Created
March 21, 2020 21:22
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Changes to train with Centermask2
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| import os | |
| import random | |
| import cv2 | |
| #from detectron2.config import get_cfg | |
| from centermask.config import get_cfg | |
| from detectron2 import model_zoo | |
| from detectron2.engine import DefaultPredictor | |
| from detectron2.utils.visualizer import Visualizer, ColorMode | |
| from detectron2.data import MetadataCatalog | |
| from loader import get_data_dicts | |
| classes = ["Blacky", "Niche"] | |
| cfg = get_cfg() | |
| cfg.merge_from_file("configs/centermask/centermask_V_99_eSE_FPN_ms_3x.yaml") | |
| cfg.DATASETS.TEST = ("cats_val",) | |
| cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth") | |
| cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2 | |
| cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 | |
| predictor = DefaultPredictor(cfg) | |
| dataset_dicts = get_data_dicts("./data/validation") | |
| for idx, d in enumerate(random.sample(dataset_dicts, 3)): | |
| im = cv2.imread(d["file_name"]) | |
| outputs = predictor(im) | |
| v = Visualizer(im[:, :, ::-1], | |
| metadata = MetadataCatalog.get("cats_val").set( | |
| thing_classes=classes, | |
| thing_colors=[(177, 205, 223), (223, 205, 177)]), | |
| scale = 0.8, | |
| instance_mode = ColorMode.IMAGE_BW | |
| ) | |
| pred_class = (outputs['instances'].pred_classes).detach()[0] | |
| pred_score = (outputs['instances'].scores).detach()[0] | |
| print(f"File: {d['file_name']}") | |
| print(f"--> Class: {classes[pred_class]}, {pred_score * 100:.2f}%") | |
| # Save image predictions | |
| v = v.draw_instance_predictions(outputs["instances"].to("cpu")) | |
| image_name = f"inference_{idx}.jpg" | |
| cv2.imwrite(image_name, v.get_image()[:, :, ::-1]) | |
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| import os | |
| from detectron2.data import MetadataCatalog, DatasetCatalog | |
| #from detectron2.config import get_cfg | |
| from centermask.config import get_cfg | |
| from detectron2 import model_zoo | |
| from detectron2.engine import DefaultTrainer | |
| from loader import get_data_dicts | |
| for d in ["train", "val"]: | |
| DatasetCatalog.register("cats_" + d, lambda d=d: get_data_dicts("./data/" + d)) | |
| MetadataCatalog.get("cats_" + d).set(thing_classes=["Blacky", "Niche"]) | |
| cats_metadata = MetadataCatalog.get("cats_train") | |
| # Configure model | |
| # TODO: Validate the configuration somehow | |
| cfg = get_cfg() | |
| #cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")) | |
| cfg.merge_from_file("configs/centermask/centermask_V_99_eSE_FPN_ms_3x.yaml") | |
| cfg.DATASETS.TRAIN = ("cats_train",) | |
| cfg.DATASETS.TEST = () | |
| cfg.DATALOADER.NUM_WORKERS = 4 | |
| #cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" | |
| cfg.MODEL.WEIGHTS = "vovnet99_ese_detectron2.pth" | |
| cfg.SOLVER.IMS_PER_BATCH = 2 | |
| cfg.SOLVER.BASE_LR = 0.00035 | |
| cfg.SOLVER.MAX_ITER = 4200 | |
| cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 | |
| cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2 | |
| os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) | |
| trainer = DefaultTrainer(cfg) | |
| trainer.resume_or_load(resume=False) | |
| trainer.train() |
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