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Keras3 and Qkeras3 Conversion to QONNX #208

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cc0ea5b
Keras3 and Qkeras3 Conversion to QONNX
nfirle Oct 18, 2025
7b1afce
deleted print statements
nfirle Oct 25, 2025
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3 changes: 2 additions & 1 deletion src/qonnx/converters/keras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,6 +135,7 @@ def _strip_qkeras_model(model):

def extract_quantizers(layer):
keras_cls_name, layer_cfg, layer_quantizers = extract_quantizers_from_layer(layer)
layer_cfg.pop("mask", None)
if layer_quantizers:
layer_quantizers = {
k: None if v == "None" else v for k, v in layer_quantizers.items()
Expand Down Expand Up @@ -256,4 +257,4 @@ def from_keras(
if output_path is not None:
onnx_model.save(output_path)

return onnx_model.model, external_storage
return onnx_model.model, external_storage
7 changes: 5 additions & 2 deletions src/qonnx/converters/qkeras/onnx.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import numpy as np
import re
from tf2onnx.late_rewriters import channel_order_rewriters
from tf2onnx.onnx_opset.math import DirectOp, MatMul
from tf2onnx.onnx_opset.nn import BiasAdd, ConvOp
Expand Down Expand Up @@ -32,12 +33,14 @@ def _extract_node_name(onnx_node, keras_quantizers):
keras_quantizers: The dictionary of all the keras quantizers

"""
onnx_name = onnx_node.name
onnx_name = onnx_node.name
keras_names = keras_quantizers.keys()
for keras_name in keras_names:
match = "/" + keras_name + "/"
if match in onnx_name:
match_keras3 = r"/" + re.escape(keras_name) + r"_\d+/"
if match in onnx_name or re.search(match_keras3, onnx_name):
return keras_name

elif "Identity" in onnx_name:
onnx_input = onnx_node.input[0]
keras_input = keras_quantizers[keras_name]["input"]
Expand Down
7 changes: 6 additions & 1 deletion src/qonnx/converters/qkeras/qlayers.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,12 @@
import qkeras

# import tensorflow as tf
from qkeras.quantizers import BaseQuantizer
try:
# New QKeras
from qkeras.base_quantizer import BaseQuantizer
except ImportError:
# Old QKeras
from qkeras.quantizers import BaseQuantizer
from qkeras.utils import REGISTERED_LAYERS as QKERAS_LAYERS


Expand Down
36 changes: 18 additions & 18 deletions tests/keras/test_keras_convert.py
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Expand Up @@ -42,13 +42,13 @@

@pytest.mark.parametrize("quantizer", act_quantizers_relu, ids=act_quantizers_relu_ids)
def test_qkeras_qactivation(quantizer, request):
x = x_in = Input((16), name="input")
x = x_in = Input(shape=(16,), name="input")
x = QActivation(activation=quantizer, name="act_0")(x)
model = Model(inputs=[x_in], outputs=[x])
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qactivation_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -118,7 +118,7 @@ def test_keras_conv2d_conversion():

def test_keras_dense_conversion():
ini = tf.keras.initializers.RandomUniform(minval=-1.0, maxval=1.0)
x = x_in = Input((15), name="input")
x = x_in = Input(shape=(15,), name="input")
x = Dense(10, kernel_initializer=ini, bias_initializer=ini, name="dense1")(x)
x = Activation("relu", name="act0_m")(x)
x = Dense(10, kernel_initializer=ini, bias_initializer=ini, activation="relu", name="dense2")(x)
Expand Down Expand Up @@ -151,7 +151,7 @@ def test_qkeras_qdense_1(quantizers, request):
# Initialize the kernel & bias to RandonUniform within the range of the quantizers
k_ini = tf.keras.initializers.RandomUniform(minval=kq.min(), maxval=kq.max())
b_ini = tf.keras.initializers.RandomUniform(minval=bq.min(), maxval=bq.max())
x = x_in = Input((16), name="input")
x = x_in = Input(shape=(16,), name="input")
x = QDense(
32,
kernel_quantizer=kq,
Expand All @@ -164,7 +164,7 @@ def test_qkeras_qdense_1(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qdense1_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand All @@ -186,7 +186,7 @@ def test_qkeras_qdense_2(quantizers, request):
# Initialize the kernel & bias to RandonUniform within the range of the quantizers
k_ini = tf.keras.initializers.RandomUniform(minval=kq.min(), maxval=kq.max())
b_ini = tf.keras.initializers.RandomUniform(minval=bq.min(), maxval=bq.max())
x = x_in = Input((16), name="input")
x = x_in = Input(shape=(16,), name="input")
x = QDense(
32,
kernel_quantizer=kq,
Expand All @@ -208,7 +208,7 @@ def test_qkeras_qdense_2(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qdense2_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand All @@ -229,7 +229,7 @@ def test_qkeras_qdense_3(quantizers, request):
# Initialize the kernel & bias to RandonUniform within the range of the quantizers
k_ini = tf.keras.initializers.RandomUniform(minval=kq.min(), maxval=kq.max())
b_ini = tf.keras.initializers.RandomUniform(minval=bq.min(), maxval=bq.max())
x = x_in = Input((16), name="input")
x = x_in = Input(shape=(16,), name="input")
x = QDense(
32,
kernel_quantizer=kq,
Expand All @@ -253,7 +253,7 @@ def test_qkeras_qdense_3(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qdense3_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand All @@ -274,7 +274,7 @@ def test_qkeras_qdense_4(quantizers, request):
# Initialize the kernel & bias to RandonUniform within the range of the quantizers
k_ini = tf.keras.initializers.RandomUniform(minval=kq.min(), maxval=kq.max())
b_ini = tf.keras.initializers.RandomUniform(minval=bq.min(), maxval=bq.max())
x = x_in = Input((16), name="input")
x = x_in = Input(shape=(16,), name="input")
x = QDense(
32,
kernel_quantizer=kq,
Expand Down Expand Up @@ -309,7 +309,7 @@ def test_qkeras_qdense_4(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qdense4_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -358,7 +358,7 @@ def test_qkeras_qconv2d_1(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 28, 28, 3)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qconv2d1_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -408,7 +408,7 @@ def test_qkeras_qconv2d_2(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 28, 28, 3)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qconv2d2_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -468,7 +468,7 @@ def test_qkeras_qconv2d_3(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 28, 28, 3)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qconv2d3_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -543,7 +543,7 @@ def test_qkeras_qconv2d_conversion_1(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 28, 28, 1)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_qconv2d_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_qconv2d_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qconv2d_conversion1_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -611,7 +611,7 @@ def test_qkeras_qconv2d_conversion_2(quantizers, request):
x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 28, 28, 1)).astype(dtype=np.float32)
y_qkeras = model.predict(x_test)

onnx_model, external_storage = from_keras(model, "test_qkeras_qconv2d_conversion", opset=9)
onnx_model, external_storage = from_keras(model, "test_qkeras_qconv2d_conversion", opset=13)
assert external_storage is None
model_path = f"model_test_qkeras_qconv2d_conversion2_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -653,7 +653,7 @@ def test_qkeras_qconv2d_conversion_2(quantizers, request):
# x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
# y_qkeras = model.predict(x_test)

# onnx_model, external_storage =from_keras(model, "test_qkeras_conversion", opset=9)
# onnx_model, external_storage =from_keras(model, "test_qkeras_conversion", opset=13)
# assert external_storage is None
# model_path = f"test_qkeras_broken1{request.node.callspec.id}.onnx"
# onnx.save(onnx_model, model_path)
Expand Down Expand Up @@ -695,7 +695,7 @@ def test_qkeras_qconv2d_conversion_2(quantizers, request):
# x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 16)).astype(dtype=np.float32)
# y_qkeras = model.predict(x_test)

# onnx_model, external_storage =from_keras(model, "test_qkeras_conversion", opset=9)
# onnx_model, external_storage =from_keras(model, "test_qkeras_conversion", opset=13)
# assert external_storage is None
# model_path = f"test_qkeras_broken2{request.node.callspec.id}.onnx"
# onnx.save(onnx_model, model_path)
Expand Down