20 lines
573 B
Python
20 lines
573 B
Python
# to start thinking about the $2,000 norm fusion bounty
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from tinygrad.tensor import Tensor
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from tinygrad.nn import Conv2d, BatchNorm2d
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from tinygrad.nn.state import get_parameters
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if __name__ == "__main__":
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with Tensor.train():
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BS, C1, H, W = 4, 16, 224, 224
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C2, K, S, P = 64, 7, 2, 1
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x = Tensor.uniform(BS, C1, H, W)
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conv = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
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bn = BatchNorm2d(C2, track_running_stats=False)
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for t in get_parameters([x, conv, bn]): t.realize()
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print("running network")
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x.sequential([conv, bn]).numpy()
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