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PaddleClas/ppcls/arch/backbone/model_zoo/shufflenet_v2.py

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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.
# reference: https://arxiv.org/abs/1807.11164
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import ParamAttr, reshape, transpose, concat, split
from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Linear
from paddle.nn.initializer import KaimingNormal
from paddle.nn.functional import swish
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ShuffleNetV2_x0_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams",
"ShuffleNetV2_x0_33":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams",
"ShuffleNetV2_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams",
"ShuffleNetV2_x1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams",
"ShuffleNetV2_x1_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams",
"ShuffleNetV2_x2_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams",
"ShuffleNetV2_swish":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
def channel_shuffle(x, groups):
batch_size, num_channels, height, width = x.shape[0:4]
channels_per_group = num_channels // groups
# reshape
x = reshape(
x=x, shape=[batch_size, groups, channels_per_group, height, width])
# transpose
x = transpose(x=x, perm=[0, 2, 1, 3, 4])
# flatten
x = reshape(x=x, shape=[batch_size, num_channels, height, width])
return x
class ConvBNLayer(Layer):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
groups=1,
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(
initializer=KaimingNormal(), name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
out_channels,
param_attr=ParamAttr(name=name + "_bn_scale"),
bias_attr=ParamAttr(name=name + "_bn_offset"),
act=act,
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 InvertedResidual(Layer):
def __init__(self,
in_channels,
out_channels,
stride,
act="relu",
name=None):
super(InvertedResidual, self).__init__()
self._conv_pw = ConvBNLayer(
in_channels=in_channels // 2,
out_channels=out_channels // 2,
kernel_size=1,
stride=1,
padding=0,
groups=1,
act=act,
name='stage_' + name + '_conv1')
self._conv_dw = ConvBNLayer(
in_channels=out_channels // 2,
out_channels=out_channels // 2,
kernel_size=3,
stride=stride,
padding=1,
groups=out_channels // 2,
act=None,
name='stage_' + name + '_conv2')
self._conv_linear = ConvBNLayer(
in_channels=out_channels // 2,
out_channels=out_channels // 2,
kernel_size=1,
stride=1,
padding=0,
groups=1,
act=act,
name='stage_' + name + '_conv3')
def forward(self, inputs):
x1, x2 = split(
inputs,
num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
axis=1)
x2 = self._conv_pw(x2)
x2 = self._conv_dw(x2)
x2 = self._conv_linear(x2)
out = concat([x1, x2], axis=1)
return channel_shuffle(out, 2)
class InvertedResidualDS(Layer):
def __init__(self,
in_channels,
out_channels,
stride,
act="relu",
name=None):
super(InvertedResidualDS, self).__init__()
# branch1
self._conv_dw_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
stride=stride,
padding=1,
groups=in_channels,
act=None,
name='stage_' + name + '_conv4')
self._conv_linear_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels // 2,
kernel_size=1,
stride=1,
padding=0,
groups=1,
act=act,
name='stage_' + name + '_conv5')
# branch2
self._conv_pw_2 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels // 2,
kernel_size=1,
stride=1,
padding=0,
groups=1,
act=act,
name='stage_' + name + '_conv1')
self._conv_dw_2 = ConvBNLayer(
in_channels=out_channels // 2,
out_channels=out_channels // 2,
kernel_size=3,
stride=stride,
padding=1,
groups=out_channels // 2,
act=None,
name='stage_' + name + '_conv2')
self._conv_linear_2 = ConvBNLayer(
in_channels=out_channels // 2,
out_channels=out_channels // 2,
kernel_size=1,
stride=1,
padding=0,
groups=1,
act=act,
name='stage_' + name + '_conv3')
def forward(self, inputs):
x1 = self._conv_dw_1(inputs)
x1 = self._conv_linear_1(x1)
x2 = self._conv_pw_2(inputs)
x2 = self._conv_dw_2(x2)
x2 = self._conv_linear_2(x2)
out = concat([x1, x2], axis=1)
return channel_shuffle(out, 2)
class ShuffleNet(Layer):
def __init__(self, class_num=1000, scale=1.0, act="relu"):
super(ShuffleNet, self).__init__()
self.scale = scale
self.class_num = class_num
stage_repeats = [4, 8, 4]
if scale == 0.25:
stage_out_channels = [-1, 24, 24, 48, 96, 512]
elif scale == 0.33:
stage_out_channels = [-1, 24, 32, 64, 128, 512]
elif scale == 0.5:
stage_out_channels = [-1, 24, 48, 96, 192, 1024]
elif scale == 1.0:
stage_out_channels = [-1, 24, 116, 232, 464, 1024]
elif scale == 1.5:
stage_out_channels = [-1, 24, 176, 352, 704, 1024]
elif scale == 2.0:
stage_out_channels = [-1, 24, 244, 488, 976, 2048]
else:
raise NotImplementedError("This scale size:[" + str(scale) +
"] is not implemented!")
# 1. conv1
self._conv1 = ConvBNLayer(
in_channels=3,
out_channels=stage_out_channels[1],
kernel_size=3,
stride=2,
padding=1,
act=act,
name='stage1_conv')
self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
# 2. bottleneck sequences
self._block_list = []
for stage_id, num_repeat in enumerate(stage_repeats):
for i in range(num_repeat):
if i == 0:
block = self.add_sublayer(
name=str(stage_id + 2) + '_' + str(i + 1),
sublayer=InvertedResidualDS(
in_channels=stage_out_channels[stage_id + 1],
out_channels=stage_out_channels[stage_id + 2],
stride=2,
act=act,
name=str(stage_id + 2) + '_' + str(i + 1)))
else:
block = self.add_sublayer(
name=str(stage_id + 2) + '_' + str(i + 1),
sublayer=InvertedResidual(
in_channels=stage_out_channels[stage_id + 2],
out_channels=stage_out_channels[stage_id + 2],
stride=1,
act=act,
name=str(stage_id + 2) + '_' + str(i + 1)))
self._block_list.append(block)
# 3. last_conv
self._last_conv = ConvBNLayer(
in_channels=stage_out_channels[-2],
out_channels=stage_out_channels[-1],
kernel_size=1,
stride=1,
padding=0,
act=act,
name='conv5')
# 4. pool
self._pool2d_avg = AdaptiveAvgPool2D(1)
self._out_c = stage_out_channels[-1]
# 5. fc
self._fc = Linear(
stage_out_channels[-1],
class_num,
weight_attr=ParamAttr(name='fc6_weights'),
bias_attr=ParamAttr(name='fc6_offset'))
def forward(self, inputs):
y = self._conv1(inputs)
y = self._max_pool(y)
for inv in self._block_list:
y = inv(y)
y = self._last_conv(y)
y = self._pool2d_avg(y)
y = paddle.flatten(y, start_axis=1, stop_axis=-1)
y = self._fc(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 ShuffleNetV2_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.25, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x0_25"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x0_33(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.33, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x0_33"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.5, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x0_5"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x1_0"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.5, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x1_5"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=2.0, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x2_0"], use_ssld=use_ssld)
return model
def ShuffleNetV2_swish(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, act="swish", **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_swish"], use_ssld=use_ssld)
return model
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