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remove unnecessary args #8

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remove unnecessary args #8

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10 changes: 3 additions & 7 deletions softnms_pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,25 +6,21 @@
import torch


def soft_nms_pytorch(dets, box_scores, sigma=0.5, thresh=0.001, cuda=0):
def soft_nms_pytorch(dets, box_scores, sigma=0.5, thresh=0.001):
"""
Build a pytorch implement of Soft NMS algorithm.
# Augments
dets: boxes coordinate tensor (format:[y1, x1, y2, x2])
box_scores: box score tensors
sigma: variance of Gaussian function
thresh: score thresh
cuda: CUDA flag
# Return
the index of the selected boxes
"""

# Indexes concatenate boxes with the last column
N = dets.shape[0]
if cuda:
indexes = torch.arange(0, N, dtype=torch.float).cuda().view(N, 1)
else:
indexes = torch.arange(0, N, dtype=torch.float).view(N, 1)
indexes = torch.arange(0, N, dtype=torch.float).view(N, 1).to(dets.device)
dets = torch.cat((dets, indexes), dim=1)

# The order of boxes coordinate is [y1,x1,y2,x2]
Expand Down Expand Up @@ -55,7 +51,7 @@ def soft_nms_pytorch(dets, box_scores, sigma=0.5, thresh=0.001, cuda=0):

w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = torch.tensor(w * h).cuda() if cuda else torch.tensor(w * h)
inter = torch.tensor(w * h).to(dets.device)
ovr = torch.div(inter, (areas[i] + areas[pos:] - inter))

# Gaussian decay
Expand Down