439 lines
14 KiB
Python
439 lines
14 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__ = ["InceptionV4"]
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class ConvBNLayer(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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stride=1,
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padding=0,
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groups=1,
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act='relu',
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = 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=stride,
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padding=padding,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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bn_name = name + "_bn"
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class InceptionStem(fluid.dygraph.Layer):
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def __init__(self):
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super(InceptionStem, self).__init__()
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self._conv_1 = ConvBNLayer(
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3, 32, 3, stride=2, act="relu", name="conv1_3x3_s2")
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self._conv_2 = ConvBNLayer(32, 32, 3, act="relu", name="conv2_3x3_s1")
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self._conv_3 = ConvBNLayer(
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32, 64, 3, padding=1, act="relu", name="conv3_3x3_s1")
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self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
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self._conv2 = ConvBNLayer(
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64, 96, 3, stride=2, act="relu", name="inception_stem1_3x3_s2")
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self._conv1_1 = ConvBNLayer(
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160, 64, 1, act="relu", name="inception_stem2_3x3_reduce")
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self._conv1_2 = ConvBNLayer(
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64, 96, 3, act="relu", name="inception_stem2_3x3")
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self._conv2_1 = ConvBNLayer(
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160, 64, 1, act="relu", name="inception_stem2_1x7_reduce")
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self._conv2_2 = ConvBNLayer(
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64,
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64, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_stem2_1x7")
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self._conv2_3 = ConvBNLayer(
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64,
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64, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_stem2_7x1")
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self._conv2_4 = ConvBNLayer(
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64, 96, 3, act="relu", name="inception_stem2_3x3_2")
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self._conv3 = ConvBNLayer(
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192, 192, 3, stride=2, act="relu", name="inception_stem3_3x3_s2")
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def forward(self, inputs):
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conv = self._conv_1(inputs)
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conv = self._conv_2(conv)
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conv = self._conv_3(conv)
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pool1 = self._pool(conv)
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conv2 = self._conv2(conv)
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concat = fluid.layers.concat([pool1, conv2], axis=1)
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conv1 = self._conv1_1(concat)
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conv1 = self._conv1_2(conv1)
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conv2 = self._conv2_1(concat)
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conv2 = self._conv2_2(conv2)
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conv2 = self._conv2_3(conv2)
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conv2 = self._conv2_4(conv2)
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concat = fluid.layers.concat([conv1, conv2], axis=1)
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conv1 = self._conv3(concat)
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pool1 = self._pool(concat)
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concat = fluid.layers.concat([conv1, pool1], axis=1)
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return concat
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class InceptionA(fluid.dygraph.Layer):
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def __init__(self, name):
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super(InceptionA, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
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self._conv1 = ConvBNLayer(
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384, 96, 1, act="relu", name="inception_a" + name + "_1x1")
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self._conv2 = ConvBNLayer(
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384, 96, 1, act="relu", name="inception_a" + name + "_1x1_2")
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self._conv3_1 = ConvBNLayer(
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384, 64, 1, act="relu", name="inception_a" + name + "_3x3_reduce")
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self._conv3_2 = ConvBNLayer(
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64,
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96,
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3,
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padding=1,
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act="relu",
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name="inception_a" + name + "_3x3")
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self._conv4_1 = ConvBNLayer(
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384,
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64,
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1,
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act="relu",
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name="inception_a" + name + "_3x3_2_reduce")
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self._conv4_2 = ConvBNLayer(
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64,
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96,
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3,
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padding=1,
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act="relu",
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name="inception_a" + name + "_3x3_2")
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self._conv4_3 = ConvBNLayer(
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96,
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96,
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3,
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padding=1,
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act="relu",
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name="inception_a" + name + "_3x3_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv1 = self._conv1(pool1)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv4 = self._conv4_1(inputs)
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conv4 = self._conv4_2(conv4)
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conv4 = self._conv4_3(conv4)
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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class ReductionA(fluid.dygraph.Layer):
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def __init__(self):
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super(ReductionA, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
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self._conv2 = ConvBNLayer(
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384, 384, 3, stride=2, act="relu", name="reduction_a_3x3")
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self._conv3_1 = ConvBNLayer(
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384, 192, 1, act="relu", name="reduction_a_3x3_2_reduce")
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self._conv3_2 = ConvBNLayer(
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192, 224, 3, padding=1, act="relu", name="reduction_a_3x3_2")
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self._conv3_3 = ConvBNLayer(
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224, 256, 3, stride=2, act="relu", name="reduction_a_3x3_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
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return concat
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class InceptionB(fluid.dygraph.Layer):
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def __init__(self, name=None):
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super(InceptionB, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
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self._conv1 = ConvBNLayer(
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1024, 128, 1, act="relu", name="inception_b" + name + "_1x1")
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self._conv2 = ConvBNLayer(
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1024, 384, 1, act="relu", name="inception_b" + name + "_1x1_2")
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self._conv3_1 = ConvBNLayer(
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1024,
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192,
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1,
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act="relu",
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name="inception_b" + name + "_1x7_reduce")
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self._conv3_2 = ConvBNLayer(
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192,
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224, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_b" + name + "_1x7")
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self._conv3_3 = ConvBNLayer(
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224,
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256, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_b" + name + "_7x1")
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self._conv4_1 = ConvBNLayer(
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1024,
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192,
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1,
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act="relu",
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name="inception_b" + name + "_7x1_2_reduce")
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self._conv4_2 = ConvBNLayer(
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192,
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192, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_b" + name + "_1x7_2")
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self._conv4_3 = ConvBNLayer(
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192,
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224, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_b" + name + "_7x1_2")
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self._conv4_4 = ConvBNLayer(
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224,
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224, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_b" + name + "_1x7_3")
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self._conv4_5 = ConvBNLayer(
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224,
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256, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_b" + name + "_7x1_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv1 = self._conv1(pool1)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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conv4 = self._conv4_1(inputs)
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conv4 = self._conv4_2(conv4)
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conv4 = self._conv4_3(conv4)
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conv4 = self._conv4_4(conv4)
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conv4 = self._conv4_5(conv4)
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concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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class ReductionB(fluid.dygraph.Layer):
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def __init__(self):
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super(ReductionB, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
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self._conv2_1 = ConvBNLayer(
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1024, 192, 1, act="relu", name="reduction_b_3x3_reduce")
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self._conv2_2 = ConvBNLayer(
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192, 192, 3, stride=2, act="relu", name="reduction_b_3x3")
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self._conv3_1 = ConvBNLayer(
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1024, 256, 1, act="relu", name="reduction_b_1x7_reduce")
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self._conv3_2 = ConvBNLayer(
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256,
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256, (1, 7),
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padding=(0, 3),
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act="relu",
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name="reduction_b_1x7")
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self._conv3_3 = ConvBNLayer(
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256,
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320, (7, 1),
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padding=(3, 0),
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act="relu",
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name="reduction_b_7x1")
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self._conv3_4 = ConvBNLayer(
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320, 320, 3, stride=2, act="relu", name="reduction_b_3x3_2")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv2 = self._conv2_1(inputs)
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conv2 = self._conv2_2(conv2)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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conv3 = self._conv3_4(conv3)
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concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
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return concat
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class InceptionC(fluid.dygraph.Layer):
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def __init__(self, name=None):
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super(InceptionC, self).__init__()
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self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
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self._conv1 = ConvBNLayer(
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1536, 256, 1, act="relu", name="inception_c" + name + "_1x1")
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self._conv2 = ConvBNLayer(
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1536, 256, 1, act="relu", name="inception_c" + name + "_1x1_2")
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self._conv3_0 = ConvBNLayer(
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1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_3")
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self._conv3_1 = ConvBNLayer(
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384,
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256, (1, 3),
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padding=(0, 1),
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act="relu",
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name="inception_c" + name + "_1x3")
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self._conv3_2 = ConvBNLayer(
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384,
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256, (3, 1),
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padding=(1, 0),
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act="relu",
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name="inception_c" + name + "_3x1")
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self._conv4_0 = ConvBNLayer(
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1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_4")
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self._conv4_00 = ConvBNLayer(
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384,
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448, (1, 3),
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padding=(0, 1),
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act="relu",
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name="inception_c" + name + "_1x3_2")
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self._conv4_000 = ConvBNLayer(
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448,
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512, (3, 1),
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padding=(1, 0),
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act="relu",
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name="inception_c" + name + "_3x1_2")
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self._conv4_1 = ConvBNLayer(
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512,
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256, (1, 3),
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padding=(0, 1),
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act="relu",
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name="inception_c" + name + "_1x3_3")
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self._conv4_2 = ConvBNLayer(
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512,
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256, (3, 1),
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padding=(1, 0),
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act="relu",
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name="inception_c" + name + "_3x1_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv1 = self._conv1(pool1)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_0(inputs)
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conv3_1 = self._conv3_1(conv3)
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conv3_2 = self._conv3_2(conv3)
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conv4 = self._conv4_0(inputs)
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conv4 = self._conv4_00(conv4)
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conv4 = self._conv4_000(conv4)
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conv4_1 = self._conv4_1(conv4)
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conv4_2 = self._conv4_2(conv4)
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concat = fluid.layers.concat(
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[conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)
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return concat
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class InceptionV4DY(fluid.dygraph.Layer):
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def __init__(self, class_dim=1000):
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super(InceptionV4DY, self).__init__()
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self._inception_stem = InceptionStem()
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self._inceptionA_1 = InceptionA(name="1")
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self._inceptionA_2 = InceptionA(name="2")
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self._inceptionA_3 = InceptionA(name="3")
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self._inceptionA_4 = InceptionA(name="4")
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self._reductionA = ReductionA()
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self._inceptionB_1 = InceptionB(name="1")
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self._inceptionB_2 = InceptionB(name="2")
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self._inceptionB_3 = InceptionB(name="3")
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self._inceptionB_4 = InceptionB(name="4")
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self._inceptionB_5 = InceptionB(name="5")
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self._inceptionB_6 = InceptionB(name="6")
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self._inceptionB_7 = InceptionB(name="7")
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self._reductionB = ReductionB()
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self._inceptionC_1 = InceptionC(name="1")
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self._inceptionC_2 = InceptionC(name="2")
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self._inceptionC_3 = InceptionC(name="3")
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self.avg_pool = Pool2D(pool_type='avg', global_pooling=True)
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self._drop = Dropout(p=0.2)
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stdv = 1.0 / math.sqrt(1536 * 1.0)
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self.out = Linear(
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1536,
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class_dim,
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param_attr=ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name="final_fc_weights"),
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bias_attr=ParamAttr(name="final_fc_offset"))
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def forward(self, inputs):
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x = self._inception_stem(inputs)
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x = self._inceptionA_1(x)
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x = self._inceptionA_2(x)
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x = self._inceptionA_3(x)
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x = self._inceptionA_4(x)
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x = self._reductionA(x)
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x = self._inceptionB_1(x)
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x = self._inceptionB_2(x)
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x = self._inceptionB_3(x)
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x = self._inceptionB_4(x)
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x = self._inceptionB_5(x)
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x = self._inceptionB_6(x)
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x = self._inceptionB_7(x)
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x = self._reductionB(x)
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x = self._inceptionC_1(x)
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x = self._inceptionC_2(x)
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x = self._inceptionC_3(x)
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x = self.avg_pool(x)
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x = fluid.layers.squeeze(x, axes=[2, 3])
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x = self._drop(x)
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x = self.out(x)
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return x
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def InceptionV4(**args):
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model = InceptionV4DY(**args)
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return model |