def main():
args = parse_args()
# build model
model = Model(radius=0.1,
bottleneck=1024,
num_pts=2048,
num_pts_observed=2048,
num_vote_train=64,
num_contrib_vote_train=10,
num_vote_test=128,
is_vote=True,
task='completion')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = torch.nn.DataParallel(model)
# load model
model_path = args.checkpoint
if not os.path.isfile(model_path):
raise ValueError(
'{} does not exist. Please provide a valid path for pretrained model!'
.format(model_path))
model.load_state_dict(torch.load(model_path))
print('Load model successfully from: {}'.format(args.checkpoint))
save_dir = 'demo_results'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
dataset = load_data(args.data_path)
model.eval()
with torch.no_grad():
for data in dataset:
pos, batch, filename = data
pos, batch = pos.to(device), batch.to(device)
pred, _ = model(None, pos, batch)
pred = pred.cpu().detach().numpy()[0]
np.save(os.path.join(save_dir, 'pred_{}'.format(filename)), pred)
print('Done.')
def main():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# parse the configuarations
parser = argparse.ArgumentParser(
description='Additioal configurations for testing',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--gpu_ids',
type=str,
default='-1',
help=
'IDs of GPUs to use (please use `,` to split multiple IDs); -1 means CPU only'
)
parser.add_argument('--tt_list',
type=str,
required=True,
help='Path to the list of testing files')
parser.add_argument('--ckpt_dir',
type=str,
required=True,
help='Name of the directory to write log')
parser.add_argument('--model_file',
type=str,
required=True,
help='Path to the model file')
parser.add_argument('--est_path',
type=str,
default='../data/estimates',
help='Path to dump estimates')
parser.add_argument(
'--write_ideal',
default=False,
action='store_true',
help=
'Whether to write ideal signals (the speech signals resynthesized from the ideal training targets; ex. for time-domain enhancement, it is the same as clean speech)'
)
args = parser.parse_args()
logger.info('Arguments in command:\n{}'.format(pprint.pformat(vars(args))))
model = Model()
model.test(args)
def test(cfg,
data,
weights=None,
batch_size=16,
img_size=608,
iou_thres=0.5,
conf_thres=0.001,
nms_thres=0.5,
save_json=True,
hyp=None,
model=None,
single_cls=False):
"""test the metrics of the trained model
:param str cfg: model cfg file
:param str data: data dict
:param str weights: weights path
:param int batch_size: batch size
:param int img_size: image size
:param float iou_thres: iou threshold
:param float conf_thres: confidence threshold
:param float nms_thres: nms threshold
:param bool save_json: Whether to save the model
:param str hyp: hyperparameter
:param str model: yolov4 model
:param bool single_cls: only one class
:return: results
"""
if model is None:
device = select_device(opt.device)
verbose = False
# Initialize model
model = Model(cfg, img_size).to(device)
# Load weights
if weights.endswith('.pt'):
checkpoint = torch.load(weights, map_location=device)
state_dict = intersect_dicts(checkpoint['model'],
model.state_dict())
model.load_state_dict(state_dict, strict=False)
elif len(weights) > 0:
load_darknet_weights(model, weights)
print(f'Loaded weights from {weights}!')
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
else:
device = next(model.parameters()).device
verbose = False
test_path = data['valid']
num_classes, names = (1, ['item']) if single_cls else (int(
data['num_classes']), data['names'])
# Dataloader
dataset = LoadImagesAndLabels(test_path, img_size, batch_size, hyp=hyp)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=8,
pin_memory=True,
collate_fn=dataset.collate_fn)
seen = 0
model.eval()
coco91class = coco80_to_coco91_class()
output_format = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets',
'Pre', 'Rec', 'mAP', 'F1')
precision, recall, f_1, mean_pre, mean_rec, mean_ap, mf1 = 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3)
json_dict, stats, aver_pre, ap_class = [], [], [], []
for batch_i, (imgs, targets, paths,
shapes) in enumerate(tqdm(dataloader, desc=output_format)):
targets = targets.to(device)
imgs = imgs.to(device) / 255.0
_, _, height, width = imgs.shape # batch size, channels, height, width
# Plot images with bounding boxes
if batch_i == 0 and not os.path.exists('test_batch0.jpg'):
plot_images(imgs=imgs,
targets=targets,
paths=paths,
fname='test_batch0.jpg')
with torch.no_grad():
inference_output, train_output = model(imgs)
if hasattr(model, 'hyp'): # if model has loss hyperparameters
loss += compute_loss(train_output, targets,
model)[1][:3].cpu() # GIoU, obj, cls
output = non_max_suppression(inference_output,
conf_thres=conf_thres,
nms_thres=nms_thres)
# Statistics per image
for i, pred in enumerate(output):
labels = targets[targets[:, 0] == i, 1:]
num_labels = len(labels)
target_class = labels[:, 0].tolist() if num_labels else []
seen += 1
if pred is None:
if num_labels:
stats.append(
([], torch.Tensor(), torch.Tensor(), target_class))
continue
# 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[i]).stem.split('_')[-1])
box = pred[:, :4].clone() # xyxy
scale_coords(imgs[i].shape[1:], box,
shapes[i][0]) # to original shape
box = xyxy2xywh(box) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for det_i, det in enumerate(pred):
json_dict.append({
'image_id':
image_id,
'category_id':
coco91class[int(det[6])],
'bbox':
[float(format(x, '.%gf' % 3)) for x in box[det_i]],
'score':
float(format(det[4], '.%gf' % 5))
})
# Clip boxes to image bounds
clip_coords(pred, (height, width))
# Assign all predictions as incorrect
correct = [0] * len(pred)
if num_labels:
detected = []
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
tbox[:, [0, 2]] *= width
tbox[:, [1, 3]] *= height
# Search for correct predictions
for j, (*pbox, _, _, pcls) in enumerate(pred):
# Break if all targets already located in image
if len(detected) == num_labels:
break
# Continue if predicted class not among image classes
if pcls.item() not in target_class:
continue
# Best iou, index between pred and targets
mask = (pcls == tcls_tensor).nonzero(
as_tuple=False).view(-1)
iou, best_iou = bbox_iou(pbox, tbox[mask]).max(0)
# If iou > threshold and class is correct mark as correct
if iou > iou_thres and mask[
best_iou] not in detected: # and pcls == target_class[bi]:
correct[j] = 1
detected.append(mask[best_iou])
# Append statistics (correct, conf, pcls, target_class)
stats.append(
(correct, pred[:, 4].cpu(), pred[:, 6].cpu(), target_class))
# Compute statistics
stats = [np.concatenate(x, 0) for x in list(zip(*stats))]
if len(stats):
precision, recall, aver_pre, f_1, ap_class = ap_per_class(*stats)
mean_pre, mean_rec, mean_ap, mf1 = precision.mean(), recall.mean(
), aver_pre.mean(), f_1.mean()
num_targets = np.bincount(
stats[3].astype(np.int64),
minlength=num_classes) # number of targets per class
else:
num_targets = torch.zeros(1)
# Print results
print_format = '%20s' + '%10.3g' * 6
print(print_format %
('all', seen, num_targets.sum(), mean_pre, mean_rec, mean_ap, mf1))
# Print results per class
if verbose and num_classes > 1 and stats:
for i, class_ in enumerate(ap_class):
print(print_format %
(names[class_], seen, num_targets[class_], precision[i],
recall[i], aver_pre[i], f_1[i]))
# Save JSON
if save_json and mean_ap and json_dict:
try:
img_ids = [
int(Path(x).stem.split('_')[-1]) for x in dataset.img_files
]
with open('results.json', 'w') as file:
json.dump(json_dict, file)
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocogt = COCO('data/coco/annotations/instances_val2017.json'
) # initialize COCO ground truth api
cocodt = cocogt.loadRes('results.json') # initialize COCO pred api
cocoeval = COCOeval(cocogt, cocodt, 'bbox')
cocoeval.params.imgIds = img_ids # [:32] # only evaluate these images
cocoeval.evaluate()
cocoeval.accumulate()
cocoeval.summarize()
mean_ap = cocoeval.stats[1] # update mAP to pycocotools mAP
except ImportError:
print(
'WARNING: missing dependency pycocotools from requirements.txt. Can not compute official COCO mAP.'
)
# Return results
maps = np.zeros(num_classes) + mean_ap
for i, class_ in enumerate(ap_class):
maps[class_] = aver_pre[i]
return (mean_pre, mean_rec, mean_ap, mf1,
*(loss / len(dataloader)).tolist()), maps
parser.add_argument(
"--checkpoint",
type=str,
help="directory which contains pretrained model (.pth)")
args = parser.parse_args()
assert args.task in ['completion', 'classification', 'segmentation']
# construct data loader
train_dataloader, test_dataloader = load_dataset(args)
model = Model(radius=args.radius,
bottleneck=args.bottleneck,
num_pts=args.num_pts,
num_pts_observed=args.num_pts_observed,
num_vote_train=args.num_vote_train,
num_contrib_vote_train=args.num_contrib_vote_train,
num_vote_test=args.num_vote_test,
is_vote=args.is_vote,
task=args.task)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = torch.nn.DataParallel(model)
# evaluation
if args.eval:
model_path = os.path.join(args.checkpoint)
if not os.path.isfile(model_path):
def fit(self,
x_train,
y_train,
x_test,
y_test,
trainable=True,
custom=True,
batch_size=32,
epochs=5):
self.create_name_fig(trainable, custom)
self.new_model()
label, indexes, counts_elements = np.unique(y_train,
return_counts=True,
return_index=True)
self.min_label = label[np.argmin(counts_elements)]
self.label = [y_train[index] for index in sorted(indexes)]
save_object(os.path.join(self.model_folder, "label.pickle"),
self.label)
save_object(os.path.join(self.model_folder, "min_label.pickle"),
self.min_label)
self.feature_extraction = PreprocessingTemplate(
self.config_dict, self.feature_extraction_folder)
corpus = np.hstack((x_train, x_test))
if self.config_dict["encoding"] == "embedding" and custom:
_, self.embedding_matrix = self.feature_extraction.fit_transform(
x_train)
else:
_ = self.feature_extraction.fit_transform(corpus)
x_train = self.feature_extraction.transform(x_train)
x_test = self.feature_extraction.transform(x_test)
self.encoder = OneHotEncoding(self.feature_extraction_folder)
self.encoder.fit(self.label)
y_test = self.encoder.transform(y_test)
self.n_out = len(label)
if self.type_model == 'LSTM':
params = {
'units_size1': 256,
'units_sizes': [128],
'dense_size1': 500,
'dense_size2': [300],
'dropout': 0.004
}
else:
params = {
'filter_sizes': [1, 2],
'nb_filter': 1024,
'dense_size1': 400,
'dense_size2': [250],
'dropout': 0.001
}
model = Model(self.type_model,
vocabulary_size=len(self.feature_extraction.vocabulary),
embedding_dim=self.config_dict['embedding_dim'],
embedding_matrix=self.embedding_matrix,
trainable=trainable,
params=params,
filename=self.model_folder)
for i in range(self.nb_dataset):
x_temp, y_temp = generate_data(x_train, y_train, self.methode,
self.threshold)
y_temp = self.encoder.transform(y_temp)
model.fit(x_train=x_temp,
y_train=y_temp,
x_test=x_test,
y_test=y_test,
batch_size=batch_size,
epochs=epochs,
i=i)
self.calculate_weight(x_test=x_test, y_test=y_test)
if self.cutoff == 'F1':
self.find_optimal_cutoff(x_test=x_test, y_test=y_test)
return
def main():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# parse the configurations
parser = argparse.ArgumentParser(
description='Additioal configurations for training',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--gpu_ids',
type=str,
default='-1',
help=
'IDs of GPUs to use (please use `,` to split multiple IDs); -1 means CPU only'
)
parser.add_argument('--tr_list',
type=str,
required=True,
help='Path to the list of training files')
parser.add_argument('--cv_file',
type=str,
required=True,
help='Path to the cross validation file')
parser.add_argument('--ckpt_dir',
type=str,
required=True,
help='Name of the directory to dump checkpoint')
parser.add_argument('--unit',
type=str,
required=True,
help='Unit of sample, can be either `seg` or `utt`')
parser.add_argument(
'--logging_period',
type=int,
default=1000,
help=
'Logging period (also the period of cross validation) represented by the number of iterations'
)
parser.add_argument('--time_log',
type=str,
default='',
help='Log file for timing batch processing')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Minibatch size')
parser.add_argument('--buffer_size',
type=int,
default=32,
help='Buffer size')
parser.add_argument('--segment_size',
type=float,
default=4.0,
help='Length of segments used for training (seconds)')
parser.add_argument('--segment_shift',
type=float,
default=1.0,
help='Shift of segments used for training (seconds)')
parser.add_argument('--lr',
type=float,
default=0.001,
help='Initial learning rate for training')
parser.add_argument('--lr_decay_factor',
type=float,
default=0.98,
help='Decaying factor of learning rate')
parser.add_argument('--lr_decay_period',
type=int,
default=2,
help='Decaying period of learning rate (epochs)')
parser.add_argument('--clip_norm',
type=float,
default=-1.0,
help='Gradient clipping (L2-norm)')
parser.add_argument('--max_n_epochs',
type=int,
default=100,
help='Maximum number of epochs')
parser.add_argument('--loss_log',
type=str,
default='loss.txt',
help='Filename of the loss log')
parser.add_argument('--resume_model',
type=str,
default='',
help='Existing model to resume training from')
args = parser.parse_args()
logger.info('Arguments in command:\n{}'.format(pprint.pformat(vars(args))))
model = Model()
model.train(args)
def main():
""" Train and test
:param opt: args
:param writer: tensorboard
:return:
"""
global opt
opt = parse()
arc = opt.arc
cfg = opt.cfg
teacher_cfg = opt.teacher_cfg
img_size = opt.img_size
epochs = opt.epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights
teacher_weights = opt.teacher_weights
multi_scale = opt.multi_scale
sparsity_training = opt.st
opt.weights = last if opt.resume else opt.weights
# Initial logging
logging.basicConfig(
format="%(message)s",
level=logging.INFO if opt.local_rank in [-1, 0] else logging.WARN)
# Train
logger.info(opt)
if opt.local_rank in [-1, 0]:
logger.info('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
writer = SummaryWriter()
# Hyperparameters
with open(opt.hyp) as f_hyp:
hyp = yaml.safe_load(f_hyp)
# data dict
with open(opt.data) as f_data:
data = yaml.safe_load(f_data)
# Distributed training initialize
device = select_device(opt.device)
if opt.local_rank != -1:
dist.init_process_group(init_method="env://", backend='nccl')
torch.cuda.set_device(opt.local_rank)
device = torch.device(f"cuda:{opt.local_rank}")
# world_size = torch.distributed.get_world_size()
init_seeds()
cuda = device.type != 'cpu'
torch.backends.cudnn.benchmark = True
if multi_scale:
img_size_min = round(img_size / 32 / 1.5) + 1
img_size_max = round(img_size / 32 * 1.5) - 1
img_size = img_size_max * 32 # initiate with maximum multi_scale size
logger.info(f'Using multi-scale {img_size_min * 32} - {img_size}')
train_path = data['train']
num_classes = int(data['num_classes']) # number of classes
# Load dataset
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
hyp=hyp,
rect=opt.rect)
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) if opt.local_rank != -1 else None
num_worker = os.cpu_count() // torch.cuda.device_count()
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=min([num_worker, batch_size, 8]),
shuffle=not (opt.rect or train_sampler),
sampler=train_sampler,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Load model
model = Model(cfg, img_size, arc=arc).to(device)
# Load teacher model
if teacher_cfg:
teacher_model = Model(teacher_cfg, img_size, arc).to(device)
# optimizer parameter groups
param_group0, param_group1 = [], []
for key, value in model.named_parameters():
if 'Conv2d.weight' in key:
param_group1.append(value)
else:
param_group0.append(value)
if opt.adam:
optimizer = optim.Adam(param_group0, lr=hyp['lr0'])
else:
optimizer = optim.SGD(param_group0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
# add param_group1 with weight_decay
optimizer.add_param_group({'params': param_group1, 'weight_decay': hyp['weight_decay']})
logger.info(f'Optimizer groups: {len(param_group1)} conv.weight, {len(param_group0)} other')
del param_group0, param_group1
start_epoch = 0
best_fitness = 0.
if weights.endswith('.pt'):
checkpoint = torch.load(weights, map_location=device)
state_dict = intersect_dicts(checkpoint['model'], model.state_dict())
model.load_state_dict(state_dict, strict=False)
print('loaded weights from', weights, '\n')
# load optimizer
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
best_fitness = checkpoint['best_fitness']
# load results
if checkpoint.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(checkpoint['training_results'])
# resume
if opt.resume:
start_epoch = checkpoint['epoch'] + 1
del checkpoint
elif len(weights) > 0:
# weights are 'yolov4.weights', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights)
logger.info(f'loaded weights from {weights}\n')
# Load teacher weights
if teacher_cfg:
if teacher_weights.endswith('.pt'):
teacher_model.load_state_dict(torch.load(teacher_weights, map_location=device)['model'])
elif teacher_weights.endswith('.weights'):
load_darknet_weights(teacher_model, teacher_weights)
else:
raise Exception('pls provide proper teacher weights for knowledge distillation')
if not mixed_precision:
teacher_model.eval()
logger.info('<......................using knowledge distillation....................>')
logger.info(f'teacher model: {teacher_weights}\n')
# Sparsity training
if opt.prune == 0:
_, _, prune_index = parse_module_index(model.module_dicts)
if sparsity_training:
logger.info('normal sparse training')
if mixed_precision:
if teacher_cfg:
[model, teacher_model], optimizer = amp.initialize([model, teacher_model], optimizer,
opt_level='O1', verbosity=1)
else:
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=1)
# SyncBatchNorm and distributed training
if cuda and opt.local_rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
model = model.to(device)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[opt.local_rank])
model.module_list = model.module.module_list
model.yolo_layers = model.module.yolo_layers
for index in prune_index:
bn_weights = gather_bn_weights(model.module_list, [index])
if opt.local_rank == 0:
writer.add_histogram('before_train_per_layer_bn_weights/hist', bn_weights.numpy(), index, bins='doane')
# Start training
model.num_classes = num_classes
model.arc = opt.arc
model.hyp = hyp
num_batch_size = len(dataloader)
# 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
results = (0, 0, 0, 0, 0, 0, 0)
start_train_time = time.time()
logger.info('Image sizes %d \n Starting training for %d epochs...', img_size, epochs)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
mean_losses = torch.zeros(4).to(device)
mean_soft_target = torch.zeros(1).to(device)
pbar = enumerate(dataloader)
logger.info(('\n %10s %10s %10s %10s %10s %10s %10s %10s'), 'Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total',
'targets', 'img_size')
if opt.local_rank in [-1, 0]:
pbar = tqdm(pbar, total=num_batch_size)
optimizer.zero_grad()
for i, (imgs, targets, _, _) in pbar: # batch -------------------------------------------------------------
num_integrated_batches = i + num_batch_size * epoch
# Adjust the learning rate
learning_rate = adjust_learning_rate(optimizer, num_integrated_batches, num_batch_size, hyp, epoch, epochs)
if i == 0 and opt.local_rank in [-1, 0]:
logger.info(f'learning rate: {learning_rate}')
imgs = imgs.to(device) / 255.0
targets = targets.to(device)
# Multi-Scale training
if multi_scale:
if num_integrated_batches / accumulate % 10 == 0:
img_size = random.randrange(img_size_min, img_size_max + 1) * 32
scale_factor = img_size / max(imgs.shape[2:])
if scale_factor != 1:
new_shape = [math.ceil(x * scale_factor / 32.) * 32 for x in imgs.shape[2:]]
imgs = F.interpolate(imgs, size=new_shape, mode='bilinear', align_corners=False)
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
# knowledge distillation
soft_target = 0
if teacher_cfg:
if mixed_precision:
with torch.no_grad():
output_teacher = teacher_model(imgs)
else:
_, output_teacher = teacher_model(imgs)
soft_target = distillation_loss(pred, output_teacher, model.num_classes, imgs.size(0))
loss += soft_target
# 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()
# Sparse the BN layer that needs pruning
if sparsity_training:
# bn_l1_regularization(model.module_list, opt.penalty_factor, cba_index, epoch, epochs)
bn_l1_regularization(model.module_list, opt.penalty_factor, prune_index, epoch, epochs)
# Accumulate gradient for x batches before optimizing
if num_integrated_batches % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
if opt.local_rank in [-1, 0]:
mean_losses = (mean_losses * i + loss_items) / (i + 1)
mean_soft_target = (mean_soft_target * i + soft_target) / (i + 1)
memory = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
description = ('%10s' * 2 + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1), '%.3gG' % memory, *mean_losses, mean_soft_target, img_size)
pbar.set_description(description)
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
# scheduler.step()
if opt.local_rank in [-1, 0]:
final_epoch = epoch + 1 == epochs
# Calculate mAP
if not (opt.notest or opt.nosave) or final_epoch:
with torch.no_grad():
results, _ = 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)
# Write epoch results
with open(results_file, 'a') as file:
# P, R, mAP, F1, test_losses=(GIoU, obj, cls)
file.write(description + '%10.3g' * 7 % results + '\n')
# Write Tensorboard results
if writer:
outputs = list(mean_losses) + list(results)
titles = ['GIoU', 'Objectness', 'Classification', 'Train loss',
'Precision', 'Recall', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification']
for output, title in zip(outputs, titles):
writer.add_scalar(title, output, epoch)
bn_weights = gather_bn_weights(model.module_list, prune_index)
writer.add_histogram('bn_weights/hist', bn_weights.numpy(), epoch, bins='doane')
# Update best mAP
fitness = results[2]
if fitness > best_fitness:
best_fitness = fitness
# Save training results
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save and opt.local_rank == 0:
with open(results_file, 'r') as file:
# Create checkpoint
checkpoint = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': file.read(),
'model': model.module.state_dict() if isinstance(
model, nn.parallel.DistributedDataParallel) else model.state_dict(),
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last checkpoint
torch.save(checkpoint, last)
# Save best checkpoint
if best_fitness == fitness:
torch.save(checkpoint, best)
# Delete checkpoint
del checkpoint
# end epoch -----------------------------------------------------------------------------------------------
# end training
if opt.local_rank in [-1, 0]:
if len(opt.name):
os.rename('results.txt', 'results_%s.txt' % opt.name)
plot_results() # save as results.png
print(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - start_train_time) / 3600:.3f} hours.\n')
if torch.cuda.device_count() > 1:
dist.destroy_process_group()
torch.cuda.empty_cache()
return results