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- from tinygrad.tensor import Tensor
- from tinygrad.nn import Conv2d, BatchNorm2d
- from tinygrad.nn.state import get_parameters
- if __name__ == "__main__":
- with Tensor.train():
- BS, C1, H, W = 4, 16, 224, 224
- C2, K, S, P = 64, 7, 2, 1
- x = Tensor.uniform(BS, C1, H, W)
- conv = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
- bn = BatchNorm2d(C2, track_running_stats=False)
- for t in get_parameters([x, conv, bn]): t.realize()
- print("running network")
- x.sequential([conv, bn]).numpy()
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