if epoch % opt.evaluation_interval == 0:
print("\n---- Evaluating Model ----")
# Evaluate the model on the validation set
precision, recall, AP, f1, ap_class = evaluate(
model,
path=valid_path,
iou_thres=0.5,
conf_thres=0.5,
nms_thres=0.5,
img_size=opt.img_size,
batch_size=8,
)
evaluation_metrics = [
("val_precision", precision.mean()),
("val_recall", recall.mean()),
("val_mAP", AP.mean()),
("val_f1", f1.mean()),
]
#logger.list_of_scalars_summary(evaluation_metrics, epoch)
# Print class APs and mAP
ap_table = [["Index", "Class name", "AP"]]
for i, c in enumerate(ap_class):
ap_table += [[c, class_names[c], "%.5f" % AP[i]]]
print(AsciiTable(ap_table).table)
print(f"---- mAP {AP.mean()}")
if epoch % opt.checkpoint_interval == 0:
torch.save(model.state_dict(),
f"checkpoints/yolov3_ckpt_%d.pth" % epoch)
if epoch % opt.evaluation_interval == 0:
print("\n---- Evaluating Model ----")
# Evaluate the model on the validation set
precision, recall, AP, f1, ap_class = evaluate(
model,
path=opt.root,
datasets=opt.testdatasets,
iou_thres=0.5,
conf_thres=0.5,
nms_thres=0.5,
img_size=opt.img_size,
batch_size=8,
)
evaluation_metrics = [
("val_precision", precision.mean()),
("val_recall", recall.mean()),
("val_mAP", AP.mean()),
("val_f1", f1.mean()),
]
#logger.list_of_scalars_summary(evaluation_metrics, epoch)
# Print class APs and mAP
ap_table = [["Index", "Class name", "AP"]]
for i, c in enumerate(ap_class):
ap_table += [[c, class_names[c], "%.5f" % AP[i]]]
print(AsciiTable(ap_table).table)
print(f"---- mAP {AP.mean()}")
if epoch % opt.checkpoint_interval == 0:
torch.save(model.state_dict(), f"checkpoints/yolov3_ckpt_%d.pth" % epoch)
def main():
# Hyperparameters parser
parser = argparse.ArgumentParser()
parser.add_argument("--year", type=str, default='2012', help="used to select training set")
parser.add_argument("--set", type=str, default='train', help="used to select training set")
parser.add_argument("--epochs", type=int, default=201, help="number of epochs")
parser.add_argument("--batch_size", type=int, default=8, help="size of each image batch")
parser.add_argument("--model_def", type=str, default="config/net/resnet_dropout.cfg", help="path to model definition file")
# parser.add_argument("--model_def", type=str, default="config/net/dqnyolo_large.cfg", help="path to model definition file")
# parser.add_argument("--model_def", type=str, default="config/net/dqnyolo_mini.cfg", help="path to model definition file")
# parser.add_argument("--model_def", type=str, default="config/net/dqnyolo_tiny.cfg", help="path to model definition file")
parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
parser.add_argument("--opt_lr", type=float, default=1e-5, help="learning rate for optimizer")
parser.add_argument("--use_gpu", default=True, help="use GPU to accelerate training")
parser.add_argument("--shuffle_train", default=True, help="shuffle the training dataset")
parser.add_argument("--checkpoint_interval", type=int, default=20, help="interval between saving model weights")
parser.add_argument("--evaluation_interval", type=int, default=10, help="interval evaluations on validation set")
# parser.add_argument("--pretrained_weights", type=str, default="data/backbone/darknet53.conv.74", help="if specified starts from checkpoint model")
# parser.add_argument("--pretrained_weights", type=str, default="logs/model/model_params_200.ckpt", help="if specified starts from checkpoint model")
parser.add_argument("--pretrained_weights", default=False, help="if specified starts from checkpoint model")
opt = parser.parse_args()
print(opt)
if opt.use_gpu is True:
if torch.cuda.is_available():
device = torch.device('cuda')
else:
raise RuntimeError("Current Torch doesn't have GPU support.")
else:
device = torch.device('cpu')
logger = SummaryWriter(exist_or_create_folder("./logs/tb/"))
# Initiate model
eval_model = Darknet(opt.model_def).to(device)
if opt.pretrained_weights:
print("Initialize model with pretrained_model")
if opt.pretrained_weights.endswith(".ckpt"):
eval_model.load_state_dict(torch.load(opt.pretrained_weights))
else:
eval_model.load_darknet_weights(opt.pretrained_weights)
else:
print("Initialize model randomly")
eval_model.apply(weights_init_normal)
# eval_model.load_state_dict(torch.load("./logs/saved_exp/master-v2/model_params_80.ckpt"))
print(eval_model)
summary(eval_model, (3, 416, 416))
learn_batch_counter = 0 # for logger update (total numbers)
batch_size = opt.batch_size
# Get dataloader
print("Begin loading train dataset ......")
t_load_data = time.time()
dataset = torchvision.datasets.VOCDetection(root='data/VOC/',
year=opt.year,
image_set=opt.set,
transforms=None,
download=True)
dataset_dict = trans_voc(dataset)
dataset = ListDataset(dataset_dict)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=opt.batch_size,
shuffle=opt.shuffle_train,
pin_memory=True,
collate_fn=dataset.collate_fn,
)
print("Complete loading train dataset in {} s".format(time.time() - t_load_data))
optimizer = torch.optim.Adam(eval_model.parameters(), lr=opt.opt_lr)
# Warmup and learning rate decay
scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.epochs)
# 5 epoch warmup, lr from 1e-5 to 1e-4, after that schedule as after_scheduler
scheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=10, total_epoch=10,
after_scheduler=scheduler_cosine)
start_time = time.time()
for i_epoch in range(opt.epochs):
eval_model.train()
for i_batch, (_, imgs, raw_targets, transform_params, tar_boxes) in enumerate(loader):
print("\n++++++++++ i_epoch-i_batch {}-{} ++++++++++".format(i_epoch, i_batch))
batch_step_counter = 0
if len(imgs) != batch_size:
print("Current batch size is smaller than opt.batch_size!")
continue
imgs = imgs.to(device)
raw_targets = raw_targets.to(device)
tar_boxes = tar_boxes.to(device)
input_img = imgs
if i_epoch == 0 and i_batch == 0:
logger.add_graph(eval_model, input_img)
# print(raw_targets)
# print(raw_targets.size())
# print(raw_targets[:, :, :, 6:].size())
# print(raw_targets[:, :, :, 0].unsqueeze(3).size())
cls_targets = torch.cat((raw_targets[:, :, :, 0].unsqueeze(3), raw_targets[:, :, :, 6:]), 3)
# print(cls_targets.size())
loss, pred = eval_model(input_img, cls_targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_step_counter += 1
learn_batch_counter += 1
print("Ep-bt: {}-{} | Loss: {}".format(i_epoch, i_batch, loss.item()))
logger.add_scalar('loss/loss', loss.item(), learn_batch_counter)
if (i_epoch + 1) % opt.checkpoint_interval == 0:
print("Saving model in epoch {}".format(i_epoch))
torch.save(eval_model.state_dict(),
exist_or_create_folder("./logs/model/model_params_{}.ckpt".format(i_epoch)))
# Evaluate the model on the validation set
if (i_epoch + 1) % opt.evaluation_interval == 0:
precision, recall, AP, f1, ap_class = evaluate(
eval_model,
[opt.year, 'val'],
[0.5, 0.5, 0.5],
batch_size,
True,
diagnosis_code=1
)
evaluation_metrics = [
("val_precision", precision.mean()),
("val_recall", recall.mean()),
("val_mAP", AP.mean()),
("val_f1", f1.mean()),
]
for tag, value in evaluation_metrics:
logger.add_scalar("val/{}".format(tag), value.item(), i_epoch)
# Print class APs and mAP
ap_table = [["Index", "Class name", "AP"]]
for i, c in enumerate(ap_class):
ap_table += [[c, val2labels(c), "%.5f" % AP[i]]]
print(AsciiTable(ap_table).table)
print(f"---- validation mAP {AP.mean()}")
# Evaluate the model on the training set
if (i_epoch + 1) % opt.evaluation_interval == 0:
precision, recall, AP, f1, ap_class = evaluate(
eval_model,
[opt.year, 'train'],
[0.5, 0.5, 0.5],
batch_size,
True,
diagnosis_code=1
)
evaluation_metrics = [
("train_precision", precision.mean()),
("train_recall", recall.mean()),
("train_mAP", AP.mean()),
("train_f1", f1.mean()),
]
for tag, value in evaluation_metrics:
logger.add_scalar("train/{}".format(tag), value.item(), i_epoch)
# Print class APs and mAP
ap_table = [["Index", "Class name", "AP"]]
for i, c in enumerate(ap_class):
ap_table += [[c, val2labels(c), "%.5f" % AP[i]]]
print(AsciiTable(ap_table).table)
print(f"---- training mAP {AP.mean()}")
# Warmup and lr decay
scheduler_warmup.step()
# Free GPU memory
torch.cuda.empty_cache()
total_train_time = time.time() - start_time
print("Training complete in {} hours".format(total_train_time / 3600))
def train(payload):
labeled = payload["labeled"]
resume_from = payload["resume_from"]
ckpt_file = payload["ckpt_file"]
# hyperparameters
batch_size = 16
epochs = 2 # just for demo
lr = 1e-2
weight_decay = 1e-2
coco = COCO("./data", Transforms(), samples=labeled, train=True)
loader = DataLoader(coco,
shuffle=True,
batch_size=batch_size,
collate_fn=collate_fn)
config_file = "yolov3.cfg"
model = Darknet(config_file).to(device)
optimizer = optim.Adam(model.parameters(),
lr=lr,
weight_decay=weight_decay)
# resume model and optimizer from previous loop
if resume_from is not None:
ckpt = torch.load(os.path.join("./log", resume_from))
model.load_state_dict(ckpt["model"])
optimizer.load_state_dict(ckpt["optimizer"])
# loss function
priors = anchors.normalize("xyxy")
loss_fn = HardNegativeMultiBoxesLoss(priors, device=device)
model.train()
for img, boxes, labels in loader:
img = img.to(device)
# 3 predictions from 3 yolo layers
output = model(img)
# batch predictions on each image
batched_prediction = []
for p in output: # (batch_size, 3, gx, gy, 85)
batch_size = p.shape[0]
p = p.view(batch_size, -1, 85)
batched_prediction.append(p)
batched_prediction = torch.cat(batched_prediction, dim=1)
# (batch_size, n_priors, 85)
# the last dim of batched_prediction represent the predicted box
# batched_prediction[...,:4] is the coordinate of the predicted bbox
# batched_prediction[...,4] is the objectness score
# batched_prediction[...,5:] is the pre-softmax class distribution
# we need to apply some transforms to the those predictions
# before we can use HardNegativeMultiBoxesLoss
# In particular, the predicted bbox need to be relative to
# normalized anchor priors
# we will define another function bbox_transform
# to do those transform, since it will be used by other processes
# as well.
# see documentation on HardNegativeMultiBoxesLoss
# on its input parameters
predicted_boxes, predicted_objectness, predicted_class_dist = bbox_transform(
batched_prediction)
loss = loss_fn(predicted_boxes, predicted_objectness,
predicted_class_dist, boxes, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# save ckpt for this loop
ckpt = {"model": model.state_dict(), "optimizer": optimizer.state_dict()}
torch.save(ckpt, os.path.join("./log", ckpt_file))
return
)
# evaluation_metrics = [
# ("val_precision", precision.mean()),
# ("val_recall", recall.mean()),
# ("val_mAP", AP.mean()),
# ("val_f1", f1.mean()),
# ]
# 输出 class APs 和 mAP
ap_table = [[
"Index", "Class name", "Precision", "Recall", "AP", "F1-score"
]]
for i, c in enumerate(ap_class):
ap_table += [[
c, class_names[c],
"%.3f" % precision[i],
"%.3f" % recall[i],
"%.3f" % AP[i],
"%.3f" % f1[i]
]]
print(AsciiTable(ap_table).table)
print(f"---- mAP {AP.mean()}")
# 根据mAP的值保存最佳模型
if AP.mean() > mAP:
mAP = AP.mean()
torch.save(
model.state_dict(),
'weights\kalete\ep' + str(epoch) + '-map%.2f' %
(AP.mean() * 100) + '-loss%.2f' % loss.item() + '.weights')
torch.cuda.empty_cache()
