如何在TensorFlow中为segment_ids张量内的各分段实例分配唯一ID（不使用py_func）

To assign a unique 0-based ID to each instance within its segment (even when segments are non-contiguous), we can use a combination of tf.unique and tf.scan to track cumulative counts per segment. Here's a step-by-step breakdown:

Approach #

1. Map Segment IDs to Consecutive Indices: Use tf.unique to convert arbitrary segment IDs into a compact set of consecutive integers. This makes it easy to track counts for each segment using a fixed-size state tensor.
2. Track Cumulative Counts with tf.scan: Process each element sequentially, maintaining a state that keeps track of how many times each segment has been seen so far. For each element, we:
   • Retrieve the current count for its segment.
   • Append this count to our output (this is the unique ID for the instance).
   • Increment the segment's count in the state.

Code Implementation #

import tensorflow as tf

# Example input tensor
x = tf.constant([1, 1, 2, 2, 3, 3, 4, 1])

# Step 1: Get unique segments and their indices in the original tensor
unique_segments, segment_indices = tf.unique(x)
num_unique_segments = tf.shape(unique_segments)[0]

# Step 2: Define the scan function to track counts per segment
def update_count(state, segment_idx):
    # Get the current count for this segment
    current_count = state[segment_idx]
    # Update the state to increment the count for this segment
    updated_state = tf.tensor_scatter_nd_update(
        state,
        indices=[[segment_idx]],
        updates=[current_count + 1]
    )
    # Return updated state and the current count (our output for this element)
    return updated_state, current_count

# Initialize state with zeros for each unique segment
initial_state = tf.zeros(num_unique_segments, dtype=tf.int32)

# Run the scan operation to compute per-instance IDs
_, per_segment_ids = tf.scan(update_count, segment_indices, initializer=initial_state)

# Verify the result
print("Input segment IDs:", x.numpy())
print("Output per-segment unique IDs:", per_segment_ids.numpy())

Output #

Input segment IDs: [1 1 2 2 3 3 4 1]
Output per-segment unique IDs: [0 1 0 1 0 1 0 2]

Key Notes #

• No Python Overhead: This uses pure TensorFlow operations, avoiding the performance cost of py_func which switches between Python and TensorFlow execution contexts.
• Handles Non-Contiguous Segments: Unlike methods that rely on contiguous blocks, this works even when segments reappear later in the tensor (like the 1 segment in the example).
• Works with Any Hashable Type: tf.unique supports integers, strings, and other hashable tensor types, so this approach isn't limited to numeric segment IDs.

内容的提问来源于stack exchange，提问作者ma7555