Add narrow_cast and narrow_by

map2map/models/__init__.py

@@ -3,7 +3,7 @@ from .vnet import VNet, VNetFat
 from .pyramid import PyramidNet
 from .patchgan import PatchGAN, PatchGAN42
 
-from .conv import narrow_like
+from .narrow import narrow_by, narrow_cast, narrow_like
 
 from .lag2eul import Lag2Eul
 

map2map/models/conv.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 
+from .narrow import narrow_like
 from .swish import Swish
 
 
@@ -114,16 +115,3 @@ class ResBlock(ConvBlock):
             x = self.act(x)
 
         return x
-
-
-def narrow_like(a, b):
-    """Narrow a to be like b.
-
-    Try to be symmetric but cut more on the right for odd difference,
-    consistent with the downsampling.
-    """
-    for d in range(2, a.dim()):
-        width = a.shape[d] - b.shape[d]
-        half_width = width // 2
-        a = a.narrow(d, half_width, a.shape[d] - width)
-    return a

map2map/models/narrow.py

@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+
+
+def narrow_by(a, c):
+    """Narrow a by size c symmetrically on all edges.
+    """
+    for d in range(2, a.dim()):
+        a = a.narrow(d, c, a.shape[d] - 2 * c)
+    return a
+
+
+def narrow_cast(*tensors):
+    """Narrow each tensor to the minimum length in each dimension.
+
+    Try to be symmetric but cut more on the right for odd difference
+    """
+    dim_max = max(a.dim() for a in tensors)
+
+    len_min = {d: min(a.shape[d] for a in tensors) for d in range(2, dim_max)}
+
+    casted_tensors = []
+    for a in tensors:
+        for d in range(2, dim_max):
+            width = a.shape[d] - len_min[d]
+            half_width = width // 2
+            a = a.narrow(d, half_width, a.shape[d] - width)
+
+        casted_tensors.append(a)
+
+    return casted_tensors
+
+
+def narrow_like(a, b):
+    """Narrow a to be like b.
+
+    Try to be symmetric but cut more on the right for odd difference
+    """
+    for d in range(2, a.dim()):
+        width = a.shape[d] - b.shape[d]
+        half_width = width // 2
+        a = a.narrow(d, half_width, a.shape[d] - width)
+    return a

map2map/models/unet.py

@@ -1,7 +1,8 @@
 import torch
 import torch.nn as nn
 
-from .conv import ConvBlock, narrow_like
+from .conv import ConvBlock
+from .narrow import narrow_like
 
 
 class UNet(nn.Module):

map2map/models/vnet.py

@@ -1,7 +1,8 @@
 import torch
 import torch.nn as nn
 
-from .conv import ConvBlock, ResBlock, narrow_like
+from .conv import ConvBlock, ResBlock
+from .narrow import narrow_like
 
 
 class VNet(nn.Module):

map2map/test.py

@@ -1,12 +1,12 @@
+import sys
 from pprint import pprint
 import numpy as np
 import torch
-import sys
 from torch.utils.data import DataLoader
 
 from .data import FieldDataset
 from . import models
-from .models import narrow_like
+from .models import narrow_cast
 from .utils import import_attr, load_model_state_dict
 
 
@@ -58,12 +58,7 @@ def test(args):
     with torch.no_grad():
         for i, (input, target) in enumerate(test_loader):
             output = model(input)
-            if args.pad > 0:  # FIXME
-                output = narrow_like(output, target)
-                input = narrow_like(input, target)
-            else:
-                target = narrow_like(target, output)
-                input = narrow_like(input, output)
+            input, output, target = narrow_cast(input, output, target)
 
             loss = criterion(output, target)
 

map2map/train.py

@@ -17,7 +17,7 @@ from torch.utils.tensorboard import SummaryWriter
 from .data import FieldDataset, GroupedRandomSampler
 from .data.figures import plt_slices
 from . import models
-from .models import (narrow_like,
+from .models import (narrow_cast,
         adv_model_wrapper, adv_criterion_wrapper,
         add_spectral_norm, rm_spectral_norm,
         InstanceNoise)
@@ -333,12 +333,15 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
         target = target.to(device, non_blocking=True)
 
         output = model(input)
+        if epoch == 0 and i == 0 and rank == 0:
+            print('input.shape =', input.shape)
+            print('output.shape =', output.shape)
+            print('target.shape =', target.shape, flush=True)
 
-        target = narrow_like(target, output)  # FIXME pad
         if hasattr(model, 'scale_factor') and model.scale_factor != 1:
             input = F.interpolate(input,
                     scale_factor=model.scale_factor, mode='nearest')
-        input = narrow_like(input, output)
+        input, output, target = narrow_cast(input, output, target)
 
         loss = criterion(output, target)
         epoch_loss[0] += loss.item()
@@ -475,11 +478,10 @@ def validate(epoch, loader, model, criterion, adv_model, adv_criterion,
 
             output = model(input)
 
-            target = narrow_like(target, output)  # FIXME pad
             if hasattr(model, 'scale_factor') and model.scale_factor != 1:
                 input = F.interpolate(input,
                         scale_factor=model.scale_factor, mode='nearest')
-            input = narrow_like(input, output)
+            input, output, target = narrow_cast(input, output, target)
 
             loss = criterion(output, target)
             epoch_loss[0] += loss.item()