upd according to core changes

aidge_quantization/unit_tests/aidge_ptq.py

@@ -29,22 +29,22 @@ labels  = np.load(gzip.GzipFile('assets/mnist_labels.npy.gz',  "r"))
 # --------------------------------------------------------------
 
 # Create the Producer node
-input_array  = np.zeros(784).astype('float32') 
+input_array  = np.zeros(784).astype('float32')
 input_tensor = aidge_core.Tensor(input_array)
 input_node = aidge_core.Producer(input_tensor, "X")
 
 # Configuration for the inputs
-input_node.get_operator().set_datatype(aidge_core.DataType.Float32)
+input_node.get_operator().set_datatype(aidge_core.dtype.float32)
 input_node.get_operator().set_backend("cpu")
 
 # Link Producer to the Graph
 input_node.add_child(aidge_model)
 
 # Configuration for the model
-aidge_model.set_datatype(aidge_core.DataType.Float32)
+aidge_model.set_datatype(aidge_core.dtype.float32)
 aidge_model.set_backend("cpu")
 
-# Create the Scheduler 
+# Create the Scheduler
 scheduler = aidge_core.SequentialScheduler(aidge_model)
 
 # --------------------------------------------------------------
@@ -55,7 +55,7 @@ def propagate(model, scheduler, sample):
     # Setup the input
     input_tensor = aidge_core.Tensor(sample)
     input_node.get_operator().set_output(0, input_tensor)
-    # Run the inference 
+    # Run the inference
     scheduler.forward(verbose=False)
     # Gather the results
     output_node = model.get_output_nodes().pop()
@@ -64,7 +64,7 @@ def propagate(model, scheduler, sample):
 
 def bake_sample(sample):
     sample = np.reshape(sample, (1, 1, 28, 28))
-    return sample.astype('float32') 
+    return sample.astype('float32')
 
 print('\n EXAMPLE INFERENCES :')
 for i in range(10):
@@ -126,12 +126,12 @@ print('\n EXAMPLE QUANTIZED INFERENCES :')
 for i in range(10):
     input_array = bake_sample(samples[i])
     output_array = propagate(aidge_model, scheduler, input_array)
-    print(labels[i] , ' -> ', np.round(output_array, 2))    
+    print(labels[i] , ' -> ', np.round(output_array, 2))
 
 # --------------------------------------------------------------
 # COMPUTE THE MODEL ACCURACY
 # --------------------------------------------------------------
-    
+
 accuracy = compute_accuracy(aidge_model, samples[0:NB_SAMPLES], labels)
 print(f'\n QUANTIZED MODEL ACCURACY : {accuracy * 100:.3f}%')