Refactor GFL & LD & LAD model

imclab · Jul 19, 2022 · 1f35b0f · 1f35b0f
1 parent 9731c4f
commit 1f35b0f
Show file tree

Hide file tree

Showing 18 changed files with 753 additions and 439 deletions.
diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
@@ -10,27 +10,30 @@
 
 # dataset settings
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
         type='RandomChoiceResize',
         scale=[(1333, 640), (1333, 800)],
-        resize_cfg=dict(type='Resize', keep_ratio=True)),
+        keep_ratio=True),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackDetInputs')
 ]
 train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
 
 # training schedule for 2x
-train_cfg = dict(max_epochs=24)
+max_epochs = 24
+train_cfg = dict(max_epochs=max_epochs)
 
 # learning rate
 param_scheduler = [
     dict(type='ConstantLR', factor=1.0 / 3, by_epoch=False, begin=0, end=500),
     dict(
         type='MultiStepLR',
         begin=0,
-        end=24,
+        end=max_epochs,
         by_epoch=True,
         milestones=[16, 22],
         gamma=0.1)

diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
@@ -2,27 +2,30 @@
 
 # dataset settings
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
         type='RandomChoiceResize',
         scale=[(1333, 640), (1333, 800)],
-        resize_cfg=dict(type='Resize', keep_ratio=True)),
+        keep_ratio=True),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackDetInputs')
 ]
 train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
 
 # training schedule for 2x
-train_cfg = dict(max_epochs=24)
+max_epochs = 24
+train_cfg = dict(max_epochs=max_epochs)
 
 # learning rate
 param_scheduler = [
     dict(type='ConstantLR', factor=1.0 / 3, by_epoch=False, begin=0, end=500),
     dict(
         type='MultiStepLR',
         begin=0,
-        end=24,
+        end=max_epochs,
         by_epoch=True,
         milestones=[16, 22],
         gamma=0.1)

diff --git a/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
@@ -24,27 +24,30 @@
 
 # dataset settings
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
         type='RandomChoiceResize',
         scale=[(1333, 640), (1333, 800)],
-        resize_cfg=dict(type='Resize', keep_ratio=True)),
+        keep_ratio=True),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackDetInputs')
 ]
 train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
 
 # training schedule for 2x
-train_cfg = dict(max_epochs=24)
+max_epochs = 24
+train_cfg = dict(max_epochs=max_epochs)
 
 # learning rate
 param_scheduler = [
     dict(type='ConstantLR', factor=1.0 / 3, by_epoch=False, begin=0, end=500),
     dict(
         type='MultiStepLR',
         begin=0,
-        end=24,
+        end=max_epochs,
         by_epoch=True,
         milestones=[16, 22],
         gamma=0.1)

diff --git a/configs/gfl/gfl_r50_fpn_1x_coco.py b/configs/gfl/gfl_r50_fpn_1x_coco.py
@@ -4,6 +4,12 @@
 ]
 model = dict(
     type='GFL',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
     backbone=dict(
         type='ResNet',
         depth=50,
@@ -53,5 +59,8 @@
         score_thr=0.05,
         nms=dict(type='nms', iou_threshold=0.6),
         max_per_img=100))
+
 # optimizer
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
diff --git a/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py
@@ -1,22 +1,30 @@
 _base_ = './gfl_r50_fpn_1x_coco.py'
+max_epochs = 24
+
 # learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=max_epochs,
+        by_epoch=True,
+        milestones=[16, 22],
+        gamma=0.1)
+]
+train_cfg = dict(max_epochs=max_epochs)
+
 # multi-scale training
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
-        type='Resize',
-        img_scale=[(1333, 480), (1333, 800)],
-        multiscale_mode='range',
+        type='RandomResize', scale=[(1333, 480), (1333, 800)],
         keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
 ]
-data = dict(train=dict(pipeline=train_pipeline))
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
diff --git a/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py
@@ -22,23 +22,30 @@
         add_extra_convs='on_output',
         num_outs=5))
 
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
+max_epochs = 24
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=max_epochs,
+        by_epoch=True,
+        milestones=[16, 22],
+        gamma=0.1)
+]
+train_cfg = dict(max_epochs=max_epochs)
+
 # multi-scale training
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
-        type='Resize',
-        img_scale=[(1333, 480), (1333, 800)],
-        multiscale_mode='range',
+        type='RandomResize', scale=[(1333, 480), (1333, 800)],
         keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
 ]
-data = dict(train=dict(pipeline=train_pipeline))
+train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
diff --git a/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py
@@ -5,6 +5,12 @@
 teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth'  # noqa
 model = dict(
     type='KnowledgeDistillationSingleStageDetector',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True,
+        pad_size_divisor=32),
     teacher_config='configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py',
     teacher_ckpt=teacher_ckpt,
     backbone=dict(
@@ -59,4 +65,6 @@
         nms=dict(type='nms', iou_threshold=0.6),
         max_per_img=100))
 
-optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
diff --git a/configs/paa/paa_r50_fpn_mstrain_3x_coco.py b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py
@@ -18,12 +18,13 @@
 train_cfg = dict(max_epochs=max_epochs)
 
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
-        type='RandomResize',
-        scale=[(1333, 640), (1333, 800)],
-        resize_cfg=dict(type='Resize', keep_ratio=True)),
+        type='RandomResize', scale=[(1333, 640), (1333, 800)],
+        keep_ratio=True),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackDetInputs')
 ]

diff --git a/configs/tood/tood_r50_fpn_mstrain_2x_coco.py b/configs/tood/tood_r50_fpn_mstrain_2x_coco.py
@@ -19,12 +19,13 @@
 
 # multi-scale training
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadImageFromFile',
+        file_client_args={{_base_.file_client_args}}),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
-        type='RandomResize',
-        scale=[(1333, 480), (1333, 800)],
-        resize_cfg=dict(type='Resize', keep_ratio=True)),
+        type='RandomResize', scale=[(1333, 480), (1333, 800)],
+        keep_ratio=True),
     dict(type='RandomFlip', prob=0.5),
     dict(type='PackDetInputs')
 ]