def predict_contacts(model, x, y, use_cuda):
b = len(x)
x, order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
logits = []
y_list = []
for i in range(b):
zi = z[i]
lp = model.predict(zi.unsqueeze(0)).view(-1)
yi = y[i].view(-1)
if use_cuda:
yi = yi.cuda()
mask = (yi < 0)
lp = lp[~mask]
yi = yi[~mask]
logits.append(lp)
y_list.append(yi)
return logits, y_list
def predict_minibatch(model, x, use_cuda):
b = len(x)
x, order = pack_sequences(x)
x = PackedSequence(x.data, x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
logits = []
for i in range(b):
zi = z[i]
lp = model.predict(zi.unsqueeze(0)).view(zi.size(0), zi.size(0))
logits.append(lp)
return logits
def contacts_grad(model, x, y, use_cuda, weight=0.5):
b = len(x)
x, order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
logits = []
for i in range(b):
zi = z[i]
lp = model.predict(zi.unsqueeze(0)).view(-1)
logits.append(lp)
logits = torch.cat(logits, 0)
y = torch.cat([yi.view(-1) for yi in y])
if use_cuda:
y = y.cuda()
mask = (y < 0)
logits = logits[~mask]
y = Variable(y[~mask])
b = y.size(0)
loss = F.binary_cross_entropy_with_logits(logits, y)
# backprop weighted loss
w_loss = loss * weight
w_loss.backward()
# calculate the recall and precision
with torch.no_grad():
p_hat = F.sigmoid(logits)
tp = torch.sum(p_hat * y).item()
gp = y.sum().item()
pp = p_hat.sum().item()
return loss.item(), tp, gp, pp, b