diff --git a/aidge_export_cpp/kernels/pad.hpp b/aidge_export_cpp/kernels/pad.hpp
index dc7629fc571ac722ed8d13937b454e96fcd59e21..158c9359c934f2735c12e47cbd186a4f30715554 100644
--- a/aidge_export_cpp/kernels/pad.hpp
+++ b/aidge_export_cpp/kernels/pad.hpp
@@ -2,10 +2,7 @@
#define __AIDGE_EXPORT_CPP_KERNELS_PAD2D__
#include "network/typedefs.hpp"
-#include "kernels/rescaling.hpp"
#include "network/utils.hpp"
-#include "kernels/macs.hpp"
-#include "kernels/activation.hpp"
// Todo add border value and border type (Reflect, Constant, Wrap...) and add the two missing pad value (bottom and right)
@@ -21,20 +18,20 @@ void convolution_forward(
Output_T* __restrict outputs
)
{
- const I *input = static_cast<const I *>(input_);
- O *output = static_cast<O *>(output_);
+ const unsigned int oySize = CHANNELS_HEIGHT + PADDING_Y + PADDING_Y;
+ const unsigned int oxSize = CHANNELS_WIDTH + PADDING_X + PADDING_X;
- const std::size_t oySize = CHANNELS_HEIGHT + PADDING_Y + PADDING_Y;
- const std::size_t oxSize = CHANNELS_WIDTH + PADDING_X + PADDING_X;
-
- for (std::uint32_t oy = 0; oy < oySize; ++oy) {
- for (std::uint32_t ox = 0; ox < oxSize; ++ox) {
+ for (unsigned int oy = 0; oy < oySize; ++oy) {
+ for (unsigned int ox = 0; ox < oxSize; ++ox) {
if (oy < PADDING_Y or oy >= CHANNELS_HEIGHT + PADDING_Y or ox < PADDING_X or ox >= CHANNELS_WIDTH + PADDING_X)
{
outputs[oy * oySize + ox] = 0.0f;
}
+ else
+ {
+ outputs[oy * oySize + ox] = inputs[(oy - PADDING_Y) * CHANNELS_HEIGHT + (ox - PADDING_X)];
+ }
- outputs[oy * oySize + ox] = input[(oy - PADDING_Y) * CHANNELS_HEIGHT + (ox - PADDING_X)];
}
}
}
diff --git a/aidge_export_cpp/kernels/pooling.hpp b/aidge_export_cpp/kernels/pooling.hpp
index 478b6a58aed45e2bce0ed1683ad113f9c7a8bffb..667e1a98cc3542cf598d223d59a767dccad6c651 100644
--- a/aidge_export_cpp/kernels/pooling.hpp
+++ b/aidge_export_cpp/kernels/pooling.hpp
@@ -7,7 +7,7 @@
#include <stdexcept>
-template<int NB_CHANNELS,
+template<int NB_CHANNELS,
int CHANNELS_HEIGHT, int CHANNELS_WIDTH,
int NB_OUTPUTS,
int OUTPUTS_HEIGHT, int OUTPUTS_WIDTH,
@@ -17,7 +17,7 @@ template<int NB_CHANNELS,
Pooling_T POOLING_TYPE,
ActivationFunction_T ACTIVATION,
typename Input_T, typename Output_T>
-__attribute__((always_inline)) inline
+__attribute__((always_inline)) inline
void pooling_forward(
const Input_T* __restrict inputs,
Output_T* __restrict outputs)
@@ -32,7 +32,7 @@ void pooling_forward(
: max(PADDING_Y - (oy * STRIDE_Y), 0);
const int syMax = (PADDING_Y == 0
&& OUTPUTS_HEIGHT == OUTPUTS_HEIGHT_NOPAD) ? POOL_HEIGHT
- : clamp(CHANNELS_HEIGHT + PADDING_Y - (oy * STRIDE_Y),
+ : clamp(CHANNELS_HEIGHT + PADDING_Y - (oy * STRIDE_Y),
0, POOL_HEIGHT);
const int iy = (oy * STRIDE_Y) - PADDING_Y;
@@ -45,7 +45,7 @@ void pooling_forward(
const int sxMax = (PADDING_X == 0
&& OUTPUTS_WIDTH == OUTPUTS_WIDTH_NOPAD)
? POOL_WIDTH
- : clamp(CHANNELS_WIDTH + PADDING_X - (ox * STRIDE_X),
+ : clamp(CHANNELS_WIDTH + PADDING_X - (ox * STRIDE_X),
0, POOL_WIDTH);
const int ix = (ox * STRIDE_X) - PADDING_X;
diff --git a/aidge_export_cpp/operators.py b/aidge_export_cpp/operators.py
index 0e3749a3592cce219f9ce8fe6888b42fea587ed1..c89236bf7b452c370d6dc6577b61a9e0b7aa9cdc 100644
--- a/aidge_export_cpp/operators.py
+++ b/aidge_export_cpp/operators.py
@@ -353,14 +353,10 @@ class BatchNorm2DCPP(ExportNodeCpp):
class Concat(ExportNodeCpp):
def __init__(self, node, mem_info):
super().__init__(node, mem_info)
-
- print(node.get_operator())
- print(dir(node.get_operator()))
- self.attributes["nb_in"] = node.get_operator().nb_inputs()
self.attributes["axis"] = node.get_operator().attr.axis
- self.config_template = str(ROOT / "templates" / "configuration" / "concat.jinja")
- self.forward_template = str(ROOT / "templates" / "forward_call" / "concat.jinja")
+ self.config_template = str(ROOT / "templates" / "configuration" / "concat_config.jinja")
+ self.forward_template = str(ROOT / "templates" / "kernel_forward" / "concat_forward.jinja")
self.include_list = []
self.kernels_to_copy = [
str(ROOT / "kernels" / "concat.hpp"),
diff --git a/aidge_export_cpp/templates/configuration/batchnorm_config.jinja b/aidge_export_cpp/templates/configuration/batchnorm_config.jinja
index bc01e3b964d3741548bb3249d88aa9034ecaa343..ae7ef5760a63689d11f6d7369e387b55b7cb3d15 100644
--- a/aidge_export_cpp/templates/configuration/batchnorm_config.jinja
+++ b/aidge_export_cpp/templates/configuration/batchnorm_config.jinja
@@ -1,6 +1,7 @@
{#- For name header -#}
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
+#include "kernels/rescaling.hpp"
{# For layer configuration -#}
{% include "./_def_io.jinja" %}
diff --git a/aidge_export_cpp/templates/configuration/concat.jinja b/aidge_export_cpp/templates/configuration/concat_config.jinja
similarity index 63%
rename from aidge_export_cpp/templates/configuration/concat.jinja
rename to aidge_export_cpp/templates/configuration/concat_config.jinja
index 8aa63156a2d890bbfb6f0c7ddce700917ccef83b..1a6637e9094c12fc04d47da4bddcee160f3c7a56 100644
--- a/aidge_export_cpp/templates/configuration/concat.jinja
+++ b/aidge_export_cpp/templates/configuration/concat_config.jinja
@@ -2,14 +2,15 @@
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
+{% include "./_def_io.jinja" %}
{% include "./_meminfo.jinja" %}
// Attributes
#define {{ name|upper }}_NB_INPUTS {{ nb_in }}
#define {{ name|upper }}_AXIS {{ axis }}
{%- for i in range(nb_in) %}
-#define {{ name|upper }}_INPUT_{{i}}_SIZE {{ in_chan[i] * in_height[i] * in_width[i] }}
+#define {{ name|upper }}_INPUT_{{i}}_SIZE {{ in_dims[i]|join('*') }}
{%- endfor %}
-#define {{ name|upper }}_OUTPUT_SIZE {{ out_chan[0] * out_height[0] * out_width[0] }}
+#define {{ name|upper }}_OUTPUT_SIZE {{ out_dims[0]|join('*')}}
#endif /* {{ name|upper }}_LAYER_H */
diff --git a/aidge_export_cpp/templates/kernel_forward/concat.jinja b/aidge_export_cpp/templates/kernel_forward/concat_forward.jinja
similarity index 86%
rename from aidge_export_cpp/templates/kernel_forward/concat.jinja
rename to aidge_export_cpp/templates/kernel_forward/concat_forward.jinja
index 46fe87e43c51c672b3d74bfbaabbb21aaac12ee7..a2f48e9b5aa563924a346eabfaec67b0c30ef38f 100644
--- a/aidge_export_cpp/templates/kernel_forward/concat.jinja
+++ b/aidge_export_cpp/templates/kernel_forward/concat_forward.jinja
@@ -1,6 +1,6 @@
{% filter indent(width=4, first=False) %}
{% include "./_mem_offset.jinja" %}
-float* {{ name|upper }}_INPUTS[] = {
+const float* {{ name|upper }}_INPUTS[] = {
{%- for i in range(nb_in) -%}
{{ in_name[i] }}{{ ", " if not loop.last else "" }}
{%- endfor -%}
@@ -12,7 +12,7 @@ unsigned int {{ name|upper }}_SIZES[] = {
{%- endfor -%}
};
-aidge_concat<float, {{ nb_in }}> (
+concat_forward<float, {{ nb_in }}> (
{{name|upper}}_AXIS,
{{ name|upper }}_INPUTS,
{{ name|upper }}_SIZES,
diff --git a/aidge_export_cpp/unit_tests/test_export.py b/aidge_export_cpp/unit_tests/test_export.py
index 20421adbe3ab90f1eba1a4df03ab141c21dc0c2d..273c9b4b0481771a2f90a8a6818787e37d28efd4 100644
--- a/aidge_export_cpp/unit_tests/test_export.py
+++ b/aidge_export_cpp/unit_tests/test_export.py
@@ -3,6 +3,8 @@ import aidge_core
import aidge_backend_cpu
import aidge_export_cpp
import numpy as np
+import operator
+from functools import reduce
import subprocess
import re
@@ -31,19 +33,8 @@ def initFiller(model):
aidge_core.constant_filler(value, 0.01)
else:
pass
-import math
-
-def normalize_random_tensor(randList):
- for index in np.ndindex(randList.shape):
- randList[index] = (math.floor(randList[index] * 21) - 10) / 10
-
- 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):
+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.
@@ -56,71 +47,18 @@ def np_init(shape, dtype=np.float32):
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_init(in_dim)) for in_dim in in_dims]
-
- scheduler.forward(data=in_tensor)
-
- export_folder = op_name + "_temp_test"
- # Export the model in C++ standalone
- aidge_core.export_utils.scheduler_export(
- scheduler,
- export_folder,
- aidge_export_cpp.ExportLibCpp,
- memory_manager=aidge_core.mem_info.generate_optimized_memory_info,
- memory_manager_args={"stats_folder": f"{export_folder}/stats", "wrapping": False }
- )
- aidge_core.export_utils.generate_main_compare_cpp(export_folder, graph_view)
- print("COMPILATION")
-
- try:
- for std_line in run_command(["make"], cwd=export_folder):
- print(std_line, end="")
- except subprocess.CalledProcessError as e:
- print(f"An error occurred: {e}\nFailed to generate export.")
- raise SystemExit(1)
- print("RUN EXPORT")
- pattern = r"Number of equal outputs: (\d+) / (\d+)"
- comparison_matched = False
- result = False
- try:
- for std_line in run_command(["./bin/run_export"], cwd=export_folder):
- print(std_line, end="")
- matches = re.findall(pattern, std_line)
- if matches:
- if comparison_matched:
- raise RuntimeError("Two comparison matched found!")
- else:
- expected, infered = map(int, matches[0])
- result = (expected == infered)
- comparison_matched = True
- except subprocess.CalledProcessError as e:
- print(f"An error occurred: {e}\nFailed to run export for comparison.")
- raise SystemExit(1)
- if not comparison_matched:
- raise RuntimeError("No comparison matched found!")
+def _np_init_ones(shape, default_value=0.01, 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.
- return result
+ :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.ones(total_elements) * default_value
+ return data.reshape(shape).astype(dtype)
class test_operator_export(unittest.TestCase):
@@ -133,7 +71,7 @@ class test_operator_export(unittest.TestCase):
def tearDown(self):
pass
- def unit_test_export(self, graph_view, op_name, in_dims):
+ def unit_test_export(self, graph_view, op_name, in_dims, random_inputs=True, random_weights=True, default_value=0.01):
"""
TODO:
* Handle multiple dataformat
@@ -146,9 +84,27 @@ class test_operator_export(unittest.TestCase):
4- Retrieve standard output and using regex to now if the results are the same
"""
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)
+
+ if (random_weights):
+ tensor = aidge_core.Tensor(_np_init(value.dims()))
+
+ node.get_operator().set_output(0, tensor)
+ else:
+ aidge_core.constant_filler(value, default_value)
+
+
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]
+ if (random_inputs):
+ in_tensor = [aidge_core.Tensor(_np_init(in_dim)) for in_dim in in_dims]
+ else:
+ in_tensor = [aidge_core.Tensor(_np_init_ones(in_dim, default_value)) for in_dim in in_dims]
+
scheduler.forward(data=in_tensor)
# Note the convention ``<op_name>_test`` is useful for gitignore to avoid pushing generated export by accident.
@@ -208,7 +164,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.Softmax(axis=1, name="sf0")
])
- self.assertTrue(unit_test_export(model, "Softmax", [[1, 10]]))
+ self.unit_test_export(model, "Softmax", [[1, 10]])
@unittest.skip("Currently this test is failing")
def test_export_FC_image_in(self):
@@ -226,7 +182,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.ReLU(name="relu0")
])
- self.assertTrue(unit_test_export(model, "ReLU", [[1, 10]]))
+ self.unit_test_export(model, "ReLU", [[1, 10]])
def test_export_add(self):
print("Add")
@@ -235,7 +191,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.Add(name="add")
])
- self.assertTrue(unit_test_export(model, "Add", [[1, 5, 5]]))
+ self.unit_test_export(model, "Add", [[1, 5, 5]])
def test_export_sub(self):
print("Sub")
@@ -244,7 +200,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.Sub(name="sub")
])
- self.assertTrue(unit_test_export(model, "Sub", [[1, 5, 5]]))
+ self.unit_test_export(model, "Sub", [[1, 5, 5]])
def test_export_mul(self):
print("Mul")
@@ -253,7 +209,16 @@ class test_operator_export(unittest.TestCase):
aidge_core.Mul(name="mul")
])
- self.assertTrue(unit_test_export(model, "Mul", [[1, 5, 5]]))
+ self.unit_test_export(model, "Mul", [[1, 5, 5]])
+
+ def test_export_concat(self):
+ print("Concat")
+ model = aidge_core.sequential([
+ aidge_core.Producer([1, 5, 5], name="producer"),
+ aidge_core.Concat(nb_inputs=2, axis=1, name="concat")
+ ])
+
+ self.unit_test_export(model, "Concat", [[1, 5, 5]])
def test_export_conv2D(self):
print("Conv2D")
@@ -261,7 +226,7 @@ class test_operator_export(unittest.TestCase):
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]]))
+ self.unit_test_export(model, "Conv2D", [[1, 3, 12, 12]], False, False)
def test_export_max_pooling(self):
print("MaxPooling2D")
@@ -269,7 +234,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.MaxPooling2D(kernel_dims=(3, 3), name="max_pool")
])
- self.assertTrue(unit_test_export(model, "MaxPooling2D", [[1, 2, 12, 12]]))
+ self.unit_test_export(model, "MaxPooling2D", [[1, 2, 12, 12]], False, False)
def test_export_avg_pooling(self):
print("AvgPooling2D")
@@ -277,7 +242,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.AvgPooling2D(kernel_dims=(3, 3), name="avg_pool")
])
- self.assertTrue(unit_test_export(model, "AvgPooling2D", [[1, 2, 12, 12]]))
+ self.unit_test_export(model, "AvgPooling2D", [[1, 2, 12, 12]], False, False)
def test_export_pad2D(self):
print("Pad2D")
@@ -285,7 +250,7 @@ class test_operator_export(unittest.TestCase):
aidge_core.Pad2D((1, 1, 1, 1), name="pad2d")
])
- self.assertTrue(unit_test_export(model, "Pad2D", [[1, 3, 10, 10]]))
+ self.unit_test_export(model, "Pad2D", [[1, 1, 10, 10]])
def test_export_batchnorm2D(self):
print("BatchNormalization2D")
@@ -293,7 +258,8 @@ class test_operator_export(unittest.TestCase):
aidge_core.BatchNorm2D(nb_features=10, epsilon=2e-5, name="bn")
])
- self.assertTrue(unit_test_export(model, "BatchNorm2D", [[1, 10, 5, 5]]))
+ self.unit_test_export(model, "BatchNorm2D", [[1, 1, 5, 5]], False, False)
+
def test_export_cpp(self):
print("Export test to do")