You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
339 lines
9.4 KiB
339 lines
9.4 KiB
# Copyright 2020-2021 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.api import ms_function
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.ops import operations as P
|
|
|
|
|
|
class ConcatV32(nn.Cell):
|
|
def __init__(self, nptype):
|
|
super(ConcatV32, self).__init__()
|
|
|
|
self.cat = P.Concat(axis=2)
|
|
self.x1 = Parameter(initializer(
|
|
Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(nptype)), [2, 2, 1]), name='x1')
|
|
self.x2 = Parameter(initializer(
|
|
Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(nptype)), [2, 2, 2]), name='x2')
|
|
|
|
@ms_function
|
|
def construct(self):
|
|
return self.cat((self.x1, self.x2))
|
|
|
|
|
|
def axis32(nptype):
|
|
context.set_context(device_target='GPU')
|
|
|
|
cat = ConcatV32(nptype)
|
|
output = cat()
|
|
expect = np.array([[[0., 0., 1.],
|
|
[1., 2., 3.]],
|
|
[[2., 4., 5.],
|
|
[3., 6., 7.]]]).astype(nptype)
|
|
assert (output.asnumpy() == expect).all()
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis32_float64():
|
|
axis32(np.float64)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis32_float32():
|
|
axis32(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis32_int16():
|
|
axis32(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis32_uint8():
|
|
axis32(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis32_bool():
|
|
axis32(np.bool)
|
|
|
|
|
|
class ConcatV43(nn.Cell):
|
|
def __init__(self, nptype):
|
|
super(ConcatV43, self).__init__()
|
|
|
|
self.cat = P.Concat(axis=3)
|
|
self.x1 = Parameter(initializer(
|
|
Tensor(np.arange(2 * 2 * 2 * 2).reshape(2, 2, 2, 2).astype(nptype)), [2, 2, 2, 2]), name='x1')
|
|
self.x2 = Parameter(initializer(
|
|
Tensor(np.arange(2 * 2 * 2 * 3).reshape(2, 2, 2, 3).astype(nptype)), [2, 2, 2, 3]), name='x2')
|
|
|
|
@ms_function
|
|
def construct(self):
|
|
return self.cat((self.x1, self.x2))
|
|
|
|
|
|
def axis43(nptype):
|
|
context.set_context(device_target='GPU')
|
|
|
|
cat = ConcatV43(nptype)
|
|
output = cat()
|
|
expect = np.array([[[[0., 1., 0., 1., 2.],
|
|
[2., 3., 3., 4., 5.]],
|
|
[[4., 5., 6., 7., 8.],
|
|
[6., 7., 9., 10., 11.]]],
|
|
[[[8., 9., 12., 13., 14.],
|
|
[10., 11., 15., 16., 17.]],
|
|
[[12., 13., 18., 19., 20.],
|
|
[14., 15., 21., 22., 23.]]]]).astype(nptype)
|
|
assert (output.asnumpy() == expect).all()
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis43_float64():
|
|
axis43(np.float64)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis43_float32():
|
|
axis43(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis43_int16():
|
|
axis43(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis43_uint8():
|
|
axis43(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis43_bool():
|
|
axis43(np.bool)
|
|
|
|
|
|
class ConcatV21(nn.Cell):
|
|
def __init__(self, nptype):
|
|
super(ConcatV21, self).__init__()
|
|
|
|
self.cat = P.Concat(axis=1)
|
|
self.x1 = Parameter(initializer(
|
|
Tensor(np.arange(2 * 2).reshape(2, 2).astype(nptype)), [2, 2]), name='x1')
|
|
self.x2 = Parameter(initializer(
|
|
Tensor(np.arange(2 * 3).reshape(2, 3).astype(nptype)), [2, 3]), name='x2')
|
|
|
|
@ms_function
|
|
def construct(self):
|
|
return self.cat((self.x1, self.x2))
|
|
|
|
|
|
def axis21(nptype):
|
|
cat = ConcatV21(nptype)
|
|
output = cat()
|
|
expect = np.array([[0., 1., 0., 1., 2.],
|
|
[2., 3., 3., 4., 5.]]).astype(nptype)
|
|
assert (output.asnumpy() == expect).all()
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis21_float64():
|
|
axis21(np.float64)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis21_float32():
|
|
axis21(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis21_int16():
|
|
axis21(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis21_uint8():
|
|
axis21(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_axis21_bool():
|
|
axis21(np.bool)
|
|
|
|
|
|
class Concat3INet(nn.Cell):
|
|
def __init__(self):
|
|
super(Concat3INet, self).__init__()
|
|
self.cat = P.Concat(axis=1)
|
|
|
|
def construct(self, x1, x2, x3):
|
|
return self.cat((x1, x2, x3))
|
|
|
|
|
|
def concat_3i(nptype):
|
|
cat = Concat3INet()
|
|
|
|
x1_np = np.random.randn(32, 4, 224, 224).astype(nptype)
|
|
x2_np = np.random.randn(32, 8, 224, 224).astype(nptype)
|
|
x3_np = np.random.randn(32, 10, 224, 224).astype(nptype)
|
|
output_np = np.concatenate((x1_np, x2_np, x3_np), axis=1)
|
|
|
|
x1_ms = Tensor(x1_np)
|
|
x2_ms = Tensor(x2_np)
|
|
x3_ms = Tensor(x3_np)
|
|
output_ms = cat(x1_ms, x2_ms, x3_ms)
|
|
|
|
error = np.ones(shape=output_np.shape) * 10e-6
|
|
diff = output_ms.asnumpy() - output_np
|
|
assert np.all(diff < error)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_3i_float64():
|
|
concat_3i(np.float64)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_3i_float32():
|
|
concat_3i(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_3i_int16():
|
|
concat_3i(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_3i_uint8():
|
|
concat_3i(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_3i_bool():
|
|
cat = Concat3INet()
|
|
|
|
x1_np = np.random.choice([True, False], (32, 4, 224, 224)).astype(np.bool)
|
|
x2_np = np.random.choice([True, False], (32, 8, 224, 224)).astype(np.bool)
|
|
x3_np = np.random.choice([True, False], (32, 10, 224, 224)).astype(np.bool)
|
|
output_np = np.concatenate((x1_np, x2_np, x3_np), axis=1)
|
|
|
|
x1_ms = Tensor(x1_np)
|
|
x2_ms = Tensor(x2_np)
|
|
x3_ms = Tensor(x3_np)
|
|
output_ms = cat(x1_ms, x2_ms, x3_ms)
|
|
|
|
assert (output_ms.asnumpy() == output_np).all()
|
|
|
|
|
|
class Concat4INet(nn.Cell):
|
|
def __init__(self):
|
|
super(Concat4INet, self).__init__()
|
|
self.cat = P.Concat(axis=1)
|
|
|
|
def construct(self, x1, x2, x3, x4):
|
|
return self.cat((x1, x2, x3, x4))
|
|
|
|
|
|
def concat_4i(nptype):
|
|
cat = Concat4INet()
|
|
|
|
x1_np = np.random.randn(32, 4, 224, 224).astype(nptype)
|
|
x2_np = np.random.randn(32, 8, 224, 224).astype(nptype)
|
|
x3_np = np.random.randn(32, 10, 224, 224).astype(nptype)
|
|
x4_np = np.random.randn(32, 5, 224, 224).astype(nptype)
|
|
output_np = np.concatenate((x1_np, x2_np, x3_np, x4_np), axis=1)
|
|
|
|
x1_ms = Tensor(x1_np)
|
|
x2_ms = Tensor(x2_np)
|
|
x3_ms = Tensor(x3_np)
|
|
x4_ms = Tensor(x4_np)
|
|
output_ms = cat(x1_ms, x2_ms, x3_ms, x4_ms)
|
|
|
|
error = np.ones(shape=output_np.shape) * 10e-6
|
|
diff = output_ms.asnumpy() - output_np
|
|
assert np.all(diff < error)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_4i_float64():
|
|
concat_4i(np.float64)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_4i_float32():
|
|
concat_4i(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_4i_int16():
|
|
concat_4i(np.int16)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_4i_uint8():
|
|
concat_4i(np.uint8)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_concat_4i_bool():
|
|
cat = Concat4INet()
|
|
|
|
x1_np = np.random.choice([True, False], (32, 4, 224, 224)).astype(np.bool)
|
|
x2_np = np.random.choice([True, False], (32, 8, 224, 224)).astype(np.bool)
|
|
x3_np = np.random.choice([True, False], (32, 10, 224, 224)).astype(np.bool)
|
|
x4_np = np.random.choice([True, False], (32, 5, 224, 224)).astype(np.bool)
|
|
output_np = np.concatenate((x1_np, x2_np, x3_np, x4_np), axis=1)
|
|
|
|
x1_ms = Tensor(x1_np)
|
|
x2_ms = Tensor(x2_np)
|
|
x3_ms = Tensor(x3_np)
|
|
x4_ms = Tensor(x4_np)
|
|
output_ms = cat(x1_ms, x2_ms, x3_ms, x4_ms)
|
|
|
|
assert (output_ms.asnumpy() == output_np).all()
|