return torch.max(margin, x)
def gv_loss(z_no_lesion, z_with_lesion):
mse_loss = nn.MSELoss(reduce=True)
nl = vae.decode(z_no_lesion)
wl = vae.decode(z_with_lesion)
gv = gv_margin(mse_loss(z_no_lesion, z_with_lesion),
torch.tensor([1]).float())
cnn_loss = 0.1 * cnn(nl)
print(f"GV: {gv.data} CNN: {cnn_loss.data}")
loss = gv + cnn_loss
return loss, nl, wl
transform = TRANSFORMER().to(device)
criterion = nn.MSELoss(size_average=True, reduce=True)
optimizer = torch.optim.Adam(transform.parameters(),
lr=LEARNING_RATE,
amsgrad=True)
for (positive, _) in positive_loader:
reconstruction, z_with_lesion = vae(
positive) #taking a positive, extracting the recon and latent z
for x in range(100):
z_no_lesion = transform(
z_with_lesion
) #transforming latent z (lesion) => latent z (no lesion)
loss, decoded_nl, decoded_wl = gv_loss(z_no_lesion, z_with_lesion)
optimizer.zero_grad()
loss.sum().backward(retain_graph=True)
# This is where your model code will be called. You may modify this code
# if required for your implementation, but it should not typically be necessary,
# and you must let the TAs know if you do so.
if args.model == 'RNN':
model = RNN(emb_size=args.emb_size, hidden_size=args.hidden_size,
seq_len=args.seq_len, batch_size=args.batch_size,
vocab_size=vocab_size, num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'GRU':
model = GRU(emb_size=args.emb_size, hidden_size=args.hidden_size,
seq_len=args.seq_len, batch_size=args.batch_size,
vocab_size=vocab_size, num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'TRANSFORMER':
if args.debug: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size, n_units=16, n_blocks=2)
else:
# Note that we're using num_layers and hidden_size to mean slightly
# different things here than in the RNNs.
# Also, the Transformer also has other hyperparameters
# (such as the number of attention heads) which can change it's behavior.
model = TRANSFORMER(vocab_size=vocab_size, n_units=args.hidden_size,
n_blocks=args.num_layers, dropout=1.-args.dp_keep_prob)
# these 3 attributes don't affect the Transformer's computations;
# they are only used in run_epoch
model.batch_size=args.batch_size
model.seq_len=args.seq_len
model.vocab_size=vocab_size
else:
print("Model type not recognized.")
batch_size=args["batch_size"],
vocab_size=vocab_size,
num_layers=args["num_layers"],
dp_keep_prob=args["dp_keep_prob"])
elif args["model"] == 'GRU':
model = GRU(emb_size=args["emb_size"],
hidden_size=args["hidden_size"],
seq_len=args["seq_len"],
batch_size=args["batch_size"],
vocab_size=vocab_size,
num_layers=args["num_layers"],
dp_keep_prob=args["dp_keep_prob"])
elif args["model"] == 'TRANSFORMER':
if args["debug"]: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size,
n_units=16,
n_blocks=2)
else:
# Note that we're using num_layers and hidden_size to mean slightly
# different things here than in the RNNs.
# Also, the Transformer also has other hyperparameters
# (such as the number of attention heads) which can change it's behavior.
model = TRANSFORMER(vocab_size=vocab_size,
n_units=args["hidden_size"],
n_blocks=args["num_layers"],
dropout=1. - args["dp_keep_prob"])
# these 3 attributes don't affect the Transformer's computations;
# they are only used in run_epoch
model.batch_size = args["batch_size"]
model.seq_len = args["seq_len"]
model.vocab_size = vocab_size
