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Add use-pinned and pinned-buffer-size options to M-RCNN training #1005

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Add use-pinned and pinned-buffer-size options to M-RCNN training #1005

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26 changes: 22 additions & 4 deletions scripts/instance/mask_rcnn/train_mask_rcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,6 +113,11 @@ def parse_args():
parser.add_argument('--kv-store', type=str, default='nccl',
help='KV store options. local, device, nccl, dist_sync, dist_device_sync, '
'dist_async are available.')
parser.add_argument('--use-pinned', action='store_true',
help='Whether to use pinned memory buffers to stage the input data.')
parser.add_argument('--pinned-buffer-size', type=int, default=0,
help='Size of the staged memory buffers for input data. If the value is <= 0, '
'the buffers will be dynamically reshaped. Default is 0.')

args = parser.parse_args()
if args.horovod:
Expand All @@ -124,6 +129,8 @@ def parse_args():
args.lr = float(args.lr) if args.lr else 0.01
args.lr_warmup = args.lr_warmup if args.lr_warmup else 1000
args.wd = float(args.wd) if args.wd else 1e-4
global use_pinned, pinned_buffer_size
use_pinned, pinned_buffer_size = args.use_pinned, args.pinned_buffer_size
return args


Expand Down Expand Up @@ -175,6 +182,7 @@ def save_params(net, logger, best_map, current_map, epoch, save_interval, prefix


def _stage_data(i, data, ctx_list, pinned_data_stage):
global pinned_buffer_size
def _get_chunk(data, storage):
s = storage.reshape(shape=(storage.size,))
s = s[:data.size]
Expand All @@ -192,7 +200,12 @@ def _get_chunk(data, storage):

for j in range(len(storage)):
if data[j].size > storage[j].size:
storage[j] = data[j].as_in_context(mx.cpu_pinned())
if data[j].size > pinned_buffer_size:
storage[j] = data[j].as_in_context(mx.cpu_pinned())
else:
storage[j] = mx.nd.zeros(shape=(pinned_buffer_size),
dtype=data[j].dtype,
ctx=mx.cpu_pinned())

return [_get_chunk(d, s) for d, s in zip(data, storage)]

Expand All @@ -204,14 +217,19 @@ def split_and_load(batch, ctx_list):
"""Split data to 1 batch each device."""
new_batch = []
for i, data in enumerate(batch):
if isinstance(data, (list, tuple)):
new_data = [x.as_in_context(ctx) for x, ctx in zip(data, ctx_list)]
if not isinstance(data, (list, tuple)):
data = [data]
global use_pinned
if use_pinned:
staged_data = _stage_data(i, data, ctx_list, pinned_data_stage)
new_data = [x.as_in_context(ctx) for x, ctx in zip(staged_data, ctx_list)]
else:
new_data = [data.as_in_context(ctx_list[0])]
new_data = [x.as_in_context(ctx) for x, ctx in zip(data, ctx_list)]
new_batch.append(new_data)
return new_batch



def validate(net, val_data, ctx, eval_metric, args):
"""Test on validation dataset."""
clipper = gcv.nn.bbox.BBoxClipToImage()
Expand Down