added FARGAN adversarial training script

dnn/torch/fargan/adv_train_fargan.py

@@ -0,0 +1,260 @@
+import os
+import argparse
+import random
+import numpy as np
+import sys
+import math as m
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import tqdm
+
+import fargan
+from dataset import FARGANDataset
+from stft_loss import *
+
+source_dir = os.path.split(os.path.abspath(__file__))[0]
+sys.path.append(os.path.join(source_dir, "../osce/"))
+
+import models as osce_models
+
+
+def fmap_loss(scores_real, scores_gen):
+    num_discs = len(scores_real)
+    loss_feat = 0
+    for k in range(num_discs):
+        num_layers = len(scores_gen[k]) - 1
+        f = 4 / num_discs / num_layers
+        for l in range(num_layers):
+            loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach())
+
+    return loss_feat
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument('features', type=str, help='path to feature file in .f32 format')
+parser.add_argument('signal', type=str, help='path to signal file in .s16 format')
+parser.add_argument('output', type=str, help='path to output folder')
+
+parser.add_argument('--suffix', type=str, help="model name suffix", default="")
+parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None)
+
+
+model_group = parser.add_argument_group(title="model parameters")
+model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256)
+model_group.add_argument('--gamma', type=float, help="Use A(z/gamma), default: 0.9", default=0.9)
+
+training_group = parser.add_argument_group(title="training parameters")
+training_group.add_argument('--batch-size', type=int, help="batch size, default: 128", default=128)
+training_group.add_argument('--lr', type=float, help='learning rate, default: 5e-4', default=5e-4)
+training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20)
+training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 60', default=60)
+training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 0.0', default=0.0)
+training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None)
+training_group.add_argument('--reg-weight', type=float, help='regression loss weight, default: 0.6', default=0.6)
+training_group.add_argument('--fmap-weight', type=float, help='feature matchin loss weight, default: 1.0', default=1.)
+
+args = parser.parse_args()
+
+if args.cuda_visible_devices != None:
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices
+
+# checkpoints
+checkpoint_dir = os.path.join(args.output, 'checkpoints')
+checkpoint = dict()
+os.makedirs(checkpoint_dir, exist_ok=True)
+
+
+# training parameters
+batch_size = args.batch_size
+lr = args.lr
+epochs = args.epochs
+sequence_length = args.sequence_length
+lr_decay = args.lr_decay
+
+adam_betas = [0.8, 0.99]
+adam_eps = 1e-8
+features_file = args.features
+signal_file = args.signal
+
+# model parameters
+cond_size  = args.cond_size
+
+
+checkpoint['batch_size'] = batch_size
+checkpoint['lr'] = lr
+checkpoint['lr_decay'] = lr_decay
+checkpoint['epochs'] = epochs
+checkpoint['sequence_length'] = sequence_length
+checkpoint['adam_betas'] = adam_betas
+
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+checkpoint['model_args']    = ()
+checkpoint['model_kwargs']  = {'cond_size': cond_size, 'gamma': args.gamma}
+print(checkpoint['model_kwargs'])
+model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs'])
+
+
+#discriminator
+disc_name = 'fdmresdisc'
+disc = osce_models.model_dict[disc_name](
+    architecture='free',
+    design='f_down',
+    fft_sizes_16k=[2**n for n in range(6, 12)],
+    freq_roi=[0, 7400],
+    max_channels=256,
+    noise_gain=0.0
+)
+
+if type(args.initial_checkpoint) != type(None):
+    checkpoint = torch.load(args.initial_checkpoint, map_location='cpu')
+    model.load_state_dict(checkpoint['state_dict'], strict=False)
+
+checkpoint['state_dict']    = model.state_dict()
+
+
+dataset = FARGANDataset(features_file, signal_file, sequence_length=sequence_length)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4)
+
+
+optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps)
+optimizer_disc = torch.optim.AdamW([p for p in disc.parameters() if p.requires_grad], lr=lr, betas=adam_betas, eps=adam_eps)
+
+
+# learning rate scheduler
+scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x))
+scheduler_disc = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer_disc, lr_lambda=lambda x : 1 / (1 + lr_decay * x))
+
+states = None
+
+spect_loss =  MultiResolutionSTFTLoss(device).to(device)
+
+if __name__ == '__main__':
+    model.to(device)
+    disc.to(device)
+
+    for epoch in range(1, epochs + 1):
+
+        m_r = 0
+        m_f = 0
+        s_r = 1
+        s_f = 1
+
+        running_cont_loss = 0
+        running_disc_loss = 0
+        running_gen_loss = 0
+        running_fmap_loss = 0
+        running_reg_loss = 0
+        running_wc = 0
+
+        print(f"training epoch {epoch}...")
+        with tqdm.tqdm(dataloader, unit='batch') as tepoch:
+            for i, (features, periods, target, lpc) in enumerate(tepoch):
+                optimizer.zero_grad()
+                features = features.to(device)
+                lpc = lpc.to(device)
+                periods = periods.to(device)
+                if True:
+                    target = target[:, :sequence_length*160]
+                    lpc = lpc[:,:sequence_length,:]
+                    features = features[:,:sequence_length+4,:]
+                    periods = periods[:,:sequence_length+4]
+                else:
+                    target=target[::2, :]
+                    lpc=lpc[::2,:]
+                    features=features[::2,:]
+                    periods=periods[::2,:]
+                target = target.to(device)
+                target = fargan.analysis_filter(target, lpc[:,:,:], gamma=args.gamma)
+
+                #nb_pre = random.randrange(1, 6)
+                nb_pre = 2
+                pre = target[:, :nb_pre*160]
+                output, _ = model(features, periods, target.size(1)//160 - nb_pre, pre=pre, states=None)
+                output = torch.cat([pre, output], -1)
+
+
+                # discriminator update
+                scores_gen = disc(output.detach().unsqueeze(1))
+                scores_real = disc(target.unsqueeze(1))
+
+                disc_loss = 0
+                for scale in scores_gen:
+                    disc_loss += ((scale[-1]) ** 2).mean()
+                    m_f = 0.9 * m_f + 0.1 * scale[-1].detach().mean().cpu().item()
+                    s_f = 0.9 * s_f + 0.1 * scale[-1].detach().std().cpu().item()
+
+                for scale in scores_real:
+                    disc_loss += ((1 - scale[-1]) ** 2).mean()
+                    m_r = 0.9 * m_r + 0.1 * scale[-1].detach().mean().cpu().item()
+                    s_r = 0.9 * s_r + 0.1 * scale[-1].detach().std().cpu().item()
+
+                disc_loss = 0.5 * disc_loss / len(scores_gen)
+                winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) )
+                running_wc += winning_chance
+
+                disc.zero_grad()
+                disc_loss.backward()
+                optimizer_disc.step()
+
+                # model update
+                scores_gen = disc(output.unsqueeze(1))
+                if False: # todo: check whether that makes a difference
+                    with torch.no_grad():
+                        scores_real = disc(target.unsqueeze(1))
+
+                cont_loss = fargan.sig_loss(target[:, nb_pre*160:nb_pre*160+80], output[:, nb_pre*160:nb_pre*160+80])
+                specc_loss = spect_loss(output, target.detach())
+                reg_loss = args.reg_weight * (.00*cont_loss + specc_loss)
+
+                loss_gen = 0
+                for scale in scores_gen:
+                    loss_gen += ((1 - scale[-1]) ** 2).mean() / len(scores_gen)
+
+                feat_loss = args.fmap_weight * fmap_loss(scores_real, scores_gen)
+
+                gen_loss = reg_loss +  feat_loss + loss_gen
+
+                model.zero_grad()
+
+
+                gen_loss.backward()
+                optimizer.step()
+
+                #model.clip_weights()
+
+                scheduler.step()
+                scheduler_disc.step()
+
+                running_cont_loss += cont_loss.detach().cpu().item()
+                running_gen_loss += loss_gen.detach().cpu().item()
+                running_disc_loss += disc_loss.detach().cpu().item()
+                running_fmap_loss += feat_loss.detach().cpu().item()
+                running_reg_loss += reg_loss.detach().cpu().item()
+
+
+
+                tepoch.set_postfix(cont_loss=f"{running_cont_loss/(i+1):8.5f}",
+                                   gen_loss=f"{running_gen_loss/(i+1):8.5f}",
+                                   disc_loss=f"{running_disc_loss/(i+1):8.5f}",
+                                   fmap_loss=f"{running_fmap_loss/(i+1):8.5f}",
+                                   reg_loss=f"{running_reg_loss/(i+1):8.5f}",
+                                   wc = f"{running_wc/(i+1):8.5f}",
+                                   )
+
+        # save checkpoint
+        checkpoint_path = os.path.join(checkpoint_dir, f'fargan{args.suffix}_adv_{epoch}.pth')
+        checkpoint['state_dict'] = model.state_dict()
+        checkpoint['disc_sate_dict'] = disc.state_dict()
+        checkpoint['loss'] = {
+            'cont': running_cont_loss / len(dataloader),
+            'gen': running_gen_loss / len(dataloader),
+            'disc': running_disc_loss / len(dataloader),
+            'fmap': running_fmap_loss / len(dataloader),
+            'reg': running_reg_loss / len(dataloader)
+        }
+        checkpoint['epoch'] = epoch
+        torch.save(checkpoint, checkpoint_path)