Skip to content

remove unnecessary args #8

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
juvekaradheesh wants to merge 1 commit into
base: master
Choose a base branch
from
Open

remove unnecessary args #8

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 3 additions & 7 deletions softnms_pytorch.py
View file
Open in desktop
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