Upload detection/cascade_rcnn_r101_fpn_config.py with huggingface_hub

detection/cascade_rcnn_r101_fpn_config.py

@@ -0,0 +1,444 @@
+auto_scale_lr = dict(base_batch_size=16, enable=False)
+backend_args = None
+data_root = './'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=1, max_keep_ckpts=1, save_best='auto', type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = '.\\best_coco_bbox_mAP_epoch_12.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+model = dict(
+    backbone=dict(
+        depth=101,
+        frozen_stages=1,
+        init_cfg=dict(checkpoint='torchvision://resnet101', type='Pretrained'),
+        norm_cfg=dict(requires_grad=True, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(
+            0,
+            1,
+            2,
+            3,
+        ),
+        style='pytorch',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        pad_size_divisor=32,
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        type='DetDataPreprocessor'),
+    neck=dict(
+        in_channels=[
+            256,
+            512,
+            1024,
+            2048,
+        ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                bbox_coder=dict(
+                    target_means=[
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                    ],
+                    target_stds=[
+                        0.1,
+                        0.1,
+                        0.2,
+                        0.2,
+                    ],
+                    type='DeltaXYWHBBoxCoder'),
+                fc_out_channels=1024,
+                in_channels=256,
+                loss_bbox=dict(beta=1.0, loss_weight=1.0, type='SmoothL1Loss'),
+                loss_cls=dict(
+                    loss_weight=1.0,
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False),
+                num_classes=80,
+                reg_class_agnostic=True,
+                roi_feat_size=7,
+                type='Shared2FCBBoxHead'),
+            dict(
+                bbox_coder=dict(
+                    target_means=[
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                    ],
+                    target_stds=[
+                        0.05,
+                        0.05,
+                        0.1,
+                        0.1,
+                    ],
+                    type='DeltaXYWHBBoxCoder'),
+                fc_out_channels=1024,
+                in_channels=256,
+                loss_bbox=dict(beta=1.0, loss_weight=1.0, type='SmoothL1Loss'),
+                loss_cls=dict(
+                    loss_weight=1.0,
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False),
+                num_classes=80,
+                reg_class_agnostic=True,
+                roi_feat_size=7,
+                type='Shared2FCBBoxHead'),
+            dict(
+                bbox_coder=dict(
+                    target_means=[
+                        0.0,
+                        0.0,
+                        0.0,
+                        0.0,
+                    ],
+                    target_stds=[
+                        0.033,
+                        0.033,
+                        0.067,
+                        0.067,
+                    ],
+                    type='DeltaXYWHBBoxCoder'),
+                fc_out_channels=1024,
+                in_channels=256,
+                loss_bbox=dict(beta=1.0, loss_weight=1.0, type='SmoothL1Loss'),
+                loss_cls=dict(
+                    loss_weight=1.0,
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False),
+                num_classes=80,
+                reg_class_agnostic=True,
+                roi_feat_size=7,
+                type='Shared2FCBBoxHead'),
+        ],
+        bbox_roi_extractor=dict(
+            featmap_strides=[
+                4,
+                8,
+                16,
+                32,
+            ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        num_stages=3,
+        stage_loss_weights=[
+            1,
+            0.5,
+            0.25,
+        ],
+        type='CascadeRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[
+                0.5,
+                1.0,
+                2.0,
+            ],
+            scales=[
+                8,
+            ],
+            strides=[
+                4,
+                8,
+                16,
+                32,
+                64,
+            ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            target_stds=[
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+            ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(
+            beta=0.1111111111111111, loss_weight=1.0, type='SmoothL1Loss'),
+        loss_cls=dict(
+            loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=[
+            dict(
+                assigner=dict(
+                    ignore_iof_thr=-1,
+                    match_low_quality=False,
+                    min_pos_iou=0.5,
+                    neg_iou_thr=0.5,
+                    pos_iou_thr=0.5,
+                    type='MaxIoUAssigner'),
+                debug=False,
+                pos_weight=-1,
+                sampler=dict(
+                    add_gt_as_proposals=True,
+                    neg_pos_ub=-1,
+                    num=512,
+                    pos_fraction=0.25,
+                    type='RandomSampler')),
+            dict(
+                assigner=dict(
+                    ignore_iof_thr=-1,
+                    match_low_quality=False,
+                    min_pos_iou=0.6,
+                    neg_iou_thr=0.6,
+                    pos_iou_thr=0.6,
+                    type='MaxIoUAssigner'),
+                debug=False,
+                pos_weight=-1,
+                sampler=dict(
+                    add_gt_as_proposals=True,
+                    neg_pos_ub=-1,
+                    num=512,
+                    pos_fraction=0.25,
+                    type='RandomSampler')),
+            dict(
+                assigner=dict(
+                    ignore_iof_thr=-1,
+                    match_low_quality=False,
+                    min_pos_iou=0.7,
+                    neg_iou_thr=0.7,
+                    pos_iou_thr=0.7,
+                    type='MaxIoUAssigner'),
+                debug=False,
+                pos_weight=-1,
+                sampler=dict(
+                    add_gt_as_proposals=True,
+                    neg_pos_ub=-1,
+                    num=512,
+                    pos_fraction=0.25,
+                    type='RandomSampler')),
+        ],
+        rpn=dict(
+            allowed_border=0,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=2000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='CascadeRCNN')
+optim_wrapper = dict(
+    loss_scale='dynamic',
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='AmpOptimWrapper')
+param_scheduler = [
+    dict(
+        begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=12,
+        gamma=0.1,
+        milestones=[
+            8,
+            11,
+        ],
+        type='MultiStepLR'),
+]
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '.\\test.json',
+        backend_args=None,
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                1333,
+                800,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_keys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file=
+    '.\\test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        meta_keys=(
+            'img_id',
+            'img_path',
+            'ori_shape',
+            'img_shape',
+            'scale_factor',
+        ),
+        type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=12, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '.\\train.json',
+        backend_args=None,
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(
+                1333,
+                800,
+            ), type='Resize'),
+            dict(
+                direction=[
+                    'horizontal',
+                    'vertical',
+                ],
+                prob=0.5,
+                type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(
+        1333,
+        800,
+    ), type='Resize'),
+    dict(direction=[
+        'horizontal',
+        'vertical',
+    ], prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=4,
+    dataset=dict(
+        ann_file=
+        '.\\test.json',
+        backend_args=None,
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(
+                1333,
+                800,
+            ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(
+                meta_ckeys=(
+                    'img_id',
+                    'img_path',
+                    'ori_shape',
+                    'img_shape',
+                    'scale_factor',
+                ),
+                type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file=
+    '.\\test.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    name='visualizer',
+    type='DetLocalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])
+work_dir = './'