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[MXNET-696] Fix undefined variable errors #11982

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Aug 9, 2018
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4 changes: 2 additions & 2 deletions example/fcn-xs/image_segmentaion.py
Original file line number Diff line number Diff line change
Expand Up @@ -93,8 +93,8 @@ def main():
model_prefix = "FCN8s_VGG16"
epoch = 19

# By default, MXNet will run on the CPU. Uncomment the line below to execute on the GPU
# ctx = mx.gpu()
# By default, MXNet will run on the CPU. Change to ctx = mx.gpu() to run on GPU.
ctx = mx.cpu()

fcnxs, fcnxs_args, fcnxs_auxs = mx.model.load_checkpoint(model_prefix, epoch)
fcnxs_args["data"] = mx.nd.array(get_data(args.input), ctx)
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17 changes: 0 additions & 17 deletions example/ssd/dataset/pycocotools/coco.py
Original file line number Diff line number Diff line change
Expand Up @@ -255,22 +255,7 @@ def showAnns(self, anns):
color.append(c)
else:
# mask
t = self.imgs[ann['image_id']]
if type(ann['segmentation']['counts']) == list:
# rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
raise NotImplementedError("maskUtils disabled!")
else:
rle = [ann['segmentation']]
# m = maskUtils.decode(rle)
raise NotImplementedError("maskUtils disabled!")
img = np.ones( (m.shape[0], m.shape[1], 3) )
if ann['iscrowd'] == 1:
color_mask = np.array([2.0,166.0,101.0])/255
if ann['iscrowd'] == 0:
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
if 'keypoints' in ann and type(ann['keypoints']) == list:
# turn skeleton into zero-based index
sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
Expand Down Expand Up @@ -430,6 +415,4 @@ def annToMask(self, ann):
:return: binary mask (numpy 2D array)
"""
rle = self.annToRLE(ann)
# m = maskUtils.decode(rle)
raise NotImplementedError("maskUtils disabled!")
return m
3 changes: 3 additions & 0 deletions example/ssd/symbol/common.py
Original file line number Diff line number Diff line change
Expand Up @@ -206,6 +206,9 @@ def multibox_layer(from_layers, num_classes, sizes=[.2, .95],
assert sizes[0] > 0 and sizes[0] < 1
assert sizes[1] > 0 and sizes[1] < 1 and sizes[1] > sizes[0]
tmp = np.linspace(sizes[0], sizes[1], num=(len(from_layers)-1))
# Ref for start_offset value:
# https://arxiv.org/abs/1512.02325
start_offset = 0.1
min_sizes = [start_offset] + tmp.tolist()
max_sizes = tmp.tolist() + [tmp[-1]+start_offset]
sizes = zip(min_sizes, max_sizes)
Expand Down