Training Models Using MMDetection

How to Train Deep Learning models using Deep Lake's integration with MMDetection

How to Train Deep Learning models using Deep Lake and MMDetection

This tutorial assumes the reader has experience training models using MMDET and has installed it successfully.

Deep Lake offers an integration with MMDetection, a popular open-source object detection toolbox based on PyTorch. The integration enables users to train models while streaming Deep Lake dataset using the transformation, training, and evaluation tools built by MMDet.

Integration Interface

Training using MMDET is typically executed using wrapper scripts like the one provided here in their repo. In the example below, we write a similar simplified wrapper script for training using a Deep Lake dataset.

The integrations with MMDET occurs in the deeplake.integrations.mmdet module. At a high-level, Deep Lake is responsible for the pytorch dataloader that streams data to the training framework, while MMDET is used for the training, transformation, and evaluation logic.

In the example script below, the user should apply the build_detector and train_detector provided by Deep Lake. The build_detector is mostly boilerplate. and the Deep Lake-related features primarily exist in train_detector.

import os
from mmcv import Config
import mmcv
from deeplake.integrations import mmdet as mmdet_deeplake
import argparse

def parse_args():

    parser = argparse.ArgumentParser(description="Deep Lake Training Using MMDET")

    parser.add_argument(
        "--cfg_file",
        type=str,
        required=True,
        help="Path for loading the config file",
    )
    parser.add_argument(
        "--validate",
        action="store_true",
        default=True,
        help="Whether to run dataset validation",
    )
    parser.add_argument(
        "--distributed",
        action="store_true",
        default=False,
        help="Whether to run distributed training",
    )
    parser.add_argument(
        "--num_classes",
        type=int,
        default=None,
        help="Number of classes in the model",
    )

    args = parser.parse_args()

    return args

if __name__ == "__main__":

    args = parse_args()
    
    # Read the config file
    cfg = Config.fromfile(args.cfg_file)

    cfg.model.bbox_head.num_classes = args.num_classes

    # Build the detector
    model = mmdet_deeplake.build_detector(cfg.model)

    # Create work_dir
    mmcv.mkdir_or_exist(os.path.abspath(cfg.work_dir))

    # Run the training
    mmdet_deeplake.train_detector(model, cfg, distributed=args.distributed, validate=args.validate)

Inputs to train_detector

Inputs to the Deep Lake train_detector are a modified MMDET config file, optional dataset objects (see below), and flags for specifying whether to perform distributed training and validation.

Modifications to the cfg file

The Deep Lake train_detector takes in a standard MMDET config file, but it also expect the inputs highlighted in the ----Deep Lake Inputs---- section in the config file below:

4KB

yolo_coco_docs_cfg.py

Open

#--------------------------------------DEEPLAKE INPUTS------------------------------------------------------------#
TOKEN = "INSERT_YOUR_DEEPLAKE_TOKEN"

data = dict(
    # samples_per_gpu=4, # Is used instead of batch_size if deeplake_dataloader is not specified below
    # workers_per_gpu=8, # Is used instead of num_workers if deeplake_dataloader is not specified below
    train=dict(
        pipeline=train_pipeline,

        # Credentials for authentication. See documendataion for deeplake.load() for details
        deeplake_path="hub://activeloop/coco-train",
        deeplake_credentials={
            "username": None,
            "password": None,
            "token": TOKEN,
            "creds": None,
        },
        #OPTIONAL - Checkout teh specified commit_id before training
        deeplake_commit_id="",
        #OPTIONAL - Loads a dataset view for training based on view_id
        deeplake_view_id="",

        # OPTIONAL - {"mmdet_key": "deep_lake_tensor",...} - Maps Deep Lake tensors to MMDET dictionary keys. 
        # If not specified, Deep Lake will auto-infer the mapping, but it might make mistakes if datasets have many tensors
        deeplake_tensors = {"img": "images", "gt_bboxes": "boxes", "gt_labels": "categories"},
        
        # OPTIONAL - Parameters to use for the Deep Lake dataloader. If unspecified, the integration uses
        # the parameters in other parts of the cfg file such as samples_per_gpu, and others.
        deeplake_dataloader = {"shuffle": True, "batch_size": 4, 'num_workers': 8}
    ),

    # Parameters as the same as for train
    val=dict(
        pipeline=test_pipeline,
        deeplake_path="hub://activeloop/coco-val",
        deeplake_credentials={
            "username": None,
            "password": None,
            "token": TOKEN,
            "creds": None,
        },
        deeplake_tensors = {"img": "images", "gt_bboxes": "boxes", "gt_labels": "categories"},
        deeplake_dataloader = {"shuffle": False, "batch_size": 1, 'num_workers': 8}
    ),
)

# Which dataloader to use
deeplake_dataloader_type = "c++"  # "c++" is available to enterprise users. Otherwise use "python"

# Which metrics to use for evaulation. In MMDET (without Deeplake), this is inferred from the dataset type.
# In the Deep Lake integration, since the format is standardized, a variety of metrics can be used for a given dataset.
deeplake_metrics_format = "COCO"

#----------------------------------END DEEPLAKE INPUTS------------------------------------------------------------#

Passing Deep Lake dataset objects to the train_detector (Optional)

The Deep Lake dataset object or dataset view can be passed to the train_detector directly, thus overwriting any dataset information in the config file. Below are the respective modifications that should be made to the training script above:

ds_train = deeplake.load(dataset_path, token, ...)
ds_train.checkout(commit_id)
ds_train_view = ds_train.query("Add query string")

mmdet_deeplake.train_detector(model, cfg, ds_train = ds_train_view, ds_val = ..., distributed = args.distributed, validate = args.validate)

Congrats! You're now able to train models using MMDET while streaming Deep Lake Datasets! 🎉