forked from PulseFocusPlatform/PulseFocusPlatform
193 lines
6.9 KiB
Python
193 lines
6.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn.initializer import KaimingUniform
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from ppdet.core.workspace import register
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from ppdet.modeling.losses import CTFocalLoss
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class ConvLayer(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=False):
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super(ConvLayer, self).__init__()
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bias_attr = False
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fan_in = ch_in * kernel_size**2
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bound = 1 / math.sqrt(fan_in)
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param_attr = paddle.ParamAttr(initializer=KaimingUniform())
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if bias:
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bias_attr = paddle.ParamAttr(
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initializer=nn.initializer.Uniform(-bound, bound))
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self.conv = nn.Conv2D(
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in_channels=ch_in,
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out_channels=ch_out,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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weight_attr=param_attr,
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bias_attr=bias_attr)
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def forward(self, inputs):
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out = self.conv(inputs)
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return out
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@register
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class CenterNetHead(nn.Layer):
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"""
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Args:
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in_channels (int): the channel number of input to CenterNetHead.
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num_classes (int): the number of classes, 80 by default.
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head_planes (int): the channel number in all head, 256 by default.
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heatmap_weight (float): the weight of heatmap loss, 1 by default.
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regress_ltrb (bool): whether to regress left/top/right/bottom or
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width/height for a box, true by default
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size_weight (float): the weight of box size loss, 0.1 by default.
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offset_weight (float): the weight of center offset loss, 1 by default.
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"""
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__shared__ = ['num_classes']
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def __init__(self,
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in_channels,
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num_classes=80,
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head_planes=256,
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heatmap_weight=1,
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regress_ltrb=True,
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size_weight=0.1,
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offset_weight=1):
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super(CenterNetHead, self).__init__()
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self.weights = {
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'heatmap': heatmap_weight,
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'size': size_weight,
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'offset': offset_weight
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}
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self.heatmap = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes,
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num_classes,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True))
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self.heatmap[2].conv.bias[:] = -2.19
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self.size = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes,
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4 if regress_ltrb else 2,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True))
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self.offset = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
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self.focal_loss = CTFocalLoss()
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@classmethod
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def from_config(cls, cfg, input_shape):
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if isinstance(input_shape, (list, tuple)):
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input_shape = input_shape[0]
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return {'in_channels': input_shape.channels}
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def forward(self, feat, inputs):
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heatmap = self.heatmap(feat)
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size = self.size(feat)
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offset = self.offset(feat)
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if self.training:
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loss = self.get_loss(heatmap, size, offset, self.weights, inputs)
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return loss
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else:
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heatmap = F.sigmoid(heatmap)
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return {'heatmap': heatmap, 'size': size, 'offset': offset}
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def get_loss(self, heatmap, size, offset, weights, inputs):
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heatmap_target = inputs['heatmap']
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size_target = inputs['size']
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offset_target = inputs['offset']
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index = inputs['index']
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mask = inputs['index_mask']
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heatmap = paddle.clip(F.sigmoid(heatmap), 1e-4, 1 - 1e-4)
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heatmap_loss = self.focal_loss(heatmap, heatmap_target)
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size = paddle.transpose(size, perm=[0, 2, 3, 1])
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size_n, size_h, size_w, size_c = size.shape
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size = paddle.reshape(size, shape=[size_n, -1, size_c])
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index = paddle.unsqueeze(index, 2)
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batch_inds = list()
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for i in range(size_n):
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batch_ind = paddle.full(
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shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
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batch_inds.append(batch_ind)
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batch_inds = paddle.concat(batch_inds, axis=0)
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index = paddle.concat(x=[batch_inds, index], axis=2)
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pos_size = paddle.gather_nd(size, index=index)
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mask = paddle.unsqueeze(mask, axis=2)
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size_mask = paddle.expand_as(mask, pos_size)
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size_mask = paddle.cast(size_mask, dtype=pos_size.dtype)
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pos_num = size_mask.sum()
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size_mask.stop_gradient = True
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size_target.stop_gradient = True
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size_loss = F.l1_loss(
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pos_size * size_mask, size_target * size_mask, reduction='sum')
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size_loss = size_loss / (pos_num + 1e-4)
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offset = paddle.transpose(offset, perm=[0, 2, 3, 1])
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offset_n, offset_h, offset_w, offset_c = offset.shape
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offset = paddle.reshape(offset, shape=[offset_n, -1, offset_c])
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pos_offset = paddle.gather_nd(offset, index=index)
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offset_mask = paddle.expand_as(mask, pos_offset)
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offset_mask = paddle.cast(offset_mask, dtype=pos_offset.dtype)
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pos_num = offset_mask.sum()
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offset_mask.stop_gradient = True
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offset_target.stop_gradient = True
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offset_loss = F.l1_loss(
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pos_offset * offset_mask,
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offset_target * offset_mask,
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reduction='sum')
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offset_loss = offset_loss / (pos_num + 1e-4)
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det_loss = weights['heatmap'] * heatmap_loss + weights[
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'size'] * size_loss + weights['offset'] * offset_loss
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return {
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'det_loss': det_loss,
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'heatmap_loss': heatmap_loss,
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'size_loss': size_loss,
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'offset_loss': offset_loss
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}
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