mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
322 lines
14 KiB
Python
322 lines
14 KiB
Python
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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
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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.initializer import MSRA
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from paddle.fluid.contrib.model_stat import summary
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__all__ = ["GhostNet", "GhostNet_0_5", "GhostNet_1_0", "GhostNet_1_3"]
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class GhostNet():
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def __init__(self, width_mult):
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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.width_mult = width_mult
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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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data_format="NCHW"):
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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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name=name+"_conv_op",
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data_format=data_format)
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x = fluid.layers.batch_norm(input=x,
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act=act,
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name=name+"_bn",
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param_attr=ParamAttr(name=name+"_bn_scale", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
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bias_attr=ParamAttr(name=name+"_bn_offset", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
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moving_mean_name=name+"_bn_mean",
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moving_variance_name=name+"_bn_variance",
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data_layout=data_format)
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return x
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def SElayer(self,
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input,
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num_channels,
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reduction_ratio=4,
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name=None):
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pool = fluid.layers.pool2d(
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input=input, pool_size=0, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = fluid.layers.fc(
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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 + '_sqz_weights'),
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bias_attr=ParamAttr(name=name + '_sqz_offset'))
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = fluid.layers.fc(
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input=squeeze,
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size=num_channels,
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act=None,
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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 + '_exc_weights'),
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bias_attr=ParamAttr(name=name + '_exc_offset'))
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#ones = fluid.layers.fill_constant(excitation.shape, "float32", 1)
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#zeros = fluid.layers.fill_constant(excitation.shape, "float32", 0)
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#excitation = fluid.layers.elementwise_max(excitation, zeros)
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# excitation = fluid.layers.elementwise_min(excitation, ones)
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excitation = fluid.layers.clip(x=excitation,
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min=0,
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max=1,
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name=name+'_clip')
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scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return scale
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def depthwise_conv(self,
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inp,
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oup,
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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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data_format="NCHW"):
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return self.conv_bn_layer(input=inp,
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num_filters=oup,
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filter_size=kernel_size,
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stride=stride,
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groups=inp.shape[1] if data_format=="NCHW" else inp.shape[-1],
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act="relu" if relu else None,
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name=name+"_dw",
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data_format=data_format)
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def GhostModule(self,
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inp,
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oup,
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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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data_format="NCHW"):
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self.oup=oup
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init_channels = int(math.ceil(oup/ratio))
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new_channels = int(init_channels*(ratio-1))
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primary_conv = self.conv_bn_layer(input=inp,
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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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data_format="NCHW")
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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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data_format=data_format)
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out = fluid.layers.concat([primary_conv, cheap_operation], axis=1, name=name+"_concat")
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# return out[:, :self.oup, :, :]
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print(self.oup)
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print(out.shape)
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return fluid.layers.slice(out, axes=[1], starts=[0], ends=[self.oup])
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def GhostBottleneck(self,
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inp,
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hidden_dim,
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oup,
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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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data_format="NCHW"):
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inp_channels = inp.shape[1]
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x = self.GhostModule(inp=inp,
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oup=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+"GhostBottle_1",
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data_format="NCHW")
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if stride==2:
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x = self.depthwise_conv(inp=x,
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oup=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+"_dw2",
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data_format="NCHW")
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if use_se:
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x = self.SElayer(input=x,
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num_channels=hidden_dim,
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name=name+"SElayer")
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x = self.GhostModule(inp=x,
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oup=oup,
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kernel_size=1,
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relu=False,
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name=name+"GhostModule_2")
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if stride==1 and inp_channels==oup:
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shortcut = inp
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else:
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shortcut = self.depthwise_conv(inp=inp,
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oup=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_conv",
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data_format="NCHW")
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shortcut = self.conv_bn_layer(input=shortcut,
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num_filters=oup,
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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_bn",
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data_format="NCHW")
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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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act=None,
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name=name+"elementwise_add")
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def net(self,
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input,
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class_dim=1000):
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#build first layer:
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output_channel = int(self._make_divisible(16*self.width_mult, 4))
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#print(output_channel)
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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="firstlayer",
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data_format="NCHW")
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input_channel = output_channel
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#build inverted residual blocks
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idx = 0
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fm = {}
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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.width_mult, 4))
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hidden_channel = int(self._make_divisible(exp_size*self.width_mult, 4))
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#print(output_channel)
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#print(hidden_channel)
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x = self.GhostBottleneck(inp=x,
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hidden_dim=hidden_channel,
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oup=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="GhostBottle_"+str(idx),
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data_format="NCHW")
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input_channel = output_channel
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fm[str(idx)] = x
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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.width_mult, 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="lastlayer",
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data_format="NCHW")
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x = fluid.layers.pool2d(input=x,
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pool_type='avg',
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global_pooling=True,
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data_format="NCHW")
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input_channel = output_channel
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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 = fluid.layers.conv2d(input=x,
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num_filters=output_channel,
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filter_size=1,
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groups=1,
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param_attr=ParamAttr(name="fc_0_w", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=False,
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name="fc_0")
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out = fluid.layers.batch_norm(input=out,
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act="relu",
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name="fc_0_bn",
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param_attr=ParamAttr(name="fc_0_bn_scale", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
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bias_attr=ParamAttr(name="fc_0_bn_offset", regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)),
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moving_mean_name="fc_0_bn_mean",
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moving_variance_name="fc_0_bn_variance",
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data_layout="NCHW")
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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_w", initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_1_bias"))
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return out, fm
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def GhostNet_0_5():
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model = GhostNet(width_mult=0.5)
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return model
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def GhostNet_1_0():
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model = GhostNet(width_mult=1.0)
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return model
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def GhostNet_1_3():
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model = GhostNet(width_mult=1.3)
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return model
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