siton bug

configs/slim/distill/yolov3_mobilenet_v1_coco_distill.yml

+_BASE_: [
+  '../../yolov3/yolov3_r34_270e_coco.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams
+
+
+slim: Distill
+distill_loss: DistillYOLOv3Loss
+
+DistillYOLOv3Loss:
+  weight: 1000

configs/slim/extensions/yolov3_mobilenet_v1_coco_distill_prune.yml

+_BASE_: [
+  '../../yolov3/yolov3_r34_270e_coco.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_r34_270e_coco.pdparams
+
+slim: DistillPrune
+
+distill_loss: DistillYOLOv3Loss
+
+DistillYOLOv3Loss:
+  weight: 1000
+
+pruner: Pruner
+
+Pruner:
+  criterion: l1_norm
+  pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
+                  'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
+                  'conv2d_34.w_0', 'conv2d_35.w_0', 'conv2d_36.w_0',
+                  'conv2d_37.w_0', 'conv2d_38.w_0', 'conv2d_39.w_0',
+                  'conv2d_41.w_0', 'conv2d_42.w_0', 'conv2d_43.w_0',
+                  'conv2d_44.w_0', 'conv2d_45.w_0', 'conv2d_46.w_0']
+  pruned_ratios: [0.5,0.5,0.5,0.5,0.5,0.5,0.7,0.7,0.7,0.7,0.7,0.7,0.8,0.8,0.8,0.8,0.8,0.8]

configs/slim/extensions/yolov3_mobilenetv1_prune_qat.yml

+# Weights of yolov3_mobilenet_v1_voc
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams
+slim: PrunerQAT
+
+PrunerQAT:
+  criterion: fpgm
+  pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
+                  'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
+                  'conv2d_34.w_0', 'conv2d_35.w_0', 'conv2d_36.w_0',
+                  'conv2d_37.w_0', 'conv2d_38.w_0', 'conv2d_39.w_0',
+                  'conv2d_41.w_0', 'conv2d_42.w_0', 'conv2d_43.w_0',
+                  'conv2d_44.w_0', 'conv2d_45.w_0', 'conv2d_46.w_0']
+  pruned_ratios: [0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.3,0.3,0.3,0.2,0.1,0.3,0.4,0.4,0.4,0.4,0.3]
+  print_prune_params: False
+  quant_config: {
+    'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
+    'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
+    'quantizable_layer_type': ['Conv2D', 'Linear']}
+  print_qat_model: True

configs/slim/ofa/ofa_picodet_demo.yml

+weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_0_pretrained.pdparams
+slim: OFA
+OFA:
+  ofa_config:
+    task: expand_ratio
+    expand_ratio: [0.5, 1]
+
+    skip_neck: True
+    skip_head: True
+
+    RunConfig:
+        # Skip the output layer of each block by layer name
+        skip_layers: ['backbone._conv1._conv','backbone.2_1._conv_linear_1._conv',
+                       'backbone.2_1._conv_linear_2._conv', 'backbone.2_1._conv_dw_mv1._conv',
+                       'backbone.2_1._conv_pw_mv1._conv', 'backbone.2_2._conv_linear._conv',
+                       'backbone.2_3._conv_linear._conv', 'backbone.3_1._conv_linear_1._conv',
+                       'backbone.3_1._conv_linear_2._conv', 'backbone.3_1._conv_dw_mv1._conv',
+                       'backbone.3_1._conv_pw_mv1._conv', 'backbone.3_2._conv_linear._conv',
+                       'backbone.3_3._conv_linear._conv', 'backbone.3_4._conv_linear._conv',
+                       'backbone.3_5._conv_linear._conv', 'backbone.3_6._conv_linear._conv',
+                       'backbone.3_7._conv_linear._conv', 'backbone.4_1._conv_linear_1._conv',
+                       'backbone.4_1._conv_linear_2._conv', 'backbone.4_1._conv_dw_mv1._conv',
+                       'backbone.4_1._conv_pw_mv1._conv', 'backbone.4_2._conv_linear._conv',
+                       'backbone.4_3._conv_linear._conv']
+
+        # For block-wise search, make layers in each block in the same search space
+        same_search_space: [
+                ['backbone.2_1._conv_dw_1._conv', 'backbone.2_1._conv_pw_2._conv',
+                 'backbone.2_1._conv_dw_2._conv', 'backbone.2_1._se.conv1', 'backbone.2_1._se.conv2'],
+                ['backbone.2_2._conv_pw._conv', 'backbone.2_2._conv_dw._conv',
+                 'backbone.2_2._se.conv1', 'backbone.2_2._se.conv2'],
+                ['backbone.2_3._conv_pw._conv', 'backbone.2_3._conv_dw._conv',
+                 'backbone.2_3._se.conv1', 'backbone.2_3._se.conv2'],
+                ['backbone.3_1._conv_dw_1._conv', 'backbone.3_1._conv_pw_2._conv',
+                 'backbone.3_1._conv_dw_2._conv', 'backbone.3_1._se.conv1', 'backbone.3_1._se.conv2'],
+                ['backbone.3_2._conv_pw._conv', 'backbone.3_2._conv_dw._conv',
+                 'backbone.3_2._se.conv1', 'backbone.3_2._se.conv2'],
+                ['backbone.3_3._conv_pw._conv', 'backbone.3_3._conv_dw._conv',
+                 'backbone.3_3._se.conv1', 'backbone.3_3._se.conv2'],
+                ['backbone.3_4._conv_pw._conv', 'backbone.3_4._conv_dw._conv',
+                 'backbone.3_4._se.conv1', 'backbone.3_4._se.conv2'],
+                ['backbone.3_5._conv_pw._conv', 'backbone.3_5._conv_dw._conv',
+                 'backbone.3_5._se.conv1', 'backbone.3_5._se.conv2'],
+                ['backbone.3_6._conv_pw._conv', 'backbone.3_6._conv_dw._conv',
+                 'backbone.3_6._se.conv1', 'backbone.3_6._se.conv2'],
+                ['backbone.3_7._conv_pw._conv', 'backbone.3_7._conv_dw._conv',
+                 'backbone.3_7._se.conv1', 'backbone.3_7._se.conv2'],
+                ['backbone.4_1._conv_dw_1._conv', 'backbone.4_1._conv_pw_2._conv',
+                 'backbone.4_1._conv_dw_2._conv', 'backbone.4_1._se.conv1', 'backbone.4_1._se.conv2'],
+                ['backbone.4_2._conv_pw._conv', 'backbone.4_2._conv_dw._conv',
+                 'backbone.4_2._se.conv1', 'backbone.4_2._se.conv2'],
+                ['backbone.4_3._conv_pw._conv', 'backbone.4_3._conv_dw._conv',
+                 'backbone.4_3._se.conv1', 'backbone.4_3._se.conv2']]
+
+        # demo expand ratio
+        # Generally, for expand ratio, float in (0, 1] is available.
+        # But please be careful if the model is complicated.
+        # For picodet, there are many split and concat, the choice of channel number is important.
+        ofa_layers:
+            'backbone.2_1._conv_dw_1._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.2_2._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.2_3._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_1._conv_dw_1._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_2._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_3._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_4._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_5._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_6._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.3_7._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.4_1._conv_dw_1._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.4_2._conv_pw._conv':
+                'expand_ratio': [0.5, 1]
+            'backbone.4_3._conv_pw._conv':
+                'expand_ratio': [0.5, 1]

configs/slim/post_quant/mask_rcnn_r50_fpn_1x_coco_ptq.yml

+weights:  https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_1x_coco.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/mcfairmot_ptq.yml

+weights:  https://paddledet.bj.bcebos.com/models/mot/mcfairmot_dla34_30e_1088x608_visdrone_vehicle_bytetracker.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/picodet_s_ptq.yml

+weights:  https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/ppyolo_mbv3_large_ptq.yml

+weights: https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/ppyolo_r50vd_dcn_ptq.yml

+weights: https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/ppyoloe_crn_s_300e_coco_ptq.yml

+weights:  https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/tinypose_128x96_ptq.yml

+weights:  https://paddledet.bj.bcebos.com/models/keypoint/tinypose_128x96.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/post_quant/yolov3_darknet53_ptq.yml

+weights: https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
+slim: PTQ
+
+PTQ:
+  ptq_config: {
+      'activation_quantizer': 'HistQuantizer',
+      'upsample_bins': 127,
+      'hist_percent': 0.999}
+  quant_batch_num: 10
+  fuse: True

configs/slim/prune/faster_rcnn_r50_fpn_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_1x_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
+                  'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
+                  'conv2d_33.w_0', 'conv2d_34.w_0', 'conv2d_35.w_0',
+                  'conv2d_36.w_0', 'conv2d_37.w_0', 'conv2d_38.w_0',
+                  'conv2d_39.w_0', 'conv2d_40.w_0', 'conv2d_41.w_0',
+                  'conv2d_42.w_0', 'conv2d_43.w_0', 'conv2d_44.w_0',
+                  'conv2d_45.w_0', 'conv2d_46.w_0', 'conv2d_47.w_0',
+                  'conv2d_48.w_0', 'conv2d_49.w_0', 'conv2d_50.w_0',
+                  'conv2d_51.w_0', 'conv2d_52.w_0']
+  pruned_ratios: [0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.3,0.3,0.3,0.2,0.1,0.3,0.4,0.4,0.4,0.4,0.3,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4]
+  print_params: False

configs/slim/prune/picodet_m_unstructured_prune_75.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams
+slim: UnstructuredPruner
+
+UnstructuredPruner:
+    stable_epochs: 0
+    pruning_epochs: 150
+    tunning_epochs: 150
+    pruning_steps: 300
+    ratio: 0.75
+    initial_ratio: 0.15
+    prune_params_type: conv1x1_only

configs/slim/prune/picodet_m_unstructured_prune_85.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams
+slim: UnstructuredPruner
+
+UnstructuredPruner:
+    stable_epochs: 0
+    pruning_epochs: 150
+    tunning_epochs: 150
+    pruning_steps: 300
+    ratio: 0.85
+    initial_ratio: 0.20
+    prune_params_type: conv1x1_only

configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_62.w_0', 'conv2d_63.w_0', 'conv2d_64.w_0',
+                  'conv2d_65.w_0', 'conv2d_66.w_0', 'conv2d_67.w_0']
+  pruned_ratios: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75]
+  print_params: True

configs/slim/prune/ppyolo_r50vd_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_56.w_0', 'conv2d_57.w_0', 'conv2d_58.w_0',
+                  'conv2d_59.w_0', 'conv2d_60.w_0', 'conv2d_61.w_0',
+                  'conv2d_63.w_0', 'conv2d_64.w_0', 'conv2d_65.w_0',
+                  'conv2d_66.w_0', 'conv2d_67.w_0', 'conv2d_68.w_0',
+                  'conv2d_70.w_0', 'conv2d_71.w_0', 'conv2d_72.w_0',
+                  'conv2d_73.w_0', 'conv2d_74.w_0', 'conv2d_75.w_0']
+  pruned_ratios: [0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875]
+  print_params: False

configs/slim/prune/yolov3_darknet_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_52.w_0', 'conv2d_53.w_0', 'conv2d_54.w_0',
+                  'conv2d_55.w_0', 'conv2d_56.w_0', 'conv2d_57.w_0',
+                  'conv2d_59.w_0', 'conv2d_60.w_0', 'conv2d_61.w_0',
+                  'conv2d_62.w_0', 'conv2d_63.w_0', 'conv2d_64.w_0',
+                  'conv2d_66.w_0', 'conv2d_67.w_0', 'conv2d_68.w_0',
+                  'conv2d_69.w_0', 'conv2d_70.w_0', 'conv2d_71.w_0']
+  pruned_ratios: [0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875]
+  print_params: True

configs/slim/prune/yolov3_prune_fpgm.yml

+# Weights of yolov3_mobilenet_v1_voc
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
+                  'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
+                  'conv2d_34.w_0', 'conv2d_35.w_0', 'conv2d_36.w_0',
+                  'conv2d_37.w_0', 'conv2d_38.w_0', 'conv2d_39.w_0',
+                  'conv2d_41.w_0', 'conv2d_42.w_0', 'conv2d_43.w_0',
+                  'conv2d_44.w_0', 'conv2d_45.w_0', 'conv2d_46.w_0']
+  pruned_ratios: [0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.3,0.3,0.3,0.2,0.1,0.3,0.4,0.4,0.4,0.4,0.3]
+  print_params: False

configs/slim/prune/yolov3_prune_l1_norm.yml

+# Weights of yolov3_mobilenet_v1_voc
+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_270e_voc.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: l1_norm
+  pruned_params: ['conv2d_27.w_0', 'conv2d_28.w_0', 'conv2d_29.w_0',
+                  'conv2d_30.w_0', 'conv2d_31.w_0', 'conv2d_32.w_0',
+                  'conv2d_34.w_0', 'conv2d_35.w_0', 'conv2d_36.w_0',
+                  'conv2d_37.w_0', 'conv2d_38.w_0', 'conv2d_39.w_0',
+                  'conv2d_41.w_0', 'conv2d_42.w_0', 'conv2d_43.w_0',
+                  'conv2d_44.w_0', 'conv2d_45.w_0', 'conv2d_46.w_0']
+  pruned_ratios: [0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.3,0.3,0.3,0.2,0.1,0.3,0.4,0.4,0.4,0.4,0.3]
+  print_params: False