e4f44b19fd
* Mark all failed ONNX layer tests as XFail
* Add additional xfailed marks
* Add one more failed tests into XFail
* Add conditions for CPU/GPU failures
* Revert "Add conditions for CPU/GPU failures"
This reverts commit 790524c59c.
* Add failures separation for CPU/GPU
* Replace all xfail with skip
513 lines
27 KiB
Python
513 lines
27 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import numpy as np
|
|
import pytest
|
|
from common.layer_test_class import check_ir_version
|
|
from common.onnx_layer_test_class import OnnxRuntimeLayerTest
|
|
|
|
from unit_tests.utils.graph import build_graph
|
|
|
|
|
|
class TestConv(OnnxRuntimeLayerTest):
|
|
def _prepare_input(self, inputs_dict):
|
|
for input in inputs_dict.keys():
|
|
inputs_dict[input] = np.random.randn(*inputs_dict[input]).astype(np.float32)
|
|
return inputs_dict
|
|
|
|
def create_net(self, shape, weights_shape, dilations, group, pads, strides, bias, ir_version,
|
|
auto_pad=None):
|
|
"""
|
|
ONNX net IR net
|
|
|
|
Input->Conv->Output => Input->Convolution
|
|
"""
|
|
|
|
#
|
|
# Create ONNX model
|
|
#
|
|
|
|
import onnx
|
|
from onnx import helper
|
|
from onnx import TensorProto
|
|
|
|
output_shape = np.array(shape)
|
|
output_shape[1] = group
|
|
_pads = np.array(pads).reshape([2, -1])
|
|
kernel_extent = np.array(dilations) * (np.array(weights_shape[2:]) - 1) + 1
|
|
spatial_val_wo_stride = shape[2:] + np.add(_pads[0, :], _pads[1, :]) - kernel_extent
|
|
output_shape[2:] = (spatial_val_wo_stride.astype(float) / strides + 1).astype(np.int64)
|
|
output_shape = output_shape.astype(int).tolist()
|
|
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
|
|
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
|
|
|
|
weights_const = np.random.randn(*weights_shape).astype(np.float32)
|
|
|
|
node_weights_def = onnx.helper.make_node(
|
|
'Constant',
|
|
inputs=[],
|
|
outputs=['weights'],
|
|
value=helper.make_tensor(
|
|
name='const_tensor',
|
|
data_type=TensorProto.FLOAT,
|
|
dims=weights_const.shape,
|
|
vals=weights_const.flatten(),
|
|
),
|
|
)
|
|
|
|
conv_args = dict(kernel_shape=weights_shape[2:],
|
|
dilations=dilations,
|
|
group=group,
|
|
strides=strides)
|
|
if pads and auto_pad not in ['SAME_UPPER', 'SAME_LOWER']:
|
|
conv_args['pads'] = pads
|
|
if auto_pad:
|
|
conv_args['auto_pad'] = auto_pad
|
|
if bias:
|
|
bias_const = np.random.randint(-10, 10, weights_shape[0]).astype(np.float32)
|
|
|
|
node_bias_def = onnx.helper.make_node(
|
|
'Constant',
|
|
inputs=[],
|
|
outputs=['bias'],
|
|
value=helper.make_tensor(
|
|
name='const_tensor',
|
|
data_type=TensorProto.FLOAT,
|
|
dims=bias_const.shape,
|
|
vals=bias_const.flatten(),
|
|
),
|
|
)
|
|
node_def = onnx.helper.make_node(
|
|
'Conv',
|
|
inputs=['input', 'weights', 'bias'],
|
|
outputs=['output'],
|
|
**conv_args
|
|
)
|
|
nodes = [node_weights_def, node_bias_def, node_def]
|
|
else:
|
|
node_def = onnx.helper.make_node(
|
|
'Conv',
|
|
inputs=['input', 'weights'],
|
|
outputs=['output'],
|
|
**conv_args
|
|
)
|
|
nodes = [node_weights_def, node_def]
|
|
|
|
# Create the graph (GraphProto)
|
|
graph_def = helper.make_graph(
|
|
nodes,
|
|
'test_model',
|
|
[input],
|
|
[output],
|
|
)
|
|
|
|
# Create the model (ModelProto)
|
|
onnx_net = helper.make_model(graph_def, producer_name='test_model')
|
|
|
|
#
|
|
# Create reference IR net
|
|
#
|
|
|
|
ref_net = None
|
|
|
|
if check_ir_version(10, None, ir_version):
|
|
if len(shape) == 3:
|
|
input_shape = shape.copy()
|
|
input_shape.insert(2, 1)
|
|
node_shape = output_shape.copy()
|
|
node_shape.insert(2, 1)
|
|
nodes_attributes = {
|
|
'input': {'kind': 'op', 'type': 'Parameter'},
|
|
'input_data': {'shape': shape, 'kind': 'data'},
|
|
'before_shape_const_indata': {'shape': [len(input_shape)], 'value': input_shape,
|
|
'kind': 'data'},
|
|
'before_shape_const': {'kind': 'op', 'type': 'Const'},
|
|
'before_shape_const_data': {'shape': [len(input_shape)], 'kind': 'data'},
|
|
'reshape_before': {'kind': 'op', 'type': 'Reshape'},
|
|
'reshape_before_data': {'shape': input_shape, 'kind': 'data'},
|
|
'kernel_indata': {'kind': 'data', 'shape': [len(weights_const.flatten())]},
|
|
'kernel': {'kind': 'op', 'type': 'Const'},
|
|
'kernel_data': {'kind': 'data', 'value': None},
|
|
'node': {'kind': 'op',
|
|
'type': 'Convolution' if group == 1 else 'GroupConvolution',
|
|
'dilations': [1, dilations[0]],
|
|
'pads_begin': [0, _pads[0, 0]], 'pads_end': [0, _pads[1, 0]]},
|
|
'node_data': {'shape': node_shape, 'kind': 'data'},
|
|
'after_shape_const_indata': {'shape': [len(output_shape)],
|
|
'value': output_shape, 'kind': 'data'},
|
|
'after_shape_const': {'kind': 'op', 'type': 'Const'},
|
|
'after_shape_const_data': {'shape': [len(output_shape)], 'kind': 'data'},
|
|
'reshape_after': {'kind': 'op', 'type': 'Reshape'},
|
|
'reshape_after_data': {'shape': output_shape, 'kind': 'data'},
|
|
'result': {'kind': 'op', 'type': 'Result'}}
|
|
edges = [('input', 'input_data'),
|
|
('input_data', 'reshape_before'),
|
|
('before_shape_const_indata', 'before_shape_const'),
|
|
('before_shape_const', 'before_shape_const_data'),
|
|
('before_shape_const_data', 'reshape_before'),
|
|
('reshape_before', 'reshape_before_data'),
|
|
('reshape_before_data', 'node'),
|
|
('kernel_indata', 'kernel'),
|
|
('kernel', 'kernel_data'),
|
|
('kernel_data', 'node'),
|
|
('node', 'node_data'),
|
|
('node_data', 'reshape_after'),
|
|
('after_shape_const_indata', 'after_shape_const'),
|
|
('after_shape_const', 'after_shape_const_data'),
|
|
('after_shape_const_data', 'reshape_after'),
|
|
('reshape_after', 'reshape_after_data')]
|
|
if bias:
|
|
nodes_attributes.update(
|
|
{'const_indata': {'kind': 'data', 'value': bias_const.flatten()},
|
|
'const': {'kind': 'op', 'type': 'Const'},
|
|
'const_data': {'kind': 'data', 'shape': None},
|
|
'bias': {'type': 'Add', 'kind': 'op'},
|
|
'bias_data': {'kind': 'data', 'shape': output_shape}})
|
|
edges += [('reshape_after_data', 'bias'),
|
|
('const_indata', 'const'),
|
|
('const', 'const_data'),
|
|
('const_data', 'bias'),
|
|
('bias', 'bias_data'),
|
|
('bias_data', 'result')]
|
|
else:
|
|
edges += [('reshape_after_data', 'result')]
|
|
ref_net = build_graph(nodes_attributes, edges)
|
|
else:
|
|
_weights_shape = weights_shape.copy()
|
|
if group != 1:
|
|
_weights_shape.insert(1, 1)
|
|
nodes_attributes = {
|
|
'input': {'kind': 'op', 'type': 'Parameter'},
|
|
'input_data': {'shape': shape, 'kind': 'data'},
|
|
'kernel_indata': {'kind': 'data', 'value': weights_const.flatten()},
|
|
'kernel': {'kind': 'op', 'type': 'Const'},
|
|
'kernel_data': {'kind': 'data', 'shape': _weights_shape},
|
|
'node': {'kind': 'op',
|
|
'type': 'Convolution' if group == 1 else 'GroupConvolution',
|
|
'dilations': dilations, 'pads_begin': _pads[0, :],
|
|
'pads_end': _pads[1, :]},
|
|
'node_data': {'shape': output_shape, 'kind': 'data'},
|
|
'result': {'kind': 'op', 'type': 'Result'}}
|
|
edges = [('input', 'input_data'),
|
|
('input_data', 'node'),
|
|
('kernel_indata', 'kernel'),
|
|
('kernel', 'kernel_data'),
|
|
('kernel_data', 'node'),
|
|
('node', 'node_data')]
|
|
|
|
if bias:
|
|
nodes_attributes.update(
|
|
{'const_indata': {'kind': 'data', 'value': bias_const.flatten()},
|
|
'const': {'kind': 'op', 'type': 'Const'},
|
|
'const_data': {'kind': 'data', 'shape': None},
|
|
'bias': {'type': 'Add', 'kind': 'op'},
|
|
'bias_data': {'kind': 'data', 'shape': output_shape}})
|
|
edges += [('node_data', 'bias'),
|
|
('const_indata', 'const'),
|
|
('const', 'const_data'),
|
|
('const_data', 'bias'),
|
|
('bias', 'bias_data'),
|
|
('bias_data', 'result')]
|
|
else:
|
|
edges += [('node_data', 'result')]
|
|
|
|
ref_net = build_graph(nodes_attributes, edges)
|
|
|
|
return onnx_net, ref_net
|
|
|
|
test_data_3D = [
|
|
dict(weights_shape=[1, 3, 3], group=1),
|
|
dict(weights_shape=[1, 3, 5], group=1),
|
|
dict(weights_shape=[3, 1, 3], group=3),
|
|
dict(weights_shape=[3, 1, 5], group=3)]
|
|
|
|
test_data_3D_autopad = [
|
|
dict(weights_shape=[1, 3, 3], group=1, pads=[1, 1], strides=[1], dilations=[1]),
|
|
dict(weights_shape=[1, 3, 3], group=1, pads=[2, 2], strides=[1], dilations=[2]),
|
|
dict(weights_shape=[1, 3, 3], group=1, pads=[1, 1], strides=[2], dilations=[1]),
|
|
dict(weights_shape=[1, 3, 3], group=1, pads=[2, 2], strides=[2], dilations=[2]),
|
|
dict(weights_shape=[1, 3, 5], group=1, pads=[2, 2], strides=[1], dilations=[1]),
|
|
dict(weights_shape=[1, 3, 5], group=1, pads=[4, 4], strides=[1], dilations=[2]),
|
|
dict(weights_shape=[1, 3, 5], group=1, pads=[2, 2], strides=[2], dilations=[1]),
|
|
dict(weights_shape=[1, 3, 5], group=1, pads=[4, 4], strides=[2], dilations=[2]),
|
|
dict(weights_shape=[3, 1, 3], group=3, pads=[1, 1], strides=[1], dilations=[1]),
|
|
dict(weights_shape=[3, 1, 3], group=3, pads=[2, 2], strides=[1], dilations=[2]),
|
|
dict(weights_shape=[3, 1, 3], group=3, pads=[1, 1], strides=[2], dilations=[1]),
|
|
dict(weights_shape=[3, 1, 3], group=3, pads=[2, 2], strides=[2], dilations=[2]),
|
|
dict(weights_shape=[3, 1, 5], group=3, pads=[2, 2], strides=[1], dilations=[1]),
|
|
dict(weights_shape=[3, 1, 5], group=3, pads=[4, 4], strides=[1], dilations=[2]),
|
|
dict(weights_shape=[3, 1, 5], group=3, pads=[2, 2], strides=[2], dilations=[1]),
|
|
dict(weights_shape=[3, 1, 5], group=3, pads=[4, 4], strides=[2], dilations=[2])]
|
|
|
|
test_data_4D_precommit = [
|
|
dict(weights_shape=[1, 3, 3, 3], group=1),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3)]
|
|
|
|
test_data_4D = [
|
|
dict(weights_shape=[1, 3, 3, 3], group=1),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3)]
|
|
|
|
test_data_4D_autopad = [
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[1, 1, 1, 1], strides=[1, 1],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[2, 2, 2, 2], strides=[1, 1],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[3, 5, 3, 5], strides=[1, 1],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[1, 1, 1, 1], strides=[2, 2],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[2, 2, 2, 2], strides=[2, 2],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[3, 5, 3, 5], strides=[2, 2],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[1, 0, 1, 0], strides=[3, 5],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[2, 0, 2, 0], strides=[3, 5],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 3], group=1, pads=[3, 3, 3, 3], strides=[3, 5],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[2, 1, 2, 1], strides=[1, 1],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[4, 2, 4, 2], strides=[1, 1],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[6, 5, 6, 5], strides=[1, 1],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[2, 1, 2, 1], strides=[2, 2],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[4, 2, 4, 2], strides=[2, 2],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[6, 5, 6, 5], strides=[2, 2],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[2, 0, 2, 0], strides=[3, 5],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[4, 0, 4, 0], strides=[3, 5],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[1, 3, 5, 3], group=1, pads=[6, 3, 6, 3], strides=[3, 5],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[1, 1, 1, 1], strides=[1, 1],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[2, 2, 2, 2], strides=[1, 1],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[3, 5, 3, 5], strides=[1, 1],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[1, 1, 1, 1], strides=[2, 2],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[2, 2, 2, 2], strides=[2, 2],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[3, 5, 3, 5], strides=[2, 2],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[1, 0, 1, 0], strides=[3, 5],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[2, 0, 2, 0], strides=[3, 5],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 3], group=3, pads=[3, 3, 3, 3], strides=[3, 5],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[1, 2, 1, 2], strides=[1, 1],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[2, 4, 2, 4], strides=[1, 1],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[3, 10, 3, 10], strides=[1, 1],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[1, 2, 1, 2], strides=[2, 2],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[2, 4, 2, 4], strides=[2, 2],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[3, 10, 3, 10], strides=[2, 2],
|
|
dilations=[3, 5]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[1, 0, 1, 0], strides=[3, 5],
|
|
dilations=[1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[2, 2, 2, 2], strides=[3, 5],
|
|
dilations=[2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 5], group=3, pads=[3, 8, 3, 8], strides=[3, 5],
|
|
dilations=[3, 5])]
|
|
|
|
test_data_5D_precommit = [
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3)]
|
|
|
|
test_data_5D = [
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3)]
|
|
|
|
test_data_5D_autopad = [
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[1, 1, 1, 1, 1, 1], strides=[1, 1, 1],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[2, 2, 2, 2, 2, 2], strides=[1, 1, 1],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[3, 4, 5, 3, 4, 5], strides=[1, 1, 1],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[1, 1, 1, 1, 1, 1], strides=[2, 2, 2],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[2, 2, 2, 2, 2, 2], strides=[2, 2, 2],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[3, 4, 5, 3, 4, 5], strides=[2, 2, 2],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[1, 1, 0, 1, 1, 0], strides=[3, 4, 5],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[2, 2, 0, 2, 2, 0], strides=[3, 4, 5],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 3, 3], group=1, pads=[3, 4, 3, 3, 4, 3], strides=[3, 4, 5],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[1, 1, 2, 1, 2, 2], strides=[1, 1, 1],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[2, 3, 4, 2, 3, 4], strides=[1, 1, 1],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[3, 6, 10, 3, 6, 10], strides=[1, 1, 1],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[1, 1, 2, 1, 2, 2], strides=[2, 2, 2],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[2, 3, 4, 2, 3, 4], strides=[2, 2, 2],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[3, 6, 10, 3, 6, 10], strides=[2, 2, 2],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[1, 1, 0, 1, 2, 0], strides=[3, 4, 5],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[2, 3, 2, 2, 3, 2], strides=[3, 4, 5],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[1, 3, 3, 4, 5], group=1, pads=[3, 6, 8, 3, 6, 8], strides=[3, 4, 5],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[1, 1, 1, 1, 1, 1], strides=[1, 1, 1],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[2, 2, 2, 2, 2, 2], strides=[1, 1, 1],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[3, 4, 5, 3, 4, 5], strides=[1, 1, 1],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[1, 1, 1, 1, 1, 1], strides=[2, 2, 2],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[2, 2, 2, 2, 2, 2], strides=[2, 2, 2],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[3, 4, 5, 3, 4, 5], strides=[2, 2, 2],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[1, 1, 0, 1, 1, 0], strides=[3, 4, 5],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[2, 2, 0, 2, 2, 0], strides=[3, 4, 5],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[3, 1, 3, 3, 3], group=3, pads=[3, 4, 3, 3, 4, 3], strides=[3, 4, 5],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[2, 1, 1, 2, 2, 1], strides=[1, 1, 1],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[4, 3, 2, 4, 3, 2], strides=[1, 1, 1],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[6, 6, 5, 6, 6, 5], strides=[1, 1, 1],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[2, 1, 1, 2, 2, 1], strides=[2, 2, 2],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[4, 3, 2, 4, 3, 2], strides=[2, 2, 2],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[6, 6, 5, 6, 6, 5], strides=[2, 2, 2],
|
|
dilations=[3, 4, 5]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[2, 1, 0, 2, 2, 0], strides=[3, 4, 5],
|
|
dilations=[1, 1, 1]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[4, 3, 0, 4, 3, 0], strides=[3, 4, 5],
|
|
dilations=[2, 2, 2]),
|
|
dict(weights_shape=[3, 1, 5, 4, 3], group=3, pads=[6, 6, 3, 6, 6, 3], strides=[3, 4, 5],
|
|
dilations=[3, 4, 5])]
|
|
|
|
@pytest.mark.parametrize("params", test_data_3D)
|
|
@pytest.mark.parametrize("dilations", [[1], [2]])
|
|
@pytest.mark.parametrize("pads", [[0, 0], [1, 1], [1, 2]])
|
|
@pytest.mark.parametrize("strides", [[1], [2]])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.nightly
|
|
def test_conv_3D(self, params, dilations, pads, strides, bias, ie_device, precision, ir_version,
|
|
temp_dir, use_old_api):
|
|
self._test(*self.create_net(**params, shape=[2, 3, 25], dilations=dilations, pads=pads,
|
|
strides=strides,
|
|
bias=bias, ir_version=ir_version),
|
|
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
|
|
|
|
@pytest.mark.parametrize("params", test_data_3D_autopad[:-1])
|
|
@pytest.mark.parametrize("auto_pad", ['SAME_UPPER', 'SAME_LOWER'])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.skip(reason='GREEN_SUITE')
|
|
def test_conv_3D_autopad(self, params, auto_pad, bias, ie_device, precision, ir_version,
|
|
temp_dir, use_old_api):
|
|
self._test(*self.create_net(**params, shape=[2, 3, 25], bias=bias, auto_pad=auto_pad,
|
|
ir_version=ir_version),
|
|
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
|
|
|
|
@pytest.mark.parametrize("params", test_data_4D_precommit)
|
|
@pytest.mark.parametrize("dilations", [[3, 5]])
|
|
@pytest.mark.parametrize("pads", [[1, 2, 3, 4]])
|
|
@pytest.mark.parametrize("strides", [[3, 5]])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.precommit
|
|
def test_conv_4D_precommit(self, params, dilations, pads, strides, bias, ie_device, precision,
|
|
ir_version, temp_dir, use_old_api):
|
|
self._test(*self.create_net(**params, shape=[2, 3, 25, 25], dilations=dilations, pads=pads,
|
|
strides=strides,
|
|
bias=bias, ir_version=ir_version),
|
|
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
|
|
|
|
@pytest.mark.parametrize("params", test_data_4D)
|
|
@pytest.mark.parametrize("dilations", [[1, 1], [2, 2], [3, 5]])
|
|
@pytest.mark.parametrize("pads", [[0, 0, 0, 0], [1, 1, 1, 1], [1, 2, 3, 4]])
|
|
@pytest.mark.parametrize("strides", [[1, 1], [2, 2], [3, 5]])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.nightly
|
|
def test_conv_4D(self, params, dilations, pads, strides, bias, ie_device, precision, ir_version,
|
|
temp_dir, use_old_api):
|
|
self._test(
|
|
*self.create_net(**params, shape=[2, 3, 25, 25], dilations=dilations, pads=pads,
|
|
strides=strides, bias=bias,
|
|
ir_version=ir_version), ie_device, precision, ir_version,
|
|
temp_dir=temp_dir, use_old_api=use_old_api)
|
|
|
|
@pytest.mark.parametrize("params", test_data_4D_autopad[:-1])
|
|
@pytest.mark.parametrize("auto_pad", ['SAME_UPPER', 'SAME_LOWER'])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.skip(reason='GREEN_SUITE')
|
|
def test_conv_4D_autopad(self, params, auto_pad, bias, ie_device, precision, ir_version,
|
|
temp_dir, use_old_api):
|
|
self._test(*self.create_net(**params, shape=[2, 3, 25, 25], bias=bias, auto_pad=auto_pad,
|
|
ir_version=ir_version), ie_device, precision, ir_version,
|
|
temp_dir=temp_dir, use_old_api=use_old_api)
|
|
|
|
@pytest.mark.parametrize("params", test_data_5D_precommit)
|
|
@pytest.mark.parametrize("dilations", [[3, 4, 5]])
|
|
@pytest.mark.parametrize("pads", [[1, 2, 3, 4, 5, 6]])
|
|
@pytest.mark.parametrize("strides", [[3, 4, 5]])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.precommit
|
|
def test_conv_5D_precommit(self, params, dilations, pads, strides, bias, ie_device, precision,
|
|
ir_version, temp_dir, use_old_api):
|
|
custom_eps_value = 1e-1 if ie_device == 'GPU' and precision == 'FP16' else None
|
|
self._test(
|
|
*self.create_net(**params, shape=[2, 3, 25, 25, 25], dilations=dilations, pads=pads,
|
|
strides=strides,
|
|
bias=bias, ir_version=ir_version),
|
|
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api, custom_eps=custom_eps_value)
|
|
|
|
@pytest.mark.parametrize("params", test_data_5D)
|
|
@pytest.mark.parametrize("dilations", [[1, 1, 1], [2, 2, 2], [3, 4, 5]])
|
|
@pytest.mark.parametrize("pads", [[0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 2, 3, 4, 5, 6]])
|
|
@pytest.mark.parametrize("strides", [[1, 1, 1], [2, 2, 2], [3, 4, 5]])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.skip(reason='GREEN_SUITE')
|
|
def test_conv_5D(self, params, dilations, pads, strides, bias, ie_device, precision, ir_version,
|
|
temp_dir, use_old_api):
|
|
self._test(
|
|
*self.create_net(**params, shape=[2, 3, 25, 25, 25], dilations=dilations, pads=pads,
|
|
strides=strides,
|
|
bias=bias, ir_version=ir_version),
|
|
ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
|
|
|
|
@pytest.mark.parametrize("params", test_data_5D_autopad[:-1])
|
|
@pytest.mark.parametrize("auto_pad", ['SAME_UPPER', 'SAME_LOWER'])
|
|
@pytest.mark.parametrize("bias", [False, True])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.skip(reason='GREEN_SUITE')
|
|
def test_conv_5D_autopad(self, params, auto_pad, bias, ie_device, precision, ir_version,
|
|
temp_dir, use_old_api):
|
|
self._test(
|
|
*self.create_net(**params, shape=[2, 3, 25, 25, 25], bias=bias, auto_pad=auto_pad,
|
|
ir_version=ir_version), ie_device, precision, ir_version,
|
|
temp_dir=temp_dir, use_old_api=use_old_api)
|