Errors while changing the basenet

[abc8350712]

When I try to change the VGG net to Resnet,it doesn't work.

I mainly change the vgg.py file like

def basenet(inputs):
    logit, endpoints =resnet_50(inputs)
    endpoints['conv4_3'] = endpoints['vgg/resnet_50/block2/unit_2']
    endpoints['fc7'] = endpoints['vgg/resnet_50/block3/unit_4']
    return endpoints['fc7'], endpoints

#try to keep the output as original net

However it dosen't work but come out:

Traceback (most recent call last): File "/home/moon/seglink-master/train_seglink.py", line 276, in tf.app.run() File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 48, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "/home/moon/seglink-master/train_seglink.py", line 271, in main train_op = create_clones(batch_queue) File "/home/moon/seglink-master/train_seglink.py", line 220, in create_clones averaged_gradients = sum_gradients(gradients) File "/home/moon/seglink-master/train_seglink.py", line 164, in sum_gradients grad = tf.add_n(grads, name = v.op.name + '_summed_gradients') File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1918, in add_n raise ValueError("inputs must be a list of at least one Tensor with the " ValueError: inputs must be a list of at least one Tensor with the same dtype and shape

My coarse renset implemention is as follow:

import tensorflow as tf
import collections

slim = tf.contrib.slim

Block = collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])

def subsample(inputs, factor, scope=None):
    if factor == 1:
        return inputs
    else:
        return slim.max_pool2d(inputs, [1, 1], stride= factor, scope=scope)

def bottleneck(inputs,
               depth,
               depth_bottleneck,
               stride,
               outputs_collections='collections',
               scope=None):

    with tf.variable_scope(scope) as sc:
        depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
        preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
        if depth==depth_in:
            shortcut = subsample(inputs, stride, 'shortcut')
        else:

            shortcut = slim.conv2d(preact, depth, [1, 1],
                                   stride=stride, normalizer_fn=None,
                                   activation_fn=None, scope='shortcut')
        residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, scope='conv1')

        residual = slim.conv2d(residual, depth_bottleneck, [3, 3], stride=stride, padding='SAME', scope='conv2')

        residual = slim.conv2d(residual, depth, [1, 1], stride=1, scope='conv3')

        output = shortcut+residual

        return slim.utils.collect_named_outputs(outputs_collections, sc.name, output)

def resnet_50(input):
    blocks = [
        Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),
        Block(
            'block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),
        Block(
            'block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 2)]),
        Block(
            'block4', bottleneck, [(2048, 512, 1)] * 3)]
    net = input
    net = slim.conv2d(net, 64, 7, stride=2, scope='conv1', padding='SAME')
    net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
    with tf.variable_scope('resnet_50'):
        for i, block in enumerate(blocks):
            with tf.variable_scope(block.scope):
                args = block.args
                for j, arg in enumerate(args):
                    depth, depth_bottleneck, stride = arg
                    net = bottleneck(net, depth, depth_bottleneck, stride, scope='unit_'+str(j))
    endpoints = slim.utils.convert_collection_to_dict('collections')
    return net, endpoints

Can you help me figure it out? Is there any example for changing basenet? Thank you! @dengdan

[dengdan]

The error implies that you have a variable defined in your model but without being used when running the train_op. Try:

def basenet(inputs):
    logit, endpoints =resnet_50(inputs)
    endpoints['conv4_3'] = endpoints['vgg/resnet_50/block2/unit_2']
    endpoints['fc7'] = logit # Maybe relu should be added on the logit: relu(logit)
    return endpoints['fc7'], endpoints

[abc8350712]

@dengdan Amazing!It can work!Thank you very much！！