I have this tensor dimension:
(batch_size, class_id, range_indices) -> (4, 3, 2)
int64
[[[1250 1302]
[1324 1374]
[1458 1572]]
[[1911 1955]
[1979 2028]
[2120 2224]]
[[2546 2599]
[2624 2668]
[2765 2871]]
[[3223 3270]
[3286 3347]
[3434 3539]]]
How do I construct densed representation with filled value with this rule:
Since there is 3 class IDs, therefore:
Therefore, it will outputting vector like this:
[0 0 0 ...(until 1250)... 1 1 1 ...(until 1302)... 0 0 0 ...(until 1324)... 2 2 2 ...(until 1374)... and so on]
Here is copiable code:
data = np.array([[[1250, 1302],
[1324, 1374],
[1458, 1572]],
[[1911, 1955],
[1979, 2028],
[2120, 2224]],
[[2546, 2599],
[2624, 2668],
[2765, 2871]],
[[3223, 3270],
[3286, 3347],
[3434, 3539]]])
Here is code generated by ChatGPT, but I'm not sure it's Numpythonic way since it's using list comprhension:
import numpy as np
# Given tensor
tensor = np.array([[[1250, 1302],
[1324, 1374],
[1458, 1572]],
[[1911, 1955],
[1979, 2028],
[2120, 2224]],
[[2546, 2599],
[2624, 2668],
[2765, 2871]],
[[3223, 3270],
[3286, 3347],
[3434, 3539]]])
# Determine the maximum value in the tensor to define the size of the output array
max_value = tensor.max()
# Create an empty array filled with zeros of size max_value + 1
dense_representation = np.zeros(max_value + 1, dtype=int)
# Generate the class_ids array, replicated for each batch
class_ids = np.tile(np.arange(1, tensor.shape[1] + 1), tensor.shape[0])
# Generate start and end indices
start_indices = tensor[:, :, 0].ravel()
end_indices = tensor[:, :, 1].ravel()
# Create an array of indices to fill
indices = np.hstack([np.arange(start, end) for start, end in zip(start_indices, end_indices)])
# Create an array of values to fill
values = np.hstack([np.full(end - start, class_id) for start, end, class_id in zip(start_indices, end_indices, class_ids)])
# Fill the dense representation array
dense_representation[indices] = values
# The resulting dense representation
print(dense_representation)
print(dense_representation[1249:1303])
print(dense_representation[1323:1375])
print(dense_representation[1457:1573])
print(dense_representation[1910:1956])
Output:
[0 0 0 ... 3 3 0]
[0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0]
[0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 0]
[0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 0]
[0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0]
IIUC, you could craft the output array with zeros, repeat, tile:
start = data[..., 0].ravel()
end = data[..., 1].ravel()
slices = [slice(a,b) for a,b in zip(start, end)]
n = end-start
out = np.zeros(data.max(), dtype='int')
out[np.r_[*slices]] = np.repeat(np.tile(np.arange(data.shape[1])+1, data.shape[0]), n)
Variant with boolean indexing:
start = data[..., 0].ravel()
end = data[..., 1].ravel()
out = np.zeros(data.max(), dtype='int')
idx = np.arange(len(out))
m = ((idx >= start[:, None]) & (idx < end[:, None])).any(axis=0)
n = end-start
out[m] = np.repeat(np.tile(np.arange(data.shape[1])+1, data.shape[0]), n)
Or:
start = data[..., 0].ravel()
end = data[..., 1].ravel()
out = np.zeros(data.max(), dtype='int')
idx = np.arange(len(out))
m1 = ((idx >= start[:, None]) & (idx < end[:, None]))
m2 = m1.any(axis=0)
nums = np.tile(np.arange(data.shape[1])+1, data.shape[0])
out[m2] = nums[m1[:, m2].argmax(0)]
Output:
[0 0 0 ... 3 3 3]