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Rename test.py to val.py (#4000)

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Glenn Jocher authored and GitHub committed 2021-07-14 15:43:54 +02:00
1 parent 8ee9fd1505
commit 720aaa65c8
11 files changed
+77 -78
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train.py
+34 -34
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@@ -32,7 +32,7 @@ from tqdm import tqdm
FILE = Path(__file__).absolute()
sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
import test # for end-of-epoch mAP
import val # for end-of-epoch mAP
from models.experimental import attempt_load
from models.yolo import Model
from utils.autoanchor import check_anchors
@@ -57,9 +57,9 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
opt,
device,
):
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, notest, nosave, workers, = \
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, = \
opt.save_dir, opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
opt.resume, opt.notest, opt.nosave, opt.workers
opt.resume, opt.noval, opt.nosave, opt.workers
# Directories
save_dir = Path(save_dir)
@@ -129,7 +129,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
with torch_distributed_zero_first(RANK):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
val_path = data_dict['val']
# Freeze
freeze = [] # parameter names to freeze (full or partial)
@@ -207,7 +207,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# 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
imgsz, imgsz_val = [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:
@@ -231,8 +231,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# Process 0
if RANK in [-1, 0]:
testloader = create_dataloader(test_path, imgsz_test, batch_size // WORLD_SIZE * 2, gs, single_cls,
hyp=hyp, cache=opt.cache_images and not notest, rect=True, rank=-1,
valloader = create_dataloader(val_path, imgsz_val, batch_size // WORLD_SIZE * 2, gs, single_cls,
hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1,
workers=workers,
pad=0.5, prefix=colorstr('val: '))[0]
@@ -276,7 +276,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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'
logger.info(f'Image sizes {imgsz} train, {imgsz_val} val\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
@@ -384,20 +384,20 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# mAP
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
final_epoch = epoch + 1 == epochs
if not notest or final_epoch: # Calculate mAP
if not noval or final_epoch: # Calculate mAP
wandb_logger.current_epoch = epoch + 1
results, maps, _ = test.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=is_coco and final_epoch,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss)
results, maps, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz_val,
model=ema.ema,
single_cls=single_cls,
dataloader=valloader,
save_dir=save_dir,
save_json=is_coco and final_epoch,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss)
# Write
with open(results_file, 'a') as f:
@@ -454,15 +454,15 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
if not evolve:
if is_coco: # COCO dataset
for m in [last, best] if best.exists() else [last]: # speed, mAP tests
results, _, _ = test.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz_test,
model=attempt_load(m, device).half(),
single_cls=single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True,
plots=False)
results, _, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz_val,
model=attempt_load(m, device).half(),
single_cls=single_cls,
dataloader=valloader,
save_dir=save_dir,
save_json=True,
plots=False)
# Strip optimizers
for f in last, best:
@@ -486,11 +486,11 @@ def parse_opt(known=False):
parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, val] image sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
@@ -538,7 +538,7 @@ def main(opt):
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, val)
opt.name = 'evolve' if opt.evolve else opt.name
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
@@ -597,7 +597,7 @@ def main(opt):
if 'anchors' not in hyp: # anchors commented in hyp.yaml
hyp['anchors'] = 3
assert LOCAL_RANK == -1, 'DDP mode not implemented for --evolve'
opt.notest, opt.nosave = True, True # only test/save final epoch
opt.noval, opt.nosave = True, True # only val/save final epoch
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
if opt.bucket: