lint changes

make_init.py

@@ -1,11 +1,15 @@
 from os.path import isdir, join
 
 root = "."
-finit = '__init__.py'
+finit = "__init__.py"
+
+
 def visitor(arg, dirname, fnames):
     fnames = [fname for fname in fnames if isdir(fname)]
     # here you could do some additional checks ...
-    print "adding %s to : %s" %(finit, dirname)
-    with open(join(dirname, finit), 'w') as file_: file_.write('')
+    print("adding %s to : %s" % (finit, dirname))
+    with open(join(dirname, finit), "w") as file_:
+        file_.write("")
+
 
-walk(root, visitor, None)
+walk(root, visitor, None)

quantum_image_processing/data_loader/mnist_data_loader.py

@@ -6,27 +6,26 @@
 def load_mnist_data():
     """
     Loads MNIST dataset from PyTorch using DataLoader.
-    :return: Train and Test DataLoader objects.
+    Return:
+        Train and Test DataLoader objects.
     """
 
     mnist_train = datasets.MNIST(
         root="../data_loader/mnist_data",
         train=True,
         download=False,
-        transform=torchvision.transforms.Compose([
-            torchvision.transforms.ToTensor(),
-            torchvision.transforms.Resize(2)
-        ])
+        transform=torchvision.transforms.Compose(
+            [torchvision.transforms.ToTensor(), torchvision.transforms.Resize(2)]
+        ),
     )
 
     mnist_test = datasets.MNIST(
         root="../data_loader/mnist_data",
         train=False,
         download=False,
-        transform=torchvision.transforms.Compose([
-            torchvision.transforms.ToTensor(),
-            torchvision.transforms.Resize(2)
-        ])
+        transform=torchvision.transforms.Compose(
+            [torchvision.transforms.ToTensor(), torchvision.transforms.Resize(2)]
+        ),
     )
 
     train_dataloader = torch.utils.data.DataLoader(
@@ -53,4 +52,3 @@ def collate_fn(batch):
         if label == 1 or label == 7:
             new_batch.append(item)
     return torch.utils.data.default_collate(new_batch)
-

quantum_image_processing/models/tensor_network_circuits/mera.py

@@ -84,7 +84,7 @@ def _apply_real_general_block(self, qubits, param_vector_copy):
 
     def mera_simple(self, complex_struct=True):
         param_vector = ParameterVector(
-            'theta',
+            "theta",
             int(self.img_dim / 2 * (self.img_dim / 2 + 1)) + 3,
         )
         param_vector_copy = param_vector
@@ -97,11 +97,13 @@ def mera_simple(self, complex_struct=True):
     # Check number of params here.
     def mera_general(self, complex_struct=True):
         if complex_struct:
-            param_vector = ParameterVector('theta', 20 * self.img_dim - 1)
+            param_vector = ParameterVector("theta", 20 * self.img_dim - 1)
             param_vector_copy = param_vector
-            return self.mera_backbone(self._apply_complex_general_block, param_vector_copy)
+            return self.mera_backbone(
+                self._apply_complex_general_block, param_vector_copy
+            )
         else:
-            param_vector = ParameterVector('theta', 10 * self.img_dim - 1)
+            param_vector = ParameterVector("theta", 10 * self.img_dim - 1)
             param_vector_copy = param_vector
             return self.mera_backbone(self._apply_real_general_block, param_vector_copy)
 

quantum_image_processing/models/tensor_network_circuits/mnist-ttn-classification.py

@@ -19,7 +19,7 @@ def data_embedding(img_dim):
     :return:
     """
 
-    params = ParameterVector('img_data', img_dim)
+    params = ParameterVector("img_data", img_dim)
     embedding = QuantumCircuit(img_dim)
     for i in range(img_dim):
         embedding.ry(params[i], i)
@@ -70,7 +70,6 @@ def callback_graph(weights, obj_func_eval):
         plt.savefig("graph.pdf")
         plt.show()
 
-
     estimator_qnn = EstimatorQNN(
         circuit=circ,
         observables=observable,

quantum_image_processing/models/tensor_network_circuits/tree_tensor_network_ttn.py

@@ -97,7 +97,7 @@ def ttn_simple(self, complex_struct=True):
 
         :return:
         """
-        param_vector = ParameterVector('theta', 2 * self.img_dim - 1)
+        param_vector = ParameterVector("theta", 2 * self.img_dim - 1)
         param_vector_copy = param_vector
 
         if complex_struct:
@@ -127,11 +127,13 @@ def ttn_general(self, complex_struct=True):
         """
         # Check number of params here.
         if complex_struct:
-            param_vector = ParameterVector('theta', 15 * self.img_dim - 1)
+            param_vector = ParameterVector("theta", 15 * self.img_dim - 1)
             param_vector_copy = param_vector
-            return self.ttn_backbone(self._apply_complex_general_block, param_vector_copy)
+            return self.ttn_backbone(
+                self._apply_complex_general_block, param_vector_copy
+            )
         else:
-            param_vector = ParameterVector('theta', 6 * self.img_dim - 1)
+            param_vector = ParameterVector("theta", 6 * self.img_dim - 1)
             param_vector_copy = param_vector
             return self.ttn_backbone(self._apply_real_general_block, param_vector_copy)
 

quantum_image_processing/representations/first-step.py

@@ -1,15 +1,16 @@
 import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
 import numpy as np
 from keras.datasets import mnist
 import tensorflow as tf
 from qiskit import QuantumCircuit
 import matplotlib.pyplot as plt
 
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
-print(f'X train: {x_train.shape}')
-print(f'Y train: {y_train.shape}')
-print(f'X test {x_test.shape}')
+print(f"X train: {x_train.shape}")
+print(f"Y train: {y_train.shape}")
+print(f"X test {x_test.shape}")
 
 # Plot image data
 # for i in range(9):
@@ -27,10 +28,10 @@
 # Plot original and resized images
 row = 3
 cols = 3
-fig, axs = plt.subplots(row, 2*cols)
+fig, axs = plt.subplots(row, 2 * cols)
 for i in range(row):
-    for j in range(0, 2*cols, 2):
-        axs[i, j].imshow(x_train[i+j])
-        axs[i, j+1].imshow(resize_x_train[i+j])
+    for j in range(0, 2 * cols, 2):
+        axs[i, j].imshow(x_train[i + j])
+        axs[i, j + 1].imshow(resize_x_train[i + j])
     # plt.imshow(resize_x_train[i], cmap=plt.get_cmap('binary_r'))
-plt.show()
+plt.show()