def window_partition(x, window_size): """ Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) """ B, H, W, C = x.shape x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows

这段代码的作用是将输入的 x 矩阵按照给定的 window_size 进行划分，划分后将每个小窗口的像素数据展平，作为输出的矩阵的行。具体来说，这里 x 的 shape 是 (B, H, W, C)，其中 B 代表 batch size，H 代表高度，W 代表宽度，C 代表通道数。函数首先将 H 和 W 分别缩小到 window_size 的倍数，然后将 H 和 W 分别划分为 window_size 大小的块，最终得到的 x 的 shape 是 (B, H // window_size, window_size, W // window_size, window_size, C)。接下来，函数将 x 的维度重新排列，使得每个小窗口的像素数据对应输出矩阵的一行，最终得到的输出矩阵的 shape 是 (num_windows*B, window_size, window_size, C)，其中 num_windows = (H // window_size) * (W // window_size) 是划分后小窗口的总数。

相关问题

class Mlp(nn.Module): """ Multilayer perceptron.""" def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x def window_partition(x, window_size): """ Args: x: (B, D, H, W, C) window_size (tuple[int]): window size Returns: windows: (B*num_windows, window_size*window_size, C) """ B, D, H, W, C = x.shape x = x.view(B, D // window_size[0], window_size[0], H // window_size[1], window_size[1], W // window_size[2], window_size[2], C) windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, reduce(mul, window_size), C) return windows def window_reverse(windows, window_size, B, D, H, W): """ Args: windows: (B*num_windows, window_size, window_size, C) window_size (tuple[int]): Window size H (int): Height of image W (int): Width of image Returns: x: (B, D, H, W, C) """ x = windows.view(B, D // window_size[0], H // window_size[1], W // window_size[2], window_size[0], window_size[1], window_size[2], -1) x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1) return x def get_window_size(x_size, window_size, shift_size=None): use_window_size = list(window_size) if shift_size is not None: use_shift_size = list(shift_size) for i in range(len(x_size)): if x_size[i] <= window_size[i]: use_window_size[i] = x_size[i] if shift_size is not None: use_shift_size[i] = 0 if shift_size is None: return tuple(use_window_size) else: return tuple(use_window_size), tuple(use_shift_size)

这是一个关于 PyTorch 的代码，其中包含了一个 MLP 模型和一些用于窗口分割和反转的函数。其中 MLP 模型包含了一个线性层和一个激活函数，以及一个 dropout 层。窗口分割和反转的函数用于将输入张量分割成多个窗口，并将这些窗口重新组合成一个张量。这些函数可以用于实现一些图像处理任务，例如局部感知机制。