MQ-Det/tools/test_net.py

# Adapted from https://github.com/microsoft/GLIP. The original liscense is:
#   Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment  # noqa F401 isort:skip

import argparse
import os

import torch
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import make_data_loader
from maskrcnn_benchmark.engine.inference import inference
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark.utils.collect_env import collect_env_info
from maskrcnn_benchmark.utils.comm import synchronize, get_rank
from maskrcnn_benchmark.utils.logger import setup_logger
from maskrcnn_benchmark.utils.miscellaneous import mkdir
from maskrcnn_benchmark.utils.stats import get_model_complexity_info


def run_test(cfg, model, distributed, log_dir):
    if distributed and hasattr(model, 'module'):
        model = model.module
    torch.cuda.empty_cache()  # TODO check if it helps
    iou_types = ("bbox",)
    if cfg.MODEL.MASK_ON:
        iou_types = iou_types + ("segm",)
    if cfg.MODEL.KEYPOINT_ON:
        iou_types = iou_types + ("keypoints",)
    dataset_names = cfg.DATASETS.TEST
    if isinstance(dataset_names[0], (list, tuple)):
        dataset_names = [dataset for group in dataset_names for dataset in group]
    output_folders = [None] * len(dataset_names)
    if log_dir:
        for idx, dataset_name in enumerate(dataset_names):
            output_folder = os.path.join(log_dir, "inference", dataset_name)
            mkdir(output_folder)
            output_folders[idx] = output_folder
    data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
    for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
        inference(
            model,
            data_loader_val,
            dataset_name=dataset_name,
            iou_types=iou_types,
            box_only=cfg.MODEL.RPN_ONLY and (cfg.MODEL.RPN_ARCHITECTURE=="RPN" or cfg.DATASETS.CLASS_AGNOSTIC),
            device=cfg.MODEL.DEVICE,
            expected_results=cfg.TEST.EXPECTED_RESULTS,
            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
            output_folder=output_folder,
            cfg=cfg
        )
        synchronize()


def main():
    parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
    parser.add_argument(
        "--config-file",
        default="/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument(
        "--weight",
        default=None,
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument("--local_rank", type=int, default=0)
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
    distributed = num_gpus > 1

    if distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )

    cfg.local_rank = args.local_rank
    cfg.num_gpus = num_gpus

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    log_dir = cfg.OUTPUT_DIR
    if args.weight:
        log_dir = os.path.join(log_dir, "eval", os.path.splitext(os.path.basename(args.weight))[0])
    if log_dir:
        mkdir(log_dir)
    logger = setup_logger("maskrcnn_benchmark", log_dir, get_rank())
    logger.info(args)
    logger.info("Using {} GPUs".format(num_gpus))
    logger.info(cfg)

    logger.info("Collecting env info (might take some time)")
    logger.info("\n" + collect_env_info())

    model = build_detection_model(cfg)
    model.to(cfg.MODEL.DEVICE)

    params, flops = get_model_complexity_info(model,
                                              (3, cfg.INPUT.MAX_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST),
                                              input_constructor=lambda x: {'images': [torch.rand(x).cuda()]})
    print("FLOPs: {}, #Parameter: {}".format(params, flops))

    checkpointer = DetectronCheckpointer(cfg, model, save_dir=cfg.OUTPUT_DIR)
    if args.weight:
        _ = checkpointer.load(args.weight, force=True)
    else:
        _ = checkpointer.load(cfg.MODEL.WEIGHT)

    run_test(cfg, model, distributed, log_dir)
    logger.info("FLOPs: {}, #Parameter: {}".format(params, flops))


if __name__ == "__main__":
    main()