[Chore] Clean unused code

examples/export_ResNet18/resnet18.py

@@ -149,7 +149,10 @@ FOLD_GRAPH      = True
 DEV_MODE      = args.dev
 AIDGE_CMP     = args.aidge_cmp
 
-IMAGENET_PATH = "/database2/ILSVRC2012/val"    # Search for ILSVRC2012
+### Add your paths here ###
+IMAGENET_PATH = "/database/ILSVRC2012/val"  # Look for ILSVRC2012/val
+VAL_PATH = "/database/ILSVRC2012/val.txt"   # File containing labels of image of val folder (Look for val.txt)
+###########################
 
 def print_cfg():
     print('\n RNG_SEED         = ', RNG_SEED)
@@ -164,7 +167,7 @@ def print_cfg():
     print(' TARGET_TYPE      = ', TARGET_TYPE)
     print(' FOLD_GRAPH       = ', FOLD_GRAPH)
     print(' USE_CUDA         = ', USE_CUDA)
-    print(' DEV_MODE        = ', DEV_MODE)
+    print(' DEV_MODE         = ', DEV_MODE)
     print(' ROUNDING         = ', ROUNDING)
 
 print_cfg()
@@ -175,8 +178,6 @@ np.random.seed(RNG_SEED)
 
 backend = "cuda" if USE_CUDA else "cpu"
 
-VAL_PATH = "/database2/ILSVRC2012/val.txt"  # File containing labels of image of val folder
-
 image_label_pairs = []
 with open(VAL_PATH, 'r') as f:
     for line in f:
@@ -185,14 +186,10 @@ with open(VAL_PATH, 'r') as f:
             image_name, label = parts
             image_label_pairs.append((image_name, int(label)))
 
-#random.shuffle(image_label_pairs)
 np.random.seed(RNG_SEED)
-#image_label_pairs = np.random.permutation(image_label_pairs).tolist()
-NB_SELECT = max(NB_TEST, NB_CALIB)  # Vérifie que NB_TEST et NB_CALIB sont fixés
+NB_SELECT = max(NB_TEST, NB_CALIB)  # Check that NB_TEST and NB_CALIB are fixed
 selected_pairs = image_label_pairs[:NB_SELECT]
 
-#selected_pairs = image_label_pairs[:max(NB_TEST, NB_CALIB)]
-
 # --------------------------------------------------------------
 # CREATE THE SAMPLES
 # --------------------------------------------------------------
@@ -222,10 +219,6 @@ for image_name, label in selected_pairs:
         except Exception as e:
             print(f"Error with image {image_path}: {e}")
 
-#print(f"Number of loaded tensors: {len(tensors)}")
-#for lbl, img_path in zip(labels, paths):
-#    print(f"Label: {lbl} -> Image Path: {img_path}")
-
 backend = "cuda" if USE_CUDA else "cpu"
 aidge_tensors = []
 for tensor in tensors:
@@ -343,13 +336,6 @@ Each time the graph has been change, it has to be reset.
 Here some Quantizer and Cast nodes have been added. 
 """
 
-""" [START Fix]
-We need first to manually add an input tensor with the correct datatype, 
-as it is not automatically done in PTQ. 
-"""
-# input_node = model.get_ordered_inputs()[0]
-# input_node[0].get_operator().set_input(0,aidge_tensors[0])
-""" [END Fix]"""
 if quantize_model:
     scheduler.reset_scheduling()
 
@@ -357,17 +343,6 @@ if quantize_model:
 # PERFORM THE EXAMPLE INFERENCES AGAIN
 # --------------------------------------------------------------
 
-
-
-#for node in model.get_input_nodes():
-#    if node.type() == "Pad2D":
-#        node.set_name("Pad2D_input")
-#
-#for node in model.get_nodes():
-#    if (node.type() == "Conv2D"):
-#        if node.get_parent(0).name() == "Pad2D_input":
-#           node.set_name("Conv2D_input")
-
 model.save("post_ptq")
 
 if (DO_EXAMPLES and quantize_model):
@@ -385,11 +360,6 @@ if (DO_EXAMPLES and quantize_model):
     print('\n MODEL ACCURACY = ', accuracy * 100, '%')
     print('\n QUANTIZED ACCURACY = ', quant_accuracy * 100, '%')
 
-    print("post ptq")
-#    output_array = propagate(model, scheduler, aidge_tensors[0])
-
-#model.log_outputs("log_outputs_post_ptq")
-
 if USE_CUDA:    
     model.set_backend("cpu")
     for aidge_tensor in aidge_tensors:
@@ -531,19 +501,13 @@ for node in model.get_nodes():
 # EXPORT THE MODEL
 # --------------------------------------------------------------
 
-
 model.save("exported_model")
 inputs_tensor = aidge_core.Tensor(np.array(aidge_tensors[0]))
-# print(np.array(inputs_tensor)[0])
-inputs_tensor.set_data_format(aidge_core.dformat.nchw)
-inputs_tensor.set_data_format(aidge_core.dformat.nhwc)
+inputs_tensor.set_data_format(aidge_core.dformat.nchw)  # Init the dataformat (default -> nchw)
+inputs_tensor.set_data_format(aidge_core.dformat.nhwc)  # Transpose the data  (nchw -> nhwc)
 if args.dtype == "int8":
     inputs_tensor.set_datatype(aidge_core.dtype.int8)
 
-#print(np.array(inputs_tensor)[0,:,:,:])
-#inputs_tensor.cpy_transpose(inputs_tensor, aidge_core.get_permutation_mapping(aidge_core.dformat.nchw, aidge_core.dformat.nhwc))
-# print(np.array(inputs_tensor)[0])
-
 aidge_export_cpp.export(EXPORT_FOLDER, 
                         model, 
                         scheduler, 
@@ -551,37 +515,3 @@ aidge_export_cpp.export(EXPORT_FOLDER,
                         inputs_tensor=inputs_tensor,
                         dev_mode = DEV_MODE,
                         aidge_cmp = AIDGE_CMP)
-#
-## --------------------------------------------------------------
-## GENERATE LABELS AND INPUTS FOR EXAMPLE INFERENCE
-## --------------------------------------------------------------
-#
-#input_label = np.array(labels).astype(np.int32).reshape(len(labels), 1)
-#generate_input_file(export_folder=EXPORT_FOLDER + "/data", 
-#                    array_name="labels",
-#                    tensor=aidge_core.Tensor(input_label))
-#
-#input_tensor = np.array(aidge_tensors[0:NB_TEST]).astype(np.int8).reshape(NB_TEST, 3, 224, 224)
-#generate_input_file(export_folder=EXPORT_FOLDER + "/data", 
-#                    array_name="inputs",
-#                    tensor=aidge_core.Tensor(input_tensor))
-#
-#
-#if TEST_MODE:
-#    input_tensor = aidge_core.Tensor(input_tensor)
-#    input_tensor.set_data_format(aidge_core.dformat.nchw)
-#    input_tensor.cpy_transpose(input_tensor, aidge_core.get_permutation_mapping(aidge_core.dformat.nchw, aidge_core.dformat.nhwc))
-#    generate_input_file(export_folder=EXPORT_FOLDER + "/data", 
-#                        array_name="inputs_ref",
-#                        tensor=input_tensor)
-#    
-## --------------------------------------------------------------
-## GENERATE DOCUMENTATION
-## --------------------------------------------------------------
-#
-#"""
-#Copy the corresponding README file into the generated export. 
-#"""
-#
-#generate_documentation(EXPORT_FOLDER, TEST_MODE)
-#
\ No newline at end of file