From 1aecd6c377800e7b6bc13f75b54c9eb443ebfbbc Mon Sep 17 00:00:00 2001
From: Gallasko <gallasko@gmail.com>
Date: Wed, 26 Mar 2025 13:14:07 +0100
Subject: [PATCH] fix: Better control over random number generation in the
model weights during unit tests
---
aidge_export_cpp/unit_tests/test_export.py | 105 ++++++++++++++-------
1 file changed, 69 insertions(+), 36 deletions(-)
diff --git a/aidge_export_cpp/unit_tests/test_export.py b/aidge_export_cpp/unit_tests/test_export.py
index c5fa99c..20421ad 100644
--- a/aidge_export_cpp/unit_tests/test_export.py
+++ b/aidge_export_cpp/unit_tests/test_export.py
@@ -39,12 +39,45 @@ def normalize_random_tensor(randList):
return aidge_core.Tensor(randList.astype(np.float32))
+import numpy as np
+import operator
+from functools import reduce
+
+def np_init(shape, dtype=np.float32):
+ """
+ Generates a NumPy array with the given shape, filled with random values between -1 and 1
+ with a step of 0.1.
+
+ :param shape: Tuple of dimensions for the array
+ :param dtype: Data type of the output array (default: np.float32)
+ :return: A NumPy array with the given shape and dtype
+ """
+ total_elements = reduce(operator.mul, shape, 1)
+ data = (np.random.randint(0, 21, size=total_elements) - 10) / 10.0
+ return data.reshape(shape).astype(dtype)
+
def unit_test_export(graph_view, op_name, in_dims):
+ # Initialize parameters (weights and biases)
+
graph_view.compile("cpu", aidge_core.dtype.float32, dims=in_dims)
+
+ for node in graph_view.get_nodes():
+ if node.type() == "Producer":
+ prod_op = node.get_operator()
+ value = prod_op.get_output(0)
+
+ # rand_tensor = aidge_core.Tensor(np_init(value.dims()))
+ # rand_tensor.set_backend(value.backend())
+ # value = rand_tensor
+
+ print(value)
+
+ aidge_core.constant_filler(value, 0.01)
+
+
scheduler = aidge_core.SequentialScheduler(graph_view)
- # in_tensor = [aidge_core.Tensor(np.random.random(in_dim).astype(np.float32)) for in_dim in in_dims]
- in_tensor = [normalize_random_tensor(np.random.rand(*in_dim)) for in_dim in in_dims]
+ in_tensor = [aidge_core.Tensor(np_init(in_dim)) for in_dim in in_dims]
scheduler.forward(data=in_tensor)
@@ -198,69 +231,69 @@ class test_operator_export(unittest.TestCase):
def test_export_add(self):
print("Add")
model = aidge_core.sequential([
- aidge_core.Producer([1, 10]),
- aidge_core.Add()
+ aidge_core.Producer([1, 5, 5], name="producer"),
+ aidge_core.Add(name="add")
])
- self.assertTrue(unit_test_export(model, "Add", [[1, 10]]))
+ self.assertTrue(unit_test_export(model, "Add", [[1, 5, 5]]))
def test_export_sub(self):
print("Sub")
model = aidge_core.sequential([
- aidge_core.Producer([1, 10]),
- aidge_core.Sub()
+ aidge_core.Producer([1, 5, 5], name="producer"),
+ aidge_core.Sub(name="sub")
])
- self.assertTrue(unit_test_export(model, "Sub", [[1, 10]]))
+ self.assertTrue(unit_test_export(model, "Sub", [[1, 5, 5]]))
def test_export_mul(self):
print("Mul")
model = aidge_core.sequential([
- aidge_core.Producer([1, 10]),
- aidge_core.Mul()
+ aidge_core.Producer([1, 5, 5], name="producer"),
+ aidge_core.Mul(name="mul")
])
- self.assertTrue(unit_test_export(model, "Mul", [[1, 10]]))
+ self.assertTrue(unit_test_export(model, "Mul", [[1, 5, 5]]))
def test_export_conv2D(self):
print("Conv2D")
model = aidge_core.sequential([
- aidge_core.Conv2D(in_channels=3, out_channels=3, kernel_dims=(3, 3))
+ aidge_core.Conv2D(in_channels=3, out_channels=3, kernel_dims=(3, 3), name="conv")
])
self.assertTrue(unit_test_export(model, "Conv2D", [[1, 3, 12, 12]]))
- # def test_export_max_pooling(self):
- # print("MaxPooling2D")
- # model = aidge_core.sequential([
- # aidge_core.MaxPooling2D(kernel_dims=(3, 3))
- # ])
+ def test_export_max_pooling(self):
+ print("MaxPooling2D")
+ model = aidge_core.sequential([
+ aidge_core.MaxPooling2D(kernel_dims=(3, 3), name="max_pool")
+ ])
- # self.assertTrue(unit_test_export(model, "MaxPooling2D", [[1, 2, 12, 12]]))
+ self.assertTrue(unit_test_export(model, "MaxPooling2D", [[1, 2, 12, 12]]))
- # def test_export_avg_pooling(self):
- # print("AvgPooling2D")
- # model = aidge_core.sequential([
- # aidge_core.AvgPooling2D(kernel_dims=(3, 3), name="avg_pool0")
- # ])
+ def test_export_avg_pooling(self):
+ print("AvgPooling2D")
+ model = aidge_core.sequential([
+ aidge_core.AvgPooling2D(kernel_dims=(3, 3), name="avg_pool")
+ ])
- # self.assertTrue(unit_test_export(model, "AvgPooling2D", [[1, 2, 12, 12]]))
+ self.assertTrue(unit_test_export(model, "AvgPooling2D", [[1, 2, 12, 12]]))
- # def test_export_pad2D(self):
- # print("Pad2D")
- # model = aidge_core.sequential([
- # aidge_core.Softmax(axis=1, name="sf0")
- # ])
+ def test_export_pad2D(self):
+ print("Pad2D")
+ model = aidge_core.sequential([
+ aidge_core.Pad2D((1, 1, 1, 1), name="pad2d")
+ ])
- # self.assertTrue(unit_test_export(model, "Softmax", [[1, 10]]))
+ self.assertTrue(unit_test_export(model, "Pad2D", [[1, 3, 10, 10]]))
- # def test_export_batchnorm2D(self):
- # print("BatchNormalization2D")
- # model = aidge_core.sequential([
- # aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5)
- # ])
+ def test_export_batchnorm2D(self):
+ print("BatchNormalization2D")
+ model = aidge_core.sequential([
+ aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5, name="bn")
+ ])
- # self.assertTrue(unit_test_export(model, "BatchNorm2D", [[1, 10]]))
+ self.assertTrue(unit_test_export(model, "BatchNorm2D", [[1, 10, 5, 5]]))
def test_export_cpp(self):
print("Export test to do")
--
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