def __init__(self, ndf, n_classes, n_rkhs, tclip=20.,
n_depth=3, encoder_size=32, use_bn=False):
super(Model, self).__init__()
self.hyperparams = {
'ndf': ndf,
'n_classes': n_classes,
'n_rkhs': n_rkhs,
'tclip': tclip,
'n_depth': n_depth,
'encoder_size': encoder_size,
'use_bn': use_bn
}
# self.n_rkhs = n_rkhs
self.tasks = ('1t5', '1t7', '5t5', '5t7', '7t7')
dummy_batch = torch.zeros((2, 3, encoder_size, encoder_size))
# encoder that provides multiscale features
self.encoder = Encoder(dummy_batch, num_channels=3, ndf=ndf,
n_rkhs=n_rkhs, n_depth=n_depth,
encoder_size=encoder_size, use_bn=use_bn)
rkhs_1, rkhs_5, _ = self.encoder(dummy_batch)
# convert for multi-gpu use
self.encoder = nn.DataParallel(self.encoder)
# configure hacky multi-gpu module for infomax costs
self.g2l_loss = LossMultiNCE(tclip=tclip)
# configure modules for classification with self-supervised features
self.evaluator = Evaluator(n_classes, ftr_1=rkhs_1)
# gather lists of self-supervised and classifier modules
self.info_modules = [self.encoder.module, self.g2l_loss]
self.class_modules = [self.evaluator]
def __init__(
self,
ndf,
n_classes,
n_rkhs,
tclip=20.0,
n_depth=3,
encoder_size=32,
use_bn=False,
):
super(Model, self).__init__()
self.hyperparams = {
"ndf": ndf,
"n_classes": n_classes,
"n_rkhs": n_rkhs,
"tclip": tclip,
"n_depth": n_depth,
"encoder_size": encoder_size,
"use_bn": use_bn,
}
# self.n_rkhs = n_rkhs
self.tasks = ("1t5", "1t7", "5t5", "5t7", "7t7")
dummy_batch = torch.zeros((2, 3, encoder_size, encoder_size))
# encoder that provides multiscale features
self.encoder = Encoder(
dummy_batch,
num_channels=3,
ndf=ndf,
n_rkhs=n_rkhs,
n_depth=n_depth,
encoder_size=encoder_size,
use_bn=use_bn,
)
rkhs_1, rkhs_5, _ = self.encoder(dummy_batch)
# convert for multi-gpu use
self.encoder = nn.DataParallel(self.encoder)
# configure hacky multi-gpu module for infomax costs
self.g2l_loss = LossMultiNCE(tclip=tclip)
# configure modules for classification with self-supervised features
self.evaluator = Evaluator(n_classes, ftr_1=rkhs_1)
# gather lists of self-supervised and classifier modules
self.info_modules = [self.encoder.module, self.g2l_loss]
self.class_modules = [self.evaluator]
def __init__(self, ndf, n_classes, n_rkhs, tclip=20.,
n_depth=3, encoder_size=32, use_bn=False, decoder_training = False): # Nawid - Added parameter to control the auxillary loss
super(Model, self).__init__()
self.hyperparams = {
'ndf': ndf,
'n_classes': n_classes,
'n_rkhs': n_rkhs,
'tclip': tclip,
'n_depth': n_depth,
'encoder_size': encoder_size,
'use_bn': use_bn
}
self.decoder_training = decoder_training # Nawid - Used to control whether the train the decoder
# self.n_rkhs = n_rkhs
self.tasks = ('1t5', '1t7', '5t5', '5t7', '7t7') # Nawid - I believe these are the features from different parts of the layer
dummy_batch = torch.zeros((2, 3, encoder_size, encoder_size)) # Nawid - Used to configure the module
# encoder that provides multiscale features
self.encoder = Encoder(dummy_batch, num_channels=3, ndf=ndf,
n_rkhs=n_rkhs, n_depth=n_depth,
encoder_size=encoder_size, use_bn=use_bn) # Nawid - Used to make the encoder
rkhs_1, rkhs_5, _ = self.encoder(dummy_batch) # Nawid -Used to make rkhs_1 which is used to provide the dimensions for the evaluator
# convert for multi-gpu use
self.encoder = nn.DataParallel(self.encoder)
# configure hacky multi-gpu module for infomax costs
self.g2l_loss = LossMultiNCE(tclip=tclip) # Nawid - Loss function
# configure modules for classification with self-supervised features
self.evaluator = Evaluator(n_classes, ftr_1=rkhs_1)
# Nawid - Configure module for decoder with self-supervised features
self.decoder = Decoder(ftr_1 =rkhs_1) # Nawid - This instantiates the decoder
# gather lists of self-supervised and classifier modules
self.info_modules = [self.encoder.module, self.g2l_loss]
self.class_modules = [self.evaluator]
# Nawid - Added module for decoder
self.decoder_modules = [self.decoder] # Nawid - Used in
def __init__(self,
ndf,
n_classes,
n_rkhs,
tclip=20.,
n_depth=3,
use_bn=False,
dataset=Dataset.STL10):
super(Model, self).__init__()
self.n_rkhs = n_rkhs
self.tasks = ('1t5', '1t7', '5t5', '5t7', '7t7')
encoder_size = self._get_encoder_size(dataset)
dummy_batch = torch.zeros((2, 3, encoder_size, encoder_size))
# encoder that provides multiscale features
self.encoder = Encoder(dummy_batch,
nc=3,
ndf=ndf,
n_rkhs=n_rkhs,
n_depth=n_depth,
encoder_size=encoder_size,
use_bn=use_bn)
rkhs_1, rkhs_5, _ = self.encoder(dummy_batch)
# convert for multi-gpu use
self.encoder = nn.DataParallel(self.encoder)
# configure hacky multi-gpu module for infomax costs
self.g2l_loss = LossMultiNCE(tclip=tclip)
# configure modules for classification with self-supervised features
self.evaluator = Evaluator(n_classes, ftr_1=rkhs_1)
# gather lists of self-supervised and classifier modules
self.info_modules = [self.encoder.module, self.g2l_loss]
self.class_modules = [self.evaluator]