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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-08 15:24:20 +02:00
parent 291c3ec9c7
commit 94669fb704
2 changed files with 4 additions and 4 deletions
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5
train.py
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@ -40,7 +40,6 @@ def train(
latest = weights + 'latest.pt' latest = weights + 'latest.pt'
best = weights + 'best.pt' best = weights + 'best.pt'
device = torch_utils.select_device() device = torch_utils.select_device()
img_size_test = img_size # image size for testing
multi_scale = not opt.single_scale multi_scale = not opt.single_scale
if multi_scale: if multi_scale:
@ -140,7 +139,7 @@ def train(
if mixed_precision: if mixed_precision:
try: try:
from apex import amp from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1') model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
except: # not installed: install help: https://github.com/NVIDIA/apex/issues/259 except: # not installed: install help: https://github.com/NVIDIA/apex/issues/259
mixed_precision = False mixed_precision = False
@ -232,7 +231,7 @@ def train(
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave) # Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1: if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
with torch.no_grad(): with torch.no_grad():
results, maps = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size_test, model=model, results, maps = test.test(cfg, data_cfg, batch_size=batch_size, img_size=opt.img_size, model=model,
conf_thres=0.1) conf_thres=0.1)
# Write epoch results # Write epoch results

3
utils/utils.py
View File

@ -430,7 +430,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):
pred = pred[(-pred[:, 4]).argsort()] pred = pred[(-pred[:, 4]).argsort()]
det_max = [] det_max = []
nms_style = 'MERGE' # 'OR' (default), 'AND', 'MERGE' (experimental) nms_style = 'SOFT' # 'OR' (default), 'AND', 'MERGE' (experimental)
for c in pred[:, -1].unique(): for c in pred[:, -1].unique():
dc = pred[pred[:, -1] == c] # select class c dc = pred[pred[:, -1] == c] # select class c
n = len(dc) n = len(dc)
@ -486,6 +486,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):
iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes iou = bbox_iou(dc[0], dc[1:]) # iou with other boxes
dc = dc[1:] dc = dc[1:]
dc[:, 4] *= torch.exp(-iou ** 2 / sigma) # decay confidences dc[:, 4] *= torch.exp(-iou ** 2 / sigma) # decay confidences
dc = dc[dc[:, 4] > nms_thres] # new line per https://github.com/ultralytics/yolov3/issues/362
if len(det_max): if len(det_max):
det_max = torch.cat(det_max) # concatenate det_max = torch.cat(det_max) # concatenate