type=float,
default=1.0,
help="Margin paramater for triplet loss")
parser.add_argument("--input-length",
"-L",
type=int,
default=2048,
help="number of input frames",
choices=[1024, 2048])
args = parser.parse_args()
print(args)
# Setup loaders, models and loss
train_loader = torch.utils.data.DataLoader(dataloaders.ImageCaptionDataset(
args.data_train, audio_conf={'target_length': args.input_length}),
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(dataloaders.ImageCaptionDataset(
args.data_val,
audio_conf={'target_length': args.input_length},
image_conf={'center_crop': True}),
batch_size=args.batch_size,
shuffle=False,
num_workers=8,
pin_memory=True)
audio_model = models.DaveNet(embedding_dim=args.input_length)
help="audio model architecture", choices=["Davenet"])
parser.add_argument("--image-model", type=str, default="VGG16",
help="image model architecture", choices=["VGG16"])
parser.add_argument("--pretrained-image-model", action="store_true",
dest="pretrained_image_model", help="Use an image network pretrained on ImageNet")
parser.add_argument("--margin", type=float, default=1.0, help="Margin paramater for triplet loss")
parser.add_argument("--input-length", "-L", type=int, default=2048,
help="number of input frames", choices=[1024, 2048])
args = parser.parse_args()
print(args)
# Setup loaders, models and loss
train_loader = torch.utils.data.DataLoader(
dataloaders.ImageCaptionDataset(args.data_train, audio_conf={'target_length': args.input_length}, image_conf={'center_crop': True}),
batch_size=args.batch_size, shuffle=True, num_workers=8, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
dataloaders.ImageCaptionDataset(args.data_val, audio_conf={'target_length': args.input_length}, image_conf={'center_crop': True}),
batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
audio_model = models.ConvX3AudioNet(input_length=args.input_length)
image_model = models.VGG16()
if bool(args.train_path):
audio_model.load_state_dict(torch.load("%s/models/best_audio_model.pth" % args.train_path), strict=False)
criterion = DotLoss()
# Set up the optimizer
args.cuda = not args.no_cuda and torch.cuda.is_available()
resume = args.resume
if args.resume:
assert(bool(args.exp_dir))
with open("%s/args.pkl" % args.exp_dir, "rb") as f:
args = pickle.load(f)
args.resume = resume
print(args)
para = {"num_workers":8, "pin_memory":True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
dataloaders.ImageCaptionDataset(args.data_train),
batch_size=args.batch_size, shuffle=True, **para)
val_loader = torch.utils.data.DataLoader(
dataloaders.ImageCaptionDataset(args.data_val, image_conf={'center_crop':True}),
batch_size=args.batch_size, shuffle=False, **para)
audio_model = models.Davenet()
image_model = models.VGG16(pretrained=args.pretrained_image_model)
if not bool(args.exp_dir):
print("exp_dir not specified, automatically creating one...")
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
args.exp_dir = "exp/Data-%s/AudioModel-%s_ImageModel-%s_Optim-%s_LR-%s_Epochs-%s_%s" % (
os.path.basename(args.data_train), args.audio_model, args.image_model, args.optim,
help="matchmap similarity function",
choices=["SISA", "MISA", "SIMA"])
args = parser.parse_args()
resume = args.resume
if args.resume:
assert (bool(args.exp_dir))
with open("%s/args.pkl" % args.exp_dir, "rb") as f:
args = pickle.load(f)
args.resume = resume
print(args)
train_loader = torch.utils.data.DataLoader(dataloaders.ImageCaptionDataset(
args.data_train),
batch_size=args.batch_size,
shuffle=True,
num_workers=8,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(dataloaders.ImageCaptionDataset(
args.data_val, image_conf={'center_crop': True}),
batch_size=args.batch_size,
shuffle=False,
num_workers=8,
pin_memory=True)
# Pick right model based on string input
models_module = __import__("models")
audio_class = getattr(models_module, args.audio_model)