Move UNet with ResBlock to VNet and Revert UNet to the previous simple version

map2map/args.py

@@ -18,6 +18,7 @@ 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')
+    parser.add_argument('--model', required=True, help='model from models')
     parser.add_argument('--criterion', default='MSELoss',
             help='model criterion from torch.nn')
     parser.add_argument('--load-state', default='', type=str,

map2map/models/__init__.py

@@ -1,2 +1,3 @@
 from .unet import UNet
+from .vnet import VNet
 from .conv import narrow_like

map2map/models/conv.py

@@ -57,22 +57,24 @@ class ConvBlock(nn.Module):
 
 class ResBlock(ConvBlock):
     """Residual convolution blocks of the form specified by `seq`. Input is
-    added to the residual followed by an activation.
+    added to the residual followed by an activation (trailing `'A'` in `seq`).
     """
     def __init__(self, in_channels, out_channels=None, mid_channels=None,
-            seq='CBACB'):
-        if 'U' in seq or 'D' in seq:
-            raise NotImplementedError('upsample and downsample layers '
-                    'not supported yet')
-
+            seq='CBACBA'):
         if out_channels is None:
             out_channels = in_channels
             self.skip = None
         else:
             self.skip = nn.Conv3d(in_channels, out_channels, 1)
 
+        if 'U' in seq or 'D' in seq:
+            raise NotImplementedError('upsample and downsample layers '
+                    'not supported yet')
+
+        assert seq[-1] == 'A', 'block must end with activation'
+
         super().__init__(in_channels, out_channels, mid_channels=mid_channels,
-                seq=seq)
+                seq=seq[:-1])
 
         self.act = nn.PReLU()
 

map2map/models/unet.py

@@ -1,39 +1,24 @@
 import torch
 import torch.nn as nn
 
-from .conv import ConvBlock, ResBlock, narrow_like
+from .conv import ConvBlock, narrow_like
 
 
 class UNet(nn.Module):
     def __init__(self, in_channels, out_channels):
         super().__init__()
 
-        self.conv_0l = nn.Sequential(
-            ConvBlock(in_channels, 64, seq='CA'),
-            ResBlock(64, seq='CBACBACB'),
-        )
-        self.down_0l = ConvBlock(64, 128, seq='DBA')
-        self.conv_1l = nn.Sequential(
-            ResBlock(128, seq='CBACB'),
-            ResBlock(128, seq='CBACB'),
-        )
-        self.down_1l = ConvBlock(128, 256, seq='DBA')
+        self.conv_0l = ConvBlock(in_channels, 64, seq='CAC')
+        self.down_0l = ConvBlock(64, 64, seq='BADBA')
+        self.conv_1l = ConvBlock(64, 64, seq='CBAC')
+        self.down_1l = ConvBlock(64, 64, seq='BADBA')
 
-        self.conv_2c = nn.Sequential(
-            ResBlock(256, seq='CBACB'),
-            ResBlock(256, seq='CBACB'),
-        )
+        self.conv_2c = ConvBlock(64, 64, seq='CBAC')
 
-        self.up_1r = ConvBlock(256, 128, seq='UBA')
-        self.conv_1r = nn.Sequential(
-            ResBlock(256, seq='CBACB'),
-            ResBlock(256, seq='CBACB'),
-        )
-        self.up_0r = ConvBlock(256, 64, seq='UBA')
-        self.conv_0r = nn.Sequential(
-            ResBlock(128, seq='CBACBAC'),
-            ConvBlock(128, out_channels, seq='C')
-        )
+        self.up_1r = ConvBlock(64, 64, seq='BAUBA')
+        self.conv_1r = ConvBlock(128, 64, seq='CBAC')
+        self.up_0r = ConvBlock(64, 64, seq='BAUBA')
+        self.conv_0r = ConvBlock(128, out_channels, seq='CAC')
 
     def forward(self, x):
         y0 = self.conv_0l(x)

map2map/models/vnet.py

@@ -0,0 +1,59 @@
+import torch
+import torch.nn as nn
+
+from .conv import ConvBlock, ResBlock, narrow_like
+
+
+class VNet(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+
+        self.conv_0l = nn.Sequential(
+            ConvBlock(in_channels, 64, seq='CA'),
+            ResBlock(64, seq='CBACBACBA'),
+        )
+        self.down_0l = ConvBlock(64, 128, seq='DBA')
+        self.conv_1l = nn.Sequential(
+            ResBlock(128, seq='CBACBA'),
+            ResBlock(128, seq='CBACBA'),
+        )
+        self.down_1l = ConvBlock(128, 256, seq='DBA')
+
+        self.conv_2c = nn.Sequential(
+            ResBlock(256, seq='CBACBA'),
+            ResBlock(256, seq='CBACBA'),
+        )
+
+        self.up_1r = ConvBlock(256, 128, seq='UBA')
+        self.conv_1r = nn.Sequential(
+            ResBlock(256, seq='CBACBA'),
+            ResBlock(256, seq='CBACBA'),
+        )
+        self.up_0r = ConvBlock(256, 64, seq='UBA')
+        self.conv_0r = nn.Sequential(
+            ResBlock(128, seq='CBACBACA'),
+            ConvBlock(128, out_channels, seq='C')
+        )
+
+    def forward(self, x):
+        y0 = self.conv_0l(x)
+        x = self.down_0l(y0)
+
+        y1 = self.conv_1l(x)
+        x = self.down_1l(y1)
+
+        x = self.conv_2c(x)
+
+        x = self.up_1r(x)
+        y1 = narrow_like(y1, x)
+        x = torch.cat([y1, x], dim=1)
+        del y1
+        x = self.conv_1r(x)
+
+        x = self.up_0r(x)
+        y0 = narrow_like(y0, x)
+        x = torch.cat([y0, x], dim=1)
+        del y0
+        x = self.conv_0r(x)
+
+        return x

map2map/test.py

@@ -3,7 +3,8 @@ import torch
 from torch.utils.data import DataLoader
 
 from .data import FieldDataset
-from .models import UNet, narrow_like
+from . import models
+from .models import narrow_like
 
 
 def test(args):
@@ -23,7 +24,7 @@ def test(args):
 
     in_channels, out_channels = test_dataset.channels
 
-    model = UNet(in_channels, out_channels)
+    model = models.__dict__[args.model](in_channels, out_channels)
     criterion = torch.nn.__dict__[args.criterion]()
 
     device = torch.device('cpu')

map2map/train.py

@@ -10,7 +10,8 @@ from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 
 from .data import FieldDataset
-from .models import UNet, narrow_like
+from . import models
+from .models import narrow_like
 
 
 def node_worker(args):
@@ -82,7 +83,7 @@ def gpu_worker(local_rank, args):
 
     in_channels, out_channels = train_dataset.channels
 
-    model = UNet(in_channels, out_channels)
+    model = models.__dict__[args.model](in_channels, out_channels)
     model.to(args.device)
     model = DistributedDataParallel(model, device_ids=[args.device])
 

scripts/dis2dis-test.slurm

@@ -37,7 +37,7 @@ tgt_files="$files"
 m2m.py test \
     --test-in-patterns "$data_root_dir/$in_dir/$test_dirs/$in_files" \
     --test-tgt-patterns "$data_root_dir/$tgt_dir/$test_dirs/$tgt_files" \
-    --norms cosmology.dis \
+    --norms cosmology.dis --model VNet \
     --batches 1 --loader-workers 0 --pad-or-crop 40 \
     --load-state best_model.pth
 

scripts/dis2dis.slurm

@@ -42,7 +42,7 @@ srun m2m.py train \
     --train-tgt-patterns "$data_root_dir/$tgt_dir/$train_dirs/$tgt_files" \
     --val-in-patterns "$data_root_dir/$in_dir/$val_dirs/$in_files" \
     --val-tgt-patterns "$data_root_dir/$tgt_dir/$val_dirs/$tgt_files" \
-    --norms cosmology.dis --augment \
+    --norms cosmology.dis --augment --model VNet \
     --epochs 1024 --batches 3 --loader-workers 3 --lr 0.001
 #    --load-state checkpoint.pth
 

scripts/vel2vel-test.slurm

@@ -37,7 +37,7 @@ tgt_files="$files"
 m2m.py test \
     --test-in-patterns "$data_root_dir/$in_dir/$test_dirs/$in_files" \
     --test-tgt-patterns "$data_root_dir/$tgt_dir/$test_dirs/$tgt_files" \
-    --norms cosmology.vel \
+    --norms cosmology.vel --model VNet \
     --batches 1 --loader-workers 0 --pad-or-crop 40 \
     --load-state best_model.pth
 

scripts/vel2vel.slurm

@@ -42,7 +42,7 @@ srun m2m.py train \
     --train-tgt-patterns "$data_root_dir/$tgt_dir/$train_dirs/$tgt_files" \
     --val-in-patterns "$data_root_dir/$in_dir/$val_dirs/$in_files" \
     --val-tgt-patterns "$data_root_dir/$tgt_dir/$val_dirs/$tgt_files" \
-    --norms cosmology.vel --augment \
+    --norms cosmology.vel --augment --model VNet \
     --epochs 1024 --batches 3 --loader-workers 3 --lr 0.001
 #    --load-state checkpoint.pth