You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
251 lines
8.3 KiB
251 lines
8.3 KiB
# Copyright (c) 2022 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 |
|
import paddle.nn as nn |
|
from paddle import ParamAttr |
|
import paddle.nn.functional as F |
|
from paddle.nn import Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm2D |
|
from paddle.nn.initializer import KaimingNormal |
|
from paddle.regularizer import L2Decay |
|
|
|
from paddlers.models.ppdet.core.workspace import register, serializable |
|
from numbers import Integral |
|
from ..shape_spec import ShapeSpec |
|
from paddlers.models.ppdet.modeling.ops import channel_shuffle |
|
|
|
__all__ = ['ShuffleNetV2'] |
|
|
|
|
|
class ConvBNLayer(nn.Layer): |
|
def __init__(self, |
|
in_channels, |
|
out_channels, |
|
kernel_size, |
|
stride, |
|
padding, |
|
groups=1, |
|
act=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()), |
|
bias_attr=False) |
|
|
|
self._batch_norm = BatchNorm2D( |
|
out_channels, |
|
weight_attr=ParamAttr(regularizer=L2Decay(0.0)), |
|
bias_attr=ParamAttr(regularizer=L2Decay(0.0))) |
|
if act == "hard_swish": |
|
act = 'hardswish' |
|
self.act = act |
|
|
|
def forward(self, inputs): |
|
y = self._conv(inputs) |
|
y = self._batch_norm(y) |
|
if self.act: |
|
y = getattr(F, self.act)(y) |
|
return y |
|
|
|
|
|
class InvertedResidual(nn.Layer): |
|
def __init__(self, in_channels, out_channels, stride, act="relu"): |
|
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) |
|
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) |
|
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) |
|
|
|
def forward(self, inputs): |
|
x1, x2 = paddle.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 = paddle.concat([x1, x2], axis=1) |
|
return channel_shuffle(out, 2) |
|
|
|
|
|
class InvertedResidualDS(nn.Layer): |
|
def __init__(self, in_channels, out_channels, stride, act="relu"): |
|
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) |
|
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) |
|
# 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) |
|
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) |
|
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) |
|
|
|
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 = paddle.concat([x1, x2], axis=1) |
|
|
|
return channel_shuffle(out, 2) |
|
|
|
|
|
@register |
|
@serializable |
|
class ShuffleNetV2(nn.Layer): |
|
def __init__(self, scale=1.0, act="relu", feature_maps=[5, 13, 17]): |
|
super(ShuffleNetV2, self).__init__() |
|
self.scale = scale |
|
if isinstance(feature_maps, Integral): |
|
feature_maps = [feature_maps] |
|
self.feature_maps = feature_maps |
|
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, 224, 488, 976, 2048] |
|
else: |
|
raise NotImplementedError("This scale size:[" + str(scale) + |
|
"] is not implemented!") |
|
|
|
self._out_channels = [] |
|
self._feature_idx = 0 |
|
# 1. conv1 |
|
self._conv1 = ConvBNLayer( |
|
in_channels=3, |
|
out_channels=stage_out_channels[1], |
|
kernel_size=3, |
|
stride=2, |
|
padding=1, |
|
act=act) |
|
self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1) |
|
self._feature_idx += 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)) |
|
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)) |
|
self._block_list.append(block) |
|
self._feature_idx += 1 |
|
self._update_out_channels(stage_out_channels[stage_id + 2], |
|
self._feature_idx, self.feature_maps) |
|
|
|
def _update_out_channels(self, channel, feature_idx, feature_maps): |
|
if feature_idx in feature_maps: |
|
self._out_channels.append(channel) |
|
|
|
def forward(self, inputs): |
|
y = self._conv1(inputs['image']) |
|
y = self._max_pool(y) |
|
outs = [] |
|
for i, inv in enumerate(self._block_list): |
|
y = inv(y) |
|
if i + 2 in self.feature_maps: |
|
outs.append(y) |
|
|
|
return outs |
|
|
|
@property |
|
def out_shape(self): |
|
return [ShapeSpec(channels=c) for c in self._out_channels]
|
|
|