如何使用 CNN 代码修复 Python “IndexError: list index out of range”

Question

I'm trying to construct a ResNet34 Encoder as part of my CNN with the following function on Python 3.7.

import tensorflow as tf

from tensorpack import *
from tensorpack.models import BatchNorm, BNReLU, Conv2D, MaxPooling, FixedUnPooling
from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
from .utils import *
import sys

sys.path.append("..") # adds higher directory to python modules path.
try: # HACK: import beyond current level, may need to restructure
    from config import Config
except ImportError:
    assert False, 'Fail to import config.py'
def res_blk(name, l, ch, ksize, count, split=1, strides=1, freeze=False):
    ch_in = l.get_shape().as_list()
    with tf.variable_scope(name):
        for i in range(0, count):
            with tf.variable_scope('block' + str(i)):  
                x = l if i == 0 else BNReLU('preact', l)
                x = Conv2D('conv1', x, ch[0], ksize[0], activation=BNReLU)
                x = Conv2D('conv2', x, ch[1], ksize[1], split=split, 
                                strides=strides if i == 0 else 1, activation=BNReLU)
                x = Conv2D('conv3', x, ch[2], ksize[2], activation=tf.identity)
                if (strides != 1 or ch_in[1] != ch[2]) and i == 0:
                    l = Conv2D('convshortcut', l, ch[2], 1, strides=strides)
                x = tf.stop_gradient(x) if freeze else x
                l = l + x
        l = BNReLU('bnlast',l)  
    return l

def encoder(i, freeze):
    d1 = Conv2D('conv0',  i, 64, 7, padding='valid', strides=1, activation=BNReLU)
    d1 = res_blk('group0', d1, [ 64,  64], [3, 3], 3, strides=1, freeze=freeze)

    d2 = res_blk('group1', d1, [128, 128], [3, 3], 4, strides=2, freeze=freeze)
    d2 = tf.stop_gradient(d2) if freeze else d2

    d3 = res_blk('group2', d2, [256, 256], [3, 3], 6, strides=2, freeze=freeze)
    d3 = tf.stop_gradient(d3) if freeze else d3

    d4 = res_blk('group3', d3, [512, 512], [3, 3], 3, strides=2, freeze=freeze)
    d4 = tf.stop_gradient(d4) if freeze else d4

    d4 = Conv2D('conv_bot',  d4, 1024, 1, padding='same')
    return [d1, d2, d3, d4]

Then i get the error

 line 67, in encoder
    d1 = res_blk('group0', d1, [ 64,  64], [3, 3], 3, strides=1, freeze=freeze)                       
  File "....", line 34, in res_blk
    x = Conv2D('conv3', x, ch[2], ksize[2], activation=tf.identity)
IndexError: list index out of range

What is the cause of this error and how can i fix it? The original code was a Resnet50 which worked fine ie the code would be

d1 = res_blk('group0', d1, [ 64, 64, 256], [1, 3, 1], 3, strides=1, freeze=freeze)

Answer 1

The key bit I found in the link you included is that "Each ResNet block is either two layers deep (used in small networks like ResNet 18, 34) or 3 layers deep (ResNet 50, 101, 152).".

The code you provided is catoring for the three layers required for ResNet 50, 101 and 152, but not accomodating the simpler two layer approach is this example, as far as I can tell.

There may be some more tweaking to do, but you should be able to just remove the final layer, similar to I have below, that would've otherwise been necessary in 50+ layer ResNet architectures.

import tensorflow as tf

from tensorpack import *
from tensorpack.models import BatchNorm, BNReLU, Conv2D, MaxPooling, FixedUnPooling
from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
from .utils import *
import sys

sys.path.append("..") # adds higher directory to python modules path.
try: # HACK: import beyond current level, may need to restructure
    from config import Config
except ImportError:
    assert False, 'Fail to import config.py'
def res_blk(name, l, ch, ksize, count, split=1, strides=1, freeze=False):
    ch_in = l.get_shape().as_list()
    with tf.variable_scope(name):
        for i in range(0, count):
            with tf.variable_scope('block' + str(i)):  
                x = l if i == 0 else BNReLU('preact', l)
                x = Conv2D('conv1', x, ch[0], ksize[0], activation=BNReLU)
                x = Conv2D('conv2', x, ch[1], ksize[1], split=split, 
                                strides=strides if i == 0 else 1, activation=BNReLU)
                x = tf.stop_gradient(x) if freeze else x
                l = l + x
        l = BNReLU('bnlast',l)  
    return l

def encoder(i, freeze):
    d1 = Conv2D('conv0',  i, 64, 7, padding='valid', strides=1, activation=BNReLU)
    d1 = res_blk('group0', d1, [ 64,  64], [3, 3], 3, strides=1, freeze=freeze)

    d2 = res_blk('group1', d1, [128, 128], [3, 3], 4, strides=2, freeze=freeze)
    d2 = tf.stop_gradient(d2) if freeze else d2

    d3 = res_blk('group2', d2, [256, 256], [3, 3], 6, strides=2, freeze=freeze)
    d3 = tf.stop_gradient(d3) if freeze else d3

    d4 = res_blk('group3', d3, [512, 512], [3, 3], 3, strides=2, freeze=freeze)
    d4 = tf.stop_gradient(d4) if freeze else d4

    d4 = Conv2D('conv_bot',  d4, 1024, 1, padding='same')
    return [d1, d2, d3, d4]