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Merge pull request #183 from wqz960/PaddleClas-dy 

add Inception, ResNeXt101_wsl, EfficientNet and other models
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ppcls/modeling/architectures/alexnet.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
import math
__all__ = ['AlexNet']
class AlexNet():
def __init__(self):
pass
def net(self, input, class_dim=1000):
stdv = 1.0 / math.sqrt(input.shape[1] * 11 * 11)
layer_name = [
"conv1", "conv2", "conv3", "conv4", "conv5", "fc6", "fc7", "fc8"
]
conv1 = fluid.layers.conv2d(
input=input,
num_filters=64,
filter_size=11,
stride=4,
padding=2,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[0] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[0] + "_weights"))
pool1 = fluid.layers.pool2d(
input=conv1,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
stdv = 1.0 / math.sqrt(pool1.shape[1] * 5 * 5)
conv2 = fluid.layers.conv2d(
input=pool1,
num_filters=192,
filter_size=5,
stride=1,
padding=2,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[1] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[1] + "_weights"))
pool2 = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
stdv = 1.0 / math.sqrt(pool2.shape[1] * 3 * 3)
conv3 = fluid.layers.conv2d(
input=pool2,
num_filters=384,
filter_size=3,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[2] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[2] + "_weights"))
stdv = 1.0 / math.sqrt(conv3.shape[1] * 3 * 3)
conv4 = fluid.layers.conv2d(
input=conv3,
num_filters=256,
filter_size=3,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[3] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[3] + "_weights"))
__all__ = ["AlexNet"]
stdv = 1.0 / math.sqrt(conv4.shape[1] * 3 * 3)
conv5 = fluid.layers.conv2d(
input=conv4,
num_filters=256,
filter_size=3,
stride=1,
padding=1,
class ConvPoolLayer(fluid.dygraph.Layer):
def __init__(self,
inputc_channels,
output_channels,
filter_size,
stride,
padding,
stdv,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[4] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[4] + "_weights"))
pool5 = fluid.layers.pool2d(
input=conv5,
pool_size=3,
act=None,
name=None):
super(ConvPoolLayer, self).__init__()
self._conv = Conv2D(num_channels=inputc_channels,
num_filters=output_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
param_attr=ParamAttr(name=name + "_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name=name + "_offset",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
act=act)
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
drop6 = fluid.layers.dropout(x=pool5, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop6.shape[1] * drop6.shape[2] *
drop6.shape[3] * 1.0)
fc6 = fluid.layers.fc(
input=drop6,
size=4096,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[5] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[5] + "_weights"))
drop7 = fluid.layers.dropout(x=fc6, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop7.shape[1] * 1.0)
fc7 = fluid.layers.fc(
input=drop7,
size=4096,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[6] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[6] + "_weights"))
stdv = 1.0 / math.sqrt(fc7.shape[1] * 1.0)
out = fluid.layers.fc(
input=fc7,
size=class_dim,
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_weights"))
return out
pool_type="max")
def forward(self, inputs):
x = self._conv(inputs)
x = self._pool(x)
return x
class AlexNetDY(fluid.dygraph.Layer):
def __init__(self, class_dim=1000):
super(AlexNetDY, self).__init__()
stdv = 1.0/math.sqrt(3*11*11)
self._conv1 = ConvPoolLayer(
3, 64, 11, 4, 2, stdv, act="relu", name="conv1")
stdv = 1.0/math.sqrt(64*5*5)
self._conv2 = ConvPoolLayer(
64, 192, 5, 1, 2, stdv, act="relu", name="conv2")
stdv = 1.0/math.sqrt(192*3*3)
self._conv3 = Conv2D(192, 384, 3, stride=1, padding=1,
param_attr=ParamAttr(name="conv3_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="conv3_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
stdv = 1.0/math.sqrt(384*3*3)
self._conv4 = Conv2D(384, 256, 3, stride=1, padding=1,
param_attr=ParamAttr(name="conv4_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="conv4_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
stdv = 1.0/math.sqrt(256*3*3)
self._conv5 = ConvPoolLayer(
256, 256, 3, 1, 1, stdv, act="relu", name="conv5")
stdv = 1.0/math.sqrt(256*6*6)
self._drop1 = Dropout(p=0.5)
self._fc6 = Linear(input_dim=256*6*6,
output_dim=4096,
param_attr=ParamAttr(name="fc6_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc6_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
self._drop2 = Dropout(p=0.5)
self._fc7 = Linear(input_dim=4096,
output_dim=4096,
param_attr=ParamAttr(name="fc7_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc7_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
self._fc8 = Linear(input_dim=4096,
output_dim=class_dim,
param_attr=ParamAttr(name="fc8_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc8_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)))
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
x = self._conv3(x)
x = self._conv4(x)
x = self._conv5(x)
x = fluid.layers.flatten(x, axis=0)
x = self._drop1(x)
x = self._fc6(x)
x = self._drop2(x)
x = self._fc7(x)
x = self._fc8(x)
return x
def AlexNet(**args):
model = AlexNetDY(**args)
return model
ppcls/modeling/architectures/darknet.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
import math
__all__ = ["DarkNet53"]
class DarkNet53():
def __init__(self):
pass
def net(self, input, class_dim=1000):
DarkNet_cfg = {53: ([1, 2, 8, 8, 4], self.basicblock)}
stages, block_func = DarkNet_cfg[53]
stages = stages[0:5]
conv1 = self.conv_bn_layer(
input,
ch_out=32,
filter_size=3,
stride=1,
padding=1,
name="yolo_input")
conv = self.downsample(
conv1, ch_out=conv1.shape[1] * 2, name="yolo_input.downsample")
for i, stage in enumerate(stages):
conv = self.layer_warp(
block_func,
conv,
32 * (2**i),
stage,
name="stage.{}".format(i))
if i < len(stages) - 1: # do not downsaple in the last stage
conv = self.downsample(
conv,
ch_out=conv.shape[1] * 2,
name="stage.{}.downsample".format(i))
pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name='fc_weights'),
bias_attr=ParamAttr(name='fc_offset'))
return out
def conv_bn_layer(self,
input,
ch_out,
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride,
padding,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=ch_out,
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
......@@ -82,39 +28,133 @@ class DarkNet53():
bias_attr=False)
bn_name = name + ".bn"
out = fluid.layers.batch_norm(
input=conv,
act='relu',
param_attr=ParamAttr(name=bn_name + '.scale'),
bias_attr=ParamAttr(name=bn_name + '.offset'),
moving_mean_name=bn_name + '.mean',
moving_variance_name=bn_name + '.var')
return out
def downsample(self,
input,
ch_out,
filter_size=3,
stride=2,
padding=1,
name=None):
return self.conv_bn_layer(
input,
ch_out=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
name=name)
def basicblock(self, input, ch_out, name=None):
conv1 = self.conv_bn_layer(input, ch_out, 1, 1, 0, name=name + ".0")
conv2 = self.conv_bn_layer(
conv1, ch_out * 2, 3, 1, 1, name=name + ".1")
out = fluid.layers.elementwise_add(x=input, y=conv2, act=None)
return out
def layer_warp(self, block_func, input, ch_out, count, name=None):
res_out = block_func(input, ch_out, name='{}.0'.format(name))
for j in range(1, count):
res_out = block_func(res_out, ch_out, name='{}.{}'.format(name, j))
return res_out
self._bn = BatchNorm(
num_channels=output_channels,
act="relu",
param_attr=ParamAttr(name=bn_name + ".scale"),
bias_attr=ParamAttr(name=bn_name + ".offset"),
moving_mean_name=bn_name + ".mean",
moving_variance_name=bn_name + ".var")
def forward(self, inputs):
x = self._conv(inputs)
x = self._bn(x)
return x
class BasicBlock(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, name=None):
super(BasicBlock, self).__init__()
self._conv1 = ConvBNLayer(
input_channels, output_channels, 1, 1, 0, name=name + ".0")
self._conv2 = ConvBNLayer(
output_channels, output_channels * 2, 3, 1, 1, name=name + ".1")
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
return fluid.layers.elementwise_add(x=inputs, y=x)
class DarkNet(fluid.dygraph.Layer):
def __init__(self, class_dim=1000):
super(DarkNet, self).__init__()
self.stages = [1, 2, 8, 8, 4]
self._conv1 = ConvBNLayer(3, 32, 3, 1, 1, name="yolo_input")
self._conv2 = ConvBNLayer(
32, 64, 3, 2, 1, name="yolo_input.downsample")
self._basic_block_01 = BasicBlock(64, 32, name="stage.0.0")
self._downsample_0 = ConvBNLayer(
64, 128, 3, 2, 1, name="stage.0.downsample")
self._basic_block_11 = BasicBlock(128, 64, name="stage.1.0")
self._basic_block_12 = BasicBlock(128, 64, name="stage.1.1")
self._downsample_1 = ConvBNLayer(
128, 256, 3, 2, 1, name="stage.1.downsample")
self._basic_block_21 = BasicBlock(256, 128, name="stage.2.0")
self._basic_block_22 = BasicBlock(256, 128, name="stage.2.1")
self._basic_block_23 = BasicBlock(256, 128, name="stage.2.2")
self._basic_block_24 = BasicBlock(256, 128, name="stage.2.3")
self._basic_block_25 = BasicBlock(256, 128, name="stage.2.4")
self._basic_block_26 = BasicBlock(256, 128, name="stage.2.5")
self._basic_block_27 = BasicBlock(256, 128, name="stage.2.6")
self._basic_block_28 = BasicBlock(256, 128, name="stage.2.7")
self._downsample_2 = ConvBNLayer(
256, 512, 3, 2, 1, name="stage.2.downsample")
self._basic_block_31 = BasicBlock(512, 256, name="stage.3.0")
self._basic_block_32 = BasicBlock(512, 256, name="stage.3.1")
self._basic_block_33 = BasicBlock(512, 256, name="stage.3.2")
self._basic_block_34 = BasicBlock(512, 256, name="stage.3.3")
self._basic_block_35 = BasicBlock(512, 256, name="stage.3.4")
self._basic_block_36 = BasicBlock(512, 256, name="stage.3.5")
self._basic_block_37 = BasicBlock(512, 256, name="stage.3.6")
self._basic_block_38 = BasicBlock(512, 256, name="stage.3.7")
self._downsample_3 = ConvBNLayer(
512, 1024, 3, 2, 1, name="stage.3.downsample")
self._basic_block_41 = BasicBlock(1024, 512, name="stage.4.0")
self._basic_block_42 = BasicBlock(1024, 512, name="stage.4.1")
self._basic_block_43 = BasicBlock(1024, 512, name="stage.4.2")
self._basic_block_44 = BasicBlock(1024, 512, name="stage.4.3")
self._pool = Pool2D(pool_type="avg", global_pooling=True)
stdv = 1.0 / math.sqrt(1024.0)
self._out = Linear(
input_dim=1024,
output_dim=class_dim,
param_attr=ParamAttr(
name="fc_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
x = self._basic_block_01(x)
x = self._downsample_0(x)
x = self._basic_block_11(x)
x = self._basic_block_12(x)
x = self._downsample_1(x)
x = self._basic_block_21(x)
x = self._basic_block_22(x)
x = self._basic_block_23(x)
x = self._basic_block_24(x)
x = self._basic_block_25(x)
x = self._basic_block_26(x)
x = self._basic_block_27(x)
x = self._basic_block_28(x)
x = self._downsample_2(x)
x = self._basic_block_31(x)
x = self._basic_block_32(x)
x = self._basic_block_33(x)
x = self._basic_block_34(x)
x = self._basic_block_35(x)
x = self._basic_block_36(x)
x = self._basic_block_37(x)
x = self._basic_block_38(x)
x = self._downsample_3(x)
x = self._basic_block_41(x)
x = self._basic_block_42(x)
x = self._basic_block_43(x)
x = self._basic_block_44(x)
x = self._pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._out(x)
return x
def DarkNet53(**args):
model = DarkNet(**args)
return model
\ No newline at end of file
ppcls/modeling/architectures/efficientnet.py
浏览文件 @ fe302aec
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
import math
import collections
import re
import math
import copy
import paddle.fluid as fluid
from .layers import conv2d, init_batch_norm_layer, init_fc_layer
__all__ = [
'EfficientNet', 'EfficientNetB0', 'EfficientNetB1', 'EfficientNetB2',
'EfficientNetB3', 'EfficientNetB4', 'EfficientNetB5', 'EfficientNetB6',
'EfficientNetB7'
'EfficientNet', 'EfficientNetB0_small', 'EfficientNetB0', 'EfficientNetB1',
'EfficientNetB2', 'EfficientNetB3', 'EfficientNetB4', 'EfficientNetB5',
'EfficientNetB6', 'EfficientNetB7'
]
GlobalParams = collections.namedtuple('GlobalParams', [
......@@ -136,59 +119,127 @@ def round_repeats(repeats, global_params):
return int(math.ceil(multiplier * repeats))
class EfficientNet():
def __init__(self,
name='b0',
padding_type='SAME',
override_params=None,
is_test=False,
use_se=True):
valid_names = ['b' + str(i) for i in range(8)]
assert name in valid_names, 'efficient name should be in b0~b7'
model_name = 'efficientnet-' + name
self._blocks_args, self._global_params = get_model_params(
model_name, override_params)
self._bn_mom = self._global_params.batch_norm_momentum
self._bn_eps = self._global_params.batch_norm_epsilon
self.is_test = is_test
self.padding_type = padding_type
self.use_se = use_se
class BlockDecoder(object):
"""
Block Decoder, straight from the official TensorFlow repository.
"""
def net(self, input, class_dim=1000, is_test=False):
@staticmethod
def _decode_block_string(block_string):
""" Gets a block through a string notation of arguments. """
assert isinstance(block_string, str)
conv = self.extract_features(input, is_test=is_test)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
out_channels = round_filters(1280, self._global_params)
conv = self.conv_bn_layer(
conv,
num_filters=out_channels,
filter_size=1,
bn_act='swish',
bn_mom=self._bn_mom,
bn_eps=self._bn_eps,
padding_type=self.padding_type,
name='',
conv_name='_conv_head',
bn_name='_bn1')
# Check stride
cond_1 = ('s' in options and len(options['s']) == 1)
cond_2 = ((len(options['s']) == 2) and
(options['s'][0] == options['s'][1]))
assert (cond_1 or cond_2)
pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
return BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
if self._global_params.dropout_rate:
pool = fluid.layers.dropout(
pool,
self._global_params.dropout_rate,
dropout_implementation='upscale_in_train')
@staticmethod
def _encode_block_string(block):
"""Encodes a block to a string."""
args = [
'r%d' % block.num_repeat, 'k%d' % block.kernel_size, 's%d%d' %
(block.strides[0], block.strides[1]), 'e%s' % block.expand_ratio,
'i%d' % block.input_filters, 'o%d' % block.output_filters
]
if 0 < block.se_ratio <= 1:
args.append('se%s' % block.se_ratio)
if block.id_skip is False:
args.append('noskip')
return '_'.join(args)
param_attr, bias_attr = init_fc_layer(class_dim, '_fc')
out = fluid.layers.fc(pool,
class_dim,
name='_fc',
param_attr=param_attr,
bias_attr=bias_attr)
return out
@staticmethod
def decode(string_list):
"""
Decode a list of string notations to specify blocks in the network.
def _drop_connect(self, inputs, prob, is_test):
string_list: list of strings, each string is a notation of block
return
list of BlockArgs namedtuples of block args
"""
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
@staticmethod
def encode(blocks_args):
"""
Encodes a list of BlockArgs to a list of strings.
:param blocks_args: a list of BlockArgs namedtuples of block args
:return: a list of strings, each string is a notation of block
"""
block_strings = []
for block in blocks_args:
block_strings.append(BlockDecoder._encode_block_string(block))
return block_strings
def initial_type(name, use_bias=False):
param_attr = ParamAttr(name=name + "_weights")
if use_bias:
bias_attr = ParamAttr(name=name + "_offset")
else:
bias_attr = False
return param_attr, bias_attr
def init_batch_norm_layer(name="batch_norm"):
param_attr = ParamAttr(name=name + "_scale")
bias_attr = ParamAttr(name=name + "_offset")
return param_attr, bias_attr
def init_fc_layer(name="fc"):
param_attr = ParamAttr(name=name + "_weights")
bias_attr = ParamAttr(name=name + "_offset")
return param_attr, bias_attr
def cal_padding(img_size, stride, filter_size, dilation=1):
"""Calculate padding size."""
if img_size % stride == 0:
out_size = max(filter_size - stride, 0)
else:
out_size = max(filter_size - (img_size % stride), 0)
return out_size // 2, out_size - out_size // 2
inp_shape = {
"b0_small": [224, 112, 112, 56, 28, 14, 14, 7],
"b0": [224, 112, 112, 56, 28, 14, 14, 7],
"b1": [240, 120, 120, 60, 30, 15, 15, 8],
"b2": [260, 130, 130, 65, 33, 17, 17, 9],
"b3": [300, 150, 150, 75, 38, 19, 19, 10],
"b4": [380, 190, 190, 95, 48, 24, 24, 12],
"b5": [456, 228, 228, 114, 57, 29, 29, 15],
"b6": [528, 264, 264, 132, 66, 33, 33, 17],
"b7": [600, 300, 300, 150, 75, 38, 38, 19]
}
def _drop_connect(inputs, prob, is_test):
if is_test:
return inputs
keep_prob = 1.0 - prob
......@@ -199,445 +250,668 @@ class EfficientNet():
output = inputs / keep_prob * binary_tensor
return output
def _expand_conv_norm(self, inputs, block_args, is_test, name=None):
# Expansion phase
oup = block_args.input_filters * \
block_args.expand_ratio # number of output channels
if block_args.expand_ratio != 1:
conv = self.conv_bn_layer(
inputs,
num_filters=oup,
filter_size=1,
bn_act=None,
bn_mom=self._bn_mom,
bn_eps=self._bn_eps,
padding_type=self.padding_type,
name=name,
conv_name=name + '_expand_conv',
bn_name='_bn0')
return conv
def _depthwise_conv_norm(self, inputs, block_args, is_test, name=None):
k = block_args.kernel_size
s = block_args.stride
if isinstance(s, list) or isinstance(s, tuple):
s = s[0]
oup = block_args.input_filters * \
block_args.expand_ratio # number of output channels
conv = self.conv_bn_layer(
inputs,
num_filters=oup,
filter_size=k,
stride=s,
num_groups=oup,
bn_act=None,
padding_type=self.padding_type,
bn_mom=self._bn_mom,
bn_eps=self._bn_eps,
name=name,
use_cudnn=False,
conv_name=name + '_depthwise_conv',
bn_name='_bn1')
class Conv2ds(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride=1,
padding=0,
groups=None,
name="conv2d",
act=None,
use_bias=False,
padding_type=None,
model_name=None,
cur_stage=None):
super(Conv2ds, self).__init__()
param_attr, bias_attr = initial_type(name=name, use_bias=use_bias)
def get_padding(filter_size, stride=1, dilation=1):
padding = ((stride - 1) + dilation * (filter_size - 1)) // 2
return padding
inps = 1 if model_name == None and cur_stage == None else inp_shape[
model_name][cur_stage]
self.need_crop = False
if padding_type == "SAME":
top_padding, bottom_padding = cal_padding(inps, stride,
filter_size)
left_padding, right_padding = cal_padding(inps, stride,
filter_size)
height_padding = bottom_padding
width_padding = right_padding
if top_padding != bottom_padding or left_padding != right_padding:
height_padding = top_padding + stride
width_padding = left_padding + stride
self.need_crop = True
padding = [height_padding, width_padding]
elif padding_type == "VALID":
height_padding = 0
width_padding = 0
padding = [height_padding, width_padding]
elif padding_type == "DYNAMIC":
padding = get_padding(filter_size, stride)
else:
padding = padding_type
self._conv = Conv2D(
input_channels,
output_channels,
filter_size,
groups=groups,
stride=stride,
act=act,
padding=padding,
param_attr=param_attr,
bias_attr=bias_attr)
return conv
def forward(self, inputs):
x = self._conv(inputs)
if self.need_crop:
x = x[:, :, 1:, 1:]
return x
def _project_conv_norm(self, inputs, block_args, is_test, name=None):
final_oup = block_args.output_filters
conv = self.conv_bn_layer(
inputs,
num_filters=final_oup,
filter_size=1,
bn_act=None,
padding_type=self.padding_type,
bn_mom=self._bn_mom,
bn_eps=self._bn_eps,
name=name,
conv_name=name + '_project_conv',
bn_name='_bn2')
return conv
def conv_bn_layer(self,
input,
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
input_channels,
filter_size,
num_filters,
output_channels,
stride=1,
num_groups=1,
padding_type="SAME",
conv_act=None,
bn_act='swish',
use_cudnn=True,
bn_act="swish",
use_bn=True,
bn_mom=0.9,
bn_eps=1e-05,
use_bias=False,
name=None,
conv_name=None,
bn_name=None):
conv = conv2d(
input=input,
num_filters=num_filters,
bn_name=None,
model_name=None,
cur_stage=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2ds(
input_channels=input_channels,
output_channels=output_channels,
filter_size=filter_size,
stride=stride,
groups=num_groups,
act=conv_act,
padding_type=padding_type,
use_cudnn=use_cudnn,
name=conv_name,
use_bias=use_bias)
if use_bn is False:
return conv
else:
use_bias=use_bias,
model_name=model_name,
cur_stage=cur_stage)
self.use_bn = use_bn
if use_bn is True:
bn_name = name + bn_name
param_attr, bias_attr = init_batch_norm_layer(bn_name)
return fluid.layers.batch_norm(
input=conv,
self._bn = BatchNorm(
num_channels=output_channels,
act=bn_act,
momentum=bn_mom,
epsilon=bn_eps,
name=bn_name,
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
momentum=0.99,
epsilon=0.001,
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance",
param_attr=param_attr,
bias_attr=bias_attr)
def _conv_stem_norm(self, inputs, is_test):
out_channels = round_filters(32, self._global_params)
bn = self.conv_bn_layer(
inputs,
num_filters=out_channels,
filter_size=3,
stride=2,
def forward(self, inputs):
if self.use_bn:
x = self._conv(inputs)
x = self._bn(x)
return x
else:
return self._conv(inputs)
class ExpandConvNorm(fluid.dygraph.Layer):
def __init__(self,
input_channels,
block_args,
padding_type,
name=None,
model_name=None,
cur_stage=None):
super(ExpandConvNorm, self).__init__()
self.oup = block_args.input_filters * block_args.expand_ratio
self.expand_ratio = block_args.expand_ratio
if self.expand_ratio != 1:
self._conv = ConvBNLayer(
input_channels,
1,
self.oup,
bn_act=None,
bn_mom=self._bn_mom,
padding_type=self.padding_type,
bn_eps=self._bn_eps,
name='',
conv_name='_conv_stem',
bn_name='_bn0')
padding_type=padding_type,
name=name,
conv_name=name + "_expand_conv",
bn_name="_bn0",
model_name=model_name,
cur_stage=cur_stage)
def forward(self, inputs):
if self.expand_ratio != 1:
return self._conv(inputs)
else:
return inputs
return bn
def mb_conv_block(self,
inputs,
class DepthwiseConvNorm(fluid.dygraph.Layer):
def __init__(self,
input_channels,
block_args,
is_test=False,
padding_type,
name=None,
model_name=None,
cur_stage=None):
super(DepthwiseConvNorm, self).__init__()
self.k = block_args.kernel_size
self.s = block_args.stride
if isinstance(self.s, list) or isinstance(self.s, tuple):
self.s = self.s[0]
oup = block_args.input_filters * block_args.expand_ratio
self._conv = ConvBNLayer(
input_channels,
self.k,
oup,
self.s,
num_groups=input_channels,
bn_act=None,
padding_type=padding_type,
name=name,
conv_name=name + "_depthwise_conv",
bn_name="_bn1",
model_name=model_name,
cur_stage=cur_stage)
def forward(self, inputs):
return self._conv(inputs)
class ProjectConvNorm(fluid.dygraph.Layer):
def __init__(self,
input_channels,
block_args,
padding_type,
name=None,
model_name=None,
cur_stage=None):
super(ProjectConvNorm, self).__init__()
final_oup = block_args.output_filters
self._conv = ConvBNLayer(
input_channels,
1,
final_oup,
bn_act=None,
padding_type=padding_type,
name=name,
conv_name=name + "_project_conv",
bn_name="_bn2",
model_name=model_name,
cur_stage=cur_stage)
def forward(self, inputs):
return self._conv(inputs)
class SEBlock(fluid.dygraph.Layer):
def __init__(self,
input_channels,
num_squeezed_channels,
oup,
padding_type,
name=None,
model_name=None,
cur_stage=None):
super(SEBlock, self).__init__()
self._pool = Pool2D(
pool_type="avg", global_pooling=True, use_cudnn=False)
self._conv1 = Conv2ds(
input_channels,
num_squeezed_channels,
1,
use_bias=True,
padding_type=padding_type,
act="swish",
name=name + "_se_reduce")
self._conv2 = Conv2ds(
num_squeezed_channels,
oup,
1,
use_bias=True,
padding_type=padding_type,
name=name + "_se_expand")
def forward(self, inputs):
x = self._pool(inputs)
x = self._conv1(x)
x = self._conv2(x)
layer_helper = LayerHelper(self.full_name(), act='sigmoid')
x = layer_helper.append_activation(x)
return fluid.layers.elementwise_mul(inputs, x)
class MbConvBlock(fluid.dygraph.Layer):
def __init__(self,
input_channels,
block_args,
padding_type,
use_se,
name=None,
drop_connect_rate=None,
name=None):
# Expansion and Depthwise Convolution
oup = block_args.input_filters * \
block_args.expand_ratio # number of output channels
has_se = self.use_se and (block_args.se_ratio is not None) and (
is_test=False,
model_name=None,
cur_stage=None):
super(MbConvBlock, self).__init__()
oup = block_args.input_filters * block_args.expand_ratio
self.block_args = block_args
self.has_se = use_se and (block_args.se_ratio is not None) and (
0 < block_args.se_ratio <= 1)
id_skip = block_args.id_skip # skip connection and drop connect
conv = inputs
if block_args.expand_ratio != 1:
conv = fluid.layers.swish(
self._expand_conv_norm(conv, block_args, is_test, name))
self.id_skip = block_args.id_skip
self.expand_ratio = block_args.expand_ratio
self.drop_connect_rate = drop_connect_rate
self.is_test = is_test
conv = fluid.layers.swish(
self._depthwise_conv_norm(conv, block_args, is_test, name))
if self.expand_ratio != 1:
self._ecn = ExpandConvNorm(
input_channels,
block_args,
padding_type=padding_type,
name=name,
model_name=model_name,
cur_stage=cur_stage)
self._dcn = DepthwiseConvNorm(
input_channels * block_args.expand_ratio,
block_args,
padding_type=padding_type,
name=name,
model_name=model_name,
cur_stage=cur_stage)
# Squeeze and Excitation
if has_se:
if self.has_se:
num_squeezed_channels = max(
1, int(block_args.input_filters * block_args.se_ratio))
conv = self.se_block(conv, num_squeezed_channels, oup, name)
self._se = SEBlock(
input_channels * block_args.expand_ratio,
num_squeezed_channels,
oup,
padding_type=padding_type,
name=name,
model_name=model_name,
cur_stage=cur_stage)
conv = self._project_conv_norm(conv, block_args, is_test, name)
self._pcn = ProjectConvNorm(
input_channels * block_args.expand_ratio,
block_args,
padding_type=padding_type,
name=name,
model_name=model_name,
cur_stage=cur_stage)
def forward(self, inputs):
x = inputs
layer_helper = LayerHelper(self.full_name(), act='swish')
if self.expand_ratio != 1:
x = self._ecn(x)
x = layer_helper.append_activation(x)
x = self._dcn(x)
x = layer_helper.append_activation(x)
if self.has_se:
x = self._se(x)
x = self._pcn(x)
if self.id_skip and \
self.block_args.stride == 1 and \
self.block_args.input_filters == self.block_args.output_filters:
if self.drop_connect_rate:
x = _drop_connect(x, self.drop_connect_rate, self.is_test)
x = fluid.layers.elementwise_add(x, inputs)
return x
class ConvStemNorm(fluid.dygraph.Layer):
def __init__(self,
input_channels,
padding_type,
_global_params,
name=None,
model_name=None,
cur_stage=None):
super(ConvStemNorm, self).__init__()
# Skip connection and drop connect
input_filters = block_args.input_filters
output_filters = block_args.output_filters
if id_skip and \
block_args.stride == 1 and \
input_filters == output_filters:
if drop_connect_rate:
conv = self._drop_connect(conv, drop_connect_rate,
self.is_test)
conv = fluid.layers.elementwise_add(conv, inputs)
return conv
def se_block(self, inputs, num_squeezed_channels, oup, name):
x_squeezed = fluid.layers.pool2d(
input=inputs,
pool_type='avg',
global_pooling=True,
use_cudnn=False)
x_squeezed = conv2d(
x_squeezed,
num_filters=num_squeezed_channels,
filter_size=1,
use_bias=True,
padding_type=self.padding_type,
act='swish',
name=name + '_se_reduce')
x_squeezed = conv2d(
x_squeezed,
num_filters=oup,
filter_size=1,
use_bias=True,
padding_type=self.padding_type,
name=name + '_se_expand')
#se_out = inputs * fluid.layers.sigmoid(x_squeezed)
se_out = fluid.layers.elementwise_mul(
inputs, fluid.layers.sigmoid(x_squeezed), axis=-1)
return se_out
output_channels = round_filters(32, _global_params)
self._conv = ConvBNLayer(
input_channels,
filter_size=3,
output_channels=output_channels,
stride=2,
bn_act=None,
padding_type=padding_type,
name="",
conv_name="_conv_stem",
bn_name="_bn0",
model_name=model_name,
cur_stage=cur_stage)
def forward(self, inputs):
return self._conv(inputs)
def extract_features(self, inputs, is_test):
""" Returns output of the final convolution layer """
conv = fluid.layers.swish(
self._conv_stem_norm(
inputs, is_test=is_test))
class ExtractFeatures(fluid.dygraph.Layer):
def __init__(self,
input_channels,
_block_args,
_global_params,
padding_type,
use_se,
is_test,
model_name=None):
super(ExtractFeatures, self).__init__()
self._global_params = _global_params
self._conv_stem = ConvStemNorm(
input_channels,
padding_type=padding_type,
_global_params=_global_params,
model_name=model_name,
cur_stage=0)
block_args_copy = copy.deepcopy(self._blocks_args)
self.block_args_copy = copy.deepcopy(_block_args)
idx = 0
block_size = 0
for block_arg in block_args_copy:
for block_arg in self.block_args_copy:
block_arg = block_arg._replace(
input_filters=round_filters(block_arg.input_filters,
self._global_params),
_global_params),
output_filters=round_filters(block_arg.output_filters,
self._global_params),
num_repeat=round_repeats(block_arg.num_repeat,
self._global_params))
_global_params),
num_repeat=round_repeats(block_arg.num_repeat, _global_params))
block_size += 1
for _ in range(block_arg.num_repeat - 1):
block_size += 1
for block_args in self._blocks_args:
# Update block input and output filters based on depth multiplier.
self.conv_seq = []
cur_stage = 1
for block_args in _block_args:
block_args = block_args._replace(
input_filters=round_filters(block_args.input_filters,
self._global_params),
_global_params),
output_filters=round_filters(block_args.output_filters,
self._global_params),
_global_params),
num_repeat=round_repeats(block_args.num_repeat,
self._global_params))
_global_params))
# The first block needs to take care of stride,
# and filter size increase.
drop_connect_rate = self._global_params.drop_connect_rate
drop_connect_rate = self._global_params.drop_connect_rate if not is_test else 0
if drop_connect_rate:
drop_connect_rate *= float(idx) / block_size
conv = self.mb_conv_block(conv, block_args, is_test,
drop_connect_rate,
'_blocks.' + str(idx) + '.')
_mc_block = self.add_sublayer(
"_blocks." + str(idx) + ".",
MbConvBlock(
block_args.input_filters,
block_args=block_args,
padding_type=padding_type,
use_se=use_se,
name="_blocks." + str(idx) + ".",
drop_connect_rate=drop_connect_rate,
model_name=model_name,
cur_stage=cur_stage))
self.conv_seq.append(_mc_block)
idx += 1
if block_args.num_repeat > 1:
block_args = block_args._replace(
input_filters=block_args.output_filters, stride=1)
for _ in range(block_args.num_repeat - 1):
drop_connect_rate = self._global_params.drop_connect_rate
drop_connect_rate = self._global_params.drop_connect_rate if not is_test else 0
if drop_connect_rate:
drop_connect_rate *= float(idx) / block_size
conv = self.mb_conv_block(conv, block_args, is_test,
drop_connect_rate,
'_blocks.' + str(idx) + '.')
_mc_block = self.add_sublayer(
"block." + str(idx) + ".",
MbConvBlock(
block_args.input_filters,
block_args,
padding_type=padding_type,
use_se=use_se,
name="_blocks." + str(idx) + ".",
drop_connect_rate=drop_connect_rate,
model_name=model_name,
cur_stage=cur_stage))
self.conv_seq.append(_mc_block)
idx += 1
cur_stage += 1
return conv
def shortcut(self, input, data_residual):
return fluid.layers.elementwise_add(input, data_residual)
class BlockDecoder(object):
"""
Block Decoder, straight from the official TensorFlow repository.
"""
@staticmethod
def _decode_block_string(block_string):
""" Gets a block through a string notation of arguments. """
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
def forward(self, inputs):
x = self._conv_stem(inputs)
layer_helper = LayerHelper(self.full_name(), act='swish')
x = layer_helper.append_activation(x)
for _mc_block in self.conv_seq:
x = _mc_block(x)
return x
# Check stride
cond_1 = ('s' in options and len(options['s']) == 1)
cond_2 = ((len(options['s']) == 2) and
(options['s'][0] == options['s'][1]))
assert (cond_1 or cond_2)
return BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
class EfficientNet(fluid.dygraph.Layer):
def __init__(self,
name="b0",
is_test=True,
padding_type="SAME",
override_params=None,
use_se=True,
class_dim=1000):
super(EfficientNet, self).__init__()
@staticmethod
def _encode_block_string(block):
"""Encodes a block to a string."""
args = [
'r%d' % block.num_repeat, 'k%d' % block.kernel_size, 's%d%d' %
(block.strides[0], block.strides[1]), 'e%s' % block.expand_ratio,
'i%d' % block.input_filters, 'o%d' % block.output_filters
]
if 0 < block.se_ratio <= 1:
args.append('se%s' % block.se_ratio)
if block.id_skip is False:
args.append('noskip')
return '_'.join(args)
model_name = 'efficientnet-' + name
self.name = name
self._block_args, self._global_params = get_model_params(
model_name, override_params)
self.padding_type = padding_type
self.use_se = use_se
self.is_test = is_test
@staticmethod
def decode(string_list):
"""
Decode a list of string notations to specify blocks in the network.
self._ef = ExtractFeatures(
3,
self._block_args,
self._global_params,
self.padding_type,
self.use_se,
self.is_test,
model_name=self.name)
output_channels = round_filters(1280, self._global_params)
if name == "b0_small" or name == "b0" or name == "b1":
oup = 320
elif name == "b2":
oup = 352
elif name == "b3":
oup = 384
elif name == "b4":
oup = 448
elif name == "b5":
oup = 512
elif name == "b6":
oup = 576
elif name == "b7":
oup = 640
self._conv = ConvBNLayer(
oup,
1,
output_channels,
bn_act="swish",
padding_type=self.padding_type,
name="",
conv_name="_conv_head",
bn_name="_bn1",
model_name=self.name,
cur_stage=7)
self._pool = Pool2D(pool_type="avg", global_pooling=True)
string_list: list of strings, each string is a notation of block
return
list of BlockArgs namedtuples of block args
"""
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
if self._global_params.dropout_rate:
self._drop = Dropout(
p=self._global_params.dropout_rate,
dropout_implementation="upscale_in_train")
@staticmethod
def encode(blocks_args):
"""
Encodes a list of BlockArgs to a list of strings.
param_attr, bias_attr = init_fc_layer("_fc")
self._fc = Linear(
output_channels,
class_dim,
param_attr=param_attr,
bias_attr=bias_attr)
:param blocks_args: a list of BlockArgs namedtuples of block args
:return: a list of strings, each string is a notation of block
"""
block_strings = []
for block in blocks_args:
block_strings.append(BlockDecoder._encode_block_string(block))
return block_strings
def forward(self, inputs):
x = self._ef(inputs)
x = self._conv(x)
x = self._pool(x)
if self._global_params.dropout_rate:
x = self._drop(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._fc(x)
return x
def EfficientNetB0(is_test=False,
padding_type='SAME',
def EfficientNetB0_small(is_test=True,
padding_type='DYNAMIC',
override_params=None,
use_se=True):
use_se=False,
**args):
model = EfficientNet(
name='b0',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB0_small(is_test=False,
padding_type='DYNAMIC',
def EfficientNetB0(is_test=False,
padding_type='SAME',
override_params=None,
use_se=False):
use_se=True,
**args):
model = EfficientNet(
name='b0',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB1(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b1',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB2(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b2',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB3(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b3',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB4(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b4',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB5(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b5',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB6(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b6',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
def EfficientNetB7(is_test=False,
padding_type='SAME',
override_params=None,
use_se=True):
use_se=True,
**args):
model = EfficientNet(
name='b7',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
use_se=use_se,
**args)
return model
\ No newline at end of file
ppcls/modeling/architectures/googlenet.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
import math
__all__ = ['GoogLeNet']
__all__ = ['GoogLeNet_DY']
def xavier(channels, filter_size, name):
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
class GoogLeNet():
def __init__(self):
return param_attr
pass
def conv_layer(self,
input,
class ConvLayer(fluid.dygraph.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
channels = input.shape[1]
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
conv = fluid.layers.conv2d(
input=input,
super(ConvLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=act,
param_attr=param_attr,
bias_attr=False,
name=name)
return conv
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
def xavier(self, channels, filter_size, name):
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
def forward(self, inputs):
y = self._conv(inputs)
return y
return param_attr
def inception(self,
input,
channels,
class Inception(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter1,
filter3R,
filter3,
......@@ -72,166 +54,155 @@ class GoogLeNet():
filter5,
proj,
name=None):
conv1 = self.conv_layer(
input=input,
num_filters=filter1,
filter_size=1,
stride=1,
act=None,
name="inception_" + name + "_1x1")
conv3r = self.conv_layer(
input=input,
num_filters=filter3R,
filter_size=1,
stride=1,
act=None,
super(Inception, self).__init__()
self._conv1 = ConvLayer(
input_channels, filter1, 1, name="inception_" + name + "_1x1")
self._conv3r = ConvLayer(
input_channels,
filter3R,
1,
name="inception_" + name + "_3x3_reduce")
conv3 = self.conv_layer(
input=conv3r,
num_filters=filter3,
filter_size=3,
stride=1,
act=None,
name="inception_" + name + "_3x3")
conv5r = self.conv_layer(
input=input,
num_filters=filter5R,
filter_size=1,
stride=1,
act=None,
self._conv3 = ConvLayer(
filter3R, filter3, 3, name="inception_" + name + "_3x3")
self._conv5r = ConvLayer(
input_channels,
filter5R,
1,
name="inception_" + name + "_5x5_reduce")
conv5 = self.conv_layer(
input=conv5r,
num_filters=filter5,
filter_size=5,
stride=1,
act=None,
name="inception_" + name + "_5x5")
pool = fluid.layers.pool2d(
input=input,
pool_size=3,
pool_stride=1,
pool_padding=1,
pool_type='max')
convprj = fluid.layers.conv2d(
input=pool,
filter_size=1,
num_filters=proj,
stride=1,
padding=0,
name="inception_" + name + "_3x3_proj",
param_attr=ParamAttr(
name="inception_" + name + "_3x3_proj_weights"),
bias_attr=False)
cat = fluid.layers.concat(input=[conv1, conv3, conv5, convprj], axis=1)
cat = fluid.layers.relu(cat)
return cat
def net(self, input, class_dim=1000):
conv = self.conv_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act=None,
name="conv1")
pool = fluid.layers.pool2d(
input=conv, pool_size=3, pool_type='max', pool_stride=2)
conv = self.conv_layer(
input=pool,
num_filters=64,
filter_size=1,
stride=1,
act=None,
name="conv2_1x1")
conv = self.conv_layer(
input=conv,
num_filters=192,
filter_size=3,
stride=1,
act=None,
name="conv2_3x3")
pool = fluid.layers.pool2d(
input=conv, pool_size=3, pool_type='max', pool_stride=2)
ince3a = self.inception(pool, 192, 64, 96, 128, 16, 32, 32, "ince3a")
ince3b = self.inception(ince3a, 256, 128, 128, 192, 32, 96, 64,
"ince3b")
pool3 = fluid.layers.pool2d(
input=ince3b, pool_size=3, pool_type='max', pool_stride=2)
ince4a = self.inception(pool3, 480, 192, 96, 208, 16, 48, 64, "ince4a")
ince4b = self.inception(ince4a, 512, 160, 112, 224, 24, 64, 64,
"ince4b")
ince4c = self.inception(ince4b, 512, 128, 128, 256, 24, 64, 64,
"ince4c")
ince4d = self.inception(ince4c, 512, 112, 144, 288, 32, 64, 64,
"ince4d")
ince4e = self.inception(ince4d, 528, 256, 160, 320, 32, 128, 128,
"ince4e")
pool4 = fluid.layers.pool2d(
input=ince4e, pool_size=3, pool_type='max', pool_stride=2)
ince5a = self.inception(pool4, 832, 256, 160, 320, 32, 128, 128,
"ince5a")
ince5b = self.inception(ince5a, 832, 384, 192, 384, 48, 128, 128,
"ince5b")
pool5 = fluid.layers.pool2d(
input=ince5b, pool_size=7, pool_type='avg', pool_stride=7)
dropout = fluid.layers.dropout(x=pool5, dropout_prob=0.4)
out = fluid.layers.fc(input=dropout,
size=class_dim,
act='softmax',
param_attr=self.xavier(1024, 1, "out"),
name="out",
bias_attr=ParamAttr(name="out_offset"))
pool_o1 = fluid.layers.pool2d(
input=ince4a, pool_size=5, pool_type='avg', pool_stride=3)
conv_o1 = self.conv_layer(
input=pool_o1,
num_filters=128,
filter_size=1,
stride=1,
act=None,
name="conv_o1")
fc_o1 = fluid.layers.fc(input=conv_o1,
size=1024,
act='relu',
param_attr=self.xavier(2048, 1, "fc_o1"),
name="fc_o1",
bias_attr=ParamAttr(name="fc_o1_offset"))
dropout_o1 = fluid.layers.dropout(x=fc_o1, dropout_prob=0.7)
out1 = fluid.layers.fc(input=dropout_o1,
size=class_dim,
act='softmax',
param_attr=self.xavier(1024, 1, "out1"),
name="out1",
bias_attr=ParamAttr(name="out1_offset"))
pool_o2 = fluid.layers.pool2d(
input=ince4d, pool_size=5, pool_type='avg', pool_stride=3)
conv_o2 = self.conv_layer(
input=pool_o2,
num_filters=128,
filter_size=1,
stride=1,
act=None,
name="conv_o2")
fc_o2 = fluid.layers.fc(input=conv_o2,
size=1024,
act='relu',
param_attr=self.xavier(2048, 1, "fc_o2"),
name="fc_o2",
self._conv5 = ConvLayer(
filter5R, filter5, 5, name="inception_" + name + "_5x5")
self._pool = Pool2D(
pool_size=3, pool_type="max", pool_stride=1, pool_padding=1)
self._convprj = ConvLayer(
input_channels, proj, 1, name="inception_" + name + "_3x3_proj")
def forward(self, inputs):
conv1 = self._conv1(inputs)
conv3r = self._conv3r(inputs)
conv3 = self._conv3(conv3r)
conv5r = self._conv5r(inputs)
conv5 = self._conv5(conv5r)
pool = self._pool(inputs)
convprj = self._convprj(pool)
cat = fluid.layers.concat([conv1, conv3, conv5, convprj], axis=1)
layer_helper = LayerHelper(self.full_name(), act="relu")
return layer_helper.append_activation(cat)
class GoogleNetDY(fluid.dygraph.Layer):
def __init__(self, class_dim=1000):
super(GoogleNetDY, self).__init__()
self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1")
self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3")
self._ince3a = Inception(
192, 192, 64, 96, 128, 16, 32, 32, name="ince3a")
self._ince3b = Inception(
256, 256, 128, 128, 192, 32, 96, 64, name="ince3b")
self._ince4a = Inception(
480, 480, 192, 96, 208, 16, 48, 64, name="ince4a")
self._ince4b = Inception(
512, 512, 160, 112, 224, 24, 64, 64, name="ince4b")
self._ince4c = Inception(
512, 512, 128, 128, 256, 24, 64, 64, name="ince4c")
self._ince4d = Inception(
512, 512, 112, 144, 288, 32, 64, 64, name="ince4d")
self._ince4e = Inception(
528, 528, 256, 160, 320, 32, 128, 128, name="ince4e")
self._ince5a = Inception(
832, 832, 256, 160, 320, 32, 128, 128, name="ince5a")
self._ince5b = Inception(
832, 832, 384, 192, 384, 48, 128, 128, name="ince5b")
self._pool_5 = Pool2D(pool_size=7, pool_type='avg', pool_stride=7)
self._drop = fluid.dygraph.Dropout(p=0.4)
self._fc_out = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"),
act="softmax")
self._pool_o1 = Pool2D(pool_size=5, pool_stride=3, pool_type="avg")
self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1")
self._fc_o1 = Linear(
1152,
1024,
param_attr=xavier(2048, 1, "fc_o1"),
bias_attr=ParamAttr(name="fc_o1_offset"),
act="relu")
self._drop_o1 = fluid.dygraph.Dropout(p=0.7)
self._out1 = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out1"),
bias_attr=ParamAttr(name="out1_offset"),
act="softmax")
self._pool_o2 = Pool2D(pool_size=5, pool_stride=3, pool_type='avg')
self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2")
self._fc_o2 = Linear(
1152,
1024,
param_attr=xavier(2048, 1, "fc_o2"),
bias_attr=ParamAttr(name="fc_o2_offset"))
dropout_o2 = fluid.layers.dropout(x=fc_o2, dropout_prob=0.7)
out2 = fluid.layers.fc(input=dropout_o2,
size=class_dim,
act='softmax',
param_attr=self.xavier(1024, 1, "out2"),
name="out2",
self._drop_o2 = fluid.dygraph.Dropout(p=0.7)
self._out2 = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out2"),
bias_attr=ParamAttr(name="out2_offset"))
# last fc layer is "out"
def forward(self, inputs):
x = self._conv(inputs)
x = self._pool(x)
x = self._conv_1(x)
x = self._conv_2(x)
x = self._pool(x)
x = self._ince3a(x)
x = self._ince3b(x)
x = self._pool(x)
ince4a = self._ince4a(x)
x = self._ince4b(ince4a)
x = self._ince4c(x)
ince4d = self._ince4d(x)
x = self._ince4e(ince4d)
x = self._pool(x)
x = self._ince5a(x)
ince5b = self._ince5b(x)
x = self._pool_5(ince5b)
x = self._drop(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
out = self._fc_out(x)
x = self._pool_o1(ince4a)
x = self._conv_o1(x)
x = fluid.layers.flatten(x)
x = self._fc_o1(x)
x = self._drop_o1(x)
out1 = self._out1(x)
x = self._pool_o2(ince4d)
x = self._conv_o2(x)
x = fluid.layers.flatten(x)
x = self._fc_o2(x)
x = self._drop_o2(x)
out2 = self._out2(x)
return [out, out1, out2]
def GoogLeNet(**args):
model = GoogleNetDY(**args)
return model
\ No newline at end of file
ppcls/modeling/architectures/inception_v4.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
import math
__all__ = ['InceptionV4']
class InceptionV4():
def __init__(self):
pass
def net(self, input, class_dim=1000):
x = self.inception_stem(input)
for i in range(4):
x = self.inceptionA(x, name=str(i + 1))
x = self.reductionA(x)
for i in range(7):
x = self.inceptionB(x, name=str(i + 1))
x = self.reductionB(x)
for i in range(3):
x = self.inceptionC(x, name=str(i + 1))
pool = fluid.layers.pool2d(
input=x, pool_type='avg', global_pooling=True)
drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
__all__ = ["InceptionV4"]
stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
out = fluid.layers.fc(
input=drop,
size=class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="final_fc_weights"),
bias_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="final_fc_offset"))
return out
def conv_bn_layer(self,
data,
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
......@@ -70,8 +16,10 @@ class InceptionV4():
groups=1,
act='relu',
name=None):
conv = fluid.layers.conv2d(
input=data,
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
......@@ -79,276 +27,413 @@ class InceptionV4():
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
name=name)
bias_attr=False)
bn_name = name + "_bn"
return fluid.layers.batch_norm(
input=conv,
self._batch_norm = BatchNorm(
num_filters,
act=act,
name=bn_name,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def inception_stem(self, data, name=None):
conv = self.conv_bn_layer(
data, 32, 3, stride=2, act='relu', name="conv1_3x3_s2")
conv = self.conv_bn_layer(conv, 32, 3, act='relu', name="conv2_3x3_s1")
conv = self.conv_bn_layer(
conv, 64, 3, padding=1, act='relu', name="conv3_3x3_s1")
pool1 = fluid.layers.pool2d(
input=conv, pool_size=3, pool_stride=2, pool_type='max')
conv2 = self.conv_bn_layer(
conv, 96, 3, stride=2, act='relu', name="inception_stem1_3x3_s2")
concat = fluid.layers.concat([pool1, conv2], axis=1)
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
conv1 = self.conv_bn_layer(
concat, 64, 1, act='relu', name="inception_stem2_3x3_reduce")
conv1 = self.conv_bn_layer(
conv1, 96, 3, act='relu', name="inception_stem2_3x3")
conv2 = self.conv_bn_layer(
concat, 64, 1, act='relu', name="inception_stem2_1x7_reduce")
conv2 = self.conv_bn_layer(
conv2,
class InceptionStem(fluid.dygraph.Layer):
def __init__(self):
super(InceptionStem, self).__init__()
self._conv_1 = ConvBNLayer(
3, 32, 3, stride=2, act="relu", name="conv1_3x3_s2")
self._conv_2 = ConvBNLayer(32, 32, 3, act="relu", name="conv2_3x3_s1")
self._conv_3 = ConvBNLayer(
32, 64, 3, padding=1, act="relu", name="conv3_3x3_s1")
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._conv2 = ConvBNLayer(
64, 96, 3, stride=2, act="relu", name="inception_stem1_3x3_s2")
self._conv1_1 = ConvBNLayer(
160, 64, 1, act="relu", name="inception_stem2_3x3_reduce")
self._conv1_2 = ConvBNLayer(
64, 96, 3, act="relu", name="inception_stem2_3x3")
self._conv2_1 = ConvBNLayer(
160, 64, 1, act="relu", name="inception_stem2_1x7_reduce")
self._conv2_2 = ConvBNLayer(
64,
64, (7, 1),
padding=(3, 0),
act='relu',
act="relu",
name="inception_stem2_1x7")
conv2 = self.conv_bn_layer(
conv2,
self._conv2_3 = ConvBNLayer(
64,
64, (1, 7),
padding=(0, 3),
act='relu',
act="relu",
name="inception_stem2_7x1")
conv2 = self.conv_bn_layer(
conv2, 96, 3, act='relu', name="inception_stem2_3x3_2")
self._conv2_4 = ConvBNLayer(
64, 96, 3, act="relu", name="inception_stem2_3x3_2")
self._conv3 = ConvBNLayer(
192, 192, 3, stride=2, act="relu", name="inception_stem3_3x3_s2")
def forward(self, inputs):
conv = self._conv_1(inputs)
conv = self._conv_2(conv)
conv = self._conv_3(conv)
pool1 = self._pool(conv)
conv2 = self._conv2(conv)
concat = fluid.layers.concat([pool1, conv2], axis=1)
conv1 = self._conv1_1(concat)
conv1 = self._conv1_2(conv1)
conv2 = self._conv2_1(concat)
conv2 = self._conv2_2(conv2)
conv2 = self._conv2_3(conv2)
conv2 = self._conv2_4(conv2)
concat = fluid.layers.concat([conv1, conv2], axis=1)
conv1 = self.conv_bn_layer(
concat,
192,
3,
stride=2,
act='relu',
name="inception_stem3_3x3_s2")
pool1 = fluid.layers.pool2d(
input=concat, pool_size=3, pool_stride=2, pool_type='max')
conv1 = self._conv3(concat)
pool1 = self._pool(concat)
concat = fluid.layers.concat([conv1, pool1], axis=1)
return concat
def inceptionA(self, data, name=None):
pool1 = fluid.layers.pool2d(
input=data, pool_size=3, pool_padding=1, pool_type='avg')
conv1 = self.conv_bn_layer(
pool1, 96, 1, act='relu', name="inception_a" + name + "_1x1")
conv2 = self.conv_bn_layer(
data, 96, 1, act='relu', name="inception_a" + name + "_1x1_2")
conv3 = self.conv_bn_layer(
data, 64, 1, act='relu', name="inception_a" + name + "_3x3_reduce")
conv3 = self.conv_bn_layer(
conv3,
class InceptionA(fluid.dygraph.Layer):
def __init__(self, name):
super(InceptionA, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
self._conv1 = ConvBNLayer(
384, 96, 1, act="relu", name="inception_a" + name + "_1x1")
self._conv2 = ConvBNLayer(
384, 96, 1, act="relu", name="inception_a" + name + "_1x1_2")
self._conv3_1 = ConvBNLayer(
384, 64, 1, act="relu", name="inception_a" + name + "_3x3_reduce")
self._conv3_2 = ConvBNLayer(
64,
96,
3,
padding=1,
act='relu',
act="relu",
name="inception_a" + name + "_3x3")
conv4 = self.conv_bn_layer(
data,
self._conv4_1 = ConvBNLayer(
384,
64,
1,
act='relu',
act="relu",
name="inception_a" + name + "_3x3_2_reduce")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_2 = ConvBNLayer(
64,
96,
3,
padding=1,
act='relu',
act="relu",
name="inception_a" + name + "_3x3_2")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_3 = ConvBNLayer(
96,
96,
3,
padding=1,
act='relu',
act="relu",
name="inception_a" + name + "_3x3_3")
concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
def forward(self, inputs):
pool1 = self._pool(inputs)
conv1 = self._conv1(pool1)
return concat
conv2 = self._conv2(inputs)
conv3 = self._conv3_1(inputs)
conv3 = self._conv3_2(conv3)
def reductionA(self, data, name=None):
pool1 = fluid.layers.pool2d(
input=data, pool_size=3, pool_stride=2, pool_type='max')
conv4 = self._conv4_1(inputs)
conv4 = self._conv4_2(conv4)
conv4 = self._conv4_3(conv4)
conv2 = self.conv_bn_layer(
data, 384, 3, stride=2, act='relu', name="reduction_a_3x3")
concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
return concat
conv3 = self.conv_bn_layer(
data, 192, 1, act='relu', name="reduction_a_3x3_2_reduce")
conv3 = self.conv_bn_layer(
conv3, 224, 3, padding=1, act='relu', name="reduction_a_3x3_2")
conv3 = self.conv_bn_layer(
conv3, 256, 3, stride=2, act='relu', name="reduction_a_3x3_3")
class ReductionA(fluid.dygraph.Layer):
def __init__(self):
super(ReductionA, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._conv2 = ConvBNLayer(
384, 384, 3, stride=2, act="relu", name="reduction_a_3x3")
self._conv3_1 = ConvBNLayer(
384, 192, 1, act="relu", name="reduction_a_3x3_2_reduce")
self._conv3_2 = ConvBNLayer(
192, 224, 3, padding=1, act="relu", name="reduction_a_3x3_2")
self._conv3_3 = ConvBNLayer(
224, 256, 3, stride=2, act="relu", name="reduction_a_3x3_3")
def forward(self, inputs):
pool1 = self._pool(inputs)
conv2 = self._conv2(inputs)
conv3 = self._conv3_1(inputs)
conv3 = self._conv3_2(conv3)
conv3 = self._conv3_3(conv3)
concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
return concat
def inceptionB(self, data, name=None):
pool1 = fluid.layers.pool2d(
input=data, pool_size=3, pool_padding=1, pool_type='avg')
conv1 = self.conv_bn_layer(
pool1, 128, 1, act='relu', name="inception_b" + name + "_1x1")
conv2 = self.conv_bn_layer(
data, 384, 1, act='relu', name="inception_b" + name + "_1x1_2")
conv3 = self.conv_bn_layer(
data,
class InceptionB(fluid.dygraph.Layer):
def __init__(self, name=None):
super(InceptionB, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
self._conv1 = ConvBNLayer(
1024, 128, 1, act="relu", name="inception_b" + name + "_1x1")
self._conv2 = ConvBNLayer(
1024, 384, 1, act="relu", name="inception_b" + name + "_1x1_2")
self._conv3_1 = ConvBNLayer(
1024,
192,
1,
act='relu',
act="relu",
name="inception_b" + name + "_1x7_reduce")
conv3 = self.conv_bn_layer(
conv3,
self._conv3_2 = ConvBNLayer(
192,
224, (1, 7),
padding=(0, 3),
act='relu',
act="relu",
name="inception_b" + name + "_1x7")
conv3 = self.conv_bn_layer(
conv3,
self._conv3_3 = ConvBNLayer(
224,
256, (7, 1),
padding=(3, 0),
act='relu',
act="relu",
name="inception_b" + name + "_7x1")
conv4 = self.conv_bn_layer(
data,
self._conv4_1 = ConvBNLayer(
1024,
192,
1,
act='relu',
act="relu",
name="inception_b" + name + "_7x1_2_reduce")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_2 = ConvBNLayer(
192,
192, (1, 7),
padding=(0, 3),
act='relu',
act="relu",
name="inception_b" + name + "_1x7_2")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_3 = ConvBNLayer(
192,
224, (7, 1),
padding=(3, 0),
act='relu',
act="relu",
name="inception_b" + name + "_7x1_2")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_4 = ConvBNLayer(
224,
224, (1, 7),
padding=(0, 3),
act='relu',
act="relu",
name="inception_b" + name + "_1x7_3")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_5 = ConvBNLayer(
224,
256, (7, 1),
padding=(3, 0),
act='relu',
act="relu",
name="inception_b" + name + "_7x1_3")
concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
def forward(self, inputs):
pool1 = self._pool(inputs)
conv1 = self._conv1(pool1)
return concat
conv2 = self._conv2(inputs)
conv3 = self._conv3_1(inputs)
conv3 = self._conv3_2(conv3)
conv3 = self._conv3_3(conv3)
conv4 = self._conv4_1(inputs)
conv4 = self._conv4_2(conv4)
conv4 = self._conv4_3(conv4)
conv4 = self._conv4_4(conv4)
conv4 = self._conv4_5(conv4)
def reductionB(self, data, name=None):
pool1 = fluid.layers.pool2d(
input=data, pool_size=3, pool_stride=2, pool_type='max')
concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
return concat
conv2 = self.conv_bn_layer(
data, 192, 1, act='relu', name="reduction_b_3x3_reduce")
conv2 = self.conv_bn_layer(
conv2, 192, 3, stride=2, act='relu', name="reduction_b_3x3")
conv3 = self.conv_bn_layer(
data, 256, 1, act='relu', name="reduction_b_1x7_reduce")
conv3 = self.conv_bn_layer(
conv3,
class ReductionB(fluid.dygraph.Layer):
def __init__(self):
super(ReductionB, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._conv2_1 = ConvBNLayer(
1024, 192, 1, act="relu", name="reduction_b_3x3_reduce")
self._conv2_2 = ConvBNLayer(
192, 192, 3, stride=2, act="relu", name="reduction_b_3x3")
self._conv3_1 = ConvBNLayer(
1024, 256, 1, act="relu", name="reduction_b_1x7_reduce")
self._conv3_2 = ConvBNLayer(
256,
256, (1, 7),
padding=(0, 3),
act='relu',
act="relu",
name="reduction_b_1x7")
conv3 = self.conv_bn_layer(
conv3,
self._conv3_3 = ConvBNLayer(
256,
320, (7, 1),
padding=(3, 0),
act='relu',
act="relu",
name="reduction_b_7x1")
conv3 = self.conv_bn_layer(
conv3, 320, 3, stride=2, act='relu', name="reduction_b_3x3_2")
self._conv3_4 = ConvBNLayer(
320, 320, 3, stride=2, act="relu", name="reduction_b_3x3_2")
def forward(self, inputs):
pool1 = self._pool(inputs)
conv2 = self._conv2_1(inputs)
conv2 = self._conv2_2(conv2)
conv3 = self._conv3_1(inputs)
conv3 = self._conv3_2(conv3)
conv3 = self._conv3_3(conv3)
conv3 = self._conv3_4(conv3)
concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
return concat
def inceptionC(self, data, name=None):
pool1 = fluid.layers.pool2d(
input=data, pool_size=3, pool_padding=1, pool_type='avg')
conv1 = self.conv_bn_layer(
pool1, 256, 1, act='relu', name="inception_c" + name + "_1x1")
conv2 = self.conv_bn_layer(
data, 256, 1, act='relu', name="inception_c" + name + "_1x1_2")
conv3 = self.conv_bn_layer(
data, 384, 1, act='relu', name="inception_c" + name + "_1x1_3")
conv3_1 = self.conv_bn_layer(
conv3,
class InceptionC(fluid.dygraph.Layer):
def __init__(self, name=None):
super(InceptionC, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
self._conv1 = ConvBNLayer(
1536, 256, 1, act="relu", name="inception_c" + name + "_1x1")
self._conv2 = ConvBNLayer(
1536, 256, 1, act="relu", name="inception_c" + name + "_1x1_2")
self._conv3_0 = ConvBNLayer(
1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_3")
self._conv3_1 = ConvBNLayer(
384,
256, (1, 3),
padding=(0, 1),
act='relu',
act="relu",
name="inception_c" + name + "_1x3")
conv3_2 = self.conv_bn_layer(
conv3,
self._conv3_2 = ConvBNLayer(
384,
256, (3, 1),
padding=(1, 0),
act='relu',
act="relu",
name="inception_c" + name + "_3x1")
conv4 = self.conv_bn_layer(
data, 384, 1, act='relu', name="inception_c" + name + "_1x1_4")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_0 = ConvBNLayer(
1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_4")
self._conv4_00 = ConvBNLayer(
384,
448, (1, 3),
padding=(0, 1),
act='relu',
act="relu",
name="inception_c" + name + "_1x3_2")
conv4 = self.conv_bn_layer(
conv4,
self._conv4_000 = ConvBNLayer(
448,
512, (3, 1),
padding=(1, 0),
act='relu',
act="relu",
name="inception_c" + name + "_3x1_2")
conv4_1 = self.conv_bn_layer(
conv4,
self._conv4_1 = ConvBNLayer(
512,
256, (1, 3),
padding=(0, 1),
act='relu',
act="relu",
name="inception_c" + name + "_1x3_3")
conv4_2 = self.conv_bn_layer(
conv4,
self._conv4_2 = ConvBNLayer(
512,
256, (3, 1),
padding=(1, 0),
act='relu',
act="relu",
name="inception_c" + name + "_3x1_3")
def forward(self, inputs):
pool1 = self._pool(inputs)
conv1 = self._conv1(pool1)
conv2 = self._conv2(inputs)
conv3 = self._conv3_0(inputs)
conv3_1 = self._conv3_1(conv3)
conv3_2 = self._conv3_2(conv3)
conv4 = self._conv4_0(inputs)
conv4 = self._conv4_00(conv4)
conv4 = self._conv4_000(conv4)
conv4_1 = self._conv4_1(conv4)
conv4_2 = self._conv4_2(conv4)
concat = fluid.layers.concat(
[conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)
return concat
class InceptionV4DY(fluid.dygraph.Layer):
def __init__(self, class_dim=1000):
super(InceptionV4DY, self).__init__()
self._inception_stem = InceptionStem()
self._inceptionA_1 = InceptionA(name="1")
self._inceptionA_2 = InceptionA(name="2")
self._inceptionA_3 = InceptionA(name="3")
self._inceptionA_4 = InceptionA(name="4")
self._reductionA = ReductionA()
self._inceptionB_1 = InceptionB(name="1")
self._inceptionB_2 = InceptionB(name="2")
self._inceptionB_3 = InceptionB(name="3")
self._inceptionB_4 = InceptionB(name="4")
self._inceptionB_5 = InceptionB(name="5")
self._inceptionB_6 = InceptionB(name="6")
self._inceptionB_7 = InceptionB(name="7")
self._reductionB = ReductionB()
self._inceptionC_1 = InceptionC(name="1")
self._inceptionC_2 = InceptionC(name="2")
self._inceptionC_3 = InceptionC(name="3")
self.avg_pool = Pool2D(pool_type='avg', global_pooling=True)
self._drop = Dropout(p=0.2)
stdv = 1.0 / math.sqrt(1536 * 1.0)
self.out = Linear(
1536,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="final_fc_weights"),
bias_attr=ParamAttr(name="final_fc_offset"))
def forward(self, inputs):
x = self._inception_stem(inputs)
x = self._inceptionA_1(x)
x = self._inceptionA_2(x)
x = self._inceptionA_3(x)
x = self._inceptionA_4(x)
x = self._reductionA(x)
x = self._inceptionB_1(x)
x = self._inceptionB_2(x)
x = self._inceptionB_3(x)
x = self._inceptionB_4(x)
x = self._inceptionB_5(x)
x = self._inceptionB_6(x)
x = self._inceptionB_7(x)
x = self._reductionB(x)
x = self._inceptionC_1(x)
x = self._inceptionC_2(x)
x = self._inceptionC_3(x)
x = self.avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._drop(x)
x = self.out(x)
return x
def InceptionV4(**args):
model = InceptionV4DY(**args)
return model
\ No newline at end of file
ppcls/modeling/architectures/resnext101_wsl.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
__all__ = [
"ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
"ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl"
]
__all__ = ["ResNeXt101_32x8d_wsl",
"ResNeXt101_wsl_32x16d_wsl",
"ResNeXt101_wsl_32x32d_wsl",
"ResNeXt101_wsl_32x48d_wsl"]
class ResNeXt101_wsl():
def __init__(self, layers=101, cardinality=32, width=48):
self.layers = layers
self.cardinality = cardinality
self.width = width
def net(self, input, class_dim=1000):
layers = self.layers
cardinality = self.cardinality
width = self.width
depth = [3, 4, 23, 3]
base_width = cardinality * width
num_filters = [base_width * i for i in [1, 2, 4, 8]]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name="conv1") #debug
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
for block in range(len(depth)):
for i in range(depth[block]):
conv_name = 'layer' + str(block + 1) + "." + str(i)
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
cardinality=cardinality,
name=conv_name)
pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name='fc.weight'),
bias_attr=fluid.param_attr.ParamAttr(name='fc.bias'))
return out
def conv_bn_layer(self,
input,
num_filters,
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
if "downsample" in name:
conv_name = name + '.0'
conv_name = name + ".0"
else:
conv_name = name
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
self._conv = Conv2D(num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
padding=(filter_size-1)//2,
groups=groups,
act=None,
param_attr=ParamAttr(name=conv_name + ".weight"),
bias_attr=False)
if "downsample" in name:
bn_name = name[:9] + 'downsample' + '.1'
bn_name = name[:9] + "downsample.1"
else:
if "conv1" == name:
bn_name = 'bn' + name[-1]
bn_name = "bn" + name[-1]
else:
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]
) + 'bn' + name[-1]
return fluid.layers.batch_norm(
input=conv,
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + "bn" + name[-1]
self._bn = BatchNorm(num_channels=output_channels,
act=act,
param_attr=ParamAttr(name=bn_name + '.weight'),
bias_attr=ParamAttr(bn_name + '.bias'),
moving_mean_name=bn_name + '.running_mean',
moving_variance_name=bn_name + '.running_var', )
def shortcut(self, input, ch_out, stride, name):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
return input
def bottleneck_block(self, input, num_filters, stride, cardinality, name):
cardinality = self.cardinality
width = self.width
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + ".conv1")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality,
act='relu',
name=name + ".conv2")
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters // (width // 8),
filter_size=1,
act=None,
name=name + ".conv3")
short = self.shortcut(
input,
num_filters // (width // 8),
stride,
name=name + ".downsample")
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
param_attr=ParamAttr(name=bn_name + ".weight"),
bias_attr=ParamAttr(name=bn_name + ".bias"),
moving_mean_name=bn_name + ".running_mean",
moving_variance_name=bn_name + ".running_var")
def forward(self, inputs):
x = self._conv(inputs)
x = self._bn(x)
return x
class ShortCut(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride, name=None):
super(ShortCut, self).__init__()
self.input_channels = input_channels
self.output_channels = output_channels
self.stride = stride
if input_channels!=output_channels or stride!=1:
self._conv = ConvBNLayer(
input_channels, output_channels, filter_size=1, stride=stride, name=name)
def forward(self, inputs):
if self.input_channels!= self.output_channels or self.stride!=1:
return self._conv(inputs)
return inputs
class BottleneckBlock(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride, cardinality, width, name):
super(BottleneckBlock, self).__init__()
self._conv0 = ConvBNLayer(
input_channels, output_channels, filter_size=1, act="relu", name=name + ".conv1")
self._conv1 = ConvBNLayer(
output_channels, output_channels, filter_size=3, act="relu", stride=stride, groups=cardinality, name=name + ".conv2")
self._conv2 = ConvBNLayer(
output_channels, output_channels//(width//8), filter_size=1, act=None, name=name + ".conv3")
self._short = ShortCut(
input_channels, output_channels//(width//8), stride=stride, name=name + ".downsample")
def forward(self, inputs):
x = self._conv0(inputs)
x = self._conv1(x)
x = self._conv2(x)
y = self._short(inputs)
return fluid.layers.elementwise_add(x, y, act="relu")
class ResNeXt101WSL(fluid.dygraph.Layer):
def __init__(self, layers=101, cardinality=32, width=48, class_dim=1000):
super(ResNeXt101WSL, self).__init__()
self.class_dim = class_dim
def ResNeXt101_32x8d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=8)
return model
def ResNeXt101_32x16d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=16)
self.layers = layers
self.cardinality = cardinality
self.width = width
self.scale = width//8
self.depth = [3, 4, 23, 3]
self.base_width = cardinality * width
num_filters = [self.base_width*i for i in [1,2,4,8]] #[256, 512, 1024, 2048]
self._conv_stem = ConvBNLayer(
3, 64, 7, stride=2, act="relu", name="conv1")
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type="max")
self._conv1_0 = BottleneckBlock(
64, num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.0")
self._conv1_1 = BottleneckBlock(
num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.1")
self._conv1_2 = BottleneckBlock(
num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.2")
self._conv2_0 = BottleneckBlock(
num_filters[0]//(width//8), num_filters[1], stride=2, cardinality=self.cardinality, width=self.width, name="layer2.0")
self._conv2_1 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.1")
self._conv2_2 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.2")
self._conv2_3 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.3")
self._conv3_0 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[2], stride=2, cardinality=self.cardinality, width=self.width, name="layer3.0")
self._conv3_1 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.1")
self._conv3_2 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.2")
self._conv3_3 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.3")
self._conv3_4 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.4")
self._conv3_5 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.5")
self._conv3_6 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.6")
self._conv3_7 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.7")
self._conv3_8 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.8")
self._conv3_9 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.9")
self._conv3_10 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.10")
self._conv3_11 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.11")
self._conv3_12 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.12")
self._conv3_13 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.13")
self._conv3_14 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.14")
self._conv3_15 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.15")
self._conv3_16 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.16")
self._conv3_17 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.17")
self._conv3_18 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.18")
self._conv3_19 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.19")
self._conv3_20 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.20")
self._conv3_21 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.21")
self._conv3_22 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.22")
self._conv4_0 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[3], stride=2, cardinality=self.cardinality, width=self.width, name="layer4.0")
self._conv4_1 = BottleneckBlock(
num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.1")
self._conv4_2 = BottleneckBlock(
num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.2")
self._avg_pool = Pool2D(pool_type="avg", global_pooling=True)
self._out = Linear(input_dim=num_filters[3]//(width//8),
output_dim=class_dim,
param_attr=ParamAttr(name="fc.weight"),
bias_attr=ParamAttr(name="fc.bias"))
def forward(self, inputs):
x = self._conv_stem(inputs)
x = self._pool(x)
x = self._conv1_0(x)
x = self._conv1_1(x)
x = self._conv1_2(x)
x = self._conv2_0(x)
x = self._conv2_1(x)
x = self._conv2_2(x)
x = self._conv2_3(x)
x = self._conv3_0(x)
x = self._conv3_1(x)
x = self._conv3_2(x)
x = self._conv3_3(x)
x = self._conv3_4(x)
x = self._conv3_5(x)
x = self._conv3_6(x)
x = self._conv3_7(x)
x = self._conv3_8(x)
x = self._conv3_9(x)
x = self._conv3_10(x)
x = self._conv3_11(x)
x = self._conv3_12(x)
x = self._conv3_13(x)
x = self._conv3_14(x)
x = self._conv3_15(x)
x = self._conv3_16(x)
x = self._conv3_17(x)
x = self._conv3_18(x)
x = self._conv3_19(x)
x = self._conv3_20(x)
x = self._conv3_21(x)
x = self._conv3_22(x)
x = self._conv4_0(x)
x = self._conv4_1(x)
x = self._conv4_2(x)
x = self._avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._out(x)
return x
def ResNeXt101_32x8d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=8, **args)
return model
def ResNeXt101_32x32d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=32)
def ResNeXt101_32x16d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=16, **args)
return model
def ResNeXt101_32x48d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=48)
def ResNeXt101_32x32d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=32, **args)
return model
def Fix_ResNeXt101_32x48d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=48)
def ResNeXt101_32x48d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=48, **args)
return model
\ No newline at end of file
ppcls/modeling/architectures/squeezenet.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
__all__ = ["SqueezeNet", "SqueezeNet1_0", "SqueezeNet1_1"]
class SqueezeNet():
def __init__(self, version='1.0'):
self.version = version
def net(self, input, class_dim=1000):
version = self.version
assert version in ['1.0', '1.1'], \
"supported version are {} but input version is {}".format(['1.0', '1.1'], version)
if version == '1.0':
conv = fluid.layers.conv2d(
input,
num_filters=96,
filter_size=7,
stride=2,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
else:
conv = fluid.layers.conv2d(
input,
num_filters=64,
filter_size=3,
stride=2,
padding=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
conv = fluid.layers.dropout(conv, dropout_prob=0.5)
conv = fluid.layers.conv2d(
conv,
num_filters=class_dim,
filter_size=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name='conv10_offset'))
conv = fluid.layers.pool2d(conv, pool_type='avg', global_pooling=True)
out = fluid.layers.flatten(conv)
return out
__all__ = ["SqueezeNet1_0", "SqueezeNet1_1"]
def make_fire_conv(self,
input,
num_filters,
class MakeFireConv(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
padding=0,
name=None):
conv = fluid.layers.conv2d(
input,
num_filters=num_filters,
filter_size=filter_size,
super(MakeFireConv, self).__init__()
self._conv = Conv2D(input_channels,
output_channels,
filter_size,
padding=padding,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + '_offset'))
return conv
act="relu",
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + "_offset"))
def forward(self, inputs):
return self._conv(inputs)
def make_fire(self,
input,
class MakeFire(fluid.dygraph.Layer):
def __init__(self,
input_channels,
squeeze_channels,
expand1x1_channels,
expand3x3_channels,
name=None):
conv = self.make_fire_conv(
input, squeeze_channels, 1, name=name + '_squeeze1x1')
conv_path1 = self.make_fire_conv(
conv, expand1x1_channels, 1, name=name + '_expand1x1')
conv_path2 = self.make_fire_conv(
conv, expand3x3_channels, 3, 1, name=name + '_expand3x3')
out = fluid.layers.concat([conv_path1, conv_path2], axis=1)
return out
super(MakeFire, self).__init__()
self._conv = MakeFireConv(input_channels,
squeeze_channels,
1,
name=name + "_squeeze1x1")
self._conv_path1 = MakeFireConv(squeeze_channels,
expand1x1_channels,
1,
name=name + "_expand1x1")
self._conv_path2 = MakeFireConv(squeeze_channels,
expand3x3_channels,
3,
padding=1,
name=name + "_expand3x3")
def forward(self, inputs):
x = self._conv(inputs)
x1 = self._conv_path1(x)
x2 = self._conv_path2(x)
return fluid.layers.concat([x1, x2], axis=1)
def SqueezeNet1_0():
model = SqueezeNet(version='1.0')
return model
class SqueezeNet(fluid.dygraph.Layer):
def __init__(self, version, class_dim=1000):
super(SqueezeNet, self).__init__()
self.version = version
if self.version == "1.0":
self._conv = Conv2D(3,
96,
7,
stride=2,
act="relu",
param_attr=ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name="conv1_offset"))
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_type="max")
self._conv1 = MakeFire(96, 16, 64, 64, name="fire2")
self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")
self._conv4 = MakeFire(256, 32, 128, 128, name="fire5")
self._conv5 = MakeFire(256, 48, 192, 192, name="fire6")
self._conv6 = MakeFire(384, 48, 192, 192, name="fire7")
self._conv7 = MakeFire(384, 64, 256, 256, name="fire8")
self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
else:
self._conv = Conv2D(3,
64,
3,
stride=2,
padding=1,
act="relu",
param_attr=ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name="conv1_offset"))
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_type="max")
self._conv1 = MakeFire(64, 16, 64, 64, name="fire2")
self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")
self._conv4 = MakeFire(256, 32, 128, 128, name="fire5")
self._conv5 = MakeFire(256, 48, 192, 192, name="fire6")
self._conv6 = MakeFire(384, 48, 192, 192, name="fire7")
self._conv7 = MakeFire(384, 64, 256, 256, name="fire8")
self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
self._drop = Dropout(p=0.5)
self._conv9 = Conv2D(512,
class_dim,
1,
act="relu",
param_attr=ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name="conv10_offset"))
self._avg_pool = Pool2D(pool_type="avg",
global_pooling=True)
def forward(self, inputs):
x = self._conv(inputs)
x = self._pool(x)
if self.version=="1.0":
x = self._conv1(x)
x = self._conv2(x)
x = self._conv3(x)
x = self._pool(x)
x = self._conv4(x)
x = self._conv5(x)
x = self._conv6(x)
x = self._conv7(x)
x = self._pool(x)
x = self._conv8(x)
else:
x = self._conv1(x)
x = self._conv2(x)
x = self._pool(x)
x = self._conv3(x)
x = self._conv4(x)
x = self._pool(x)
x = self._conv5(x)
x = self._conv6(x)
x = self._conv7(x)
x = self._conv8(x)
x = self._drop(x)
x = self._conv9(x)
x = self._avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2,3])
return x
def SqueezeNet1_0(**args):
model = SqueezeNet(version="1.0", **args)
return model
def SqueezeNet1_1():
model = SqueezeNet(version='1.1')
def SqueezeNet1_1(**args):
model = SqueezeNet(version="1.1", **args)
return model
\ No newline at end of file
ppcls/modeling/architectures/vgg.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
__all__ = ["VGGNet", "VGG11", "VGG13", "VGG16", "VGG19"]
class VGGNet():
def __init__(self, layers=16):
self.layers = layers
def net(self, input, class_dim=1000):
layers = self.layers
vgg_spec = {
11: ([1, 1, 2, 2, 2]),
13: ([2, 2, 2, 2, 2]),
16: ([2, 2, 3, 3, 3]),
19: ([2, 2, 4, 4, 4])
}
assert layers in vgg_spec.keys(), \
"supported layers are {} but input layer is {}".format(vgg_spec.keys(), layers)
nums = vgg_spec[layers]
conv1 = self.conv_block(input, 64, nums[0], name="conv1_")
conv2 = self.conv_block(conv1, 128, nums[1], name="conv2_")
conv3 = self.conv_block(conv2, 256, nums[2], name="conv3_")
conv4 = self.conv_block(conv3, 512, nums[3], name="conv4_")
conv5 = self.conv_block(conv4, 512, nums[4], name="conv5_")
fc_dim = 4096
fc_name = ["fc6", "fc7", "fc8"]
fc1 = fluid.layers.fc(
input=conv5,
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"))
fc1 = fluid.layers.dropout(x=fc1, dropout_prob=0.5)
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"))
fc2 = fluid.layers.dropout(x=fc2, dropout_prob=0.5)
out = fluid.layers.fc(
input=fc2,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
name=fc_name[2] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_offset"))
return out
def conv_block(self, input, num_filter, groups, name=None):
conv = input
for i in range(groups):
conv = fluid.layers.conv2d(
input=conv,
num_filters=num_filter,
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"]
class ConvBlock(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
groups,
name=None):
super(ConvBlock, self).__init__()
self.groups = groups
self._conv_1 = Conv2D(num_channels=input_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
name=name + str(i + 1) + "_weights"),
act="relu",
param_attr=ParamAttr(name=name + "1_weights"),
bias_attr=False)
return fluid.layers.pool2d(
input=conv, pool_size=2, pool_type='max', pool_stride=2)
if groups == 2 or groups == 3 or groups == 4:
self._conv_2 = Conv2D(num_channels=output_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "2_weights"),
bias_attr=False)
if groups == 3 or groups == 4:
self._conv_3 = Conv2D(num_channels=output_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "3_weights"),
bias_attr=False)
if groups == 4:
self._conv_4 = Conv2D(num_channels=output_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "4_weights"),
bias_attr=False)
self._pool = Pool2D(pool_size=2,
pool_type="max",
pool_stride=2)
def forward(self, inputs):
x = self._conv_1(inputs)
if self.groups == 2 or self.groups == 3 or self.groups == 4:
x = self._conv_2(x)
if self.groups == 3 or self.groups == 4 :
x = self._conv_3(x)
if self.groups == 4:
x = self._conv_4(x)
x = self._pool(x)
return x
class VGGNet(fluid.dygraph.Layer):
def __init__(self, layers=11, class_dim=1000):
super(VGGNet, self).__init__()
def VGG11():
model = VGGNet(layers=11)
self.layers = layers
self.vgg_configure = {11: [1, 1, 2, 2, 2],
13: [2, 2, 2, 2, 2],
16: [2, 2, 3, 3, 3],
19: [2, 2, 4, 4, 4]}
assert self.layers in self.vgg_configure.keys(), \
"supported layers are {} but input layer is {}".format(vgg_configure.keys(), layers)
self.groups = self.vgg_configure[self.layers]
self._conv_block_1 = ConvBlock(3, 64, self.groups[0], name="conv1_")
self._conv_block_2 = ConvBlock(64, 128, self.groups[1], name="conv2_")
self._conv_block_3 = ConvBlock(128, 256, self.groups[2], name="conv3_")
self._conv_block_4 = ConvBlock(256, 512, self.groups[3], name="conv4_")
self._conv_block_5 = ConvBlock(512, 512, self.groups[4], name="conv5_")
self._drop = fluid.dygraph.Dropout(p=0.5)
self._fc1 = Linear(input_dim=7*7*512,
output_dim=4096,
act="relu",
param_attr=ParamAttr(name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
self._fc2 = Linear(input_dim=4096,
output_dim=4096,
act="relu",
param_attr=ParamAttr(name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
self._out = Linear(input_dim=4096,
output_dim=class_dim,
param_attr=ParamAttr(name="fc8_weights"),
bias_attr=ParamAttr(name="fc8_offset"))
def forward(self, inputs):
x = self._conv_block_1(inputs)
x = self._conv_block_2(x)
x = self._conv_block_3(x)
x = self._conv_block_4(x)
x = self._conv_block_5(x)
x = fluid.layers.flatten(x, axis=0)
x = self._fc1(x)
x = self._drop(x)
x = self._fc2(x)
x = self._drop(x)
x = self._out(x)
return x
def VGG11(**args):
model = VGGNet(layers=11, **args)
return model
def VGG13():
model = VGGNet(layers=13)
def VGG13(**args):
model = VGGNet(layers=13, **args)
return model
def VGG16():
model = VGGNet(layers=16)
def VGG16(**args):
model = VGGNet(layers=16, **args)
return model
def VGG19():
model = VGGNet(layers=19)
def VGG19(**args):
model = VGGNet(layers=19, **args)
return model
\ No newline at end of file
ppcls/modeling/architectures/xception.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import sys
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
import math
__all__ = ['Xception', 'Xception41', 'Xception65', 'Xception71']
__all__ = ['Xception41', 'Xception65', 'Xception71']
class Xception(object):
"""Xception"""
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
def __init__(self, entry_flow_block_num=3, middle_flow_block_num=8):
self.entry_flow_block_num = entry_flow_block_num
self.middle_flow_block_num = middle_flow_block_num
return
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
bn_name = "bn_" + name
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def net(self, input, class_dim=1000):
conv = self.entry_flow(input, self.entry_flow_block_num)
conv = self.middle_flow(conv, self.middle_flow_block_num)
conv = self.exit_flow(conv, class_dim)
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
return conv
def entry_flow(self, input, block_num=3):
'''xception entry_flow'''
name = "entry_flow"
conv = self.conv_bn_layer(
input=input,
num_filters=32,
filter_size=3,
stride=2,
act='relu',
name=name + "_conv1")
conv = self.conv_bn_layer(
input=conv,
num_filters=64,
filter_size=3,
stride=1,
act='relu',
name=name + "_conv2")
class SeparableConv(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride=1, name=None):
super(SeparableConv, self).__init__()
if block_num == 3:
relu_first = [False, True, True]
num_filters = [128, 256, 728]
stride = [2, 2, 2]
elif block_num == 5:
relu_first = [False, True, True, True, True]
num_filters = [128, 256, 256, 728, 728]
stride = [2, 1, 2, 1, 2]
else:
sys.exit(-1)
self._pointwise_conv = ConvBNLayer(
input_channels, output_channels, 1, name=name + "_sep")
self._depthwise_conv = ConvBNLayer(
output_channels,
output_channels,
3,
stride=stride,
groups=output_channels,
name=name + "_dw")
for block in range(block_num):
curr_name = "{}_{}".format(name, block)
conv = self.entry_flow_bottleneck_block(
conv,
num_filters=num_filters[block],
name=curr_name,
stride=stride[block],
relu_first=relu_first[block])
def forward(self, inputs):
x = self._pointwise_conv(inputs)
x = self._depthwise_conv(x)
return x
return conv
def entry_flow_bottleneck_block(self,
input,
num_filters,
name,
class EntryFlowBottleneckBlock(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
stride=2,
name=None,
relu_first=False):
'''entry_flow_bottleneck_block'''
short = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
super(EntryFlowBottleneckBlock, self).__init__()
self.relu_first = relu_first
self._short = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=1,
stride=stride,
padding=0,
act=None,
param_attr=ParamAttr(name + "_branch1_weights"),
bias_attr=False)
self._conv1 = SeparableConv(
input_channels,
output_channels,
stride=1,
name=name + "_branch2a_weights")
self._conv2 = SeparableConv(
output_channels,
output_channels,
stride=1,
name=name + "_branch2b_weights")
self._pool = Pool2D(
pool_size=3, pool_stride=stride, pool_padding=1, pool_type="max")
def forward(self, inputs):
conv0 = inputs
short = self._short(inputs)
layer_helper = LayerHelper(self.full_name(), act="relu")
if self.relu_first:
conv0 = layer_helper.append_activation(conv0)
conv1 = self._conv1(conv0)
conv2 = layer_helper.append_activation(conv1)
conv2 = self._conv2(conv2)
pool = self._pool(conv2)
return fluid.layers.elementwise_add(x=short, y=pool)
conv0 = input
if relu_first:
conv0 = fluid.layers.relu(conv0)
conv1 = self.separable_conv(
conv0, num_filters, stride=1, name=name + "_branch2a_weights")
conv2 = fluid.layers.relu(conv1)
conv2 = self.separable_conv(
conv2, num_filters, stride=1, name=name + "_branch2b_weights")
pool = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=stride,
pool_padding=1,
pool_type='max')
class EntryFlow(fluid.dygraph.Layer):
def __init__(self, block_num=3):
super(EntryFlow, self).__init__()
return fluid.layers.elementwise_add(x=short, y=pool)
name = "entry_flow"
self.block_num = block_num
self._conv1 = ConvBNLayer(
3, 32, 3, stride=2, act="relu", name=name + "_conv1")
self._conv2 = ConvBNLayer(32, 64, 3, act="relu", name=name + "_conv2")
if block_num == 3:
self._conv_0 = EntryFlowBottleneckBlock(
64, 128, stride=2, name=name + "_0", relu_first=False)
self._conv_1 = EntryFlowBottleneckBlock(
128, 256, stride=2, name=name + "_1", relu_first=True)
self._conv_2 = EntryFlowBottleneckBlock(
256, 728, stride=2, name=name + "_2", relu_first=True)
elif block_num == 5:
self._conv_0 = EntryFlowBottleneckBlock(
64, 128, stride=2, name=name + "_0", relu_first=False)
self._conv_1 = EntryFlowBottleneckBlock(
128, 256, stride=1, name=name + "_1", relu_first=True)
self._conv_2 = EntryFlowBottleneckBlock(
256, 256, stride=2, name=name + "_2", relu_first=True)
self._conv_3 = EntryFlowBottleneckBlock(
256, 728, stride=1, name=name + "_3", relu_first=True)
self._conv_4 = EntryFlowBottleneckBlock(
728, 728, stride=2, name=name + "_4", relu_first=True)
else:
sys.exit(-1)
def middle_flow(self, input, block_num=8):
'''xception middle_flow'''
num_filters = 728
conv = input
for block in range(block_num):
name = "middle_flow_{}".format(block)
conv = self.middle_flow_bottleneck_block(conv, num_filters, name)
return conv
def middle_flow_bottleneck_block(self, input, num_filters, name):
'''middle_flow_bottleneck_block'''
conv0 = fluid.layers.relu(input)
conv0 = self.separable_conv(
conv0,
num_filters=num_filters,
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
if self.block_num == 3:
x = self._conv_0(x)
x = self._conv_1(x)
x = self._conv_2(x)
elif self.block_num == 5:
x = self._conv_0(x)
x = self._conv_1(x)
x = self._conv_2(x)
x = self._conv_3(x)
x = self._conv_4(x)
return x
class MiddleFlowBottleneckBlock(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, name):
super(MiddleFlowBottleneckBlock, self).__init__()
self._conv_0 = SeparableConv(
input_channels,
output_channels,
stride=1,
name=name + "_branch2a_weights")
conv1 = fluid.layers.relu(conv0)
conv1 = self.separable_conv(
conv1,
num_filters=num_filters,
self._conv_1 = SeparableConv(
output_channels,
output_channels,
stride=1,
name=name + "_branch2b_weights")
conv2 = fluid.layers.relu(conv1)
conv2 = self.separable_conv(
conv2,
num_filters=num_filters,
self._conv_2 = SeparableConv(
output_channels,
output_channels,
stride=1,
name=name + "_branch2c_weights")
return fluid.layers.elementwise_add(x=input, y=conv2)
def exit_flow(self, input, class_dim):
'''xception exit flow'''
name = "exit_flow"
num_filters1 = 728
num_filters2 = 1024
conv0 = self.exit_flow_bottleneck_block(
input, num_filters1, num_filters2, name=name + "_1")
conv1 = self.separable_conv(
conv0, num_filters=1536, stride=1, name=name + "_2")
conv1 = fluid.layers.relu(conv1)
conv2 = self.separable_conv(
conv1, num_filters=2048, stride=1, name=name + "_3")
conv2 = fluid.layers.relu(conv2)
pool = fluid.layers.pool2d(
input=conv2, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
name='fc_weights',
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
return out
def exit_flow_bottleneck_block(self, input, num_filters1, num_filters2,
def forward(self, inputs):
layer_helper = LayerHelper(self.full_name(), act="relu")
conv0 = layer_helper.append_activation(inputs)
conv0 = self._conv_0(conv0)
conv1 = layer_helper.append_activation(conv0)
conv1 = self._conv_1(conv1)
conv2 = layer_helper.append_activation(conv1)
conv2 = self._conv_2(conv2)
return fluid.layers.elementwise_add(x=inputs, y=conv2)
class MiddleFlow(fluid.dygraph.Layer):
def __init__(self, block_num=8):
super(MiddleFlow, self).__init__()
self.block_num = block_num
self._conv_0 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_0")
self._conv_1 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_1")
self._conv_2 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_2")
self._conv_3 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_3")
self._conv_4 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_4")
self._conv_5 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_5")
self._conv_6 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_6")
self._conv_7 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_7")
if block_num == 16:
self._conv_8 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_8")
self._conv_9 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_9")
self._conv_10 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_10")
self._conv_11 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_11")
self._conv_12 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_12")
self._conv_13 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_13")
self._conv_14 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_14")
self._conv_15 = MiddleFlowBottleneckBlock(
728, 728, name="middle_flow_15")
def forward(self, inputs):
x = self._conv_0(inputs)
x = self._conv_1(x)
x = self._conv_2(x)
x = self._conv_3(x)
x = self._conv_4(x)
x = self._conv_5(x)
x = self._conv_6(x)
x = self._conv_7(x)
if self.block_num == 16:
x = self._conv_8(x)
x = self._conv_9(x)
x = self._conv_10(x)
x = self._conv_11(x)
x = self._conv_12(x)
x = self._conv_13(x)
x = self._conv_14(x)
x = self._conv_15(x)
return x
class ExitFlowBottleneckBlock(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels1, output_channels2,
name):
'''entry_flow_bottleneck_block'''
short = fluid.layers.conv2d(
input=input,
num_filters=num_filters2,
super(ExitFlowBottleneckBlock, self).__init__()
self._short = Conv2D(
num_channels=input_channels,
num_filters=output_channels2,
filter_size=1,
stride=2,
padding=0,
act=None,
param_attr=ParamAttr(name + "_branch1_weights"),
bias_attr=False)
conv0 = fluid.layers.relu(input)
conv1 = self.separable_conv(
conv0, num_filters1, stride=1, name=name + "_branch2a_weights")
conv2 = fluid.layers.relu(conv1)
conv2 = self.separable_conv(
conv2, num_filters2, stride=1, name=name + "_branch2b_weights")
pool = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
self._conv_1 = SeparableConv(
input_channels,
output_channels1,
stride=1,
name=name + "_branch2a_weights")
self._conv_2 = SeparableConv(
output_channels1,
output_channels2,
stride=1,
name=name + "_branch2b_weights")
self._pool = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type="max")
def forward(self, inputs):
short = self._short(inputs)
layer_helper = LayerHelper(self.full_name(), act="relu")
conv0 = layer_helper.append_activation(inputs)
conv1 = self._conv_1(conv0)
conv2 = layer_helper.append_activation(conv1)
conv2 = self._conv_2(conv2)
pool = self._pool(conv2)
return fluid.layers.elementwise_add(x=short, y=pool)
def separable_conv(self, input, num_filters, stride=1, name=None):
"""separable_conv"""
pointwise_conv = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters,
stride=1,
name=name + "_sep")
depthwise_conv = self.conv_bn_layer(
input=pointwise_conv,
filter_size=3,
num_filters=num_filters,
stride=stride,
groups=num_filters,
use_cudnn=False,
name=name + "_dw")
class ExitFlow(fluid.dygraph.Layer):
def __init__(self, class_dim):
super(ExitFlow, self).__init__()
return depthwise_conv
name = "exit_flow"
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
use_cudnn=True,
name=None):
"""conv_bn_layer"""
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
use_cudnn=use_cudnn)
self._conv_0 = ExitFlowBottleneckBlock(
728, 728, 1024, name=name + "_1")
self._conv_1 = SeparableConv(1024, 1536, stride=1, name=name + "_2")
self._conv_2 = SeparableConv(1536, 2048, stride=1, name=name + "_3")
self._pool = Pool2D(pool_type="avg", global_pooling=True)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self._out = Linear(
2048,
class_dim,
param_attr=ParamAttr(
name="fc_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
layer_helper = LayerHelper(self.full_name(), act="relu")
conv0 = self._conv_0(inputs)
conv1 = self._conv_1(conv0)
conv1 = layer_helper.append_activation(conv1)
conv2 = self._conv_2(conv1)
conv2 = layer_helper.append_activation(conv2)
pool = self._pool(conv2)
pool = fluid.layers.reshape(pool, [0, -1])
out = self._out(pool)
return out
bn_name = "bn_" + name
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
class Xception(fluid.dygraph.Layer):
def __init__(self,
entry_flow_block_num=3,
middle_flow_block_num=8,
class_dim=1000):
super(Xception, self).__init__()
self.entry_flow_block_num = entry_flow_block_num
self.middle_flow_block_num = middle_flow_block_num
self._entry_flow = EntryFlow(entry_flow_block_num)
self._middle_flow = MiddleFlow(middle_flow_block_num)
self._exit_flow = ExitFlow(class_dim)
def forward(self, inputs):
x = self._entry_flow(inputs)
x = self._middle_flow(x)
x = self._exit_flow(x)
return x
def Xception41():
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8)
def Xception41(**args):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **args)
return model
def Xception65():
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16)
def Xception65(**args):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **args)
return model
def Xception71():
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16)
def Xception71(**args):
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **args)
return model
\ No newline at end of file
ppcls/modeling/architectures/xception_deeplab.py
浏览文件 @ fe302aec
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
import paddle
import math
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from .model_libs import scope, name_scope
from .model_libs import bn, bn_relu, relu
from .model_libs import conv
from .model_libs import seperate_conv
__all__ = ['Xception41_deeplab', 'Xception65_deeplab', 'Xception71_deeplab']
__all__ = ["Xception41_deeplab", "Xception65_deeplab", "Xception71_deeplab"]
def check_data(data, number):
......@@ -54,32 +31,24 @@ def check_points(count, points):
return (True if count == points else False)
class Xception():
def __init__(self, backbone="xception_65"):
self.bottleneck_params = self.gen_bottleneck_params(backbone)
self.backbone = backbone
def gen_bottleneck_params(self, backbone='xception_65'):
def gen_bottleneck_params(backbone='xception_65'):
if backbone == 'xception_65':
bottleneck_params = {
"entry_flow": (3, [2, 2, 2], [128, 256, 728]),
"middle_flow": (16, 1, 728),
"exit_flow":
(2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
"exit_flow": (2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
}
elif backbone == 'xception_41':
bottleneck_params = {
"entry_flow": (3, [2, 2, 2], [128, 256, 728]),
"middle_flow": (8, 1, 728),
"exit_flow":
(2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
"exit_flow": (2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
}
elif backbone == 'xception_71':
bottleneck_params = {
"entry_flow": (5, [2, 1, 2, 1, 2], [128, 256, 256, 728, 728]),
"middle_flow": (16, 1, 728),
"exit_flow":
(2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
"exit_flow": (2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
}
else:
raise Exception(
......@@ -87,234 +56,330 @@ class Xception():
)
return bottleneck_params
def net(self,
input,
output_stride=32,
class_dim=1000,
end_points=None,
decode_points=None):
self.stride = 2
self.block_point = 0
self.output_stride = output_stride
self.decode_points = decode_points
self.short_cuts = dict()
with scope(self.backbone):
# Entry flow
data = self.entry_flow(input)
if check_points(self.block_point, end_points):
return data, self.short_cuts
# Middle flow
data = self.middle_flow(data)
if check_points(self.block_point, end_points):
return data, self.short_cuts
# Exit flow
data = self.exit_flow(data)
if check_points(self.block_point, end_points):
return data, self.short_cuts
data = fluid.layers.reduce_mean(data, [2, 3], keep_dim=True)
data = fluid.layers.dropout(data, 0.5)
stdv = 1.0 / math.sqrt(data.shape[1] * 1.0)
with scope("logit"):
out = fluid.layers.fc(
input=data,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
name='fc_weights',
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=fluid.param_attr.ParamAttr(name='fc_bias'))
return out
def entry_flow(self, data):
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=None,
initializer=fluid.initializer.TruncatedNormal(
loc=0.0, scale=0.09))
with scope("entry_flow"):
with scope("conv1"):
data = bn_relu(
conv(
data,
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride=1,
padding=0,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
param_attr=ParamAttr(name=name + "/weights"),
bias_attr=False)
self._bn = BatchNorm(
num_channels=output_channels,
act=act,
epsilon=1e-3,
momentum=0.99,
param_attr=ParamAttr(name=name + "/BatchNorm/gamma"),
bias_attr=ParamAttr(name=name + "/BatchNorm/beta"),
moving_mean_name=name + "/BatchNorm/moving_mean",
moving_variance_name=name + "/BatchNorm/moving_variance")
def forward(self, inputs):
return self._bn(self._conv(inputs))
class Seperate_Conv(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
stride,
filter,
dilation=1,
act=None,
name=None):
super(Seperate_Conv, self).__init__()
self._conv1 = Conv2D(
num_channels=input_channels,
num_filters=input_channels,
filter_size=filter,
stride=stride,
groups=input_channels,
padding=(filter) // 2 * dilation,
dilation=dilation,
param_attr=ParamAttr(name=name + "/depthwise/weights"),
bias_attr=False)
self._bn1 = BatchNorm(
input_channels,
act=act,
epsilon=1e-3,
momentum=0.99,
param_attr=ParamAttr(name=name + "/depthwise/BatchNorm/gamma"),
bias_attr=ParamAttr(name=name + "/depthwise/BatchNorm/beta"),
moving_mean_name=name + "/depthwise/BatchNorm/moving_mean",
moving_variance_name=name + "/depthwise/BatchNorm/moving_variance")
self._conv2 = Conv2D(
input_channels,
output_channels,
1,
stride=1,
groups=1,
padding=0,
param_attr=ParamAttr(name=name + "/pointwise/weights"),
bias_attr=False)
self._bn2 = BatchNorm(
output_channels,
act=act,
epsilon=1e-3,
momentum=0.99,
param_attr=ParamAttr(name=name + "/pointwise/BatchNorm/gamma"),
bias_attr=ParamAttr(name=name + "/pointwise/BatchNorm/beta"),
moving_mean_name=name + "/pointwise/BatchNorm/moving_mean",
moving_variance_name=name + "/pointwise/BatchNorm/moving_variance")
def forward(self, inputs):
x = self._conv1(inputs)
x = self._bn1(x)
x = self._conv2(x)
x = self._bn2(x)
return x
class Xception_Block(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
strides=1,
filter_size=3,
dilation=1,
skip_conv=True,
has_skip=True,
activation_fn_in_separable_conv=False,
name=None):
super(Xception_Block, self).__init__()
repeat_number = 3
output_channels = check_data(output_channels, repeat_number)
filter_size = check_data(filter_size, repeat_number)
strides = check_data(strides, repeat_number)
self.has_skip = has_skip
self.skip_conv = skip_conv
self.activation_fn_in_separable_conv = activation_fn_in_separable_conv
if not activation_fn_in_separable_conv:
self._conv1 = Seperate_Conv(
input_channels,
output_channels[0],
stride=strides[0],
filter=filter_size[0],
dilation=dilation,
name=name + "/separable_conv1")
self._conv2 = Seperate_Conv(
output_channels[0],
output_channels[1],
stride=strides[1],
filter=filter_size[1],
dilation=dilation,
name=name + "/separable_conv2")
self._conv3 = Seperate_Conv(
output_channels[1],
output_channels[2],
stride=strides[2],
filter=filter_size[2],
dilation=dilation,
name=name + "/separable_conv3")
else:
self._conv1 = Seperate_Conv(
input_channels,
output_channels[0],
stride=strides[0],
filter=filter_size[0],
act="relu",
dilation=dilation,
name=name + "/separable_conv1")
self._conv2 = Seperate_Conv(
output_channels[0],
output_channels[1],
stride=strides[1],
filter=filter_size[1],
act="relu",
dilation=dilation,
name=name + "/separable_conv2")
self._conv3 = Seperate_Conv(
output_channels[1],
output_channels[2],
stride=strides[2],
filter=filter_size[2],
act="relu",
dilation=dilation,
name=name + "/separable_conv3")
if has_skip and skip_conv:
self._short = ConvBNLayer(
input_channels,
output_channels[-1],
1,
stride=strides[-1],
padding=0,
name=name + "/shortcut")
def forward(self, inputs):
layer_helper = LayerHelper(self.full_name(), act='relu')
if not self.activation_fn_in_separable_conv:
x = layer_helper.append_activation(inputs)
x = self._conv1(x)
x = layer_helper.append_activation(x)
x = self._conv2(x)
x = layer_helper.append_activation(x)
x = self._conv3(x)
else:
x = self._conv1(inputs)
x = self._conv2(x)
x = self._conv3(x)
if self.has_skip is False:
return x
if self.skip_conv:
skip = self._short(inputs)
else:
skip = inputs
return fluid.layers.elementwise_add(x, skip)
class XceptionDeeplab(fluid.dygraph.Layer):
def __init__(self, backbone, class_dim=1000):
super(XceptionDeeplab, self).__init__()
bottleneck_params = gen_bottleneck_params(backbone)
self.backbone = backbone
self._conv1 = ConvBNLayer(
3,
32,
3,
stride=2,
padding=1,
param_attr=param_attr))
with scope("conv2"):
data = bn_relu(
conv(
data,
act="relu",
name=self.backbone + "/entry_flow/conv1")
self._conv2 = ConvBNLayer(
32,
64,
3,
stride=1,
padding=1,
param_attr=param_attr))
act="relu",
name=self.backbone + "/entry_flow/conv2")
self.block_num = bottleneck_params["entry_flow"][0]
self.strides = bottleneck_params["entry_flow"][1]
self.chns = bottleneck_params["entry_flow"][2]
self.strides = check_data(self.strides, self.block_num)
self.chns = check_data(self.chns, self.block_num)
# get entry flow params
block_num = self.bottleneck_params["entry_flow"][0]
strides = self.bottleneck_params["entry_flow"][1]
chns = self.bottleneck_params["entry_flow"][2]
strides = check_data(strides, block_num)
chns = check_data(chns, block_num)
self.entry_flow = []
self.middle_flow = []
# params to control your flow
self.stride = 2
self.output_stride = 32
s = self.stride
block_point = self.block_point
output_stride = self.output_stride
with scope("entry_flow"):
for i in range(block_num):
block_point = block_point + 1
with scope("block" + str(i + 1)):
stride = strides[i] if check_stride(s * strides[i],
output_stride) else 1
data, short_cuts = self.xception_block(data, chns[i],
[1, 1, stride])
s = s * stride
if check_points(block_point, self.decode_points):
self.short_cuts[block_point] = short_cuts[1]
for i in range(self.block_num):
stride = self.strides[i] if check_stride(s * self.strides[i],
self.output_stride) else 1
xception_block = self.add_sublayer(
self.backbone + "/entry_flow/block" + str(i + 1),
Xception_Block(
input_channels=64 if i == 0 else self.chns[i - 1],
output_channels=self.chns[i],
strides=[1, 1, self.stride],
name=self.backbone + "/entry_flow/block" + str(i + 1)))
self.entry_flow.append(xception_block)
s = s * stride
self.stride = s
self.block_point = block_point
return data
def middle_flow(self, data):
block_num = self.bottleneck_params["middle_flow"][0]
strides = self.bottleneck_params["middle_flow"][1]
chns = self.bottleneck_params["middle_flow"][2]
strides = check_data(strides, block_num)
chns = check_data(chns, block_num)
# params to control your flow
self.block_num = bottleneck_params["middle_flow"][0]
self.strides = bottleneck_params["middle_flow"][1]
self.chns = bottleneck_params["middle_flow"][2]
self.strides = check_data(self.strides, self.block_num)
self.chns = check_data(self.chns, self.block_num)
s = self.stride
block_point = self.block_point
output_stride = self.output_stride
with scope("middle_flow"):
for i in range(block_num):
block_point = block_point + 1
with scope("block" + str(i + 1)):
stride = strides[i] if check_stride(s * strides[i],
output_stride) else 1
data, short_cuts = self.xception_block(
data, chns[i], [1, 1, strides[i]], skip_conv=False)
s = s * stride
if check_points(block_point, self.decode_points):
self.short_cuts[block_point] = short_cuts[1]
for i in range(self.block_num):
stride = self.strides[i] if check_stride(s * self.strides[i],
self.output_stride) else 1
xception_block = self.add_sublayer(
self.backbone + "/middle_flow/block" + str(i + 1),
Xception_Block(
input_channels=728,
output_channels=728,
strides=[1, 1, self.strides[i]],
skip_conv=False,
name=self.backbone + "/middle_flow/block" + str(i + 1)))
self.middle_flow.append(xception_block)
s = s * stride
self.stride = s
self.block_point = block_point
return data
def exit_flow(self, data):
block_num = self.bottleneck_params["exit_flow"][0]
strides = self.bottleneck_params["exit_flow"][1]
chns = self.bottleneck_params["exit_flow"][2]
strides = check_data(strides, block_num)
chns = check_data(chns, block_num)
assert (block_num == 2)
# params to control your flow
self.block_num = bottleneck_params["exit_flow"][0]
self.strides = bottleneck_params["exit_flow"][1]
self.chns = bottleneck_params["exit_flow"][2]
self.strides = check_data(self.strides, self.block_num)
self.chns = check_data(self.chns, self.block_num)
s = self.stride
block_point = self.block_point
output_stride = self.output_stride
with scope("exit_flow"):
with scope('block1'):
block_point += 1
stride = strides[0] if check_stride(s * strides[0],
output_stride) else 1
data, short_cuts = self.xception_block(data, chns[0],
[1, 1, stride])
stride = self.strides[0] if check_stride(s * self.strides[0],
self.output_stride) else 1
self._exit_flow_1 = Xception_Block(
728,
self.chns[0], [1, 1, stride],
name=self.backbone + "/exit_flow/block1")
s = s * stride
if check_points(block_point, self.decode_points):
self.short_cuts[block_point] = short_cuts[1]
with scope('block2'):
block_point += 1
stride = strides[1] if check_stride(s * strides[1],
output_stride) else 1
data, short_cuts = self.xception_block(
data,
chns[1], [1, 1, stride],
stride = self.strides[1] if check_stride(s * self.strides[1],
self.output_stride) else 1
self._exit_flow_2 = Xception_Block(
self.chns[0][-1],
self.chns[1], [1, 1, stride],
dilation=2,
has_skip=False,
activation_fn_in_separable_conv=True)
activation_fn_in_separable_conv=True,
name=self.backbone + "/exit_flow/block2")
s = s * stride
if check_points(block_point, self.decode_points):
self.short_cuts[block_point] = short_cuts[1]
self.stride = s
self.block_point = block_point
return data
def xception_block(self,
input,
channels,
strides=1,
filters=3,
dilation=1,
skip_conv=True,
has_skip=True,
activation_fn_in_separable_conv=False):
repeat_number = 3
channels = check_data(channels, repeat_number)
filters = check_data(filters, repeat_number)
strides = check_data(strides, repeat_number)
data = input
results = []
for i in range(repeat_number):
with scope('separable_conv' + str(i + 1)):
if not activation_fn_in_separable_conv:
data = relu(data)
data = seperate_conv(
data,
channels[i],
strides[i],
filters[i],
dilation=dilation)
else:
data = seperate_conv(
data,
channels[i],
strides[i],
filters[i],
dilation=dilation,
act=relu)
results.append(data)
if not has_skip:
return data, results
if skip_conv:
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=None,
initializer=fluid.initializer.TruncatedNormal(
loc=0.0, scale=0.09))
with scope('shortcut'):
skip = bn(
conv(
input,
channels[-1],
1,
strides[-1],
groups=1,
padding=0,
param_attr=param_attr))
else:
skip = input
return data + skip, results
def Xception41_deeplab():
model = Xception("xception_41")
self._drop = Dropout(p=0.5)
self._pool = Pool2D(pool_type="avg", global_pooling=True)
self._fc = Linear(
self.chns[1][-1],
class_dim,
param_attr=ParamAttr(name="fc_weights"),
bias_attr=ParamAttr(name="fc_bias"))
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
for ef in self.entry_flow:
x = ef(x)
for mf in self.middle_flow:
x = mf(x)
x = self._exit_flow_1(x)
x = self._exit_flow_2(x)
x = self._drop(x)
x = self._pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._fc(x)
return x
def Xception41_deeplab(**args):
model = XceptionDeeplab('xception_41', **args)
return model
def Xception65_deeplab():
model = Xception("xception_65")
def Xception65_deeplab(**args):
model = XceptionDeeplab("xception_65", **args)
return model
def Xception71_deeplab():
model = Xception("xception_71")
def Xception71_deeplab(**args):
model = XceptionDeeplab("xception_71", **args)
return model
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