From 961952520ea33dcb51cbe7c82418f99dc27843f6 Mon Sep 17 00:00:00 2001
From: Axel Farrugia <axel.farrugia@cea.fr>
Date: Thu, 24 Apr 2025 13:35:08 +0200
Subject: [PATCH] [Chore] Clean unused code
---
examples/export_ResNet18/resnet18.py | 86 +++-------------------------
1 file changed, 8 insertions(+), 78 deletions(-)
diff --git a/examples/export_ResNet18/resnet18.py b/examples/export_ResNet18/resnet18.py
index 6ca0223..354e474 100644
--- a/examples/export_ResNet18/resnet18.py
+++ b/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
--
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