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Clear Cuda cache before training loop and validate loop starts #835

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Clear Cuda cache before training loop and validate loop starts #835

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e0d2fcd
Clear cuda cache before training loop and validate loop starts
dumbmonkey2 Mar 27, 2024
2832925
Improve validation loop speed
dumbmonkey2 Mar 27, 2024
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2 changes: 2 additions & 0 deletions imageai/Detection/Custom/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -218,6 +218,8 @@ def trainModel(self) -> None:
os.makedirs(self.__output_models_dir, exist_ok=True)
os.makedirs(self.__output_json_dir, exist_ok=True)

torch.cuda.empty_cache()

mp, mr, map50, map50_95, best_fitness = 0, 0, 0, 0, 0.0
nbs = 64 # norminal batch size
nb = len(self.__train_loader) # number of batches
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10 changes: 6 additions & 4 deletions imageai/Detection/Custom/yolo/validate.py
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Expand Up @@ -56,10 +56,12 @@ def run(model, val_dataloader, num_class, net_dim=416, nms_thresh=0.6, objectnes
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for [email protected]:0.95
niou = iouv.numel()

p, r, f1, mp, mr, map50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
stats, ap, ap_class = [], [], []
torch.cuda.empty_cache()

mp, mr, map50, map = 0.0, 0.0, 0.0, 0.0
stats, ap = [], []

for batch_i, (im, targets) in tqdm(enumerate(val_dataloader)):
for im, targets in tqdm(val_dataloader):
im = im.to(device)
targets = targets.to(device)
nb = im.shape[0] # batch
Expand Down Expand Up @@ -108,7 +110,7 @@ def run(model, val_dataloader, num_class, net_dim=416, nms_thresh=0.6, objectnes
# Compute metrics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
p, r, ap, f1, ap_class = ap_per_class(*stats)
p, r, ap, _, _ = ap_per_class(*stats)
ap50, ap = ap[:, 0], ap.mean(1) # [email protected], [email protected]:0.95
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()

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