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optimize nms #1

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3ca4c75
optimize nms in IPEX
chunyuan-w Dec 11, 2020
8fcccda
update device name to ipex.DEVICE
chunyuan-w Jan 29, 2021
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4 changes: 2 additions & 2 deletions others/cloud/single_stage_detector/pytorch/infer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -198,7 +198,7 @@ def coco_eval(model, val_dataloader, cocoGt, encoder, inv_map, args):
inference_time.update(time.time() - start_time)
end_time = time.time()
try:
results = encoder.decode_batch(ploc.to('cpu'), plabel.to('cpu'), 0.50, 200,device=device)
results = encoder.decode_batch(ploc, plabel, 0.50, 200,device=device)
except:
print("No object detected in idx: {}".format(idx))
continue
Expand Down Expand Up @@ -255,7 +255,7 @@ def coco_eval(model, val_dataloader, cocoGt, encoder, inv_map, args):
inference_time.update(time.time() - start_time)
end_time = time.time()
try:
results = encoder.decode_batch(ploc.to('cpu'), plabel.to('cpu'), 0.50, 200,device=device)
results = encoder.decode_batch(ploc, plabel, 0.50, 200,device=device)
except:
print("No object detected in idx: {}".format(idx))
continue
Expand Down
27 changes: 26 additions & 1 deletion others/cloud/single_stage_detector/pytorch/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,11 @@
import pickle
from math import sqrt, ceil

if os.environ.get('USE_IPEX') == "1":
import intel_pytorch_extension as ipex

from intel_pytorch_extension import batch_score_nms

# This function is from https://github.com/kuangliu/pytorch-ssd.
def calc_iou_tensor(box1, box2):
""" Calculation of IoU based on two boxes tensor,
Expand Down Expand Up @@ -122,6 +127,9 @@ def scale_back_batch(self, bboxes_in, scores_in,device):
if bboxes_in.device == torch.device("cpu"):
self.dboxes = self.dboxes.cpu()
self.dboxes_xywh = self.dboxes_xywh.cpu()
elif bboxes_in.device == torch.device(ipex.DEVICE):
self.dboxes = self.dboxes.to(ipex.DEVICE)
self.dboxes_xywh = self.dboxes_xywh.to(ipex.DEVICE)
else:
self.dboxes = self.dboxes.cuda(device)
self.dboxes_xywh = self.dboxes_xywh.cuda(device)
Expand Down Expand Up @@ -155,10 +163,27 @@ def decode_batch(self, bboxes_in, scores_in, criteria = 0.45, max_output=200,de
for bbox, prob in zip(bboxes.split(1, 0), probs.split(1, 0)):
bbox = bbox.squeeze(0)
prob = prob.squeeze(0)
output.append(self.decode_single(bbox, prob, criteria, max_output))
if bbox.device == torch.device(ipex.DEVICE):
output.append(self.decode_single_ipex(bbox, prob, criteria, max_output))
else:
output.append(self.decode_single(bbox, prob, criteria, max_output))
#print(output[-1])
return output

# perform non-maximum suppression for IPEX tensor
def decode_single_ipex(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
# Reference to https://github.com/amdegroot/ssd.pytorch

bboxes_out = []
scores_out = []
labels_out = []

bboxes_out, labels_out, scores_out = batch_score_nms(bboxes_in, scores_in, criteria)

_, max_ids = scores_out.sort(dim=0)
max_ids = max_ids[-max_output:]
return bboxes_out[max_ids, :], labels_out[max_ids], scores_out[max_ids]

# perform non-maximum suppression
def decode_single(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
# Reference to https://github.com/amdegroot/ssd.pytorch
Expand Down