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- import unittest
- from super_gradients.training.datasets.dataset_interfaces.dataset_interface import CoCoDetectionDatasetInterface
- from super_gradients.training.metrics.detection_metrics import DetectionMetrics
- from super_gradients.training import Trainer
- from super_gradients.training.models.detection_models.yolo_base import YoloPostPredictionCallback
- from super_gradients.training.utils.detection_utils import CrowdDetectionCollateFN, DetectionCollateFN, \
- DetectionTargetsFormat
- class TestDatasetStatisticsTensorboardLogger(unittest.TestCase):
- def test_dataset_statistics_tensorboard_logger(self):
- """
- ** IMPORTANT NOTE **
- This test is not the usual fail/pass test - it is a visual test. The success criteria is your own visual check
- After launching the test, follow the log the see where was the tensorboard opened. open the tensorboard in your
- browser and make sure the text and plots in the tensorboard are as expected.
- """
- # Create dataset
- dataset = CoCoDetectionDatasetInterface(dataset_params={"data_dir": "/data/coco",
- "train_subdir": "images/train2017",
- "val_subdir": "images/val2017",
- "train_json_file": "instances_train2017.json",
- "val_json_file": "instances_val2017.json",
- "batch_size": 16,
- "val_batch_size": 128,
- "val_image_size": 640,
- "train_image_size": 640,
- "hgain": 5,
- "sgain": 30,
- "vgain": 30,
- "mixup_prob": 1.0,
- "degrees": 10.,
- "shear": 2.0,
- "flip_prob": 0.5,
- "hsv_prob": 1.0,
- "mosaic_scale": [0.1, 2],
- "mixup_scale": [0.5, 1.5],
- "mosaic_prob": 1.,
- "translate": 0.1,
- "val_collate_fn": CrowdDetectionCollateFN(),
- "train_collate_fn": DetectionCollateFN(),
- "cache_dir_path": None,
- "cache_train_images": False,
- "cache_val_images": False,
- "targets_format": DetectionTargetsFormat.LABEL_CXCYWH,
- "with_crowd": True,
- "filter_box_candidates": False,
- "wh_thr": 0,
- "ar_thr": 0,
- "area_thr": 0
- })
- trainer = Trainer('dataset_statistics_visual_test',
- model_checkpoints_location='local',
- post_prediction_callback=YoloPostPredictionCallback())
- trainer.connect_dataset_interface(dataset, data_loader_num_workers=8)
- trainer.build_model("yolox_s")
- training_params = {"max_epochs": 1, # we dont really need the actual training to run
- "lr_mode": "cosine",
- "initial_lr": 0.01,
- "loss": "yolox_loss",
- "criterion_params": {"strides": [8, 16, 32], "num_classes": 80},
- "dataset_statistics": True,
- "launch_tensorboard": True,
- "valid_metrics_list": [
- DetectionMetrics(post_prediction_callback=YoloPostPredictionCallback(),
- normalize_targets=True,
- num_cls=80)],
- "loss_logging_items_names": ["iou", "obj", "cls", "l1", "num_fg", "Loss"],
- "metric_to_watch": "mAP@0.50:0.95",
- }
- trainer.train(training_params=training_params)
- if __name__ == '__main__':
- unittest.main()
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