def main():
args = parser.parse_args()
if os.path.isfile(args.out_h5data):
raise Exception("Experiment name " + args.out_h5data + " already exists.")
transforms = []
# TODO Encode body points also differentially to some joint not only hand wrt wrist
if args.dif_encoding:
transforms.append(WristDifference())
transforms.append(ChestDifference())
# TODO Change Normalization scheme to fixed bone dist
if args.normalize:
transforms.append(NormalizeFixedFactor(1280))
if args.predict == "right_index":
n_input = 12 + 17
n_output = 4
transforms.append(BuildIndexItem())
elif args.predict == "right_3fingers":
n_input = 12 + 9
n_output = 12
transforms.append(Build3fingerItem())
elif args.predict == "right_hand":
# n_input = 12
n_input = 8
n_output = 21
transforms.append(BuildRightHandItem())
else:
raise ValueError()
transforms = torchvision.transforms.Compose(transforms)
if "Text" in args.model:
dataset = TextPoseH5Dataset(args.valid_h5data, args.valid_textdata, args.max_frames, transforms, selection=args.frames_selection,
use_rand_tokens=args.rand_tokens)
else:
dataset = FastPoseDataset(args.data, args.max_frames, transforms)
loader = DataLoader(dataset, batch_size=128, collate_fn=collate_function_h5)
if args.model == "Conv":
model = ConvModel(args.conv_channels, "ReLU", pos_emb=args.conv_pos_emb)
elif args.model == "ConvTransformerEncoder":
model = ConvTransformerEncoder(args, 21 * 2)
elif args.model == "TransformerEnc":
model = TransformerEnc(ninp=12*2, nhead=4, nhid=128, nout=21*2,
nlayers=4, dropout=args.transformer_dropout)
elif args.model == "TextPoseTransformer":
model = TextPoseTransformer(n_tokens=1000, n_joints=n_input, joints_dim=2, nhead=4,
nhid=128, nout=n_output*2, n_enc_layers=4, n_dec_layers=4,
dropout=args.transformer_dropout)
else:
raise ValueError()
model.load_state_dict(torch.load(args.model_checkpoint))
infer_utterance_h5(model, loader, args)
def main():
args = parser.parse_args()
if os.path.isdir(args.output_folder):
raise Exception("Experiment name " + args.output_folder +
" already exists.")
os.mkdir(args.output_folder)
with open(args.output_folder + "/args.pckl", "wb") as f:
pickle.dump(args, f)
transform = None
if args.normalize:
transform = NormalizeFixedFactor(1280)
# utterance_dict = build_dataset_structure(args.utterance_folder)
# metadata_structure = [utterance_dict]
if "Text" in args.model:
dataset = FastTextPoseDataset(args.data,
args.max_frames,
transform,
use_rand_tokens=args.rand_tokens)
else:
dataset = FastPoseDataset(args.data, args.max_frames, transform)
loader = DataLoader(dataset, batch_size=1, collate_fn=collate_function)
if args.model == "Conv":
model = ConvModel(args.conv_channels,
activation="ReLU",
pos_emb=args.conv_pos_emb)
elif args.model == "TransformerEncoder":
model = TransformerEncoder(args, 100)
elif args.model == "ConvTransformerEncoder":
model = ConvTransformerEncoder(args, 21 * 2)
elif args.model == "TransformerEnc":
model = TransformerEnc(ninp=12 * 2,
nhead=4,
nhid=100,
nout=21 * 2,
nlayers=4,
dropout=0.0)
elif args.model == "TextPoseTransformer":
model = TextPoseTransformer(n_tokens=1000,
n_joints=12,
joints_dim=2,
nhead=4,
nhid=128,
nout=21 * 2,
n_enc_layers=4,
n_dec_layers=4,
dropout=args.transformer_dropout)
else:
raise ValueError()
model.load_state_dict(torch.load(args.model_checkpoint))
infer_utterance(model, loader, args)
if __name__ == '__main__':
args = parser.parse_args()
print(args)
if args.resume:
assert (bool(args.exp))
with open("%s/args.pckl" % args.exp, "rb") as f:
args = pickle.load(f)
args.resume = True
pass
transform = None
# TODO Change Normalization scheme to fixed bone dist
if args.normalize:
transform = NormalizeFixedFactor(1280)
train_dataset = FastPoseDataset(args.train_data, args.max_frames,
transform)
valid_dataset = FastPoseDataset(args.valid_data, args.max_frames,
transform)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
collate_fn=collate_function)
valid_dataloader = DataLoader(valid_dataset,
batch_size=args.batch_size,
collate_fn=collate_function)
if args.model == "Conv":
model = ConvModel(args.conv_channels,
with open("%s/args.pckl" % args.exp, "rb") as f:
args = pickle.load(f)
args.resume = True
# try:
# _ = args.rand_tokens
# except:
# args.rand_tokens = True
transforms = []
# TODO Encode body points also differentially to some joint not only hand wrt wrist
if args.dif_encoding:
transforms.append(WristDifference())
transforms.append(ChestDifference())
# TODO Change Normalization scheme to fixed bone dist
if args.normalize:
transforms.append(NormalizeFixedFactor(1280))
if args.predict == "right_index":
n_input = 12 + 17
n_output = 4
transforms.append(BuildIndexItem())
elif args.predict == "right_3fingers":
n_input = 12 + 9
n_output = 12
transforms.append(Build3fingerItem())
elif args.predict == "right_hand":
n_input = 8
n_output = 21
transforms.append(BuildRightHandItem())
else:
raise ValueError()