Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2019-08-23 00:36:48 +02:00
parent 0040c85b9a
commit ff7f73b642
1 changed files with 5 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
train.py
View File

@ -112,9 +112,9 @@ def train(cfg,
cutoff = -1 # backbone reaches to cutoff layer cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0 start_epoch = 0
best_fitness = 0. best_fitness = 0.
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
if opt.resume or opt.transfer: # Load previously saved model if opt.resume or opt.transfer: # Load previously saved model
if opt.transfer: # Transfer learning if opt.transfer: # Transfer learning
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device) chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255}, model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
strict=False) strict=False)
@ -208,7 +208,8 @@ def train(cfg,
maps = np.zeros(nc) # mAP per class maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
t0 = time.time() t0 = time.time()
for epoch in range(start_epoch, epochs): for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train() model.train()
print(('\n' + '%10s' * 9) % print(('\n' + '%10s' * 9) %
('Epoch', 'gpu_mem', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size')) ('Epoch', 'gpu_mem', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size'))
@ -232,12 +233,12 @@ def train(cfg,
mloss = torch.zeros(5).to(device) # mean losses mloss = torch.zeros(5).to(device) # mean losses
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = (i + nb * epoch) # number integrated batches (since train start)
imgs = imgs.to(device) imgs = imgs.to(device)
targets = targets.to(device) targets = targets.to(device)
# Multi-Scale training # Multi-Scale training
ni = (i + nb * epoch) # number integrated batches (since train start)
if multi_scale: if multi_scale:
if ni / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches if ni / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32 img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32