forked from mindspore-Ecosystem/mindspore
149 lines
4.4 KiB
Python
149 lines
4.4 KiB
Python
# Copyright 2020 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.
|
|
# ============================================================================
|
|
""" test nn.Dense """
|
|
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 _executor
|
|
from ..ut_filter import non_graph_engine
|
|
|
|
|
|
def test_dense_none():
|
|
with pytest.raises(TypeError):
|
|
nn.Dense(3, 2, None, None)
|
|
|
|
|
|
@non_graph_engine
|
|
def test_dense_str_activation():
|
|
dense = nn.Dense(1, 1, activation='relu')
|
|
assert isinstance(dense.activation, nn.ReLU)
|
|
|
|
input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
|
|
dense(input_data)
|
|
|
|
|
|
def test_dense_weight_error():
|
|
dim_error = Tensor(np.array([[[0.1], [0.3], [0.6]], [[0.4], [0.5], [0.2]]]))
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(3, 2, dim_error)
|
|
|
|
shape_error = Tensor(np.array([[0.1, 0.3, 0.6], [0.4, 0.5, 0.2]]))
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(2, 2, shape_error)
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(3, 3, shape_error)
|
|
|
|
|
|
def test_dense_bias_error():
|
|
dim_error = Tensor(np.array([[0.5, 0.3]]))
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(3, 2, bias_init=dim_error)
|
|
|
|
shape_error = Tensor(np.array([0.5, 0.3, 0.4]))
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(3, 2, bias_init=shape_error)
|
|
|
|
|
|
def test_dense_channels_error():
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(3, 0)
|
|
|
|
with pytest.raises(ValueError):
|
|
nn.Dense(-1, 2)
|
|
|
|
|
|
class Net(nn.Cell):
|
|
""" Net definition """
|
|
|
|
def __init__(self,
|
|
input_channels,
|
|
output_channels,
|
|
weight='normal',
|
|
bias='zeros',
|
|
has_bias=True,
|
|
activation=None):
|
|
super(Net, self).__init__()
|
|
self.dense = nn.Dense(input_channels,
|
|
output_channels,
|
|
weight,
|
|
bias,
|
|
has_bias,
|
|
activation=activation)
|
|
|
|
def construct(self, input_x):
|
|
return self.dense(input_x)
|
|
|
|
|
|
def test_compile():
|
|
""" test_compile """
|
|
# has bias
|
|
weight = Tensor(np.random.randint(0, 255, [8, 64]).astype(np.float32))
|
|
bias = Tensor(np.random.randint(0, 255, [8]).astype(np.float32))
|
|
net = Net(64, 8, weight=weight, bias=bias)
|
|
input_data = Tensor(np.random.randint(0, 255, [128, 64]).astype(np.float32))
|
|
_executor.compile(net, input_data)
|
|
|
|
# training
|
|
net_train = Net(64, 8, weight=weight, bias=bias)
|
|
net_train.set_train()
|
|
_executor.compile(net_train, input_data)
|
|
|
|
|
|
def test_compile_2():
|
|
""" test_compile_2 """
|
|
# no bias
|
|
weight = Tensor(np.random.randint(0, 255, [8, 64]).astype(np.float32))
|
|
net = Net(64, 8, weight=weight, has_bias=False)
|
|
input_data = Tensor(np.random.randint(0, 255, [128, 64]).astype(np.float32))
|
|
_executor.compile(net, input_data)
|
|
|
|
# training
|
|
net_train = Net(64, 8, weight=weight, has_bias=False)
|
|
net_train.set_train()
|
|
_executor.compile(net_train, input_data)
|
|
|
|
|
|
def test_compile_3():
|
|
""" test_compile_3 """
|
|
# test for Graph mode
|
|
# has bias
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
net = Net(128, 10)
|
|
input_data = Tensor(np.random.randint(0, 255, [128, 128]).astype(np.float32))
|
|
_executor.compile(net, input_data)
|
|
|
|
# training
|
|
net_train = Net(128, 10)
|
|
net_train.set_train()
|
|
_executor.compile(net_train, input_data)
|
|
|
|
|
|
def test_compile_4():
|
|
""" test_compile_4 """
|
|
# test for Graph mode
|
|
# no bias
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
net = Net(128, 10, has_bias=False)
|
|
input_data = Tensor(np.random.randint(0, 255, [128, 128]).astype(np.float32))
|
|
_executor.compile(net, input_data)
|
|
|
|
# training
|
|
net_train = Net(128, 10, has_bias=False)
|
|
net_train.set_train()
|
|
_executor.compile(net_train, input_data)
|