forked from PulseFocusPlatform/PulseFocusPlatform
310 lines
12 KiB
Python
310 lines
12 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
|
|
import paddle
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
from paddle import ParamAttr
|
|
from paddle.nn.initializer import Constant, Normal
|
|
from paddle.regularizer import L2Decay
|
|
from ppdet.core.workspace import register
|
|
from ppdet.modeling.layers import DeformableConvV2, LiteConv
|
|
import numpy as np
|
|
|
|
|
|
@register
|
|
class HMHead(nn.Layer):
|
|
"""
|
|
Args:
|
|
ch_in (int): The channel number of input Tensor.
|
|
ch_out (int): The channel number of output Tensor.
|
|
num_classes (int): Number of classes.
|
|
conv_num (int): The convolution number of hm_feat.
|
|
dcn_head(bool): whether use dcn in head. False by default.
|
|
lite_head(bool): whether use lite version. False by default.
|
|
norm_type (string): norm type, 'sync_bn', 'bn', 'gn' are optional.
|
|
bn by default
|
|
|
|
Return:
|
|
Heatmap head output
|
|
"""
|
|
__shared__ = ['num_classes', 'norm_type']
|
|
|
|
def __init__(
|
|
self,
|
|
ch_in,
|
|
ch_out=128,
|
|
num_classes=80,
|
|
conv_num=2,
|
|
dcn_head=False,
|
|
lite_head=False,
|
|
norm_type='bn', ):
|
|
super(HMHead, self).__init__()
|
|
head_conv = nn.Sequential()
|
|
for i in range(conv_num):
|
|
name = 'conv.{}'.format(i)
|
|
if lite_head:
|
|
lite_name = 'hm.' + name
|
|
head_conv.add_sublayer(
|
|
lite_name,
|
|
LiteConv(
|
|
in_channels=ch_in if i == 0 else ch_out,
|
|
out_channels=ch_out,
|
|
norm_type=norm_type))
|
|
head_conv.add_sublayer(lite_name + '.act', nn.ReLU6())
|
|
else:
|
|
if dcn_head:
|
|
head_conv.add_sublayer(
|
|
name,
|
|
DeformableConvV2(
|
|
in_channels=ch_in if i == 0 else ch_out,
|
|
out_channels=ch_out,
|
|
kernel_size=3,
|
|
weight_attr=ParamAttr(initializer=Normal(0, 0.01))))
|
|
else:
|
|
head_conv.add_sublayer(
|
|
name,
|
|
nn.Conv2D(
|
|
in_channels=ch_in if i == 0 else ch_out,
|
|
out_channels=ch_out,
|
|
kernel_size=3,
|
|
padding=1,
|
|
weight_attr=ParamAttr(initializer=Normal(0, 0.01)),
|
|
bias_attr=ParamAttr(
|
|
learning_rate=2., regularizer=L2Decay(0.))))
|
|
head_conv.add_sublayer(name + '.act', nn.ReLU())
|
|
self.feat = head_conv
|
|
bias_init = float(-np.log((1 - 0.01) / 0.01))
|
|
self.head = nn.Conv2D(
|
|
in_channels=ch_out,
|
|
out_channels=num_classes,
|
|
kernel_size=1,
|
|
weight_attr=ParamAttr(initializer=Normal(0, 0.01)),
|
|
bias_attr=ParamAttr(
|
|
learning_rate=2.,
|
|
regularizer=L2Decay(0.),
|
|
initializer=Constant(bias_init)))
|
|
|
|
def forward(self, feat):
|
|
out = self.feat(feat)
|
|
out = self.head(out)
|
|
return out
|
|
|
|
|
|
@register
|
|
class WHHead(nn.Layer):
|
|
"""
|
|
Args:
|
|
ch_in (int): The channel number of input Tensor.
|
|
ch_out (int): The channel number of output Tensor.
|
|
conv_num (int): The convolution number of wh_feat.
|
|
dcn_head(bool): whether use dcn in head. False by default.
|
|
lite_head(bool): whether use lite version. False by default.
|
|
norm_type (string): norm type, 'sync_bn', 'bn', 'gn' are optional.
|
|
bn by default
|
|
Return:
|
|
Width & Height head output
|
|
"""
|
|
__shared__ = ['norm_type']
|
|
|
|
def __init__(self,
|
|
ch_in,
|
|
ch_out=64,
|
|
conv_num=2,
|
|
dcn_head=False,
|
|
lite_head=False,
|
|
norm_type='bn'):
|
|
super(WHHead, self).__init__()
|
|
head_conv = nn.Sequential()
|
|
for i in range(conv_num):
|
|
name = 'conv.{}'.format(i)
|
|
if lite_head:
|
|
lite_name = 'wh.' + name
|
|
head_conv.add_sublayer(
|
|
lite_name,
|
|
LiteConv(
|
|
in_channels=ch_in if i == 0 else ch_out,
|
|
out_channels=ch_out,
|
|
norm_type=norm_type))
|
|
head_conv.add_sublayer(lite_name + '.act', nn.ReLU6())
|
|
else:
|
|
if dcn_head:
|
|
head_conv.add_sublayer(
|
|
name,
|
|
DeformableConvV2(
|
|
in_channels=ch_in if i == 0 else ch_out,
|
|
out_channels=ch_out,
|
|
kernel_size=3,
|
|
weight_attr=ParamAttr(initializer=Normal(0, 0.01))))
|
|
else:
|
|
head_conv.add_sublayer(
|
|
name,
|
|
nn.Conv2D(
|
|
in_channels=ch_in if i == 0 else ch_out,
|
|
out_channels=ch_out,
|
|
kernel_size=3,
|
|
padding=1,
|
|
weight_attr=ParamAttr(initializer=Normal(0, 0.01)),
|
|
bias_attr=ParamAttr(
|
|
learning_rate=2., regularizer=L2Decay(0.))))
|
|
head_conv.add_sublayer(name + '.act', nn.ReLU())
|
|
|
|
self.feat = head_conv
|
|
self.head = nn.Conv2D(
|
|
in_channels=ch_out,
|
|
out_channels=4,
|
|
kernel_size=1,
|
|
weight_attr=ParamAttr(initializer=Normal(0, 0.001)),
|
|
bias_attr=ParamAttr(
|
|
learning_rate=2., regularizer=L2Decay(0.)))
|
|
|
|
def forward(self, feat):
|
|
out = self.feat(feat)
|
|
out = self.head(out)
|
|
out = F.relu(out)
|
|
return out
|
|
|
|
|
|
@register
|
|
class TTFHead(nn.Layer):
|
|
"""
|
|
TTFHead
|
|
Args:
|
|
in_channels (int): the channel number of input to TTFHead.
|
|
num_classes (int): the number of classes, 80 by default.
|
|
hm_head_planes (int): the channel number in heatmap head,
|
|
128 by default.
|
|
wh_head_planes (int): the channel number in width & height head,
|
|
64 by default.
|
|
hm_head_conv_num (int): the number of convolution in heatmap head,
|
|
2 by default.
|
|
wh_head_conv_num (int): the number of convolution in width & height
|
|
head, 2 by default.
|
|
hm_loss (object): Instance of 'CTFocalLoss'.
|
|
wh_loss (object): Instance of 'GIoULoss'.
|
|
wh_offset_base (float): the base offset of width and height,
|
|
16.0 by default.
|
|
down_ratio (int): the actual down_ratio is calculated by base_down_ratio
|
|
(default 16) and the number of upsample layers.
|
|
lite_head(bool): whether use lite version. False by default.
|
|
norm_type (string): norm type, 'sync_bn', 'bn', 'gn' are optional.
|
|
bn by default
|
|
ags_module(bool): whether use AGS module to reweight location feature.
|
|
false by default.
|
|
|
|
"""
|
|
|
|
__shared__ = ['num_classes', 'down_ratio', 'norm_type']
|
|
__inject__ = ['hm_loss', 'wh_loss']
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
num_classes=80,
|
|
hm_head_planes=128,
|
|
wh_head_planes=64,
|
|
hm_head_conv_num=2,
|
|
wh_head_conv_num=2,
|
|
hm_loss='CTFocalLoss',
|
|
wh_loss='GIoULoss',
|
|
wh_offset_base=16.,
|
|
down_ratio=4,
|
|
dcn_head=False,
|
|
lite_head=False,
|
|
norm_type='bn',
|
|
ags_module=False):
|
|
super(TTFHead, self).__init__()
|
|
self.in_channels = in_channels
|
|
self.hm_head = HMHead(in_channels, hm_head_planes, num_classes,
|
|
hm_head_conv_num, dcn_head, lite_head, norm_type)
|
|
self.wh_head = WHHead(in_channels, wh_head_planes, wh_head_conv_num,
|
|
dcn_head, lite_head, norm_type)
|
|
self.hm_loss = hm_loss
|
|
self.wh_loss = wh_loss
|
|
|
|
self.wh_offset_base = wh_offset_base
|
|
self.down_ratio = down_ratio
|
|
self.ags_module = ags_module
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, input_shape):
|
|
if isinstance(input_shape, (list, tuple)):
|
|
input_shape = input_shape[0]
|
|
return {'in_channels': input_shape.channels, }
|
|
|
|
def forward(self, feats):
|
|
hm = self.hm_head(feats)
|
|
wh = self.wh_head(feats) * self.wh_offset_base
|
|
return hm, wh
|
|
|
|
def filter_box_by_weight(self, pred, target, weight):
|
|
"""
|
|
Filter out boxes where ttf_reg_weight is 0, only keep positive samples.
|
|
"""
|
|
index = paddle.nonzero(weight > 0)
|
|
index.stop_gradient = True
|
|
weight = paddle.gather_nd(weight, index)
|
|
pred = paddle.gather_nd(pred, index)
|
|
target = paddle.gather_nd(target, index)
|
|
return pred, target, weight
|
|
|
|
def filter_loc_by_weight(self, score, weight):
|
|
index = paddle.nonzero(weight > 0)
|
|
index.stop_gradient = True
|
|
score = paddle.gather_nd(score, index)
|
|
return score
|
|
|
|
def get_loss(self, pred_hm, pred_wh, target_hm, box_target, target_weight):
|
|
pred_hm = paddle.clip(F.sigmoid(pred_hm), 1e-4, 1 - 1e-4)
|
|
hm_loss = self.hm_loss(pred_hm, target_hm)
|
|
H, W = target_hm.shape[2:]
|
|
mask = paddle.reshape(target_weight, [-1, H, W])
|
|
avg_factor = paddle.sum(mask) + 1e-4
|
|
|
|
base_step = self.down_ratio
|
|
shifts_x = paddle.arange(0, W * base_step, base_step, dtype='int32')
|
|
shifts_y = paddle.arange(0, H * base_step, base_step, dtype='int32')
|
|
shift_y, shift_x = paddle.tensor.meshgrid([shifts_y, shifts_x])
|
|
base_loc = paddle.stack([shift_x, shift_y], axis=0)
|
|
base_loc.stop_gradient = True
|
|
|
|
pred_boxes = paddle.concat(
|
|
[0 - pred_wh[:, 0:2, :, :] + base_loc, pred_wh[:, 2:4] + base_loc],
|
|
axis=1)
|
|
pred_boxes = paddle.transpose(pred_boxes, [0, 2, 3, 1])
|
|
boxes = paddle.transpose(box_target, [0, 2, 3, 1])
|
|
boxes.stop_gradient = True
|
|
|
|
if self.ags_module:
|
|
pred_hm_max = paddle.max(pred_hm, axis=1, keepdim=True)
|
|
pred_hm_max_softmax = F.softmax(pred_hm_max, axis=1)
|
|
pred_hm_max_softmax = paddle.transpose(pred_hm_max_softmax,
|
|
[0, 2, 3, 1])
|
|
pred_hm_max_softmax = self.filter_loc_by_weight(pred_hm_max_softmax,
|
|
mask)
|
|
else:
|
|
pred_hm_max_softmax = None
|
|
|
|
pred_boxes, boxes, mask = self.filter_box_by_weight(pred_boxes, boxes,
|
|
mask)
|
|
mask.stop_gradient = True
|
|
wh_loss = self.wh_loss(
|
|
pred_boxes,
|
|
boxes,
|
|
iou_weight=mask.unsqueeze(1),
|
|
loc_reweight=pred_hm_max_softmax)
|
|
wh_loss = wh_loss / avg_factor
|
|
|
|
ttf_loss = {'hm_loss': hm_loss, 'wh_loss': wh_loss}
|
|
return ttf_loss
|