280 lines
12 KiB
Python
280 lines
12 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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__all__ = ["GhostNet", "GhostNet_x0_5", "GhostNet_x1_0", "GhostNet_x1_3"]
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class GhostNet():
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def __init__(self, scale):
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cfgs = [
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# k, t, c, SE, s
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[3, 16, 16, 0, 1],
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[3, 48, 24, 0, 2],
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[3, 72, 24, 0, 1],
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[5, 72, 40, 1, 2],
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[5, 120, 40, 1, 1],
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[3, 240, 80, 0, 2],
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[3, 200, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 184, 80, 0, 1],
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[3, 480, 112, 1, 1],
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[3, 672, 112, 1, 1],
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[5, 672, 160, 1, 2],
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[5, 960, 160, 0, 1],
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[5, 960, 160, 1, 1],
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[5, 960, 160, 0, 1],
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[5, 960, 160, 1, 1]
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]
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self.cfgs = cfgs
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self.scale = scale
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def net(self, input, class_dim=1000):
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# build first layer:
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output_channel = int(self._make_divisible(16 * self.scale, 4))
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x = self.conv_bn_layer(input=input,
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num_filters=output_channel,
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filter_size=3,
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stride=2,
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groups=1,
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act="relu",
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name="conv1")
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# build inverted residual blocks
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idx = 0
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for k, exp_size, c, use_se, s in self.cfgs:
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output_channel = int(self._make_divisible(c * self.scale, 4))
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hidden_channel = int(self._make_divisible(exp_size * self.scale, 4))
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x = self.ghost_bottleneck(input=x,
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hidden_dim=hidden_channel,
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output=output_channel,
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kernel_size=k,
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stride=s,
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use_se=use_se,
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name="_ghostbottleneck_" + str(idx))
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idx += 1
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# build last several layers
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output_channel = int(self._make_divisible(exp_size * self.scale, 4))
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x = self.conv_bn_layer(input=x,
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num_filters=output_channel,
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filter_size=1,
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stride=1,
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groups=1,
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act="relu",
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name="conv_last")
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x = fluid.layers.pool2d(input=x, pool_type='avg', global_pooling=True)
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output_channel = 1280
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stdv = 1.0 / math.sqrt(x.shape[1] * 1.0)
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out = self.conv_bn_layer(input=x,
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num_filters=output_channel,
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filter_size=1,
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stride=1,
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act="relu",
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name="fc_0")
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out = fluid.layers.dropout(x=out, dropout_prob=0.2)
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stdv = 1.0 / math.sqrt(out.shape[1] * 1.0)
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out = fluid.layers.fc(input=out,
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size=class_dim,
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param_attr=ParamAttr(name="fc_1_weights",
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_1_offset"))
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return out
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def _make_divisible(self, v, divisor, min_value=None):
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by 8
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It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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def conv_bn_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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x = fluid.layers.conv2d(input=input,
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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=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(
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initializer=fluid.initializer.MSRA(), name=name + "_weights"),
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bias_attr=False)
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bn_name = name + "_bn"
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x = fluid.layers.batch_norm(input=x,
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act=act,
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param_attr=ParamAttr(
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name=bn_name + "_scale",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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bias_attr=ParamAttr(
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name=bn_name + "_offset",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=name + "_variance")
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return x
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def se_block(self, input, num_channels, reduction_ratio=4, name=None):
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pool = fluid.layers.pool2d(input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = fluid.layers.fc(input=pool,
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size=num_channels // reduction_ratio,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_1_weights'),
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bias_attr=ParamAttr(name=name + '_1_offset'))
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = fluid.layers.fc(input=squeeze,
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size=num_channels,
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act="hard_sigmoid",
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_2_weights'),
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bias_attr=ParamAttr(name=name + '_2_offset'))
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#excitation = fluid.layers.clip(x=excitation, min=0, max=1)
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print("using hardsigmoid")
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se_scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return se_scale
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def depthwise_conv(self,
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input,
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output,
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kernel_size,
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stride=1,
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relu=False,
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name=None):
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return self.conv_bn_layer(input=input,
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num_filters=output,
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filter_size=kernel_size,
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stride=stride,
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groups=input.shape[1],
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act="relu" if relu else None,
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name=name + "_depthwise")
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def ghost_module(self,
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input,
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output,
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kernel_size=1,
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ratio=2,
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dw_size=3,
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stride=1,
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relu=True,
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name=None):
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self.output = output
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init_channels = int(math.ceil(output / ratio))
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new_channels = int(init_channels * (ratio - 1))
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primary_conv = self.conv_bn_layer(input=input,
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num_filters=init_channels,
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filter_size=kernel_size,
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stride=stride,
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groups=1,
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act="relu" if relu else None,
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name=name + "_primary_conv")
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cheap_operation = self.conv_bn_layer(input=primary_conv,
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num_filters=new_channels,
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filter_size=dw_size,
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stride=1,
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groups=init_channels,
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act="relu" if relu else None,
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name=name + "_cheap_operation")
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out = fluid.layers.concat([primary_conv, cheap_operation], axis=1)
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return out
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def ghost_bottleneck(self,
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input,
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hidden_dim,
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output,
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kernel_size,
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stride,
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use_se,
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name=None):
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inp_channels = input.shape[1]
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x = self.ghost_module(input=input,
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output=hidden_dim,
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kernel_size=1,
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stride=1,
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relu=True,
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name=name + "_ghost_module_1")
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if stride == 2:
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x = self.depthwise_conv(input=x,
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output=hidden_dim,
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kernel_size=kernel_size,
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stride=stride,
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relu=False,
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name=name + "_depthwise")
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if use_se:
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x = self.se_block(input=x, num_channels=hidden_dim, name=name + "_se")
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x = self.ghost_module(input=x,
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output=output,
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kernel_size=1,
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relu=False,
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name=name + "_ghost_module_2")
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if stride == 1 and inp_channels == output:
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shortcut = input
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else:
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shortcut = self.depthwise_conv(input=input,
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output=inp_channels,
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kernel_size=kernel_size,
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stride=stride,
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relu=False,
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name=name + "_shortcut_depthwise")
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shortcut = self.conv_bn_layer(input=shortcut,
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num_filters=output,
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filter_size=1,
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stride=1,
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groups=1,
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act=None,
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name=name + "_shortcut_conv")
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return fluid.layers.elementwise_add(x=x,
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y=shortcut,
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axis=-1)
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def GhostNet_x0_5():
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model = GhostNet(scale=0.5)
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return model
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def GhostNet_x1_0():
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model = GhostNet(scale=1.0)
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return model
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def GhostNet_x1_3():
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model = GhostNet(scale=1.3)
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return model
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if __name__ == "__main__":
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image = fluid.data(name='image', shape=[16, 3, 224, 224], dtype='float32')
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model = GhostNet_x1_0()
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out = model.net(input=image, class_dim=1000)
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test_program = fluid.default_main_program().clone(for_test=True)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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#lltotal_flops_params, is_quantize = summary(test_program)
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fluid.save(test_program, "ghostnet1_3") |