Upload cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_flower.py

cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_flower.py

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+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_swin_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        embed_dim=96,
+        depths=[2, 2, 18, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        ape=False,
+        drop_path_rate=0.2,
+        patch_norm=True,
+        use_checkpoint=False
+    ),
+    neck=dict(in_channels=[96, 192, 384, 768]),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='ConvFCBBoxHead',
+                num_shared_convs=4,
+                num_shared_fcs=1,
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                #num_classes=80,
+                num_classes=3,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=False,
+                reg_decoded_bbox=True,
+                norm_cfg=dict(type='SyncBN', requires_grad=True),
+                loss_cls=dict(
+                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
+            dict(
+                type='ConvFCBBoxHead',
+                num_shared_convs=4,
+                num_shared_fcs=1,
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                #num_classes=80,
+                num_classes=3,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=False,
+                reg_decoded_bbox=True,
+                norm_cfg=dict(type='SyncBN', requires_grad=True),
+                loss_cls=dict(
+                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
+            dict(
+                type='ConvFCBBoxHead',
+                num_shared_convs=4,
+                num_shared_fcs=1,
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                #num_classes=80,
+                num_classes=3,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=False,
+                reg_decoded_bbox=True,
+                norm_cfg=dict(type='SyncBN', requires_grad=True),
+                loss_cls=dict(
+                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
+        ]))
+
+dataset_type = 'COCODataset'
+classes = ('bud','flower','fruit',)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    #dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='LoadAnnotations', with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='AutoAugment',
+         policies=[
+             [
+                 dict(type='Resize',
+                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                                 (736, 1333), (768, 1333), (800, 1333)],
+                      multiscale_mode='value',
+                      keep_ratio=True)
+             ],
+             [
+                 dict(type='Resize',
+                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                      multiscale_mode='value',
+                      keep_ratio=True),
+                 dict(type='RandomCrop',
+                      crop_type='absolute_range',
+                      crop_size=(384, 600),
+                      allow_negative_crop=True),
+                 dict(type='Resize',
+                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                 (576, 1333), (608, 1333), (640, 1333),
+                                 (672, 1333), (704, 1333), (736, 1333),
+                                 (768, 1333), (800, 1333)],
+                      multiscale_mode='value',
+                      override=True,
+                      keep_ratio=True)
+             ]
+         ]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    #dict(type='Collect', keys=['img']),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+    #dict(type='Collect', keys=['img', 'gt_labels', 'gt_masks']),
+    #dict(type='Collect', keys=['img']),
+]
+
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        #img_scale=(1333, 800),
+        img_scale=(800, 1333),
+        flip=False,
+    transforms=[
+    dict(type='Resize', keep_ratio=True),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img']),             # do not pass gt_label while testing
+    ])
+]
+'''
+data = dict(train=dict(pipeline=train_pipeline),
+            test=dict(
+        img_prefix='/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/mmdetection/data/coco/images/',
+        classes=classes,
+        ann_file='/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/mmdetection/data/coco/annotations/flowers/test.json'))
+'''
+data = dict(
+       #train=dict(
+       train=dict(
+          img_prefix='/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/mmdetection/data/coco/images/',
+          classes=classes,
+          ann_file='/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/mmdetection/data/coco/annotations/flowers/train.json',          pipeline=train_pipeline),
+       
+       test=dict(             # test data config
+        #type=dataset_type,
+        img_prefix='/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/mmdetection/data/coco/images/',
+        classes=classes,
+        ann_file='/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/mmdetection/data/coco/annotations/flowers/test.json',
+        pipeline=test_pipeline)
+      
+        )
+        #)
+
+
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
+                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
+                                                 'relative_position_bias_table': dict(decay_mult=0.),
+                                                 'norm': dict(decay_mult=0.)}))
+lr_config = dict(step=[27, 33])
+runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
+#runner = dict(type='EpochBasedRunnerAmp', max_epochs=72)
+
+# do not use mmdet version fp16
+fp16 = None
+optimizer_config = dict(
+    type="DistOptimizerHook",
+    update_interval=1,
+    grad_clip=None,
+    coalesce=True,
+    bucket_size_mb=-1,
+    use_fp16=True,
+)
+
+load_from = '/projectnb/ds549/students/kmn5409/tertiary_task/Swin/Swin-Transformer-Object-Detection/cascade_mask_rcnn_swin_small_patch4_window7.pth'