def __init__(self):
super().__init__(dataset='paired_mnist')
self.mine_enc = MnistCNNEncoder(out_dim=self.z_dim)
self.mine_fc = FCDiscriminator(in_dim=2 * self.z_dim)
# classifier to measure accuracy of generation
self.mnist_clf = MNISTClassifier()
self.mnist_clf.load_state_dict(
torch.load('../saved_models/mnist_clf.ckpt'))
self.mnist_clf.eval()
self.mnist_clf.freeze()
def __init__(self, dataset='paired_mnist'):
super(BiAAE, self).__init__()
self.dataset = dataset
if self.dataset == 'paired_mnist':
self.z_dim = 16
self.joint_dim = 4
self.loss_rec_lambda_x = 10
self.loss_rec_lambda_y = 10
self.loss_normal_lambda = 0.3
self.loss_indep_lambda_x = 1
self.loss_indep_lambda_y = 1
self.loss_mse_lambda = 0.1
self.discr_steps = 1
self.gen_steps = 1
self.enc_x = MnistCNNEncoder(out_dim=self.z_dim)
self.enc_y = MnistCNNEncoder(out_dim=self.z_dim)
self.dec_x = MnistCNNDecoder(in_dim=self.z_dim)
self.dec_y = MnistCNNDecoder(in_dim=self.z_dim)
self.discr = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
self.discr_indep_x = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
self.discr_indep_y = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
elif self.dataset == 'lincs_rnn':
self.z_dim = 20
self.joint_dim = 10
self.loss_rec_lambda_x = 5
self.loss_rec_lambda_y = 1
self.loss_normal_lambda = 0.5
self.loss_indep_lambda_x = 0.5
self.loss_indep_lambda_y = 0.5
self.loss_mse_lambda = 0.5
self.discr_steps = 3
self.gen_steps = 1
rnn_1 = RNNEncoder(out_dim=88)
rnn_1.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
self.enc_x = FinetunedEncoder(rnn_1, out_dim=self.z_dim)
self.enc_y = ExprDiffEncoder(out_dim=self.z_dim)
rnn_2 = RNNDecoder(in_dim=44)
rnn_2.load_state_dict(
torch.load('../saved_models/rnn_dec.ckpt',
map_location='cuda:1'))
self.dec_x = FinetunedDecoder(rnn_2, in_dim=self.z_dim)
self.dec_y = ExprDiffDecoder(in_dim=self.z_dim)
self.discr = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
self.discr_indep_x = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
self.discr_indep_y = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
elif self.dataset == 'lincs_rnn_reverse':
self.z_dim = 20
self.joint_dim = 10
self.loss_rec_lambda_x = 1
self.loss_rec_lambda_y = 0.2
self.loss_normal_lambda = 0.5
self.loss_indep_lambda_x = 0.5
self.loss_indep_lambda_y = 0.5
self.loss_mse_lambda = 0.5
self.discr_steps = 3
self.gen_steps = 1
rnn_1 = RNNEncoder(out_dim=88)
rnn_1.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
self.enc_y = FinetunedEncoder(rnn_1, out_dim=self.z_dim)
self.enc_x = ExprDiffEncoder(out_dim=self.z_dim)
rnn_2 = RNNDecoder(in_dim=44)
rnn_2.load_state_dict(
torch.load('../saved_models/rnn_dec.ckpt',
map_location='cuda:1'))
self.dec_y = FinetunedDecoder(rnn_2, in_dim=self.z_dim)
self.dec_x = ExprDiffDecoder(in_dim=self.z_dim)
self.discr = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
self.discr_indep_x = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
self.discr_indep_y = FCDiscriminator(in_dim=2 * self.z_dim -
self.joint_dim,
use_sigmoid=False)
def __init__(self, dataset='paired_mnist'):
super(VCCA, self).__init__()
self.dataset = dataset
if self.dataset == 'paired_mnist':
self.z_dim = 16
self.joint_dim = 4
self.enc_x = MnistCNNEncoder(out_dim=2 *
(self.z_dim - self.joint_dim))
self.enc_y = MnistCNNEncoder(out_dim=2 * self.z_dim)
self.loss_rec_lambda_x = 10
self.loss_rec_lambda_y = 10
self.beta = 0.01
self.dec_x = MnistCNNDecoder(in_dim=self.z_dim)
self.dec_y = MnistCNNDecoder(in_dim=self.z_dim)
elif self.dataset == 'lincs_rnn':
self.z_dim = 20
self.joint_dim = 10
rnn_1 = RNNEncoder(out_dim=88)
rnn_1.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
self.enc_x = FinetunedEncoder(rnn_1,
out_dim=2 *
(self.z_dim - self.joint_dim))
self.enc_y = ExprDiffEncoder(out_dim=2 * self.z_dim)
self.loss_rec_lambda_x = 5
self.loss_rec_lambda_y = 1
self.beta = 1
rnn_2 = RNNDecoder(in_dim=44)
rnn_2.load_state_dict(
torch.load('../saved_models/rnn_dec.ckpt',
map_location='cuda:1'))
self.dec_x = FinetunedDecoder(rnn_2, in_dim=self.z_dim)
self.dec_y = ExprDiffDecoder(in_dim=self.z_dim)
elif self.dataset == 'lincs_rnn_reverse':
self.z_dim = 20
self.joint_dim = 10
rnn_1 = RNNEncoder(out_dim=88)
rnn_1.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
self.enc_y = FinetunedEncoder(rnn_1, out_dim=2 * self.z_dim)
self.enc_x = ExprDiffEncoder(out_dim=2 *
(self.z_dim - self.joint_dim))
self.loss_rec_lambda_x = 1
self.loss_rec_lambda_y = 0.2
self.beta = 1
rnn_2 = RNNDecoder(in_dim=44)
rnn_2.load_state_dict(
torch.load('../saved_models/rnn_dec.ckpt',
map_location='cuda:1'))
self.dec_y = FinetunedDecoder(rnn_2, in_dim=self.z_dim)
self.dec_x = ExprDiffDecoder(in_dim=self.z_dim)
def __init__(self, dataset='paired_mnist'):
super(CVAE, self).__init__()
self.dataset = dataset
if self.dataset == 'paired_mnist':
self.z_dim = 8
self.loss_rec_lambda_x = 10
self.loss_rec_lambda_y = 10
self.beta = 0.01
self.enc_y = MnistCNNEncoder(out_dim=2 * self.z_dim)
self.enc_xy = JointEncoder(MnistCNNEncoder(out_dim=2 * self.z_dim,
short_tail=True),
MnistCNNEncoder(out_dim=2 * self.z_dim,
short_tail=True),
out_dim=2 * self.z_dim)
self.dec_x_cond = ConditionedDecoder(
dec=MnistCNNDecoder(in_dim=self.z_dim + self.z_dim),
cond=MnistCNNEncoder(out_dim=self.z_dim, short_tail=True))
elif self.dataset == 'lincs_rnn':
self.z_dim = 10
self.loss_rec_lambda_x = 5
self.loss_rec_lambda_y = 1
self.beta = 1
rnn_1 = RNNEncoder(out_dim=88)
rnn_1.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
enc_x = FinetunedEncoder(rnn_1, out_dim=2 * self.z_dim)
self.enc_xy = JointEncoder(enc_x,
ExprDiffEncoder(out_dim=2 * self.z_dim),
out_dim=2 * self.z_dim)
self.enc_y = ExprDiffEncoder(out_dim=2 * self.z_dim)
rnn_2 = RNNDecoder(in_dim=44)
rnn_2.load_state_dict(
torch.load('../saved_models/rnn_dec.ckpt',
map_location='cuda:1'))
dec_x = FinetunedDecoder(rnn_2, in_dim=2 * self.z_dim)
self.dec_x_cond = ConditionedDecoder(
dec=dec_x, cond=ExprDiffEncoder(out_dim=self.z_dim))
elif self.dataset == 'lincs_rnn_reverse':
self.z_dim = 10
self.loss_rec_lambda_x = 1
self.loss_rec_lambda_y = 0.2
self.beta = 1
rnn_1 = RNNEncoder(out_dim=88)
rnn_1.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
self.enc_y = FinetunedEncoder(rnn_1, out_dim=2 * self.z_dim)
self.enc_xy = JointEncoder(ExprDiffEncoder(out_dim=2 * self.z_dim),
FinetunedEncoder(rnn_1,
out_dim=2 *
self.z_dim),
out_dim=2 * self.z_dim)
rnn_2 = RNNEncoder(out_dim=88)
rnn_2.load_state_dict(
torch.load('../saved_models/rnn_enc.ckpt',
map_location='cuda:1'))
self.dec_x_cond = ConditionedDecoder(
dec=ExprDiffDecoder(in_dim=2 * self.z_dim),
cond=FinetunedEncoder(rnn_2, out_dim=self.z_dim))