forked from PulseFocusPlatform/PulseFocusPlatform
116 lines
4.3 KiB
Python
116 lines
4.3 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 numpy as np
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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, Uniform
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from ppdet.core.workspace import register
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from ppdet.modeling.heads.centernet_head import ConvLayer
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__all__ = ['FairMOTEmbeddingHead']
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@register
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class FairMOTEmbeddingHead(nn.Layer):
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"""
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Args:
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in_channels (int): the channel number of input to FairMOTEmbeddingHead.
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ch_head (int): the channel of features before fed into embedding, 256 by default.
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ch_emb (int): the channel of the embedding feature, 128 by default.
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num_identifiers (int): the number of identifiers, 14455 by default.
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"""
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def __init__(self,
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in_channels,
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ch_head=256,
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ch_emb=128,
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num_identifiers=14455):
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super(FairMOTEmbeddingHead, self).__init__()
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self.reid = nn.Sequential(
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ConvLayer(
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in_channels, ch_head, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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ch_head, ch_emb, kernel_size=1, stride=1, padding=0, bias=True))
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param_attr = paddle.ParamAttr(initializer=KaimingUniform())
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bound = 1 / math.sqrt(ch_emb)
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bias_attr = paddle.ParamAttr(initializer=Uniform(-bound, bound))
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self.classifier = nn.Linear(
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ch_emb,
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num_identifiers,
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weight_attr=param_attr,
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bias_attr=bias_attr)
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self.reid_loss = nn.CrossEntropyLoss(ignore_index=-1, reduction='sum')
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# When num_identifiers is 1, emb_scale is set as 1
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self.emb_scale = math.sqrt(2) * math.log(
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num_identifiers - 1) if num_identifiers > 1 else 1
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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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reid_feat = self.reid(feat)
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if self.training:
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loss = self.get_loss(reid_feat, inputs)
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return loss
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else:
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reid_feat = F.normalize(reid_feat)
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return reid_feat
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def get_loss(self, feat, inputs):
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index = inputs['index']
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mask = inputs['index_mask']
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target = inputs['reid']
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target = paddle.masked_select(target, mask > 0)
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target = paddle.unsqueeze(target, 1)
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feat = paddle.transpose(feat, perm=[0, 2, 3, 1])
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feat_n, feat_h, feat_w, feat_c = feat.shape
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feat = paddle.reshape(feat, shape=[feat_n, -1, feat_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(feat_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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feat = paddle.gather_nd(feat, index=index)
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mask = paddle.unsqueeze(mask, axis=2)
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mask = paddle.expand_as(mask, feat)
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mask.stop_gradient = True
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feat = paddle.masked_select(feat, mask > 0)
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feat = paddle.reshape(feat, shape=[-1, feat_c])
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feat = F.normalize(feat)
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feat = self.emb_scale * feat
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logit = self.classifier(feat)
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target.stop_gradient = True
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loss = self.reid_loss(logit, target)
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valid = (target != self.reid_loss.ignore_index)
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valid.stop_gradient = True
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count = paddle.sum((paddle.cast(valid, dtype=np.int32)))
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count.stop_gradient = True
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if count > 0:
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loss = loss / count
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return loss
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