# 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.
# reference: https://arxiv.org/abs/1610.02357
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
import sys
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Xception41":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams",
"Xception65":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams",
"Xception71":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_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):
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 = "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 forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class SeparableConv(nn.Layer):
def __init__(self, input_channels, output_channels, stride=1, name=None):
super(SeparableConv, self).__init__()
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")
def forward(self, inputs):
x = self._pointwise_conv(inputs)
x = self._depthwise_conv(x)
return x
class EntryFlowBottleneckBlock(nn.Layer):
def __init__(self,
input_channels,
output_channels,
stride=2,
name=None,
relu_first=False):
super(EntryFlowBottleneckBlock, self).__init__()
self.relu_first = relu_first
self._short = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=1,
stride=stride,
padding=0,
weight_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 = MaxPool2D(kernel_size=3, stride=stride, padding=1)
def forward(self, inputs):
conv0 = inputs
short = self._short(inputs)
if self.relu_first:
conv0 = F.relu(conv0)
conv1 = self._conv1(conv0)
conv2 = F.relu(conv1)
conv2 = self._conv2(conv2)
pool = self._pool(conv2)
return paddle.add(x=short, y=pool)
class EntryFlow(nn.Layer):
def __init__(self, block_num=3):
super(EntryFlow, self).__init__()
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 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(nn.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")
self._conv_1 = SeparableConv(
output_channels,
output_channels,
stride=1,
name=name + "_branch2b_weights")
self._conv_2 = SeparableConv(
output_channels,
output_channels,
stride=1,
name=name + "_branch2c_weights")
def forward(self, inputs):
conv0 = F.relu(inputs)
conv0 = self._conv_0(conv0)
conv1 = F.relu(conv0)
conv1 = self._conv_1(conv1)
conv2 = F.relu(conv1)
conv2 = self._conv_2(conv2)
return paddle.add(x=inputs, y=conv2)
class MiddleFlow(nn.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(nn.Layer):
def __init__(self, input_channels, output_channels1, output_channels2,
name):
super(ExitFlowBottleneckBlock, self).__init__()
self._short = Conv2D(
in_channels=input_channels,
out_channels=output_channels2,
kernel_size=1,
stride=2,
padding=0,
weight_attr=ParamAttr(name + "_branch1_weights"),
bias_attr=False)
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 = MaxPool2D(kernel_size=3, stride=2, padding=1)
def forward(self, inputs):
short = self._short(inputs)
conv0 = F.relu(inputs)
conv1 = self._conv_1(conv0)
conv2 = F.relu(conv1)
conv2 = self._conv_2(conv2)
pool = self._pool(conv2)
return paddle.add(x=short, y=pool)
class ExitFlow(nn.Layer):
def __init__(self, class_num):
super(ExitFlow, self).__init__()
name = "exit_flow"
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 = AdaptiveAvgPool2D(1)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self._out = Linear(
2048,
class_num,
weight_attr=ParamAttr(
name="fc_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
conv0 = self._conv_0(inputs)
conv1 = self._conv_1(conv0)
conv1 = F.relu(conv1)
conv2 = self._conv_2(conv1)
conv2 = F.relu(conv2)
pool = self._pool(conv2)
pool = paddle.flatten(pool, start_axis=1, stop_axis=-1)
out = self._out(pool)
return out
class Xception(nn.Layer):
def __init__(self,
entry_flow_block_num=3,
middle_flow_block_num=8,
class_num=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_num)
def forward(self, inputs):
x = self._entry_flow(inputs)
x = self._middle_flow(x)
x = self._exit_flow(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 Xception41(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception41"], use_ssld=use_ssld)
return model
def Xception65(pretrained=False, use_ssld=False, **kwargs):
model = Xception(
entry_flow_block_num=3, middle_flow_block_num=16, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception65"], use_ssld=use_ssld)
return model
def Xception71(pretrained=False, use_ssld=False, **kwargs):
model = Xception(
entry_flow_block_num=5, middle_flow_block_num=16, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception71"], use_ssld=use_ssld)
return model