forked from mindspore-Ecosystem/mindspore
169 lines
5.1 KiB
Python
169 lines
5.1 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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"""
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@File : test_adapter.py
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@Author:
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@Date : 2019-03-20
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@Desc : test mindspore compile method
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"""
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import logging
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.ops import operations as P
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log = logging.getLogger("test")
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log.setLevel(level=logging.ERROR)
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def conv3x3(in_channels, out_channels, stride=1, padding=1):
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"""3x3 convolution """
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weight = Tensor(np.ones([out_channels, in_channels, 3, 3]).astype(np.float32))
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=3, stride=stride,
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padding=padding, weight_init=weight)
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def conv1x1(in_channels, out_channels, stride=1, padding=0):
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"""1x1 convolution"""
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weight = Tensor(np.ones([out_channels, in_channels, 1, 1]).astype(np.float32))
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return nn.Conv2d(in_channels, out_channels,
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kernel_size=1, stride=stride,
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padding=padding, weight_init=weight)
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class ResidualBlock(nn.Cell):
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"""
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residual Block
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"""
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expansion = 4
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def __init__(self,
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in_channels,
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out_channels,
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stride=1,
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down_sample=False):
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super(ResidualBlock, self).__init__()
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out_chls = out_channels // self.expansion
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self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
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self.bn1 = nn.BatchNorm2d(out_chls)
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self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
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self.bn2 = nn.BatchNorm2d(out_chls)
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self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
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self.bn3 = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU()
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self.downsample = down_sample
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if self.downsample:
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self.conv_down_sample = conv1x1(in_channels, out_channels,
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stride=stride, padding=0)
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self.bn_down_sample = nn.BatchNorm2d(out_channels)
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self.add = P.TensorAdd()
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def construct(self, x):
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample:
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identity = self.conv_down_sample(identity)
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identity = self.bn_down_sample(identity)
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out = self.add(out, identity)
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out = self.relu(out)
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return out
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class ResNet(nn.Cell):
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""" ResNet definition """
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def __init__(self, tensor):
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super(ResNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
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self.bn1 = nn.BatchNorm2d(64)
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self.weight = Parameter(tensor, name='w')
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def construct(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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return x
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class LeNet(nn.Cell):
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""" LeNet definition """
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def __init__(self):
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super(LeNet, self).__init__()
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self.relu = nn.ReLU()
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weight1 = Tensor(np.ones([6, 1, 5, 5]).astype(np.float32) * 0.01)
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weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
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self.conv1 = nn.Conv2d(1, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid')
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self.pool = nn.MaxPool2d(2)
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self.flatten = nn.Flatten()
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fcweight1 = Tensor(np.ones([120, 16 * 5 * 5]).astype(np.float32) * 0.01)
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fcweight2 = Tensor(np.ones([84, 120]).astype(np.float32) * 0.01)
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fcweight3 = Tensor(np.ones([10, 84]).astype(np.float32) * 0.01)
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self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=fcweight1)
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self.fc2 = nn.Dense(120, 84, weight_init=fcweight2)
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self.fc3 = nn.Dense(84, 10, weight_init=fcweight3)
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def construct(self, input_x):
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output = self.conv1(input_x)
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output = self.relu(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.pool(output)
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output = self.flatten(output)
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output = self.fc1(output)
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output = self.fc2(output)
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output = self.fc3(output)
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return output
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def loss_func(x):
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return x
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def optimizer(x):
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return x
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self, dim):
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super(Net, self).__init__()
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self.softmax = nn.Softmax(dim)
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def construct(self, input_x):
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return self.softmax(input_x)
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