GAN 训练：由于 Tensor.set_shape 错误，无法计算初始分数

Question

我需要计算在我训练的 GAN 中生成的图像的起始分数。

我指的是这个 OpenAI repository 中的代码。

但是，我在 _init_inception 函数快结束时遇到错误。错误是 ValueError: Tensor._shape cannot be assigned, use Tensor.set_shape instead.

所以我按照建议使用 Tensor.set_shape 解决了这个问题。但是现在，我得到了一个不同的错误，即 TypeError: set_shape() missing 1 required positional argument: 'shape'，即使我将 new_shape 作为变量传递。原来 new_shape 由于某种原因仍然是一个空列表，并且不被 Tensor.set_shape 接受。

解决这个问题以获得初始分数的正确方法是什么？为了您的方便，下面提供了完整的代码。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import sys
import tarfile

import numpy as np
from six.moves import urllib
import tensorflow as tf
import glob
import scipy.misc
import math
import sys

MODEL_DIR = '/tmp/imagenet'
DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
softmax = None

# Call this function with list of images. Each of elements should be a 
# numpy array with values ranging from 0 to 255.
def get_inception_score(images, splits=10):
  assert(type(images) == list)
  assert(type(images[0]) == np.ndarray)
  assert(len(images[0].shape) == 3)
  assert(np.max(images[0]) > 10)
  assert(np.min(images[0]) >= 0.0)
  inps = []
  for img in images:
    img = img.astype(np.float32)
    inps.append(np.expand_dims(img, 0))
  bs = 100
  with tf.Session() as sess:
    preds = []
    n_batches = int(math.ceil(float(len(inps)) / float(bs)))
    for i in range(n_batches):
        sys.stdout.write(".")
        sys.stdout.flush()
        inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
        inp = np.concatenate(inp, 0)
        pred = sess.run(softmax, {'ExpandDims:0': inp})
        preds.append(pred)
    preds = np.concatenate(preds, 0)
    scores = []
    for i in range(splits):
      part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
      kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
      kl = np.mean(np.sum(kl, 1))
      scores.append(np.exp(kl))
    return np.mean(scores), np.std(scores)

# This function is called automatically.
def _init_inception():
  global softmax
  if not os.path.exists(MODEL_DIR):
    os.makedirs(MODEL_DIR)
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(MODEL_DIR, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
  tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
  with tf.gfile.FastGFile(os.path.join(
      MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')
  # Works with an arbitrary minibatch size.
  with tf.Session() as sess:
    pool3 = sess.graph.get_tensor_by_name('pool_3:0')
    ops = pool3.graph.get_operations()
    for op_idx, op in enumerate(ops):
        for o in op.outputs:
            shape = o.get_shape()
            shape = [s.value for s in shape]
            new_shape = []
            for j, s in enumerate(shape):
                if s == 1 and j == 0:
                    new_shape.append(None)
                else:
                    new_shape.append(s)
            o._shape = tf.TensorShape(new_shape)
    w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
    logits = tf.matmul(tf.squeeze(pool3), w)
    softmax = tf.nn.softmax(logits)

if softmax is None:
    _init_inception()

Answer 1

我已经在此处提交了解决此问题的拉取请求：

https://github.com/openai/improved-gan/pull/31

您正在寻找的修复程序是 o.set_shape(tf.TensorShape(new_shape))

而不是 o.set_shape(new_shape)。

GAN 训练：由于 Tensor.set_shape 错误，无法计算初始分数

GAN Training: Cannot calculate inception score because of Tensor.set_shape error

python

image-generation

deep-learning

tensorflow