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Commit 34e96530 authored by Gallas Gaye's avatar Gallas Gaye
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fix: Some unit test failing

parent 25eec3cb
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......@@ -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)];
}
}
}
......
......@@ -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;
......
......@@ -368,14 +368,10 @@ 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"),
......
{#- For name header -#}
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
#include "kernels/rescaling.hpp"
{# For layer configuration -#}
{% include "./_def_io.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 */
{% 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,
......
......@@ -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")
......
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