Adding to categorical function

Normally the PyTorch labels for classification are in integer numbers associated with classes.
For the MixMatch algorithm, the labels need to be in one hot format.
The to_categorical function was taken from Keras code, is able to handle batches.
num_clases=10 for future CIFAR test.
Inside of the algorithm y is converted to one hot encode format. 
At the end of the MixMatch phase, the labels p and q are converted back to the natural classes format in PyTorch

Author: fperdigon

layer.py

@@ -44,9 +44,31 @@ def label_guessing(model, ub, K):
 
     return (sum/K).cpu().detach().numpy()
 
+def to_categorical(y, num_classes=None, dtype='float32'):
+    """Converts a class vector (integers) to binary class matrix.
+    E.g. for use with categorical_crossentropy.
+    Taken from Keras code
+    https://github.com/keras-team/keras/blob/master/keras/utils/np_utils.py#L9
+    """
+
+    y = np.array(y, dtype='int')
+    input_shape = y.shape
+    if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
+        input_shape = tuple(input_shape[:-1])
+    y = y.ravel()
+    if not num_classes:
+        num_classes = np.max(y) + 1
+    n = y.shape[0]
+    categorical = np.zeros((n, num_classes), dtype=dtype)
+    categorical[np.arange(n), y] = 1
+    output_shape = input_shape + (num_classes,)
+    categorical = np.reshape(categorical, output_shape)
+    return categorical
+
 
 def mixmatch(x, y, u, model, augment_fn, T=0.5, K=2, alpha=0.75):
     xb = augment_fn(x)
+    y = to_categorical(y, num_classes=10) # Converting to one hot encode, num_clases=10 for future CIFAR test
     ub = [augment_fn(u) for _ in range(K)]
     avg_probs = label_guessing(model, ub, K)
     qb = sharpen(avg_probs, T)
@@ -57,6 +79,10 @@ def mixmatch(x, y, u, model, augment_fn, T=0.5, K=2, alpha=0.75):
     Wy = np.concatenate([qb, y], axis=0)[indices]
     X, p = mixup_mod(xb, Wx[:len(xb)], y, Wy[:len(xb)], alpha)
     U, q = mixup_mod(Ux, Wx[len(xb):], Uy, Wy[len(xb):], alpha)
+    
+    # One hot decode for PyTorch labels compability
+    p = p.argmax(axis=1)
+    q = q.argmax(axis=1)
     return X, p, U, q