An example of DFF¶
model = dict(
type='DFF', # The name of video detector
detector=dict(
# Please refer to https://mmdetection.readthedocs.io/en/latest/tutorials/config.html#an-example-of-mask-r-cnn for detailed comments of detector.
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3,),
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='ChannelMapper',
in_channels=[2048],
out_channels=512,
kernel_size=3),
rpn_head=dict(
type='RPNHead',
in_channels=512,
feat_channels=512,
anchor_generator=dict(
type='AnchorGenerator',
scales=[4, 8, 16, 32],
ratios=[0.5, 1.0, 2.0],
strides=[16]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111,
loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=512,
featmap_strides=[16]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=512,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=30,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.2, 0.2, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=6000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_across_levels=False,
nms_pre=6000,
nms_post=300,
max_num=300,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.0001,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))),
motion=dict(
type='FlowNetSimple', # The name of motion model
img_scale_factor=0.5, # the scale factor to downsample/upsample the input image of motion model
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmtracking/pretrained_weights/flownet_simple.pth' # noqa: E501
)), # The pretrained weights of FlowNetSimple
train_cfg=None,
test_cfg=dict(key_frame_interval=10)) # The interval of key frame during testing
dataset_type = 'ImagenetVIDDataset' # Dataset type, this will be used to define the dataset
data_root = 'data/ILSVRC/' # Root path of data
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models
std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models
to_rgb=True) # The channel orders of image used to pre-training the pre-trained backbone models
train_pipeline = [ # Training pipeline
dict(type='LoadMultiImagesFromFile'), # First pipeline to load multi images from files path
dict(
type='SeqLoadAnnotations', # Second pipeline to load annotations for multi images
with_bbox=True, # Whether to use bounding box, True for detection
with_track=True), # Whether to use instance ids, True for detection
dict(type='SeqResize', # Augmentation pipeline that resize the multi images and their annotations
img_scale=(1000, 600), # The largest scale of image
keep_ratio=True), # whether to keep the ratio between height and width.
dict(
type='SeqRandomFlip', # Augmentation pipeline that flip the multi images and their annotations
share_params=True,
flip_ratio=0.5), # The ratio or probability to flip
dict(
type='SeqNormalize', # Augmentation pipeline that normalize the input multi images
mean=[123.675, 116.28, 103.53], # These keys are the same of img_norm_cfg since the
std=[58.395, 57.12, 57.375], # keys of img_norm_cfg are used here as arguments
to_rgb=True),
dict(type='SeqPad', # Padding config
size_divisor=16), # The number the padded images should be divisible
dict(
type='VideoCollect', # Pipeline that decides which keys in the data should be passed to the video detector
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_instance_ids']),
dict(type='ConcatVideoReferences'), # Pipeline that concats references images
dict(type='SeqDefaultFormatBundle', # Default format bundle to gather data in the pipeline
ref_prefix='ref') # The prefix key for reference images.
]
test_pipeline = [
dict(type='LoadImageFromFile'), # First pipeline to load images from file path
dict(
type='MultiScaleFlipAug', # An encapsulation that encapsulates the testing augmentations
img_scale=(1000, 600), # Decides the largest scale for testing, used for the Resize pipeline
flip=False, # Whether to flip images during testing
transforms=[
dict(type='Resize', # Use resize augmentation
keep_ratio=True),
# Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above.
dict(type='RandomFlip'), # Thought RandomFlip is added in pipeline, it is not used because flip=False
dict(
type='Normalize', # Normalization config, the values are from img_norm_cfg
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=16), # Padding config to pad images divisible by 16.
dict(type='ImageToTensor', keys=['img']), # convert image to tensor
dict(type='VideoCollect', keys=['img']) # Collect pipeline that collect necessary keys for testing.
])
]
data = dict(
samples_per_gpu=1, # Batch size of a single GPU
workers_per_gpu=2, # Worker to pre-fetch data for each single GPU
train=[
dict( # Train dataset config
type='ImagenetVIDDataset', # Type of dataset
ann_file='data/ILSVRCannotations/imagenet_vid_train.json', # Path of annotation file
img_prefix='data/ILSVRCData/VID', # Prefix of image path
ref_img_sampler=dict( # configuration for sampling reference images
num_ref_imgs=1,
frame_range=9,
filter_key_img=False,
method='uniform'),
pipeline=train_pipeline), # pipeline, this is passed by the train_pipeline created before.
dict(
type='ImagenetVIDDataset',
load_as_video=False,
ann_file='data/ILSVRCannotations/imagenet_det_30plus1cls.json',
img_prefix='data/ILSVRCData/DET',
ref_img_sampler=dict(
num_ref_imgs=1,
frame_range=0,
filter_key_img=False,
method='uniform'),
pipeline=train_pipeline)
],
val=dict( # Validation dataset config
type='ImagenetVIDDataset',
ann_file='data/ILSVRCannotations/imagenet_vid_val.json',
img_prefix='data/ILSVRCData/VID',
ref_img_sampler=None,
pipeline=test_pipeline, # Pipeline is passed by test_pipeline created before
test_mode=True),
test=dict( # Test dataset config, modify the ann_file for test-dev/test submission
type='ImagenetVIDDataset',
ann_file='data/ILSVRCannotations/imagenet_vid_val.json',
img_prefix='data/ILSVRCData/VID',
ref_img_sampler=None,
pipeline=test_pipeline, # Pipeline is passed by test_pipeline created before
test_mode=True))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9,
weight_decay=0.0001) # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
optimizer_config = dict(grad_clip=dict(max_norm=35,
norm_type=2)) # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details.
checkpoint_config = dict(
interval=1) # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False),
dict(type='WandbLoggerHook', by_epoch=False,
init_kwargs={'entity': "OpenMMLab",
'project': "MMTracking",
'config': cfg_dict}),
])
dist_params = dict(backend='nccl',
port='29500') # Parameters to setup distributed training, the port is set to 29500 by default
log_level = 'INFO' # The level of logging.
load_from = None # load models as a pre-trained model from a given path. This will not resume training.
resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
workflow = [('train',
1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 7 epochs according to the total_epochs.
lr_config = dict( # Learning rate scheduler config used to register LrUpdater hook
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.3333333333333333,
step=[2, 5])
total_epochs = 7 # Total epochs to train the model
evaluation = dict(metric=['bbox'], interval=7) # The config to build the evaluation hook
work_dir = '../mmtrack_output/tmp' # Directory to save the model checkpoints and logs for the current experiments.