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jblitzar
/
github-python-corpus

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# ones like dist to use all indexes
ones_dist = torch.ones_like(dist).to(device)
output, target, ce_output, neg_num = f.joint(img=x_lab, dist=ones_dist)
l_pxcontrast = nn.CrossEntropyLoss(reduction="mean")(output, target)
if cur_iter % args.print_every == 0:
acc = (ce_output.max(1)[1] == y_lab).float().mean()
print("p(x)Contrast {}:{:>d} loss={:>14.9f}, acc={:>14.9f}".format(epoch, cur_iter, l_pxcontrast.item(), acc.item()))
logger.record_dict({"l_pxcontrast": l_pxcontrast.cpu().data.item(), "acc_pxcontrast": acc.item()})
L += args.pxycontrast * l_pxcontrast
# log p(x|y) using sgld
if args.pxysgld > 0:
x_q_lab = sample_q(f, replay_buffer, y=y_lab)
fp, fq = f(x_lab).mean(), f(x_q_lab).mean()
l_pxysgld = -(fp - fq)
if cur_iter % args.print_every == 0:
print("p(x|y)SGLD | {}:{:>d} loss={:>14.9f} f(x_p_d)={:>14.9f} f(x_q)={:>14.9f}".format(epoch, i, l_pxysgld.item(), fp, fq))
logger.record_dict({"l_pxysgld": l_pxysgld.cpu().data.item()})
L += args.pxsgld * l_pxysgld
# log p(x|y) using contrastive learning
if args.pxycontrast > 0:
output, target, ce_output, neg_num = f.joint(img=x_lab, dist=dist)
l_pxycontrast = nn.CrossEntropyLoss(reduction="mean")(output, target)
if cur_iter % args.print_every == 0:
acc = (ce_output.max(1)[1] == y_lab).float().mean()
print("p(x|y)Contrast {}:{:>d} loss={:>14.9f}, acc={:>14.9f}".format(epoch, cur_iter, l_pxycontrast.item(), acc.item()))
logger.record_dict({"l_pxycontrast": l_pxycontrast.cpu().data.item(), "acc_pxycontrast": acc.item()})
L += args.pxycontrast * l_pxycontrast
# SGLD training of log q(x) may diverge
# break here and record information to restart
if L.abs().item() > 1e8:
print("restart epoch: {}".format(epoch))
print("save dir: {}".format(args.log_dir))
print("id: {}".format(args.id))
print("steps: {}".format(args.n_steps))
print("seed: {}".format(args.seed))
print("exp prefix: {}".format(args.exp_prefix))
sys.stdout = sys.__stdout__
sys.stderr = sys.__stderr__
print("restart epoch: {}".format(epoch))
print("save dir: {}".format(args.log_dir))
print("id: {}".format(args.id))
print("steps: {}".format(args.n_steps))
print("seed: {}".format(args.seed))
print("exp prefix: {}".format(args.exp_prefix))
assert False, "shit loss explode..."
optim.zero_grad()
L.backward()
optim.step()
cur_iter += 1
if epoch % args.plot_every == 0:
if args.plot_uncond:
if args.class_cond_p_x_sample:
assert not args.uncond, "can only draw class-conditional samples if EBM is class-cond"
y_q = torch.randint(0, args.n_classes, (args.sgld_batch_size,)).to(device)
x_q = sample_q(f, replay_buffer, y=y_q)
plot("{}/x_q_{}_{:>06d}.png".format(args.log_dir, epoch, i), x_q)
if args.plot_contrast:
x_q = sample_q(f, replay_buffer, y=y_q, contrast=True)
plot("{}/contrast_x_q_{}_{:>06d}.png".format(args.log_dir, epoch, i), x_q)
else:
x_q = sample_q(f, replay_buffer)
plot("{}/x_q_{}_{:>06d}.png".format(args.log_dir, epoch, i), x_q)
if args.plot_contrast:
x_q = sample_q(f, replay_buffer, contrast=True)
plot("{}/contrast_x_q_{}_{:>06d}.png".format(args.log_dir, epoch, i), x_q)
if args.plot_cond: # generate class-conditional samples
y = torch.arange(0, args.n_classes)[None].repeat(args.n_classes, 1).transpose(1, 0).contiguous().view(-1).to(device)
x_q_y = sample_q(f, replay_buffer, y=y)
plot("{}/x_q_y{}_{:>06d}.png".format(args.log_dir, epoch, i), x_q_y)
if args.plot_contrast:
y = torch.arange(0, args.n_classes)[None].repeat(args.n_classes, 1).transpose(1, 0).contiguous().view(-1).to(device)
x_q_y = sample_q(f, replay_buffer, y=y, contrast=True)
plot("{}/contrast_x_q_y_{}_{:>06d}.png".format(args.log_dir, epoch, i), x_q_y)
if args.ckpt_every > 0 and epoch % args.ckpt_every == 0:
checkpoint(f, replay_buffer, f"ckpt_{epoch}.pt", args)
if epoch % args.eval_every == 0:
# Validation set
correct, val_loss = eval_classification(f, dload_valid)
if correct > best_valid_acc:
best_valid_acc = correct
print("Best Valid!: {}".format(correct))
checkpoint(f, replay_buffer, "best_valid_ckpt.pt", args)
# Test set
correct, test_loss = eval_classification(f, dload_test)
print("Epoch {}: Valid Loss {}, Valid Acc {}".format(epoch, val_loss, correct))
print("Epoch {}: Test Loss {}, Test Acc {}".format(epoch, test_loss, correct))
f.train()
logger.record_dict(
{
"Epoch": epoch,
"Valid Loss": val_loss,
"Valid Acc": correct.detach().cpu().numpy(),
"Test Loss": test_loss,