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Run black autoformatter
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@@ -2,12 +2,13 @@ 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=False)
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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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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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@@ -15,11 +16,13 @@ def gen_batch_norm_test_result(input):
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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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@@ -32,11 +35,13 @@ if __name__ == "__main__":
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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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@@ -47,6 +52,6 @@ if __name__ == "__main__":
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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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