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timm_efficientdet: benchmark coverage for custom devices #2374

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timm_efficientdet: benchmark coverage for custom devices #2374

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b762d61
timm_efficientdet:benchmark coverage for custom devices
weishi-deng Jul 12, 2024
5319312
Merge branch 'main' into timm_efficientdet
weishi-deng Sep 25, 2024
67f6c90
update effdet
weishi-deng Sep 26, 2024
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7 changes: 2 additions & 5 deletions torchbenchmark/models/timm_efficientdet/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,10 +49,6 @@ class Model(BenchmarkModel):

def __init__(self, test, device, batch_size=None, extra_args=[]):
super().__init__(test=test, device=device, batch_size=batch_size, extra_args=extra_args)
if not device == "cuda":
# Only implemented on CUDA because the original model code explicitly calls the `Tensor.cuda()` API
# https://github.com/rwightman/efficientdet-pytorch/blob/9cb43186711d28bd41f82f132818c65663b33c1f/effdet/data/loader.py#L114
raise NotImplementedError("The original model code forces the use of CUDA.")
# generate arguments
args = get_args()
# setup train and eval batch size
Expand Down Expand Up @@ -136,7 +132,8 @@ def __init__(self, test, device, batch_size=None, extra_args=[]):
mean=input_config['mean'],
std=input_config['std'],
num_workers=args.workers,
pin_mem=args.pin_mem)
pin_mem=args.pin_mem,
device=args.device)
self.loader = prefetch(self.loader, self.device, self.NUM_OF_BATCHES)
self.args = args
# Only run 1 epoch
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2 changes: 2 additions & 0 deletions torchbenchmark/models/timm_efficientdet/loader.py
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Expand Up @@ -63,6 +63,7 @@ def create_datasets_and_loaders(
anchor_labeler=labeler,
transform_fn=transform_train_fn,
collate_fn=collate_fn,
device=args.device
)

if args.val_skip > 1:
Expand All @@ -83,6 +84,7 @@ def create_datasets_and_loaders(
anchor_labeler=labeler,
transform_fn=transform_eval_fn,
collate_fn=collate_fn,
device=args.device
)

evaluator = create_evaluator(args.dataset, loader_eval.dataset, distributed=args.distributed, pred_yxyx=False)
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