def init_encoder(self):
dummy_batch = torch.zeros((2, 3, self.hparams.patch_size, self.hparams.patch_size))
encoder_name = self.hparams.encoder_name
if encoder_name == "cpc_encoder":
return cpc_resnet101(dummy_batch)
return torchvision_ssl_encoder(encoder_name, return_all_feature_maps=self.hparams.task == "amdim")
def init_encoder(self):
dummy_batch = torch.zeros((2, 3, self.hparams.patch_size, self.hparams.patch_size))
encoder_name = self.hparams.encoder
if encoder_name == 'cpc_encoder':
return CPCResNet101(dummy_batch)
else:
return torchvision_ssl_encoder(encoder_name, return_all_feature_maps=self.hparams.task == 'amdim')
def __init__(self, encoder=None):
super().__init__()
if encoder is None:
encoder = torchvision_ssl_encoder('resnet50')
# Encoder
self.encoder = encoder
# Projector
self.projector = MLP()
# Predictor
self.predictor = MLP(input_dim=256)
def __init__(self, encoder='resnet50', encoder_out_dim=2048, projector_hidden_size=4096, projector_out_dim=256):
super().__init__()
if isinstance(encoder, str):
encoder = torchvision_ssl_encoder(encoder)
# Encoder
self.encoder = encoder
# Projector
self.projector = MLP(encoder_out_dim, projector_hidden_size, projector_out_dim)
# Predictor
self.predictor = MLP(projector_out_dim, projector_hidden_size, projector_out_dim)
def __init__(
self,
encoder: Optional[nn.Module] = None,
input_dim: int = 2048,
hidden_size: int = 4096,
output_dim: int = 256,
) -> None:
super().__init__()
if encoder is None:
encoder = torchvision_ssl_encoder('resnet50')
# Encoder
self.encoder = encoder
# Projector
self.projector = MLP(input_dim, hidden_size, output_dim)
# Predictor
self.predictor = MLP(output_dim, hidden_size, output_dim)
def init_encoder(self):
dummy_batch = torch.zeros((2, self.hparams.image_channels, self.hparams.image_height,
self.hparams.image_height))
encoder_name = self.hparams.encoder
if encoder_name == 'amdim_encoder':
encoder = AMDIMEncoder(
dummy_batch,
num_channels=self.hparams.image_channels,
encoder_feature_dim=self.hparams.encoder_feature_dim,
embedding_fx_dim=self.hparams.embedding_fx_dim,
conv_block_depth=self.hparams.conv_block_depth,
encoder_size=self.hparams.image_height,
use_bn=self.hparams.use_bn
)
encoder.init_weights()
return encoder
else:
return torchvision_ssl_encoder(encoder_name, return_all_feature_maps=True)
def init_encoder(self):
encoder_name = self.hparams.encoder
return torchvision_ssl_encoder(
encoder_name, return_all_feature_maps=self.hparams.task == "amdim")
