elif isinstance(dim, int): shift = x.shape[dim] // 2

这是一个关于 Python 代码的问题，我可以回答。这段代码的作用是计算 x 在维度 dim 上的中心位置，并将其赋值给 shift 变量。其中 isinstance(dim, int) 是用来判断 dim 是否为整数类型的语句，// 代表整除运算符。

相关问题

img = Image.open('C:\yingxiang\DJI_20230322140516_0026_V.JPG') # 获取 EXIF 数据 exif_data = img._getexif() # 获取方位元素信息 orientation = exif_data.get(274) # 方向信息标签为274 if orientation == 1: # 没有方向信息 pass elif orientation == 2: img = img.transpose(Image.FLIP_LEFT_RIGHT) elif orientation == 3: img = img.rotate(180) elif orientation == 4: img = img.rotate(180).transpose(Image.FLIP_LEFT_RIGHT) elif orientation == 5: img = img.rotate(-90).transpose(Image.FLIP_LEFT_RIGHT) elif orientation == 6: img = img.rotate(-90) elif orientation == 7: img = img.rotate(90).transpose(Image.FLIP_LEFT_RIGHT) elif orientation == 8: img = img.rotate(90) # 获取航向角、俯仰角和滚转角 roll = exif_data.get(204) / 100.0 # 滚转角标签为204，单位为1/100度 pitch = exif_data.get(206) / 100.0 # 俯仰角标签为206，单位为1/100度 yaw = exif_data.get(208) / 100.0 # 航向角标签为208，单位为1/100度 print(roll,pitch,yaw)这段代码怎么改可以确保变量不为空，再进行除法运算

你可以使用异常处理来确保变量不为空，再进行除法运算。例如，你可以使用try-except语句来捕获可能引发ZeroDivisionError或TypeError异常的情况，并在出现异常时进行处理。具体代码如下所示：

img = Image.open('C:\yingxiang\DJI_20230322140516_0026_V.JPG')

# 获取 EXIF 数据
exif_data = img._getexif()

# 获取方位元素信息
orientation = exif_data.get(274)

# 方向信息标签为274
if orientation == 1:
    # 没有方向信息
    pass
elif orientation == 2:
    img = img.transpose(Image.FLIP_LEFT_RIGHT)
elif orientation == 3:
    img = img.rotate(180)
elif orientation == 4:
    img = img.rotate(180).transpose(Image.FLIP_LEFT_RIGHT)
elif orientation == 5:
    img = img.rotate(-90).transpose(Image.FLIP_LEFT_RIGHT)
elif orientation == 6:
    img = img.rotate(-90)
elif orientation == 7:
    img = img.rotate(90).transpose(Image.FLIP_LEFT_RIGHT)
elif orientation == 8:
    img = img.rotate(90)

# 获取航向角、俯仰角和滚转角
try:
    roll = exif_data.get(204) / 100.0  # 滚转角标签为204，单位为1/100度
    pitch = exif_data.get(206) / 100.0  # 俯仰角标签为206，单位为1/100度
    yaw = exif_data.get(208) / 100.0  # 航向角标签为208，单位为1/100度
except (ZeroDivisionError, TypeError):
    print("发生除零或类型错误异常，无法计算角度值。")
    roll, pitch, yaw = None, None, None

print(roll, pitch, yaw)

在这个代码中，我们使用try-except语句来捕获可能引发ZeroDivisionError或TypeError异常的情况。如果出现异常，我们会在控制台上打印一条错误消息，并将变量roll、pitch、yaw设置为None。这样可以确保变量不为空，避免了除以0或类型错误的情况。

class Partial_conv3(nn.Module): def __init__(self, dim, n_div, forward): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x 在这段代码中插入全局平均池化做通道增强的模块

可以将全局平均池化放在每个 forward 函数的最后，对每个 channel 进行平均池化操作，然后将结果与未被改变的 channel 拼接起来，得到增强后的输出。修改后的代码如下：

class Partial_conv3(nn.Module):
    def __init__(self, dim, n_div, forward):
        super().__init__()
        self.dim_conv3 = dim // n_div
        self.dim_untouched = dim - self.dim_conv3
        self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))  # 添加全局平均池化层
        if forward == 'slicing':
            self.forward = self.forward_slicing
        elif forward == 'split_cat':
            self.forward = self.forward_split_cat
        else:
            raise NotImplementedError
    
    def forward_slicing(self, x: Tensor) -> Tensor:
        # only for inference
        x = x.clone()  # !!! Keep the original input intact for the residual connection later
        x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
        x[:, :self.dim_conv3, :, :] = self.global_avg_pool(x[:, :self.dim_conv3, :, :])  # 添加全局平均池化
        x = torch.cat((x, x[:, self.dim_conv3:, :, :]), 1)
        return x
    
    def forward_split_cat(self, x: Tensor) -> Tensor:
        x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1)
        x1 = self.partial_conv3(x1)
        x1 = self.global_avg_pool(x1)  # 添加全局平均池化
        x = torch.cat((x1, x2), 1)
        return x