forked from mindspore-Ecosystem/mindspore
182 lines
6.1 KiB
Python
182 lines
6.1 KiB
Python
# Copyright 2022 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 pytest
|
|
import numpy as np
|
|
import mindspore.context as context
|
|
import mindspore.ops as ops
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops.functional import vmap
|
|
|
|
|
|
class FuncNet(nn.Cell):
|
|
def __init__(self, paddings):
|
|
super(FuncNet, self).__init__()
|
|
self.paddings = paddings
|
|
|
|
def construct(self, x):
|
|
return ops.pad(x, self.paddings)
|
|
|
|
|
|
class TensorNet(nn.Cell):
|
|
def __init__(self, paddings):
|
|
super(TensorNet, self).__init__()
|
|
self.paddings = paddings
|
|
|
|
def construct(self, x):
|
|
return x.pad(self.paddings)
|
|
|
|
|
|
class GradNet(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradNet, self).__init__()
|
|
self.network = network
|
|
self.grad = ops.GradOperation()
|
|
|
|
def construct(self, x):
|
|
return self.grad(self.network)(x)
|
|
|
|
|
|
def run_case(x, paddings, expect, mode="functional"):
|
|
if mode == "functional":
|
|
net = FuncNet(paddings)
|
|
else:
|
|
net = TensorNet(paddings)
|
|
out_ms = net(Tensor(x))
|
|
assert np.allclose(expect, out_ms.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pad_function_cpu():
|
|
"""
|
|
Feature: test ops.Pad functional interface.
|
|
Description: paddings has negative values.
|
|
Expectation: the result match with numpy result.
|
|
"""
|
|
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
|
|
# case1: padding value are non negative
|
|
paddings1 = ((0, 1), (1, 0))
|
|
expect1 = np.array([[0, 1, 2, 3], [0, 4, 5, 6], [0, 7, 8, 9], [0, 0, 0, 0]], dtype=np.float32)
|
|
# case2: padding value are non positive
|
|
paddings2 = ((-1, 0), (-1, -1))
|
|
expect2 = np.array([[5], [8]], dtype=np.float32)
|
|
# case3: padding with positive and negative value
|
|
paddings3 = ((-1, 1), (1, -1))
|
|
expect3 = np.array([[0, 4, 5], [0, 7, 8], [0, 0, 0]], dtype=np.float32)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
run_case(x, paddings1, expect1)
|
|
run_case(x, paddings2, expect2)
|
|
run_case(x, paddings3, expect3)
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
|
run_case(x, paddings1, expect1)
|
|
run_case(x, paddings2, expect2)
|
|
run_case(x, paddings3, expect3)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pad_function_grad_cpu():
|
|
"""
|
|
Feature: test ops.Pad functional interface backward.
|
|
Description: paddings has negative values.
|
|
Expectation: the result match with numpy result.
|
|
"""
|
|
paddings = ((1, -1), (1, -1))
|
|
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
|
|
expect = np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=np.float32)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
net = GradNet(FuncNet(paddings))
|
|
out_ms = net(Tensor(x))
|
|
assert np.allclose(expect, out_ms.asnumpy())
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
|
net = GradNet(FuncNet(paddings))
|
|
out_ms = net(Tensor(x))
|
|
assert np.allclose(expect, out_ms.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pad_tensor_cpu():
|
|
"""
|
|
Feature: test ops.Pad tensor interface.
|
|
Description: paddings with different values.
|
|
Expectation: the result match with numpy result.
|
|
"""
|
|
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
|
|
# case1: padding value are non negative
|
|
paddings1 = ((1, 1), (1, 1))
|
|
expect1 = np.pad(x, paddings1, "constant", constant_values=0).astype(x.dtype)
|
|
# case2: padding value are non positive
|
|
paddings2 = ((-1, -1), (0, -1))
|
|
expect2 = np.array([[4, 5]], dtype=np.float32)
|
|
# case3: padding with positive and negative value
|
|
paddings3 = ((1, -1), (-1, 1))
|
|
expect3 = np.array([[0, 0, 0], [2, 3, 0], [5, 6, 0]], dtype=np.float32)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
run_case(x, paddings1, expect1, "tensor")
|
|
run_case(x, paddings2, expect2, "tensor")
|
|
run_case(x, paddings3, expect3, "tensor")
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
|
run_case(x, paddings1, expect1, "tensor")
|
|
run_case(x, paddings2, expect2, "tensor")
|
|
run_case(x, paddings3, expect3, "tensor")
|
|
|
|
|
|
def vmap_case():
|
|
class Net(nn.Cell):
|
|
def __init__(self, paddings):
|
|
super(Net, self).__init__()
|
|
self.pad = ops.Pad(paddings)
|
|
|
|
def construct(self, x):
|
|
return self.pad(x)
|
|
|
|
# single vmap case
|
|
x_np = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
|
|
expect = np.array([[0, 1, 2, 3, 0], [0, 4, 5, 6, 0], [0, 7, 8, 9, 0]], dtype=np.float32)
|
|
out_ms = vmap(Net(((1, 1),)), 0, 0)(Tensor(x_np))
|
|
assert np.allclose(expect, out_ms.asnumpy())
|
|
# nested vmap case
|
|
x_np1 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], dtype=np.float32)
|
|
expect1 = np.array([[[0, 1, 2, 0], [0, 3, 4, 0]], [[0, 5, 6, 0], [0, 7, 8, 0]]], dtype=np.float32)
|
|
out_ms1 = vmap(vmap(Net(((1, 1),)), 0, 0), 0, 0)(Tensor(x_np1))
|
|
assert np.allclose(expect1, out_ms1.asnumpy())
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_pad_vmap_cpu():
|
|
"""
|
|
Feature: test ops.Pad vmap.
|
|
Description: inputs with batch.
|
|
Expectation: the result match with expect
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
vmap_case()
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
|
vmap_case()
|