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135 lines
4.1 KiB
135 lines
4.1 KiB
# Copyright (c) 2019 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
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class SimpleImgConvPool(fluid.dygraph.Layer):
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def __init__(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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dtype='float32',
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param_attr=None,
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bias_attr=None):
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super(SimpleImgConvPool, self).__init__()
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self._conv2d = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_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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param_attr=param_attr,
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bias_attr=bias_attr,
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use_cudnn=use_cudnn,
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dtype=dtype,
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act=act)
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self._pool2d = Pool2D(
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pool_size=pool_size,
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pool_type=pool_type,
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pool_stride=pool_stride,
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pool_padding=pool_padding,
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global_pooling=global_pooling,
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use_cudnn=use_cudnn)
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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(fluid.dygraph.Layer):
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def __init__(self, dtype="float32"):
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super(MNIST, self).__init__()
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self._simple_img_conv_pool_1 = SimpleImgConvPool(
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num_channels=3,
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num_filters=20,
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filter_size=5,
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pool_size=2,
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pool_stride=2,
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act="relu",
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dtype=dtype,
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use_cudnn=True)
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self._simple_img_conv_pool_2 = SimpleImgConvPool(
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num_channels=20,
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num_filters=50,
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filter_size=5,
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pool_size=2,
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pool_stride=2,
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act="relu",
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dtype=dtype,
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use_cudnn=True)
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self.pool_2_shape = 50 * 53 * 53
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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._linear = Linear(
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self.pool_2_shape,
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10,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=scale)),
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act="softmax",
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dtype=dtype)
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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 = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape])
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cost = self._linear(x)
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loss = fluid.layers.cross_entropy(cost, label)
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avg_loss = fluid.layers.mean(loss)
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return avg_loss
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class TestMnist(unittest.TestCase):
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def test_mnist_fp16(self):
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if not fluid.is_compiled_with_cuda():
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return
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x = np.random.randn(1, 3, 224, 224).astype("float16")
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y = np.random.randint(10, size=[1, 1], dtype="int64")
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with fluid.dygraph.guard(fluid.CUDAPlace(0)):
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model = MNIST(dtype="float16")
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x = fluid.dygraph.to_variable(x)
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y = fluid.dygraph.to_variable(y)
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loss = model(x, y)
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print(loss.numpy())
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if __name__ == "__main__":
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unittest.main()
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