chore: include reshape in FHE execution

app.py

@@ -187,7 +187,7 @@ def encrypt(user_id, input_image, filter_name):
     client = get_client(user_id, filter_name)
 
     # Pre-process, encrypt and serialize the image
-    encrypted_image = client.pre_process_encrypt_serialize(input_image)
+    encrypted_image = client.encrypt_serialize(input_image)
 
     # Compute the input's size in Megabytes
     encrypted_input_size = len(encrypted_image) / 1000000

client_server_interface.py

@@ -118,20 +118,17 @@ class FHEClient:
         """
         return self.client.evaluation_keys.serialize()
 
-    def pre_process_encrypt_serialize(self, input_image):
-        """Pre-process, encrypt and serialize the input image in the clear.
+    def encrypt_serialize(self, input_image):
+        """Encrypt and serialize the input image in the clear.
 
         Args:
-            input_image (numpy.ndarray): The image to pre-process, encrypt and serialize.
+            input_image (numpy.ndarray): The image to encrypt and serialize.
 
         Returns:
             bytes: The pre-processed, encrypted and serialized image.
         """
-        # Pre-process the image
-        preprocessed_image = self.filter.pre_processing(input_image)
-
         # Encrypt the image
-        encrypted_image = self.client.encrypt(preprocessed_image)
+        encrypted_image = self.client.encrypt(input_image)
 
         # Serialize the encrypted image to be sent to the server
         serialized_encrypted_image = self.client.specs.serialize_public_args(encrypted_image)

filters.py

@@ -52,7 +52,7 @@ class TorchRotate(nn.Module):
         Returns:
             torch.Tensor: The rotated image.
         """
-        return x.transpose(2, 3)
+        return x.transpose(0, 1)
 
 
 class TorchConv(nn.Module):
@@ -102,15 +102,26 @@ class TorchConv(nn.Module):
                 kernel_shape[0],
                 kernel_shape[1],
             )
+
+
         else:
             raise ValueError(
                 "Wrong kernel shape, only 1D or 2D kernels are accepted. Got kernel of shape "
                 f"{kernel_shape}"
             )
 
+        # Reshape the image. This is done because Torch convolutions and Numpy arrays (for PIL 
+        # display) don't follow the same shape conventions. More precisely, x is of shape 
+        # (Width, Height, Channels) while the conv2d operator requires an input of shape 
+        # (Batch, Channels, Height, Width)
+        x = x.transpose(2, 0).unsqueeze(axis=0)
+
         # Apply the convolution
         x = nn.functional.conv2d(x, kernel, stride=stride, groups=self.groups)
 
+        # Reshape the output back to the original shape (Width, Height, Channels)
+        x = x.transpose(1, 3).reshape((x.shape[2], x.shape[3], self.n_out_channels))
+
         # Subtract a given threshold if given
         if self.threshold is not None:
             x -= self.threshold
@@ -212,8 +223,11 @@ class Filter:
         # This version's compiler only handles tuples of 1-batch array as inputset, meaning we need
         # to define the inputset as a Tuple[np.ndarray[shape=(1, 3, H, W)]]  
         np.random.seed(42)
+        # inputset = tuple(
+        #     np.random.randint(0, 256, size=((1, 3) + INPUT_SHAPE), dtype=np.int64) for _ in range(100)
+        # )
         inputset = tuple(
-            np.random.randint(0, 256, size=((1, 3) + INPUT_SHAPE), dtype=np.int64) for _ in range(100)
+            np.random.randint(0, 256, size=(INPUT_SHAPE + (3, )), dtype=np.int64) for _ in range(100)
         )
 
         # Convert the Torch module to a Numpy module
@@ -239,22 +253,6 @@ class Filter:
 
         return self.fhe_circuit
 
-    def pre_processing(self, input_image):
-        """Apply pre-processing to the encrypted input images.
-
-        Args:
-            input_image (np.ndarray): The image to pre-process.
-
-        Returns:
-            input_image (np.ndarray): The pre-processed image.
-        """
-        # Reshape the inputs found in inputset. This is done because Torch and Numpy don't follow
-        # the same shape conventions.
-        # Additionally, make sure the input images are made of integers only
-        input_image = np.expand_dims(input_image.transpose(2, 0, 1), axis=0).astype(np.int64)
-
-        return input_image
-
     def post_processing(self, output_image):
         """Apply post-processing to the encrypted output images.
 
@@ -271,10 +269,6 @@ class Filter:
         # Clip the image's values to proper RGB standards as filters don't handle such constraints
         output_image = output_image.clip(0, 255)
 
-        # Reshape the inputs found in inputset. This is done because Torch and Numpy don't follow
-        # the same shape conventions.
-        output_image = output_image.transpose(0, 2, 3, 1).squeeze(0)
-
         # Gradio requires all images to follow a RGB format
         if self.repeat_out_channels:
             output_image = output_image.repeat(3, axis=2)

filters/black and white/deployment/client.zip

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filters/black and white/deployment/server.zip

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filters/blur/deployment/client.zip

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filters/blur/deployment/server.zip

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filters/identity/deployment/client.zip

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filters/identity/deployment/server.zip

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filters/inverted/deployment/client.zip

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filters/inverted/deployment/server.zip

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filters/ridge detection/deployment/client.zip

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filters/ridge detection/deployment/server.zip

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filters/rotate/deployment/client.zip

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filters/rotate/deployment/server.zip

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filters/sharpen/deployment/client.zip

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