First model version

app.py

@@ -8,24 +8,24 @@ os.system('python setup.py build develop --user')
 import cv2
 import pandas as pd
 import gradio as gr
-from tools.demo import TextDemo
+from det_demo import DetDemo
 from maskrcnn_benchmark.config import cfg
 
 
 def infer(filepath):
-    cfg.merge_from_file('./configs/ctw/r50_baseline.yaml')
+    cfg.merge_from_file('./configs/det/r50_baseline.yaml')
     # manual override some options
     cfg.merge_from_list(["MODEL.DEVICE", "cpu"])
 
-    text_demo = TextDemo(
+    det_demo = DetDemo(
         cfg,
         min_image_size=800,
         confidence_threshold=0.7,
         output_polygon=True
     )
     image = cv2.imread(filepath)
-    result_polygons, result_masks = text_demo.run_on_opencv_image(image)
-    image = text_demo.visualization(image, result_polygons, result_masks)
+    result_polygons, result_masks = det_demo.run_on_opencv_image(image)
+    image = det_demo.visualization(image, result_polygons, result_masks)
     cv2.imwrite('result.jpg', image)
     return 'result.jpg'#, pd.DataFrame(result_words)
 

test_contour.sh

@@ -1,3 +0,0 @@
-export NGPUS=1
-CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py \
-	--config-file "configs/ctw/r50_baseline.yaml" 

tools/test_net.py

@@ -1,95 +0,0 @@
-# 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
-
-
-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("--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://"
-        )
-        synchronize()
-
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-
-    save_dir = ""
-    logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
-    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)
-
-    output_dir = cfg.OUTPUT_DIR
-    checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
-    _ = checkpointer.load(cfg.MODEL.WEIGHT)
-
-    iou_types = ("bbox",)
-    if cfg.MODEL.BOUNDARY_ON:
-        iou_types = iou_types + ("bo",)
-    output_folders = [None] * len(cfg.DATASETS.TEST)
-    dataset_names = cfg.DATASETS.TEST
-    if cfg.OUTPUT_DIR:
-        for idx, dataset_name in enumerate(dataset_names):
-            output_folder = os.path.join(cfg.OUTPUT_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=False if cfg.MODEL.FCOS_ON or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
-            device=cfg.MODEL.DEVICE,
-            expected_results=cfg.TEST.EXPECTED_RESULTS,
-            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
-            output_folder=output_folder,
-        )
-        synchronize()
-
-
-if __name__ == "__main__":
-    main()

tools/train_net.py

@@ -1,174 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-r"""
-Basic training script for PyTorch
-"""
-
-# 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.solver import make_lr_scheduler
-from maskrcnn_benchmark.solver import make_optimizer
-from maskrcnn_benchmark.engine.inference import inference
-from maskrcnn_benchmark.engine.trainer import do_train
-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.imports import import_file
-from maskrcnn_benchmark.utils.logger import setup_logger
-from maskrcnn_benchmark.utils.miscellaneous import mkdir
-
-
-def train(cfg, local_rank, distributed):
-    model = build_detection_model(cfg)
-    device = torch.device(cfg.MODEL.DEVICE)
-    model.to(device)
-
-    optimizer = make_optimizer(cfg, model)
-    scheduler = make_lr_scheduler(cfg, optimizer)
-
-    if distributed:
-        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[local_rank], output_device=local_rank,
-            # this should be removed if we update BatchNorm stats
-            broadcast_buffers=False,
-        )
-
-    arguments = {}
-    arguments["iteration"] = 0
-
-    output_dir = cfg.OUTPUT_DIR
-
-    save_to_disk = get_rank() == 0
-    checkpointer = DetectronCheckpointer(
-        cfg, model, optimizer, scheduler, output_dir, save_to_disk
-    )
-    extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
-    arguments.update(extra_checkpoint_data)
-
-    data_loader = make_data_loader(
-        cfg,
-        is_train=True,
-        is_distributed=distributed,
-        start_iter=arguments["iteration"],
-    )
-
-    checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
-
-    do_train(
-        model,
-        data_loader,
-        optimizer,
-        scheduler,
-        checkpointer,
-        device,
-        checkpoint_period,
-        arguments,
-    )
-
-    return model
-
-
-def run_test(cfg, model, distributed):
-    if distributed:
-        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",)
-    output_folders = [None] * len(cfg.DATASETS.TEST)
-    dataset_names = cfg.DATASETS.TEST
-    if cfg.OUTPUT_DIR:
-        for idx, dataset_name in enumerate(dataset_names):
-            output_folder = os.path.join(cfg.OUTPUT_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=False if cfg.MODEL.FCOS_ON or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
-            device=cfg.MODEL.DEVICE,
-            expected_results=cfg.TEST.EXPECTED_RESULTS,
-            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
-            output_folder=output_folder,
-        )
-        synchronize()
-
-
-def main():
-    parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
-    parser.add_argument(
-        "--config-file",
-        default="",
-        metavar="FILE",
-        help="path to config file",
-        type=str,
-    )
-    parser.add_argument("--local_rank", type=int, default=0)
-    parser.add_argument(
-        "--skip-test",
-        dest="skip_test",
-        help="Do not test the final model",
-        action="store_true",
-    )
-    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
-    args.distributed = num_gpus > 1
-
-    if args.distributed:
-        torch.cuda.set_device(args.local_rank)
-        torch.distributed.init_process_group(
-            backend="nccl", init_method="env://"
-        )
-        synchronize()
-
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-
-    output_dir = cfg.OUTPUT_DIR
-    if output_dir:
-        mkdir(output_dir)
-
-    logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
-    logger.info("Using {} GPUs".format(num_gpus))
-    logger.info(args)
-
-    logger.info("Collecting env info (might take some time)")
-    logger.info("\n" + collect_env_info())
-
-    logger.info("Loaded configuration file {}".format(args.config_file))
-    with open(args.config_file, "r") as cf:
-        config_str = "\n" + cf.read()
-        logger.info(config_str)
-    logger.info("Running with config:\n{}".format(cfg))
-
-    model = train(cfg, args.local_rank, args.distributed)
-
-    if not args.skip_test:
-        run_test(cfg, model, args.distributed)
-
-
-if __name__ == "__main__":
-    main()

train_contour.sh

@@ -1,6 +0,0 @@
-# export NCCL_P2P_DISABLE=1
-export NGPUS=1
-CUDA_VISIBLE_DEVICES=1 python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py \
-	--config-file "configs/ic/r50_baseline.yaml" \
-	--skip-test
-