upd leras ops

core/leras/ops/__init__.py

@@ -137,7 +137,39 @@ def resize2d_bilinear(x, size=2):
     return x
 nn.resize2d_bilinear = resize2d_bilinear
 
+def resize2d_nearest(x, size=2):
+    if size in [-1,0,1]:
+        return x
+        
+    """
+    if size > 0:
+        raise Exception("")
+    else:
+        if nn.data_format == "NCHW":
+            x = x[:,:,::-size,::-size]
+        else:
+            x = x[:,::-size,::-size,:]
+    return x
+    """
+        
+    h = x.shape[nn.conv2d_spatial_axes[0]].value
+    w = x.shape[nn.conv2d_spatial_axes[1]].value
 
+    if nn.data_format == "NCHW":
+        x = tf.transpose(x, (0,2,3,1))
+
+    if size > 0:
+        new_size = (h*size,w*size)
+    else:
+        new_size = (h//-size,w//-size)
+
+    x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+
+    if nn.data_format == "NCHW":
+        x = tf.transpose(x, (0,3,1,2))
+
+    return x
+nn.resize2d_nearest = resize2d_nearest
 
 def flatten(x):
     if nn.data_format == "NHWC":
@@ -237,7 +269,9 @@ def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03
         img_dtype = img1.dtype
         img1 = tf.cast(img1, tf.float32)
         img2 = tf.cast(img2, tf.float32)
-
+    
+    filter_size = max(1, filter_size)
+    
     kernel = np.arange(0, filter_size, dtype=np.float32)
     kernel -= (filter_size - 1 ) / 2.0
     kernel = kernel**2