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readme_rtdetr.txt
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readme_rtdetr.txt
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
----------------------- paddle语法转pytorch -----------------------
class SPP(nn.Layer):
def __init__(...):
super(SPP, self).__init__()
self.pool = []
self.data_format = data_format
for i, size in enumerate(pool_size):
pool = self.add_sublayer(
'pool{}'.format(i),
nn.MaxPool2D(
kernel_size=size,
stride=1,
padding=size // 2,
data_format=data_format,
ceil_mode=False))
self.pool.append(pool)
self.conv = ConvBNLayer(ch_in, ch_out, k, padding=k // 2, act=act)
转成（见 custom_pan.py）:
class SPP(nn.Module):
def __init__(...):
super(SPP, self).__init__()
self.pool = []
self.data_format = data_format
for i, size in enumerate(pool_size):
name = 'pool{}'.format(i)
pool = nn.MaxPool2d(
kernel_size=size,
stride=1,
padding=size // 2,
ceil_mode=False)
self.add_module(name, pool)
self.pool.append(pool)
self.conv = ConvBNLayer(ch_in, ch_out, k, padding=k // 2, act=act, act_name=act_name)
即
pool = self.add_sublayer('pool{}'.format(i), nn.MaxPool2D(...))
拆分成
name = 'pool{}'.format(i)
pool = nn.MaxPool2d(...)
self.add_module(name, pool)
这3句代码
----------------------- 转换权重 -----------------------
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_dec3_6x_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_r34vd_dec4_6x_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_m_6x_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_r101vd_6x_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_l_6x_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_x_6x_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
复现训练时请仔细核对每个参数的 lr、 L2Decay
forward流程：
骨干网络出来3个张量，形状是[N, 512, 80, 80], [N, 1024, 40, 40], [N, 2048, 20, 20],
进入 HybridEncoder,
先分别用3个conv(1x1卷积)+bn进行降维(相关层名字是input_proj)，形状变成[N, 256, 80, 80], [N, 256, 40, 40], [N, 256, 20, 20], 256是hidden_dim,
mmdet/models/losses/detr_loss.py
_get_loss_bbox()
可能是boxes被inplace操作，导致无法训练。
与原版的一些不同：
# attn_mask 会进入 MultiHeadAttention, 和 QK^T/sqrt(dk) 相加。
# attn_mask 如果是 True, 则变成 0 , attn_mask 如果是 False, 则变成 负无穷 .
device = attn_mask.device
attn_mask = torch.where(
attn_mask.to(torch.bool),
torch.zeros(attn_mask.shape, dtype=tgt.dtype, device=device),
torch.ones(attn_mask.shape, dtype=tgt.dtype, device=device) * -100000.)
# 这里和paddle不同，暂时设为较大的数
# anchors = torch.where(valid_mask, anchors, paddle.to_tensor(float("inf")))
anchors = torch.where(valid_mask, anchors, torch.ones_like(anchors) * 100000.)
encoder_layer(TransformerLayer) 被放进 HybridEncoder 的 self.encoder(nn.ModuleList)
self.encoder里有1个元素，类型是 TransformerEncoder(encoder_layer, num_encoder_layers)
python tools/convert_weights.py -f exps/rtdetr/rtdetr_r18vd_6x_coco.py -c rtdetr_r18vd_dec3_6x_coco.pdparams -oc rtdetr_r18vd_dec3_6x_coco.pth -nc 80 --device gpu
python tools/convert_weights.py -f exps/rtdetr/rtdetr_r50vd_6x_coco.py -c rtdetr_r50vd_6x_coco.pdparams -oc rtdetr_r50vd_6x_coco.pth -nc 80 --device gpu
python tools/convert_weights.py -f exps/rtdetr/rtdetr_r50vd_6x_coco.py -c ResNet50_vd_ssld_v2_pretrained.pdparams -oc ResNet50_vd_ssld_v2_pretrained.pth -nc 80 --only_backbone True
----------------------- 预测 -----------------------
python tools/demo.py image -f exps/rtdetr/rtdetr_r18vd_6x_coco.py -c rtdetr_r18vd_dec3_6x_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
python tools/demo.py image -f exps/rtdetr/rtdetr_r50vd_6x_coco.py -c rtdetr_r50vd_6x_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
----------------------- 训练 -----------------------
----------------------- 迁移学习，带上-c（--ckpt）参数读取预训练模型。 -----------------------
后台启动：
nohup xxx > ppyolo.log 2>&1 &
- - - - - - - - - - - - - - - - - - - - - -
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/rtdetr/rtdetr_r18vd_6x_voc2012.py -d 2 -b 24 -eb 24 -w 4 -ew 4 -lrs 0.1 -c rtdetr_r18vd_dec3_6x_coco.pth > rtdetr_r18vd_6x_voc2012.log 2>&1 &
python tools/train.py -f exps/rtdetr/rtdetr_r18vd_6x_voc2012.py -d 2 -b 24 -eb 24 -w 4 -ew 4 -lrs 1.0 -c rtdetr_r18vd_dec3_6x_coco.pth
python tools/train.py -f exps/rtdetr/rtdetr_r18vd_6x_voc2012.py -d 1 -b 24 -eb 24 -w 4 -ew 4 -lrs 0.1 -c rtdetr_r18vd_dec3_6x_coco.pth
python tools/train.py -f exps/rtdetr/rtdetr_r18vd_6x_voc2012.py -d 1 -b 2 -eb 2 -w 0 -ew 0 -c rtdetr_r18vd_dec3_6x_coco.pth
----------------------- 恢复训练（加上参数--resume） -----------------------
----------------------- 评估 -----------------------
python tools/eval.py -f exps/rtdetr/rtdetr_r50vd_6x_coco.py -d 1 -b 8 -w 4 -c rtdetr_r50vd_6x_coco.pth --tsize 640
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.532
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.714
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.578
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.348
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.580
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.701
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.391
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.656
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.722
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.547
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.765
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.880
----------------------- 导出为ONNX -----------------------
----------------------- 导出为TensorRT -----------------------
----------------------- 复现COCO上的精度 -----------------------