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