text stringlengths 1 93.6k |
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bs = args.sgld_batch_size if y is None else y.size(0)
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# generate initial samples and buffer inds of those samples (if buffer is used)
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init_sample, buffer_inds = sample_p_0(replay_buffer, bs=bs, y=y)
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x_k = init_sample.clone()
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x_k.requires_grad = True
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# sgld
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for k in range(n_steps):
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if not contrast:
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energy = f(x_k, y=y).sum()
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else:
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if y is not None:
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dist = smooth_one_hot(y, args.n_classes, args.smoothing)
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else:
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dist = torch.ones((bs, args.n_classes)).to(device)
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output, target, ce_output, neg_num = f.joint(img=x_k, dist=dist, evaluation=True)
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energy = -1.0 * nn.CrossEntropyLoss(reduction="mean")(output, target)
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f_prime = torch.autograd.grad(energy, [x_k], retain_graph=True)[0]
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x_k.data += args.sgld_lr * f_prime + args.sgld_std * torch.randn_like(x_k)
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f.train()
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final_samples = x_k.detach()
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# update replay buffer
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if len(replay_buffer) > 0:
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replay_buffer[buffer_inds] = final_samples.cpu()
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return final_samples
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return sample_q
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def main(args):
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# Setup datasets
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dload_train, dload_train_labeled, dload_valid, dload_test = get_data(args)
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# Model and buffer
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sample_q = get_sample_q(args)
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f, replay_buffer = get_model_and_buffer(args, sample_q)
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# Setup Optimizer
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params = f.class_output.parameters() if args.clf_only else f.parameters()
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if args.optimizer == "adam":
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optim = torch.optim.Adam(params, lr=args.lr, betas=[0.9, 0.999], weight_decay=args.weight_decay)
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else:
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optim = torch.optim.SGD(params, lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
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best_valid_acc = 0.0
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cur_iter = 0
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for epoch in range(args.start_epoch, args.n_epochs):
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# Decay lr
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if epoch in args.decay_epochs:
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for param_group in optim.param_groups:
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new_lr = param_group["lr"] * args.decay_rate
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param_group["lr"] = new_lr
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# Load data
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for i, (x_p_d, _) in tqdm(enumerate(dload_train)):
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# Warmup
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if cur_iter <= args.warmup_iters:
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lr = args.lr * cur_iter / float(args.warmup_iters)
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for param_group in optim.param_groups:
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param_group["lr"] = lr
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x_p_d = x_p_d.to(device)
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x_lab, y_lab = dload_train_labeled.__next__()
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x_lab, y_lab = x_lab.to(device), y_lab.to(device)
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# Label smoothing
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dist = smooth_one_hot(y_lab, args.n_classes, args.smoothing)
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L = 0.0
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# log p(y|x) cross entropy loss
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if args.pyxce > 0:
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logits = f.classify(x_lab)
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l_pyxce = KHotCrossEntropyLoss()(logits, dist)
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if cur_iter % args.print_every == 0:
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acc = (logits.max(1)[1] == y_lab).float().mean()
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print("p(y|x)CE {}:{:>d} loss={:>14.9f}, acc={:>14.9f}".format(epoch, cur_iter, l_pyxce.item(), acc.item()))
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logger.record_dict({"l_pyxce": l_pyxce.cpu().data.item(), "acc_pyxce": acc.item()})
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L += args.pyxce * l_pyxce
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# log p(x) using sgld
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if args.pxsgld > 0:
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if args.class_cond_p_x_sample:
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assert not args.uncond, "can only draw class-conditional samples if EBM is class-cond"
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y_q = torch.randint(0, args.n_classes, (args.sgld_batch_size,)).to(device)
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x_q = sample_q(f, replay_buffer, y=y_q)
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else:
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x_q = sample_q(f, replay_buffer) # sample from log-sumexp
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fp_all = f(x_p_d)
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fq_all = f(x_q)
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fp = fp_all.mean()
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fq = fq_all.mean()
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l_pxsgld = -(fp - fq)
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if cur_iter % args.print_every == 0:
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print("p(x)SGLD | {}:{:>d} loss={:>14.9f} f(x_p_d)={:>14.9f} f(x_q)={:>14.9f}".format(epoch, i, l_pxsgld, fp, fq))
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logger.record_dict({"l_pxsgld": l_pxsgld.cpu().data.item()})
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L += args.pxsgld * l_pxsgld
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# log p(x) using contrastive learning
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if args.pxcontrast > 0:
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