Add linear resamplers

map2map/models/__init__.py

@@ -4,6 +4,7 @@ from .pyramid import PyramidNet
 from .patchgan import PatchGAN, PatchGAN42
 
 from .narrow import narrow_by, narrow_cast, narrow_like
+from .resample import resample, Resampler
 
 from .lag2eul import Lag2Eul
 

map2map/models/resample.py

@@ -0,0 +1,46 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .narrow import narrow_by
+
+
+def resample(x, scale_factor, narrow=True):
+    modes = {1: 'linear', 2: 'bilinear', 3: 'trilinear'}
+    ndim = x.dim() - 2
+    mode = modes[ndim]
+
+    x = F.interpolate(x, scale_factor=scale_factor,
+                      mode=mode, align_corners=False)
+
+    if scale_factor > 1 and narrow == True:
+        edges = round(scale_factor) // 2
+        edges = max(edges, 1)
+        x = narrow_by(x, edges)
+
+    return x
+
+
+class Resampler(nn.Module):
+    """Resampling, upsampling or downsampling.
+
+    By default discard the inaccurate edges when upsampling.
+    """
+    def __init__(self, ndim, scale_factor, narrow=True):
+        super().__init__()
+
+        modes = {1: 'linear', 2: 'bilinear', 3: 'trilinear'}
+        self.mode = modes[ndim]
+
+        self.scale_factor = scale_factor
+        self.narrow = narrow
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor,
+                          mode=self.mode, align_corners=False)
+
+        if self.scale_factor > 1 and self.narrow == True:
+            edges = round(self.scale_factor) // 2
+            edges = max(edges, 1)
+            x = narrow_by(x, edges)
+
+        return x

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_cast,
+from .models import (narrow_cast, resample
         adv_model_wrapper, adv_criterion_wrapper,
         add_spectral_norm, rm_spectral_norm,
         InstanceNoise)
@@ -338,9 +338,9 @@ def train(epoch, loader, model, criterion, optimizer, scheduler,
             print('output.shape =', output.shape)
             print('target.shape =', target.shape, flush=True)
 
-        if hasattr(model, 'scale_factor') and model.scale_factor != 1:
-            input = F.interpolate(input,
-                    scale_factor=model.scale_factor, mode='nearest')
+        if (hasattr(model.module, 'scale_factor')
+                and model.module.scale_factor != 1):
+            input = resample(input, model.module.scale_factor, narrow=False)
         input, output, target = narrow_cast(input, output, target)
 
         loss = criterion(output, target)
@@ -478,9 +478,9 @@ def validate(epoch, loader, model, criterion, adv_model, adv_criterion,
 
             output = model(input)
 
-            if hasattr(model, 'scale_factor') and model.scale_factor != 1:
-                input = F.interpolate(input,
-                        scale_factor=model.scale_factor, mode='nearest')
+            if (hasattr(model.module, 'scale_factor')
+                    and model.module.scale_factor != 1):
+                input = resample(input, model.module.scale_factor, narrow=False)
             input, output, target = narrow_cast(input, output, target)
 
             loss = criterion(output, target)