forked from OSSInnovation/mindspore
137 lines
5.3 KiB
Python
137 lines
5.3 KiB
Python
# Copyright 2019 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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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor, Model, ms_function
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.ops import operations as P
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context.set_context(device_target="Ascend")
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input_channel = 2048
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output_channel = 512
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num_class = 10
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batch_size = 32
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class MsWrapper(nn.Cell):
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def __init__(self, network):
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super(MsWrapper, self).__init__(auto_prefix=False)
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self._network = network
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@ms_function
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def construct(self, *args):
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return self._network(*args)
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def me_train_tensor(net, input_np, label_np, epoch_size=2):
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loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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opt = nn.Momentum(Tensor(np.array([0.1])), Tensor(np.array([0.9])),
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filter(lambda x: x.requires_grad, net.get_parameters()))
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context.set_context(mode=context.GRAPH_MODE)
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Model(net, loss, opt)
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_network = nn.WithLossCell(net, loss)
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_train_net = MsWrapper(nn.TrainOneStepCell(_network, opt))
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_train_net.set_train()
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for epoch in range(0, epoch_size):
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print(f"epoch %d" % (epoch))
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output = _train_net(Tensor(input_np), Tensor(label_np))
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print(output.asnumpy())
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def test_conv_bn_add_relu_fusion():
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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.conv = nn.Conv2d(input_channel, output_channel,
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kernel_size=1, stride=1, padding=0, has_bias=False, pad_mode="same")
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self.conv1 = nn.Conv2d(input_channel, output_channel,
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kernel_size=1, stride=1, padding=0, has_bias=False, pad_mode="same")
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self.bn = nn.BatchNorm2d(output_channel, momentum=0.1, eps=0.0001)
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self.add = P.TensorAdd()
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self.relu = P.ReLU()
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self.mean = P.ReduceMean(keep_dims=True)
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self.reshape = P.Reshape()
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self.dense = nn.Dense(output_channel, num_class)
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def construct(self, input_x):
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output = self.conv(input_x)
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output = self.bn(output)
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output = self.add(output, self.conv1(input_x))
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output = self.relu(output)
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output = self.mean(output, (-2, -1))
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output = self.reshape(output, (batch_size, output_channel))
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output = self.dense(output)
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return output
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net = Net()
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input_np = np.ones([batch_size, input_channel, 7, 7]).astype(np.float32) * 0.01
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label_np = np.ones([batch_size]).astype(np.int32)
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me_train_tensor(net, input_np, label_np)
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def test_conv_bn_relu_fusion():
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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.conv = nn.Conv2d(input_channel, output_channel,
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kernel_size=1, stride=1, padding=0, has_bias=False, pad_mode="same")
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self.bn = nn.BatchNorm2d(output_channel, momentum=0.1, eps=0.0001)
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self.relu = P.ReLU()
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self.mean = P.ReduceMean(keep_dims=True)
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self.reshape = P.Reshape()
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self.dense = nn.Dense(output_channel, num_class)
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def construct(self, input_x):
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output = self.conv(input_x)
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output = self.bn(output)
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output = self.relu(output)
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output = self.mean(output, (-2, -1))
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output = self.reshape(output, (batch_size, output_channel))
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output = self.dense(output)
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return output
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net = Net()
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input_np = np.ones([batch_size, input_channel, 7, 7]).astype(np.float32) * 0.01
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label_np = np.ones([batch_size]).astype(np.int32)
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me_train_tensor(net, input_np, label_np)
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def test_conv_bn_fusion():
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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.conv = nn.Conv2d(input_channel, output_channel,
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kernel_size=1, stride=1, padding=0, has_bias=False, pad_mode="same")
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self.bn = nn.BatchNorm2d(output_channel, momentum=0.1, eps=0.0001)
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self.mean = P.ReduceMean(keep_dims=True)
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self.reshape = P.Reshape()
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self.dense = nn.Dense(output_channel, num_class)
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def construct(self, input_x):
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output = self.conv(input_x)
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output = self.bn(output)
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output = self.mean(output, (-2, -1))
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output = self.reshape(output, (batch_size, output_channel))
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output = self.dense(output)
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return output
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net = Net()
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input_np = np.ones([batch_size, input_channel, 7, 7]).astype(np.float32) * 0.01
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label_np = np.ones([batch_size]).astype(np.int32)
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me_train_tensor(net, input_np, label_np)
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