366 lines
10 KiB
Python
366 lines
10 KiB
Python
# 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/1409.4842
|
|
|
|
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 ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
|
|
|
|
MODEL_URLS = {
|
|
"GoogLeNet":
|
|
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
|
|
}
|
|
|
|
__all__ = list(MODEL_URLS.keys())
|
|
|
|
|
|
def xavier(channels, filter_size, name):
|
|
stdv = (3.0 / (filter_size**2 * channels))**0.5
|
|
param_attr = ParamAttr(
|
|
initializer=Uniform(-stdv, stdv), name=name + "_weights")
|
|
return param_attr
|
|
|
|
|
|
class ConvLayer(nn.Layer):
|
|
def __init__(self,
|
|
num_channels,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
groups=1,
|
|
act=None,
|
|
name=None,
|
|
data_format="NCHW"):
|
|
super(ConvLayer, 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)
|
|
|
|
def forward(self, inputs):
|
|
y = self._conv(inputs)
|
|
return y
|
|
|
|
|
|
class Inception(nn.Layer):
|
|
def __init__(self,
|
|
input_channels,
|
|
output_channels,
|
|
filter1,
|
|
filter3R,
|
|
filter3,
|
|
filter5R,
|
|
filter5,
|
|
proj,
|
|
name=None,
|
|
data_format="NCHW"):
|
|
super(Inception, self).__init__()
|
|
self.data_format = data_format
|
|
|
|
self._conv1 = ConvLayer(
|
|
input_channels,
|
|
filter1,
|
|
1,
|
|
name="inception_" + name + "_1x1",
|
|
data_format=data_format)
|
|
self._conv3r = ConvLayer(
|
|
input_channels,
|
|
filter3R,
|
|
1,
|
|
name="inception_" + name + "_3x3_reduce",
|
|
data_format=data_format)
|
|
self._conv3 = ConvLayer(
|
|
filter3R,
|
|
filter3,
|
|
3,
|
|
name="inception_" + name + "_3x3",
|
|
data_format=data_format)
|
|
self._conv5r = ConvLayer(
|
|
input_channels,
|
|
filter5R,
|
|
1,
|
|
name="inception_" + name + "_5x5_reduce",
|
|
data_format=data_format)
|
|
self._conv5 = ConvLayer(
|
|
filter5R,
|
|
filter5,
|
|
5,
|
|
name="inception_" + name + "_5x5",
|
|
data_format=data_format)
|
|
self._pool = MaxPool2D(
|
|
kernel_size=3, stride=1, padding=1, data_format=data_format)
|
|
|
|
self._convprj = ConvLayer(
|
|
input_channels,
|
|
proj,
|
|
1,
|
|
name="inception_" + name + "_3x3_proj",
|
|
data_format=data_format)
|
|
|
|
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)
|
|
|
|
if self.data_format == "NHWC":
|
|
cat = paddle.concat([conv1, conv3, conv5, convprj], axis=3)
|
|
else:
|
|
cat = paddle.concat([conv1, conv3, conv5, convprj], axis=1)
|
|
cat = F.relu(cat)
|
|
return cat
|
|
|
|
|
|
class GoogLeNetDY(nn.Layer):
|
|
def __init__(self, class_num=1000, data_format="NCHW"):
|
|
super(GoogLeNetDY, self).__init__()
|
|
self.data_format = data_format
|
|
self._conv = ConvLayer(
|
|
3, 64, 7, 2, name="conv1", data_format=data_format)
|
|
self._pool = MaxPool2D(
|
|
kernel_size=3, stride=2, data_format=data_format)
|
|
self._conv_1 = ConvLayer(
|
|
64, 64, 1, name="conv2_1x1", data_format=data_format)
|
|
self._conv_2 = ConvLayer(
|
|
64, 192, 3, name="conv2_3x3", data_format=data_format)
|
|
|
|
self._ince3a = Inception(
|
|
192,
|
|
192,
|
|
64,
|
|
96,
|
|
128,
|
|
16,
|
|
32,
|
|
32,
|
|
name="ince3a",
|
|
data_format=data_format)
|
|
self._ince3b = Inception(
|
|
256,
|
|
256,
|
|
128,
|
|
128,
|
|
192,
|
|
32,
|
|
96,
|
|
64,
|
|
name="ince3b",
|
|
data_format=data_format)
|
|
|
|
self._ince4a = Inception(
|
|
480,
|
|
480,
|
|
192,
|
|
96,
|
|
208,
|
|
16,
|
|
48,
|
|
64,
|
|
name="ince4a",
|
|
data_format=data_format)
|
|
self._ince4b = Inception(
|
|
512,
|
|
512,
|
|
160,
|
|
112,
|
|
224,
|
|
24,
|
|
64,
|
|
64,
|
|
name="ince4b",
|
|
data_format=data_format)
|
|
self._ince4c = Inception(
|
|
512,
|
|
512,
|
|
128,
|
|
128,
|
|
256,
|
|
24,
|
|
64,
|
|
64,
|
|
name="ince4c",
|
|
data_format=data_format)
|
|
self._ince4d = Inception(
|
|
512,
|
|
512,
|
|
112,
|
|
144,
|
|
288,
|
|
32,
|
|
64,
|
|
64,
|
|
name="ince4d",
|
|
data_format=data_format)
|
|
self._ince4e = Inception(
|
|
528,
|
|
528,
|
|
256,
|
|
160,
|
|
320,
|
|
32,
|
|
128,
|
|
128,
|
|
name="ince4e",
|
|
data_format=data_format)
|
|
|
|
self._ince5a = Inception(
|
|
832,
|
|
832,
|
|
256,
|
|
160,
|
|
320,
|
|
32,
|
|
128,
|
|
128,
|
|
name="ince5a",
|
|
data_format=data_format)
|
|
self._ince5b = Inception(
|
|
832,
|
|
832,
|
|
384,
|
|
192,
|
|
384,
|
|
48,
|
|
128,
|
|
128,
|
|
name="ince5b",
|
|
data_format=data_format)
|
|
|
|
self._pool_5 = AdaptiveAvgPool2D(1, data_format=data_format)
|
|
|
|
self._drop = Dropout(p=0.4, mode="downscale_in_infer")
|
|
self.flatten = nn.Flatten()
|
|
self._fc_out = Linear(
|
|
1024,
|
|
class_num,
|
|
weight_attr=xavier(1024, 1, "out"),
|
|
bias_attr=ParamAttr(name="out_offset"))
|
|
self._pool_o1 = AvgPool2D(
|
|
kernel_size=5, stride=3, data_format=data_format)
|
|
self._conv_o1 = ConvLayer(
|
|
512, 128, 1, name="conv_o1", data_format=data_format)
|
|
self._fc_o1 = Linear(
|
|
1152,
|
|
1024,
|
|
weight_attr=xavier(2048, 1, "fc_o1"),
|
|
bias_attr=ParamAttr(name="fc_o1_offset"))
|
|
self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
|
|
self._out1 = Linear(
|
|
1024,
|
|
class_num,
|
|
weight_attr=xavier(1024, 1, "out1"),
|
|
bias_attr=ParamAttr(name="out1_offset"))
|
|
self._pool_o2 = AvgPool2D(
|
|
kernel_size=5, stride=3, data_format=data_format)
|
|
self._conv_o2 = ConvLayer(
|
|
528, 128, 1, name="conv_o2", data_format=data_format)
|
|
self._fc_o2 = Linear(
|
|
1152,
|
|
1024,
|
|
weight_attr=xavier(2048, 1, "fc_o2"),
|
|
bias_attr=ParamAttr(name="fc_o2_offset"))
|
|
self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
|
|
self._out2 = Linear(
|
|
1024,
|
|
class_num,
|
|
weight_attr=xavier(1024, 1, "out2"),
|
|
bias_attr=ParamAttr(name="out2_offset"))
|
|
|
|
def forward(self, inputs):
|
|
if self.data_format == "NHWC":
|
|
inputs = paddle.transpose(inputs, [0, 2, 3, 1])
|
|
inputs.stop_gradient = True
|
|
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 = self.flatten(x)
|
|
out = self._fc_out(x)
|
|
|
|
x = self._pool_o1(ince4a)
|
|
x = self._conv_o1(x)
|
|
x = self.flatten(x)
|
|
x = self._fc_o1(x)
|
|
x = F.relu(x)
|
|
x = self._drop_o1(x)
|
|
out1 = self._out1(x)
|
|
|
|
x = self._pool_o2(ince4d)
|
|
x = self._conv_o2(x)
|
|
x = self.flatten(x)
|
|
x = self._fc_o2(x)
|
|
x = self._drop_o2(x)
|
|
out2 = self._out2(x)
|
|
return [out, out1, out2]
|
|
|
|
|
|
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 GoogLeNet(pretrained=False, use_ssld=False, **kwargs):
|
|
model = GoogLeNetDY(**kwargs)
|
|
_load_pretrained(
|
|
pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld)
|
|
return model
|