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vgg.py
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vgg.py
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# coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
__all__ = ['VGG']
class VGG(object):
"""
VGG, see https://arxiv.org/abs/1409.1556
Args:
depth (int): the VGG net depth (16 or 19)
normalizations (list): params list of init scale in l2 norm, skip init
scale if param is -1.
with_extra_blocks (bool): whether or not extra blocks should be added
extra_block_filters (list): in each extra block, params:
[in_channel, out_channel, padding_size, stride_size, filter_size]
class_dim (int): number of class while classification
"""
def __init__(self,
depth=16,
with_extra_blocks=False,
normalizations=[20., -1, -1, -1, -1, -1],
extra_block_filters=[[256, 512, 1, 2, 3], [128, 256, 1, 2, 3], [128, 256, 0, 1, 3],
[128, 256, 0, 1, 3]],
class_dim=1000):
assert depth in [16, 19], "depth {} not in [16, 19]"
self.depth = depth
self.depth_cfg = {16: [2, 2, 3, 3, 3], 19: [2, 2, 4, 4, 4]}
self.with_extra_blocks = with_extra_blocks
self.normalizations = normalizations
self.extra_block_filters = extra_block_filters
self.class_dim = class_dim
def __call__(self, input):
layers = []
layers += self._vgg_block(input)
if not self.with_extra_blocks:
return layers[-1]
layers += self._add_extras_block(layers[-1])
norm_cfg = self.normalizations
for k, v in enumerate(layers):
if not norm_cfg[k] == -1:
layers[k] = self._l2_norm_scale(v, init_scale=norm_cfg[k])
return layers
def _vgg_block(self, input):
nums = self.depth_cfg[self.depth]
vgg_base = [64, 128, 256, 512, 512]
conv = input
res_layer = []
layers = []
for k, v in enumerate(vgg_base):
conv = self._conv_block(conv, v, nums[k], name="conv{}_".format(k + 1))
layers.append(conv)
if self.with_extra_blocks:
if k == 4:
conv = self._pooling_block(conv, 3, 1, pool_padding=1)
else:
conv = self._pooling_block(conv, 2, 2)
else:
conv = self._pooling_block(conv, 2, 2)
if not self.with_extra_blocks:
fc_dim = 4096
fc_name = ["fc6", "fc7", "fc8"]
fc1 = fluid.layers.fc(
input=conv,
size=fc_dim,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_offset"))
fc2 = fluid.layers.fc(
input=fc1,
size=fc_dim,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_offset"))
out = fluid.layers.fc(
input=fc2,
size=self.class_dim,
param_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_offset"))
out = fluid.layers.softmax(out)
res_layer.append(out)
return [out]
else:
fc6 = self._conv_layer(conv, 1024, 3, 1, 6, dilation=6, name="fc6")
fc7 = self._conv_layer(fc6, 1024, 1, 1, 0, name="fc7")
return [layers[3], fc7]
def _add_extras_block(self, input):
cfg = self.extra_block_filters
conv = input
layers = []
for k, v in enumerate(cfg):
assert len(v) == 5, "extra_block_filters size not fix"
conv = self._extra_block(conv, v[0], v[1], v[2], v[3], v[4], name="conv{}_".format(6 + k))
layers.append(conv)
return layers
def _conv_block(self, input, num_filter, groups, name=None):
conv = input
for i in range(groups):
conv = self._conv_layer(
input=conv,
num_filters=num_filter,
filter_size=3,
stride=1,
padding=1,
act='relu',
name=name + str(i + 1))
return conv
def _extra_block(self, input, num_filters1, num_filters2, padding_size, stride_size, filter_size, name=None):
# 1x1 conv
conv_1 = self._conv_layer(
input=input, num_filters=int(num_filters1), filter_size=1, stride=1, act='relu', padding=0, name=name + "1")
# 3x3 conv
conv_2 = self._conv_layer(
input=conv_1,
num_filters=int(num_filters2),
filter_size=filter_size,
stride=stride_size,
act='relu',
padding=padding_size,
name=name + "2")
return conv_2
def _conv_layer(self,
input,
num_filters,
filter_size,
stride,
padding,
dilation=1,
act='relu',
use_cudnn=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
dilation=dilation,
act=act,
use_cudnn=use_cudnn,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + "_biases") if self.with_extra_blocks else False,
name=name + '.conv2d.output.1')
return conv
def _pooling_block(self, conv, pool_size, pool_stride, pool_padding=0, ceil_mode=True):
pool = fluid.layers.pool2d(
input=conv,
pool_size=pool_size,
pool_type='max',
pool_stride=pool_stride,
pool_padding=pool_padding,
ceil_mode=ceil_mode)
return pool
def _l2_norm_scale(self, input, init_scale=1.0, channel_shared=False):
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.initializer import Constant
helper = LayerHelper("Scale")
l2_norm = fluid.layers.l2_normalize(input, axis=1) # l2 norm along channel
shape = [1] if channel_shared else [input.shape[1]]
scale = helper.create_parameter(
attr=helper.param_attr, shape=shape, dtype=input.dtype, default_initializer=Constant(init_scale))
out = fluid.layers.elementwise_mul(
x=l2_norm, y=scale, axis=-1 if channel_shared else 1, name="conv4_3_norm_scale")
return out