update application file

configs/_base_/faster-rcnn_r50_fpn_1x_coco.py

@@ -168,71 +168,52 @@ test_pipeline = [
 
 evaluation = dict(interval=1, metric='bbox')
 
-data_root = 'data/' # dataset root
-
-train_batch_size_per_gpu = 16
-train_num_workers = 1
-
-max_epochs = 105
-base_lr = 0.00001
-
-
-metainfo = {
-    'classes': ('orgaquant', ),
-    'palette': [
-        (220, 20, 60),
-    ]
-}
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu,
-    num_workers=train_num_workers,
-    dataset=dict(
-        data_root=data_root,
-        metainfo=metainfo,
-        data_prefix=dict(img='train/'),
-        ann_file='train.json'))
-
-val_dataloader = dict(
-    dataset=dict(
-        data_root=data_root,
-        metainfo=metainfo,
-        data_prefix=dict(img='val/'),
-        ann_file='val.json'))
-
-test_dataloader = val_dataloader
-
-val_evaluator = dict(ann_file=data_root + 'val.json')
-
-test_evaluator = val_evaluator
-
-model = dict(
-    roi_head=dict(
-        bbox_head=dict(num_classes=1)))
-
-    
-    
-# optimizer
-optim_wrapper = dict(
-    _delete_=True,
-    type='OptimWrapper',
-    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
-    paramwise_cfg=dict(
-        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
-
-default_hooks = dict(
-    checkpoint=dict(
-        interval=5,
-        max_keep_ckpts=2,  # only keep latest 2 checkpoints
-        save_best='auto'
-    ),
-    logger=dict(type='LoggerHook', interval=5))
-
-
-# load COCO pre-trained weight
-
-# load_from = './work_dirs/faster-rcnn_r50_fpn_organoid/best_coco_bbox_mAP_epoch_12.pth'
-
-
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
-visualizer = dict(vis_backends=[dict(type='LocalVisBackend'),dict(type='TensorboardVisBackend')])
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/'
+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='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(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']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'train.json',
+        img_prefix=data_root + 'train/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')