parser.add_argument('--skip_validation', action='store_true')
parser.add_argument(
'--fp16',
action='store_true',
help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
parser.add_argument(
'--fp16_scale',
type=float,
default=1024.,
help=
'Loss scaling, positive power of 2 values can improve fp16 convergence.'
)
tools.add_arguments_for_module(parser,
models,
argument_for_class='model',
default='FlowNet2S')
tools.add_arguments_for_module(parser,
losses,
argument_for_class='loss',
default='MultiScale')
tools.add_arguments_for_module(parser,
torch.optim,
argument_for_class='optimizer',
default='Adam',
skip_params=['params'])
tools.add_arguments_for_module(
parser,
parser.add_argument('--blocktest', action='store_true')
parser.add_argument(
'--fp16',
action='store_true',
help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
parser.add_argument(
'--fp16_scale',
type=float,
default=1024.,
help=
'Loss scaling, positive power of 2 values can improve fp16 convergence.'
)
tools.add_arguments_for_module(parser,
models,
argument_for_class='model',
default='FlowNet2')
tools.add_arguments_for_module(parser,
losses,
argument_for_class='loss',
default='L1Loss')
tools.add_arguments_for_module(parser,
torch.optim,
argument_for_class='optimizer',
default='Adam',
skip_params=['params'])
tools.add_arguments_for_module(
parser,
required=False,
default='./pretrained/FlowNet2_checkpoint.pth.tar',
type=str,
help='path to latest checkpoint')
parser.add_argument(
'--hdf5_input_path',
required=True,
type=str,
help='path to HDF5 file containing images to extract flow')
parser.add_argument('--hdf5_frames_dset',
required=False,
default='frames_',
type=str,
help='name of HDF5 dataset storing the video frames')
tools.add_arguments_for_module(parser,
models,
argument_for_class='model',
default='FlowNet2')
######################################################################################
######################################################################################
args = parser.parse_args()
args.model_class = tools.module_to_dict(models)[args.model]
args.cuda = not args.no_cuda and torch.cuda.is_available()
################################################################################
################################################################################
# Extract all the images into temp directory
tmp_image_dir = '/tmp/flownet2/'
cortex.utils.mkdirs(tmp_image_dir)
