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class C3STR(C3):
# C3 module with SwinTransformerBlock()
def init(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().init(c1, c2, c2, n, shortcut, g, e)
c_ = int(c2 * e)
num_heads = c_ // 32
self.m = SwinTransformerBlock(c_, c_, num_heads, n)
super().init(c1, c2, c2, n, shortcut, g, e) <-- maybe there has two c2? and correct solution is super().init(c1, c2, n, shortcut, g, e) that model can trainning.
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