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fix: replace max calibration batches default value with None #216

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merged 1 commit into from
Sep 9, 2022
Merged

fix: replace max calibration batches default value with None #216

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2 changes: 1 addition & 1 deletion alonet/torch2trt/base_exporter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -402,7 +402,7 @@ def add_argparse_args(parent_parser):
parser.add_argument("--verbose", action="store_true", help="Helpful when debugging")
parser.add_argument("--profiling_verbosity", default=0, type=int, help="Helpful when profiling the engine (default: %(default)s)")
parser.add_argument("--calibration_batch_size", type=int, default=8, help="Calibration data batch size (default: %(default)s)")
parser.add_argument("--limit_calibration_batches", type=int, default=10, help="Limits number of batches (default: %(default)s)")
parser.add_argument("--limit_calibration_batches", type=int, default=None, help="Limits number of batches (default: %(default)s)")
parser.add_argument("--cache_file", type=str, default="calib.bin", help="Path to caliaration cache file (default: %(default)s)")
parser.add_argument(
"--calibrator",
Expand Down
10 changes: 5 additions & 5 deletions alonet/torch2trt/calibrator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,8 +65,6 @@ class DataBatchStreamer:
>>> s_dataStreamer = DataBatchStreamer(dataset=s_calib)
>>> m_dataStreamer = DataBatchStreamer(dataset=m_calib)
"""
FTYPES = ["torch.Tensor", "ndarray", "aloscene.Frame"]

def __init__(
self,
dataset=None,
Expand All @@ -76,7 +74,8 @@ def __init__(
):
for sample in dataset[0]:
if not isinstance(sample, (torch.Tensor, np.ndarray, Frame)):
raise TypeError(f"unknown sample type, expected samples to be instance of {' or '.join(self.FTYPES)} got {sample.__class__.__name__} instead")
ftypes = ["torch.Tensor", "ndarray", "aloscene.Frame"]
raise TypeError(f"unknown sample type, expected samples to be instance of {' or '.join(ftypes)} got {sample.__class__.__name__} instead")

self.batch_idx = 0
self.dataset = dataset
Expand Down Expand Up @@ -105,7 +104,8 @@ def convert_frame(frame):
elif isinstance(frame, np.ndarray):
pass
else:
raise TypeError(f"Unknown sample type, expected samples to be instance of {' or '.join(self.FTYPES)} got {frame.__class__.__name__}.")
ftypes = ["torch.Tensor", "ndarray", "aloscene.Frame"]
raise TypeError(f"Unknown sample type, expected samples to be instance of {' or '.join(ftypes)} got {frame.__class__.__name__}.")
return frame

def next_(self):
Expand All @@ -127,7 +127,7 @@ def next_(self):
return None

def __len__(self):
return max_batch
return self.max_batch


class BaseCalibrator:
Expand Down