interp_idx =interp_idx.unsqueeze(1).repeat(1,1 ,feature.shape[1])
时间: 2024-04-23 19:24:37 浏览: 15
这行代码的作用是将`interp_idx`沿着第二个维度(即第二维)进行复制,复制的次数为`feature.shape[1]`。具体而言,`interp_idx.unsqueeze(1)`将`interp_idx`的第二个维度扩展为1,也就是将形状为`(a,b)`的张量变为形状为`(a,1,b)`的张量,然后`.repeat(1,1,feature.shape[1])`将该张量在第二个维度上复制`feature.shape[1]`次,最终得到的张量形状为`(a,1,feature.shape[1])`。
这个操作常用于将一个张量的某个维度扩展到和其他张量的维度相同,以便进行后续的运算。在这里,`interp_idx`和`feature`可能是某个模型的输入,而该模型要求它们在某个维度上的大小相同,因此需要将`interp_idx`扩展到`feature`的维度上,以便进行后续的计算。
相关问题
import numpy as np from numpy import int64 def bilinear_interp_baseline(a: np.ndarray, b: np.ndarray) -> np.ndarray: """ This is the baseline implementation of bilinear interpolation without vectorization. - a is a ND array with shape [N, H1, W1, C], dtype = int64 - b is a ND array with shape [N, H2, W2, 2], dtype = float64 - return a ND array with shape [N, H2, W2, C], dtype = int64 """ # Get axis size from ndarray shape N, H1, W1, C = a.shape N1, H2, W2, _ = b.shape assert N == N1 # Do iteration res = np.empty((N, H2, W2, C), dtype=int64) for n in range(N): for i in range(H2): for j in range(W2): x, y = b[n, i, j] x_idx, y_idx = int(np.floor(x)), int(np.floor(y)) _x, _y = x - x_idx, y - y_idx # For simplicity, we assume all x are in [0, H1 - 1), all y are in [0, W1 - 1) res[n, i, j] = a[n, x_idx, y_idx] * (1 - _x) * (1 - _y) + a[n, x_idx + 1, y_idx] * _x * (1 - _y) + \ a[n, x_idx, y_idx + 1] * (1 - _x) * _y + a[n, x_idx + 1, y_idx + 1] * _x * _y return res 将这段代码改成向量化的方式
下面是将代码改写为向量化方式的版本:
```python
import numpy as np
from numpy import int64
def bilinear_interp_vectorized(a: np.ndarray, b: np.ndarray) -> np.ndarray:
"""
This is the vectorized implementation of bilinear interpolation.
- a is a ND array with shape [N, H1, W1, C], dtype = int64
- b is a ND array with shape [N, H2, W2, 2], dtype = float64
- return a ND array with shape [N, H2, W2, C], dtype = int64
"""
# Get axis size from ndarray shape
N, H1, W1, C = a.shape
N1, H2, W2, _ = b.shape
assert N == N1
# Calculate the indices for interpolation
x_idx = np.floor(b[:, :, :, 0]).astype(int)
y_idx = np.floor(b[:, :, :, 1]).astype(int)
x = b[:, :, :, 0] - x_idx
y = b[:, :, :, 1] - y_idx
# Calculate the interpolation values
res = a[:, x_idx, y_idx] * (1 - x) * (1 - y) + a[:, x_idx + 1, y_idx] * x * (1 - y) + \
a[:, x_idx, y_idx + 1] * (1 - x) * y + a[:, x_idx + 1, y_idx + 1] * x * y
return res.astype(int64)
```
这个向量化的版本通过使用数组切片和广播操作,避免了显式的循环,从而提高了代码的执行效率。
import numpy as np from numpy import int64 def bilinear_interp_vectorized(a: np.ndarray, b: np.ndarray) -> np.ndarray: """ This is the vectorized implementation of bilinear interpolation. - a is a ND array with shape [N, H1, W1, C], dtype = int64 - b is a ND array with shape [N, H2, W2, 2], dtype = float64 - return a ND array with shape [N, H2, W2, C], dtype = int64 """ # Get axis size from ndarray shape N, H1, W1, C = a.shape N1, H2, W2, _ = b.shape assert N == N1 # Calculate the indices for interpolation x_idx = np.floor(b[:, :, :, 0]).astype(int) y_idx = np.floor(b[:, :, :, 1]).astype(int) x = b[:, :, :, 0] - x_idx y = b[:, :, :, 1] - y_idx # Calculate the interpolation values res = a[:, x_idx, y_idx] * (1 - x) * (1 - y) + a[:, x_idx + 1, y_idx] * x * (1 - y) + a[:, x_idx, y_idx + 1] * (1 - x) * y + a[:, x_idx + 1, y_idx + 1] * x * y return res.astype(int64) 在res = a[:, x_idx, y_idx] * (1 - x) * (1 - y) + a[:, x_idx + 1, y_idx] * x * (1 - y) + a[:, x_idx, y_idx + 1] * (1 - x) * y + a[:, x_idx + 1, y_idx + 1] * x * y发生错误operands could not be broadcast together with shapes (8,8,720,1280,4) (8,720,1280)
这个错误是由于形状不匹配导致的。你可以在计算 `res` 之前,先调整 `x_idx` 和 `y_idx` 的形状,使其与 `a` 的形状相匹配。可以使用 `np.expand_dims` 函数来添加额外的维度。修改代码如下:
```python
x_idx = np.expand_dims(x_idx, axis=-1)
y_idx = np.expand_dims(y_idx, axis=-1)
```
然后再重新计算 `res`。
希望这可以解决你的问题!如果还有其他问题,请随时提问。
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