wehub-resource-sync
138 lines
3.8 KiB
Python
138 lines
3.8 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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import numpy as np
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import paddle
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from legacy_test.test_dist_base import (
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TestParallelDyGraphRunnerBase,
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runtime_main,
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)
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class SimpleImgConvPool(paddle.nn.Layer):
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def __init__(
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self,
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num_channels,
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num_filters,
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filter_size,
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pool_size,
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pool_stride,
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pool_padding=0,
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pool_type='max',
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global_pooling=False,
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conv_stride=1,
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conv_padding=0,
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conv_dilation=1,
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conv_groups=1,
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act=None,
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use_cudnn=False,
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param_attr=None,
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bias_attr=None,
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):
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super().__init__()
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self._conv2d = paddle.nn.Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=conv_stride,
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padding=conv_padding,
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dilation=conv_dilation,
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groups=conv_groups,
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weight_attr=None,
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bias_attr=None,
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)
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self._pool2d = paddle.nn.MaxPool2D(
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kernel_size=pool_size,
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stride=pool_stride,
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padding=pool_padding,
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)
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def forward(self, inputs):
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x = self._conv2d(inputs)
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x = self._pool2d(x)
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return x
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class MNIST(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._simple_img_conv_pool_1 = SimpleImgConvPool(
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1, 20, 5, 2, 2, act="relu"
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)
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self._simple_img_conv_pool_2 = SimpleImgConvPool(
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20, 50, 5, 2, 2, act="relu"
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)
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self.pool_2_shape = 50 * 4 * 4
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SIZE = 10
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scale = (2.0 / (self.pool_2_shape**2 * SIZE)) ** 0.5
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self._fc = paddle.nn.Linear(
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self.pool_2_shape,
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10,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Normal(mean=0.0, std=scale)
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),
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)
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self.act = paddle.nn.Softmax()
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def forward(self, inputs, label):
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x = self._simple_img_conv_pool_1(inputs)
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x = self._simple_img_conv_pool_2(x)
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x = paddle.reshape(x, shape=[-1, self.pool_2_shape])
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cost = self._fc(x)
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loss = paddle.nn.functional.cross_entropy(
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self.act(cost), label, reduction='none', use_softmax=False
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)
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avg_loss = paddle.mean(loss)
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return avg_loss
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class TestMnist(TestParallelDyGraphRunnerBase):
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def get_model(self):
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model = MNIST()
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=2, drop_last=True
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)
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opt = paddle.optimizer.Adam(
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learning_rate=1e-3, parameters=model.parameters()
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)
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return model, train_reader, opt
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def run_one_loop(self, model, opt, data):
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batch_size = len(data)
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dy_x_data = np.array([x[0].reshape(1, 28, 28) for x in data]).astype(
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'float32'
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)
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y_data = (
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np.array([x[1] for x in data])
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.astype('int64')
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.reshape(batch_size, 1)
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)
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img = paddle.to_tensor(dy_x_data)
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label = paddle.to_tensor(y_data)
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label.stop_gradient = True
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avg_loss = model(img, label)
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return avg_loss
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if __name__ == "__main__":
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runtime_main(TestMnist)
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