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@Lechtr
Lechtr committed Mar 23, 2021
1 parent 174ce8a commit bfa2cd6
Showing 1 changed file with 84 additions and 79 deletions.
163 changes: 84 additions & 79 deletions train.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@

import argparse
import logging
import math
Expand Down Expand Up @@ -33,6 +34,7 @@
from utils.loss import ComputeLoss
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
from utils.wandb_logging.wandb_utils import WandbLogger, resume_and_get_id, check_wandb_config_file



Expand All @@ -49,7 +51,7 @@
logger = logging.getLogger(__name__)


def train(hyp, opt, device, tb_writer=None, wandb=None):
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
Expand All @@ -73,10 +75,17 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
is_coco = opt.data.endswith('coco.yaml')

# Logging- Doing this before checking the dataset. Might update data_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)
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
loggers = {'wandb': wandb_logger.wandb} # loggers dict
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
Expand All @@ -95,6 +104,10 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
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
train_path = data_dict['train']
test_path = data_dict['val']

# Freeze
freeze = [] # parameter names to freeze (full or partial)
Expand Down Expand Up @@ -138,17 +151,6 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)

# Logging
if rank in [-1, 0] and wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(config=opt, resume="allow",
project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
name=save_dir.stem,
entity=opt.entity,
group=opt.wandb_group,
id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
loggers = {'wandb': wandb} # loggers dict

# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None

Expand Down Expand Up @@ -363,9 +365,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
# 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:
wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')
if x.exists()]}, commit=False)
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 ----------------------------------------------------------------------------------------------------
Expand All @@ -380,17 +382,19 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
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(opt.data,
batch_size=batch_size * 2,
wandb_logger.current_epoch = epoch + 1
results, maps, times = test.test(data_dict,
batch_size=total_batch_size,
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,
log_imgs=opt.log_imgs if wandb else 0,
compute_loss=compute_loss)
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco)

# Write
with open(results_file, 'a') as f:
Expand All @@ -406,8 +410,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
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:
wandb.log({tag: x}, step=epoch, commit=tag == tags[-1]) # W&B
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, mAP@.5, mAP@.5-.95]
Expand All @@ -432,36 +436,29 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
'wandb_id': wandb_run.id if wandb else None}
'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
wandb_logger.end_epoch(best_result=best_fitness == fi)

# end epoch ----------------------------------------------------------------------------------------------------
# end training

if rank in [-1, 0]:
# Strip optimizers
final = best if best.exists() else last # final model
for f in last, best:
if f.exists():
strip_optimizer(f)
if opt.bucket:
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload

# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
if wandb_logger.wandb:
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
if (save_dir / f).exists()]})
if opt.log_artifacts:
wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem)

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
Expand All @@ -476,13 +473,24 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
dataloader=testloader,
save_dir=save_dir,
save_json=True,
plots=False)
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: # 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'])
else:
dist.destroy_process_group()

wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
wandb_logger.finish_run()
return results


Expand Down Expand Up @@ -529,15 +537,17 @@ def train_ray_tune(config):
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model')
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
parser.add_argument('--project', default='runs/train', help='save to project/name')
parser.add_argument('--entity', default=None, help='W&B entity')
parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--quad', action='store_true', help='quad dataloader, experimental for >640 image sizes')
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')

parser.add_argument('--wandb-group', type=str, default='no_group', help='JF: grouping for wandb logging')
parser.add_argument('--raytune', action='store_true', help='JF: optimize with ray tune')
Expand All @@ -553,7 +563,8 @@ def train_ray_tune(config):
check_requirements()

# Resume
if opt.resume: # resume an interrupted run
wandb_run = resume_and_get_id(opt)
if opt.resume and not wandb_run: # resume an interrupted run
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
apriori = opt.global_rank, opt.local_rank
Expand Down Expand Up @@ -586,12 +597,6 @@ def train_ray_tune(config):

# Train
logger.info(opt)
try:
import wandb
except ImportError:
wandb = None
prefix = colorstr('wandb: ')
logger.info(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
if not opt.evolve:
tb_writer = None # init loggers
if opt.global_rank in [-1, 0]:
Expand All @@ -600,34 +605,34 @@ def train_ray_tune(config):



### Joshua Friedrich
### Ray Tune
if opt.raytune:
print("raytune")
global_hyp = hyp
global_opt = opt
global_device = device
global_tb_writer = tb_writer
global_wandb = wandb
### Joshua Friedrich
### Ray Tune
if opt.raytune:
print("raytune")
global_hyp = hyp
global_opt = opt
global_device = device
global_tb_writer = tb_writer
# global_wandb = wandb

train_ray_tune({
"alpha": tune.grid_search([0.001, 0.01, 0.1]),
"beta": tune.choice([1, 2, 3])
})

exit(5)

analysis = tune.run(
train_ray_tune,
metric=fitness,
mode=max,
config={
train_ray_tune({
"alpha": tune.grid_search([0.001, 0.01, 0.1]),
"beta": tune.choice([1, 2, 3])
},
fail_fast=True)
else:
train(hyp, opt, device, tb_writer, wandb)
})

exit(5)

analysis = tune.run(
train_ray_tune,
metric=fitness,
mode=max,
config={
"alpha": tune.grid_search([0.001, 0.01, 0.1]),
"beta": tune.choice([1, 2, 3])
},
fail_fast=True)
else:
train(hyp, opt, device, tb_writer)



Expand Down Expand Up @@ -703,7 +708,7 @@ def train_ray_tune(config):
hyp[k] = round(hyp[k], 5) # significant digits

# Train mutation
results = train(hyp.copy(), opt, device, wandb=wandb)
results = train(hyp.copy(), opt, device)

# Write mutation results
print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
Expand Down

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