:
|
||||
test_params.update(mo_params)
|
||||
self._test_by_ref_graph(temp_dir, test_params, graph_ref, compare_tensor_names=False)
|
||||
|
||||
|
||||
class TestPaddleConversionParams(CommonMOConvertTest):
|
||||
paddle_is_imported = False
|
||||
try:
|
||||
import paddle
|
||||
paddle_is_imported = True
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
test_data = [
|
||||
{'params_test': {'input': paddle.shape(paddle.to_tensor(np.random.rand(2, 3, 4)))},
|
||||
'fw_model': make_pd_hapi_graph_model([1, 2]),
|
||||
'ref_model': make_ref_graph_model([2, 3, 4])},
|
||||
{'params_test': {'input': paddle.to_tensor(np.random.rand(5, 6)).shape},
|
||||
'fw_model': make_pd_hapi_graph_model([1, 2, 3]),
|
||||
'ref_model': make_ref_graph_model([5, 6])},
|
||||
{'params_test': {'input': (paddle.to_tensor(np.random.rand(4, 2, 7)).shape, paddle.int32)},
|
||||
'fw_model': make_pd_hapi_graph_model([2, 3]),
|
||||
'ref_model': make_ref_graph_model([4, 2, 7], np.int32)},
|
||||
] if paddle_is_imported else []
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.nightly
|
||||
def test_conversion_params(self, params, ie_device, precision, ir_version,
|
||||
temp_dir, use_new_frontend, use_old_api):
|
||||
fw_model = params['fw_model']
|
||||
test_params = params['params_test']
|
||||
ref_model = params['ref_model']
|
||||
|
||||
test_params.update({'input_model': fw_model})
|
||||
self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
|
||||
|
|
|
|||
|
|
@ -1145,3 +1145,75 @@ class ConvertRaises(unittest.TestCase):
|
|||
with self.assertRaisesRegex(Exception, ".*Cannot recognize input model.*"):
|
||||
with tempfile.NamedTemporaryFile() as tmpfile:
|
||||
convert_model(tmpfile.name)
|
||||
|
||||
|
||||
def create_model_three_inputs():
|
||||
from torch import nn
|
||||
|
||||
class NeuralNetwork(nn.Module):
|
||||
def __init__(self):
|
||||
super(NeuralNetwork, self).__init__()
|
||||
self.linear_relu_stack = nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Sigmoid(),
|
||||
)
|
||||
|
||||
def forward(self, x, y, z):
|
||||
out = self.linear_relu_stack(x + y + z),
|
||||
return out
|
||||
return NeuralNetwork()
|
||||
|
||||
|
||||
def make_ref_model_three_inputs(shape, dtype=np.float32):
|
||||
x = ov.opset8.parameter(PartialShape(
|
||||
shape), name="x", dtype=dtype)
|
||||
y = ov.opset8.parameter(PartialShape(
|
||||
shape), name="y", dtype=dtype)
|
||||
z = ov.opset8.parameter(PartialShape(
|
||||
shape), name="z", dtype=dtype)
|
||||
add1 = ov.opset8.add(x, y)
|
||||
add2 = ov.opset8.add(add1, z)
|
||||
|
||||
relu = ov.opset8.relu(add2)
|
||||
|
||||
if dtype not in [np.float32, Type.dynamic]:
|
||||
relu = ov.opset8.convert(relu, np.float32)
|
||||
|
||||
sigm = ov.opset8.sigmoid(relu)
|
||||
|
||||
parameter_list = [x, y, z]
|
||||
model = Model([sigm], parameter_list, "test")
|
||||
return model
|
||||
|
||||
|
||||
class TestPytorchConversionParams(CommonMOConvertTest):
|
||||
|
||||
test_data = [
|
||||
{'params_test': {'input': [(torch.Size([2, 3, 4]), torch.float32),
|
||||
(torch.empty(2, 3, 4).size(), torch.float32),
|
||||
(torch.empty(2, 3, 4).shape, torch.float32)]},
|
||||
'fw_model': create_model_three_inputs(),
|
||||
'ref_model': make_ref_model_three_inputs([2,3,4], np.float32)},
|
||||
{'params_test': {'input': [(torch.Size([5, 2]), torch.int32),
|
||||
(torch.empty(5, 2).size(), torch.int32),
|
||||
(torch.empty(5, 2).shape, torch.int32)]},
|
||||
'fw_model': create_model_three_inputs(),
|
||||
'ref_model': make_ref_model_three_inputs([5, 2], np.int32)},
|
||||
{'params_test': {'input': [(torch.Size([1, 3, 5]), torch.float32)]},
|
||||
'fw_model': make_pt_model_one_input(),
|
||||
'ref_model': make_ref_pt_model_one_input([1, 3, 5], np.float32)},
|
||||
{'params_test': {'input': [(torch.empty(7, 3).size(), torch.int32)]},
|
||||
'fw_model': make_pt_model_one_input(),
|
||||
'ref_model': make_ref_pt_model_one_input([7, 3], np.int32)},
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.nightly
|
||||
def test_conversion_params(self, params, ie_device, precision, ir_version,
|
||||
temp_dir, use_new_frontend, use_old_api):
|
||||
fw_model = params['fw_model']
|
||||
test_params = params['params_test']
|
||||
ref_model = params['ref_model']
|
||||
|
||||
test_params.update({'input_model': fw_model})
|
||||
self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
|
||||
|
|
|
|||
|
|
@ -10,11 +10,10 @@ from openvino.runtime import PartialShape, Model, Dimension
|
|||
|
||||
from common.mo_convert_test_class import CommonMOConvertTest
|
||||
from common.layer_test_class import CommonLayerTest
|
||||
import tensorflow as tf
|
||||
|
||||
|
||||
def create_tf_graph_def(tmp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
|
|
@ -41,8 +40,6 @@ def create_tf_graph_def(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_model(temp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
tf.keras.backend.clear_session()
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
|
|
@ -69,8 +66,6 @@ def create_keras_model(temp_dir):
|
|||
|
||||
|
||||
def create_tf1_wrap_function(tmp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
def f(x, y):
|
||||
return tf.nn.sigmoid(tf.nn.relu(x + y))
|
||||
|
||||
|
|
@ -91,7 +86,6 @@ def create_tf1_wrap_function(tmp_dir):
|
|||
|
||||
|
||||
def create_tf_session(tmp_dir):
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.eager.context import graph_mode
|
||||
|
||||
with graph_mode():
|
||||
|
|
@ -119,8 +113,6 @@ def create_tf_session(tmp_dir):
|
|||
|
||||
|
||||
def create_tf_module(tmp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
class Net(tf.Module):
|
||||
def __init__(self, name=None):
|
||||
super(Net, self).__init__(name=name)
|
||||
|
|
@ -143,8 +135,6 @@ def create_tf_module(tmp_dir):
|
|||
|
||||
|
||||
def create_tf_module_dynamic(tmp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
class Net(tf.Module):
|
||||
def __init__(self, name=None):
|
||||
super(Net, self).__init__(name=name)
|
||||
|
|
@ -169,7 +159,6 @@ def create_tf_module_dynamic(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_layer(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -193,7 +182,6 @@ def create_keras_layer(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_layer_dynamic(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -219,8 +207,6 @@ def create_keras_layer_dynamic(tmp_dir):
|
|||
|
||||
|
||||
def create_tf_checkpoint(tmp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
input_names = ["Input1", "Input2"]
|
||||
input_shape = [1, 2, 3]
|
||||
|
||||
|
|
@ -245,8 +231,6 @@ def create_tf_checkpoint(tmp_dir):
|
|||
|
||||
|
||||
def create_tf_function(temp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
@tf.function(
|
||||
input_signature=[tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32),
|
||||
tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32)])
|
||||
|
|
@ -268,8 +252,6 @@ def create_tf_function(temp_dir):
|
|||
|
||||
|
||||
def create_tf_graph(temp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
|
|
@ -296,8 +278,6 @@ def create_tf_graph(temp_dir):
|
|||
|
||||
|
||||
def create_tf_saved_model_dir(temp_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
input_names = ["Input1", "Input2"]
|
||||
input_shape = [1, 2, 3]
|
||||
|
||||
|
|
@ -322,7 +302,6 @@ def create_tf_saved_model_dir(temp_dir):
|
|||
|
||||
|
||||
def create_tf_stateful_partioned_call_net(temp_dir):
|
||||
import tensorflow as tf
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
data_shape = [1, 1, 10, 10]
|
||||
|
|
@ -359,7 +338,6 @@ def create_tf_stateful_partioned_call_net(temp_dir):
|
|||
|
||||
|
||||
def create_keras_layer_input_list():
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -386,7 +364,6 @@ def create_keras_layer_input_list():
|
|||
|
||||
|
||||
def create_keras_layer_input_list_one_inp():
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -408,7 +385,6 @@ def create_keras_layer_input_list_one_inp():
|
|||
|
||||
|
||||
def create_keras_layer_input_dict():
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -434,7 +410,6 @@ def create_keras_layer_input_dict():
|
|||
|
||||
|
||||
def create_keras_layer_input_dict_one_inp():
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.keras.layers.Layer):
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -522,7 +497,6 @@ def create_keras_layer_with_input_shapes_case4(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_layer_with_tf_function_call(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.Module):
|
||||
def __init__(self):
|
||||
super(LayerModel, self).__init__()
|
||||
|
|
@ -538,7 +512,6 @@ def create_keras_layer_with_tf_function_call(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_layer_with_tf_function_call_default_compressed_to_fp16(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.Module):
|
||||
def __init__(self):
|
||||
super(LayerModel, self).__init__()
|
||||
|
|
@ -554,7 +527,6 @@ def create_keras_layer_with_tf_function_call_default_compressed_to_fp16(tmp_dir)
|
|||
|
||||
|
||||
def create_keras_layer_with_tf_function_call_no_signature(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.Module):
|
||||
def __init__(self):
|
||||
super(LayerModel, self).__init__()
|
||||
|
|
@ -572,7 +544,6 @@ def create_keras_layer_with_tf_function_call_no_signature(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_layer_with_tf_function_call_no_signature_single_input(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.Module):
|
||||
def __init__(self):
|
||||
super(LayerModel, self).__init__()
|
||||
|
|
@ -590,7 +561,6 @@ def create_keras_layer_with_tf_function_call_no_signature_single_input(tmp_dir):
|
|||
|
||||
|
||||
def create_keras_layer_with_string_tensor(tmp_dir):
|
||||
import tensorflow as tf
|
||||
class LayerModel(tf.Module):
|
||||
def __init__(self):
|
||||
super(LayerModel, self).__init__()
|
||||
|
|
@ -612,6 +582,69 @@ def create_keras_layer_with_string_tensor(tmp_dir):
|
|||
return model, model_ref, {}
|
||||
|
||||
|
||||
def create_tf_model_three_inputs(shape=[1, 2, 3, 4], type=tf.float32):
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
inp1 = tf.compat.v1.placeholder(type, shape, 'Input1')
|
||||
inp2 = tf.compat.v1.placeholder(type, shape, 'Input2')
|
||||
inp3 = tf.compat.v1.placeholder(type, shape, 'Input3')
|
||||
|
||||
relu1 = tf.nn.relu(inp1, name='Relu1')
|
||||
relu2 = tf.nn.relu(inp2, name='Relu2')
|
||||
relu3 = tf.nn.relu(inp3, name='Relu3')
|
||||
|
||||
add = relu1 + relu2 + relu3
|
||||
|
||||
tf.compat.v1.global_variables_initializer()
|
||||
tf_net = sess.graph
|
||||
return tf_net
|
||||
|
||||
|
||||
def create_ref_model_three_inputs(shape=[1, 2, 3, 4], dtype=np.float32):
|
||||
inp1 = ov.opset8.parameter(PartialShape(
|
||||
shape), name="Input1", dtype=dtype)
|
||||
inp2 = ov.opset8.parameter(PartialShape(
|
||||
shape), name="Input2", dtype=dtype)
|
||||
inp3 = ov.opset8.parameter(PartialShape(
|
||||
shape), name="Input3", dtype=dtype)
|
||||
|
||||
relu1 = ov.opset8.relu(inp1)
|
||||
relu2 = ov.opset8.relu(inp2)
|
||||
relu3 = ov.opset8.relu(inp3)
|
||||
|
||||
add1 = ov.opset8.add(relu1, relu2)
|
||||
add2 = ov.opset8.add(add1, relu3)
|
||||
|
||||
parameter_list = [inp1, inp2, inp3]
|
||||
model = Model([add2], parameter_list, "test")
|
||||
return model
|
||||
|
||||
|
||||
def create_tf_model_single_input(shape=[1, 2, 3, 4], type=tf.float32):
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
inp = tf.compat.v1.placeholder(type, shape, 'Input')
|
||||
relu = tf.nn.relu(inp, name='Relu')
|
||||
output = tf.nn.sigmoid(relu, name='Sigmoid')
|
||||
|
||||
tf.compat.v1.global_variables_initializer()
|
||||
tf_net = sess.graph
|
||||
|
||||
return tf_net
|
||||
|
||||
|
||||
def create_ref_model_single_input(shape=[1, 2, 3, 4], dtype=np.float32):
|
||||
inp = ov.opset8.parameter(PartialShape(
|
||||
shape), name="Input", dtype=dtype)
|
||||
relu = ov.opset8.relu(inp)
|
||||
sigm = ov.opset8.sigmoid(relu)
|
||||
parameter_list = [inp]
|
||||
model = Model([sigm], parameter_list, "test")
|
||||
return model
|
||||
|
||||
|
||||
class TestMoConvertTF(CommonMOConvertTest):
|
||||
test_data = [
|
||||
# TF2
|
||||
|
|
@ -667,7 +700,6 @@ class TestMoConvertTF(CommonMOConvertTest):
|
|||
self._test_by_ref_graph(temp_dir, test_params, graph_ref, compare_tensor_names=False)
|
||||
|
||||
def test_zero_copy(self, ie_device, precision, ir_version, temp_dir):
|
||||
import tensorflow as tf
|
||||
from openvino.tools.ovc import convert_model
|
||||
from openvino.runtime import compile_model
|
||||
class LayerModel(tf.Module):
|
||||
|
|
@ -716,7 +748,6 @@ class TestMoConvertTF(CommonMOConvertTest):
|
|||
assert np.array_equal(ov_infer2['Identity:0'], [ 0., 8., 16.])
|
||||
|
||||
def test_turn_off_sharing(self, ie_device, precision, ir_version, temp_dir):
|
||||
import tensorflow as tf
|
||||
from openvino.tools.ovc import convert_model
|
||||
from openvino.runtime import compile_model
|
||||
class LayerModel(tf.Module):
|
||||
|
|
@ -767,7 +798,6 @@ class TestMoConvertTF(CommonMOConvertTest):
|
|||
def test_memory_loss(self, ie_device, precision, ir_version, temp_dir):
|
||||
# This test checks that the memory allocated for constants
|
||||
# is not lost after returning the model from convert_model() method.
|
||||
import tensorflow as tf
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
from openvino.tools.ovc import convert_model
|
||||
|
|
@ -820,7 +850,6 @@ class TestMoConvertTF(CommonMOConvertTest):
|
|||
assert CommonLayerTest().compare_ie_results_with_framework(ov_infer1, {"add:0": [2.6, 9.6, 12.4]}, eps)
|
||||
|
||||
def test_scalar(self, ie_device, precision, ir_version, temp_dir):
|
||||
import tensorflow as tf
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
from openvino.tools.ovc import convert_model
|
||||
|
|
@ -861,7 +890,6 @@ class TestMoConvertTF(CommonMOConvertTest):
|
|||
assert CommonLayerTest().compare_ie_results_with_framework(ov_infer, {"Identity:0": 3.2}, eps)
|
||||
|
||||
def test_unnamed_variable(self, ie_device, precision, ir_version, temp_dir):
|
||||
import tensorflow as tf
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
from openvino.tools.ovc import convert_model
|
||||
|
|
@ -905,7 +933,6 @@ class TFConvertTest(unittest.TestCase):
|
|||
@pytest.mark.nightly
|
||||
@pytest.mark.precommit
|
||||
def test_tf_function_no_signature(self):
|
||||
import tensorflow as tf
|
||||
from openvino.tools.ovc import convert_model
|
||||
|
||||
@tf.function()
|
||||
|
|
@ -920,8 +947,6 @@ class TFConvertTest(unittest.TestCase):
|
|||
class TestTFLoadByModel(unittest.TestCase):
|
||||
def test_load_by_model_tf_graph_iterator(self):
|
||||
def simple_tf_model():
|
||||
import tensorflow as tf
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
with tf.compat.v1.Session() as sess:
|
||||
|
|
@ -955,3 +980,37 @@ class TestTFConvertRaises(unittest.TestCase):
|
|||
# check that it accepts specified names as is without parsing into 2 different inputs
|
||||
with self.assertRaisesRegex(Exception, 'No node with name Input1\[1, 2, 3\],Input2\[1, 2, 3\]'):
|
||||
convert_model(tf_model, input='Input1[1, 2, 3],Input2[1, 2, 3]')
|
||||
|
||||
|
||||
class TestTFConversionParams(CommonMOConvertTest):
|
||||
test_data = [
|
||||
{'params_test': {'input': [tf.shape(tf.zeros((2, 3, 4))), tf.zeros((2, 3, 4)).shape, tf.TensorShape((2, 3, 4))]},
|
||||
'fw_model': create_tf_model_three_inputs([1, 2, 3, 4]),
|
||||
'ref_model': create_ref_model_three_inputs([2, 3, 4])},
|
||||
{'params_test': {'input': [tf.float32, tf.float32, tf.float32]},
|
||||
'fw_model': create_tf_model_three_inputs([2, 3], tf.int32),
|
||||
'ref_model': create_ref_model_three_inputs([2, 3], np.float32)},
|
||||
{'params_test': {'input': tf.shape(tf.zeros((5, 8, 2)))},
|
||||
'fw_model': create_tf_model_single_input(),
|
||||
'ref_model': create_ref_model_single_input([5, 8, 2])},
|
||||
{'params_test': {'input': tf.zeros((9, 2)).shape},
|
||||
'fw_model': create_tf_model_single_input(),
|
||||
'ref_model': create_ref_model_single_input([9, 2])},
|
||||
{'params_test': {'input': tf.TensorShape((4, 8, 3))},
|
||||
'fw_model': create_tf_model_single_input(),
|
||||
'ref_model': create_ref_model_single_input([4, 8, 3])},
|
||||
{'params_test': {'input': tf.int32},
|
||||
'fw_model': create_tf_model_single_input(),
|
||||
'ref_model': create_ref_model_single_input([1, 2, 3, 4], np.int32)}
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.nightly
|
||||
def test_mo_convert_tf_model(self, params, ie_device, precision, ir_version,
|
||||
temp_dir, use_new_frontend, use_old_api):
|
||||
fw_model = params['fw_model']
|
||||
test_params = params['params_test']
|
||||
ref_model = params['ref_model']
|
||||
|
||||
test_params.update({'input_model': fw_model})
|
||||
self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
|
||||
|
|
@ -14,22 +14,13 @@ from openvino.runtime import PartialShape, Dimension, Shape, Type # pylint: dis
|
|||
import openvino
|
||||
from openvino.tools.ovc.error import Error
|
||||
from openvino.tools.ovc.help import get_convert_model_help_specifics
|
||||
from openvino.tools.ovc.moc_frontend.shape_utils import to_partial_shape, is_shape_type
|
||||
from openvino.tools.ovc.moc_frontend.type_utils import to_ov_type, is_type
|
||||
from openvino.tools.ovc.utils import get_mo_root_dir
|
||||
|
||||
# Helper class for storing input cut information
|
||||
_InputCutInfo = namedtuple("InputCutInfo", ["name", "shape", "type", "value"], defaults=[None, None, None, None])
|
||||
|
||||
def is_shape_type(value):
|
||||
if isinstance(value, PartialShape):
|
||||
return True
|
||||
if isinstance(value, Shape):
|
||||
return True
|
||||
if isinstance(value, list) or isinstance(value, tuple):
|
||||
for dim in value:
|
||||
if not (isinstance(dim, Dimension) or isinstance(dim, int)):
|
||||
return False
|
||||
return True
|
||||
return False
|
||||
|
||||
def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type, type]):
|
||||
"""
|
||||
|
|
@ -43,7 +34,7 @@ def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type,
|
|||
if isinstance(input, (tuple, list)) or is_shape_type(input):
|
||||
# If input represents list with shape, wrap it to list. Single PartialShape also goes to this condition.
|
||||
# Check of all dimensions will be in is_shape_type(val) method below
|
||||
if len(input) > 0 and isinstance(input[0], (int, Dimension)) or isinstance(input, PartialShape):
|
||||
if is_shape_type(input):
|
||||
input = [input]
|
||||
|
||||
# Check values of tuple or list and collect to InputCutInfo
|
||||
|
|
@ -55,14 +46,14 @@ def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type,
|
|||
if name is not None:
|
||||
raise Exception("More than one input name provided: {}".format(input))
|
||||
name = val
|
||||
elif isinstance(val, (type, Type)):
|
||||
elif is_type(val):
|
||||
if inp_type is not None:
|
||||
raise Exception("More than one input type provided: {}".format(input))
|
||||
inp_type = val
|
||||
inp_type = to_ov_type(val)
|
||||
elif is_shape_type(val) or val is None:
|
||||
if shape is not None:
|
||||
raise Exception("More than one input shape provided: {}".format(input))
|
||||
shape = PartialShape(val) if val is not None else None
|
||||
shape = to_partial_shape(val) if val is not None else None
|
||||
else:
|
||||
raise Exception("Incorrect input parameters provided. Expected tuple with input name, "
|
||||
"input type or input shape. Got unknown object: {}".format(val))
|
||||
|
|
@ -72,14 +63,16 @@ def single_input_to_input_cut_info(input: [str, tuple, list, PartialShape, Type,
|
|||
inp_type,
|
||||
None)
|
||||
# Case when only type is set
|
||||
if isinstance(input, (type, Type)):
|
||||
return _InputCutInfo(None, None, input, None) # pylint: disable=no-member
|
||||
if is_type(input):
|
||||
return _InputCutInfo(None, None, to_ov_type(input), None) # pylint: disable=no-member
|
||||
|
||||
# We don't expect here single unnamed value. If list of int is set it is considered as shape.
|
||||
# Setting of value is expected only using InputCutInfo or string analog.
|
||||
|
||||
raise Exception("Unexpected object provided for input. Expected tuple, Shape, PartialShape, Type or str. Got {}".format(type(input)))
|
||||
|
||||
|
||||
|
||||
def is_single_input(input: [tuple, list]):
|
||||
"""
|
||||
Checks if input has parameters for single input.
|
||||
|
|
@ -94,14 +87,14 @@ def is_single_input(input: [tuple, list]):
|
|||
if name is not None:
|
||||
return False
|
||||
name = val
|
||||
elif isinstance(val, (type, Type)):
|
||||
elif is_type(val):
|
||||
if inp_type is not None:
|
||||
return False
|
||||
inp_type = val
|
||||
inp_type = to_ov_type(val)
|
||||
elif is_shape_type(val):
|
||||
if shape is not None:
|
||||
return False
|
||||
shape = PartialShape(val)
|
||||
shape = to_partial_shape(val)
|
||||
else:
|
||||
return False
|
||||
return True
|
||||
|
|
|
|||
|
|
@ -1,8 +1,11 @@
|
|||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
from openvino.runtime import PartialShape, Dimension # pylint: disable=no-name-in-module,import-error
|
||||
from openvino.runtime import Shape, PartialShape, Dimension # pylint: disable=no-name-in-module,import-error
|
||||
|
||||
from openvino.tools.ovc.error import Error
|
||||
|
||||
|
||||
|
|
@ -62,3 +65,46 @@ def get_dynamic_dims(shape: [PartialShape, list, tuple]):
|
|||
dynamic_dims.append(idx)
|
||||
|
||||
return dynamic_dims
|
||||
|
||||
|
||||
def tensor_to_int_list(tensor):
|
||||
assert hasattr(tensor, 'numpy'), "Could not get value of provided tensor: {}".format(tensor)
|
||||
tensor_numpy = tensor.numpy()
|
||||
assert tensor_numpy.dtype == np.int32, "Unexpected type of provided tensor. Expected int32, got: {}".format(
|
||||
tensor_numpy.dtype)
|
||||
return tensor_numpy.tolist()
|
||||
|
||||
def to_partial_shape(shape):
|
||||
if 'tensorflow' in sys.modules:
|
||||
import tensorflow as tf
|
||||
if isinstance(shape, tf.Tensor):
|
||||
return PartialShape(tensor_to_int_list(shape))
|
||||
if isinstance(shape, tf.TensorShape):
|
||||
return PartialShape(list(shape))
|
||||
if 'paddle' in sys.modules:
|
||||
import paddle
|
||||
if isinstance(shape, paddle.Tensor):
|
||||
return PartialShape(tensor_to_int_list(shape))
|
||||
return PartialShape(shape)
|
||||
|
||||
|
||||
def is_shape_type(value):
|
||||
if isinstance(value, PartialShape):
|
||||
return True
|
||||
if 'tensorflow' in sys.modules:
|
||||
import tensorflow as tf
|
||||
if isinstance(value, (tf.TensorShape, tf.Tensor)):
|
||||
return True
|
||||
if 'paddle' in sys.modules:
|
||||
import paddle
|
||||
if isinstance(value, paddle.Tensor):
|
||||
return True
|
||||
if isinstance(value, Shape):
|
||||
return True
|
||||
if isinstance(value, list) or isinstance(value, tuple):
|
||||
for dim in value:
|
||||
if not (isinstance(dim, Dimension) or isinstance(dim, int)):
|
||||
return False
|
||||
return True
|
||||
return False
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,81 @@
|
|||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import sys
|
||||
|
||||
from openvino.runtime import Type
|
||||
|
||||
import openvino as ov
|
||||
|
||||
|
||||
def is_type(val):
|
||||
if isinstance(val, (type, Type)):
|
||||
return True
|
||||
if 'tensorflow' in sys.modules:
|
||||
import tensorflow as tf
|
||||
if isinstance(val, tf.dtypes.DType):
|
||||
return True
|
||||
if 'torch' in sys.modules:
|
||||
import torch
|
||||
if isinstance(val, torch.dtype):
|
||||
return True
|
||||
if 'paddle' in sys.modules:
|
||||
import paddle
|
||||
if isinstance(val, paddle.dtype):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def to_ov_type(val):
|
||||
if isinstance(val, Type):
|
||||
return val
|
||||
if isinstance(val, type):
|
||||
return Type(val)
|
||||
if 'tensorflow' in sys.modules:
|
||||
import tensorflow as tf
|
||||
if isinstance(val, tf.dtypes.DType):
|
||||
return Type(val.as_numpy_dtype())
|
||||
if 'torch' in sys.modules:
|
||||
import torch
|
||||
|
||||
if isinstance(val, torch.dtype):
|
||||
torch_to_ov_type = {
|
||||
torch.float32: ov.Type.f32,
|
||||
torch.float16: ov.Type.f16,
|
||||
torch.float64: ov.Type.f64,
|
||||
torch.bfloat16: ov.Type.bf16,
|
||||
torch.uint8: ov.Type.u8,
|
||||
torch.int8: ov.Type.i8,
|
||||
torch.int16: ov.Type.i16,
|
||||
torch.int32: ov.Type.i32,
|
||||
torch.int64: ov.Type.i64,
|
||||
torch.bool: ov.Type.boolean,
|
||||
}
|
||||
if val not in torch_to_ov_type:
|
||||
raise Exception("The provided data time is not supported {}.".format(val))
|
||||
|
||||
return torch_to_ov_type[val]
|
||||
|
||||
if 'paddle' in sys.modules:
|
||||
import paddle
|
||||
|
||||
if isinstance(val, paddle.dtype):
|
||||
paddle_to_ov_type = {
|
||||
paddle.float32: ov.Type.f32,
|
||||
paddle.float16: ov.Type.f16,
|
||||
paddle.float64: ov.Type.f64,
|
||||
paddle.bfloat16: ov.Type.bf16,
|
||||
paddle.uint8: ov.Type.u8,
|
||||
paddle.int8: ov.Type.i8,
|
||||
paddle.int16: ov.Type.i16,
|
||||
paddle.int32: ov.Type.i32,
|
||||
paddle.int64: ov.Type.i64,
|
||||
paddle.bool: ov.Type.boolean,
|
||||
}
|
||||
|
||||
if val not in paddle_to_ov_type:
|
||||
raise Exception("The provided data time is not supported {}.".format(val))
|
||||
|
||||
return paddle_to_ov_type[val]
|
||||
raise Exception("Unexpected type object. Expected ov.Type, np.dtype, tf.dtypes.DType. Got {}".format(type(val)))
|
||||
|
||||
|
|
@ -22,6 +22,7 @@ openvino/tools/ovc/moc_frontend/pipeline.py
|
|||
openvino/tools/ovc/moc_frontend/preprocessing.py
|
||||
openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
|
||||
openvino/tools/ovc/moc_frontend/shape_utils.py
|
||||
openvino/tools/ovc/moc_frontend/type_utils.py
|
||||
openvino/tools/ovc/ovc.py
|
||||
openvino/tools/ovc/telemetry_params.py
|
||||
openvino/tools/ovc/telemetry_stub.py
|
||||
|
|
|
|||
Loading…
Reference in New Issue