Incorrect receptive field

[d-01]

A description of what the bug is

There are two problems with a receptive field:

1. An effective receptive field size (see tests below) doesn't match a calculated size (receptive_field attribute).
2. TCN layer with dilation=1 have holes (blind spots) in a receptive field.

A snippet

The test code demonstrates which positions in the input sequence affect the output.

Test code (Google Colab):

import numpy as np
from tcn import TCN
import tensorflow as tf  # 2.8.0

def test_inference(seq, tcn):
    '''Perform inference with TCN layer, print input and output.'''
    batch = np.asarray(seq, dtype=np.float32)[None, ..., None]
    output = tcn(batch).numpy().squeeze()
    print('input: {}; output: {:.4f}'.format(' '.join(map(str, seq)), output))
    
tf.random.set_seed(333)
tcn = TCN(nb_filters=1, kernel_size=2, dilations=(1, 2), return_sequences=False)
print(f'k={tcn.kernel_size}, dilations={tcn.dilations}, receptive_field={tcn.receptive_field}')

TEST_SEQ_LEN = 10
for i in range(TEST_SEQ_LEN):
    print(i, end='. ')
    test_seq = list(range(TEST_SEQ_LEN))
    test_seq[i] = 0
    test_inference(test_seq[1:][::-1], tcn)

Actual result

Output (with comments):

k=2, dilations=(1, 2), receptive_field=7

0. input: 9 8 7 6 5 4 3 2 1; output: 8.5691  base output (unmodified)
1. input: 9 8 7 6 5 4 3 2 0; output: 9.537   changed
2. input: 9 8 7 6 5 4 3 0 1; output: 8.5691  does not reflect changes in input
3. input: 9 8 7 6 5 4 0 2 1; output: 7.0454  changed
4. input: 9 8 7 6 5 0 3 2 1; output: 8.5691  does not reflect changes in input
5. input: 9 8 7 6 0 4 3 2 1; output: 2.9412  changed
6. input: 9 8 7 0 5 4 3 2 1; output: 8.5691  does not reflect changes in input
7. input: 9 8 0 6 5 4 3 2 1; output: 8.5691  does not reflect changes in input
8. input: 9 0 7 6 5 4 3 2 1; output: 8.5691  outside of receptive field
9. input: 0 8 7 6 5 4 3 2 1; output: 8.5691  outside of receptive field
                  _   _   _                  elements model "sees" in the input
              * * * * * * *                  receptive field indicator

• Actual receptive field size is 5, not 7.
• 2nd and 4th positions in the receptive field do not affect the output.

Expected result

Every element in the input sequence covered by receptive field affects the output.

Dependencies

• environment – Google Colab
• python – 3.7.12 (default, Jan 15 2022, 18:48:18)
• tensorflow – 2.8.0
• keras-tcn – 3.4.0

[d-01]

Sorry for bothering, it was my mistake.

While testing, the relu activation should be disabled (activation=None). The output will be:

k=2, dilations=(1, 2), receptive_field=7
0. input: 9 8 7 6 5 4 3 2 1; output: 12.6189
1. input: 9 8 7 6 5 4 3 2 0; output: 13.6868  *
2. input: 9 8 7 6 5 4 3 0 1; output: 12.3591  *
3. input: 9 8 7 6 5 4 0 2 1; output: 15.2741  *
4. input: 9 8 7 6 5 0 3 2 1; output: 5.9221   *
5. input: 9 8 7 6 0 4 3 2 1; output: 22.6492  *
6. input: 9 8 7 0 5 4 3 2 1; output: -8.8503  *
7. input: 9 8 0 6 5 4 3 2 1; output: 14.6726  *
8. input: 9 0 7 6 5 4 3 2 1; output: 12.6189
9. input: 0 8 7 6 5 4 3 2 1; output: 12.6189

I realized it after implemented TCN myself and got the same "bug" ¯\(ツ)/¯.