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ppcls/arch/backbone/model_zoo/hrnet.py 已删除 100644 → 0
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# 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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"HRNet_W18_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_pretrained.pdparams",
"HRNet_W30_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W30_C_pretrained.pdparams",
"HRNet_W32_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W32_C_pretrained.pdparams",
"HRNet_W40_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams",
"HRNet_W44_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams",
"HRNet_W48_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams",
"HRNet_W64_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act="relu",
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
bn_name = name + '_bn'
self._batch_norm = BatchNorm(
num_filters,
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')
def forward(self, input):
y = self._conv(input)
y = self._batch_norm(y)
return y
class Layer1(nn.Layer):
def __init__(self, num_channels, has_se=False, name=None):
super(Layer1, self).__init__()
self.bottleneck_block_list = []
for i in range(4):
bottleneck_block = self.add_sublayer(
"bb_{}_{}".format(name, i + 1),
BottleneckBlock(
num_channels=num_channels if i == 0 else 256,
num_filters=64,
has_se=has_se,
stride=1,
downsample=True if i == 0 else False,
name=name + '_' + str(i + 1)))
self.bottleneck_block_list.append(bottleneck_block)
def forward(self, input):
conv = input
for block_func in self.bottleneck_block_list:
conv = block_func(conv)
return conv
class TransitionLayer(nn.Layer):
def __init__(self, in_channels, out_channels, name=None):
super(TransitionLayer, self).__init__()
num_in = len(in_channels)
num_out = len(out_channels)
out = []
self.conv_bn_func_list = []
for i in range(num_out):
residual = None
if i < num_in:
if in_channels[i] != out_channels[i]:
residual = self.add_sublayer(
"transition_{}_layer_{}".format(name, i + 1),
ConvBNLayer(
num_channels=in_channels[i],
num_filters=out_channels[i],
filter_size=3,
name=name + '_layer_' + str(i + 1)))
else:
residual = self.add_sublayer(
"transition_{}_layer_{}".format(name, i + 1),
ConvBNLayer(
num_channels=in_channels[-1],
num_filters=out_channels[i],
filter_size=3,
stride=2,
name=name + '_layer_' + str(i + 1)))
self.conv_bn_func_list.append(residual)
def forward(self, input):
outs = []
for idx, conv_bn_func in enumerate(self.conv_bn_func_list):
if conv_bn_func is None:
outs.append(input[idx])
else:
if idx < len(input):
outs.append(conv_bn_func(input[idx]))
else:
outs.append(conv_bn_func(input[-1]))
return outs
class Branches(nn.Layer):
def __init__(self,
block_num,
in_channels,
out_channels,
has_se=False,
name=None):
super(Branches, self).__init__()
self.basic_block_list = []
for i in range(len(out_channels)):
self.basic_block_list.append([])
for j in range(block_num):
in_ch = in_channels[i] if j == 0 else out_channels[i]
basic_block_func = self.add_sublayer(
"bb_{}_branch_layer_{}_{}".format(name, i + 1, j + 1),
BasicBlock(
num_channels=in_ch,
num_filters=out_channels[i],
has_se=has_se,
name=name + '_branch_layer_' + str(i + 1) + '_' +
str(j + 1)))
self.basic_block_list[i].append(basic_block_func)
def forward(self, inputs):
outs = []
for idx, input in enumerate(inputs):
conv = input
basic_block_list = self.basic_block_list[idx]
for basic_block_func in basic_block_list:
conv = basic_block_func(conv)
outs.append(conv)
return outs
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
has_se,
stride=1,
downsample=False,
name=None):
super(BottleneckBlock, self).__init__()
self.has_se = has_se
self.downsample = downsample
self.conv1 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act="relu",
name=name + "_conv1", )
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act="relu",
name=name + "_conv2")
self.conv3 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_conv3")
if self.downsample:
self.conv_down = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_downsample")
if self.has_se:
self.se = SELayer(
num_channels=num_filters * 4,
num_filters=num_filters * 4,
reduction_ratio=16,
name='fc' + name)
def forward(self, input):
residual = input
conv1 = self.conv1(input)
conv2 = self.conv2(conv1)
conv3 = self.conv3(conv2)
if self.downsample:
residual = self.conv_down(input)
if self.has_se:
conv3 = self.se(conv3)
y = paddle.add(x=residual, y=conv3)
y = F.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride=1,
has_se=False,
downsample=False,
name=None):
super(BasicBlock, self).__init__()
self.has_se = has_se
self.downsample = downsample
self.conv1 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=3,
stride=stride,
act="relu",
name=name + "_conv1")
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=1,
act=None,
name=name + "_conv2")
if self.downsample:
self.conv_down = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
act="relu",
name=name + "_downsample")
if self.has_se:
self.se = SELayer(
num_channels=num_filters,
num_filters=num_filters,
reduction_ratio=16,
name='fc' + name)
def forward(self, input):
residual = input
conv1 = self.conv1(input)
conv2 = self.conv2(conv1)
if self.downsample:
residual = self.conv_down(input)
if self.has_se:
conv2 = self.se(conv2)
y = paddle.add(x=residual, y=conv2)
y = F.relu(y)
return y
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
med_ch = int(num_channels / reduction_ratio)
stdv = 1.0 / math.sqrt(num_channels * 1.0)
self.squeeze = Linear(
num_channels,
med_ch,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
med_ch,
num_filters,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
def forward(self, input):
pool = self.pool2d_gap(input)
pool = paddle.squeeze(pool, axis=[2, 3])
squeeze = self.squeeze(pool)
squeeze = F.relu(squeeze)
excitation = self.excitation(squeeze)
excitation = F.sigmoid(excitation)
excitation = paddle.unsqueeze(excitation, axis=[2, 3])
out = input * excitation
return out
class Stage(nn.Layer):
def __init__(self,
num_channels,
num_modules,
num_filters,
has_se=False,
multi_scale_output=True,
name=None):
super(Stage, self).__init__()
self._num_modules = num_modules
self.stage_func_list = []
for i in range(num_modules):
if i == num_modules - 1 and not multi_scale_output:
stage_func = self.add_sublayer(
"stage_{}_{}".format(name, i + 1),
HighResolutionModule(
num_channels=num_channels,
num_filters=num_filters,
has_se=has_se,
multi_scale_output=False,
name=name + '_' + str(i + 1)))
else:
stage_func = self.add_sublayer(
"stage_{}_{}".format(name, i + 1),
HighResolutionModule(
num_channels=num_channels,
num_filters=num_filters,
has_se=has_se,
name=name + '_' + str(i + 1)))
self.stage_func_list.append(stage_func)
def forward(self, input):
out = input
for idx in range(self._num_modules):
out = self.stage_func_list[idx](out)
return out
class HighResolutionModule(nn.Layer):
def __init__(self,
num_channels,
num_filters,
has_se=False,
multi_scale_output=True,
name=None):
super(HighResolutionModule, self).__init__()
self.branches_func = Branches(
block_num=4,
in_channels=num_channels,
out_channels=num_filters,
has_se=has_se,
name=name)
self.fuse_func = FuseLayers(
in_channels=num_filters,
out_channels=num_filters,
multi_scale_output=multi_scale_output,
name=name)
def forward(self, input):
out = self.branches_func(input)
out = self.fuse_func(out)
return out
class FuseLayers(nn.Layer):
def __init__(self,
in_channels,
out_channels,
multi_scale_output=True,
name=None):
super(FuseLayers, self).__init__()
self._actual_ch = len(in_channels) if multi_scale_output else 1
self._in_channels = in_channels
self.residual_func_list = []
for i in range(self._actual_ch):
for j in range(len(in_channels)):
residual_func = None
if j > i:
residual_func = self.add_sublayer(
"residual_{}_layer_{}_{}".format(name, i + 1, j + 1),
ConvBNLayer(
num_channels=in_channels[j],
num_filters=out_channels[i],
filter_size=1,
stride=1,
act=None,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1)))
self.residual_func_list.append(residual_func)
elif j < i:
pre_num_filters = in_channels[j]
for k in range(i - j):
if k == i - j - 1:
residual_func = self.add_sublayer(
"residual_{}_layer_{}_{}_{}".format(
name, i + 1, j + 1, k + 1),
ConvBNLayer(
num_channels=pre_num_filters,
num_filters=out_channels[i],
filter_size=3,
stride=2,
act=None,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1)))
pre_num_filters = out_channels[i]
else:
residual_func = self.add_sublayer(
"residual_{}_layer_{}_{}_{}".format(
name, i + 1, j + 1, k + 1),
ConvBNLayer(
num_channels=pre_num_filters,
num_filters=out_channels[j],
filter_size=3,
stride=2,
act="relu",
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1)))
pre_num_filters = out_channels[j]
self.residual_func_list.append(residual_func)
def forward(self, input):
outs = []
residual_func_idx = 0
for i in range(self._actual_ch):
residual = input[i]
for j in range(len(self._in_channels)):
if j > i:
y = self.residual_func_list[residual_func_idx](input[j])
residual_func_idx += 1
y = F.upsample(y, scale_factor=2**(j - i), mode="nearest")
residual = paddle.add(x=residual, y=y)
elif j < i:
y = input[j]
for k in range(i - j):
y = self.residual_func_list[residual_func_idx](y)
residual_func_idx += 1
residual = paddle.add(x=residual, y=y)
residual = F.relu(residual)
outs.append(residual)
return outs
class LastClsOut(nn.Layer):
def __init__(self,
num_channel_list,
has_se,
num_filters_list=[32, 64, 128, 256],
name=None):
super(LastClsOut, self).__init__()
self.func_list = []
for idx in range(len(num_channel_list)):
func = self.add_sublayer(
"conv_{}_conv_{}".format(name, idx + 1),
BottleneckBlock(
num_channels=num_channel_list[idx],
num_filters=num_filters_list[idx],
has_se=has_se,
downsample=True,
name=name + 'conv_' + str(idx + 1)))
self.func_list.append(func)
def forward(self, inputs):
outs = []
for idx, input in enumerate(inputs):
out = self.func_list[idx](input)
outs.append(out)
return outs
class HRNet(nn.Layer):
def __init__(self, width=18, has_se=False, class_dim=1000):
super(HRNet, self).__init__()
self.width = width
self.has_se = has_se
self.channels = {
18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]],
30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]],
40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]],
48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]],
60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]],
64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]]
}
self._class_dim = class_dim
channels_2, channels_3, channels_4 = self.channels[width]
num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3
self.conv_layer1_1 = ConvBNLayer(
num_channels=3,
num_filters=64,
filter_size=3,
stride=2,
act='relu',
name="layer1_1")
self.conv_layer1_2 = ConvBNLayer(
num_channels=64,
num_filters=64,
filter_size=3,
stride=2,
act='relu',
name="layer1_2")
self.la1 = Layer1(num_channels=64, has_se=has_se, name="layer2")
self.tr1 = TransitionLayer(
in_channels=[256], out_channels=channels_2, name="tr1")
self.st2 = Stage(
num_channels=channels_2,
num_modules=num_modules_2,
num_filters=channels_2,
has_se=self.has_se,
name="st2")
self.tr2 = TransitionLayer(
in_channels=channels_2, out_channels=channels_3, name="tr2")
self.st3 = Stage(
num_channels=channels_3,
num_modules=num_modules_3,
num_filters=channels_3,
has_se=self.has_se,
name="st3")
self.tr3 = TransitionLayer(
in_channels=channels_3, out_channels=channels_4, name="tr3")
self.st4 = Stage(
num_channels=channels_4,
num_modules=num_modules_4,
num_filters=channels_4,
has_se=self.has_se,
name="st4")
# classification
num_filters_list = [32, 64, 128, 256]
self.last_cls = LastClsOut(
num_channel_list=channels_4,
has_se=self.has_se,
num_filters_list=num_filters_list,
name="cls_head", )
last_num_filters = [256, 512, 1024]
self.cls_head_conv_list = []
for idx in range(3):
self.cls_head_conv_list.append(
self.add_sublayer(
"cls_head_add{}".format(idx + 1),
ConvBNLayer(
num_channels=num_filters_list[idx] * 4,
num_filters=last_num_filters[idx],
filter_size=3,
stride=2,
name="cls_head_add" + str(idx + 1))))
self.conv_last = ConvBNLayer(
num_channels=1024,
num_filters=2048,
filter_size=1,
stride=1,
name="cls_head_last_conv")
self.pool2d_avg = AdaptiveAvgPool2D(1)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = Linear(
2048,
class_dim,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, input):
conv1 = self.conv_layer1_1(input)
conv2 = self.conv_layer1_2(conv1)
la1 = self.la1(conv2)
tr1 = self.tr1([la1])
st2 = self.st2(tr1)
tr2 = self.tr2(st2)
st3 = self.st3(tr2)
tr3 = self.tr3(st3)
st4 = self.st4(tr3)
last_cls = self.last_cls(st4)
y = last_cls[0]
for idx in range(3):
y = paddle.add(last_cls[idx + 1], self.cls_head_conv_list[idx](y))
y = self.conv_last(y)
y = self.pool2d_avg(y)
y = paddle.reshape(y, shape=[-1, y.shape[1]])
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def HRNet_W18_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=18, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W18_C"], use_ssld=use_ssld)
return model
def HRNet_W30_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=30, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W30_C"], use_ssld=use_ssld)
return model
def HRNet_W32_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=32, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W32_C"], use_ssld=use_ssld)
return model
def HRNet_W40_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=40, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W40_C"], use_ssld=use_ssld)
return model
def HRNet_W44_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=44, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W44_C"], use_ssld=use_ssld)
return model
def HRNet_W48_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=48, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W48_C"], use_ssld=use_ssld)
return model
def HRNet_W64_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=64, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W64_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W64_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=64, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["SE_HRNet_W64_C"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/inception_v3.py 已删除 100644 → 0
浏览文件 @ 4fb05ee4
# copyright (c) 2021 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
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"InceptionV3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
padding=0,
groups=1,
act="relu",
name=None):
super(ConvBNLayer, self).__init__()
self.conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name+"_weights"),
bias_attr=False)
self.batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=name+"_bn_scale"),
bias_attr=ParamAttr(name=name+"_bn_offset"),
moving_mean_name=name+"_bn_mean",
moving_variance_name=name+"_bn_variance")
def forward(self, inputs):
y = self.conv(inputs)
y = self.batch_norm(y)
return y
class InceptionStem(nn.Layer):
def __init__(self):
super(InceptionStem, self).__init__()
self.conv_1a_3x3 = ConvBNLayer(num_channels=3,
num_filters=32,
filter_size=3,
stride=2,
act="relu",
name="conv_1a_3x3")
self.conv_2a_3x3 = ConvBNLayer(num_channels=32,
num_filters=32,
filter_size=3,
stride=1,
act="relu",
name="conv_2a_3x3")
self.conv_2b_3x3 = ConvBNLayer(num_channels=32,
num_filters=64,
filter_size=3,
padding=1,
act="relu",
name="conv_2b_3x3")
self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self.conv_3b_1x1 = ConvBNLayer(num_channels=64,
num_filters=80,
filter_size=1,
act="relu",
name="conv_3b_1x1")
self.conv_4a_3x3 = ConvBNLayer(num_channels=80,
num_filters=192,
filter_size=3,
act="relu",
name="conv_4a_3x3")
def forward(self, x):
y = self.conv_1a_3x3(x)
y = self.conv_2a_3x3(y)
y = self.conv_2b_3x3(y)
y = self.maxpool(y)
y = self.conv_3b_1x1(y)
y = self.conv_4a_3x3(y)
y = self.maxpool(y)
return y
class InceptionA(nn.Layer):
def __init__(self, num_channels, pool_features, name=None):
super(InceptionA, self).__init__()
self.branch1x1 = ConvBNLayer(num_channels=num_channels,
num_filters=64,
filter_size=1,
act="relu",
name="inception_a_branch1x1_"+name)
self.branch5x5_1 = ConvBNLayer(num_channels=num_channels,
num_filters=48,
filter_size=1,
act="relu",
name="inception_a_branch5x5_1_"+name)
self.branch5x5_2 = ConvBNLayer(num_channels=48,
num_filters=64,
filter_size=5,
padding=2,
act="relu",
name="inception_a_branch5x5_2_"+name)
self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=64,
filter_size=1,
act="relu",
name="inception_a_branch3x3dbl_1_"+name)
self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_a_branch3x3dbl_2_"+name)
self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_a_branch3x3dbl_3_"+name)
self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
num_filters=pool_features,
filter_size=1,
act="relu",
name="inception_a_branch_pool_"+name)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = self.branch_pool(x)
branch_pool = self.branch_pool_conv(branch_pool)
outputs = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
return outputs
class InceptionB(nn.Layer):
def __init__(self, num_channels, name=None):
super(InceptionB, self).__init__()
self.branch3x3 = ConvBNLayer(num_channels=num_channels,
num_filters=384,
filter_size=3,
stride=2,
act="relu",
name="inception_b_branch3x3_"+name)
self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=64,
filter_size=1,
act="relu",
name="inception_b_branch3x3dbl_1_"+name)
self.branch3x3dbl_2 = ConvBNLayer(num_channels=64,
num_filters=96,
filter_size=3,
padding=1,
act="relu",
name="inception_b_branch3x3dbl_2_"+name)
self.branch3x3dbl_3 = ConvBNLayer(num_channels=96,
num_filters=96,
filter_size=3,
stride=2,
act="relu",
name="inception_b_branch3x3dbl_3_"+name)
self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
def forward(self, x):
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = self.branch_pool(x)
outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
return outputs
class InceptionC(nn.Layer):
def __init__(self, num_channels, channels_7x7, name=None):
super(InceptionC, self).__init__()
self.branch1x1 = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_c_branch1x1_"+name)
self.branch7x7_1 = ConvBNLayer(num_channels=num_channels,
num_filters=channels_7x7,
filter_size=1,
stride=1,
act="relu",
name="inception_c_branch7x7_1_"+name)
self.branch7x7_2 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(1, 7),
stride=1,
padding=(0, 3),
act="relu",
name="inception_c_branch7x7_2_"+name)
self.branch7x7_3 = ConvBNLayer(num_channels=channels_7x7,
num_filters=192,
filter_size=(7, 1),
stride=1,
padding=(3, 0),
act="relu",
name="inception_c_branch7x7_3_"+name)
self.branch7x7dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=channels_7x7,
filter_size=1,
act="relu",
name="inception_c_branch7x7dbl_1_"+name)
self.branch7x7dbl_2 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(7, 1),
padding = (3, 0),
act="relu",
name="inception_c_branch7x7dbl_2_"+name)
self.branch7x7dbl_3 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(1, 7),
padding = (0, 3),
act="relu",
name="inception_c_branch7x7dbl_3_"+name)
self.branch7x7dbl_4 = ConvBNLayer(num_channels=channels_7x7,
num_filters=channels_7x7,
filter_size=(7, 1),
padding = (3, 0),
act="relu",
name="inception_c_branch7x7dbl_4_"+name)
self.branch7x7dbl_5 = ConvBNLayer(num_channels=channels_7x7,
num_filters=192,
filter_size=(1, 7),
padding = (0, 3),
act="relu",
name="inception_c_branch7x7dbl_5_"+name)
self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_c_branch_pool_"+name)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = self.branch_pool(x)
branch_pool = self.branch_pool_conv(branch_pool)
outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
return outputs
class InceptionD(nn.Layer):
def __init__(self, num_channels, name=None):
super(InceptionD, self).__init__()
self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_d_branch3x3_1_"+name)
self.branch3x3_2 = ConvBNLayer(num_channels=192,
num_filters=320,
filter_size=3,
stride=2,
act="relu",
name="inception_d_branch3x3_2_"+name)
self.branch7x7x3_1 = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_d_branch7x7x3_1_"+name)
self.branch7x7x3_2 = ConvBNLayer(num_channels=192,
num_filters=192,
filter_size=(1, 7),
padding=(0, 3),
act="relu",
name="inception_d_branch7x7x3_2_"+name)
self.branch7x7x3_3 = ConvBNLayer(num_channels=192,
num_filters=192,
filter_size=(7, 1),
padding=(3, 0),
act="relu",
name="inception_d_branch7x7x3_3_"+name)
self.branch7x7x3_4 = ConvBNLayer(num_channels=192,
num_filters=192,
filter_size=3,
stride=2,
act="relu",
name="inception_d_branch7x7x3_4_"+name)
self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
def forward(self, x):
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
branch7x7x3 = self.branch7x7x3_1(x)
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
branch_pool = self.branch_pool(x)
outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
return outputs
class InceptionE(nn.Layer):
def __init__(self, num_channels, name=None):
super(InceptionE, self).__init__()
self.branch1x1 = ConvBNLayer(num_channels=num_channels,
num_filters=320,
filter_size=1,
act="relu",
name="inception_e_branch1x1_"+name)
self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
num_filters=384,
filter_size=1,
act="relu",
name="inception_e_branch3x3_1_"+name)
self.branch3x3_2a = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(1, 3),
padding=(0, 1),
act="relu",
name="inception_e_branch3x3_2a_"+name)
self.branch3x3_2b = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(3, 1),
padding=(1, 0),
act="relu",
name="inception_e_branch3x3_2b_"+name)
self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
num_filters=448,
filter_size=1,
act="relu",
name="inception_e_branch3x3dbl_1_"+name)
self.branch3x3dbl_2 = ConvBNLayer(num_channels=448,
num_filters=384,
filter_size=3,
padding=1,
act="relu",
name="inception_e_branch3x3dbl_2_"+name)
self.branch3x3dbl_3a = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(1, 3),
padding=(0, 1),
act="relu",
name="inception_e_branch3x3dbl_3a_"+name)
self.branch3x3dbl_3b = ConvBNLayer(num_channels=384,
num_filters=384,
filter_size=(3, 1),
padding=(1, 0),
act="relu",
name="inception_e_branch3x3dbl_3b_"+name)
self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
num_filters=192,
filter_size=1,
act="relu",
name="inception_e_branch_pool_"+name)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = paddle.concat(branch3x3, axis=1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = paddle.concat(branch3x3dbl, axis=1)
branch_pool = self.branch_pool(x)
branch_pool = self.branch_pool_conv(branch_pool)
outputs = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
return outputs
class Inception_V3(nn.Layer):
def __init__(self, class_dim=1000):
super(Inception_V3, self).__init__()
self.inception_a_list = [[192, 256, 288], [32, 64, 64]]
self.inception_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]]
self.inception_stem = InceptionStem()
self.inception_block_list = []
for i in range(len(self.inception_a_list[0])):
inception_a = self.add_sublayer("inception_a_"+str(i+1),
InceptionA(self.inception_a_list[0][i],
self.inception_a_list[1][i],
name=str(i+1)))
self.inception_block_list.append(inception_a)
inception_b = self.add_sublayer("nception_b_1",
InceptionB(288, name="1"))
self.inception_block_list.append(inception_b)
for i in range(len(self.inception_c_list[0])):
inception_c = self.add_sublayer("inception_c_"+str(i+1),
InceptionC(self.inception_c_list[0][i],
self.inception_c_list[1][i],
name=str(i+1)))
self.inception_block_list.append(inception_c)
inception_d = self.add_sublayer("inception_d_1",
InceptionD(768, name="1"))
self.inception_block_list.append(inception_d)
inception_e = self.add_sublayer("inception_e_1",
InceptionE(1280, name="1"))
self.inception_block_list.append(inception_e)
inception_e = self.add_sublayer("inception_e_2",
InceptionE(2048, name="2"))
self.inception_block_list.append(inception_e)
self.gap = AdaptiveAvgPool2D(1)
self.drop = Dropout(p=0.2, mode="downscale_in_infer")
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = Linear(
2048,
class_dim,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, x):
y = self.inception_stem(x)
for inception_block in self.inception_block_list:
y = inception_block(y)
y = self.gap(y)
y = paddle.reshape(y, shape=[-1, 2048])
y = self.drop(y)
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def InceptionV3(pretrained=False, use_ssld=False, **kwargs):
model = Inception_V3(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV3"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/mobilenet_v1.py 已删除 100644 → 0
浏览文件 @ 4fb05ee4
# 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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import KaimingNormal
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"MobileNetV1_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_25_pretrained.pdparams",
"MobileNetV1_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_5_pretrained.pdparams",
"MobileNetV1_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_75_pretrained.pdparams",
"MobileNetV1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
act='relu',
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(
initializer=KaimingNormal(), name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name + "_bn_scale"),
bias_attr=ParamAttr(name + "_bn_offset"),
moving_mean_name=name + "_bn_mean",
moving_variance_name=name + "_bn_variance")
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class DepthwiseSeparable(nn.Layer):
def __init__(self,
num_channels,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
name=None):
super(DepthwiseSeparable, self).__init__()
self._depthwise_conv = ConvBNLayer(
num_channels=num_channels,
num_filters=int(num_filters1 * scale),
filter_size=3,
stride=stride,
padding=1,
num_groups=int(num_groups * scale),
name=name + "_dw")
self._pointwise_conv = ConvBNLayer(
num_channels=int(num_filters1 * scale),
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
padding=0,
name=name + "_sep")
def forward(self, inputs):
y = self._depthwise_conv(inputs)
y = self._pointwise_conv(y)
return y
class MobileNet(nn.Layer):
def __init__(self, scale=1.0, class_dim=1000):
super(MobileNet, self).__init__()
self.scale = scale
self.block_list = []
self.conv1 = ConvBNLayer(
num_channels=3,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1,
name="conv1")
conv2_1 = self.add_sublayer(
"conv2_1",
sublayer=DepthwiseSeparable(
num_channels=int(32 * scale),
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale,
name="conv2_1"))
self.block_list.append(conv2_1)
conv2_2 = self.add_sublayer(
"conv2_2",
sublayer=DepthwiseSeparable(
num_channels=int(64 * scale),
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2,
scale=scale,
name="conv2_2"))
self.block_list.append(conv2_2)
conv3_1 = self.add_sublayer(
"conv3_1",
sublayer=DepthwiseSeparable(
num_channels=int(128 * scale),
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale,
name="conv3_1"))
self.block_list.append(conv3_1)
conv3_2 = self.add_sublayer(
"conv3_2",
sublayer=DepthwiseSeparable(
num_channels=int(128 * scale),
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2,
scale=scale,
name="conv3_2"))
self.block_list.append(conv3_2)
conv4_1 = self.add_sublayer(
"conv4_1",
sublayer=DepthwiseSeparable(
num_channels=int(256 * scale),
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale,
name="conv4_1"))
self.block_list.append(conv4_1)
conv4_2 = self.add_sublayer(
"conv4_2",
sublayer=DepthwiseSeparable(
num_channels=int(256 * scale),
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2,
scale=scale,
name="conv4_2"))
self.block_list.append(conv4_2)
for i in range(5):
conv5 = self.add_sublayer(
"conv5_" + str(i + 1),
sublayer=DepthwiseSeparable(
num_channels=int(512 * scale),
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
scale=scale,
name="conv5_" + str(i + 1)))
self.block_list.append(conv5)
conv5_6 = self.add_sublayer(
"conv5_6",
sublayer=DepthwiseSeparable(
num_channels=int(512 * scale),
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2,
scale=scale,
name="conv5_6"))
self.block_list.append(conv5_6)
conv6 = self.add_sublayer(
"conv6",
sublayer=DepthwiseSeparable(
num_channels=int(1024 * scale),
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1,
scale=scale,
name="conv6"))
self.block_list.append(conv6)
self.pool2d_avg = AdaptiveAvgPool2D(1)
self.out = Linear(
int(1024 * scale),
class_dim,
weight_attr=ParamAttr(
initializer=KaimingNormal(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
def forward(self, inputs):
y = self.conv1(inputs)
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = paddle.flatten(y, start_axis=1, stop_axis=-1)
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV1_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_25"], use_ssld=use_ssld)
return model
def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"], use_ssld=use_ssld)
return model
def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"], use_ssld=use_ssld)
return model
def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1"], use_ssld=use_ssld)
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/mobilenet_v3.py 已删除 100644 → 0
浏览文件 @ 4fb05ee4
# 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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.functional import hardswish, hardsigmoid
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.regularizer import L2Decay
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"MobileNetV3_small_x0_35": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams",
"MobileNetV3_small_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams",
"MobileNetV3_small_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams",
"MobileNetV3_small_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_pretrained.pdparams",
"MobileNetV3_small_x1_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_25_pretrained.pdparams",
"MobileNetV3_large_x0_35": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_35_pretrained.pdparams",
"MobileNetV3_large_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams",
"MobileNetV3_large_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_75_pretrained.pdparams",
"MobileNetV3_large_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams",
"MobileNetV3_large_x1_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_25_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
def make_divisible(v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
class MobileNetV3(nn.Layer):
def __init__(self,
scale=1.0,
model_name="small",
dropout_prob=0.2,
class_dim=1000):
super(MobileNetV3, self).__init__()
inplanes = 16
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, False, "relu", 1],
[3, 64, 24, False, "relu", 2],
[3, 72, 24, False, "relu", 1],
[5, 72, 40, True, "relu", 2],
[5, 120, 40, True, "relu", 1],
[5, 120, 40, True, "relu", 1],
[3, 240, 80, False, "hardswish", 2],
[3, 200, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 184, 80, False, "hardswish", 1],
[3, 480, 112, True, "hardswish", 1],
[3, 672, 112, True, "hardswish", 1],
[5, 672, 160, True, "hardswish", 2],
[5, 960, 160, True, "hardswish", 1],
[5, 960, 160, True, "hardswish", 1],
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
[3, 16, 16, True, "relu", 2],
[3, 72, 24, False, "relu", 2],
[3, 88, 24, False, "relu", 1],
[5, 96, 40, True, "hardswish", 2],
[5, 240, 40, True, "hardswish", 1],
[5, 240, 40, True, "hardswish", 1],
[5, 120, 48, True, "hardswish", 1],
[5, 144, 48, True, "hardswish", 1],
[5, 288, 96, True, "hardswish", 2],
[5, 576, 96, True, "hardswish", 1],
[5, 576, 96, True, "hardswish", 1],
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
else:
raise NotImplementedError(
"mode[{}_model] is not implemented!".format(model_name))
self.conv1 = ConvBNLayer(
in_c=3,
out_c=make_divisible(inplanes * scale),
filter_size=3,
stride=2,
padding=1,
num_groups=1,
if_act=True,
act="hardswish",
name="conv1")
self.block_list = []
i = 0
inplanes = make_divisible(inplanes * scale)
for (k, exp, c, se, nl, s) in self.cfg:
block = self.add_sublayer(
"conv" + str(i + 2),
ResidualUnit(
in_c=inplanes,
mid_c=make_divisible(scale * exp),
out_c=make_divisible(scale * c),
filter_size=k,
stride=s,
use_se=se,
act=nl,
name="conv" + str(i + 2)))
self.block_list.append(block)
inplanes = make_divisible(scale * c)
i += 1
self.last_second_conv = ConvBNLayer(
in_c=inplanes,
out_c=make_divisible(scale * self.cls_ch_squeeze),
filter_size=1,
stride=1,
padding=0,
num_groups=1,
if_act=True,
act="hardswish",
name="conv_last")
self.pool = AdaptiveAvgPool2D(1)
self.last_conv = Conv2D(
in_channels=make_divisible(scale * self.cls_ch_squeeze),
out_channels=self.cls_ch_expand,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(name="last_1x1_conv_weights"),
bias_attr=False)
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
self.out = Linear(
self.cls_ch_expand,
class_dim,
weight_attr=ParamAttr("fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
x = self.conv1(inputs)
for block in self.block_list:
x = block(x)
x = self.last_second_conv(x)
x = self.pool(x)
x = self.last_conv(x)
x = hardswish(x)
x = self.dropout(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.out(x)
return x
class ConvBNLayer(nn.Layer):
def __init__(self,
in_c,
out_c,
filter_size,
stride,
padding,
num_groups=1,
if_act=True,
act=None,
use_cudnn=True,
name=""):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.conv = Conv2D(
in_channels=in_c,
out_channels=out_c,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
self.bn = BatchNorm(
num_channels=out_c,
act=None,
param_attr=ParamAttr(
name=name + "_bn_scale", regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(
name=name + "_bn_offset", regularizer=L2Decay(0.0)),
moving_mean_name=name + "_bn_mean",
moving_variance_name=name + "_bn_variance")
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.if_act:
if self.act == "relu":
x = F.relu(x)
elif self.act == "hardswish":
x = hardswish(x)
else:
print("The activation function is selected incorrectly.")
exit()
return x
class ResidualUnit(nn.Layer):
def __init__(self,
in_c,
mid_c,
out_c,
filter_size,
stride,
use_se,
act=None,
name=''):
super(ResidualUnit, self).__init__()
self.if_shortcut = stride == 1 and in_c == out_c
self.if_se = use_se
self.expand_conv = ConvBNLayer(
in_c=in_c,
out_c=mid_c,
filter_size=1,
stride=1,
padding=0,
if_act=True,
act=act,
name=name + "_expand")
self.bottleneck_conv = ConvBNLayer(
in_c=mid_c,
out_c=mid_c,
filter_size=filter_size,
stride=stride,
padding=int((filter_size - 1) // 2),
num_groups=mid_c,
if_act=True,
act=act,
name=name + "_depthwise")
if self.if_se:
self.mid_se = SEModule(mid_c, name=name + "_se")
self.linear_conv = ConvBNLayer(
in_c=mid_c,
out_c=out_c,
filter_size=1,
stride=1,
padding=0,
if_act=False,
act=None,
name=name + "_linear")
def forward(self, inputs):
x = self.expand_conv(inputs)
x = self.bottleneck_conv(x)
if self.if_se:
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = paddle.add(inputs, x)
return x
class SEModule(nn.Layer):
def __init__(self, channel, reduction=4, name=""):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2D(1)
self.conv1 = Conv2D(
in_channels=channel,
out_channels=channel // reduction,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(name=name + "_1_weights"),
bias_attr=ParamAttr(name=name + "_1_offset"))
self.conv2 = Conv2D(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0,
weight_attr=ParamAttr(name + "_2_weights"),
bias_attr=ParamAttr(name=name + "_2_offset"))
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hardsigmoid(outputs, slope=0.2, offset=0.5)
return paddle.multiply(x=inputs, y=outputs)
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV3_small_x0_35(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=0.35, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_35"], use_ssld=use_ssld)
return model
def MobileNetV3_small_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_5"], use_ssld=use_ssld)
return model
def MobileNetV3_small_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_75"], use_ssld=use_ssld)
return model
def MobileNetV3_small_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_0"], use_ssld=use_ssld)
return model
def MobileNetV3_small_x1_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=1.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_25"], use_ssld=use_ssld)
return model
def MobileNetV3_large_x0_35(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=0.35, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_35"], use_ssld=use_ssld)
return model
def MobileNetV3_large_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_5"], use_ssld=use_ssld)
return model
def MobileNetV3_large_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_75"], use_ssld=use_ssld)
return model
def MobileNetV3_large_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_0"], use_ssld=use_ssld)
return model
def MobileNetV3_large_x1_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=1.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_25"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnet.py 已删除 100644 → 0
浏览文件 @ 4fb05ee4
# 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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"ResNet18": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams",
"ResNet34": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams",
"ResNet50": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams",
"ResNet101": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_pretrained.pdparams",
"ResNet152": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None,
data_format="NCHW"):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
data_format=data_format)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self._batch_norm = BatchNorm(
num_filters,
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",
data_layout=data_format)
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
shortcut=True,
name=None,
data_format="NCHW"):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act="relu",
name=name + "_branch2a",
data_format=data_format)
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act="relu",
name=name + "_branch2b",
data_format=data_format)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_branch2c",
data_format=data_format)
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
stride=stride,
name=name + "_branch1",
data_format=data_format)
self.shortcut = shortcut
self._num_channels_out = num_filters * 4
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
shortcut=True,
name=None,
data_format="NCHW"):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=3,
stride=stride,
act="relu",
name=name + "_branch2a",
data_format=data_format)
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b",
data_format=data_format)
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
stride=stride,
name=name + "_branch1",
data_format=data_format)
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
class ResNet(nn.Layer):
def __init__(self, layers=50, class_dim=1000, input_image_channel=3, data_format="NCHW"):
super(ResNet, self).__init__()
self.layers = layers
self.data_format = data_format
self.input_image_channel = input_image_channel
supported_layers = [18, 34, 50, 101, 152]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
num_channels = [64, 256, 512,
1024] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.conv = ConvBNLayer(
num_channels=self.input_image_channel,
num_filters=64,
filter_size=7,
stride=2,
act="relu",
name="conv1",
data_format=self.data_format)
self.pool2d_max = MaxPool2D(
kernel_size=3,
stride=2,
padding=1,
data_format=self.data_format)
self.block_list = []
if layers >= 50:
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
bottleneck_block = self.add_sublayer(
conv_name,
BottleneckBlock(
num_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
name=conv_name,
data_format=self.data_format))
self.block_list.append(bottleneck_block)
shortcut = True
else:
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
basic_block = self.add_sublayer(
conv_name,
BasicBlock(
num_channels=num_channels[block]
if i == 0 else num_filters[block],
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
name=conv_name,
data_format=self.data_format))
self.block_list.append(basic_block)
shortcut = True
self.pool2d_avg = AdaptiveAvgPool2D(1, data_format=self.data_format)
self.pool2d_avg_channels = num_channels[-1] * 2
stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
def forward(self, inputs):
with paddle.static.amp.fp16_guard():
if self.data_format == "NHWC":
inputs = paddle.tensor.transpose(inputs, [0, 2, 3, 1])
inputs.stop_gradient = True
y = self.conv(inputs)
y = self.pool2d_max(y)
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNet18(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=18, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet18"], use_ssld=use_ssld)
return model
def ResNet34(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet34"], use_ssld=use_ssld)
return model
def ResNet50(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50"], use_ssld=use_ssld)
return model
def ResNet101(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=101, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101"], use_ssld=use_ssld)
return model
def ResNet152(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=152, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnet_vd.py 已删除 100644 → 0
浏览文件 @ 4fb05ee4
# 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 numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNet18_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams",
"ResNet34_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_pretrained.pdparams",
"ResNet50_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams",
"ResNet101_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_pretrained.pdparams",
"ResNet152_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_vd_pretrained.pdparams",
"ResNet200_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet200_vd_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
lr_mult=1.0,
name=None):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(
name=name + "_weights", learning_rate=lr_mult),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(
name=bn_name + '_scale', learning_rate=lr_mult),
bias_attr=ParamAttr(
bn_name + '_offset', learning_rate=lr_mult),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def forward(self, inputs):
if self.is_vd_mode:
inputs = self._pool2d_avg(inputs)
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
shortcut=True,
if_first=False,
lr_mult=1.0,
name=None):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu',
lr_mult=lr_mult,
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu',
lr_mult=lr_mult,
name=name + "_branch2b")
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None,
lr_mult=lr_mult,
name=name + "_branch2c")
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
stride=1,
is_vd_mode=False if if_first else True,
lr_mult=lr_mult,
name=name + "_branch1")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
shortcut=True,
if_first=False,
lr_mult=1.0,
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu',
lr_mult=lr_mult,
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
act=None,
lr_mult=lr_mult,
name=name + "_branch2b")
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
stride=1,
is_vd_mode=False if if_first else True,
lr_mult=lr_mult,
name=name + "_branch1")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
class ResNet_vd(nn.Layer):
def __init__(self,
layers=50,
class_dim=1000,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
super(ResNet_vd, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
self.lr_mult_list = lr_mult_list
assert isinstance(self.lr_mult_list, (
list, tuple
)), "lr_mult_list should be in (list, tuple) but got {}".format(
type(self.lr_mult_list))
assert len(
self.lr_mult_list
) == 5, "lr_mult_list length should should be 5 but got {}".format(
len(self.lr_mult_list))
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512,
1024] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.conv1_1 = ConvBNLayer(
num_channels=3,
num_filters=32,
filter_size=3,
stride=2,
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_1")
self.conv1_2 = ConvBNLayer(
num_channels=32,
num_filters=32,
filter_size=3,
stride=1,
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_2")
self.conv1_3 = ConvBNLayer(
num_channels=32,
num_filters=64,
filter_size=3,
stride=1,
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_3")
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
if layers in [101, 152, 200] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
num_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
lr_mult=self.lr_mult_list[block + 1],
name=conv_name))
self.block_list.append(bottleneck_block)
shortcut = True
else:
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BasicBlock(
num_channels=num_channels[block]
if i == 0 else num_filters[block],
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name,
lr_mult=self.lr_mult_list[block + 1]))
self.block_list.append(basic_block)
shortcut = True
self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
def forward(self, inputs):
y = self.conv1_1(inputs)
y = self.conv1_2(y)
y = self.conv1_3(y)
y = self.pool2d_max(y)
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=18, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet18_vd"], use_ssld=use_ssld)
return model
def ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet34_vd"], use_ssld=use_ssld)
return model
def ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50_vd"], use_ssld=use_ssld)
return model
def ResNet101_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=101, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101_vd"], use_ssld=use_ssld)
return model
def ResNet152_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=152, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152_vd"], use_ssld=use_ssld)
return model
def ResNet200_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=200, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet200_vd"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/vgg.py 已删除 100644 → 0
浏览文件 @ 4fb05ee4
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"VGG11": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams",
"VGG13": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams",
"VGG16": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams",
"VGG19": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBlock(nn.Layer):
def __init__(self, input_channels, output_channels, groups, name=None):
super(ConvBlock, self).__init__()
self.groups = groups
self._conv_1 = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "1_weights"),
bias_attr=False)
if groups == 2 or groups == 3 or groups == 4:
self._conv_2 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "2_weights"),
bias_attr=False)
if groups == 3 or groups == 4:
self._conv_3 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "3_weights"),
bias_attr=False)
if groups == 4:
self._conv_4 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "4_weights"),
bias_attr=False)
self._pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
x = self._conv_1(inputs)
x = F.relu(x)
if self.groups == 2 or self.groups == 3 or self.groups == 4:
x = self._conv_2(x)
x = F.relu(x)
if self.groups == 3 or self.groups == 4:
x = self._conv_3(x)
x = F.relu(x)
if self.groups == 4:
x = self._conv_4(x)
x = F.relu(x)
x = self._pool(x)
return x
class VGGNet(nn.Layer):
def __init__(self, layers=11, stop_grad_layers=0, class_dim=1000):
super(VGGNet, self).__init__()
self.layers = layers
self.stop_grad_layers = stop_grad_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(
self.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_")
for idx, block in enumerate([
self._conv_block_1, self._conv_block_2, self._conv_block_3,
self._conv_block_4, self._conv_block_5
]):
if self.stop_grad_layers >= idx + 1:
for param in block.parameters():
param.trainable = False
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
self._fc1 = Linear(
7 * 7 * 512,
4096,
weight_attr=ParamAttr(name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
self._fc2 = Linear(
4096,
4096,
weight_attr=ParamAttr(name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
self._out = Linear(
4096,
class_dim,
weight_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 = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self._fc1(x)
x = F.relu(x)
x = self._drop(x)
x = self._fc2(x)
x = F.relu(x)
x = self._drop(x)
x = self._out(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def VGG11(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=11, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG11"], use_ssld=use_ssld)
return model
def VGG13(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=13, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG13"], use_ssld=use_ssld)
return model
def VGG16(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG16"], use_ssld=use_ssld)
return model
def VGG19(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=19, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG19"], use_ssld=use_ssld)
return model
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