def __init__(self, model, autobalance=False):
super(ComputeLoss, self).__init__()
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([h['cls_pw']], device=device))
BCEobj = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([h['obj_pw']], device=device))
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
self.cp, self.cn = smooth_BCE(eps=0.0)
# Focal loss
g = h['fl_gamma'] # focal loss gamma
if g > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
self.balance = {
2: [4.0, 1.0],
3: [3.67, 1.0, 0.43],
4: [3.78, 1.0, 0.39, 0.22],
5: [3.88, 1.0, 0.37, 0.17, 0.10]
}[det.nl]
# self.balance = [1.0] * det.nl
self.ssi = (det.stride == 16).nonzero(
as_tuple=False).item() # stride 16 index
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
for k in 'na', 'nc', 'nl', 'anchors':
setattr(self, k, getattr(det, k))
def __init__(self, model, autobalance=False):
super(ComputeLoss, self).__init__()
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(
[h['cls_pw']], device=device)) # class loss
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(
[h['obj_pw']], device=device)) # object loss
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
# Bag of freebies : inference 중에 추가적인 computational cost없이 성능을 향상시킬 수 있는 기법
self.cp, self.cn = smooth_BCE(eps=0.0) # positive, negative
# Focal loss
g = h['fl_gamma'] # focal loss gamma
if g > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(
BCEobj, g) # class, object loss 둘다 Focalloss로 BCE를 들고 감
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
self.balance = {
3: [4.0, 1.0, 0.4]
}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
self.ssi = list(
det.stride).index(16) if autobalance else 0 # stride 16 index
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
for k in 'na', 'nc', 'nl', 'anchors':
setattr(self, k, getattr(det, k))
def __init__(self, model, autobalance=False):
self.sort_obj_iou = False
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([h['cls_pw']], device=device))
BCEobj = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([h['obj_pw']], device=device))
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
self.cp, self.cn = smooth_BCE(eps=h.get(
'label_smoothing', 0.0)) # positive, negative BCE targets
# Focal loss
g = h['fl_gamma'] # focal loss gamma
if g > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
self.balance = {
3: [4.0, 1.0, 0.4]
}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
self.ssi = list(
det.stride).index(16) if autobalance else 0 # stride 16 index
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
for k in 'na', 'nc', 'nl', 'anchors':
setattr(self, k, getattr(det, k))
def __init__(self, model, recipe):
self.enabled = bool(recipe)
self.model = model.module if is_parallel(model) else model
self.recipe = recipe
self.manager = ScheduledModifierManager.from_yaml(
recipe) if self.enabled else None
self.logger = None
def build_targets(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
na, nt = det.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(8, device=targets.device) # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
g = 0.5 # bias
off = torch.tensor([[0, 0],
# [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
], device=targets.device).float() * g # offsets
for i in range(det.nl):
anchors = det.anchors[i]
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xywh gain
# Match targets to anchors
t = targets * gain
if nt:
# Matches
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j),))
t = t.repeat((off.shape[0], 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gtheta = t[:, 6:7] # theta
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 7].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh, gtheta), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch
def build_targets(p, targets, model):
nt = targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(6, device=targets.device) # normalized to gridspace gain
off = torch.tensor([[1, 0], [0, 1], [-1, 0], [0, -1]],
device=targets.device).float() # overlap offsets
g = 0.5 # offset
multi_gpu = is_parallel(model)
for i, jj in enumerate(
model.module.yolo_layers if multi_gpu else model.yolo_layers):
# get number of grid points and anchor vec for this yolo layer
anchors = model.module.module_list[
jj].anchor_vec if multi_gpu else model.module_list[jj].anchor_vec
gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
a, t, offsets = [], targets * gain, 0
if nt:
na = anchors.shape[0] # number of anchors
at = torch.arange(na).view(na, 1).repeat(
1, nt) # anchor tensor, same as .repeat_interleave(nt)
r = t[None, :, 4:6] / anchors[:, None] # wh ratio
j = torch.max(r,
1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n) = wh_iou(anchors(3,2), gwh(n,2))
a, t = at[j], t.repeat(na, 1, 1)[j] # filter
# overlaps
gxy = t[:, 2:4] # grid xy
z = torch.zeros_like(gxy)
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxy % 1. > (1 - g)) & (gxy < (gain[[2, 3]] - 1.))).T
a, t = torch.cat((a, a[j], a[k], a[l], a[m]), 0), torch.cat(
(t, t[j], t[k], t[l], t[m]), 0)
offsets = torch.cat((z, z[j] + off[0], z[k] + off[1],
z[l] + off[2], z[m] + off[3]), 0) * g
# Define
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
#indices.append((b, a, gj, gi)) # image, anchor, grid indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0,
gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch
def build_targets_freeanchor(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
# print(targets.shape)
det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
nt = targets.shape[0] # number of anchors, targets
tcls, indices, anch = [], [], []
gain = torch.ones(6, device=targets.device) # normalized to gridspace gain
# ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
# targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
g = 0.5 # bias
# print(p[0].shape)
for i in range(len(p)):
# anchors = det.anchors[i]
# print(p[i].shape)
gain[2:6] = torch.tensor(p[i].shape)[[2, 1, 2, 1]] # xyxy gain
# print(gain)
# Match targets to anchors
if nt:
t = targets * gain
r = t[:, 4:6] # wh ratio
if i ==0:
j = torch.max(t[:,4:6],1)[0] < 16 # compare
elif i==1:
j = torch.max(t[:,4:6],1)[0] >6
j1_ = torch.max(t[:,4:6],1)[0]<12
j = j & j1_
else:
j = torch.max(t[:,4:6],1)[0] >6
# print(j)
# print(torch.max(t[:,4:6],0)[0] < 2)
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
offsets = 0
b, c = t[:,:2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gxy = torch.round(gxy)
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
indices.append((b, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, grid indices
# tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(gwh) # anchors
tcls.append(c) # class
return tcls, indices, anch
def check_anchors(dataset, model, thr=4.0, imgsz=640):
# Check anchor fit to data, recompute if necessary
prefix = colorstr('autoanchor: ')
print(f'\n{prefix}Analyzing anchors... ', end='')
m = model.module.detection if is_parallel(model) else model.detection
shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
scale = np.random.uniform(0.9, 1.1,
size=(shapes.shape[0], 1)) # augment scale
wh = torch.tensor(
np.concatenate([
l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)
])).float() # wh
def metric(k): # compute metric
r = wh[:, None] / k[None]
x = torch.min(r, 1. / r).min(2)[0] # ratio metric
best = x.max(1)[0] # best_x
aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold
bpr = (best > 1. / thr).float().mean() # best possible recall
return bpr, aat
bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
print(
f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}',
end='')
if bpr < 0.98: # threshold to recompute
print('. Attempting to improve anchors, please wait...')
na = m.anchor_grid.numel() // 2 # number of anchors
new_anchors = kmean_anchors(dataset,
n=na,
img_size=imgsz,
thr=thr,
gen=1000,
verbose=False)
new_bpr = metric(new_anchors.reshape(-1, 2))[0]
if new_bpr > bpr: # replace anchors
new_anchors = torch.tensor(
new_anchors, device=m.anchors.device).type_as(m.anchors)
m.anchor_grid[:] = new_anchors.clone().view_as(
m.anchor_grid) # for inference
m.anchors[:] = new_anchors.clone().view_as(
m.anchors) / m.stride.to(m.anchors.device).view(-1, 1,
1) # loss
check_anchor_order(m)
print(
f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.'
)
else:
print(
f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.'
)
print('') # newline
def create_checkpoint(epoch, model, optimizer, ema, sparseml_wrapper,
**kwargs):
pickle = not sparseml_wrapper.qat_active(
epoch) # qat does not support pickled exports
ckpt_model = deepcopy(
model.module if is_parallel(model) else model).float()
yaml = ckpt_model.yaml
if not pickle:
ckpt_model = ckpt_model.state_dict()
return {
'epoch': epoch,
'model': ckpt_model,
'optimizer': optimizer.state_dict(),
'yaml': yaml,
**ema.state_dict(pickle),
**sparseml_wrapper.state_dict(),
**kwargs
}
def build_targets(p, targets, model):
# 1.将targets 重复3遍(3=层anchor数目)，也就是将每个gt bbox复制变成独立的3份，方便和每个位置的3个anchor单独匹配
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
na, nt = det.na, targets.shape[0] # number of anchors, number of targets
tcls, tbox, indices, anch, ttar = [], [], [], [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
# anchor=3个数,将target变成3*target格式，方便后面计算loss
# anchor索引，后面有用，用于表示当前bbox和当前层的哪个anchor匹配
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(
1, nt) # same as .repeat_interleave(nt)
# 先repeat和当前层anchor个数一样，相当于每个bbox变成了3个，然后和3个anchor单独进行匹配
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]),
2) # append anchor indices
g = 0.5 # bias # 网格中心偏移
# 附近的4个网格
off = torch.tensor(
[
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
],
device=targets.device).float() * g # offsets
for i in range(det.nl): # 3个输出分支
anchors = det.anchors[i] # 当前分支anchor
# p是网络输出值， 1 1 特征图大小 特征图大小 特征图大小 特征图大小 1
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]
# 2.对每个输出层单独匹配。首先将targets变成anchor尺度，方便计算；然后将target wh shape和anchor的wh计算比例，如果其最大比例过大，则说明匹配度不高，将该bbox过滤，在当前层认为是bg
# Match targets to anchors targets的xywh本身是归一化尺度，故需要变成特征图尺度
t = targets * gain
if nt:
# 计算当前target的wh和anchor的wh之间的比值
# 如果w和h的最大比例大于预设阈值model.hyp['anchor_t']=4，则说明当前target和anchor匹配度不高，不应该强制回归，应该把target丢弃
# 主要是把shape和anchor匹配度不高的label去掉，这其实也说明了该物体的大小比较极端，要么太大，要么太小，要么wh差距很大
# 基于shape过滤后，就会出现某些bbox仅仅和当前层的几个anchor匹配，即可能出现某些bbox仅仅和其中某个匹配，而不是和当前位置的所有anchor匹配
r = t[:, :, 4:6] / anchors[:, None] # wh ratio 不考虑xy坐标
j = torch.max(r,
1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter 注意过滤规则是没有考虑xy的，也就是当前bbox的wh是和所有anchor计算的
# 3.计算最近的2个邻居网格
# https://www.kaggle.com/c/global-wheat-detection/discussion/172436
# 网格的3个附近点，不再是落在哪个网格就计算该网格的anchor，而是依靠中心点的情况
# 选择最近的3个网格，作为落脚点，可以极大增加正样本数
# 也就是对于保留的bbox，最少有3个anchor匹配，最多9个
gxy = t[:, 2:4] # grid xy label的中心点坐标
gxi = gain[[2, 3]] - gxy # inverse
# 这两个条件可以选择出最靠近的2个邻居，再加上自己，就是3个
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
# 5是因为预设的off是5个，现在选择出最近的3个（包括0, 0也就是自己）
t = t.repeat(
(5, 1, 1))[j] # (label个数x3,7) 附近的2个网格anchor,都算该bbox的anchor点
offsets = (torch.zeros_like(gxy)[None] +
off[:, None])[j] # 选择出最近的3个
else:
t = targets[0]
offsets = 0
# 4.对每个bbox找出对应的正样本anchor，其中包括b表示当前bbox属于batch内部的第几张图片，a表示当前bbox和当前层的第几个anchor匹配上，gi,gj是对应的负责预测该bbox的网格坐标，
# gxy是不考虑offset或者说yolov3里面设定的该Bbox的负责预测网格，gwh是对应的归一化bbox wh，c是该Bbox类别
# 按照yolov3,则直接（gxy - 0.5）.long()即可得到网格坐标
# 但是这里考虑了附近网格，即采用了跨网格预测，估offsets不再是0.5而是2个邻居
# 所以xy回归范围也变了，故xy预测输出不再是0-1，而是-1~1，加上offset偏移，则为-0.5-1.5
# 由于shape过滤规则,宽高范围也也不再是任意范围，而是0-4，因为超过4倍比例是算不匹配的anchor,所以最大是4
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long() # 当前label落在哪个网格上
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 6].long() # anchor indices
indices.append(
(b, a, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices(j,i)
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
ttar.append(torch.cat((gxy, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch, ttar
def train(hyp, opt, device, tb_writer=None):
logger.info(
colorstr("hyperparameters: ") + ", ".join(f"{k}={v}"
for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = (
Path(opt.save_dir), # save_dir
opt.epochs,
opt.batch_size,
opt.total_batch_size,
opt.weights,
opt.global_rank,
)
# Directories
wdir = save_dir / "weights"
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / "last.pt"
best = wdir / "best.pt"
results_file = save_dir / "results.txt"
# Save run settings
with open(save_dir / "hyp.yaml", "w") as f:
yaml.safe_dump(hyp, f, sort_keys=False)
with open(save_dir / "opt.yaml", "w") as f:
yaml.safe_dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != "cpu"
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.safe_load(f) # data dict
is_coco = opt.data.endswith("coco.yaml")
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {"wandb": None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = (torch.load(weights).get("wandb_id")
if weights.endswith(".pt") and os.path.isfile(weights) else
None)
wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
loggers["wandb"] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = (
opt.weights,
opt.epochs,
opt.hyp,
) # WandbLogger might update weights, epochs if resuming
nc = 1 if opt.single_cls else int(data_dict["nc"]) # number of classes
names = (["item"] if opt.single_cls and len(data_dict["names"]) != 1 else
data_dict["names"]) # class names
assert len(names) == nc, "%g names found for nc=%g dataset in %s" % (
len(names),
nc,
opt.data,
) # check
# Model
pretrained = weights.endswith(".pt")
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt["model"].yaml,
ch=3,
nc=nc,
anchors=hyp.get("anchors")).to(device) # create
exclude = (["anchor"] if
(opt.cfg or hyp.get("anchors")) and not opt.resume else []
) # exclude keys
state_dict = ckpt["model"].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
"Transferred %g/%g items from %s" %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc,
anchors=hyp.get("anchors")).to(device) # create
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
# -------------- SageMaker の train と val ------------- #
#train_path = data_dict["train"]
#test_path = data_dict["val"]
data_path = opt.data_dir
train_path = data_path + "/train2017.txt"
test_path = data_path + "/val2017.txt"
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print("freezing %s" % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp["weight_decay"] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp["lr0"],
betas=(hyp["momentum"],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp["lr0"],
momentum=hyp["momentum"],
nesterov=True)
optimizer.add_param_group({
"params": pg1,
"weight_decay": hyp["weight_decay"]
}) # add pg1 with weight_decay
optimizer.add_param_group({"params": pg2}) # add pg2 (biases)
logger.info("Optimizer groups: %g .bias, %g conv.weight, %g other" %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = (lambda x: (1 - x / (epochs - 1)) *
(1.0 - hyp["lrf"]) + hyp["lrf"]) # linear
else:
lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt["optimizer"] is not None:
optimizer.load_state_dict(ckpt["optimizer"])
best_fitness = ckpt["best_fitness"]
# EMA
if ema and ckpt.get("ema"):
ema.ema.load_state_dict(ckpt["ema"].float().state_dict())
ema.updates = ckpt["updates"]
# Results
if ckpt.get("training_results") is not None:
results_file.write_text(
ckpt["training_results"]) # write results.txt
# Epochs
start_epoch = ckpt["epoch"] + 1
if opt.resume:
assert (
start_epoch > 0
), "%s training to %g epochs is finished, nothing to resume." % (
weights,
epochs,
)
if epochs < start_epoch:
logger.info(
"%s has been trained for %g epochs. Fine-tuning for %g additional epochs."
% (weights, ckpt["epoch"], epochs))
epochs += ckpt["epoch"] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.model[
-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info("Using SyncBatchNorm()")
# Trainloader
## ここで dataset 作ってる、
dataloader, dataset = create_dataloader(
train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr("train: "),
)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert (
mlc < nc
), "Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g" % (
mlc,
nc,
opt.data,
nc - 1,
)
# Process 0
if rank in [-1, 0]:
testloader = create_dataloader(
test_path,
imgsz_test,
batch_size * 2,
gs,
opt, # testloader
hyp=hyp,
cache=opt.cache_images and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers,
pad=0.5,
prefix=colorstr("val: "),
)[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram("classes", c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp["anchor_t"],
imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
# DDP mode
if cuda and rank != -1:
## 変更ポイント3、ラップ
model = DDP(
model,
device_ids=[opt.local_rank],
output_device=opt.local_rank,
# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
find_unused_parameters=any(
isinstance(layer, nn.MultiheadAttention)
for layer in model.modules()),
)
# Model parameters
hyp["box"] *= 3.0 / nl # scale to layers
hyp["cls"] *= nc / 80.0 * 3.0 / nl # scale to classes and layers
hyp["obj"] *= (imgsz / 640)**2 * 3.0 / nl # scale to image size and layers
hyp["label_smoothing"] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = (
labels_to_class_weights(dataset.labels, nc).to(device) * nc
) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp["warmup_epochs"] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
logger.info(f"Image sizes {imgsz} train, {imgsz_test} test\n"
f"Using {dataloader.num_workers} dataloader workers\n"
f"Logging results to {save_dir}\n"
f"Starting training for {epochs} epochs...")
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
print(epoch)
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = (model.class_weights.cpu().numpy() * (1 - maps)**2 / nc
) # class weights
iw = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=cw) # image weights
dataset.indices = random.choices(
range(dataset.n), weights=iw,
k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
("\n" + "%10s" * 8) % ("Epoch", "gpu_mem", "box", "obj", "cls",
"total", "labels", "img_size"))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs,
targets,
paths,
_,
) in (
pbar
): # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
print(f'batch: {ni}')
imgs = (imgs.to(device, non_blocking=True).float() / 255.0
) # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x["lr"] = np.interp(
ni,
xi,
[
hyp["warmup_bias_lr"] if j == 2 else 0.0,
x["initial_lr"] * lf(epoch),
],
)
if "momentum" in x:
x["momentum"] = np.interp(
ni, xi, [hyp["warmup_momentum"], hyp["momentum"]])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode="bilinear",
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= (opt.world_size
) # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.0
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9
if torch.cuda.is_available() else 0) # (GB)
s = ("%10s" * 2 + "%10.4g" * 6) % (
"%g/%g" % (epoch, epochs - 1),
mem,
*mloss,
targets.shape[0],
imgs.shape[-1],
)
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f"train_batch{ni}.jpg" # filename
Thread(target=plot_images,
args=(imgs, targets, paths, f),
daemon=True).start()
#if tb_writer:
# tb_writer.add_graph(
# torch.jit.trace(model, imgs, strict=False), []
# ) # add model graph
# # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
elif plots and ni == 10 and wandb_logger.wandb:
wandb_logger.log({
"Mosaics": [
wandb_logger.wandb.Image(str(x), caption=x.name)
for x in save_dir.glob("train*.jpg") if x.exists()
]
})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x["lr"] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
# 変更ポイント5 の dist.get_rank()==0 はここで反映されている
if rank in [-1, 0]:
# mAP
ema.update_attr(
model,
include=[
"yaml", "nc", "hyp", "gr", "names", "stride",
"class_weights"
],
)
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
wandb_logger.current_epoch = epoch + 1
results, maps, times = test.test(
data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco,
)
# Write
with open(results_file, "a") as f:
f.write(s + "%10.4g" * 7 % results +
"\n") # append metrics, val_loss
# Log
tags = [
"train/box_loss",
"train/obj_loss",
"train/cls_loss", # train loss
"metrics/precision",
"metrics/recall",
"metrics/mAP_0.5",
"metrics/mAP_0.5:0.95",
"val/box_loss",
"val/obj_loss",
"val/cls_loss", # val loss
"x/lr0",
"x/lr1",
"x/lr2",
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb_logger.wandb:
wandb_logger.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
wandb_logger.end_epoch(best_result=best_fitness == fi)
# Save model
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
ckpt = {
"epoch":
epoch,
"best_fitness":
best_fitness,
"training_results":
results_file.read_text(),
"model":
deepcopy(
model.module if is_parallel(model) else model).half(),
"ema":
deepcopy(ema.ema).half(),
"updates":
ema.updates,
"optimizer":
optimizer.state_dict(),
"wandb_id":
wandb_logger.wandb_run.id if wandb_logger.wandb else None,
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if wandb_logger.wandb:
if ((epoch + 1) % opt.save_period == 0
and not final_epoch) and opt.save_period != -1:
wandb_logger.log_model(last.parent,
opt,
epoch,
fi,
best_model=best_fitness == fi)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb_logger.wandb:
files = [
"results.png",
"confusion_matrix.png",
*[f"{x}_curve.png" for x in ("F1", "PR", "P", "R")],
]
wandb_logger.log({
"Results": [
wandb_logger.wandb.Image(str(save_dir / f), caption=f)
for f in files if (save_dir / f).exists()
]
})
# Test best.pt
logger.info("%g epochs completed in %.3f hours.\n" %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith("coco.yaml") and nc == 80: # if COCO
for m in (last,
best) if best.exists() else (last): # speed, mAP tests
results, _, _ = test.test(
opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=0.001,
iou_thres=0.7,
model=attempt_load(m, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True,
plots=False,
is_coco=is_coco,
)
# Strip optimizers
final = best if best.exists() else last # final model
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f"gsutil cp {final} gs://{opt.bucket}/weights") # upload
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
wandb_logger.wandb.log_artifact(
str(final),
type="model",
name="run_" + wandb_logger.wandb_run.id + "_model",
aliases=["last", "best", "stripped"],
)
wandb_logger.finish_run()
else:
dist.destroy_process_group()
torch.cuda.empty_cache()
return results
def train(hyp, opt, device, tb_writer=None, wandb=None):
logger.info(f'Hyperparameters {hyp}')
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(
data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if hyp.get('anchors'):
ckpt['model'].yaml['anchors'] = round(
hyp['anchors']) # force autoanchor
detector_args = {}
detector_args['conf_thres'] = 0.1
detector_args['iou_thres'] = 0.6
model = refine_yolo(opt.cfg or ckpt['model'].yaml,
ch=3,
nc=nc,
detector_args=detector_args).to(device) # create
exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [
] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
'Transferred %g/%g items from %s' %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp[
'lrf']) + hyp['lrf'] # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Logging
if wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(
config=opt,
resume="allow",
project='YOLOv3'
if opt.project == 'runs/train' else Path(opt.project).stem,
name=save_dir.stem,
id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
loggers = {'wandb': wandb} # loggers dict
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weights, epochs)
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path,
imgsz_test,
total_batch_size,
gs,
opt,
hyp=hyp,
cache=opt.cache_images
and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers)[0] # testloader
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
# Thread(target=plot_labels, args=(labels, save_dir, loggers), daemon=True).start()
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test\n'
'Using %g dataloader workers\nLogging results to %s\n'
'Starting training for %g epochs...' %
(imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (
1 - maps)**2 # class weights
iw = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=cw) # image weights
dataset.indices = random.choices(
range(dataset.n), weights=iw,
k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
# print(device)
# exit()
mloss = torch.zeros(5, device=device) # mean losses=
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
('\n' + '%10s' * 9) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls',
'total', 'refine', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
# ig = imgs[0].permute(1,2,0).numpy().copy()
# x1 = int(targets[2])
# x2 = int(targets[3])
# x3 = int(targets[4])
# x4 = int(targets[5])
# cv2.line(ig,(x1,x2),(x3,x4),(0,255,255),2)
# cv2.imwrite('yanzheng/1.jpg',ig)
# print('xxxxxxxxxxxx')
# exit()
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
# torch.autograd.set_detect_anomaly(True)
(detect_res, pred), feature = model(imgs,
refine=True) # forward
loss, loss_items = compute_loss(
pred, targets.to(device),
model) # loss scaled by batch_size
refine_loss = torch.zeros(1, device=device)
refine_ = False
if epoch > 2:
if is_parallel(model):
res, boxes = model.module.detector_(
detect_res, feature)
model.module.refine_net = model.module.refine_net.to(
device)
res = model.module.refine_net(res, boxes)
else:
res, boxes = model.detector_(detect_res, feature)
model.refine_net = model.module.refine_net.to(device)
res = model.refine_net(res, boxes)
refine_loss, refine_loss_items = compute_loss_refinenet(
res, targets.to(device), boxes, model, imgs)
####
loss += refine_loss
loss_items = torch.cat((loss_items, refine_loss.detach()))
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 +
'%10.4g' * 7) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
Thread(target=plot_images,
args=(imgs, targets, paths, f),
daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 3 and wandb:
wandb.log({
"Mosaics": [
wandb.Image(str(x), caption=x.name)
for x in save_dir.glob('train*.jpg')
]
})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(
model,
include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test_.test(
opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
plots=plots and final_epoch,
log_imgs=opt.log_imgs if wandb else 0)
# Write
with open(results_file, 'a') as f:
f.write(
s + '%10.4g' * 7 % results +
'\n') # P, R, [email protected], [email protected], val_loss(box, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Log
tags = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss',
'trian/refile_loss' # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95',
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'
] # params
for x, tag in zip(
list(mloss[:-2]) + list(mloss[-1:-2]) + list(results) + lr,
tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb:
wandb.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
ema.ema,
'optimizer':
None if final_epoch else optimizer.state_dict(),
'wandb_id':
wandb_run.id if wandb else None
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
n = opt.name if opt.name.isnumeric() else ''
fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file],
[flast, fbest, fresults]):
if f1.exists():
os.rename(f1, f2) # rename
if str(f2).endswith('.pt'): # is *.pt
strip_optimizer(f2) # strip optimizer
os.system(
'gsutil cp %s gs://%s/weights' %
(f2, opt.bucket)) if opt.bucket else None # upload
# Finish
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
files = [
'results.png', 'precision_recall_curve.png',
'confusion_matrix.png'
]
wandb.log({
"Results": [
wandb.Image(str(save_dir / f), caption=f)
for f in files if (save_dir / f).exists()
]
})
logger.info('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
else:
dist.destroy_process_group()
wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
return results
def build_targets(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
na, nt = det.na, targets.shape[0] # number of anchors, targets
# print(na, nt)
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
# print(gain.shape, ai.shape, targets.shape)
g = 0.5 # bias
off = torch.tensor([[0, 0],
[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
], device=targets.device).float() * g # offsets
for i in range(det.nl):
anchors = det.anchors[i]
# print(anchors.shape)
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
# print(targets[:, :, :6].shape, targets[:, :, -1][:,:,None].shape)
t = torch.cat((targets[:, :, :6], targets[:, :, -1][:,:,None]), 2) * gain
# print(gain[2:4].repeat(68).shape, targets[:, :, 7:].shape)
landmark_xy = targets[:, :, 6:16] * gain[2:4].repeat(5)
print("162:", landmark_xy.shape)
if nt:
# Matches
# print(anchors[:, None].shape)
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
# print("torch max:", torch.max(r, 1. / r).shape)
j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
# print(j.shape, t.shape)
t = t[j] # filter
landmark_xy = landmark_xy[j]
print("174:", landmark_xy.shape)
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
# print(j.shape, t.repeat((5, 1, 1)).shape)
t = t.repeat((5, 1, 1))[j]
landmark_xy = landmark_xy.repeat((5, 1, 1))[j]
print("185:", landmark_xy.shape)
# print(t.shape)
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
# print(torch.zeros_like(gxy)[None].shape, off[:, None].shape, (torch.zeros_like(gxy)[None] + off[:, None]).shape)
else:
t = targets[0]
landmark_xy = landmark_xy[0]
print("No?", t.shape, landmark_xy.shape)
offsets = 0
# Define
b, c = t[:, :2].long().T # image, class
# print("image ??", b, c)
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
# aaaaa = torch.tensor([0., 0.2, 0.7, 1., 1.2, 4.8])
# print(aaaaa.long())
gij = (gxy - offsets).long()
# print("gxy:", gxy)
# print("gij:", gij)
# print("offset:", offsets)
gi, gj = gij.T # grid xy indices
# print(gij.shape, gij.repeat(1,68).shape)
glandmarks = landmark_xy - gij.repeat(1,5)
print("208:", glandmarks.shape)
# print(t[100000000000000])
# Append
a = t[:, 6].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh, glandmarks), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch
def train(hyp, opt, device, tb_writer=None):
logger.info(
colorstr('hyperparameters: ') + ', '.join(f'{k}={v}'
for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {'wandb': None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = torch.load(weights).get('wandb_id') if weights.endswith(
'.pt') and os.path.isfile(weights) else None
wandb_logger = WandbLogger(opt,
Path(opt.save_dir).stem, run_id, data_dict)
loggers['wandb'] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
# Model
pretrained = weights.endswith('.pt')
model = SR_Model(opt.cfg, ch=3).to(device)
# Freeze
freeze = [
] # parameter names to freeze (full or partial) 'model.%s.' % x for x in range(8)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
optimizer = optim.Adam(model.parameters(),
lr=hyp['lr0'],
betas=(hyp['momentum'], 0.999))
scheduler = lr_scheduler.StepLR(optimizer,
step_size=hyp['lr_decay'],
gamma=hyp['gamma'])
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Trainloader
# DIV2k dataset
dataloader, dataset = create_SRdataloader(opt,
train=True,
batch_size=opt.batch_size,
rank=rank,
world_size=opt.world_size,
workers=opt.workers)
nb = len(dataloader)
scaler = amp.GradScaler(enabled=cuda)
scheduler.last_epoch = start_epoch - 1 # do not move
sr_loss = SR_Loss(opt, device)
testloader, _ = create_SRdataloader(opt,
train=False,
batch_size=1,
rank=rank,
world_size=opt.world_size,
workers=opt.workers)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
mloss = torch.zeros(1, device=device)
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
('\n' + '%10s' * 4) % ('Epoch', 'gpu_mem', 'loss', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (lr, hr, _) in pbar:
ni = i + nb * epoch # number integrated batches (since train start)
idx_scale = opt.scale
lr = lr.to(device).float()
hr = hr.to(device).float()
# Forward
with amp.autocast(enabled=cuda):
pred = model(lr) # forward
loss = sr_loss(pred, hr) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 2) % ('%g/%g' %
(epoch, epochs - 1), mem,
mloss, lr.shape[-1])
pbar.set_description(s)
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
learning_rate = [x['lr']
for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# PSNR
ema.update_attr(
model,
include=['yaml', 'nc', 'hyp', 'gr', 'names', 'class_weights'])
final_epoch = epoch + 1 == epochs
model.eval()
with torch.no_grad():
for idx_scale, scale in enumerate(opt.scale):
eval_acc = 0
#testloader.dataset.set_scale(idx_scale)
pbar = enumerate(testloader)
pbar = tqdm(pbar, total=len(testloader))
for idx_img, (lr, hr, filename) in pbar:
lr = lr.to(device).float()
hr = hr.to(device).float()
filename = filename[0]
pred = model(lr, idx_scale)
pred = quantize(pred, opt.rgb_range)
save_list = [pred]
eval_acc += calc_psnr(pred, hr, scale, opt.rgb_range)
save_list.extend([lr, hr])
# PSNR 로그로 표시
results = eval_acc / len(testloader)
logger.info(f'[DIV2K x{opt.scale}]\tPSNR: {results}')
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 1 % (results) +
'\n') # append metrics, val_loss
# Update best PSNR
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
ckpt = {
'epoch': epoch,
'best_fitness': best_fitness,
'training_results': results_file.read_text(),
'model':
deepcopy(model.module if is_parallel(model) else model).half(),
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
'wandb_id':
wandb_logger.wandb_run.id if wandb_logger.wandb else None
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if wandb_logger.wandb:
if ((epoch + 1) % opt.save_period == 0
and not final_epoch) and opt.save_period != -1:
wandb_logger.log_model(last.parent,
opt,
epoch,
fi,
best_model=best_fitness == fi)
del ckpt
def train(hyp, opt, device):
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
do_semi = opt.do_semi
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True)
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve #create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader)
nc = 1 if opt.single_cls else int(data_dict['nc']) #number of classes
names = ['item'] if opt.single_cls and len(
data_dict['names']) != 1 else data_dict['names']
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data)
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) #load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml,
ch=3,
nc=nc,
anchors=hyp.get('anchors')).to(device) #create
exclude = [
'anchor'
] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [
] #exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) #intersect
model.load_state_dict(state_dict, strict=False) #load
else:
model = Model(opt.cfg, ch=3, nc=nc,
anchors=hyp.get('anchors')).to(device)
with torch_distributed_zero_first(rank):
check_dataset(data_dict) #check
train_path = data_dict['train']
test_path = data_dict['val']
# Optimizer
nbs = 64
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply dacay
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust betal to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
del pg0, pg1, pg2
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp[
'lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs)
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Results
if ckpt.get('training_results') is not None:
results_file.write_text(
ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weight, epochs)
if epochs < start_epoch:
epochs += ckpt['epoch']
del ckpt, state_dict
# Image sizes
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.model[
-1].nl # number of detection layer (used for scaling hyp['obj])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(
device)
# Trainloader
if do_semi:
dataloader, dataset, unlabeldataloader = create_dataloader(
train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr('train: '),
do_semi=opt.do_semi)
else:
dataloader, dataset = create_dataloader(
train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr('train: '),
do_semi=opt.do_semi)
# Train teacher model
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# process 0
if rank in [-1, 0]:
testloader = create_dataloader(
test_path,
imgsz_test,
batch_size * 2,
gs,
opt, # testloader
hyp=hyp,
cache=opt.cache_images and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers,
pad=0.5,
prefix=colorstr('val: '),
do_semi=False)[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
# DDP mode
if cuda and rank != 1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=opt.local_rank,
find_unused_parameters=any(
isinstance(layer, nn.MultiheadAttention)
for layer in model.modules()))
# Model parameters
hyp['box'] *= 3. / nl # scale to layers
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640)**2 * 3. / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(
dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Train teacher model --> burn in
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
burnin_epochs = epochs / 2
# burn in
for epoch in range(start_epoch,
burnin_epochs): # epoch-------------------------
model.train()
nb = len(dataloader)
mloss = torch.zeros(4, device=device) # mean loss
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb)
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar:
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warm up
if ni <= [0, nw]:
xi = [0, nw]
accumulate = max(
1, np.interp(ni, xi, [1, nbs / total_batch_size].round()))
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_item = compute_loss(
pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between device in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# print
if rank in [-1, 0]:
mloss = (mloss * i + loss_item) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
ema.update_attr(model,
include=[
'yaml', 'nc', 'hyp', 'gr', 'names', 'stride',
'class_weights'
])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50
and final_epoch,
plots=plots and final_epoch,
compute_loss=compute_loss)
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, mAP@50, [email protected]]
if fi > best_fitness:
best_fitness = fi
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
results_file.read_text(),
'model':
deepcopy(model.module if is_parallel(model) else model).half(),
'ema':
deepcopy(ema.ema).half(),
'updates':
ema.updates,
'optimizer':
optimizer.state_dict()
}
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------
# end warm up
# get persudo label
# STAC
# first apply weak augmentation on unlabeled dataset then use teacher net to predict the persudo labels
# Then apply strong augmentation on unlabeled dataset, use student net to get the logists and compute the unlabeled loss.
model.eval()
img = []
target = []
Path = []
imgsz = opt.img_size
for idx, batch in tqdm(enumerate(unlabeldataloader),
total=len(unlabeldataloader)):
imgs0, _, path, _ = batch # from uint8 to float16
with torch.no_grad():
pred = model(imgs0.to(device, non_blocking=True).float() /
255.0)[0]
gn = torch.tensor(imgs0.shape)[[3, 2, 3, 2]]
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
for index, pre in enumerate(pred):
predict_number = len(pre)
if predict_number == 0:
continue
Class = pre[:, 5].view(predict_number, 1).cpu()
XYWH = (xyxy2xywh(pre[:, :4])).cpu()
XYWH /= gn
pre = torch.cat((torch.zeros(predict_number, 1), Class, XYWH),
dim=1)
img.append(imgs0[index])
target.append(pre)
Path.append(path[index])
unlabeldataset = semiDataset(img, target, Path)
del img, targets, Path
model.train()
def build_targets(prediction, targets, model):
"""
Build targets for compute_loss()
Args:
prediction:
targets: image, class, x, y, w, h
model:
Returns:
clsT:
boxT:
indices:
anchor:
"""
clsT, boxT, indices, anchor = [], [], [], []
gain = torch.ones(7,
device=targets.device) # normalized to grid space gain
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
anchorNum, targetNum = det.na, targets.shape[
0] # number of anchors, targets
ai = torch.arange(anchorNum, device=targets.device).float().view(
anchorNum, 1).repeat(1, targetNum)
targets = torch.cat((targets.repeat(anchorNum, 1, 1), ai[:, :, None]),
2) # append anchor indices
bias = 0.5 # ↓ 表示5个偏移，原点不动、往右、往下、往左、往上
off = torch.tensor([
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1],
],
device=targets.device).float() * bias # offsets
for i in range(det.nl):
anchors = det.anchors[i]
gain[2:6] = torch.tensor(prediction[i].shape)[[3, 2, 3,
2]] # xyxy gain
# Match targets to anchors
t = targets * gain # 将 gt 的 cx, cy, w, h 换算到当前 feature map 对应尺寸和 anchor 匹配
if targetNum:
# Matches
radio = t[:, :, 4:6] / anchors[:, None]
j = torch.max(
radio, 1. / radio).max(2)[0] < model.hyp['anchor_t'] # compare
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < bias) & (gxy > 1.)).T
l, m = ((gxi % 1. < bias) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 6].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0,
gain[2] - 1))) # image, anchor, grid indices
boxT.append(torch.cat((gxy - gij, gwh), 1)) # box
anchor.append(anchors[a]) # anchors
clsT.append(c) # class
return clsT, boxT, indices, anchor
def build_targets(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
na, nt = det.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch, landmarks, lmks_mask = [], [], [], [], [], []
#gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
gain = torch.ones(15, device=targets.device)
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(
1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]),
2) # append anchor indices
g = 0.5 # bias
off = torch.tensor(
[
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
],
device=targets.device).float() * g # offsets
for i in range(det.nl):
anchors = det.anchors[i]
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
#landmarks 10
gain[6:14] = torch.tensor(p[i].shape)[[3, 2, 3, 2, 3, 2, 3,
2]] # xyxy gain
# Match targets to anchors
t = targets * gain
if nt:
# Matches
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
j = torch.max(r,
1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 14].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0,
gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
#landmarks
lks = t[:, 6:14]
#lks_mask = lks > 0
#lks_mask = lks_mask.float()
lks_mask = torch.where(lks < 0, torch.full_like(lks, 0.),
torch.full_like(lks, 1.0))
#应该是关键点的坐标除以anch的宽高才对，便于模型学习。使用gwh会导致不同关键点的编码不同，没有统一的参考标准
lks[:, [0, 1]] = (lks[:, [0, 1]] - gij)
lks[:, [2, 3]] = (lks[:, [2, 3]] - gij)
lks[:, [4, 5]] = (lks[:, [4, 5]] - gij)
lks[:, [6, 7]] = (lks[:, [6, 7]] - gij)
'''
#anch_w = torch.ones(5, device=targets.device).fill_(anchors[0][0])
#anch_wh = torch.ones(5, device=targets.device)
anch_f_0 = (a == 0).unsqueeze(1).repeat(1, 5)
anch_f_1 = (a == 1).unsqueeze(1).repeat(1, 5)
anch_f_2 = (a == 2).unsqueeze(1).repeat(1, 5)
lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_0, lks[:, [0, 2, 4, 6, 8]] / anchors[0][0], lks[:, [0, 2, 4, 6, 8]])
lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_1, lks[:, [0, 2, 4, 6, 8]] / anchors[1][0], lks[:, [0, 2, 4, 6, 8]])
lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_2, lks[:, [0, 2, 4, 6, 8]] / anchors[2][0], lks[:, [0, 2, 4, 6, 8]])
lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_0, lks[:, [1, 3, 5, 7, 9]] / anchors[0][1], lks[:, [1, 3, 5, 7, 9]])
lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_1, lks[:, [1, 3, 5, 7, 9]] / anchors[1][1], lks[:, [1, 3, 5, 7, 9]])
lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_2, lks[:, [1, 3, 5, 7, 9]] / anchors[2][1], lks[:, [1, 3, 5, 7, 9]])
#new_lks = lks[lks_mask>0]
#print('new_lks: min --- ', torch.min(new_lks), ' max --- ', torch.max(new_lks))
lks_mask_1 = torch.where(lks < -3, torch.full_like(lks, 0.), torch.full_like(lks, 1.0))
lks_mask_2 = torch.where(lks > 3, torch.full_like(lks, 0.), torch.full_like(lks, 1.0))
lks_mask_new = lks_mask * lks_mask_1 * lks_mask_2
lks_mask_new[:, 0] = lks_mask_new[:, 0] * lks_mask_new[:, 1]
lks_mask_new[:, 1] = lks_mask_new[:, 0] * lks_mask_new[:, 1]
lks_mask_new[:, 2] = lks_mask_new[:, 2] * lks_mask_new[:, 3]
lks_mask_new[:, 3] = lks_mask_new[:, 2] * lks_mask_new[:, 3]
lks_mask_new[:, 4] = lks_mask_new[:, 4] * lks_mask_new[:, 5]
lks_mask_new[:, 5] = lks_mask_new[:, 4] * lks_mask_new[:, 5]
lks_mask_new[:, 6] = lks_mask_new[:, 6] * lks_mask_new[:, 7]
lks_mask_new[:, 7] = lks_mask_new[:, 6] * lks_mask_new[:, 7]
lks_mask_new[:, 8] = lks_mask_new[:, 8] * lks_mask_new[:, 9]
lks_mask_new[:, 9] = lks_mask_new[:, 8] * lks_mask_new[:, 9]
'''
lks_mask_new = lks_mask
lmks_mask.append(lks_mask_new)
landmarks.append(lks)
#print('lks: ', lks.size())
return tcls, tbox, indices, anch, landmarks, lmks_mask
def build_targets_agument(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[-1] if is_parallel(model) else model.model[
-1] # Detect() module
nt = targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(6, device=targets.device) # normalized to gridspace gain
g = 0.5 # bias
off = torch.tensor(
[
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
],
device=targets.device).float() * g # offsets
threshold = [0.25, 0.5]
for i in range(det.nl):
if i == 0:
gain[2:6] = torch.tensor(p[i].shape)[[2, 1, 2, 1]] # xyxy gain
# Match targets to anchors
t = targets * gain
j = torch.max(t[..., 4:6], 1)[0] < threshold[i] * p[i].shape[2]
t = t[j] # filter
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((off.shape[0], 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# b, c = t[:, :2].long().T # image, class
# gxy = t[:, 2:4] # grid xy
# gxy_ = torch.round(gxy-0.5)
# gwh = t[:, 4:6] # grid wh
# gij = gxy_.long()
# gi, gj = gij.T # grid xy indices
# Append
indices.append(
(b, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
tcls.append(c) # class
if i == 1:
gain[2:6] = torch.tensor(p[i].shape)[[2, 1, 2, 1]] # xyxy gain
# Match targets to anchors
t = targets * gain
j = torch.max(t[..., 4:6], 1)[0] > threshold[0] * p[i].shape[2]
j1_ = torch.max(t[..., 4:6], 1)[0] < threshold[1] * p[i].shape[2]
j = j & j1_
t = t[j] # filter
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((off.shape[0], 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
indices.append(
(b, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
tcls.append(c) # class
if i == 2:
gain[2:6] = torch.tensor(p[i].shape)[[2, 1, 2, 1]] # xyxy gain
# Match targets to anchors
t = targets * gain
# print(t[...,4:6].shape)
j = torch.max(t[..., 4:6], 1)[0] > threshold[1] * p[i].shape[2]
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((off.shape[0], 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
indices.append(
(b, gj.clamp_(0, gain[3] - 1),
gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
tcls.append(c) # class
return tcls, tbox, indices, anch
def train(hyp, opt, device, tb_writer=None):
logger.info(
colorstr('hyperparameters: ') + ', '.join(f'{k}={v}'
for k, v in hyp.items()))
print(
colorstr('hyperparameters: ') + ', '.join(f'{k}={v}'
for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
shutil.copyfile(os.path.basename(__file__),
os.path.join(str(save_dir), os.path.basename(__file__)))
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
is_coco = opt.data.endswith('coco.yaml')
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {'wandb': None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = torch.load(weights).get('wandb_id') if weights.endswith(
'.pt') and os.path.isfile(weights) else None
wandb_logger = WandbLogger(opt,
Path(opt.save_dir).stem, run_id, data_dict)
loggers['wandb'] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if opt.single_cls and len(
data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if 'yolov5' in opt.cfg:
model = V5Centernet(opt.cfg,
num_classes=nc,
pretrained=weights,
device=device).to(device)
else:
model = V5Dual(opt.cfg,
num_classes=nc,
pretrained=weights,
device=device).to(device)
# model = FlexibleModel(model_config=opt.cfg).to(device)
if pretrained:
# weights = torch.load(pretrained)
# self.model.load_state_dict(weights)
exclude = [] # exclude keys
ckpt = torch.load(weights, map_location=device) # load checkpoint
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
print('Transferred %g/%g items from %s' %
(len(state_dict), len(model.state_dict()), weights)) # report
del ckpt, state_dict
# segLoss = FocalLoss()
segLoss = nn.MSELoss()
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
print(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
# optimizer = MADGRAD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'])
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
print('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp[
'lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
# if pretrained:
if 0:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Results
if ckpt.get('training_results') is not None:
results_file.write_text(
ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weights, epochs)
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
print(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
if hasattr(model, 'model'):
gs = max(int(model.model.stride.max()), 32) # grid size (max stride)
try:
nl = model.model.model[-1].nl
except:
nl = model.model.detection.nl # number of detection layers (used for scaling hyp['obj'])
else:
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.detection.nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
print('Using SyncBatchNorm()')
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr('train: '))
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
# testloader = create_dataloader(test_path, imgsz_test, batch_size*2, gs, opt, # testloader
# hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
# world_size=opt.world_size, workers=opt.workers,
# pad=0.5, prefix=colorstr('val: '))[0]
testloader = create_dataloader(
test_path,
imgsz_test,
batch_size * 2,
gs,
opt, # testloader
hyp=hyp,
cache=opt.cache_images and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers,
pad=0.5,
prefix=colorstr('val: '))[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
# DDP mode
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# Model parameters
hyp['box'] *= 3. / nl # scale to layers
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640)**2 * 3. / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(
dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
print(f'Image sizes {imgsz} train, {imgsz_test} test\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
criterion = torch.nn.BCEWithLogitsLoss()
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (
1 - maps)**2 / nc # class weights
iw = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=cw) # image weights
dataset.indices = random.choices(
range(dataset.n), weights=iw,
k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 11) %
('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total',
'labels', 'img_size', 'hm loss', 'lo_loss', 'loss'))
print(('\n' + '%10s' * 11) %
('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels',
'img_size', 'hm loss', 'lo_loss', 'loss'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, hms, targets, paths, _, logt
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
hms = hms.to(device, non_blocking=True).float()
# print(hms.shape, imgs.shape)
# if i>10:
# break
# print('output_layer====output_layer: ',torch.max(hms))
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred, seg_out, logits = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# print(seg_out.shape)
seg_out = _sigmoid(seg_out)
# seg_out = seg_out[:,0,:,:]
hms = torch.unsqueeze(hms, 1)
# print(hms.shape, seg_out.shape)
# print(targets.shape, imgs.shape, logits.shape, paths.shape)
# logits = torch.clip(logits, -11, 11)
# print(logits)
logit_loss = criterion(logits, logt.to(device))
hm_loss = segLoss(seg_out, hms)
# print(hm_loss)
ratio = sigmoid_rampup(epoch, int(40 * epochs / 50))
ratio = 10 * (1 - ratio)
loss = 1 * loss + ratio * hm_loss + 0.5 * logit_loss
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 9) % (
'%g/%g' %
(epoch, epochs - 1), mem, *mloss, targets.shape[0],
imgs.shape[-1], hm_loss, logit_loss, loss)
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
Thread(target=plot_images,
args=(imgs, hms, targets, paths, f),
daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 10 and wandb_logger.wandb:
wandb_logger.log({
"Mosaics": [
wandb_logger.wandb.Image(str(x), caption=x.name)
for x in save_dir.glob('train*.jpg') if x.exists()
]
})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
ema.update_attr(model,
include=[
'yaml', 'nc', 'hyp', 'gr', 'names', 'stride',
'class_weights'
])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
wandb_logger.current_epoch = epoch + 1
results, maps, times = test.test(data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50
and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results +
'\n') # append metrics, val_loss
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Log
tags = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95',
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb_logger.wandb:
wandb_logger.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
wandb_logger.end_epoch(best_result=best_fitness == fi)
# Save model
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
results_file.read_text(),
'model':
deepcopy(
model.module if is_parallel(model) else model).half(),
'ema':
deepcopy(ema.ema).half(),
'updates':
ema.updates,
'optimizer':
optimizer.state_dict(),
'wandb_id':
wandb_logger.wandb_run.id if wandb_logger.wandb else None
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if wandb_logger.wandb:
if ((epoch + 1) % opt.save_period == 0
and not final_epoch) and opt.save_period != -1:
wandb_logger.log_model(last.parent,
opt,
epoch,
fi,
best_model=best_fitness == fi)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb_logger.wandb:
files = [
'results.png', 'confusion_matrix.png',
*[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]
]
wandb_logger.log({
"Results": [
wandb_logger.wandb.Image(str(save_dir / f), caption=f)
for f in files if (save_dir / f).exists()
]
})
# Test best.pt
logger.info('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
print('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
for m in (last,
best) if best.exists() else (last): # speed, mAP tests
results, _, _ = test.test(opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=0.001,
iou_thres=0.7,
model=attempt_load(m, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True,
plots=False,
is_coco=is_coco)
# Strip optimizers
final = best if best.exists() else last # final model
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
wandb_logger.wandb.log_artifact(
str(final),
type='model',
name='run_' + wandb_logger.wandb_run.id + '_model',
aliases=['last', 'best', 'stripped'])
wandb_logger.finish_run()
else:
dist.destroy_process_group()
torch.cuda.empty_cache()
return results
def train(hyp, opt, device, tb_writer=None):
logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.safe_dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.safe_dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.safe_load(f) # data dict
is_coco = opt.data.endswith('coco.yaml')
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {'wandb': None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
loggers['wandb'] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# modal stage model
modal_stage_model = None
if opt.modal_stage_model is not None and opt.modal_stage_model != "":
modal_ckpt = torch.load(opt.modal_stage_model, map_location=device)
modal_stage_model = Model(modal_ckpt['model'].yaml, ch=3, nc=nc).to(device)
state_dict = modal_ckpt['model'].float().state_dict()
state_dict = intersect_dicts(state_dict, modal_stage_model.state_dict(), exclude=[])
modal_stage_model.load_state_dict(state_dict, strict=False)
logger.info('Transferred %g/%g items from %s for modal stage model' % (len(state_dict), len(modal_stage_model.state_dict()), opt.modal_stage_model))
modal_stage_model.eval()
# Model
if modal_stage_model is not None:
input_ch = 3 + nc
else:
input_ch = 3
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml, ch=input_ch, nc=nc, anchors=hyp.get('anchors')).to(device) # create
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=input_ch, nc=nc, anchors=hyp.get('anchors')).to(device) # create
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
val_paths = dict()
if type(data_dict['val']) == str:
val_paths[MASTER_VAL_NAME] = data_dict['val']
elif len(data_dict['val']) == 1:
val_paths[MASTER_VAL_NAME] = data_dict['val']
else:
if MASTER_VAL_NAME not in data_dict['val']:
raise ValueError(f"When you use multiple validation sets, one MUST be named '{MASTER_VAL_NAME}'. This is the val set" +
f" we will use for early stopping/model selection. Your data yaml file ({opt.data}) does NOT " +
"conform to this requirement. Please fix it.")
for k, v in data_dict['val'].items():
# in this case, the yaml file has several datasets under the key 'val'
val_paths[k] = v
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Results
if ckpt.get('training_results') is not None:
results_file.write_text(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
if epochs < start_epoch:
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers,
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
if dataset.single_labelset:
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
else:
mlc = np.concatenate([dataset.labels[i]['amodal'] for i in range(len(dataset.labels))], 0)[:, 0].max() # max label class, as taken from *amodal* labels
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
val_loaders = dict()
for k, v in val_paths.items():
val_loaders[k] = create_dataloader(v, imgsz_test, batch_size * 2, gs, opt,
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
world_size=opt.world_size, workers=opt.workers,
pad=0.5, prefix=colorstr(f"val/{k}: "))[0]
if not opt.resume:
if dataset.single_labelset:
labels = np.concatenate(dataset.labels, 0)
else:
# use the class distribution from the amodal labels for this generated histogram
labels = np.concatenate([dataset.labels[i]['amodal'] for i in range(len(dataset.labels))], 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank,
# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules()))
# Model parameters
hyp['box'] *= 3. / nl # scale to layers
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
# init tensorboardx tags
train_tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss']
lr_tags = ['x/lr0', 'x/lr1', 'x/lr2']
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
if not dataset.single_labelset:
raise NotImplementedError("We don't support image weighting in the modal/amodal label case yet, but it can be added.")
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
"""
In the modal/amodal use case, 'targets' is a dictionary of tensors, so you should choose
targets['modal'] or targets['amodal'] depending on your needs.
"""
# TODO: change this logic when ready!
if isinstance(targets, dict):
targets = targets['amodal']
if modal_stage_model is not None:
with torch.no_grad():
img_shape = (imgs.shape[2], imgs.shape[3])
boxes, _ = modal_stage_model.forward(imgs)
pixel_map = predicted_bboxes_to_pixel_map(boxes, img_shape)
imgs = torch.cat([imgs, pixel_map], dim=1)
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph
elif plots and ni == 10 and wandb_logger.wandb:
wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
save_dir.glob('train*.jpg') if x.exists()]})
# SEND RESULTS TO TBX
if ni % nb != 0 and ni % opt.tbx_report_train_every_n_batches == 1:
# ni % nb != 0 condition to avoid double-logging anything, ... == 1 condition to log information (almost) right away
for x, tag in zip(mloss[:-1], train_tags):
tb_writer.add_scalar(tag, x, ni)
# init tensorboardx 'lr' list
lr = [x['lr'] for x in optimizer.param_groups] # gather learning rates
for x, tag in zip(lr, lr_tags):
tb_writer.add_scalar(tag, x, ni)
# TODO: if we want to update val loss more frequently, do it here
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
wandb_logger.current_epoch = epoch + 1
for val_loader_name in val_loaders.keys():
temp_results, temp_maps, temp_times = test.test(data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=val_loaders[val_loader_name],
save_dir=save_dir,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco,
modal_stage_model=modal_stage_model)
if val_loader_name != MASTER_VAL_NAME:
# Log tbx metrics for all non-master validation sets
tbx_tags = ['precision', 'recall', 'mAP_0.5', 'mAP_0.5:0.95',
'box_loss', 'obj_loss', 'cls_loss']
for x, tag in zip(list(temp_results), tbx_tags):
if tb_writer:
tb_writer.add_scalar(f"{val_loader_name}/{tag}", x, nb * (epoch + 1))
if wandb_logger.wandb:
wandb_logger.log({f"{val_loader_name}/{tag}": x}) # W&B
else:
results = temp_results
maps = temp_maps
times = temp_times
# Log
all_tbx_tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
# IMPORTANT: when looking at tbx results, the metrics under 'metrics' are calculated on the MASTER VAL SET.
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
f"{MASTER_VAL_NAME}/box_loss", f"{MASTER_VAL_NAME}/obj_loss", f"{MASTER_VAL_NAME}/cls_loss", # val loss
'x/lr0', 'x/lr1', 'x/lr2'] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, all_tbx_tags):
# TBX UPDATES
if tb_writer:
tb_writer.add_scalar(tag, x, nb * (epoch + 1)) # tensorboard
if wandb_logger.wandb:
wandb_logger.log({tag: x}) # W&B
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
wandb_logger.end_epoch(best_result=best_fitness == fi)
# Save model
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': results_file.read_text(),
'model': deepcopy(model.module if is_parallel(model) else model).half(),
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if wandb_logger.wandb:
if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
wandb_logger.log_model(
last.parent, opt, epoch, fi, best_model=best_fitness == fi)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb_logger.wandb:
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
if (save_dir / f).exists()]})
# Test best.pt
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
for m in (last, best) if best.exists() else (last): # speed, mAP tests
results, _, _ = test.test(opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=0.001,
iou_thres=0.7,
model=attempt_load(m, device).half(),
single_cls=opt.single_cls,
dataloader=val_loaders[MASTER_VAL_NAME],
save_dir=save_dir,
save_json=True,
plots=False,
is_coco=is_coco,
modal_stage_model=modal_stage_model)
# Strip optimizers
final = best if best.exists() else last # final model
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
wandb_logger.wandb.log_artifact(str(final), type='model',
name='run_' + wandb_logger.wandb_run.id + '_model',
aliases=['last', 'best', 'stripped'])
wandb_logger.finish_run()
else:
dist.destroy_process_group()
torch.cuda.empty_cache()
return results