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Add non square pooling and batch norm tests

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This commit is contained in:
Matúš Námešný
2024-05-20 22:16:00 +02:00
parent 6dca8ccd3c
commit 10e73638b6
7 changed files with 533 additions and 173 deletions
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63
tools/batch_norm_test.py
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@@ -2,30 +2,51 @@ import torch
from utils import print_cpp_vector
batch_norm = torch.nn.BatchNorm2d(2, track_running_stats=False)
def gen_batch_norm_test_result(input):
weights = torch.Tensor([0.63508, 0.64903])
biases= torch.Tensor([0.25079, 0.66841])
batch_norm = torch.nn.BatchNorm2d(2, track_running_stats=False)
batch_norm.weight = torch.nn.Parameter(weights)
batch_norm.bias = torch.nn.Parameter(biases)
weights = torch.Tensor([0.63508, 0.64903])
biases= torch.Tensor([0.25079, 0.66841])
input = torch.Tensor([
# Channel 0
0.38899, 0.80478, 0.48836, 0.97381,
0.57508, 0.60835, 0.65467, 0.00168,
0.65869, 0.74235, 0.17928, 0.70349,
0.15524, 0.38664, 0.23411, 0.7137,
# Channel 1
0.32473, 0.15698, 0.314, 0.60888,
0.80268, 0.99766, 0.93694, 0.89237,
0.13449, 0.27367, 0.53036, 0.18962,
0.57672, 0.48364, 0.10863, 0.0571
]).reshape(1, 2, 4, 4)
batch_norm.weight = torch.nn.Parameter(weights)
batch_norm.bias = torch.nn.Parameter(biases)
output = batch_norm(input)
print_cpp_vector(output.flatten())
output = batch_norm(input)
print_cpp_vector(output.flatten())
print(batch_norm.running_mean)
print(batch_norm.running_var)
if __name__ == "__main__":
print("Generating test results...")
print("Batch norm test:")
input = torch.Tensor([
# Channel 0
0.38899, 0.80478, 0.48836, 0.97381,
0.57508, 0.60835, 0.65467, 0.00168,
0.65869, 0.74235, 0.17928, 0.70349,
0.15524, 0.38664, 0.23411, 0.7137,
# Channel 1
0.32473, 0.15698, 0.314, 0.60888,
0.80268, 0.99766, 0.93694, 0.89237,
0.13449, 0.27367, 0.53036, 0.18962,
0.57672, 0.48364, 0.10863, 0.0571
]).reshape(1, 2, 4, 4)
gen_batch_norm_test_result(input)
print("Batch norm test non square input:")
input = torch.Tensor([
0.38899, 0.80478, 0.48836, 0.97381, 0.21567, 0.92312,
0.57508, 0.60835, 0.65467, 0.00168, 0.31567, 0.71345,
0.65869, 0.74235, 0.17928, 0.70349, 0.12856, 0.95645,
0.15524, 0.38664, 0.23411, 0.7137, 0.26789, 0.83412,
0.32473, 0.15698, 0.314, 0.60888, 0.23145, 0.78945,
0.80268, 0.99766, 0.93694, 0.89237, 0.61234, 0.92314,
0.13449, 0.27367, 0.53036, 0.18962, 0.45623, 0.14523,
0.57672, 0.48364, 0.10863, 0.0571, 0.78934, 0.67545
]).reshape(1, 2, 4, 6)
gen_batch_norm_test_result(input)

86
tools/pooling_test.py
View File

@@ -14,6 +14,19 @@ def _get_pool_input():
0.532, 0.819, 0.732, 0.850
]).reshape(1, 2, 4, 4)
def _get_pool_input_non_square():
return torch.Tensor([
0.573, 0.619, 0.732, 0.055, 0.123, 0.234,
0.243, 0.316, 0.573, 0.619, 0.456, 0.789,
0.712, 0.055, 0.243, 0.316, 0.654, 0.987,
0.573, 0.619, 0.742, 0.055, 0.321, 0.654,
0.473, 0.919, 0.107, 0.073, 0.321, 0.654,
0.073, 0.362, 0.973, 0.059, 0.654, 0.987,
0.473, 0.455, 0.283, 0.416, 0.789, 0.123,
0.532, 0.819, 0.732, 0.850, 0.987, 0.321
]).reshape(1, 2, 4, 6)
def gen_max_pool_test_result():
input = _get_pool_input()
@@ -23,6 +36,33 @@ def gen_max_pool_test_result():
print_cpp_vector(output)
def gen_max_pool_non_square_input_test_result():
input = _get_pool_input_non_square()
output = torch.nn.MaxPool2d(kernel_size=2, stride=2)(input)
output = torch.flatten(output)
print_cpp_vector(output)
def gen_max_non_square_pool_test_result():
input = _get_pool_input()
output = torch.nn.MaxPool2d(kernel_size=(2, 3), stride=2)(input)
output = torch.flatten(output)
print_cpp_vector(output)
def gen_max_pool_non_square_stride_test_result():
input = _get_pool_input()
output = torch.nn.MaxPool2d(kernel_size=2, stride=(1, 2))(input)
output = torch.flatten(output)
print_cpp_vector(output)
def gen_avg_pool_test_result():
input = _get_pool_input()
@@ -33,9 +73,55 @@ def gen_avg_pool_test_result():
print_cpp_vector(output)
def gen_avg_pool_non_square_input_test_result():
input = _get_pool_input_non_square()
output = torch.nn.AvgPool2d(kernel_size=2, stride=2)(input)
output = torch.flatten(output)
print_cpp_vector(output)
def gen_avg_non_square_pool_test_result():
input = _get_pool_input()
output = torch.nn.AvgPool2d(kernel_size=(2, 3), stride=2)(input)
output = torch.flatten(output)
print_cpp_vector(output)
def gen_avg_pool_non_square_stride_test_result():
input = _get_pool_input()
output = torch.nn.AvgPool2d(kernel_size=2, stride=(1, 2))(input)
output = torch.flatten(output)
print_cpp_vector(output)
if __name__ == "__main__":
print("Generating test results...")
print("Max pool test:")
gen_max_pool_test_result()
print("Max pool non square input test:")
gen_max_pool_non_square_input_test_result()
print("Max non square pool test:")
gen_max_non_square_pool_test_result()
print("Max pool non square stride test:")
gen_max_pool_non_square_stride_test_result()
print("--------------")
print("Avg pool test:")
gen_avg_pool_test_result()
print("Avg pool non square input test:")
gen_avg_pool_non_square_input_test_result()
print("Avg non square pool test:")
gen_avg_non_square_pool_test_result()
print("Avg pool non square stride test:")
gen_avg_pool_non_square_stride_test_result()