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64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import torch
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from utils import print_cpp_vector
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def gen_batch_norm_test_result(input):
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batch_norm = torch.nn.BatchNorm2d(2, track_running_stats=True)
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weights = torch.Tensor([0.63508, 0.64903])
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biases = torch.Tensor([0.25079, 0.66841])
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batch_norm.weight = torch.nn.Parameter(weights)
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batch_norm.bias = torch.nn.Parameter(biases)
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batch_norm.running_mean = torch.Tensor([0.5, 0.5])
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batch_norm.running_var = torch.Tensor([1.0, 1.0])
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batch_norm.eval()
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output = batch_norm(input)
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print_cpp_vector(output.flatten())
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if __name__ == "__main__":
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print("Generating test results...")
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print("Batch norm test:")
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# fmt: off
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input = torch.Tensor([
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# Channel 0
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0.38899, 0.80478, 0.48836, 0.97381,
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0.57508, 0.60835, 0.65467, 0.00168,
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0.65869, 0.74235, 0.17928, 0.70349,
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0.15524, 0.38664, 0.23411, 0.7137,
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# Channel 1
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0.32473, 0.15698, 0.314, 0.60888,
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0.80268, 0.99766, 0.93694, 0.89237,
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0.13449, 0.27367, 0.53036, 0.18962,
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0.57672, 0.48364, 0.10863, 0.0571
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]).reshape(1, 2, 4, 4)
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# fmt: on
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gen_batch_norm_test_result(input)
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print("Batch norm test non square input:")
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# fmt: off
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input = torch.Tensor([
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0.38899, 0.80478, 0.48836, 0.97381, 0.21567, 0.92312,
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0.57508, 0.60835, 0.65467, 0.00168, 0.31567, 0.71345,
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0.65869, 0.74235, 0.17928, 0.70349, 0.12856, 0.95645,
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0.15524, 0.38664, 0.23411, 0.7137, 0.26789, 0.83412,
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0.32473, 0.15698, 0.314, 0.60888, 0.23145, 0.78945,
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0.80268, 0.99766, 0.93694, 0.89237, 0.61234, 0.92314,
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0.13449, 0.27367, 0.53036, 0.18962, 0.45623, 0.14523,
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0.57672, 0.48364, 0.10863, 0.0571, 0.78934, 0.67545
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]).reshape(1, 2, 4, 6)
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# fmt: on
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gen_batch_norm_test_result(input)
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