From 5122fd751640229ebad467afb5908e5f90ac47a8 Mon Sep 17 00:00:00 2001 From: Chilicyy Date: Fri, 6 Jan 2023 17:58:15 +0800 Subject: [PATCH] update code --- assets/speed_comparision_v3.png | Bin 0 -> 498653 bytes configs/experiment/yolov6t.py | 55 +++ configs/experiment/yolov6t_finetune.py | 55 +++ configs/yolov6l6.py | 62 ++++ configs/yolov6l6_finetune.py | 62 ++++ configs/yolov6m6.py | 61 ++++ configs/yolov6m6_finetune.py | 61 ++++ configs/yolov6n6.py | 56 +++ configs/yolov6n6_finetune.py | 56 +++ configs/yolov6s6.py | 56 +++ configs/yolov6s6_finetune.py | 56 +++ docs/Train_coco_data.md | 106 ++++++ yolov6/models/heads/effidehead_distill_ns.py | 259 +++++++++++++ yolov6/models/heads/effidehead_fuseab.py | 333 +++++++++++++++++ yolov6/models/losses/loss.py | 269 ++++++++++++++ yolov6/models/losses/loss_distill.py | 360 +++++++++++++++++++ yolov6/models/losses/loss_distill_ns.py | 348 ++++++++++++++++++ yolov6/models/losses/loss_fuseab.py | 241 +++++++++++++ 18 files changed, 2496 insertions(+) create mode 100644 assets/speed_comparision_v3.png create mode 100644 configs/experiment/yolov6t.py create mode 100644 configs/experiment/yolov6t_finetune.py create mode 100644 configs/yolov6l6.py create mode 100644 configs/yolov6l6_finetune.py create mode 100644 configs/yolov6m6.py create mode 100644 configs/yolov6m6_finetune.py create mode 100644 configs/yolov6n6.py create mode 100644 configs/yolov6n6_finetune.py create mode 100644 configs/yolov6s6.py create mode 100644 configs/yolov6s6_finetune.py create mode 100644 docs/Train_coco_data.md create mode 100644 yolov6/models/heads/effidehead_distill_ns.py create mode 100644 yolov6/models/heads/effidehead_fuseab.py create mode 100644 yolov6/models/losses/loss.py create mode 100644 yolov6/models/losses/loss_distill.py create mode 100644 yolov6/models/losses/loss_distill_ns.py create mode 100644 yolov6/models/losses/loss_fuseab.py diff --git a/assets/speed_comparision_v3.png b/assets/speed_comparision_v3.png new file mode 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00000000..afacd436 --- /dev/null +++ b/configs/experiment/yolov6t.py @@ -0,0 +1,55 @@ +# YOLOv6t model +model = dict( + type='YOLOv6t', + pretrained=None, + depth_multiple=0.33, + width_multiple=0.375, + backbone=dict( + type='EfficientRep', + num_repeats=[1, 6, 12, 18, 6], + out_channels=[64, 128, 256, 512, 1024], + ), + neck=dict( + type='RepPANNeck', + num_repeats=[12, 12, 12, 12], + out_channels=[256, 128, 128, 256, 256, 512], + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512], + num_layers=3, + begin_indices=24, + anchors=1, + out_indices=[17, 20, 23], + strides=[8, 16, 32], + iou_type='siou', + use_dfl=False, + reg_max=0 #if use_dfl is False, please set reg_max to 0 + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.01, + lrf=0.01, + momentum=0.937, + weight_decay=0.0005, + warmup_epochs=3.0, + warmup_momentum=0.8, + warmup_bias_lr=0.1 +) + +data_aug = dict( + hsv_h=0.015, + hsv_s=0.7, + hsv_v=0.4, + degrees=0.0, + translate=0.1, + scale=0.5, + shear=0.0, + flipud=0.0, + fliplr=0.5, + mosaic=1.0, + mixup=0.0, +) diff --git a/configs/experiment/yolov6t_finetune.py b/configs/experiment/yolov6t_finetune.py new file mode 100644 index 00000000..8be47416 --- /dev/null +++ b/configs/experiment/yolov6t_finetune.py @@ -0,0 +1,55 @@ +# YOLOv6t model +model = dict( + type='YOLOv6t', + pretrained='weights/yolov6t.pt', + depth_multiple=0.33, + width_multiple=0.375, + backbone=dict( + type='EfficientRep', + num_repeats=[1, 6, 12, 18, 6], + out_channels=[64, 128, 256, 512, 1024], + ), + neck=dict( + type='RepPANNeck', + num_repeats=[12, 12, 12, 12], + out_channels=[256, 128, 128, 256, 256, 512], + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512], + num_layers=3, + begin_indices=24, + anchors=1, + out_indices=[17, 20, 23], + strides=[8, 16, 32], + iou_type='siou', + use_dfl=False, + reg_max=0 #if use_dfl is False, please set reg_max to 0 + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.0032, + lrf=0.12, + momentum=0.843, + weight_decay=0.00036, + warmup_epochs=2.0, + warmup_momentum=0.5, + warmup_bias_lr=0.05 +) + +data_aug = dict( + hsv_h=0.0138, + hsv_s=0.664, + hsv_v=0.464, + degrees=0.373, + translate=0.245, + scale=0.898, + shear=0.602, + flipud=0.00856, + fliplr=0.5, + mosaic=1.0, + mixup=0.243, +) diff --git a/configs/yolov6l6.py b/configs/yolov6l6.py new file mode 100644 index 00000000..a5c3ef68 --- /dev/null +++ b/configs/yolov6l6.py @@ -0,0 +1,62 @@ +# YOLOv6l6 model +model = dict( + type='YOLOv6l6', + pretrained=None, + depth_multiple=1.0, + width_multiple=1.0, + backbone=dict( + type='CSPBepBackbone_P6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + csp_e=float(1)/2, + fuse_P2=True, + ), + neck=dict( + type='CSPRepBiFPANNeck_P6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + csp_e=float(1)/2, + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='giou', + use_dfl=True, + reg_max=16, #if use_dfl is False, please set reg_max to 0 + distill_weight={ + 'class': 1.0, + 'dfl': 1.0, + }, + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.01, + lrf=0.01, + momentum=0.937, + weight_decay=0.0005, + warmup_epochs=3.0, + warmup_momentum=0.8, + warmup_bias_lr=0.1 +) + +data_aug = dict( + hsv_h=0.015, + hsv_s=0.7, + hsv_v=0.4, + degrees=0.0, + translate=0.1, + scale=0.9, + shear=0.0, + flipud=0.0, + fliplr=0.5, + mosaic=1.0, + mixup=0.2, +) +training_mode = "conv_silu" \ No newline at end of file diff --git a/configs/yolov6l6_finetune.py b/configs/yolov6l6_finetune.py new file mode 100644 index 00000000..a500d7fc --- /dev/null +++ b/configs/yolov6l6_finetune.py @@ -0,0 +1,62 @@ +# YOLOv6l6 model +model = dict( + type='YOLOv6l6', + pretrained='weights/yolov6l6.pt', + depth_multiple=1.0, + width_multiple=1.0, + backbone=dict( + type='CSPBepBackbone_P6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + csp_e=float(1)/2, + fuse_P2=True, + ), + neck=dict( + type='CSPRepBiFPANNeck_P6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + csp_e=float(1)/2, + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='giou', + use_dfl=True, + reg_max=16, #if use_dfl is False, please set reg_max to 0 + distill_weight={ + 'class': 1.0, + 'dfl': 1.0, + }, + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.0032, + lrf=0.12, + momentum=0.843, + weight_decay=0.00036, + warmup_epochs=2.0, + warmup_momentum=0.5, + warmup_bias_lr=0.05 +) + +data_aug = dict( + hsv_h=0.0138, + hsv_s=0.664, + hsv_v=0.464, + degrees=0.373, + translate=0.245, + scale=0.898, + shear=0.602, + flipud=0.00856, + fliplr=0.5, + mosaic=1.0, + mixup=0.243, +) +training_mode = "conv_silu" \ No newline at end of file diff --git a/configs/yolov6m6.py b/configs/yolov6m6.py new file mode 100644 index 00000000..2d8d7a42 --- /dev/null +++ b/configs/yolov6m6.py @@ -0,0 +1,61 @@ +# YOLOv6m6 model +model = dict( + type='YOLOv6m6', + pretrained=None, + depth_multiple=0.60, + width_multiple=0.75, + backbone=dict( + type='CSPBepBackbone_P6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + csp_e=float(2)/3, + fuse_P2=True, + ), + neck=dict( + type='CSPRepBiFPANNeck_P6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + csp_e=float(2)/3, + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='giou', + use_dfl=True, + reg_max=16, #if use_dfl is False, please set reg_max to 0 + distill_weight={ + 'class': 1.0, + 'dfl': 1.0, + }, + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.01, + lrf=0.01, + momentum=0.937, + weight_decay=0.0005, + warmup_epochs=3.0, + warmup_momentum=0.8, + warmup_bias_lr=0.1 +) + +data_aug = dict( + hsv_h=0.015, + hsv_s=0.7, + hsv_v=0.4, + degrees=0.0, + translate=0.1, + scale=0.9, + shear=0.0, + flipud=0.0, + fliplr=0.5, + mosaic=1.0, + mixup=0.1, +) \ No newline at end of file diff --git a/configs/yolov6m6_finetune.py b/configs/yolov6m6_finetune.py new file mode 100644 index 00000000..91d9a21c --- /dev/null +++ b/configs/yolov6m6_finetune.py @@ -0,0 +1,61 @@ +# YOLOv6m6 model +model = dict( + type='YOLOv6m6', + pretrained='weights/yolov6m6.pt', + depth_multiple=0.60, + width_multiple=0.75, + backbone=dict( + type='CSPBepBackbone_P6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + csp_e=float(2)/3, + fuse_P2=True, + ), + neck=dict( + type='CSPRepBiFPANNeck_P6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + csp_e=float(2)/3, + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='giou', + use_dfl=True, + reg_max=16, #if use_dfl is False, please set reg_max to 0 + distill_weight={ + 'class': 1.0, + 'dfl': 1.0, + }, + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.0032, + lrf=0.12, + momentum=0.843, + weight_decay=0.00036, + warmup_epochs=2.0, + warmup_momentum=0.5, + warmup_bias_lr=0.05 +) + +data_aug = dict( + hsv_h=0.0138, + hsv_s=0.664, + hsv_v=0.464, + degrees=0.373, + translate=0.245, + scale=0.898, + shear=0.602, + flipud=0.00856, + fliplr=0.5, + mosaic=1.0, + mixup=0.243, +) \ No newline at end of file diff --git a/configs/yolov6n6.py b/configs/yolov6n6.py new file mode 100644 index 00000000..0abe3a44 --- /dev/null +++ b/configs/yolov6n6.py @@ -0,0 +1,56 @@ +# YOLOv6n model +model = dict( + type='YOLOv6n6', + pretrained=None, + depth_multiple=0.33, + width_multiple=0.25, + backbone=dict( + type='EfficientRep6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + fuse_P2=True, # if use RepBiFPANNeck6, please set fuse_P2 to True. + cspsppf=True, + ), + neck=dict( + type='RepBiFPANNeck6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='siou', + use_dfl=False, + reg_max=0 #if use_dfl is False, please set reg_max to 0 + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.02, + lrf=0.01, + momentum=0.937, + weight_decay=0.0005, + warmup_epochs=3.0, + warmup_momentum=0.8, + warmup_bias_lr=0.1 +) + +data_aug = dict( + hsv_h=0.015, + hsv_s=0.7, + hsv_v=0.4, + degrees=0.0, + translate=0.1, + scale=0.5, + shear=0.0, + flipud=0.0, + fliplr=0.5, + mosaic=1.0, + mixup=0.0, +) diff --git a/configs/yolov6n6_finetune.py b/configs/yolov6n6_finetune.py new file mode 100644 index 00000000..01100f0f --- /dev/null +++ b/configs/yolov6n6_finetune.py @@ -0,0 +1,56 @@ +# YOLOv6n model +model = dict( + type='YOLOv6n6', + pretrained='weights/yolov6n6.pt', + depth_multiple=0.33, + width_multiple=0.25, + backbone=dict( + type='EfficientRep6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + fuse_P2=True, # if use RepBiFPANNeck6, please set fuse_P2 to True. + cspsppf=True, + ), + neck=dict( + type='RepBiFPANNeck6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='siou', + use_dfl=False, + reg_max=0 #if use_dfl is False, please set reg_max to 0 + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.0032, + lrf=0.12, + momentum=0.843, + weight_decay=0.00036, + warmup_epochs=2.0, + warmup_momentum=0.5, + warmup_bias_lr=0.05 +) + +data_aug = dict( + hsv_h=0.0138, + hsv_s=0.664, + hsv_v=0.464, + degrees=0.373, + translate=0.245, + scale=0.898, + shear=0.602, + flipud=0.00856, + fliplr=0.5, + mosaic=1.0, + mixup=0.243, +) diff --git a/configs/yolov6s6.py b/configs/yolov6s6.py new file mode 100644 index 00000000..091bfffc --- /dev/null +++ b/configs/yolov6s6.py @@ -0,0 +1,56 @@ +# YOLOv6n model +model = dict( + type='YOLOv6s6', + pretrained=None, + depth_multiple=0.33, + width_multiple=0.50, + backbone=dict( + type='EfficientRep6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + fuse_P2=True, # if use RepBiFPANNeck6, please set fuse_P2 to True. + cspsppf=True, + ), + neck=dict( + type='RepBiFPANNeck6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='giou', + use_dfl=False, + reg_max=0 #if use_dfl is False, please set reg_max to 0 + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.01, + lrf=0.01, + momentum=0.937, + weight_decay=0.0005, + warmup_epochs=3.0, + warmup_momentum=0.8, + warmup_bias_lr=0.1 +) + +data_aug = dict( + hsv_h=0.015, + hsv_s=0.7, + hsv_v=0.4, + degrees=0.0, + translate=0.1, + scale=0.5, + shear=0.0, + flipud=0.0, + fliplr=0.5, + mosaic=1.0, + mixup=0.0, +) diff --git a/configs/yolov6s6_finetune.py b/configs/yolov6s6_finetune.py new file mode 100644 index 00000000..955a1f44 --- /dev/null +++ b/configs/yolov6s6_finetune.py @@ -0,0 +1,56 @@ +# YOLOv6n model +model = dict( + type='YOLOv6s6', + pretrained='weights/yolov6s6.pt', + depth_multiple=0.33, + width_multiple=0.50, + backbone=dict( + type='EfficientRep6', + num_repeats=[1, 6, 12, 18, 6, 6], + out_channels=[64, 128, 256, 512, 768, 1024], + fuse_P2=True, # if use RepBiFPANNeck6, please set fuse_P2 to True. + cspsppf=True, + ), + neck=dict( + type='RepBiFPANNeck6', + num_repeats=[12, 12, 12, 12, 12, 12], + out_channels=[512, 256, 128, 256, 512, 1024], + ), + head=dict( + type='EffiDeHead', + in_channels=[128, 256, 512, 1024], + num_layers=4, + anchors=1, + strides=[8, 16, 32, 64], + atss_warmup_epoch=4, + iou_type='giou', + use_dfl=False, + reg_max=0 #if use_dfl is False, please set reg_max to 0 + ) +) + +solver = dict( + optim='SGD', + lr_scheduler='Cosine', + lr0=0.0032, + lrf=0.12, + momentum=0.843, + weight_decay=0.00036, + warmup_epochs=2.0, + warmup_momentum=0.5, + warmup_bias_lr=0.05 +) + +data_aug = dict( + hsv_h=0.0138, + hsv_s=0.664, + hsv_v=0.464, + degrees=0.373, + translate=0.245, + scale=0.898, + shear=0.602, + flipud=0.00856, + fliplr=0.5, + mosaic=1.0, + mixup=0.243, +) \ No newline at end of file diff --git a/docs/Train_coco_data.md b/docs/Train_coco_data.md new file mode 100644 index 00000000..15b662b1 --- /dev/null +++ b/docs/Train_coco_data.md @@ -0,0 +1,106 @@ +# Train COCO Dataset + +This guidence shows the training commands for reproducing our results on COCO Dataset. + +## For P5 models + +#### YOLOv6-N + +```shell +# Step 1: Training a base model +# Be sure to open use_dfl mode in config file (use_dfl=True, reg_max=16) + +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 128 \ + --conf configs/yolov6n.py \ + --data data/coco.yaml \ + --epoch 300 \ + --fuse_ab \ + --device 0,1,2,3,4,5,6,7 \ + --name yolov6n_coco + +# Step 2: Self-distillation training +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 128 \ + --conf configs/yolov6n.py \ + --data data/coco.yaml \ + --epoch 300 \ + --device 0,1,2,3,4,5,6,7 \ + --distill \ + --teacher_model_path runs/train/yolov6n_coco/weights/best_ckpt.pt \ + --name yolov6n_coco +``` + + +#### YOLOv6-S/M/L + +```shell +# Step 1: Training a base model +# Be sure to open use_dfl mode in config file (use_dfl=True, reg_max=16) + +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 256 \ + --conf configs/yolov6s.py \ # yolov6m/yolov6l + --data data/coco.yaml \ + --epoch 300 \ + --fuse_ab \ + --device 0,1,2,3,4,5,6,7 \ + --name yolov6s_coco # yolov6m_coco/yolov6l_coco + +# Step 2: Self-distillation training +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 256 \ # 128 for distillation of yolov6l + --conf configs/yolov6s.py \ # yolov6m/yolov6l + --data data/coco.yaml \ + --epoch 300 \ + --device 0,1,2,3,4,5,6,7 \ + --distill \ + --teacher_model_path runs/train/yolov6s_coco/weights/best_ckpt.pt \ + --name yolov6s_coco # yolov6m_coco/yolov6l_coco + +``` + +## For P6 models + +#### YOLOv6-N6/S6 + +```shell +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 128 \ + --img 1280 \ + --conf configs/yolov6s6.py \ # yolov6n6 + --data data/coco.yaml \ + --epoch 300 \ + --bs_per_gpu 16 \ + --device 0,1,2,3,4,5,6,7 \ + --name yolov6s6_coco # yolov6n6_coco + +``` + + +#### YOLOv6-M6/L6 + +```shell +# Step 1: Training a base model +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 128 \ + --conf configs/yolov6l6.py \ # yolov6m6 + --data data/coco.yaml \ + --epoch 300 \ + --bs_per_gpu 16 \ + --device 0,1,2,3,4,5,6,7 \ + --name yolov6l6_coco # yolov6m6_coco + +# Step 2: Self-distillation training +python -m torch.distributed.launch --nproc_per_node 8 tools/train.py \ + --batch 128 \ + --conf configs/yolov6l6.py \ # yolov6m6 + --data data/coco.yaml \ + --epoch 300 \ + --bs_per_gpu 16 \ + --device 0,1,2,3,4,5,6,7 \ + --distill \ + --teacher_model_path runs/train/yolov6l6_coco/weights/best_ckpt.pt \ + --name yolov6l6_coco # yolov6m6_coco + +``` diff --git a/yolov6/models/heads/effidehead_distill_ns.py b/yolov6/models/heads/effidehead_distill_ns.py new file mode 100644 index 00000000..4450cf2c --- /dev/null +++ b/yolov6/models/heads/effidehead_distill_ns.py @@ -0,0 +1,259 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +from yolov6.layers.common import * +from yolov6.assigners.anchor_generator import generate_anchors +from yolov6.utils.general import dist2bbox + + +class Detect(nn.Module): + '''Efficient Decoupled Head for Cost-free Distillation.(FOR NANO/SMALL MODEL) + ''' + def __init__(self, num_classes=80, num_layers=3, inplace=True, head_layers=None, use_dfl=True, reg_max=16): # detection layer + super().__init__() + assert head_layers is not None + self.nc = num_classes # number of classes + self.no = num_classes + 5 # number of outputs per anchor + self.nl = num_layers # number of detection layers + self.grid = [torch.zeros(1)] * num_layers + self.prior_prob = 1e-2 + self.inplace = inplace + stride = [8, 16, 32] # strides computed during build + self.stride = torch.tensor(stride) + self.use_dfl = use_dfl + self.reg_max = reg_max + self.proj_conv = nn.Conv2d(self.reg_max + 1, 1, 1, bias=False) + self.grid_cell_offset = 0.5 + self.grid_cell_size = 5.0 + + # Init decouple head + self.stems = nn.ModuleList() + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + self.cls_preds = nn.ModuleList() + self.reg_preds = nn.ModuleList() + self.reg_preds_lrtb = nn.ModuleList() + + # Efficient decoupled head layers + for i in range(num_layers): + idx = i*6 + self.stems.append(head_layers[idx]) + self.cls_convs.append(head_layers[idx+1]) + self.reg_convs.append(head_layers[idx+2]) + self.cls_preds.append(head_layers[idx+3]) + self.reg_preds.append(head_layers[idx+4]) + self.reg_preds_lrtb.append(head_layers[idx+5]) + + def initialize_biases(self): + + for conv in self.cls_preds: + b = conv.bias.view(-1, ) + b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob)) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + for conv in self.reg_preds: + b = conv.bias.view(-1, ) + b.data.fill_(1.0) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + for conv in self.reg_preds_lrtb: + b = conv.bias.view(-1, ) + b.data.fill_(1.0) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + self.proj = nn.Parameter(torch.linspace(0, self.reg_max, self.reg_max + 1), requires_grad=False) + self.proj_conv.weight = nn.Parameter(self.proj.view([1, self.reg_max + 1, 1, 1]).clone().detach(), + requires_grad=False) + + def forward(self, x): + if self.training: + cls_score_list = [] + reg_distri_list = [] + reg_lrtb_list = [] + + for i in range(self.nl): + x[i] = self.stems[i](x[i]) + cls_x = x[i] + reg_x = x[i] + cls_feat = self.cls_convs[i](cls_x) + cls_output = self.cls_preds[i](cls_feat) + reg_feat = self.reg_convs[i](reg_x) + reg_output = self.reg_preds[i](reg_feat) + reg_output_lrtb = self.reg_preds_lrtb[i](reg_feat) + + cls_output = torch.sigmoid(cls_output) + cls_score_list.append(cls_output.flatten(2).permute((0, 2, 1))) + reg_distri_list.append(reg_output.flatten(2).permute((0, 2, 1))) + reg_lrtb_list.append(reg_output_lrtb.flatten(2).permute((0, 2, 1))) + + cls_score_list = torch.cat(cls_score_list, axis=1) + reg_distri_list = torch.cat(reg_distri_list, axis=1) + reg_lrtb_list = torch.cat(reg_lrtb_list, axis=1) + + return x, cls_score_list, reg_distri_list, reg_lrtb_list + else: + cls_score_list = [] + reg_lrtb_list = [] + anchor_points, stride_tensor = generate_anchors( + x, self.stride, self.grid_cell_size, self.grid_cell_offset, device=x[0].device, is_eval=True, mode='af') + + for i in range(self.nl): + b, _, h, w = x[i].shape + l = h * w + x[i] = self.stems[i](x[i]) + cls_x = x[i] + reg_x = x[i] + cls_feat = self.cls_convs[i](cls_x) + cls_output = self.cls_preds[i](cls_feat) + reg_feat = self.reg_convs[i](reg_x) + reg_output_lrtb = self.reg_preds_lrtb[i](reg_feat) + + cls_output = torch.sigmoid(cls_output) + cls_score_list.append(cls_output.reshape([b, self.nc, l])) + reg_lrtb_list.append(reg_output_lrtb.reshape([b, 4, l])) + + cls_score_list = torch.cat(cls_score_list, axis=-1).permute(0, 2, 1) + reg_lrtb_list = torch.cat(reg_lrtb_list, axis=-1).permute(0, 2, 1) + + pred_bboxes = dist2bbox(reg_lrtb_list, anchor_points, box_format='xywh') + pred_bboxes *= stride_tensor + return torch.cat( + [ + pred_bboxes, + torch.ones((b, pred_bboxes.shape[1], 1), device=pred_bboxes.device, dtype=pred_bboxes.dtype), + cls_score_list + ], + axis=-1) + + +def build_effidehead_layer(channels_list, num_anchors, num_classes, reg_max=16): + head_layers = nn.Sequential( + # stem0 + Conv( + in_channels=channels_list[6], + out_channels=channels_list[6], + kernel_size=1, + stride=1 + ), + # cls_conv0 + Conv( + in_channels=channels_list[6], + out_channels=channels_list[6], + kernel_size=3, + stride=1 + ), + # reg_conv0 + Conv( + in_channels=channels_list[6], + out_channels=channels_list[6], + kernel_size=3, + stride=1 + ), + # cls_pred0 + nn.Conv2d( + in_channels=channels_list[6], + out_channels=num_classes * num_anchors, + kernel_size=1 + ), + # reg_pred0 + nn.Conv2d( + in_channels=channels_list[6], + out_channels=4 * (reg_max + num_anchors), + kernel_size=1 + ), + # reg_pred0_1 + nn.Conv2d( + in_channels=channels_list[6], + out_channels=4 * (num_anchors), + kernel_size=1 + ), + # stem1 + Conv( + in_channels=channels_list[8], + out_channels=channels_list[8], + kernel_size=1, + stride=1 + ), + # cls_conv1 + Conv( + in_channels=channels_list[8], + out_channels=channels_list[8], + kernel_size=3, + stride=1 + ), + # reg_conv1 + Conv( + in_channels=channels_list[8], + out_channels=channels_list[8], + kernel_size=3, + stride=1 + ), + # cls_pred1 + nn.Conv2d( + in_channels=channels_list[8], + out_channels=num_classes * num_anchors, + kernel_size=1 + ), + # reg_pred1 + nn.Conv2d( + in_channels=channels_list[8], + out_channels=4 * (reg_max + num_anchors), + kernel_size=1 + ), + # reg_pred1_1 + nn.Conv2d( + in_channels=channels_list[8], + out_channels=4 * (num_anchors), + kernel_size=1 + ), + # stem2 + Conv( + in_channels=channels_list[10], + out_channels=channels_list[10], + kernel_size=1, + stride=1 + ), + # cls_conv2 + Conv( + in_channels=channels_list[10], + out_channels=channels_list[10], + kernel_size=3, + stride=1 + ), + # reg_conv2 + Conv( + in_channels=channels_list[10], + out_channels=channels_list[10], + kernel_size=3, + stride=1 + ), + # cls_pred2 + nn.Conv2d( + in_channels=channels_list[10], + out_channels=num_classes * num_anchors, + kernel_size=1 + ), + # reg_pred2 + nn.Conv2d( + in_channels=channels_list[10], + out_channels=4 * (reg_max + num_anchors), + kernel_size=1 + ), + # reg_pred2_1 + nn.Conv2d( + in_channels=channels_list[10], + out_channels=4 * (num_anchors), + kernel_size=1 + ) + ) + return head_layers \ No newline at end of file diff --git a/yolov6/models/heads/effidehead_fuseab.py b/yolov6/models/heads/effidehead_fuseab.py new file mode 100644 index 00000000..f30f126d --- /dev/null +++ b/yolov6/models/heads/effidehead_fuseab.py @@ -0,0 +1,333 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +from yolov6.layers.common import * +from yolov6.assigners.anchor_generator import generate_anchors +from yolov6.utils.general import dist2bbox + + +class Detect(nn.Module): + '''Efficient Decoupled Head for fusing anchor-base branches. + ''' + def __init__(self, num_classes=80, anchors=None, num_layers=3, inplace=True, head_layers=None, use_dfl=True, reg_max=16): # detection layer + super().__init__() + assert head_layers is not None + self.nc = num_classes # number of classes + self.no = num_classes + 5 # number of outputs per anchor + self.nl = num_layers # number of detection layers + if isinstance(anchors, (list, tuple)): + self.na = len(anchors[0]) // 2 + else: + self.na = anchors + self.grid = [torch.zeros(1)] * num_layers + self.prior_prob = 1e-2 + self.inplace = inplace + stride = [8, 16, 32] if num_layers == 3 else [8, 16, 32, 64] # strides computed during build + self.stride = torch.tensor(stride) + self.use_dfl = use_dfl + self.reg_max = reg_max + self.proj_conv = nn.Conv2d(self.reg_max + 1, 1, 1, bias=False) + self.grid_cell_offset = 0.5 + self.grid_cell_size = 5.0 + self.anchors_init= ((torch.tensor(anchors) / self.stride[:,None])).reshape(self.nl, self.na, 2) + + # Init decouple head + self.stems = nn.ModuleList() + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + self.cls_preds_af = nn.ModuleList() + self.reg_preds_af = nn.ModuleList() + self.cls_preds_ab = nn.ModuleList() + self.reg_preds_ab = nn.ModuleList() + + # Efficient decoupled head layers + for i in range(num_layers): + idx = i*7 + self.stems.append(head_layers[idx]) + self.cls_convs.append(head_layers[idx+1]) + self.reg_convs.append(head_layers[idx+2]) + self.cls_preds_af.append(head_layers[idx+3]) + self.reg_preds_af.append(head_layers[idx+4]) + self.cls_preds_ab.append(head_layers[idx+5]) + self.reg_preds_ab.append(head_layers[idx+6]) + + def initialize_biases(self): + + for conv in self.cls_preds_af: + b = conv.bias.view(-1, ) + b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob)) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + for conv in self.cls_preds_ab: + b = conv.bias.view(-1, ) + b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob)) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + for conv in self.reg_preds_af: + b = conv.bias.view(-1, ) + b.data.fill_(1.0) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + for conv in self.reg_preds_ab: + b = conv.bias.view(-1, ) + b.data.fill_(1.0) + conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + w = conv.weight + w.data.fill_(0.) + conv.weight = torch.nn.Parameter(w, requires_grad=True) + + self.proj = nn.Parameter(torch.linspace(0, self.reg_max, self.reg_max + 1), requires_grad=False) + self.proj_conv.weight = nn.Parameter(self.proj.view([1, self.reg_max + 1, 1, 1]).clone().detach(), + requires_grad=False) + + def forward(self, x): + if self.training: + device = x[0].device + cls_score_list_af = [] + reg_dist_list_af = [] + cls_score_list_ab = [] + reg_dist_list_ab = [] + + for i in range(self.nl): + b, _, h, w = x[i].shape + l = h * w + + x[i] = self.stems[i](x[i]) + cls_x = x[i] + reg_x = x[i] + + cls_feat = self.cls_convs[i](cls_x) + reg_feat = self.reg_convs[i](reg_x) + + #anchor_base + cls_output_ab = self.cls_preds_ab[i](cls_feat) + reg_output_ab = self.reg_preds_ab[i](reg_feat) + + cls_output_ab = torch.sigmoid(cls_output_ab) + cls_output_ab = cls_output_ab.reshape(b, self.na, -1, h, w).permute(0,1,3,4,2) + cls_score_list_ab.append(cls_output_ab.flatten(1,3)) + + reg_output_ab = reg_output_ab.reshape(b, self.na, -1, h, w).permute(0,1,3,4,2) + reg_output_ab[..., 2:4] = ((reg_output_ab[..., 2:4].sigmoid() * 2) ** 2 ) * (self.anchors_init[i].reshape(1, self.na, 1, 1, 2).to(device)) + reg_dist_list_ab.append(reg_output_ab.flatten(1,3)) + + #anchor_free + cls_output_af = self.cls_preds_af[i](cls_feat) + reg_output_af = self.reg_preds_af[i](reg_feat) + + cls_output_af = torch.sigmoid(cls_output_af) + cls_score_list_af.append(cls_output_af.flatten(2).permute((0, 2, 1))) + reg_dist_list_af.append(reg_output_af.flatten(2).permute((0, 2, 1))) + + + cls_score_list_ab = torch.cat(cls_score_list_ab, axis=1) + reg_dist_list_ab = torch.cat(reg_dist_list_ab, axis=1) + cls_score_list_af = torch.cat(cls_score_list_af, axis=1) + reg_dist_list_af = torch.cat(reg_dist_list_af, axis=1) + + return x, cls_score_list_ab, reg_dist_list_ab, cls_score_list_af, reg_dist_list_af + + else: + device = x[0].device + cls_score_list_af = [] + reg_dist_list_af = [] + + for i in range(self.nl): + b, _, h, w = x[i].shape + l = h * w + + x[i] = self.stems[i](x[i]) + cls_x = x[i] + reg_x = x[i] + + cls_feat = self.cls_convs[i](cls_x) + reg_feat = self.reg_convs[i](reg_x) + + #anchor_free + cls_output_af = self.cls_preds_af[i](cls_feat) + reg_output_af = self.reg_preds_af[i](reg_feat) + + if self.use_dfl: + reg_output_af = reg_output_af.reshape([-1, 4, self.reg_max + 1, l]).permute(0, 2, 1, 3) + reg_output_af = self.proj_conv(F.softmax(reg_output_af, dim=1)) + + cls_output_af = torch.sigmoid(cls_output_af) + cls_score_list_af.append(cls_output_af.reshape([b, self.nc, l])) + reg_dist_list_af.append(reg_output_af.reshape([b, 4, l])) + + cls_score_list_af = torch.cat(cls_score_list_af, axis=-1).permute(0, 2, 1) + reg_dist_list_af = torch.cat(reg_dist_list_af, axis=-1).permute(0, 2, 1) + + + #anchor_free + anchor_points_af, stride_tensor_af = generate_anchors( + x, self.stride, self.grid_cell_size, self.grid_cell_offset, device=x[0].device, is_eval=True, mode='af') + + pred_bboxes_af = dist2bbox(reg_dist_list_af, anchor_points_af, box_format='xywh') + pred_bboxes_af *= stride_tensor_af + + pred_bboxes = pred_bboxes_af + cls_score_list = cls_score_list_af + + return torch.cat( + [ + pred_bboxes, + torch.ones((b, pred_bboxes.shape[1], 1), device=pred_bboxes.device, dtype=pred_bboxes.dtype), + cls_score_list + ], + axis=-1) + + +def build_effidehead_layer(channels_list, num_anchors, num_classes, reg_max=16, num_layers=3): + + chx = [6, 8, 10] if num_layers == 3 else [8, 9, 10, 11] + + head_layers = nn.Sequential( + # stem0 + Conv( + in_channels=channels_list[chx[0]], + out_channels=channels_list[chx[0]], + kernel_size=1, + stride=1 + ), + # cls_conv0 + Conv( + in_channels=channels_list[chx[0]], + out_channels=channels_list[chx[0]], + kernel_size=3, + stride=1 + ), + # reg_conv0 + Conv( + in_channels=channels_list[chx[0]], + out_channels=channels_list[chx[0]], + kernel_size=3, + stride=1 + ), + # cls_pred0_af + nn.Conv2d( + in_channels=channels_list[chx[0]], + out_channels=num_classes, + kernel_size=1 + ), + # reg_pred0_af + nn.Conv2d( + in_channels=channels_list[chx[0]], + out_channels=4 * (reg_max + 1), + kernel_size=1 + ), + # cls_pred0_3ab + nn.Conv2d( + in_channels=channels_list[chx[0]], + out_channels=num_classes * num_anchors, + kernel_size=1 + ), + # reg_pred0_3ab + nn.Conv2d( + in_channels=channels_list[chx[0]], + out_channels=4 * num_anchors, + kernel_size=1 + ), + # stem1 + Conv( + in_channels=channels_list[chx[1]], + out_channels=channels_list[chx[1]], + kernel_size=1, + stride=1 + ), + # cls_conv1 + Conv( + in_channels=channels_list[chx[1]], + out_channels=channels_list[chx[1]], + kernel_size=3, + stride=1 + ), + # reg_conv1 + Conv( + in_channels=channels_list[chx[1]], + out_channels=channels_list[chx[1]], + kernel_size=3, + stride=1 + ), + # cls_pred1_af + nn.Conv2d( + in_channels=channels_list[chx[1]], + out_channels=num_classes, + kernel_size=1 + ), + # reg_pred1_af + nn.Conv2d( + in_channels=channels_list[chx[1]], + out_channels=4 * (reg_max + 1), + kernel_size=1 + ), + # cls_pred1_3ab + nn.Conv2d( + in_channels=channels_list[chx[1]], + out_channels=num_classes * num_anchors, + kernel_size=1 + ), + # reg_pred1_3ab + nn.Conv2d( + in_channels=channels_list[chx[1]], + out_channels=4 * num_anchors, + kernel_size=1 + ), + # stem2 + Conv( + in_channels=channels_list[chx[2]], + out_channels=channels_list[chx[2]], + kernel_size=1, + stride=1 + ), + # cls_conv2 + Conv( + in_channels=channels_list[chx[2]], + out_channels=channels_list[chx[2]], + kernel_size=3, + stride=1 + ), + # reg_conv2 + Conv( + in_channels=channels_list[chx[2]], + out_channels=channels_list[chx[2]], + kernel_size=3, + stride=1 + ), + # cls_pred2_af + nn.Conv2d( + in_channels=channels_list[chx[2]], + out_channels=num_classes, + kernel_size=1 + ), + # reg_pred2_af + nn.Conv2d( + in_channels=channels_list[chx[2]], + out_channels=4 * (reg_max + 1), + kernel_size=1 + ), + # cls_pred2_3ab + nn.Conv2d( + in_channels=channels_list[chx[2]], + out_channels=num_classes * num_anchors, + kernel_size=1 + ), + # reg_pred2_3ab + nn.Conv2d( + in_channels=channels_list[chx[2]], + out_channels=4 * num_anchors, + kernel_size=1 + ), + ) + + return head_layers diff --git a/yolov6/models/losses/loss.py b/yolov6/models/losses/loss.py new file mode 100644 index 00000000..7f23636f --- /dev/null +++ b/yolov6/models/losses/loss.py @@ -0,0 +1,269 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import torch +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from yolov6.assigners.anchor_generator import generate_anchors +from yolov6.utils.general import dist2bbox, bbox2dist, xywh2xyxy, box_iou +from yolov6.utils.figure_iou import IOUloss +from yolov6.assigners.atss_assigner import ATSSAssigner +from yolov6.assigners.tal_assigner import TaskAlignedAssigner + +class ComputeLoss: + '''Loss computation func.''' + def __init__(self, + fpn_strides=[8, 16, 32], + grid_cell_size=5.0, + grid_cell_offset=0.5, + num_classes=80, + ori_img_size=640, + warmup_epoch=4, + use_dfl=True, + reg_max=16, + iou_type='giou', + loss_weight={ + 'class': 1.0, + 'iou': 2.5, + 'dfl': 0.5} + ): + + self.fpn_strides = fpn_strides + self.grid_cell_size = grid_cell_size + self.grid_cell_offset = grid_cell_offset + self.num_classes = num_classes + self.ori_img_size = ori_img_size + + self.warmup_epoch = warmup_epoch + self.warmup_assigner = ATSSAssigner(9, num_classes=self.num_classes) + self.formal_assigner = TaskAlignedAssigner(topk=13, num_classes=self.num_classes, alpha=1.0, beta=6.0) + + self.use_dfl = use_dfl + self.reg_max = reg_max + self.proj = nn.Parameter(torch.linspace(0, self.reg_max, self.reg_max + 1), requires_grad=False) + self.iou_type = iou_type + self.varifocal_loss = VarifocalLoss().cuda() + self.bbox_loss = BboxLoss(self.num_classes, self.reg_max, self.use_dfl, self.iou_type).cuda() + self.loss_weight = loss_weight + + def __call__( + self, + outputs, + targets, + epoch_num, + step_num + ): + + feats, pred_scores, pred_distri = outputs + anchors, anchor_points, n_anchors_list, stride_tensor = \ + generate_anchors(feats, self.fpn_strides, self.grid_cell_size, self.grid_cell_offset, device=feats[0].device) + + assert pred_scores.type() == pred_distri.type() + gt_bboxes_scale = torch.full((1,4), self.ori_img_size).type_as(pred_scores) + batch_size = pred_scores.shape[0] + + # targets + targets =self.preprocess(targets, batch_size, gt_bboxes_scale) + gt_labels = targets[:, :, :1] + gt_bboxes = targets[:, :, 1:] #xyxy + mask_gt = (gt_bboxes.sum(-1, keepdim=True) > 0).float() + + # pboxes + anchor_points_s = anchor_points / stride_tensor + pred_bboxes = self.bbox_decode(anchor_points_s, pred_distri) #xyxy + + try: + if epoch_num < self.warmup_epoch: + target_labels, target_bboxes, target_scores, fg_mask = \ + self.warmup_assigner( + anchors, + n_anchors_list, + gt_labels, + gt_bboxes, + mask_gt, + pred_bboxes.detach() * stride_tensor) + else: + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + pred_scores.detach(), + pred_bboxes.detach() * stride_tensor, + anchor_points, + gt_labels, + gt_bboxes, + mask_gt) + + except RuntimeError: + print( + "OOM RuntimeError is raised due to the huge memory cost during label assignment. \ + CPU mode is applied in this batch. If you want to avoid this issue, \ + try to reduce the batch size or image size." + ) + torch.cuda.empty_cache() + print("------------CPU Mode for This Batch-------------") + if epoch_num < self.warmup_epoch: + _anchors = anchors.cpu().float() + _n_anchors_list = n_anchors_list + _gt_labels = gt_labels.cpu().float() + _gt_bboxes = gt_bboxes.cpu().float() + _mask_gt = mask_gt.cpu().float() + _pred_bboxes = pred_bboxes.detach().cpu().float() + _stride_tensor = stride_tensor.cpu().float() + + target_labels, target_bboxes, target_scores, fg_mask = \ + self.warmup_assigner( + _anchors, + _n_anchors_list, + _gt_labels, + _gt_bboxes, + _mask_gt, + _pred_bboxes * _stride_tensor) + + else: + _pred_scores = pred_scores.detach().cpu().float() + _pred_bboxes = pred_bboxes.detach().cpu().float() + _anchor_points = anchor_points.cpu().float() + _gt_labels = gt_labels.cpu().float() + _gt_bboxes = gt_bboxes.cpu().float() + _mask_gt = mask_gt.cpu().float() + _stride_tensor = stride_tensor.cpu().float() + + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + _pred_scores, + _pred_bboxes * _stride_tensor, + _anchor_points, + _gt_labels, + _gt_bboxes, + _mask_gt) + + target_labels = target_labels.cuda() + target_bboxes = target_bboxes.cuda() + target_scores = target_scores.cuda() + fg_mask = fg_mask.cuda() + #Dynamic release GPU memory + if step_num % 10 == 0: + torch.cuda.empty_cache() + + # rescale bbox + target_bboxes /= stride_tensor + + # cls loss + target_labels = torch.where(fg_mask > 0, target_labels, torch.full_like(target_labels, self.num_classes)) + one_hot_label = F.one_hot(target_labels.long(), self.num_classes + 1)[..., :-1] + loss_cls = self.varifocal_loss(pred_scores, target_scores, one_hot_label) + + target_scores_sum = target_scores.sum() + # avoid devide zero error, devide by zero will cause loss to be inf or nan. + # if target_scores_sum is 0, loss_cls equals to 0 alson + if target_scores_sum > 0: + loss_cls /= target_scores_sum + + # bbox loss + loss_iou, loss_dfl = self.bbox_loss(pred_distri, pred_bboxes, anchor_points_s, target_bboxes, + target_scores, target_scores_sum, fg_mask) + + loss = self.loss_weight['class'] * loss_cls + \ + self.loss_weight['iou'] * loss_iou + \ + self.loss_weight['dfl'] * loss_dfl + + return loss, \ + torch.cat(((self.loss_weight['iou'] * loss_iou).unsqueeze(0), + (self.loss_weight['dfl'] * loss_dfl).unsqueeze(0), + (self.loss_weight['class'] * loss_cls).unsqueeze(0))).detach() + + def preprocess(self, targets, batch_size, scale_tensor): + targets_list = np.zeros((batch_size, 1, 5)).tolist() + for i, item in enumerate(targets.cpu().numpy().tolist()): + targets_list[int(item[0])].append(item[1:]) + max_len = max((len(l) for l in targets_list)) + targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device) + batch_target = targets[:, :, 1:5].mul_(scale_tensor) + targets[..., 1:] = xywh2xyxy(batch_target) + return targets + + def bbox_decode(self, anchor_points, pred_dist): + if self.use_dfl: + batch_size, n_anchors, _ = pred_dist.shape + pred_dist = F.softmax(pred_dist.view(batch_size, n_anchors, 4, self.reg_max + 1), dim=-1).matmul(self.proj.to(pred_dist.device)) + return dist2bbox(pred_dist, anchor_points) + + +class VarifocalLoss(nn.Module): + def __init__(self): + super(VarifocalLoss, self).__init__() + + def forward(self, pred_score,gt_score, label, alpha=0.75, gamma=2.0): + + weight = alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label + with torch.cuda.amp.autocast(enabled=False): + loss = (F.binary_cross_entropy(pred_score.float(), gt_score.float(), reduction='none') * weight).sum() + + return loss + + +class BboxLoss(nn.Module): + def __init__(self, num_classes, reg_max, use_dfl=False, iou_type='giou'): + super(BboxLoss, self).__init__() + self.num_classes = num_classes + self.iou_loss = IOUloss(box_format='xyxy', iou_type=iou_type, eps=1e-10) + self.reg_max = reg_max + self.use_dfl = use_dfl + + def forward(self, pred_dist, pred_bboxes, anchor_points, + target_bboxes, target_scores, target_scores_sum, fg_mask): + + # select positive samples mask + num_pos = fg_mask.sum() + if num_pos > 0: + # iou loss + bbox_mask = fg_mask.unsqueeze(-1).repeat([1, 1, 4]) + pred_bboxes_pos = torch.masked_select(pred_bboxes, + bbox_mask).reshape([-1, 4]) + target_bboxes_pos = torch.masked_select( + target_bboxes, bbox_mask).reshape([-1, 4]) + bbox_weight = torch.masked_select( + target_scores.sum(-1), fg_mask).unsqueeze(-1) + loss_iou = self.iou_loss(pred_bboxes_pos, + target_bboxes_pos) * bbox_weight + if target_scores_sum == 0: + loss_iou = loss_iou.sum() + else: + loss_iou = loss_iou.sum() / target_scores_sum + + # dfl loss + if self.use_dfl: + dist_mask = fg_mask.unsqueeze(-1).repeat( + [1, 1, (self.reg_max + 1) * 4]) + pred_dist_pos = torch.masked_select( + pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1]) + target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max) + target_ltrb_pos = torch.masked_select( + target_ltrb, bbox_mask).reshape([-1, 4]) + loss_dfl = self._df_loss(pred_dist_pos, + target_ltrb_pos) * bbox_weight + if target_scores_sum == 0: + loss_dfl = loss_dfl.sum() + else: + loss_dfl = loss_dfl.sum() / target_scores_sum + else: + loss_dfl = pred_dist.sum() * 0. + + else: + loss_iou = pred_dist.sum() * 0. + loss_dfl = pred_dist.sum() * 0. + + return loss_iou, loss_dfl + + def _df_loss(self, pred_dist, target): + target_left = target.to(torch.long) + target_right = target_left + 1 + weight_left = target_right.to(torch.float) - target + weight_right = 1 - weight_left + loss_left = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_left.view(-1), reduction='none').view( + target_left.shape) * weight_left + loss_right = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_right.view(-1), reduction='none').view( + target_left.shape) * weight_right + return (loss_left + loss_right).mean(-1, keepdim=True) diff --git a/yolov6/models/losses/loss_distill.py b/yolov6/models/losses/loss_distill.py new file mode 100644 index 00000000..1b88a066 --- /dev/null +++ b/yolov6/models/losses/loss_distill.py @@ -0,0 +1,360 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import torch +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from yolov6.assigners.anchor_generator import generate_anchors +from yolov6.utils.general import dist2bbox, bbox2dist, xywh2xyxy +from yolov6.utils.figure_iou import IOUloss +from yolov6.assigners.atss_assigner import ATSSAssigner +from yolov6.assigners.tal_assigner import TaskAlignedAssigner + + +class ComputeLoss: + '''Loss computation func.''' + def __init__(self, + fpn_strides=[8, 16, 32], + grid_cell_size=5.0, + grid_cell_offset=0.5, + num_classes=80, + ori_img_size=640, + warmup_epoch=0, + use_dfl=True, + reg_max=16, + iou_type='giou', + loss_weight={ + 'class': 1.0, + 'iou': 2.5, + 'dfl': 0.5, + 'cwd': 10.0}, + distill_feat = False, + distill_weight={ + 'class': 1.0, + 'dfl': 1.0, + } + ): + + self.fpn_strides = fpn_strides + self.grid_cell_size = grid_cell_size + self.grid_cell_offset = grid_cell_offset + self.num_classes = num_classes + self.ori_img_size = ori_img_size + + self.warmup_epoch = warmup_epoch + self.warmup_assigner = ATSSAssigner(9, num_classes=self.num_classes) + self.formal_assigner = TaskAlignedAssigner(topk=13, num_classes=self.num_classes, alpha=1.0, beta=6.0) + + self.use_dfl = use_dfl + self.reg_max = reg_max + self.proj = nn.Parameter(torch.linspace(0, self.reg_max, self.reg_max + 1), requires_grad=False) + self.iou_type = iou_type + self.varifocal_loss = VarifocalLoss().cuda() + self.bbox_loss = BboxLoss(self.num_classes, self.reg_max, self.use_dfl, self.iou_type).cuda() + self.loss_weight = loss_weight + self.distill_feat = distill_feat + self.distill_weight = distill_weight + + def __call__( + self, + outputs, + t_outputs, + s_featmaps, + t_featmaps, + targets, + epoch_num, + max_epoch, + temperature, + step_num + ): + + feats, pred_scores, pred_distri = outputs + t_feats, t_pred_scores, t_pred_distri = t_outputs[0], t_outputs[-2], t_outputs[-1] + anchors, anchor_points, n_anchors_list, stride_tensor = \ + generate_anchors(feats, self.fpn_strides, self.grid_cell_size, self.grid_cell_offset, device=feats[0].device) + t_anchors, t_anchor_points, t_n_anchors_list, t_stride_tensor = \ + generate_anchors(t_feats, self.fpn_strides, self.grid_cell_size, self.grid_cell_offset, device=feats[0].device) + + assert pred_scores.type() == pred_distri.type() + gt_bboxes_scale = torch.full((1,4), self.ori_img_size).type_as(pred_scores) + batch_size = pred_scores.shape[0] + + # targets + targets =self.preprocess(targets, batch_size, gt_bboxes_scale) + gt_labels = targets[:, :, :1] + gt_bboxes = targets[:, :, 1:] #xyxy + mask_gt = (gt_bboxes.sum(-1, keepdim=True) > 0).float() + + # pboxes + anchor_points_s = anchor_points / stride_tensor + pred_bboxes = self.bbox_decode(anchor_points_s, pred_distri) #xyxy + t_anchor_points_s = t_anchor_points / t_stride_tensor + t_pred_bboxes = self.bbox_decode(t_anchor_points_s, t_pred_distri) #xyxy + + try: + if epoch_num < self.warmup_epoch: + target_labels, target_bboxes, target_scores, fg_mask = \ + self.warmup_assigner( + anchors, + n_anchors_list, + gt_labels, + gt_bboxes, + mask_gt, + pred_bboxes.detach() * stride_tensor) + else: + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + pred_scores.detach(), + pred_bboxes.detach() * stride_tensor, + anchor_points, + gt_labels, + gt_bboxes, + mask_gt) + + except RuntimeError: + print( + "OOM RuntimeError is raised due to the huge memory cost during label assignment. \ + CPU mode is applied in this batch. If you want to avoid this issue, \ + try to reduce the batch size or image size." + ) + torch.cuda.empty_cache() + print("------------CPU Mode for This Batch-------------") + if epoch_num < self.warmup_epoch: + _anchors = anchors.cpu().float() + _n_anchors_list = n_anchors_list + _gt_labels = gt_labels.cpu().float() + _gt_bboxes = gt_bboxes.cpu().float() + _mask_gt = mask_gt.cpu().float() + _pred_bboxes = pred_bboxes.detach().cpu().float() + _stride_tensor = stride_tensor.cpu().float() + + target_labels, target_bboxes, target_scores, fg_mask = \ + self.warmup_assigner( + _anchors, + _n_anchors_list, + _gt_labels, + _gt_bboxes, + _mask_gt, + _pred_bboxes * _stride_tensor) + + else: + _pred_scores = pred_scores.detach().cpu().float() + _pred_bboxes = pred_bboxes.detach().cpu().float() + _anchor_points = anchor_points.cpu().float() + _gt_labels = gt_labels.cpu().float() + _gt_bboxes = gt_bboxes.cpu().float() + _mask_gt = mask_gt.cpu().float() + _stride_tensor = stride_tensor.cpu().float() + + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + _pred_scores, + _pred_bboxes * _stride_tensor, + _anchor_points, + _gt_labels, + _gt_bboxes, + _mask_gt) + + target_labels = target_labels.cuda() + target_bboxes = target_bboxes.cuda() + target_scores = target_scores.cuda() + fg_mask = fg_mask.cuda() + + #Dynamic release GPU memory + if step_num % 10 == 0: + torch.cuda.empty_cache() + + # rescale bbox + target_bboxes /= stride_tensor + + # cls loss + target_labels = torch.where(fg_mask > 0, target_labels, torch.full_like(target_labels, self.num_classes)) + one_hot_label = F.one_hot(target_labels, self.num_classes + 1)[..., :-1] + loss_cls = self.varifocal_loss(pred_scores, target_scores, one_hot_label) + + target_scores_sum = target_scores.sum() + # avoid devide zero error, devide by zero will cause loss to be inf or nan. + if target_scores_sum > 0: + loss_cls /= target_scores_sum + + # bbox loss + loss_iou, loss_dfl, d_loss_dfl = self.bbox_loss(pred_distri, pred_bboxes, t_pred_distri, t_pred_bboxes, temperature, anchor_points_s, + target_bboxes, target_scores, target_scores_sum, fg_mask) + + logits_student = pred_scores + logits_teacher = t_pred_scores + distill_num_classes = self.num_classes + d_loss_cls = self.distill_loss_cls(logits_student, logits_teacher, distill_num_classes, temperature) + if self.distill_feat: + d_loss_cw = self.distill_loss_cw(s_featmaps, t_featmaps) + else: + d_loss_cw = torch.tensor(0.).to(feats[0].device) + import math + distill_weightdecay = ((1 - math.cos(epoch_num * math.pi / max_epoch)) / 2) * (0.01- 1) + 1 + d_loss_dfl *= distill_weightdecay + d_loss_cls *= distill_weightdecay + d_loss_cw *= distill_weightdecay + loss_cls_all = loss_cls + d_loss_cls * self.distill_weight['class'] + loss_dfl_all = loss_dfl + d_loss_dfl * self.distill_weight['dfl'] + loss = self.loss_weight['class'] * loss_cls_all + \ + self.loss_weight['iou'] * loss_iou + \ + self.loss_weight['dfl'] * loss_dfl_all + \ + self.loss_weight['cwd'] * d_loss_cw + + return loss, \ + torch.cat(((self.loss_weight['iou'] * loss_iou).unsqueeze(0), + (self.loss_weight['dfl'] * loss_dfl_all).unsqueeze(0), + (self.loss_weight['class'] * loss_cls_all).unsqueeze(0), + (self.loss_weight['cwd'] * d_loss_cw).unsqueeze(0))).detach() + + def distill_loss_cls(self, logits_student, logits_teacher, num_classes, temperature=20): + logits_student = logits_student.view(-1, num_classes) + logits_teacher = logits_teacher.view(-1, num_classes) + pred_student = F.softmax(logits_student / temperature, dim=1) + pred_teacher = F.softmax(logits_teacher / temperature, dim=1) + log_pred_student = torch.log(pred_student) + + d_loss_cls = F.kl_div(log_pred_student, pred_teacher, reduction="sum") + d_loss_cls *= temperature**2 + return d_loss_cls + def distill_loss_cw(self, s_feats, t_feats, temperature=1): + N,C,H,W = s_feats[0].shape + # print(N,C,H,W) + loss_cw = F.kl_div(F.log_softmax(s_feats[0].view(N,C,H*W)/temperature, dim=2), + F.log_softmax(t_feats[0].view(N,C,H*W).detach()/temperature, dim=2), + reduction='sum', + log_target=True) * (temperature * temperature)/ (N*C) + + N,C,H,W = s_feats[1].shape + # print(N,C,H,W) + loss_cw += F.kl_div(F.log_softmax(s_feats[1].view(N,C,H*W)/temperature, dim=2), + F.log_softmax(t_feats[1].view(N,C,H*W).detach()/temperature, dim=2), + reduction='sum', + log_target=True) * (temperature * temperature)/ (N*C) + + N,C,H,W = s_feats[2].shape + # print(N,C,H,W) + loss_cw += F.kl_div(F.log_softmax(s_feats[2].view(N,C,H*W)/temperature, dim=2), + F.log_softmax(t_feats[2].view(N,C,H*W).detach()/temperature, dim=2), + reduction='sum', + log_target=True) * (temperature * temperature)/ (N*C) + # print(loss_cw) + return loss_cw + + def preprocess(self, targets, batch_size, scale_tensor): + targets_list = np.zeros((batch_size, 1, 5)).tolist() + for i, item in enumerate(targets.cpu().numpy().tolist()): + targets_list[int(item[0])].append(item[1:]) + max_len = max((len(l) for l in targets_list)) + targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device) + batch_target = targets[:, :, 1:5].mul_(scale_tensor) + targets[..., 1:] = xywh2xyxy(batch_target) + return targets + + def bbox_decode(self, anchor_points, pred_dist): + if self.use_dfl: + batch_size, n_anchors, _ = pred_dist.shape + pred_dist = F.softmax(pred_dist.view(batch_size, n_anchors, 4, self.reg_max + 1), dim=-1).matmul(self.proj.to(pred_dist.device)) + return dist2bbox(pred_dist, anchor_points) + + +class VarifocalLoss(nn.Module): + def __init__(self): + super(VarifocalLoss, self).__init__() + + def forward(self, pred_score,gt_score, label, alpha=0.75, gamma=2.0): + + weight = alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label + with torch.cuda.amp.autocast(enabled=False): + loss = (F.binary_cross_entropy(pred_score.float(), gt_score.float(), reduction='none') * weight).sum() + + return loss + + +class BboxLoss(nn.Module): + def __init__(self, num_classes, reg_max, use_dfl=False, iou_type='giou'): + super(BboxLoss, self).__init__() + self.num_classes = num_classes + self.iou_loss = IOUloss(box_format='xyxy', iou_type=iou_type, eps=1e-10) + self.reg_max = reg_max + self.use_dfl = use_dfl + + def forward(self, pred_dist, pred_bboxes, t_pred_dist, t_pred_bboxes, temperature, anchor_points, + target_bboxes, target_scores, target_scores_sum, fg_mask): + # select positive samples mask + num_pos = fg_mask.sum() + if num_pos > 0: + # iou loss + bbox_mask = fg_mask.unsqueeze(-1).repeat([1, 1, 4]) + pred_bboxes_pos = torch.masked_select(pred_bboxes, + bbox_mask).reshape([-1, 4]) + t_pred_bboxes_pos = torch.masked_select(t_pred_bboxes, + bbox_mask).reshape([-1, 4]) + target_bboxes_pos = torch.masked_select( + target_bboxes, bbox_mask).reshape([-1, 4]) + bbox_weight = torch.masked_select( + target_scores.sum(-1), fg_mask).unsqueeze(-1) + loss_iou = self.iou_loss(pred_bboxes_pos, + target_bboxes_pos) * bbox_weight + if target_scores_sum == 0: + loss_iou = loss_iou.sum() + else: + loss_iou = loss_iou.sum() / target_scores_sum + + # dfl loss + if self.use_dfl: + dist_mask = fg_mask.unsqueeze(-1).repeat( + [1, 1, (self.reg_max + 1) * 4]) + pred_dist_pos = torch.masked_select( + pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1]) + t_pred_dist_pos = torch.masked_select( + t_pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1]) + target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max) + target_ltrb_pos = torch.masked_select( + target_ltrb, bbox_mask).reshape([-1, 4]) + loss_dfl = self._df_loss(pred_dist_pos, + target_ltrb_pos) * bbox_weight + d_loss_dfl = self.distill_loss_dfl(pred_dist_pos, t_pred_dist_pos, temperature) * bbox_weight + if target_scores_sum == 0: + loss_dfl = loss_dfl.sum() + d_loss_dfl = d_loss_dfl.sum() + else: + loss_dfl = loss_dfl.sum() / target_scores_sum + d_loss_dfl = d_loss_dfl.sum() / target_scores_sum + else: + loss_dfl = pred_dist.sum() * 0. + d_loss_dfl = pred_dist.sum() * 0. + + else: + + loss_iou = pred_dist.sum() * 0. + loss_dfl = pred_dist.sum() * 0. + d_loss_dfl = pred_dist.sum() * 0. + + return loss_iou, loss_dfl, d_loss_dfl + + def _df_loss(self, pred_dist, target): + target_left = target.to(torch.long) + target_right = target_left + 1 + weight_left = target_right.to(torch.float) - target + weight_right = 1 - weight_left + loss_left = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_left.view(-1), reduction='none').view( + target_left.shape) * weight_left + loss_right = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_right.view(-1), reduction='none').view( + target_left.shape) * weight_right + return (loss_left + loss_right).mean(-1, keepdim=True) + + def distill_loss_dfl(self, logits_student, logits_teacher, temperature=20): + + logits_student = logits_student.view(-1,17) + logits_teacher = logits_teacher.view(-1,17) + pred_student = F.softmax(logits_student / temperature, dim=1) + pred_teacher = F.softmax(logits_teacher / temperature, dim=1) + log_pred_student = torch.log(pred_student) + + d_loss_dfl = F.kl_div(log_pred_student, pred_teacher, reduction="none").sum(1).mean() + d_loss_dfl *= temperature**2 + return d_loss_dfl diff --git a/yolov6/models/losses/loss_distill_ns.py b/yolov6/models/losses/loss_distill_ns.py new file mode 100644 index 00000000..be51bdd8 --- /dev/null +++ b/yolov6/models/losses/loss_distill_ns.py @@ -0,0 +1,348 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import torch +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from yolov6.assigners.anchor_generator import generate_anchors +from yolov6.utils.general import dist2bbox, bbox2dist, xywh2xyxy +from yolov6.utils.figure_iou import IOUloss +from yolov6.assigners.atss_assigner import ATSSAssigner +from yolov6.assigners.tal_assigner import TaskAlignedAssigner + + +class ComputeLoss: + '''Loss computation func.''' + def __init__(self, + fpn_strides=[8, 16, 32], + grid_cell_size=5.0, + grid_cell_offset=0.5, + num_classes=80, + ori_img_size=640, + warmup_epoch=0, + use_dfl=True, + reg_max=16, + iou_type='giou', + loss_weight={ + 'class': 1.0, + 'iou': 2.5, + 'dfl': 0.5, + 'cwd': 10.0}, + distill_feat = False, + distill_weight={ + 'class': 1.0, + 'dfl': 1.0, + } + ): + + self.fpn_strides = fpn_strides + self.grid_cell_size = grid_cell_size + self.grid_cell_offset = grid_cell_offset + self.num_classes = num_classes + self.ori_img_size = ori_img_size + + self.warmup_epoch = warmup_epoch + self.formal_assigner = TaskAlignedAssigner(topk=13, num_classes=self.num_classes, alpha=1.0, beta=6.0) + + self.use_dfl = use_dfl + self.reg_max = reg_max + self.proj = nn.Parameter(torch.linspace(0, self.reg_max, self.reg_max + 1), requires_grad=False) + self.iou_type = iou_type + self.varifocal_loss = VarifocalLoss().cuda() + self.bbox_loss = BboxLoss(self.num_classes, self.reg_max, self.use_dfl, self.iou_type).cuda() + self.loss_weight = loss_weight + self.distill_feat = distill_feat + self.distill_weight = distill_weight + + def __call__( + self, + outputs, + t_outputs, + s_featmaps, + t_featmaps, + targets, + epoch_num, + max_epoch, + temperature, + step_num + ): + + feats, pred_scores, pred_distri, pred_lrtb = outputs + t_feats, t_pred_scores, t_pred_distri = t_outputs[0], t_outputs[-2], t_outputs[-1] + anchors, anchor_points, n_anchors_list, stride_tensor = \ + generate_anchors(feats, self.fpn_strides, self.grid_cell_size, self.grid_cell_offset, device=feats[0].device) + t_anchors, t_anchor_points, t_n_anchors_list, t_stride_tensor = \ + generate_anchors(t_feats, self.fpn_strides, self.grid_cell_size, self.grid_cell_offset, device=feats[0].device) + + assert pred_scores.type() == pred_distri.type() + gt_bboxes_scale = torch.full((1,4), self.ori_img_size).type_as(pred_scores) + batch_size = pred_scores.shape[0] + + # targets + targets =self.preprocess(targets, batch_size, gt_bboxes_scale) + gt_labels = targets[:, :, :1] + gt_bboxes = targets[:, :, 1:] #xyxy + mask_gt = (gt_bboxes.sum(-1, keepdim=True) > 0).float() + + # pboxes + anchor_points_s = anchor_points / stride_tensor + pred_bboxes = self.bbox_decode(anchor_points_s, pred_distri) #xyxy #distri branch + pred_bboxes_lrtb = dist2bbox(pred_lrtb, anchor_points_s) #iou branch + t_anchor_points_s = t_anchor_points / t_stride_tensor + t_pred_bboxes = self.bbox_decode(t_anchor_points_s, t_pred_distri) #xyxy + try: + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + pred_scores.detach(), + pred_bboxes.detach() * stride_tensor, + anchor_points, + gt_labels, + gt_bboxes, + mask_gt) + + except RuntimeError: + print( + "OOM RuntimeError is raised due to the huge memory cost during label assignment. \ + CPU mode is applied in this batch. If you want to avoid this issue, \ + try to reduce the batch size or image size." + ) + torch.cuda.empty_cache() + print("------------CPU Mode for This Batch-------------") + _pred_scores = pred_scores.detach().cpu().float() + _pred_bboxes = pred_bboxes.detach().cpu().float() + _anchor_points = anchor_points.cpu().float() + _gt_labels = gt_labels.cpu().float() + _gt_bboxes = gt_bboxes.cpu().float() + _mask_gt = mask_gt.cpu().float() + _stride_tensor = stride_tensor.cpu().float() + + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + _pred_scores, + _pred_bboxes * _stride_tensor, + _anchor_points, + _gt_labels, + _gt_bboxes, + _mask_gt) + + target_labels = target_labels.cuda() + target_bboxes = target_bboxes.cuda() + target_scores = target_scores.cuda() + fg_mask = fg_mask.cuda() + + #Dynamic release GPU memory + if step_num % 10 == 0: + torch.cuda.empty_cache() + + # rescale bbox + target_bboxes /= stride_tensor + + # cls loss + target_labels = torch.where(fg_mask > 0, target_labels, torch.full_like(target_labels, self.num_classes)) + one_hot_label = F.one_hot(target_labels, self.num_classes + 1)[..., :-1] + loss_cls = self.varifocal_loss(pred_scores, target_scores, one_hot_label) + + target_scores_sum = target_scores.sum() + # avoid devide zero error, devide by zero will cause loss to be inf or nan. + if target_scores_sum > 0: + loss_cls /= target_scores_sum + + # bbox loss + loss_iou, loss_dfl, d_loss_dfl = self.bbox_loss(pred_distri, + pred_bboxes_lrtb, + pred_bboxes, + t_pred_distri, + t_pred_bboxes, + temperature, + anchor_points_s, + target_bboxes, + target_scores, + target_scores_sum, + fg_mask) + + logits_student = pred_scores + logits_teacher = t_pred_scores + distill_num_classes = self.num_classes + d_loss_cls = self.distill_loss_cls(logits_student, logits_teacher, distill_num_classes, temperature) + if self.distill_feat: + d_loss_cw = self.distill_loss_cw(s_featmaps, t_featmaps) + else: + d_loss_cw = torch.tensor(0.).to(feats[0].device) + import math + distill_weightdecay = ((1 - math.cos(epoch_num * math.pi / max_epoch)) / 2) * (0.01- 1) + 1 + d_loss_dfl *= distill_weightdecay + d_loss_cls *= distill_weightdecay + d_loss_cw *= distill_weightdecay + loss_cls_all = loss_cls + d_loss_cls * self.distill_weight['class'] + loss_dfl_all = loss_dfl + d_loss_dfl * self.distill_weight['dfl'] + loss = self.loss_weight['class'] * loss_cls_all + \ + self.loss_weight['iou'] * loss_iou + \ + self.loss_weight['dfl'] * loss_dfl_all + \ + self.loss_weight['cwd'] * d_loss_cw + + return loss, \ + torch.cat(((self.loss_weight['iou'] * loss_iou).unsqueeze(0), + (self.loss_weight['dfl'] * loss_dfl_all).unsqueeze(0), + (self.loss_weight['class'] * loss_cls_all).unsqueeze(0), + (self.loss_weight['cwd'] * d_loss_cw).unsqueeze(0))).detach() + + def distill_loss_cls(self, logits_student, logits_teacher, num_classes, temperature=20): + logits_student = logits_student.view(-1, num_classes) + logits_teacher = logits_teacher.view(-1, num_classes) + pred_student = F.softmax(logits_student / temperature, dim=1) + pred_teacher = F.softmax(logits_teacher / temperature, dim=1) + log_pred_student = torch.log(pred_student) + + d_loss_cls = F.kl_div(log_pred_student, pred_teacher, reduction="sum") + d_loss_cls *= temperature**2 + return d_loss_cls + + def distill_loss_cw(self, s_feats, t_feats, temperature=1): + N,C,H,W = s_feats[0].shape + # print(N,C,H,W) + loss_cw = F.kl_div(F.log_softmax(s_feats[0].view(N,C,H*W)/temperature, dim=2), + F.log_softmax(t_feats[0].view(N,C,H*W).detach()/temperature, dim=2), + reduction='sum', + log_target=True) * (temperature * temperature)/ (N*C) + + N,C,H,W = s_feats[1].shape + # print(N,C,H,W) + loss_cw += F.kl_div(F.log_softmax(s_feats[1].view(N,C,H*W)/temperature, dim=2), + F.log_softmax(t_feats[1].view(N,C,H*W).detach()/temperature, dim=2), + reduction='sum', + log_target=True) * (temperature * temperature)/ (N*C) + + N,C,H,W = s_feats[2].shape + # print(N,C,H,W) + loss_cw += F.kl_div(F.log_softmax(s_feats[2].view(N,C,H*W)/temperature, dim=2), + F.log_softmax(t_feats[2].view(N,C,H*W).detach()/temperature, dim=2), + reduction='sum', + log_target=True) * (temperature * temperature)/ (N*C) + # print(loss_cw) + return loss_cw + + def preprocess(self, targets, batch_size, scale_tensor): + targets_list = np.zeros((batch_size, 1, 5)).tolist() + for i, item in enumerate(targets.cpu().numpy().tolist()): + targets_list[int(item[0])].append(item[1:]) + max_len = max((len(l) for l in targets_list)) + targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device) + batch_target = targets[:, :, 1:5].mul_(scale_tensor) + targets[..., 1:] = xywh2xyxy(batch_target) + return targets + + def bbox_decode(self, anchor_points, pred_dist): + if self.use_dfl: + batch_size, n_anchors, _ = pred_dist.shape + pred_dist = F.softmax(pred_dist.view(batch_size, n_anchors, 4, self.reg_max + 1), dim=-1).matmul(self.proj.to(pred_dist.device)) + return dist2bbox(pred_dist, anchor_points) + + +class VarifocalLoss(nn.Module): + def __init__(self): + super(VarifocalLoss, self).__init__() + + def forward(self, pred_score,gt_score, label, alpha=0.75, gamma=2.0): + + weight = alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label + with torch.cuda.amp.autocast(enabled=False): + loss = (F.binary_cross_entropy(pred_score.float(), gt_score.float(), reduction='none') * weight).sum() + + return loss + + +class BboxLoss(nn.Module): + def __init__(self, num_classes, reg_max, use_dfl=False, iou_type='giou'): + super(BboxLoss, self).__init__() + self.num_classes = num_classes + self.iou_loss = IOUloss(box_format='xyxy', iou_type=iou_type, eps=1e-10) + self.reg_max = reg_max + self.use_dfl = use_dfl + + def forward(self, pred_dist, pred_bboxes_lrtb, pred_bboxes, t_pred_dist, t_pred_bboxes, temperature, anchor_points, + target_bboxes, target_scores, target_scores_sum, fg_mask): + # select positive samples mask + num_pos = fg_mask.sum() + if num_pos > 0: + # iou loss + bbox_mask = fg_mask.unsqueeze(-1).repeat([1, 1, 4]) + pred_bboxes_pos = torch.masked_select(pred_bboxes, + bbox_mask).reshape([-1, 4]) + pred_bboxes_lrtb_pos = torch.masked_select(pred_bboxes_lrtb, + bbox_mask).reshape([-1, 4]) + t_pred_bboxes_pos = torch.masked_select(t_pred_bboxes, + bbox_mask).reshape([-1, 4]) + target_bboxes_pos = torch.masked_select( + target_bboxes, bbox_mask).reshape([-1, 4]) + bbox_weight = torch.masked_select( + target_scores.sum(-1), fg_mask).unsqueeze(-1) + loss_iou = self.iou_loss(pred_bboxes_pos, + target_bboxes_pos) * bbox_weight + loss_iou_lrtb = self.iou_loss(pred_bboxes_lrtb_pos, + target_bboxes_pos) * bbox_weight + + if target_scores_sum == 0: + loss_iou = loss_iou.sum() + loss_iou_lrtb = loss_iou_lrtb.sum() + else: + loss_iou = loss_iou.sum() / target_scores_sum + loss_iou_lrtb = loss_iou_lrtb.sum() / target_scores_sum + + # dfl loss + if self.use_dfl: + dist_mask = fg_mask.unsqueeze(-1).repeat( + [1, 1, (self.reg_max + 1) * 4]) + pred_dist_pos = torch.masked_select( + pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1]) + t_pred_dist_pos = torch.masked_select( + t_pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1]) + target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max) + target_ltrb_pos = torch.masked_select( + target_ltrb, bbox_mask).reshape([-1, 4]) + loss_dfl = self._df_loss(pred_dist_pos, + target_ltrb_pos) * bbox_weight + d_loss_dfl = self.distill_loss_dfl(pred_dist_pos, t_pred_dist_pos, temperature) * bbox_weight + if target_scores_sum == 0: + loss_dfl = loss_dfl.sum() + d_loss_dfl = d_loss_dfl.sum() + else: + loss_dfl = loss_dfl.sum() / target_scores_sum + d_loss_dfl = d_loss_dfl.sum() / target_scores_sum + else: + loss_dfl = pred_dist.sum() * 0. + d_loss_dfl = pred_dist.sum() * 0. + + else: + + loss_iou = pred_dist.sum() * 0. + loss_dfl = pred_dist.sum() * 0. + d_loss_dfl = pred_dist.sum() * 0. + loss_iou_lrtb = pred_dist.sum() * 0. + + return (loss_iou + loss_iou_lrtb), loss_dfl, d_loss_dfl + + def _df_loss(self, pred_dist, target): + target_left = target.to(torch.long) + target_right = target_left + 1 + weight_left = target_right.to(torch.float) - target + weight_right = 1 - weight_left + loss_left = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_left.view(-1), reduction='none').view( + target_left.shape) * weight_left + loss_right = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_right.view(-1), reduction='none').view( + target_left.shape) * weight_right + return (loss_left + loss_right).mean(-1, keepdim=True) + + def distill_loss_dfl(self, logits_student, logits_teacher, temperature=20): + + logits_student = logits_student.view(-1,17) + logits_teacher = logits_teacher.view(-1,17) + pred_student = F.softmax(logits_student / temperature, dim=1) + pred_teacher = F.softmax(logits_teacher / temperature, dim=1) + log_pred_student = torch.log(pred_student) + + d_loss_dfl = F.kl_div(log_pred_student, pred_teacher, reduction="none").sum(1).mean() + d_loss_dfl *= temperature**2 + return d_loss_dfl \ No newline at end of file diff --git a/yolov6/models/losses/loss_fuseab.py b/yolov6/models/losses/loss_fuseab.py new file mode 100644 index 00000000..34e37f3b --- /dev/null +++ b/yolov6/models/losses/loss_fuseab.py @@ -0,0 +1,241 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- + +import torch +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from yolov6.assigners.anchor_generator import generate_anchors +from yolov6.utils.general import dist2bbox, bbox2dist, xywh2xyxy, box_iou +from yolov6.utils.figure_iou import IOUloss +from yolov6.assigners.tal_assigner import TaskAlignedAssigner + + +class ComputeLoss: + '''Loss computation func.''' + def __init__(self, + fpn_strides=[8, 16, 32], + grid_cell_size=5.0, + grid_cell_offset=0.5, + num_classes=80, + ori_img_size=640, + warmup_epoch=0, + use_dfl=True, + reg_max=16, + iou_type='giou', + loss_weight={ + 'class': 1.0, + 'iou': 2.5, + 'dfl': 0.5} + ): + + self.fpn_strides = fpn_strides + self.grid_cell_size = grid_cell_size + self.grid_cell_offset = grid_cell_offset + self.num_classes = num_classes + self.ori_img_size = ori_img_size + + self.warmup_epoch = warmup_epoch + self.formal_assigner = TaskAlignedAssigner(topk=26, num_classes=self.num_classes, alpha=1.0, beta=6.0) + + self.use_dfl = use_dfl + self.reg_max = reg_max + self.proj = nn.Parameter(torch.linspace(0, self.reg_max, self.reg_max + 1), requires_grad=False) + self.iou_type = iou_type + self.varifocal_loss = VarifocalLoss().cuda() + self.bbox_loss = BboxLoss(self.num_classes, self.reg_max, self.use_dfl, self.iou_type).cuda() + self.loss_weight = loss_weight + + def __call__( + self, + outputs, + targets, + epoch_num, + step_num + ): + + feats, pred_scores, pred_distri = outputs + anchors, anchor_points, n_anchors_list, stride_tensor = \ + generate_anchors(feats, self.fpn_strides, self.grid_cell_size, self.grid_cell_offset, device=feats[0].device, is_eval=False, mode='ab') + + assert pred_scores.type() == pred_distri.type() + gt_bboxes_scale = torch.full((1,4), self.ori_img_size).type_as(pred_scores) + batch_size = pred_scores.shape[0] + + # targets + targets =self.preprocess(targets, batch_size, gt_bboxes_scale) + gt_labels = targets[:, :, :1] + gt_bboxes = targets[:, :, 1:] #xyxy + mask_gt = (gt_bboxes.sum(-1, keepdim=True) > 0).float() + + # pboxes + anchor_points_s = anchor_points / stride_tensor + pred_distri[..., :2] += anchor_points_s + pred_bboxes = xywh2xyxy(pred_distri) + + try: + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + pred_scores.detach(), + pred_bboxes.detach() * stride_tensor, + anchor_points, + gt_labels, + gt_bboxes, + mask_gt) + + except RuntimeError: + print( + "OOM RuntimeError is raised due to the huge memory cost during label assignment. \ + CPU mode is applied in this batch. If you want to avoid this issue, \ + try to reduce the batch size or image size." + ) + torch.cuda.empty_cache() + print("------------CPU Mode for This Batch-------------") + + _pred_scores = pred_scores.detach().cpu().float() + _pred_bboxes = pred_bboxes.detach().cpu().float() + _anchor_points = anchor_points.cpu().float() + _gt_labels = gt_labels.cpu().float() + _gt_bboxes = gt_bboxes.cpu().float() + _mask_gt = mask_gt.cpu().float() + _stride_tensor = stride_tensor.cpu().float() + + target_labels, target_bboxes, target_scores, fg_mask = \ + self.formal_assigner( + _pred_scores, + _pred_bboxes * _stride_tensor, + _anchor_points, + _gt_labels, + _gt_bboxes, + _mask_gt) + + target_labels = target_labels.cuda() + target_bboxes = target_bboxes.cuda() + target_scores = target_scores.cuda() + fg_mask = fg_mask.cuda() + #Dynamic release GPU memory + if step_num % 10 == 0: + torch.cuda.empty_cache() + + # rescale bbox + target_bboxes /= stride_tensor + + # cls loss + target_labels = torch.where(fg_mask > 0, target_labels, torch.full_like(target_labels, self.num_classes)) + one_hot_label = F.one_hot(target_labels.long(), self.num_classes + 1)[..., :-1] + loss_cls = self.varifocal_loss(pred_scores, target_scores, one_hot_label) + + target_scores_sum = target_scores.sum() + # avoid devide zero error, devide by zero will cause loss to be inf or nan. + # if target_scores_sum is 0, loss_cls equals to 0 alson + if target_scores_sum > 0: + loss_cls /= target_scores_sum + + # bbox loss + loss_iou, loss_dfl = self.bbox_loss(pred_distri, pred_bboxes, anchor_points_s, target_bboxes, + target_scores, target_scores_sum, fg_mask) + + loss = self.loss_weight['class'] * loss_cls + \ + self.loss_weight['iou'] * loss_iou + \ + self.loss_weight['dfl'] * loss_dfl + + return loss, \ + torch.cat(((self.loss_weight['iou'] * loss_iou).unsqueeze(0), + (self.loss_weight['dfl'] * loss_dfl).unsqueeze(0), + (self.loss_weight['class'] * loss_cls).unsqueeze(0))).detach() + + def preprocess(self, targets, batch_size, scale_tensor): + targets_list = np.zeros((batch_size, 1, 5)).tolist() + for i, item in enumerate(targets.cpu().numpy().tolist()): + targets_list[int(item[0])].append(item[1:]) + max_len = max((len(l) for l in targets_list)) + targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device) + batch_target = targets[:, :, 1:5].mul_(scale_tensor) + targets[..., 1:] = xywh2xyxy(batch_target) + return targets + + def bbox_decode(self, anchor_points, pred_dist): + if self.use_dfl: + batch_size, n_anchors, _ = pred_dist.shape + pred_dist = F.softmax(pred_dist.view(batch_size, n_anchors, 4, self.reg_max + 1), dim=-1).matmul(self.proj.to(pred_dist.device)) + return dist2bbox(pred_dist, anchor_points) + + +class VarifocalLoss(nn.Module): + def __init__(self): + super(VarifocalLoss, self).__init__() + + def forward(self, pred_score,gt_score, label, alpha=0.75, gamma=2.0): + + weight = alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label + with torch.cuda.amp.autocast(enabled=False): + loss = (F.binary_cross_entropy(pred_score.float(), gt_score.float(), reduction='none') * weight).sum() + + return loss + + +class BboxLoss(nn.Module): + def __init__(self, num_classes, reg_max, use_dfl=False, iou_type='giou'): + super(BboxLoss, self).__init__() + self.num_classes = num_classes + self.iou_loss = IOUloss(box_format='xyxy', iou_type=iou_type, eps=1e-10) + self.reg_max = reg_max + self.use_dfl = use_dfl + + def forward(self, pred_dist, pred_bboxes, anchor_points, + target_bboxes, target_scores, target_scores_sum, fg_mask): + + # select positive samples mask + num_pos = fg_mask.sum() + if num_pos > 0: + # iou loss + bbox_mask = fg_mask.unsqueeze(-1).repeat([1, 1, 4]) + pred_bboxes_pos = torch.masked_select(pred_bboxes, + bbox_mask).reshape([-1, 4]) + target_bboxes_pos = torch.masked_select( + target_bboxes, bbox_mask).reshape([-1, 4]) + bbox_weight = torch.masked_select( + target_scores.sum(-1), fg_mask).unsqueeze(-1) + loss_iou = self.iou_loss(pred_bboxes_pos, + target_bboxes_pos) * bbox_weight + if target_scores_sum == 0: + loss_iou = loss_iou.sum() + else: + loss_iou = loss_iou.sum() / target_scores_sum + + # dfl loss + if self.use_dfl: + dist_mask = fg_mask.unsqueeze(-1).repeat( + [1, 1, (self.reg_max + 1) * 4]) + pred_dist_pos = torch.masked_select( + pred_dist, dist_mask).reshape([-1, 4, self.reg_max + 1]) + target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max) + target_ltrb_pos = torch.masked_select( + target_ltrb, bbox_mask).reshape([-1, 4]) + loss_dfl = self._df_loss(pred_dist_pos, + target_ltrb_pos) * bbox_weight + if target_scores_sum == 0: + loss_dfl = loss_dfl.sum() + else: + loss_dfl = loss_dfl.sum() / target_scores_sum + else: + loss_dfl = pred_dist.sum() * 0. + + else: + loss_iou = pred_dist.sum() * 0. + loss_dfl = pred_dist.sum() * 0. + + return loss_iou, loss_dfl + + def _df_loss(self, pred_dist, target): + target_left = target.to(torch.long) + target_right = target_left + 1 + weight_left = target_right.to(torch.float) - target + weight_right = 1 - weight_left + loss_left = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_left.view(-1), reduction='none').view( + target_left.shape) * weight_left + loss_right = F.cross_entropy( + pred_dist.view(-1, self.reg_max + 1), target_right.view(-1), reduction='none').view( + target_left.shape) * weight_right + return (loss_left + loss_right).mean(-1, keepdim=True)