Customize VID Models

We basically categorize model components into 3 types.

• detector: usually a detector to detect objects from an image, e.g., Faster R-CNN.
• motion: the component to compute motion information between two images, e.g., FlowNetSimple.
• aggregator: the component for aggregating features from multi images, e.g., EmbedAggregator.

Add a new detector

Please refer to tutorial in mmdetection for developping a new detector.

Add a new motion model

1. Define a motion model (e.g. MyFlowNet)

Create a new file mmtrack/models/motion/my_flownet.py.

from mmcv.runner import BaseModule

from ..builder import MOTION

@MOTION.register_module()
class MyFlowNet(BaseModule):

    def __init__(self,
                arg1,
                arg2):
        pass

    def forward(self, inputs):
        # implementation is ignored
        pass

2. Import the module

You can either add the following line to mmtrack/models/motion/__init__.py,

from .my_flownet import MyFlowNet

or alternatively add

custom_imports = dict(
    imports=['mmtrack.models.motion.my_flownet.py'],
    allow_failed_imports=False)

to the config file and avoid modifying the original code.

3. Modify the config file

motion=dict(
    type='MyFlowNet',
    arg1=xxx,
    arg2=xxx)

Add a new aggregator

1. Define a aggregator (e.g. MyAggregator)

Create a new file mmtrack/models/aggregators/my_aggregator.py.

from mmcv.runner import BaseModule

from ..builder import AGGREGATORS

@AGGREGATORS.register_module()
class MyAggregator(BaseModule):

    def __init__(self,
                arg1,
                arg2):
        pass

    def forward(self, inputs):
        # implementation is ignored
        pass

2. Import the module

You can either add the following line to mmtrack/models/aggregators/__init__.py,

from .my_aggregator import MyAggregator

or alternatively add

custom_imports = dict(
    imports=['mmtrack.models.aggregators.my_aggregator.py'],
    allow_failed_imports=False)

to the config file and avoid modifying the original code.

3. Modify the config file

aggregator=dict(
    type='MyAggregator',
    arg1=xxx,
    arg2=xxx)