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Fixed the numpy floating point type error #240

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Fixed the numpy floating point type error #240

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2 changes: 1 addition & 1 deletion test_partseg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,7 @@ def log_string(str):
mean_shape_ious = np.mean(list(shape_ious.values()))
test_metrics['accuracy'] = total_correct / float(total_seen)
test_metrics['class_avg_accuracy'] = np.mean(
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float))
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float32))
for cat in sorted(shape_ious.keys()):
log_string('eval mIoU of %s %f' % (cat + ' ' * (14 - len(cat)), shape_ious[cat]))
test_metrics['class_avg_iou'] = mean_shape_ious
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6 changes: 3 additions & 3 deletions test_semseg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,7 @@ def log_string(str):
total_correct_class[l] += total_correct_class_tmp[l]
total_iou_deno_class[l] += total_iou_deno_class_tmp[l]

iou_map = np.array(total_correct_class_tmp) / (np.array(total_iou_deno_class_tmp, dtype=np.float) + 1e-6)
iou_map = np.array(total_correct_class_tmp) / (np.array(total_iou_deno_class_tmp, dtype=np.float32) + 1e-6)
print(iou_map)
arr = np.array(total_seen_class_tmp)
tmp_iou = np.mean(iou_map[arr != 0])
Expand All @@ -182,7 +182,7 @@ def log_string(str):
fout.close()
fout_gt.close()

IoU = np.array(total_correct_class) / (np.array(total_iou_deno_class, dtype=np.float) + 1e-6)
IoU = np.array(total_correct_class) / (np.array(total_iou_deno_class, dtype=np.float32) + 1e-6)
iou_per_class_str = '------- IoU --------\n'
for l in range(NUM_CLASSES):
iou_per_class_str += 'class %s, IoU: %.3f \n' % (
Expand All @@ -191,7 +191,7 @@ def log_string(str):
log_string(iou_per_class_str)
log_string('eval point avg class IoU: %f' % np.mean(IoU))
log_string('eval whole scene point avg class acc: %f' % (
np.mean(np.array(total_correct_class) / (np.array(total_seen_class, dtype=np.float) + 1e-6))))
np.mean(np.array(total_correct_class) / (np.array(total_seen_class, dtype=np.float32) + 1e-6))))
log_string('eval whole scene point accuracy: %f' % (
np.sum(total_correct_class) / float(np.sum(total_seen_class) + 1e-6)))

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1 change: 1 addition & 0 deletions train_classification.py
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Expand Up @@ -40,6 +40,7 @@ def parse_args():
parser.add_argument('--use_normals', action='store_true', default=False, help='use normals')
parser.add_argument('--process_data', action='store_true', default=False, help='save data offline')
parser.add_argument('--use_uniform_sample', action='store_true', default=False, help='use uniform sampiling')
parser.add_argument('--num_workers', type=int, default=0, help='dataloader num_workers')
return parser.parse_args()


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7 changes: 4 additions & 3 deletions train_partseg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,6 +58,7 @@ def parse_args():
parser.add_argument('--normal', action='store_true', default=False, help='use normals')
parser.add_argument('--step_size', type=int, default=20, help='decay step for lr decay')
parser.add_argument('--lr_decay', type=float, default=0.5, help='decay rate for lr decay')
parser.add_argument('--num_workers', type=int, default=0, help='dataloader num_workers')

return parser.parse_args()

Expand Down Expand Up @@ -101,9 +102,9 @@ def log_string(str):
root = 'data/shapenetcore_partanno_segmentation_benchmark_v0_normal/'

TRAIN_DATASET = PartNormalDataset(root=root, npoints=args.npoint, split='trainval', normal_channel=args.normal)
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=10, drop_last=True)
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, drop_last=True)
TEST_DATASET = PartNormalDataset(root=root, npoints=args.npoint, split='test', normal_channel=args.normal)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=10)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
log_string("The number of training data is: %d" % len(TRAIN_DATASET))
log_string("The number of test data is: %d" % len(TEST_DATASET))

Expand Down Expand Up @@ -264,7 +265,7 @@ def bn_momentum_adjust(m, momentum):
mean_shape_ious = np.mean(list(shape_ious.values()))
test_metrics['accuracy'] = total_correct / float(total_seen)
test_metrics['class_avg_accuracy'] = np.mean(
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float))
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float32))
for cat in sorted(shape_ious.keys()):
log_string('eval mIoU of %s %f' % (cat + ' ' * (14 - len(cat)), shape_ious[cat]))
test_metrics['class_avg_iou'] = mean_shape_ious
Expand Down
10 changes: 6 additions & 4 deletions train_semseg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,8 @@ def parse_args():
parser.add_argument('--step_size', type=int, default=10, help='Decay step for lr decay [default: every 10 epochs]')
parser.add_argument('--lr_decay', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')
parser.add_argument('--test_area', type=int, default=5, help='Which area to use for test, option: 1-6 [default: 5]')
parser.add_argument('--num_workers', type=int, default=0, help='dataloader num_workers')


return parser.parse_args()

Expand Down Expand Up @@ -98,10 +100,10 @@ def log_string(str):
print("start loading test data ...")
TEST_DATASET = S3DISDataset(split='test', data_root=root, num_point=NUM_POINT, test_area=args.test_area, block_size=1.0, sample_rate=1.0, transform=None)

trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=BATCH_SIZE, shuffle=True, num_workers=10,
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=BATCH_SIZE, shuffle=True, num_workers=args.num_workers,
pin_memory=True, drop_last=True,
worker_init_fn=lambda x: np.random.seed(x + int(time.time())))
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=BATCH_SIZE, shuffle=False, num_workers=10,
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=BATCH_SIZE, shuffle=False, num_workers=args.num_workers,
pin_memory=True, drop_last=True)
weights = torch.Tensor(TRAIN_DATASET.labelweights).cuda()

Expand Down Expand Up @@ -255,12 +257,12 @@ def bn_momentum_adjust(m, momentum):
total_iou_deno_class[l] += np.sum(((pred_val == l) | (batch_label == l)))

labelweights = labelweights.astype(np.float32) / np.sum(labelweights.astype(np.float32))
mIoU = np.mean(np.array(total_correct_class) / (np.array(total_iou_deno_class, dtype=np.float) + 1e-6))
mIoU = np.mean(np.array(total_correct_class) / (np.array(total_iou_deno_class, dtype=np.float32) + 1e-6))
log_string('eval mean loss: %f' % (loss_sum / float(num_batches)))
log_string('eval point avg class IoU: %f' % (mIoU))
log_string('eval point accuracy: %f' % (total_correct / float(total_seen)))
log_string('eval point avg class acc: %f' % (
np.mean(np.array(total_correct_class) / (np.array(total_seen_class, dtype=np.float) + 1e-6))))
np.mean(np.array(total_correct_class) / (np.array(total_seen_class, dtype=np.float32) + 1e-6))))

iou_per_class_str = '------- IoU --------\n'
for l in range(NUM_CLASSES):
Expand Down