From 2e5458246c6c8d881a058dec212290e981a51cba Mon Sep 17 00:00:00 2001
From: "michael.divia" <michael.divia@etu.hesge.ch>
Date: Wed, 9 Apr 2025 16:34:03 +0200
Subject: [PATCH] Standartise everything
---
Python/convert_onnx.py | 42 -------------
python/convert_onnx.py | 37 ++++++++++++
python/hailo_compile.py | 59 +++++++++++++++++++
{Python => python}/pokedex_ResNet50.py | 2 +-
{Python => python}/pokedex_Xception.py | 2 +-
.../pokedex_rpi.py | 18 +++++-
.../pokedex_test.py | 32 +++++++---
7 files changed, 136 insertions(+), 56 deletions(-)
delete mode 100644 Python/convert_onnx.py
create mode 100644 python/convert_onnx.py
create mode 100644 python/hailo_compile.py
rename {Python => python}/pokedex_ResNet50.py (98%)
rename {Python => python}/pokedex_Xception.py (98%)
rename Python/predict_pokemon.py => python/pokedex_rpi.py (71%)
rename Python/test_ResNet50.py => python/pokedex_test.py (67%)
diff --git a/Python/convert_onnx.py b/Python/convert_onnx.py
deleted file mode 100644
index d59ebff..0000000
--- a/Python/convert_onnx.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import tensorflow as tf
-import tf2onnx
-import argparse
-
-# WHAT ?
-parser = argparse.ArgumentParser(description="WHAT ?!")
-parser.add_argument("--model", choices=["1", "2"], default="1",
- help="1 = ResNet50, 2 = Xception")
-args = parser.parse_args()
-
-# Load Sequential model
-if args.model == "1":
- seq_model = tf.keras.models.load_model("../models/ResNet50/pokemon_resnet50.h5", compile=False)
-elif args.model == "2":
- seq_model = tf.keras.models.load_model("../models/ResNet50/pokemon_xception.h5", compile=False)
-
-# Create input layer with same shape
-inputs = tf.keras.Input(shape=(224, 224, 3), name="input")
-
-# Call the Sequential model as a function
-outputs = seq_model(inputs)
-
-# Wrap in Functional model
-model = tf.keras.Model(inputs=inputs, outputs=outputs)
-
-# Convert to ONNX
-spec = (tf.TensorSpec((1, 224, 224, 3), tf.float32, name="input"),)
-
-if args.model == "1":
- onnx_model, _ = tf2onnx.convert.from_keras(
- model,
- input_signature=spec,
- opset=13,
- output_path="../models/ResNet50/pokemon_resnet50.onnx"
- )
-elif args.model == "2":
- onnx_model, _ = tf2onnx.convert.from_keras(
- model,
- input_signature=spec,
- opset=13,
- output_path="../models/ResNet50/pokemon_xception.onnx"
- )
\ No newline at end of file
diff --git a/python/convert_onnx.py b/python/convert_onnx.py
new file mode 100644
index 0000000..5cc6061
--- /dev/null
+++ b/python/convert_onnx.py
@@ -0,0 +1,37 @@
+import tensorflow as tf
+import tf2onnx
+import argparse
+
+# --- WHAT ? ---
+parser = argparse.ArgumentParser(description="WHAT ?!")
+parser.add_argument("--model", choices=["1", "2"], required=True, help="1 = ResNet50, 2 = Xception")
+args = parser.parse_args()
+
+# Paths
+if args.model == "1":
+ h5_path = "../models/ResNet50/pokedex_ResNet50.h5"
+ onnx_path = "../models/ResNet50/pokedex_ResNet50.onnx"
+elif args.model == "2":
+ h5_path = "../models/Xception/pokedex_Xception.h5"
+ onnx_path = "../models/ResNet50/pokedex_Xception.onnx"
+
+# --- Load Sequential model ---
+seq_model = tf.keras.models.load_model(h5_path, compile=False)
+
+# --- Create input layer with same shape ---
+inputs = tf.keras.Input(shape=(224, 224, 3), name="input")
+
+# --- Call the Sequential model as a function ---
+outputs = seq_model(inputs)
+
+# --- Wrap in Functional model ---
+model = tf.keras.Model(inputs=inputs, outputs=outputs)
+
+# --- Convert to ONNX ---
+spec = (tf.TensorSpec((1, 224, 224, 3), tf.float32, name="input"),)
+onnx_model, _ = tf2onnx.convert.from_keras(
+ model,
+ input_signature=spec,
+ opset=13,
+ output_path=onnx_path
+)
\ No newline at end of file
diff --git a/python/hailo_compile.py b/python/hailo_compile.py
new file mode 100644
index 0000000..2f25e2c
--- /dev/null
+++ b/python/hailo_compile.py
@@ -0,0 +1,59 @@
+import os
+import subprocess
+import argparse
+import onnx
+
+parser = argparse.ArgumentParser(description="Convert, parse, optimize and compile model for Hailo.")
+parser.add_argument("--model", choices=["1", "2"], required=True, help="1 = ResNet50, 2 = Xception")
+parser.add_argument("--calib", default="--use-random-calib-set", help="Calibration data path or '--use-random-calib-set'")
+args = parser.parse_args()
+
+def get_onnx_io_names(onnx_path):
+ model = onnx.load(onnx_path)
+ input_name = model.graph.input[0].name
+ output_name = model.graph.output[0].name
+ return input_name, output_name
+
+# Paths
+if args.model == "1":
+ base_model_path = "../models/ResNet50"
+ model = "pokedex_ResNet50"
+elif args.model == "2":
+ base_model_path = "../models/Xception"
+ model = "pokedex_Xception"
+
+onnx_path = os.path.join(base_model_path, f"pokedex_{model}.onnx")
+har_path = os.path.join(base_model_path, f"{model}.har")
+optimized_har_path = os.path.join(base_model_path, f"{model}_optimized.har")
+hef_path = os.path.join(base_model_path, f"{model}.hef")
+
+# Node names
+start_node, end_node = get_onnx_io_names(onnx_path)
+print(f"-- Using start_node: {start_node}, end_node: {end_node}")
+
+# Step 1: Parse
+print(f"-- Parsing {onnx_path}...")
+subprocess.run([
+ "hailo", "parser", "onnx", onnx_path,
+ "--start-node-names", start_node,
+ "--end-nodes-names", end_node,
+ "--hw-arch", "hailo8l"
+])
+
+# Step 2: Optimize
+print(f"-- Optimizing to {optimized_har_path}...")
+optimize_cmd = [
+ "hailo", "optimize", har_path,
+ "--hw-arch", "hailo8l",
+ "--use-random-calib-set"
+]
+subprocess.run(optimize_cmd)
+
+# Step 3: Compile
+print(f"-- Compiling to {hef_path}...")
+subprocess.run([
+ "hailo", "compiler", optimized_har_path,
+ "--hw-arch", "hailo8l",
+ "--performance",
+])
+print("-- Done.")
diff --git a/Python/pokedex_ResNet50.py b/python/pokedex_ResNet50.py
similarity index 98%
rename from Python/pokedex_ResNet50.py
rename to python/pokedex_ResNet50.py
index 5724038..dad6c96 100644
--- a/Python/pokedex_ResNet50.py
+++ b/python/pokedex_ResNet50.py
@@ -117,7 +117,7 @@ model.fit(
)
# --- Save the model ---
-model_h5_path = os.path.join(model_output_path, "pokemon_resnet50.h5")
+model_h5_path = os.path.join(model_output_path, "pokedex_ResNet50.h5")
model.save(model_h5_path)
print(f"Model saved to {model_h5_path}")
diff --git a/Python/pokedex_Xception.py b/python/pokedex_Xception.py
similarity index 98%
rename from Python/pokedex_Xception.py
rename to python/pokedex_Xception.py
index 9878540..55d425c 100644
--- a/Python/pokedex_Xception.py
+++ b/python/pokedex_Xception.py
@@ -135,7 +135,7 @@ model.fit(
)
# --- Save the model ---
-model_h5_path = os.path.join(model_output_path, "pokemon_xception.h5")
+model_h5_path = os.path.join(model_output_path, "pokedex_Xception.h5")
model.save(model_h5_path)
print(f"Model saved to {model_h5_path}")
diff --git a/Python/predict_pokemon.py b/python/pokedex_rpi.py
similarity index 71%
rename from Python/predict_pokemon.py
rename to python/pokedex_rpi.py
index d4a931a..3f197ca 100644
--- a/Python/predict_pokemon.py
+++ b/python/pokedex_rpi.py
@@ -2,13 +2,25 @@ import cv2
import numpy as np
import json
from hailo_platform.pyhailort import HailoRT
+import argparse
+
+# --- WHAT ? ---
+parser = argparse.ArgumentParser(description="WHAT ?!")
+parser.add_argument("--model", choices=["1", "2"], required=True, help="1 = ResNet50, 2 = Xception")
+args = parser.parse_args()
+
+# Paths
+if args.model == "1":
+ hef_path = "../models/ResNet50/pokedex_ResNet50.hef"
+ json_path = "../models/ResNet50/class_names.json"
+elif args.model == "2":
+ hef_path = "../models/Xception/pokedex_Xception.hef"
+ json_path = "../models/Xception/class_names.json"
# Load class names
-with open("../models/ResNet50/class_names.json", "r") as f:
+with open(json_path, "r") as f:
class_names = json.load(f)
-# --- Load HEF and configure device ---
-hef_path = "../models/ResNet50/resnet50.hef"
device = HailoRT.Device()
hef = HailoRT.Hef(hef_path)
configured_network_group = device.create_hef_group(hef)
diff --git a/Python/test_ResNet50.py b/python/pokedex_test.py
similarity index 67%
rename from Python/test_ResNet50.py
rename to python/pokedex_test.py
index 04dd172..4d3e1b8 100644
--- a/Python/test_ResNet50.py
+++ b/python/pokedex_test.py
@@ -5,19 +5,33 @@ import numpy as np
import os
import random
import json
+import argparse
-# === Load class names from JSON ===
-with open("../models/ResNet50/class_names.json", "r") as f:
+# --- WHAT ? ---
+parser = argparse.ArgumentParser(description="WHAT ?!")
+parser.add_argument("--model", choices=["1", "2"], required=True, help="1 = ResNet50, 2 = Xception")
+args = parser.parse_args()
+
+# Paths
+if args.model == "1":
+ h5_path = "../models/ResNet50/pokedex_ResNet50.h5"
+ json_path = "../models/ResNet50/class_names.json"
+elif args.model == "2":
+ h5_path = "../models/Xception/pokedex_Xception.h5"
+ json_path = "../models/Xception/class_names.json"
+
+# --- Load class names from JSON ---
+with open(json_path, "r") as f:
class_names = json.load(f)
class_names = [class_names[i] for i in range(len(class_names))] # convert to list
-# === Load trained model ===
-model = keras.models.load_model("../models/ResNet50/pokemon_resnet50.h5")
+# --- Load trained model ---
+model = keras.models.load_model(h5_path)
-# === Paths ===
+# --- Paths ---
base_path = "../Combined_Dataset"
-# === Prepare 2x2 Plot ===
+# --- Prepare 2x2 Plot ---
plt.figure(figsize=(10, 10))
for i in range(4):
@@ -30,13 +44,13 @@ for i in range(4):
])
img_path = os.path.join(class_folder, random_image)
- # === Load & Preprocess Image ===
+ # --- Load & Preprocess Image ---
img = keras.utils.load_img(img_path, target_size=(224, 224)) # resize to match model input
img_array = keras.utils.img_to_array(img)
img_array = img_array / 255.0 # normalize if your model expects it
img_array = tf.expand_dims(img_array, 0)
- # === Predict ===
+ # --- Predict ---
predictions = model.predict(img_array, verbose=0)
probabilities = tf.nn.softmax(predictions[0])
predicted_class_index = np.argmax(probabilities)
@@ -46,7 +60,7 @@ for i in range(4):
# Compare with actual
is_correct = predicted_label == random_class
- # === Plot ===
+ # --- Plot ---
ax = plt.subplot(2, 2, i + 1)
plt.imshow(img)
plt.axis("off")
--
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