num_workers=min(num_gpu * 8, 16))
val_generator = data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=min(num_gpu * 8, 16))
# This is just to check that labels are correctly encoded.
for pos in range(20):
img1, target1 = train_dataset.__getitem__(pos)
gt_color = (0, 0, 255) #red
# Get numpy image in opencv format
np_img = preprocess.post_process_image(img1)
# get bounding boxes: xyxy + class + confidence
img_h, img_w, _ = np_img.shape
target_bboxes = preprocess.decode_label(target1, img_h, img_w)
draw_all_bboxes(np_img, target_bboxes, preprocess, gt_color,
"images_transformed/gt_{}.jpg".format(pos))
import os
os._exit(1)
# Enable anomaly detection for debugging purpose
torch.autograd.set_detect_anomaly(True)
model = YOLO((channels, height, width), S, B, C)
if num_gpu > 1:
print("Let's use", num_gpu, "GPUs!")
model = torch.nn.DataParallel(model)
model = model.to(device)
preds = model(batch_x)
# compute loss
test_loss = loss_func(preds, batch_y)
accumulated_test_loss.append(test_loss)
name = "test_results/images/test{}.jpg".format(it + 1)
img = batch_x.clone().view((channels, height, width))
pred = preds.clone().view((S, S, B * 5 + C))
target = batch_y.clone().view((S, S, B * 5 + C))
# Get numpy image in opencv format
np_img = preprocess.post_process_image(img)
# get bounding boxes: xyxy + class + confidence
img_h, img_w, _ = np_img.shape
target_bboxes = preprocess.decode_label(target, img_h, img_w)
pred_bboxes = preprocess.decode_label(pred, img_h, img_w)
draw_all_bboxes(np_img, target_bboxes, preprocess, (0, 0, 255), name)
draw_all_bboxes(np_img, pred_bboxes, preprocess, (0, 255, 0), name)
# write results to disk
gt_file_name = "test_results/gt/test{}.txt".format(it + 1)
write_result_to_disk(gt_file_name, target_bboxes, type="gt")
pred_file_name = "test_results/pred/test{}.txt".format(it + 1)
write_result_to_disk(pred_file_name, pred_bboxes, type="pred")
it += 1
duration = time.time() - start_timestamp
# Epoch losses
