Bienqq
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car-detection-bayes
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updates

This commit is contained in:
glenn-jocher 2019-07-01 14:48:44 +02:00
parent 09d065711a
commit 5e2b802f68
3 changed files with 39 additions and 54 deletions
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24
detect.py
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@ -127,20 +127,18 @@ if __name__ == '__main__':
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold') parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression') parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
parser.add_argument('--fourcc', type=str, default='mp4v', help='specifies the fourcc code for output video encoding (make sure ffmpeg supports specified fourcc codec)') parser.add_argument('--fourcc', type=str, default='mp4v', help='fourcc output video codec (verify ffmpeg support)')
parser.add_argument('--output', type=str, default='output',help='specifies the output path for images and videos') parser.add_argument('--output', type=str, default='output', help='specifies the output path for images and videos')
opt = parser.parse_args() opt = parser.parse_args()
print(opt) print(opt)
with torch.no_grad(): with torch.no_grad():
detect( detect(opt.cfg,
opt.cfg, opt.data_cfg,
opt.data_cfg, opt.weights,
opt.weights, images=opt.images,
images=opt.images, img_size=opt.img_size,
img_size=opt.img_size, conf_thres=opt.conf_thres,
conf_thres=opt.conf_thres, nms_thres=opt.nms_thres,
nms_thres=opt.nms_thres, fourcc=opt.fourcc,
fourcc=opt.fourcc, output=opt.output)
output=opt.output
)

20
test.py
View File

@ -201,14 +201,12 @@ if __name__ == '__main__':
print(opt) print(opt)
with torch.no_grad(): with torch.no_grad():
mAP = test( mAP = test(opt.cfg,
opt.cfg, opt.data_cfg,
opt.data_cfg, opt.weights,
opt.weights, opt.batch_size,
opt.batch_size, opt.img_size,
opt.img_size, opt.iou_thres,
opt.iou_thres, opt.conf_thres,
opt.conf_thres, opt.nms_thres,
opt.nms_thres, opt.save_json)
opt.save_json
)

49
train.py
View File

@ -46,12 +46,10 @@ def train(
cfg, cfg,
data_cfg, data_cfg,
img_size=416, img_size=416,
resume=False,
epochs=100, # 500200 batches at bs 16, 117263 images = 273 epochs epochs=100, # 500200 batches at bs 16, 117263 images = 273 epochs
batch_size=8, batch_size=8,
accumulate=8, # effective bs = batch_size * accumulate = 8 * 8 = 64 accumulate=8, # effective bs = batch_size * accumulate = 8 * 8 = 64
freeze_backbone=False, freeze_backbone=False,
transfer=False # Transfer learning (train only YOLO layers)
): ):
init_seeds() init_seeds()
weights = 'weights' + os.sep weights = 'weights' + os.sep
@ -81,8 +79,8 @@ def train(
start_epoch = 0 start_epoch = 0
best_loss = float('inf') best_loss = float('inf')
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255) nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
if resume: # Load previously saved model if opt.resume or opt.transfer: # Load previously saved model
if transfer: # Transfer learning if opt.transfer: # Transfer learning
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)
@ -138,7 +136,11 @@ def train(
# Initialize distributed training # Initialize distributed training
if torch.cuda.device_count() > 1: if torch.cuda.device_count() > 1:
dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank) dist.init_process_group(backend='nccl', # 'distributed backend'
init_method='tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(model) model = torch.nn.parallel.DistributedDataParallel(model)
# sampler = torch.utils.data.distributed.DistributedSampler(dataset) # sampler = torch.utils.data.distributed.DistributedSampler(dataset)
@ -308,10 +310,6 @@ if __name__ == '__main__':
parser.add_argument('--resume', action='store_true', help='resume training flag') parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--transfer', action='store_true', help='transfer learning flag') parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers') parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
parser.add_argument('--backend', default='nccl', type=str, help='distributed backend')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss') parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss')
@ -325,16 +323,12 @@ if __name__ == '__main__':
opt.nosave = True # only save final checkpoint opt.nosave = True # only save final checkpoint
# Train # Train
results = train( results = train(opt.cfg,
opt.cfg, opt.data_cfg,
opt.data_cfg, img_size=opt.img_size,
img_size=opt.img_size, epochs=opt.epochs,
resume=opt.resume or opt.transfer, batch_size=opt.batch_size,
transfer=opt.transfer, accumulate=opt.accumulate)
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate,
)
# Evolve hyperparameters (optional) # Evolve hyperparameters (optional)
if opt.evolve: if opt.evolve:
@ -361,16 +355,12 @@ if __name__ == '__main__':
hyp[k] = np.clip(hyp[k], v[0], v[1]) hyp[k] = np.clip(hyp[k], v[0], v[1])
# Determine mutation fitness # Determine mutation fitness
results = train( results = train(opt.cfg,
opt.cfg, opt.data_cfg,
opt.data_cfg, img_size=opt.img_size,
img_size=opt.img_size, epochs=opt.epochs,
resume=opt.resume or opt.transfer, batch_size=opt.batch_size,
transfer=opt.transfer, accumulate=opt.accumulate)
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate,
)
mutation_fitness = results[2] mutation_fitness = results[2]
# Write mutation results # Write mutation results
@ -378,7 +368,6 @@ if __name__ == '__main__':
# Update hyperparameters if fitness improved # Update hyperparameters if fitness improved
if mutation_fitness > best_fitness: if mutation_fitness > best_fitness:
# Fitness improved!
print('Fitness improved!') print('Fitness improved!')
best_fitness = mutation_fitness best_fitness = mutation_fitness
else: else: