Ankle/yolov5_birdproject
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Fixed access 'names' from a DistributedDataParallel module (#11023)

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imyhxy authored and GitHub committed 2023-02-20 11:23:13 +01:00
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commit 4db6757ef9
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classify/train.py
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@@ -44,7 +44,7 @@ from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirecto
check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save) check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save)
from utils.loggers import GenericLogger from utils.loggers import GenericLogger
from utils.plots import imshow_cls from utils.plots import imshow_cls
from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP, from utils.torch_utils import (ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP,
smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first) smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
@@ -260,7 +260,7 @@ def train(opt, device):
# Plot examples # Plot examples
images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels
pred = torch.max(ema.ema(images.to(device)), 1)[1] pred = torch.max(ema.ema(images.to(device)), 1)[1]
file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg') file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / 'test_images.jpg')
# Log results # Log results
meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()} meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()}