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Update inception_v3.py

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Felix 2021-05-28 16:20:06 +08:00 committed by GitHub
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ppcls/arch/backbone/legendary_models/inception_v3.py
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@ -1,4 +1,4 @@
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# 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.
@ -12,28 +12,32 @@
# 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
from __future__ import absolute_import, division, 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.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain
__all__ = ["InceptionV3"]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
# InceptionV3 config
# key: inception blocks
# value: conv num in different blocks
MODEL_URLS = {
"InceptionV3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams",
}
__all__ = MODEL_URLS.keys()
'''
InceptionV3 config: dict.
key: inception blocks of InceptionV3.
values: conv num in different blocks.
'''
NET_CONFIG = {
'inception_a':[[192, 256, 288], [32, 64, 64]],
'inception_b':[288],
@ -42,7 +46,6 @@ NET_CONFIG = {
'inception_e':[1280,2048]
}
class ConvBNLayer(TheseusLayer):
def __init__(self,
num_channels,
@ -53,7 +56,7 @@ class ConvBNLayer(TheseusLayer):
groups=1,
act="relu"):
super(ConvBNLayer, self).__init__()
self.act = act
self.conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
@ -63,13 +66,15 @@ class ConvBNLayer(TheseusLayer):
groups=groups,
bias_attr=False)
self.batch_norm = BatchNorm(
num_filters,
act=act)
num_filters)
self.relu = nn.ReLU()
def forward(self, inputs):
y = self.conv(inputs)
y = self.batch_norm(y)
return y
def forward(self, x):
x = self.conv(x)
x = self.batch_norm(x)
if self.act:
x = self.relu(x)
return x
class InceptionStem(TheseusLayer):
def __init__(self):
@ -100,14 +105,14 @@ class InceptionStem(TheseusLayer):
filter_size=3,
act="relu")
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
x = self.conv_1a_3x3(x)
x = self.conv_2a_3x3(x)
x = self.conv_2b_3x3(x)
x = self.maxpool(x)
x = self.conv_3b_1x1(x)
x = self.conv_4a_3x3(x)
x = self.maxpool(x)
return x
class InceptionA(TheseusLayer):
@ -158,8 +163,8 @@ class InceptionA(TheseusLayer):
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
x = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
return x
class InceptionB(TheseusLayer):
@ -195,9 +200,9 @@ class InceptionB(TheseusLayer):
branch_pool = self.branch_pool(x)
outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
x = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
return outputs
return x
class InceptionC(TheseusLayer):
def __init__(self, num_channels, channels_7x7):
@ -273,9 +278,9 @@ class InceptionC(TheseusLayer):
branch_pool = self.branch_pool(x)
branch_pool = self.branch_pool_conv(branch_pool)
outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
x = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
return outputs
return x
class InceptionD(TheseusLayer):
def __init__(self, num_channels):
@ -321,8 +326,8 @@ class InceptionD(TheseusLayer):
branch_pool = self.branch_pool(x)
outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
return outputs
x = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
return x
class InceptionE(TheseusLayer):
def __init__(self, num_channels):
@ -391,12 +396,20 @@ class InceptionE(TheseusLayer):
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
x = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
return x
class Inception_V3(TheseusLayer):
"""
Inception_V3
Args:
config: dict. config of Inception_V3.
class_num: int=1000. The number of classes.
pretrained: (True or False) or path of pretrained_model. Whether to load the pretrained model.
Returns:
model: nn.Layer. Specific Inception_V3 model depends on args.
"""
def __init__(self,
config,
class_num=1000,
@ -409,7 +422,8 @@ class Inception_V3(TheseusLayer):
self.inception_b_list = config['inception_b']
self.inception_d_list = config['inception_d']
self.inception_e_list = config ['inception_e']
self.pretrained = pretrained
self.inception_stem = InceptionStem()
self.inception_block_list = nn.LayerList()
@ -445,20 +459,15 @@ class Inception_V3(TheseusLayer):
initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr())
if pretrained is not None:
load_dygraph_pretrain(self, pretrained)
def forward(self, x):
y = self.inception_stem(x)
x = 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
x = inception_block(x)
x = self.gap(x)
x = paddle.reshape(x, shape=[-1, 2048])
x = self.drop(x)
x = self.out(x)
return x
def InceptionV3(**kwargs):
@ -467,10 +476,19 @@ def InceptionV3(**kwargs):
Args:
kwargs:
class_num: int=1000. Output dim of last fc layer.
pretrained: str, pretrained model file
pretrained: bool or str, default: bool=False. Whether to load the pretrained model.
Returns:
model: nn.Layer. Specific `InceptionV3` model
"""
model = Inception_V3(NET_CONFIG, **kwargs)
if isinstance(model.pretrained, bool):
if model.pretrained is True:
load_dygraph_pretrain_from_url(model, MODEL_URLS["InceptionV3"])
elif isinstance(model.pretrained, str):
load_dygraph_pretrain(model, model.pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type")
return model