add segm model

mmdet/segm/mmdet_dd-person_mask2former.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ff5ebfaacfef171c8043ef867c1035a2ab9949eca9813c1028746e85e035915
+size 528418860

mmdet/segm/mmdet_dd-person_mask2former.py

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+dataset_type = 'CocoDataset'
+data_root = 'data/dd-person_mask2former/'
+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', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Resize',
+        img_scale=(1024, 1024),
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_size=(1024, 1024),
+        crop_type='absolute',
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(
+        type='FilterAnnotations', min_gt_bbox_wh=(1e-05, 1e-05), by_mask=True),
+    dict(
+        type='Pad',
+        size=(1024, 1024),
+        pad_val=dict(img=(128, 128, 128), masks=0, seg=255)),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='DefaultFormatBundle', img_to_float=True),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+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='Pad',
+                size_divisor=32,
+                pad_val=dict(img=(128, 128, 128), masks=0, seg=255)),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=1,
+    train=dict(
+        type='CocoDataset',
+        ann_file='data/dd-person_mask2former/annotations/train.json',
+        img_prefix='data/dd-person_mask2former/train/',
+        pipeline=[
+            dict(type='LoadImageFromFile', to_float32=True),
+            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Resize',
+                img_scale=(1024, 1024),
+                ratio_range=(0.1, 2.0),
+                multiscale_mode='range',
+                keep_ratio=True),
+            dict(
+                type='RandomCrop',
+                crop_size=(1024, 1024),
+                crop_type='absolute',
+                recompute_bbox=True,
+                allow_negative_crop=True),
+            dict(
+                type='FilterAnnotations',
+                min_gt_bbox_wh=(1e-05, 1e-05),
+                by_mask=True),
+            dict(
+                type='Pad',
+                size=(1024, 1024),
+                pad_val=dict(img=(128, 128, 128), masks=0, seg=255)),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='DefaultFormatBundle', img_to_float=True),
+            dict(
+                type='Collect',
+                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+        ]),
+    val=dict(
+        type='CocoDataset',
+        ann_file='data/dd-person_mask2former/annotations/val.json',
+        img_prefix='data/dd-person_mask2former/val/',
+        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='Pad',
+                        size_divisor=32,
+                        pad_val=dict(img=(128, 128, 128), masks=0, seg=255)),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(
+        type='CocoDataset',
+        ann_file='data/dd-person_mask2former/annotations/val.json',
+        img_prefix='data/dd-person_mask2former/val/',
+        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='Pad',
+                        size_divisor=32,
+                        pad_val=dict(img=(128, 128, 128), masks=0, seg=255)),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]))
+evaluation = dict(
+    interval=2000,
+    metric=['bbox', 'segm'],
+    dynamic_intervals=[(400001, 400000)])
+checkpoint_config = dict(
+    interval=2000, by_epoch=False, save_last=True, max_keep_ckpts=10)
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook', by_epoch=False)
+    ])
+custom_hooks = [dict(type='NumClassCheckHook')]
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = 'checkpoints/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth'
+resume_from = 'checkpoints/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth'
+workflow = [('train', 2000)]
+opencv_num_threads = 0
+mp_start_method = 'fork'
+auto_scale_lr = dict(enable=False, base_batch_size=16)
+num_things_classes = 1
+num_stuff_classes = 0
+num_classes = 1
+model = dict(
+    type='Mask2Former',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    panoptic_head=dict(
+        type='Mask2FormerHead',
+        in_channels=[256, 512, 1024, 2048],
+        strides=[4, 8, 16, 32],
+        feat_channels=256,
+        out_channels=256,
+        num_things_classes=1,
+        num_stuff_classes=0,
+        num_queries=100,
+        num_transformer_feat_level=3,
+        pixel_decoder=dict(
+            type='MSDeformAttnPixelDecoder',
+            num_outs=3,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        num_levels=3,
+                        num_points=4,
+                        im2col_step=64,
+                        dropout=0.0,
+                        batch_first=False,
+                        norm_cfg=None,
+                        init_cfg=None),
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=256,
+                        feedforward_channels=1024,
+                        num_fcs=2,
+                        ffn_drop=0.0,
+                        act_cfg=dict(type='ReLU', inplace=True)),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=128, normalize=True),
+            init_cfg=None),
+        enforce_decoder_input_project=False,
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        transformer_decoder=dict(
+            type='DetrTransformerDecoder',
+            return_intermediate=True,
+            num_layers=9,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=256,
+                    num_heads=8,
+                    attn_drop=0.0,
+                    proj_drop=0.0,
+                    dropout_layer=None,
+                    batch_first=False),
+                ffn_cfgs=dict(
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    num_fcs=2,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    ffn_drop=0.0,
+                    dropout_layer=None,
+                    add_identity=True),
+                feedforward_channels=2048,
+                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
+                                 'ffn', 'norm')),
+            init_cfg=None),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            loss_weight=2.0,
+            reduction='mean',
+            class_weight=[1.0, 0.1]),
+        loss_mask=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='mean',
+            loss_weight=5.0),
+        loss_dice=dict(
+            type='DiceLoss',
+            use_sigmoid=True,
+            activate=True,
+            reduction='mean',
+            naive_dice=True,
+            eps=1.0,
+            loss_weight=5.0)),
+    panoptic_fusion_head=dict(
+        type='MaskFormerFusionHead',
+        num_things_classes=1,
+        num_stuff_classes=0,
+        loss_panoptic=None,
+        init_cfg=None),
+    train_cfg=dict(
+        num_points=12544,
+        oversample_ratio=3.0,
+        importance_sample_ratio=0.75,
+        assigner=dict(
+            type='MaskHungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=2.0),
+            mask_cost=dict(
+                type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
+            dice_cost=dict(
+                type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),
+        sampler=dict(type='MaskPseudoSampler')),
+    test_cfg=dict(
+        panoptic_on=False,
+        semantic_on=False,
+        instance_on=True,
+        max_per_image=100,
+        iou_thr=0.8,
+        filter_low_score=True),
+    init_cfg=None)
+image_size = (1024, 1024)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    eps=1e-08,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(
+        custom_keys=dict(
+            backbone=dict(lr_mult=0.1, decay_mult=1.0),
+            query_embed=dict(lr_mult=1.0, decay_mult=0.0),
+            query_feat=dict(lr_mult=1.0, decay_mult=0.0),
+            level_embed=dict(lr_mult=1.0, decay_mult=0.0)),
+        norm_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
+lr_config = dict(
+    policy='step',
+    gamma=0.1,
+    by_epoch=False,
+    step=[327778, 355092],
+    warmup='linear',
+    warmup_by_epoch=False,
+    warmup_ratio=1.0,
+    warmup_iters=10)
+max_iters = 400000
+runner = dict(type='IterBasedRunner', max_iters=400000)
+interval = 2000
+dynamic_intervals = [(400001, 400000)]
+pad_cfg = dict(img=(128, 128, 128), masks=0, seg=255)
+work_dir = './work_dirs\dd-person_mask2former'
+auto_resume = False
+gpu_ids = [0]