mirror of
https://github.com/lordmathis/CUDANet.git
synced 2025-11-05 17:34:21 +00:00
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import torch
|
|
import numpy as np
|
|
|
|
# Define input and kernel data as tensors
|
|
input_data = torch.tensor([
|
|
0.823, 0.217, 0.435, 0.981, 0.742,
|
|
0.109, 0.518, 0.374, 0.681, 0.147,
|
|
0.956, 0.729, 0.654, 0.087, 0.392,
|
|
0.784, 0.921, 0.543, 0.231, 0.816,
|
|
0.472, 0.614, 0.102, 0.987, 0.398,
|
|
0.051, 0.756, 0.841, 0.293, 0.128,
|
|
0.417, 0.632, 0.095, 0.184, 0.529,
|
|
0.871, 0.958, 0.213, 0.347, 0.725,
|
|
0.461, 0.012, 0.278, 0.195, 0.649,
|
|
0.853, 0.707, 0.988, 0.988, 0.322,
|
|
0.345, 0.123, 0.789, 0.123, 0.456,
|
|
0.456, 0.789, 0.123, 0.345, 0.123,
|
|
0.789, 0.123, 0.345, 0.123, 0.456,
|
|
0.123, 0.345, 0.123, 0.789, 0.123,
|
|
0.345, 0.123, 0.789, 0.123, 0.456
|
|
], dtype=torch.float)
|
|
|
|
kernel_data = torch.tensor([
|
|
0.128, 0.754, 0.987,
|
|
0.321, 0.412, 0.635,
|
|
0.298, 0.017, 0.845,
|
|
0.514, 0.729, 0.952,
|
|
0.684, 0.378, 0.159,
|
|
0.823, 0.547, 0.216,
|
|
0.983, 0.231, 0.456,
|
|
0.178, 0.654, 0.821,
|
|
0.345, 0.987, 0.123,
|
|
0.789, 0.543, 0.210,
|
|
0.012, 0.371, 0.638,
|
|
0.456, 0.198, 0.907,
|
|
0.101, 0.432, 0.759,
|
|
0.234, 0.567, 0.890,
|
|
0.543, 0.876, 0.219,
|
|
0.345, 0.678, 0.011,
|
|
0.678, 0.011, 0.345,
|
|
0.011, 0.345, 0.678
|
|
], dtype=torch.float)
|
|
|
|
# Reshape input data to a 4D tensor (batch_size, channels, height, width)
|
|
input_data = input_data.reshape(1, 3, 5, 5)
|
|
|
|
# Define the convolution layer
|
|
conv2d = torch.nn.Conv2d(in_channels=3, out_channels=2, kernel_size=3, padding=1, bias=False)
|
|
|
|
# Set the weights of the convolution layer
|
|
conv2d.weight = torch.nn.Parameter(kernel_data.reshape(2, 3, 3, 3))
|
|
|
|
# Perform the convolution
|
|
output = conv2d(input_data)
|
|
|
|
# Print the output as cpp vector
|
|
output = torch.flatten(output)
|
|
print("std::vector<float> expected = {", end="")
|
|
for i in range(len(output)):
|
|
if i != 0:
|
|
print(", ", end="")
|
|
print(str(round(output[i].item(), 5)) + "f", end="")
|
|
print("};")
|