def __init__(self,
model_cfg='yolov5s.yaml',
ch=3,
nc=None): # model, input channels, number of classes
super(Model, self).__init__()
if type(model_cfg) is dict:
self.md = model_cfg # model dict
else: # is *.yaml
with open(model_cfg) as f:
self.md = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc:
self.md['nc'] = nc # override yaml value
self.model, self.save = parse_model(self.md,
ch=[ch]) # model, savelist, ch_out
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
m.stride = torch.tensor([
64 / x.shape[-2] for x in self.forward(torch.zeros(1, ch, 64, 64))
]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
self.stride = m.stride
# Init weights, biases
torch_utils.initialize_weights(self)
self._initialize_biases() # only run once
torch_utils.model_info(self)
print('')
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
print('Fusing layers...')
for m in self.model.modules():
if type(m) is Conv:
m.conv = torch_utils.fuse_conv_and_bn(m.conv,
m.bn) # update conv
m.bn = None # remove batchnorm
m.forward = m.fuseforward # update forward
torch_utils.model_info(self)
def __init__(self,
model_cfg='yolov5s.yaml',
ch=3,
nc=None): # model, input channels, number of classes
super(Model, self).__init__()
if type(model_cfg) is dict:
self.md = model_cfg # model dict
else: # is *.yaml
import yaml # for torch hub
with open(model_cfg) as f:
self.md = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc and nc != self.md['nc']:
print('Overriding %s nc=%g with nc=%g' %
(model_cfg, self.md['nc'], nc))
self.md['nc'] = nc # override yaml value
self.model, self.save = parse_model(self.md,
ch=[ch]) # model, savelist, ch_out
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 128 # 2x min stride
m.stride = torch.tensor([
s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))
]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
torch_utils.initialize_weights(self)
self._initialize_biases() # only run once
torch_utils.model_info(self)
print('')
def info(self, verbose=False, img_size=640): # print model information
model_info(self, verbose, img_size)
def info(self, verbose=False): # print model information
model_info(self, verbose)
def info(self, verbose=False):
torch_utils.model_info(self, verbose)
def info(self): # print model information
model_info(self)
def train_aux_for_MFCP(cfg, backbone, neck, data_loader, weights, aux_weight,
hyp, device, resume, epochs):
init_seeds()
batch_size = data_loader.batch_size
accumulate = 64 // batch_size
img_size = data_loader.dataset.img_size
model = Darknet(cfg).to(device)
model_chkpt = torch.load(weights, map_location=device)
model.load_state_dict(model_chkpt['model'], strict=True)
del model_chkpt
aux_util = AuxNetUtils(model, hyp, backbone, neck)
hook_util = HookUtils()
start_epoch = 0
aux_model_list = []
pg0, pg1 = [], [] # optimizer parameter groups
for layer in aux_util.aux_in_layer:
aux_model = aux_util.creat_aux_model(layer, img_size)
aux_model.to(device)
for k, v in dict(aux_model.named_parameters()).items():
if 'Conv2d.weight' in k:
pg1 += [v] # parameter group 1 (apply weight_decay)
else:
pg0 += [v]
aux_model_list.append(aux_model)
optimizer = optim.SGD(pg0,
lr=aux_util.hyp['lr0'],
momentum=aux_util.hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': aux_util.hyp['weight_decay']
})
del pg0, pg1
if resume:
chkpt = torch.load(aux_weight, map_location=device)
for i, layer in enumerate(aux_util.aux_in_layer):
if isinstance(layer, str):
aux_model_list[i].load_state_dict(
chkpt['aux_in{}'.format(layer)], strict=True)
else:
aux_model_list[i].load_state_dict(chkpt['aux_in{}'.format(
layer[0])],
strict=True)
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
start_epoch = chkpt['epoch'] + 1
scheduler = lr_scheduler.MultiStepLR(
optimizer, milestones=[epochs // 3, 2 * epochs // 3], gamma=0.1)
scheduler.last_epoch = start_epoch - 1
handles = [] # 结束训练后handle需要回收
for name, child in model.module_list.named_children():
if name in aux_util.aux_in_layer:
handles.append(
child.register_forward_hook(hook_util.hook_origin_output))
elif name in aux_util.aux_in_layer[1] or name in aux_util.aux_in_layer[
2]:
handles.append(
child.register_forward_hook(hook_util.hook_origin_output))
if device.type != 'cpu' and torch.cuda.device_count() > 1:
for i, aux_model in enumerate(aux_model_list):
aux_model_list[i] = nn.parallel.DistributedDataParallel(
aux_model, find_unused_parameters=True)
model = nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers
nb = len(data_loader)
model.nc = 80
model.hyp = aux_util.hyp
model.arc = 'default'
for aux_model in aux_model_list:
model_info(aux_model, report='summary')
print('Starting training for %g epochs...' % epochs)
for epoch in range(start_epoch, epochs):
for aux_model in aux_model_list:
aux_model.train()
print(('\n' + '%10s' * 8) % ('Stage', 'Epoch', 'gpu_mem', 'AuxID',
'DIoU', 'cls', 'total', 'targets'))
# -----------------start batch-----------------
pbar = tqdm(enumerate(data_loader), total=nb)
for i, (imgs, targets, _, _) in pbar:
if len(targets) == 0:
continue
ni = i + nb * epoch
imgs = imgs.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
with torch.no_grad():
_ = model(imgs)
hook_util.cat_to_gpu0()
for aux_idx, aux_model in enumerate(aux_model_list):
if aux_idx == 0:
pred = aux_model(hook_util.origin_features['gpu0'][0])
elif aux_idx == 1:
pred = aux_model(hook_util.origin_features['gpu0'][1],
hook_util.origin_features['gpu0'][-1])
elif aux_idx == 2:
pred = aux_model(hook_util.origin_features['gpu0'][2],
hook_util.origin_features['gpu0'][-2])
else:
pred = aux_model(hook_util.origin_features['gpu0'][3])
loss, loss_items = compute_loss_for_MFCP(
pred, targets, aux_model)
loss *= batch_size / 64
loss.backward()
mem = torch.cuda.memory_cached(
) / 1E9 if torch.cuda.is_available() else 0 # (GB)
s = ('%10s' * 3 + '%10.3g' * 5) % ('Train Aux', '%g/%g' %
(epoch, epochs - 1),
'%.3gG' % mem, aux_idx,
*loss_items, len(targets))
pbar.set_description(s)
# 每个batch后要把hook_out内容清除
hook_util.clean_hook_out()
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
# -----------------end batches-----------------
scheduler.step()
final_epoch = epoch + 1 == epochs
chkpt = {
'epoch': epoch,
'optimizer': None if final_epoch else optimizer.state_dict()
}
for i, layer in enumerate(aux_util.aux_in_layer):
if isinstance(layer, str):
chkpt['aux_in{}'.format(
layer
)] = aux_model_list[i].module.state_dict() if type(
aux_model_list[i]
) is nn.parallel.DistributedDataParallel else aux_model_list[
i].state_dict()
else:
chkpt['aux_in{}'.format(
layer[0]
)] = aux_model_list[i].module.state_dict() if type(
aux_model_list[i]
) is nn.parallel.DistributedDataParallel else aux_model_list[
i].state_dict()
torch.save(chkpt, aux_weight)
torch.save(chkpt, "../weights/aux-coco.pt")
del chkpt
with open("aux_result.txt", 'a') as f:
f.write(s + '\n')
# 最后要把hook全部删除
for handle in handles:
handle.remove()
torch.cuda.empty_cache()
print("ONLY consider CONV layers: ")
print("total number of zeros: {}, non-zeros: {}, zero sparsity is: {:.4f}".format(
total_zeros, total_nonzeros, total_zeros / (total_zeros + total_nonzeros)))
print("only consider conv layers, compression rate is: {:.4f}".format(
(total_zeros + total_nonzeros) / total_nonzeros))
print("===========================================================================\n\n")
return comp_ratio
if __name__ == '__main__':
print("Check Dense model: ")
model = Darknet(cfg = 'cfg/csdarknet53s-panet-spp.cfg',img_size=(320,320))
n_po, macso = torch_utils.model_info(model, verbose=False)
print("Check 8x prunned model: ")
state_dict = torch.load('weights/best8x-514.pt')
model.load_state_dict(state_dict["model"])
n_p8x, macs8x = model.prunnedinfo()
print("parameters compression rate: %g, flops compression rate: %g" % (n_po/n_p8x, macso/macs8x)) ##flops=2*macs
test_sparsity(model)
print("Check 14x prunned model: ")
state_dict = torch.load('weights/best14x-49.pt')
model.load_state_dict(state_dict["model"])
n_p14x, macs14x = model.prunnedinfo()
print("parameters compression rate: %g, flops compression rate: %g" % (n_po/n_p14x, macso/macs14x)) ##flops=2*macs
test_sparsity(model)
cb_handler=cb_handler)
# %%
# lr_find(learner)
# %%
# learner.recorder.plot()
# %%
fit_one_cycle(learner, 1, max_lr=1e-2)
# %%
from utils.torch_utils import model_info
model_info(model)
learner.model
learner.layer_groups
learner.lr_range(slice(1e-6, 1e-4))
from fastai.callbacks import hooks
hooks.model_summary(learner)
# %%
model_state = model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict()
torch.save({'model': model_state}, "weights/fastai.pt")
def info(self, verbose=False):
"""打印模型的信息
:param verbose:
:return:
"""
torch_utils.model_info(self, verbose)
def info(self, verbose=False):
model_info(self, verbose)
def train():
cfg = opt.cfg
data = opt.data
img_size = opt.img_size
epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
if 'pw' not in opt.arch: # remove BCELoss positive weights
param['cls_pw'] = 1.
param['obj_pw'] = 1.
# Initialize
init_seeds()
multi_scale = opt.multi_scale
if multi_scale:
img_sz_min = round(img_size / 32 / 1.5) + 1
img_sz_max = round(img_size / 32 * 1.5) - 1
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale {} - {}'.format(img_sz_min * 32, img_size))
# Configure run
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
nc = int(data_dict['classes']) # number of classes
# Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# Initialize model
model = Darknet(cfg, arch=opt.arch).to(device)
# Optimizer
pg0, pg1 = [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if 'Conv2d.weight' in k:
pg1 += [v] # parameter group 1 (apply weight_decay)
else:
pg0 += [v] # parameter group 0
if opt.adam:
optimizer = optim.Adam(pg0, lr=param['lr0'])
# optimizer = AdaBound(pg0, lr=param['lr0'], final_lr=0.1)
else:
optimizer = optim.SGD(pg0, lr=param['lr0'], momentum=param['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': param['weight_decay']}) # add pg1 with weight_decay
del pg0, pg1
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_fitness = float('inf')
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
# if opt.transfer:
chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
model.load_state_dict(chkpt['model'], strict=False)
# else:
# model.load_state_dict(chkpt['model'])
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
cutoff = load_darknet_weights(model, weights)
if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
if opt.prebias:
for p in optimizer.param_groups:
# lower param count allows more aggressive training
# settings: i.e. SGD ~0.1 lr0, ~0.9 momentum
p['lr'] *= 100 # lr gain
if p.get('momentum') is not None: # for SGD but not Adam
p['momentum'] *= 0.9
for p in model.parameters():
if opt.prebias and p.numel() == nf: # train (yolo biases)
p.requires_grad = True
elif opt.transfer and p.shape[0] == nf: # train (yolo biases+weights)
p.requires_grad = True
else: # freeze layer
p.requires_grad = False
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (param['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (param['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.MultiStepLR(
# optimizer, milestones=range(59, 70, 1), gamma=0.8,
# ) # gradual fall to 0.1*lr0
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[round(opt.epochs * x) for x in [0.8, 0.9]],
gamma=0.1,
)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
# Initialize distributed training
if torch.cuda.device_count() > 1:
dist.init_process_group(backend='nccl', # 'distributed backend'
init_method='tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(model)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(
train_path,
img_size,
batch_size,
augment=True,
param=param, # augmentation hyperparameters
rect=opt.rect, # rectangular training
image_weights=opt.img_weights,
cache_labels=True if epochs > 10 else False,
cache_images=False if opt.prebias else opt.cache_images,
)
# Dataloader
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 16]),
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn,
)
# Start training
model.nc = nc # attach number of classes to model
model.arch = opt.arch # attach yolo architecture
model.param = param # attach hyperparameters to model
# model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
torch_utils.model_info(model, report='summary') # 'full' or 'summary'
nb = len(dataloader)
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
print('Starting {} for {} epochs...'.format('prebias' if opt.prebias else 'training', epochs))
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
print(('{:>10s}' * 8).format(
'Epoch', 'gpu_mem', 'GIoU', 'obj',
'cls', 'total', 'targets', 'img_size',
))
# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
freeze_backbone = False
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device)
targets = targets.to(device)
# Multi-Scale training
if multi_scale:
if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Plot images with bounding boxes
if ni == 0:
fname = 'train_batch{}.jpg'.format(i)
plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
if tb_writer:
tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
# Hyperparameter burn-in
# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
# if ni <= n_burn:
# for m in model.named_modules():
# if m[0].endswith('BatchNorm2d'):
# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
# g = (i / n_burn) ** 4 # gain rises from 0 - 1
# for x in optimizer.param_groups:
# x['lr'] = param['lr0'] * g
# x['weight_decay'] = param['weight_decay'] * g
# Run model
preds = model(imgs)
# Compute loss
loss, loss_items = compute_loss(preds, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
s = ('{:>10s}' * 2 + '{:10.3g}' * 6).format(
'{:g}/{:g}'.format(epoch, epochs - 1),
'{:.3g}G'.format(mem), *mloss, len(targets), img_size)
pbar.set_description(s)
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
final_epoch = epoch + 1 == epochs
if opt.prebias:
print_model_biases(model)
else:
# Calculate mAP (always test final epoch, skip first 10 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch:
with torch.no_grad():
results, maps = test.test(
cfg,
data,
batch_size=batch_size,
img_size=opt.img_size,
model=model,
conf_thres=0.001 if final_epoch and epoch > 0 else 0.1, # 0.1 for speed
save_json=final_epoch and epoch > 0 and 'coco.data' in data,
)
# Write epoch results
with open(results_file, 'a') as f:
f.write(s + ('%10.3g' * 7).format(results) + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
# Write Tensorboard results
if tb_writer:
x = list(mloss) + list(results)
titles = [
'GIoU', 'Objectness', 'Classification', 'Train loss',
'Precision', 'Recall', 'mAP', 'F1', 'val GIoU',
'val Objectness', 'val Classification',
]
for xi, title in zip(x, titles):
tb_writer.add_scalar(title, xi, epoch)
# Update best mAP
fitness = sum(results[4:]) # total loss
if fitness < best_fitness:
best_fitness = fitness
# Save training results
save = (not opt.nosave) or (final_epoch and not opt.evolve) or opt.prebias
if save:
with open(results_file, 'r') as f:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last checkpoint
torch.save(chkpt, last)
# Save best checkpoint
if best_fitness == fitness:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, wdir + 'backup{}.pt'.format(epoch))
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if len(opt.name) and not opt.prebias:
fresults = 'results{}.txt'.format(opt.name)
flast = 'last{}.pt'.format(opt.name)
fbest = 'best{}.pt'.format(opt.name)
os.rename('results.txt', fresults)
os.rename(wdir + 'last.pt', wdir + flast) if os.path.exists(wdir + 'last.pt') else None
os.rename(wdir + 'best.pt', wdir + fbest) if os.path.exists(wdir + 'best.pt') else None
# save to cloud
if opt.bucket:
os.system('gsutil cp {} {} gs://{}'.format(fresults, wdir + flast, opt.bucket))
plot_results() # save as results.png
print('{} epochs completed in {:.3f} hours.\n'.format(
epoch - start_epoch + 1,
(time.time() - t0) / 3600),
)
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
def train():
cfg = opt.cfg
data = opt.data
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
imgsz_min, imgsz_max, img_size_test = opt.img_size # img sizes (min, max, test)
# Image Sizes
gs = 64 # (pixels) grid size
print(imgsz_min, gs)
assert math.fmod(
imgsz_min,
gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
opt.multi_scale |= imgsz_min != imgsz_max # multi if different (min, max)
if opt.multi_scale:
if imgsz_min == imgsz_max:
imgsz_min //= 1.5
imgsz_max //= 0.667
grid_min, grid_max = imgsz_min // gs, imgsz_max // gs
imgsz_max = grid_max * gs # initialize with maximum multi_scale size
print('Using multi-scale %g - %g' % (grid_min * gs, imgsz_max))
img_size = int(imgsz_max)
# Configure run
init_seeds()
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
test_path = data_dict['valid']
nc = 1 if opt.single_cls else int(
data_dict['classes']) # number of classes
hyp['cls'] *= nc / 80 # update coco-tuned hyp['cls'] to current dataset
# Remove previous results
for f in glob.glob('*_batch*.png') + glob.glob(results_file):
os.remove(f)
# Initialize model
model = Darknet(cfg).to(device)
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters'])
print("Yolo filters", nf)
# Optimizer
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
# v.requires_grad = True if v.shape[0] == nf else False
# if not v.requires_grad:
# continue
if '.bias' in k:
pg2 += [v] # biases
elif 'Conv2d.weight' in k:
pg1 += [v] # apply weight_decay
else:
pg0 += [v] # all else
if opt.adam:
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
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
start_epoch = 0
best_fitness = 0.0
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
try:
chkpt['model'] = {
k: v
for k, v in chkpt['model'].items()
if model.state_dict()[k].numel() == v.numel()
}
model.load_state_dict(chkpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
"See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Scheduler https://github.com/ultralytics/yolov3/issues/238
lf = lambda x: (
((1 + math.cos(x * math.pi / epochs)) / 2
)**1.0) * 0.95 + 0.05 # cosine https://arxiv.org/pdf/1812.01187.pdf
scheduler = lr_scheduler.LambdaLR(optimizer,
lr_lambda=lf,
last_epoch=start_epoch - 1)
# scheduler = lr_scheduler.MultiStepLR(optimizer, [round(epochs * x) for x in [0.8, 0.9]], 0.1, start_epoch - 1)
# Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, '.-', label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count(
) > 1 and torch.distributed.is_available():
dist.init_process_group(
backend='nccl', # 'distributed backend'
init_method=
'tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
print("img_size", img_size)
# Dataset
dataset = LoadImagesAndLabels(
train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
cache_images=opt.cache_images,
single_cls=opt.single_cls)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=nw,
shuffle=not opt.
rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(
test_path,
img_size_test,
batch_size,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Model parameters
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
# Model EMA
ema = torch_utils.ModelEMA(model, decay=0.3)
# Start training
nb = len(dataloader) # number of batches
n_burn = max(3 * nb,
500) # burn-in iterations, max(3 epochs, 500 iterations)
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs)
model_info(model, verbose=True)
# exit(1)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 -
maps)**2 # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w)
dataset.indices = random.choices(range(dataset.n),
weights=image_weights,
k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls',
'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Burn-in
if ni <= n_burn * 2:
model.gr = np.interp(
ni, [0, n_burn * 2],
[0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
if ni == n_burn: # burnin complete
print_model_biases(model)
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, [0, n_burn],
[0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, [0, n_burn],
[0.9, hyp['momentum']])
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 1 == 0: # adjust img_size (67% - 150%) every 1 batch
img_size = random.randrange(grid_min, grid_max + 1) * gs
sf = img_size / max(imgs.shape[2:]) # scale factor
# print("multiscale ",sf)
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Optimize accumulated gradient
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
ema.update(model)
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() /
1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' %
(epoch, epochs - 1), mem,
*mloss, len(targets), img_size)
pbar.set_description(s)
# Plot images with bounding boxes
if ni < 1:
f = 'train_batch%g.png' % i # filename
plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
if tb_writer:
tb_writer.add_image(f,
cv2.imread(f)[:, :, ::-1],
dataformats='HWC')
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
ema.update_attr(model)
final_epoch = epoch + 1 == epochs
if (not opt.notest
and epoch % 10 == 9) or final_epoch: # Calculate mAP
is_coco = any([
x in data
for x in ['coco.data', 'coco2014.data', 'coco2017.data']
]) and model.nc == 80
results, maps = test.test(
cfg,
data,
batch_size=batch_size,
img_size=img_size_test,
# model=ema.ema,
model=model,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader)
# Write epoch results
with open(results_file, 'a') as f:
f.write(s + '%10.3g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' %
(opt.bucket, opt.name))
# Write Tensorboard results
if tb_writer:
tags = [
'train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5',
'metrics/F1', 'val/giou_loss', 'val/obj_loss', 'val/cls_loss'
]
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save training results
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f:
# Create checkpoint
chkpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
# 'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
'model':
model.module.state_dict()
if hasattr(model, 'module') else model.state_dict(),
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last checkpoint
torch.save(chkpt, last)
# Save best checkpoint
if (best_fitness == fi) and not final_epoch:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
# if epoch > 0 and epoch % 10 == 0:
# torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
n = opt.name
if len(n):
n = '_' + n if not n.isnumeric() else n
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'],
[flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
ispt = f2.endswith('.pt') # is *.pt
strip_optimizer(f2) if ispt else None # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (
f2, opt.bucket)) if opt.bucket and ispt else None # upload
if not opt.evolve:
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1,
(time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
def test(
data,
weights=None,
batch_size=16,
imgsz=640,
conf_thres=0.001,
iou_thres=0.6, # for NMS
save_json=False,
verbose=False,
model=None,
dataloader=None,
logdir='./runs',
merge=False):
# Initialize/load model and set device
if model is None:
training = False
device = torch_utils.select_device(opt.device, batch_size=batch_size)
# Remove previous
for f in glob.glob(os.path.join(logdir, 'test_batch*.jpg')):
os.remove(f)
# Load model
model = torch.load(
weights, map_location=device)['model'].float() # load to FP32
torch_utils.model_info(model)
model.fuse()
model.to(device)
# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
else: # called by train.py
training = True
device = next(model.parameters()).device # get model device
# Half
half = device.type != 'cpu' and torch.cuda.device_count(
) == 1 # half precision only supported on single-GPU
half = False
if half:
model.half() # to FP16
# Configure
model.eval()
with open(data) as f:
data = yaml.load(f, Loader=yaml.FullLoader) # model dict
nc = int(data['num_classes']) # number of classes
iouv = torch.linspace(0.5, 0.95,
10).to(device) # iou vector for [email protected]:0.95
niou = iouv.numel()
losser = YoloLoss(model)
# Dataloader
if dataloader is None: # not training
merge = opt.merge # use Merge NMS
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
path = data['test'] if opt.task == 'test' else data[
'val'] # path to val/test images
dataloader = kitti.create_dataloader(path,
imgsz,
batch_size,
int(max(model.stride)),
config=None,
augment=False,
cache=False,
pad=0.5,
rect=True)[0]
seen = 0
names = data['names']
kitti8class = data_utils.kitti8_classes()
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
'[email protected]', '[email protected]:.95')
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], []
for batch_i, (img, targets, paths,
shapes) in enumerate(tqdm.tqdm(dataloader, desc=s)):
targets.delete_by_mask()
targets.to_float32()
targ = ParamList(targets.size, True)
targ.copy_from(targets)
img_id = targets.get_field('img_id')
classes = targets.get_field('class')
bboxes = targets.get_field('bbox')
targets = torch.cat(
[img_id.unsqueeze(-1),
classes.unsqueeze(-1), bboxes], dim=-1)
img = img.to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
# img /= 1.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
whwh = torch.Tensor([width, height, width, height]).to(device)
# Disable gradients
with torch.no_grad():
# Run model
t = torch_utils.time_synchronized()
inf_out, train_out = model(img) # inference and training outputs
t0 += torch_utils.time_synchronized() - t
# Compute loss
if training: # if model has loss hyperparameters
# loss += calc_loss([x.float() for x in train_out], targets, model)[1][:3] # GIoU, obj, cls
loss += losser([x.float() for x in train_out], targ)[1][:3]
# Run NMS
t = torch_utils.time_synchronized()
output = postprocess.apply_nms(inf_out,
nc,
conf_thres=conf_thres,
iou_thres=iou_thres,
merge=merge)
t1 += torch_utils.time_synchronized() - t
# Statistics per image
for si, pred in enumerate(output):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
seen += 1
if pred is None:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls))
continue
# Append to text file
# with open('test.txt', 'a') as file:
# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]
# Clip boxes to image bounds
utils.clip_coords(pred, (height, width))
# Append to pycocotools JSON dictionary
if save_json:
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(Path(paths[si]).stem.split('_')[-1])
box = pred[:, :4].clone() # xyxy
utils.scale_coords(img[si].shape[1:], box, shapes[si][0],
shapes[si][1]) # to original shape
box = data_utils.xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
jdict.append({
'image_id': image_id,
'category_id': kitti8class[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)
})
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0],
niou,
dtype=torch.bool,
device=device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = data_utils.xywh2xyxy(labels[:, 1:5]) * whwh
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(
-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(
-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = metrics_utils.box_iou(
pred[pi, :4],
tbox[ti]).max(1) # best ious, indices
# Append detections
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d not in detected:
detected.append(d)
correct[
pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(
detected
) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
stats.append(
(correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
if batch_i < 1:
f = os.path.join(logdir,
'test_batch%g_gt.jpg' % batch_i) # filename
visual_utils.plot_images(img, targets, paths, f,
names) # ground truth
f = os.path.join(logdir, 'test_batch%g_pred.jpg' % batch_i)
visual_utils.plot_images(img,
utils.output_to_target(
output, width, height), paths, f,
names) # predictions
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats):
p, r, ap, f1, ap_class = metrics_utils.ap_per_class(*stats)
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(
1) # [P, R, [email protected], [email protected]:0.95]
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(np.int64),
minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
pf = '%20s' + '%12.3g' * 6 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
# Print results per class
if verbose and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
# Print speeds
t = tuple(x / seen * 1E3
for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
if not training:
print(
'Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g'
% t)
# Save JSON
if save_json and map50 and len(jdict):
imgIds = [
int(Path(x).stem.split('_')[-1])
for x in dataloader.dataset.img_files
]
f = 'detections_val2017_%s_results.json' % \
(weights.split(os.sep)[-1].replace('.pt', '') if weights else '') # filename
print('\nCOCO mAP with pycocotools... saving %s...' % f)
with open(f, 'w') as file:
json.dump(jdict, file)
try:
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoGt = COCO(
glob.glob('../coco/annotations/instances_val*.json')
[0]) # initialize COCO ground truth api
cocoDt = cocoGt.loadRes(f) # initialize COCO pred api
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds # image IDs to evaluate
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
map, map50 = cocoEval.stats[:
2] # update results ([email protected]:0.95, [email protected])
except:
print(
'WARNING: pycocotools must be installed with numpy==1.17 to run correctly. '
'See https://github.com/cocodataset/cocoapi/issues/356')
# Return results
model.float() # for training
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map50, map,
*(loss.cpu() / len(dataloader)).tolist()), maps, t
