forked from mindspore-Ecosystem/mindspore
261 lines
8.7 KiB
Python
261 lines
8.7 KiB
Python
# Copyright 2020 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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""" test_initializer """
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import math
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from functools import reduce
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import numpy as np
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import pytest as py
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from scipy import stats
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import mindspore as ms
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import mindspore.common.initializer as init
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.parameter import Parameter
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from mindspore.common.tensor import Tensor
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from mindspore.nn import Conv2d
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from mindspore.ops import operations as P
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from ..ut_filter import non_graph_engine
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# pylint: disable=W0212
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# W0212: protected-access
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class InitTwo(init.Initializer):
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"""Initialize the array to two."""
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def _initialize(self, arr):
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init._assignment(arr, 2)
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def _check_value(tensor, value_min, value_max):
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nd = tensor.asnumpy()
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for ele in nd.flatten():
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if value_min <= ele <= value_max:
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continue
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raise ValueError('value_min = %d, ele = %d, value_max = %d'
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% (value_min, ele, value_max))
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def _check_uniform(tensor, boundary_a, boundary_b):
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samples = tensor.asnumpy().reshape((-1))
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_, p = stats.kstest(samples, 'uniform', (boundary_a, (boundary_b - boundary_a)))
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print("p-value is %f" % p)
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return p > 0.0001
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def test_init_Initializer():
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tensor = init.initializer(InitTwo(), [2, 2], ms.int32)
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assert tensor.shape == (2, 2)
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_check_value(tensor.to_tensor(), 2, 2)
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def test_init_tensor():
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tensor = ms.Tensor(np.zeros([1, 2, 3]))
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tensor = init.initializer(tensor, [1, 2, 3], ms.float32)
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assert tensor.shape == (1, 2, 3)
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def test_init_zero_default_dtype():
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tensor = init.initializer(init.Zero(), [2, 2])
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assert tensor.dtype == ms.float32
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_check_value(tensor.to_tensor(), 0, 0)
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def test_init_zero():
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tensor = init.initializer(init.Zero(), [2, 2], ms.float32)
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_check_value(tensor.to_tensor(), 0, 0)
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def test_init_zero_alias_default_dtype():
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tensor = init.initializer('zeros', [1, 2])
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assert tensor.dtype == ms.float32
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_check_value(tensor.to_tensor(), 0, 0)
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def test_init_zero_alias():
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tensor = init.initializer('zeros', [1, 2], ms.float32)
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_check_value(tensor.to_tensor(), 0, 0)
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def test_init_one():
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tensor = init.initializer(init.One(), [2, 2], ms.float32)
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_check_value(tensor.to_tensor(), 1, 1)
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def test_init_one_alias():
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tensor = init.initializer('ones', [1, 2], ms.float32)
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_check_value(tensor.to_tensor(), 1, 1)
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def test_init_constant():
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tensor = init.initializer(init.Constant(1), [2, 2], ms.float32)
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_check_value(tensor.to_tensor(), 1, 1)
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def test_init_uniform():
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scale = 10
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tensor = init.initializer(init.Uniform(scale=scale), [5, 4], ms.float32)
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_check_value(tensor.to_tensor(), -scale, scale)
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def test_init_uniform_alias():
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scale = 100
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tensor = init.initializer('uniform', [5, 4], ms.float32)
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_check_value(tensor.to_tensor(), -scale, scale)
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def test_init_normal():
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tensor = init.initializer(init.Normal(), [5, 4], ms.float32)
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assert isinstance(tensor, init.Normal), 'Normal init failed!'
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def test_init_truncated_normal():
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tensor = init.initializer(init.TruncatedNormal(), [5, 4], ms.float32)
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assert isinstance(tensor, init.TruncatedNormal), 'TruncatedNormal init failed!'
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def test_init_normal_alias():
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tensor = init.initializer('normal', [5, 4], ms.float32)
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assert isinstance(tensor, init.Normal), 'Normal init failed!'
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def test_init_truncatednormal_alias():
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tensor = init.initializer('truncatednormal', [5, 4], ms.float32)
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assert isinstance(tensor, init.TruncatedNormal), 'TruncatedNormal init failed!'
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def test_init_abnormal():
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with py.raises(TypeError):
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init.initializer([''], [5, 4], ms.float32)
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def test_initializer_reinit():
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weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16)
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assert weights.dtype == ms.float16
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assert weights.shape == (10, 1, 10, 10)
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weights = init.initializer(weights)
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assert weights.dtype == ms.float16
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assert weights.shape == (10, 1, 10, 10)
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weights.shape = None
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weights = init.initializer(weights, (10, 1))
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assert weights.dtype == ms.float16
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assert weights.shape == (10, 1)
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def test_init_xavier_uniform():
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""" test_init_xavier_uniform """
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gain = 1.2
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tensor1 = init.initializer(init.XavierUniform(gain=gain), [20, 22], ms.float32).to_tensor()
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tensor2 = init.initializer(init.XavierUniform(), [20, 22], ms.float32).to_tensor()
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tensor3 = init.initializer(init.XavierUniform(gain=gain), [20, 22, 5, 5], ms.float32).to_tensor()
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tensor4 = init.initializer(init.XavierUniform(), [20, 22, 5, 5], ms.float32).to_tensor()
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tensor5 = init.initializer('xavier_uniform', [20, 22, 5, 5], ms.float32).to_tensor()
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tensor6 = init.initializer('xavier_uniform', [20, 22], ms.float32).to_tensor()
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tensor_dict = {tensor1: gain, tensor2: None, tensor3: gain, tensor4: None, tensor5: None, tensor6: None}
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for tensor, gain_value in tensor_dict.items():
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if gain_value is None:
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gain_value = 1
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shape = tensor.asnumpy().shape
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if len(shape) > 2:
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s = reduce(lambda x, y: x * y, shape[2:])
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else:
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s = 1
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n_in = shape[1] * s
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n_out = shape[0] * s
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std = gain_value * math.sqrt(2 / (n_in + n_out))
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boundary = std * math.sqrt(3)
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assert _check_uniform(tensor, -boundary, boundary)
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def test_init_xavier_uniform_error():
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with py.raises(ValueError):
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init.initializer(init.XavierUniform(), [6], ms.float32).to_tensor()
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def test_init_he_uniform():
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""" test_init_he_uniform """
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tensor1 = init.initializer(init.HeUniform(), [20, 22], ms.float32)
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tensor2 = init.initializer(init.HeUniform(), [20, 22, 5, 5], ms.float32)
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tensor3 = init.initializer('he_uniform', [20, 22, 5, 5], ms.float32)
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tensor4 = init.initializer('he_uniform', [20, 22], ms.float32)
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tensors = [tensor1.to_tensor(), tensor2.to_tensor(), tensor3.to_tensor(), tensor4.to_tensor()]
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for tensor in tensors:
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shape = tensor.asnumpy().shape
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if len(shape) > 2:
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s = reduce(lambda x, y: x * y, shape[2:])
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else:
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s = 1
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n_in = shape[1] * s
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std = math.sqrt(2 / n_in)
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boundary = std * math.sqrt(3)
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assert _check_uniform(tensor, -boundary, boundary)
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def test_init_he_uniform_error():
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with py.raises(ValueError):
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init.initializer(init.HeUniform(), [6], ms.float32).to_tensor()
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def test_conv2d_abnormal_kernel_negative():
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kernel = np.random.randn(64, 3, 7, 7).astype(np.float32)
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with py.raises(ValueError):
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ms.Model(
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Conv2d(in_channels=3, out_channels=64, kernel_size=-7, stride=3,
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padding=0, weight_init=ms.Tensor(kernel)))
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@non_graph_engine
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def test_conv2d_abnormal_kernel_normal():
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kernel = np.random.randn(64, 3, 7, 7).astype(np.float32)
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input_data = np.random.randn(32, 3, 224, 112).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE)
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model = ms.Model(
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Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=3,
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padding=0, weight_init=ms.Tensor(kernel)))
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model.predict(ms.Tensor(input_data))
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@non_graph_engine
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def test_conv2d_abnormal_kernel_truncated_normal():
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input_data = init.initializer(init.TruncatedNormal(), [64, 3, 7, 7], ms.float32).to_tensor()
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context.set_context(mode=context.GRAPH_MODE)
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model = ms.Model(
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Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=3,
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padding=0, weight_init="truncatednormal"))
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model.predict(input_data)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.add = P.TensorAdd()
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self.t1 = Parameter(init.initializer('uniform', [5, 4], ms.float32), name="w1")
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self.t2 = Parameter(init.initializer(init.TruncatedNormal(), [5, 4], ms.float32), name="w2")
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def construct(self, x):
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z = self.add(x, self.t1)
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z = self.add(z, self.t2)
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return z
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def test_weight_shape():
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
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a = np.arange(20).reshape(5, 4)
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t = Tensor(a, dtype=ms.float32)
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net = Net()
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out = net(t)
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print(out)
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