Add optional adversary model and make validation optional

map2map/args.py

@@ -17,17 +17,19 @@ def get_args():
 
 def add_common_args(parser):
     parser.add_argument('--norms', type=str_list, help='comma-sep. list '
-            'of normalization functions from data.norms')
+            'of normalization functions from .data.norms')
     parser.add_argument('--crop', type=int,
             help='size to crop the input and target data')
     parser.add_argument('--pad', default=0, type=int,
-            help='pad the input data assuming periodic boundary condition')
+            help='size to pad the input data beyond the crop size, assuming '
+            'periodic boundary condition')
 
-    parser.add_argument('--model', required=True, help='model from models')
+    parser.add_argument('--model', required=True, type=str,
-    parser.add_argument('--criterion', default='MSELoss',
+            help='model from .models')
+    parser.add_argument('--criterion', default='MSELoss', type=str,
             help='model criterion from torch.nn')
     parser.add_argument('--load-state', default='', type=str,
-            help='path to load model, optimizer, rng, etc.')
+            help='path to load the states of model, optimizer, rng, etc.')
 
     parser.add_argument('--batches', default=1, type=int,
             help='mini-batch size, per GPU in training or in total in testing')
@@ -46,16 +48,23 @@ def add_train_args(parser):
             help='comma-sep. list of glob patterns for training input data')
     parser.add_argument('--train-tgt-patterns', type=str_list, required=True,
             help='comma-sep. list of glob patterns for training target data')
-    parser.add_argument('--val-in-patterns', type=str_list, required=True,
+    parser.add_argument('--val-in-patterns', type=str_list,
             help='comma-sep. list of glob patterns for validation input data')
-    parser.add_argument('--val-tgt-patterns', type=str_list, required=True,
+    parser.add_argument('--val-tgt-patterns', type=str_list,
             help='comma-sep. list of glob patterns for validation target data')
     parser.add_argument('--augment', action='store_true',
             help='enable training data augmentation')
 
+    parser.add_argument('--adv-model', type=str,
+            help='enable adversary model from .models')
+    parser.add_argument('--adv-criterion', default='BCEWithLogitsLoss', type=str,
+            help='adversary criterion from torch.nn')
+    parser.add_argument('--cgan', action='store_true',
+            help='enable conditional GAN')
+
     parser.add_argument('--epochs', default=128, type=int,
             help='total number of epochs to run')
-    parser.add_argument('--optimizer', default='Adam',
+    parser.add_argument('--optimizer', default='Adam', type=str,
             help='optimizer from torch.optim')
     parser.add_argument('--lr', default=0.001, type=float,
             help='initial learning rate')
@@ -63,6 +72,10 @@ def add_train_args(parser):
 #            help='momentum')
     parser.add_argument('--weight-decay', default=0., type=float,
             help='weight decay')
+    parser.add_argument('--adv-lr', type=float,
+            help='initial adversary learning rate')
+    parser.add_argument('--adv-weight-decay', type=float,
+            help='adversary weight decay')
     parser.add_argument('--seed', default=42, type=int,
             help='seed for initializing training')
 
@@ -70,7 +83,7 @@ def add_train_args(parser):
             help='enable data division among GPUs, useful with cache')
     parser.add_argument('--dist-backend', default='nccl', type=str,
             choices=['gloo', 'nccl'], help='distributed backend')
-    parser.add_argument('--log-interval', default=20, type=int,
+    parser.add_argument('--log-interval', default=100, type=int,
             help='interval between logging training loss')
 
 

map2map/data/fields.py

@@ -37,6 +37,8 @@ class FieldDataset(Dataset):
         assert len(self.in_files) == len(self.tgt_files), \
                 'input and target sample sizes do not match'
 
+        assert len(self.in_files) > 0, 'file not found'
+
         if div_data:
             files = len(self.in_files) // world_size
             self.in_files = self.in_files[rank * files : (rank + 1) * files]
@@ -151,7 +153,7 @@ def flip(fields, axes, ndim):
 
     new_fields = []
     for x in fields:
-        if x.size(0) == ndim:  # flip vector components
+        if x.shape[0] == ndim:  # flip vector components
             x[axes] = - x[axes]
 
         axes = (1 + axes).tolist()
@@ -167,7 +169,7 @@ def perm(fields, axes, ndim):
 
     new_fields = []
     for x in fields:
-        if x.size(0) == ndim:  # permutate vector components
+        if x.shape[0] == ndim:  # permutate vector components
             x = x[axes]
 
         axes = [0] + (1 + axes).tolist()

map2map/models/conv.py

@@ -39,7 +39,9 @@ class ConvBlock(nn.Module):
             in_channels, out_channels = self._setup_conv()
             return nn.Conv3d(in_channels, out_channels, self.kernel_size)
         elif l == 'B':
-            return nn.BatchNorm3d(self.bn_channels)
+            #return nn.BatchNorm3d(self.bn_channels)
+            #return nn.InstanceNorm3d(self.bn_channels, affine=True, track_running_stats=True)
+            return nn.InstanceNorm3d(self.bn_channels)
         elif l == 'A':
             return Swish()
         else:
@@ -109,8 +111,8 @@ def narrow_like(a, b):
     Try to be symmetric but cut more on the right for odd difference,
     consistent with the downsampling.
     """
-    for dim in range(2, 5):
+    for d in range(2, a.dim()):
-        width = a.size(dim) - b.size(dim)
+        width = a.shape[d] - b.shape[d]
         half_width = width // 2
-        a = a.narrow(dim, half_width, a.size(dim) - width)
+        a = a.narrow(d, half_width, a.shape[d] - width)
     return a

map2map/train.py

@@ -1,8 +1,9 @@
 import os
 import shutil
 import torch
+import torch.nn.functional as F
+import torch.distributed as dist
 from torch.multiprocessing import spawn
-from torch.distributed import init_process_group, destroy_process_group, all_reduce
 from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data import DataLoader
@@ -35,7 +36,7 @@ def gpu_worker(local_rank, args):
 
     args.rank = args.gpus_per_node * args.node + local_rank
 
-    init_process_group(
+    dist.init_process_group(
         backend=args.dist_backend,
         init_method='env://',
         world_size=args.world_size,
@@ -59,11 +60,14 @@ def gpu_worker(local_rank, args):
         pin_memory=True
     )
 
+    args.val = args.val_in_patterns is not None and \
+            args.val_tgt_patterns is not None
+    if args.val:
         val_dataset = FieldDataset(
             in_patterns=args.val_in_patterns,
             tgt_patterns=args.val_tgt_patterns,
             augment=False,
-        **{k:v for k, v in vars(args).items() if k != 'augment'},
+            **{k: v for k, v in vars(args).items() if k != 'augment'},
         )
         if not args.div_data:
             #val_sampler = DistributedSampler(val_dataset, shuffle=False)
@@ -93,17 +97,50 @@ def gpu_worker(local_rank, args):
         model.parameters(),
         lr=args.lr,
         #momentum=args.momentum,
+        betas=(0.5, 0.999),
         weight_decay=args.weight_decay,
     )
     scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
             factor=0.5, patience=3, verbose=True)
 
+    adv_model = adv_criterion = adv_optimizer = adv_scheduler = None
+    args.adv = args.adv_model is not None
+    if args.adv:
+        adv_model = getattr(models, args.adv_model)
+        adv_model = adv_model(in_channels + out_channels
+                if args.cgan else out_channels, 1)
+        adv_model.to(args.device)
+        adv_model = DistributedDataParallel(adv_model, device_ids=[args.device])
+
+        adv_criterion = getattr(torch.nn, args.adv_criterion)
+        adv_criterion = adv_criterion()
+        adv_criterion.to(args.device)
+
+        if args.adv_lr is None:
+            args.adv_lr = args.lr
+        if args.adv_weight_decay is None:
+            args.adv_weight_decay = args.weight_decay
+
+        adv_optimizer = getattr(torch.optim, args.optimizer)
+        adv_optimizer = adv_optimizer(
+            adv_model.parameters(),
+            lr=args.adv_lr,
+            betas=(0.5, 0.999),
+            weight_decay=args.adv_weight_decay,
+        )
+        adv_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(adv_optimizer,
+                factor=0.5, patience=3, verbose=True)
+
     if args.load_state:
         state = torch.load(args.load_state, map_location=args.device)
         args.start_epoch = state['epoch']
         model.module.load_state_dict(state['model'])
         optimizer.load_state_dict(state['optimizer'])
         scheduler.load_state_dict(state['scheduler'])
+        if 'adv_model' in state and args.adv:
+            adv_model.module.load_state_dict(state['adv_model'])
+            adv_optimizer.load_state_dict(state['adv_optimizer'])
+            adv_scheduler.load_state_dict(state['adv_scheduler'])
         torch.set_rng_state(state['rng'].cpu())  # move rng state back
         if args.rank == 0:
             min_loss = state['min_loss']
@@ -111,6 +148,15 @@ def gpu_worker(local_rank, args):
                 state['epoch'], args.load_state))
         del state
     else:
+#        def init_weights(m):
+#            classname = m.__class__.__name__
+#            if isinstance(m, (torch.nn.Conv3d, torch.nn.ConvTranspose3d)):
+#                m.weight.data.normal_(0.0, 0.02)
+#            elif isinstance(m, torch.nn.BatchNorm3d):
+#                m.weight.data.normal_(1.0, 0.02)
+#                m.bias.data.fill_(0)
+#        model.apply(init_weights)
+#
         args.start_epoch = 0
         if args.rank == 0:
             min_loss = None
@@ -119,47 +165,68 @@ def gpu_worker(local_rank, args):
 
     if args.rank == 0:
         args.logger = SummaryWriter()
-        #hparam = {k: v if isinstance(v, (int, float, str, bool, torch.Tensor))
-        #        else str(v) for k, v in vars(args).items()}
-        #args.logger.add_hparams(hparam_dict=hparam, metric_dict={})
 
     for epoch in range(args.start_epoch, args.epochs):
         if not args.div_data:
             train_sampler.set_epoch(epoch)
-        train(epoch, train_loader, model, criterion, optimizer, scheduler, args)
 
-        val_loss = validate(epoch, val_loader, model, criterion, args)
+        train_loss = train(epoch, train_loader,
+            model, criterion, optimizer, scheduler,
+            adv_model, adv_criterion, adv_optimizer, adv_scheduler,
+            args)
+        epoch_loss = train_loss
 
-        scheduler.step(val_loss)
+        if args.val:
+            val_loss = validate(epoch, val_loader,
+                model, criterion, adv_model, adv_criterion,
+                args)
+            epoch_loss = val_loss
+
+        scheduler.step(epoch_loss[0])
 
         if args.rank == 0:
             print(end='', flush=True)
             args.logger.close()
 
+            is_best = min_loss is None or epoch_loss[0] < min_loss[0]
+            if is_best:
+                min_loss = epoch_loss
+
             state = {
                 'epoch': epoch + 1,
                 'model': model.module.state_dict(),
-                'optimizer' : optimizer.state_dict(),
+                'optimizer': optimizer.state_dict(),
-                'scheduler' : scheduler.state_dict(),
+                'scheduler': scheduler.state_dict(),
-                'rng' : torch.get_rng_state(),
+                'rng': torch.get_rng_state(),
                 'min_loss': min_loss,
             }
+            if args.adv:
+                state.update({
+                    'adv_model': adv_model.module.state_dict(),
+                    'adv_optimizer': adv_optimizer.state_dict(),
+                    'adv_scheduler': adv_scheduler.state_dict(),
+                })
             ckpt_file = 'checkpoint.pth'
             best_file = 'best_model_{}.pth'
             torch.save(state, ckpt_file)
             del state
 
-            if min_loss is None or val_loss < min_loss:
+            if is_best:
-                min_loss = val_loss
                 shutil.copyfile(ckpt_file, best_file.format(epoch + 1))
                 #if os.path.isfile(best_file.format(epoch)):
                 #    os.remove(best_file.format(epoch))
 
-    destroy_process_group()
+    dist.destroy_process_group()
 
 
-def train(epoch, loader, model, criterion, optimizer, scheduler, args):
+def train(epoch, loader, model, criterion, optimizer, scheduler,
+        adv_model, adv_criterion, adv_optimizer, adv_scheduler, args):
     model.train()
+    if args.adv:
+        adv_model.train()
+
+    # loss, loss_adv, adv_loss, adv_loss_fake, adv_loss_real
+    epoch_loss = torch.zeros(5, dtype=torch.float64, device=args.device)
 
     for i, (input, target) in enumerate(loader):
         input = input.to(args.device, non_blocking=True)
@@ -169,40 +236,158 @@ def train(epoch, loader, model, criterion, optimizer, scheduler, args):
         target = narrow_like(target, output)  # FIXME pad
 
         loss = criterion(output, target)
+        epoch_loss[0] += loss.item()
+
+        if args.adv:
+            if args.cgan:
+                if hasattr(model, 'scale_factor') and model.scale_factor != 1:
+                    input = F.interpolate(input,
+                            scale_factor=model.scale_factor, mode='trilinear')
+                input = narrow_like(input, output)
+                output = torch.cat([input, output], dim=1)
+                target = torch.cat([input, target], dim=1)
+
+            # discriminator
+#
+#            outtgt = torch.cat([output.detach(), target], dim=0)
+#
+#            eval_outtgt = adv_model(outtgt)
+#
+#            fake = torch.zeros(1, dtype=torch.float32, device=args.device)
+#            fake = fake.expand_as(output.shape[0] + eval_outtgt.shape[1:])
+#            real = torch.ones(1, dtype=torch.float32, device=args.device)
+#            real = real.expand_as(target.shape[0] + eval_outtgt.shape[1:])
+#            fakereal = torch.cat([fake, real], dim=0)
+
+            eval_out = adv_model(output.detach())
+            fake = torch.zeros(1, dtype=torch.float32,
+                    device=args.device).expand_as(eval_out)
+            adv_loss_fake = adv_criterion(eval_out, fake)  # FIXME try min
+            epoch_loss[3] += adv_loss_fake.item()
+
+            eval_tgt = adv_model(target)
+            real = torch.ones(1, dtype=torch.float32,
+                    device=args.device).expand_as(eval_tgt)
+            adv_loss_real = adv_criterion(eval_tgt, real)  # FIXME try min
+            epoch_loss[4] += adv_loss_real.item()
+
+            adv_loss = 0.5 * (adv_loss_fake + adv_loss_real)
+            epoch_loss[2] += adv_loss.item()
+
+            adv_optimizer.zero_grad()
+            adv_loss = 0.001 * adv_loss + 0.999 * adv_loss.item()
+            adv_loss.backward()
+            adv_optimizer.step()
+
+            # generator adversarial loss
+
+            eval_out = adv_model(output)
+            loss_adv = adv_criterion(eval_out, real)  # FIXME try min
+            epoch_loss[1] += loss_adv.item()
+
+#            loss_fac = loss.item() / (loss.item() + loss_adv.item())
+#            loss = 0.5 * (loss * (1 + loss_fac) + loss_adv * loss_fac)  # FIXME does this work?
+            loss += 0.001 * (loss_adv - loss_adv.item())
 
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()
 
-        #if scheduler is not None:  # for batch scheduler
-            #scheduler.step()
-
         batch = epoch * len(loader) + i + 1
         if batch % args.log_interval == 0:
-            all_reduce(loss)
+            dist.all_reduce(loss)
             loss /= args.world_size
             if args.rank == 0:
-                args.logger.add_scalar('loss/train', loss.item(), global_step=batch)
+                args.logger.add_scalar('loss/batch/train', loss.item(),
+                        global_step=batch)
+                if args.adv:
+                    args.logger.add_scalar('loss/batch/train/adv/G',
+                            loss_adv.item(), global_step=batch)
+                    args.logger.add_scalars('loss/batch/train/adv/D', {
+                            'total': adv_loss.item(),
+                            'fake': adv_loss_fake.item(),
+                            'real': adv_loss_real.item(),
+                        }, global_step=batch)
+
+    dist.all_reduce(epoch_loss)
+    epoch_loss /= len(loader) * args.world_size
+    if args.rank == 0:
+        args.logger.add_scalar('loss/epoch/train', epoch_loss[0],
+                global_step=epoch+1)
+        if args.adv:
+            args.logger.add_scalar('loss/epoch/train/adv/G', epoch_loss[1],
+                    global_step=epoch+1)
+            args.logger.add_scalars('loss/epoch/train/adv/D', {
+                    'total': epoch_loss[2],
+                    'fake': epoch_loss[3],
+                    'real': epoch_loss[4],
+                }, global_step=epoch+1)
+
+    return epoch_loss
 
 
-def validate(epoch, loader, model, criterion, args):
+def validate(epoch, loader, model, criterion, adv_model, adv_criterion, args):
     model.eval()
+    if args.adv:
+        adv_model.eval()
 
-    loss = 0
+    # loss, loss_adv, adv_loss, adv_loss_fake, adv_loss_real
+    epoch_loss = torch.zeros(5, dtype=torch.float64, device=args.device)
 
     with torch.no_grad():
-        for i, (input, target) in enumerate(loader):
+        for input, target in loader:
             input = input.to(args.device, non_blocking=True)
             target = target.to(args.device, non_blocking=True)
 
             output = model(input)
             target = narrow_like(target, output)  # FIXME pad
 
-            loss += criterion(output, target)
+            loss = criterion(output, target)
+            epoch_loss[0] += loss.item()
 
-    all_reduce(loss)
+            if args.adv:
-    loss /= len(loader) * args.world_size
+                if args.cgan:
+                    if hasattr(model, 'scale_factor') and model.scale_factor != 1:
+                        input = F.interpolate(input,
+                                scale_factor=model.scale_factor, mode='trilinear')
+                    input = narrow_like(input, output)
+                    output = torch.cat([input, output], dim=1)
+                    target = torch.cat([input, target], dim=1)
+
+                # discriminator
+
+                eval_out = adv_model(output)
+                fake = torch.zeros(1, dtype=torch.float32,
+                        device=args.device).expand_as(eval_out)  # FIXME criterion wrapper: both D&G; min reduction; expand_as
+                adv_loss_fake = adv_criterion(eval_out, fake)  # FIXME try min
+                epoch_loss[3] += adv_loss_fake.item()
+
+                eval_tgt = adv_model(target)
+                real = torch.ones(1, dtype=torch.float32,
+                        device=args.device).expand_as(eval_tgt)
+                adv_loss_real = adv_criterion(eval_tgt, real)  # FIXME try min
+                epoch_loss[4] += adv_loss_real.item()
+
+                adv_loss = 0.5 * (adv_loss_fake + adv_loss_real)
+                epoch_loss[2] += adv_loss.item()
+
+                # generator adversarial loss
+
+                loss_adv = adv_criterion(eval_out, real)  # FIXME try min
+                epoch_loss[1] += loss_adv.item()
+
+    dist.all_reduce(epoch_loss)
+    epoch_loss /= len(loader) * args.world_size
     if args.rank == 0:
-        args.logger.add_scalar('loss/val', loss.item(), global_step=epoch+1)
+        args.logger.add_scalar('loss/epoch/val', epoch_loss[0],
+                global_step=epoch+1)
+        if args.adv:
+            args.logger.add_scalar('loss/epoch/val/adv/G', epoch_loss[1],
+                    global_step=epoch+1)
+            args.logger.add_scalars('loss/epoch/val/adv/D', {
+                    'total': epoch_loss[2],
+                    'fake': epoch_loss[3],
+                    'real': epoch_loss[4],
+                }, global_step=epoch+1)
 
-    return loss.item()
+    return epoch_loss