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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from mmcv.transforms import RandomChoice, RandomChoiceResize | |
| from mmcv.transforms.loading import LoadImageFromFile | |
| from mmengine.config import read_base | |
| from mmengine.model.weight_init import PretrainedInit | |
| from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper | |
| from mmengine.optim.scheduler.lr_scheduler import MultiStepLR | |
| from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop | |
| from torch.nn.modules.activation import ReLU | |
| from torch.nn.modules.batchnorm import BatchNorm2d | |
| from torch.optim.adamw import AdamW | |
| from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs, | |
| RandomCrop, RandomFlip, Resize) | |
| from mmdet.models import (DETR, ChannelMapper, DetDataPreprocessor, DETRHead, | |
| ResNet) | |
| from mmdet.models.losses.cross_entropy_loss import CrossEntropyLoss | |
| from mmdet.models.losses.iou_loss import GIoULoss | |
| from mmdet.models.losses.smooth_l1_loss import L1Loss | |
| from mmdet.models.task_modules import (BBoxL1Cost, ClassificationCost, | |
| HungarianAssigner, IoUCost) | |
| with read_base(): | |
| from .._base_.datasets.coco_detection import * | |
| from .._base_.default_runtime import * | |
| model = dict( | |
| type=DETR, | |
| num_queries=100, | |
| 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=1), | |
| backbone=dict( | |
| type=ResNet, | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(3, ), | |
| frozen_stages=1, | |
| norm_cfg=dict(type=BatchNorm2d, requires_grad=False), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict( | |
| type=PretrainedInit, checkpoint='torchvision://resnet50')), | |
| neck=dict( | |
| type=ChannelMapper, | |
| in_channels=[2048], | |
| kernel_size=1, | |
| out_channels=256, | |
| act_cfg=None, | |
| norm_cfg=None, | |
| num_outs=1), | |
| encoder=dict( # DetrTransformerEncoder | |
| num_layers=6, | |
| layer_cfg=dict( # DetrTransformerEncoderLayer | |
| self_attn_cfg=dict( # MultiheadAttention | |
| embed_dims=256, | |
| num_heads=8, | |
| dropout=0.1, | |
| batch_first=True), | |
| ffn_cfg=dict( | |
| embed_dims=256, | |
| feedforward_channels=2048, | |
| num_fcs=2, | |
| ffn_drop=0.1, | |
| act_cfg=dict(type=ReLU, inplace=True)))), | |
| decoder=dict( # DetrTransformerDecoder | |
| num_layers=6, | |
| layer_cfg=dict( # DetrTransformerDecoderLayer | |
| self_attn_cfg=dict( # MultiheadAttention | |
| embed_dims=256, | |
| num_heads=8, | |
| dropout=0.1, | |
| batch_first=True), | |
| cross_attn_cfg=dict( # MultiheadAttention | |
| embed_dims=256, | |
| num_heads=8, | |
| dropout=0.1, | |
| batch_first=True), | |
| ffn_cfg=dict( | |
| embed_dims=256, | |
| feedforward_channels=2048, | |
| num_fcs=2, | |
| ffn_drop=0.1, | |
| act_cfg=dict(type=ReLU, inplace=True))), | |
| return_intermediate=True), | |
| positional_encoding=dict(num_feats=128, normalize=True), | |
| bbox_head=dict( | |
| type=DETRHead, | |
| num_classes=80, | |
| embed_dims=256, | |
| loss_cls=dict( | |
| type=CrossEntropyLoss, | |
| bg_cls_weight=0.1, | |
| use_sigmoid=False, | |
| loss_weight=1.0, | |
| class_weight=1.0), | |
| loss_bbox=dict(type=L1Loss, loss_weight=5.0), | |
| loss_iou=dict(type=GIoULoss, loss_weight=2.0)), | |
| # training and testing settings | |
| train_cfg=dict( | |
| assigner=dict( | |
| type=HungarianAssigner, | |
| match_costs=[ | |
| dict(type=ClassificationCost, weight=1.), | |
| dict(type=BBoxL1Cost, weight=5.0, box_format='xywh'), | |
| dict(type=IoUCost, iou_mode='giou', weight=2.0) | |
| ])), | |
| test_cfg=dict(max_per_img=100)) | |
| # train_pipeline, NOTE the img_scale and the Pad's size_divisor is different | |
| # from the default setting in mmdet. | |
| train_pipeline = [ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=LoadAnnotations, with_bbox=True), | |
| dict(type=RandomFlip, prob=0.5), | |
| dict( | |
| type=RandomChoice, | |
| transforms=[[ | |
| dict( | |
| type=RandomChoiceResize, | |
| resize_type=Resize, | |
| scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), | |
| (608, 1333), (640, 1333), (672, 1333), (704, 1333), | |
| (736, 1333), (768, 1333), (800, 1333)], | |
| keep_ratio=True) | |
| ], | |
| [ | |
| dict( | |
| type=RandomChoiceResize, | |
| resize_type=Resize, | |
| scales=[(400, 1333), (500, 1333), (600, 1333)], | |
| keep_ratio=True), | |
| dict( | |
| type=RandomCrop, | |
| crop_type='absolute_range', | |
| crop_size=(384, 600), | |
| allow_negative_crop=True), | |
| dict( | |
| type=RandomChoiceResize, | |
| resize_type=Resize, | |
| scales=[(480, 1333), (512, 1333), (544, 1333), | |
| (576, 1333), (608, 1333), (640, 1333), | |
| (672, 1333), (704, 1333), (736, 1333), | |
| (768, 1333), (800, 1333)], | |
| keep_ratio=True) | |
| ]]), | |
| dict(type=PackDetInputs) | |
| ] | |
| train_dataloader.update(dataset=dict(pipeline=train_pipeline)) | |
| # optimizer | |
| optim_wrapper = dict( | |
| type=OptimWrapper, | |
| optimizer=dict(type=AdamW, lr=0.0001, weight_decay=0.0001), | |
| clip_grad=dict(max_norm=0.1, norm_type=2), | |
| paramwise_cfg=dict( | |
| custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) | |
| # learning policy | |
| max_epochs = 150 | |
| train_cfg = dict( | |
| type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1) | |
| val_cfg = dict(type=ValLoop) | |
| test_cfg = dict(type=TestLoop) | |
| param_scheduler = [ | |
| dict( | |
| type=MultiStepLR, | |
| begin=0, | |
| end=max_epochs, | |
| by_epoch=True, | |
| milestones=[100], | |
| gamma=0.1) | |
| ] | |
| # NOTE: `auto_scale_lr` is for automatically scaling LR, | |
| # USER SHOULD NOT CHANGE ITS VALUES. | |
| # base_batch_size = (8 GPUs) x (2 samples per GPU) | |
| auto_scale_lr = dict(base_batch_size=16) | |