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fix segmentation testing and support CPU #888

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fix segmentation testing and support CPU #888

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9d46a4b
fix segmentation testing and support CPU
wkcn Jul 29, 2019
d86aaad
update no cuda config
wkcn Jul 29, 2019
f02a744
change the axis of argmax
wkcn Jul 30, 2019
1cafa23
Merge branch 'master' into fix_segmentation_test
wkcn Aug 20, 2019
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5 changes: 3 additions & 2 deletions scripts/segmentation/test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,8 +130,8 @@ def test(model, args, input_transform):
im_paths = dsts
predicts = evaluator.parallel_forward(data)
for predict, impath in zip(predicts, im_paths):
predict = mx.nd.squeeze(mx.nd.argmax(predict[0], 1)).asnumpy() + \
testset.pred_offset
predict = mx.nd.squeeze(mx.nd.argmax(predict, 1), axis=0).\
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This will break existing segmentation model, which outputs a list by default.

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@wkcn wkcn Aug 2, 2019 •
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What are the elements in the list predict?
I think predicts is a list of NDArray, and predict is a NDArray whose shape is (1, num_cls, height, width)

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predicts is a list of list of NDArray

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Thank you! I will check it.

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@zhanghang1989
Hi, I found the type of predicts is tuple, and the type of predict is NDArray.

Test script: python test.py --dataset pascal_voc --model-zoo psp_resnet101_voc

asnumpy() + testset.pred_offset
mask = get_color_pallete(predict, args.dataset)
outname = os.path.splitext(impath)[0] + '.png'
mask.save(os.path.join(outdir, outname))
Expand Down Expand Up @@ -194,6 +194,7 @@ def benchmarking(model, args):

if __name__ == "__main__":
args = parse_args()
args.test_batch_size = max(1, args.ngpus)
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Just want to know why args.test_batch_size here is dependent on args.ngpus. Also since args.batch_size is already defined, is it possible to use this one directly instead of adding a new one like test_batch_size?

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Thank you for the review! I have removed this line, and I will update the code later.
The test_batch_size is defined in train.py, and it is the batch size of testing dataset. For testing script, we should keep the consistency with training procedure.

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Thank you for the review! I have removed this line, and I will update the code later.
The test_batch_size is defined in train.py, and it is the batch size of testing dataset. For testing script, we should keep the consistency with training procedure.

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I don't know why the program gets stuck. I will check it later.

logging.basicConfig()
logger = logging.getLogger('logger')
logger.setLevel(logging.INFO)
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2 changes: 2 additions & 0 deletions scripts/segmentation/train.py
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Expand Up @@ -88,6 +88,8 @@ def parse_args():
# the parser
args = parser.parse_args()
# handle contexts
if args.ngpus == 0:
args.no_cuda = True
if args.no_cuda:
print('Using CPU')
args.kvstore = 'local'
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