104 lines
4.2 KiB
Python
104 lines
4.2 KiB
Python
import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
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import math
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__all__ = ["AlexNet"]
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class ConvPoolLayer(fluid.dygraph.Layer):
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def __init__(self,
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inputc_channels,
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output_channels,
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filter_size,
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stride,
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padding,
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stdv,
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groups=1,
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act=None,
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name=None):
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super(ConvPoolLayer, self).__init__()
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self._conv = Conv2D(num_channels=inputc_channels,
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num_filters=output_channels,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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groups=groups,
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param_attr=ParamAttr(name=name + "_weights",
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name=name + "_offset",
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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act=act)
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self._pool = Pool2D(pool_size=3,
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pool_stride=2,
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pool_padding=0,
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pool_type="max")
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def forward(self, inputs):
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x = self._conv(inputs)
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x = self._pool(x)
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return x
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class AlexNetDY(fluid.dygraph.Layer):
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def __init__(self, class_dim=1000):
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super(AlexNetDY, self).__init__()
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stdv = 1.0/math.sqrt(3*11*11)
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self._conv1 = ConvPoolLayer(
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3, 64, 11, 4, 2, stdv, act="relu", name="conv1")
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stdv = 1.0/math.sqrt(64*5*5)
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self._conv2 = ConvPoolLayer(
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64, 192, 5, 1, 2, stdv, act="relu", name="conv2")
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stdv = 1.0/math.sqrt(192*3*3)
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self._conv3 = Conv2D(192, 384, 3, stride=1, padding=1,
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param_attr=ParamAttr(name="conv3_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="conv3_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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act="relu")
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stdv = 1.0/math.sqrt(384*3*3)
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self._conv4 = Conv2D(384, 256, 3, stride=1, padding=1,
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param_attr=ParamAttr(name="conv4_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="conv4_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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act="relu")
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stdv = 1.0/math.sqrt(256*3*3)
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self._conv5 = ConvPoolLayer(
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256, 256, 3, 1, 1, stdv, act="relu", name="conv5")
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stdv = 1.0/math.sqrt(256*6*6)
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self._drop1 = Dropout(p=0.5)
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self._fc6 = Linear(input_dim=256*6*6,
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output_dim=4096,
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param_attr=ParamAttr(name="fc6_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc6_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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act="relu")
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self._drop2 = Dropout(p=0.5)
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self._fc7 = Linear(input_dim=4096,
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output_dim=4096,
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param_attr=ParamAttr(name="fc7_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc7_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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act="relu")
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self._fc8 = Linear(input_dim=4096,
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output_dim=class_dim,
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param_attr=ParamAttr(name="fc8_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc8_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)))
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def forward(self, inputs):
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x = self._conv1(inputs)
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x = self._conv2(x)
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x = self._conv3(x)
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x = self._conv4(x)
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x = self._conv5(x)
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x = fluid.layers.flatten(x, axis=0)
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x = self._drop1(x)
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x = self._fc6(x)
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x = self._drop2(x)
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x = self._fc7(x)
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x = self._fc8(x)
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return x
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def AlexNet(**args):
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model = AlexNetDY(**args)
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return model
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