forked from mindspore-Ecosystem/mindspore
178 lines
6.6 KiB
Python
178 lines
6.6 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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from operator import mul
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from functools import reduce
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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import mindspore.context as context
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from mindspore.ops import operations as P
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from mindspore.ops.functional import vmap
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from mindspore.common import dtype as ms_type
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def check_dropout_nd_by_keep_prob(func_name, input_x, output, output_mask, keep_prob):
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"""
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Feature: check mindspore Dropout2D or Dropout3D's output and mask.
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Description: output shape, mask shap and keep_pro will be checked.
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Expectation: match to mindspore Dropout2D or Dropout3D.
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"""
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# Check input, output, mask all have same shape
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assert input_x.shape == output.shape == output_mask.shape
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data_type = input_x.dtype
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loss = 1e-6
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if data_type == np.float16:
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loss = 1e-3
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data_shape = input_x.shape
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rank = len(data_shape)
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features = 1
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if func_name == "dropout2d":
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# HW
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features = features * data_shape[-2] * data_shape[-1]
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channels = reduce(mul, data_shape[: rank - 2])
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else:
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# DHW
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features = features * data_shape[-3] * data_shape[-2] * data_shape[-1]
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channels = reduce(mul, data_shape[: rank - 3])
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if keep_prob == 0.0:
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input_x_by_keep_prob = input_x.astype(data_type).reshape(channels, features)
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else:
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input_x_by_keep_prob = (input_x / keep_prob).astype(data_type).reshape(channels, features)
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output_reshape = output.reshape(channels, features)
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mask_reshape = output_mask.reshape(channels, features)
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# Check each channel is entirely True or False and output match to input_x
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for channel in range(channels):
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if np.all(output_reshape[channel] == 0):
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assert int(np.all(mask_reshape[channel])) == 0
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else:
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assert np.all(mask_reshape[channel])
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np.allclose(input_x_by_keep_prob[channel], output_reshape[channel], loss, loss)
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class DropoutNdVMapNet(nn.Cell):
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def __init__(self, forward_net, in_axes, out_axes):
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super(DropoutNdVMapNet, self).__init__()
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self.net = forward_net
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self.in_axes = in_axes
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self.out_axes = out_axes
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def construct(self, input_x):
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return vmap(self.net, self.in_axes, self.out_axes)(input_x)
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class Dropout3DNet(nn.Cell):
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def __init__(self, keep_prob):
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super(Dropout3DNet, self).__init__()
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self.drop = P.Dropout3D(keep_prob)
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def construct(self, x):
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return self.drop(x)
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class Dropout2DNet(nn.Cell):
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def __init__(self, keep_prob):
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super(Dropout2DNet, self).__init__()
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self.drop = P.Dropout2D(keep_prob)
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def construct(self, x):
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return self.drop(x)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("func_name", ["dropout2d", "dropout3d"])
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@pytest.mark.parametrize("keep_prob", [0.0, 0.4, 1.0])
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@pytest.mark.parametrize("data_type", [np.int8, np.int16, np.int32, np.int64, np.float16, np.float32, np.float64])
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def test_dropout_nd(func_name, data_type, keep_prob):
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"""
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Feature: Test Dropout2D and Dropout3D.
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Description: The input shape is 4d or 5d.
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Expectation: check it by function check_dropout_nd_by_keep_prob.
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"""
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if func_name == "dropout2d":
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dropout_nd = Dropout2DNet(keep_prob)
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data_shape = (32, 16, 4, 5)
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else:
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data_shape = (32, 16, 2, 5, 4)
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dropout_nd = Dropout3DNet(keep_prob)
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input_data = np.ones(data_shape).astype(data_type)
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output, mask = dropout_nd(Tensor(input_data))
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context.set_context(mode=context.GRAPH_MODE)
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check_dropout_nd_by_keep_prob(func_name, input_data, output.asnumpy(), mask.asnumpy(), keep_prob)
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context.set_context(mode=context.PYNATIVE_MODE)
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output, mask = dropout_nd(Tensor(input_data))
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check_dropout_nd_by_keep_prob(func_name, input_data, output.asnumpy(), mask.asnumpy(), keep_prob)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("func_name", ["dropout2d", "dropout3d"])
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def test_dropout_nd_vmap(func_name):
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"""
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Feature: Test dropout2d or dropout3d Vmap on CPU.
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Description: The output shape match to input shape.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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data_type = np.float32
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in_axes = 0
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out_axes = 0
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keep_prob = 0.4
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if func_name == "dropout2d":
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data_shape = (5, 32, 16, 4, 5)
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dropout = Dropout2DNet(keep_prob)
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else:
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data_shape = (10, 5, 32, 16, 4, 5)
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dropout = Dropout3DNet(keep_prob)
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input_x = np.ones(data_shape).astype(data_type)
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output, mask = DropoutNdVMapNet(dropout, in_axes, out_axes)(Tensor(input_x))
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check_dropout_nd_by_keep_prob(func_name, input_x, output.asnumpy(), mask.asnumpy(), keep_prob)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("func_name", ["dropout2d", "dropout3d"])
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def test_dropout_nd_dy_shape(func_name):
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"""
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Feature: Test dropout2d or dropout3d Dynamic Shape.
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Description: The output shape match to input shape.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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ms_data_type = ms_type.float32
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data_type = np.float32
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keep_prob = 0.4
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if func_name == "dropout2d":
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data_shape = (32, 16, 4, 5)
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dropout = Dropout2DNet(keep_prob)
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input_dyn = Tensor(shape=[32, 16, 4, None], dtype=ms_data_type)
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else:
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data_shape = (32, 16, 2, 5, 4)
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dropout = Dropout3DNet(keep_prob)
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input_dyn = Tensor(shape=[32, 16, 2, 5, None], dtype=ms_data_type)
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input_x = np.ones(data_shape).astype(data_type)
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dropout.set_inputs(input_dyn)
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output, mask = dropout(Tensor(input_x))
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check_dropout_nd_by_keep_prob(func_name, input_x, output.asnumpy(), mask.asnumpy(), keep_prob)
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context.set_context(mode=context.PYNATIVE_MODE)
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dropout.set_inputs(input_dyn)
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output, mask = dropout(Tensor(input_x))
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check_dropout_nd_by_keep_prob(func_name, input_x, output.asnumpy(), mask.asnumpy(), keep_prob)
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