我需要计算训练有素的 GAN 中生成图像的初始分数。
我指的是这个 OpenAI存储库中的代码。
但是，我在函数即将结束时遇到错误_init_inception。错误是ValueError: Tensor._shape cannot be assigned, use Tensor.set_shape instead。
所以我Tensor.set_shape按照建议通过使用来解决这个问题。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new_shape作为变量传入。事实证明，new_shape由于某种原因，它仍然是一个空列表，并且不被Tensor.set_shape.
解决这个问题以获得我的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()