def main():
parser = argparse.ArgumentParser(description="Evaluates a fluid network")
parser.add_argument("--trainscript",
type=str,
required=True,
help="The python training script.")
parser.add_argument(
"--checkpoint_iter",
type=int,
required=False,
help="The checkpoint iteration. The default is the last checkpoint.")
parser.add_argument("--frame-skip",
type=int,
default=5,
help="The frame skip. Default is 5.")
args = parser.parse_args()
global trainscript
module_name = os.path.splitext(os.path.basename(args.trainscript))[0]
sys.path.append('.')
trainscript = importlib.import_module(module_name)
# get a list of checkpoints
checkpoint_files = glob(
os.path.join(trainscript.train_dir, 'checkpoints', 'ckpt-*.index'))
all_checkpoints = sorted([
(int(re.match('.*ckpt-(\d+)\.index',
x).group(1)), os.path.splitext(x)[0])
for x in checkpoint_files
])
# select the checkpoint
if args.checkpoint_iter is not None:
checkpoint = dict(all_checkpoints)[args.checkpoint_iter]
else:
checkpoint = all_checkpoints[-1]
output_path = args.trainscript + '_eval_{}.json'.format(checkpoint[0])
if os.path.isfile(output_path):
print('Printing previously computed results for :', checkpoint)
fluid_errors = FluidErrors()
fluid_errors.load(output_path)
else:
print('evaluating :', checkpoint)
fluid_errors = FluidErrors()
eval_checkpoint(checkpoint[1], trainscript.val_files, fluid_errors,
args)
fluid_errors.save(output_path)
print_errors(fluid_errors)
return 0
def main():
parser = argparse.ArgumentParser(
description="Evaluates a fluid network",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--trainscript",
type=str,
required=True,
help="The python training script.")
parser.add_argument(
"--checkpoint_iter",
type=int,
required=False,
help="The checkpoint iteration. The default is the last checkpoint.")
parser.add_argument(
"--weights",
type=str,
required=False,
help="If set uses the specified weights file instead of a checkpoint.")
parser.add_argument("--frame-skip",
type=int,
default=5,
help="The frame skip. Default is 5.")
parser.add_argument("--device",
type=str,
default="cuda",
help="The device to use. Applies only for torch.")
args = parser.parse_args()
global trainscript
module_name = os.path.splitext(os.path.basename(args.trainscript))[0]
sys.path.append('.')
trainscript = importlib.import_module(module_name)
if args.weights is not None:
print('evaluating :', args.weights)
output_path = args.weights + '_eval.json'
if os.path.isfile(output_path):
print('Printing previously computed results for :', args.weights)
fluid_errors = FluidErrors()
fluid_errors.load(output_path)
else:
fluid_errors = FluidErrors()
eval_checkpoint(args.weights, trainscript.val_files, fluid_errors,
args)
fluid_errors.save(output_path)
else:
# get a list of checkpoints
# tensorflow checkpoints
checkpoint_files = glob(
os.path.join(trainscript.train_dir, 'checkpoints', 'ckpt-*.index'))
# torch checkpoints
checkpoint_files.extend(
glob(os.path.join(trainscript.train_dir, 'checkpoints',
'ckpt-*.pt')))
all_checkpoints = sorted([
(int(re.match('.*ckpt-(\d+)\.(pt|index)', x).group(1)), x)
for x in checkpoint_files
])
# select the checkpoint
if args.checkpoint_iter is not None:
checkpoint = dict(all_checkpoints)[args.checkpoint_iter]
else:
checkpoint = all_checkpoints[-1]
output_path = args.trainscript + '_eval_{}.json'.format(checkpoint[0])
if os.path.isfile(output_path):
print('Printing previously computed results for :', checkpoint)
fluid_errors = FluidErrors()
fluid_errors.load(output_path)
else:
print('evaluating :', checkpoint)
fluid_errors = FluidErrors()
eval_checkpoint(checkpoint[1], trainscript.val_files, fluid_errors,
args)
fluid_errors.save(output_path)
print_errors(fluid_errors)
return 0