我正在尝试将现有光学流网络的输出从numpy数组转换回张量，以便可以通过可微分的插值网络运行它。
PWC-Net代码获取两个相同大小的输入图像并计算流对应关系。如果这是一对图像，我认为流程将是x和y的像素位移。尺寸为[1，h，W，2]。 但是，一批中可以有不同数量的图像对。因此我们可以将其称为b来表示批量大小，因此4D体积将变为[b，h，w，2]。
https://github.com/philferriere/tfoptflow/blob/master/tfoptflow/pwcnet_predict_from_img_pairs.py
通过使用到张量
pred_labels_tensor = tf.convert_to_tensor(pred_labels)
我已经阅读了这些
Convert Python sequence to NumPy array, filling missing values
How to input a list of lists with different sizes in tf.data.Dataset
但是我仍然不明白我需要做些什么才能使它工作。
我还研究了该文件运行的代码，它确实使用了np.asarray。
这两个链接使我认为这与列表列表或可能需要一些零有关。我该如何找出问题所在？而我该如何解决呢？
从此python文件中：- https://github.com/philferriere/tfoptflow/blob/master/tfoptflow/pwcnet_predict_from_img_pairs.py
要重现此问题，您可以使用github下载中提供的现有示例，并使用此代码代替for循环。
image_path1 = f'./samples/mpisintel_test_clean_ambush_1_frame_0001.png'
image_path2 = f'./samples/mpisintel_test_clean_ambush_1_frame_0002.png'
image1, image2 = imread(image_path1), imread(image_path2)
img_pairs.append((image1, image2))
image_path1 = f'./samples/mpisintel_test_clean_ambush_1_frame_0003.png'
image_path2 = f'./samples/mpisintel_test_clean_ambush_1_frame_0004.png'
image1, image2 = imread(image_path1), imread(image_path2)
img_pairs.append((image1, image2))
pred_labels = nn.predict_from_img_pairs(img_pairs, batch_size=1, verbose=False)
pred_labels_tensor = tf.convert_to_tensor(pred_labels)
我期望得到张量输出，但是，我在MSVSCode的终端中收到此错误：-
ValueError: Argument must be a dense tensor: [array([[[ 0.32990038, -0.11566047],
[ 0.35661912, -0.09227534],
[ 0.38333783, -0.06889021],
...,
[-0.1237613 , 0.07946336],
[-0.1237613 , 0.07946336],
[-0.1237613 , 0.07946336]],
[[ 0.34405386, -0.09286585],
[ 0.36766803, -0.07679807],
[ 0.39128217, -0.06073029],
...,
[-0.10938472, 0.08551764],
[-0.10938472, 0.08551764],
[-0.10938472, 0.08551764]],
[[ 0.35820735, -0.07007124],
[ 0.37871695, -0.0613208 ],
[ 0.39922655, -0.05257037],
...,
[-0.09500815, 0.09157193],
[-0.09500815, 0.09157193],
[-0.09500815, 0.09157193]],
...,
[[ 0.9003515 , 1.0893728 ],
[ 0.93065804, 1.0662789 ],
[ 0.96096456, 1.0431851 ],
...,
[ 0.43580785, 0.02744378],
[ 0.43580785, 0.02744378],
[ 0.43580785, 0.02744378]],
[[ 0.9003515 , 1.0893728 ],
[ 0.93065804, 1.0662789 ],
[ 0.96096456, 1.0431851 ],
...,
[ 0.43580785, 0.02744378],
[ 0.43580785, 0.02744378],
[ 0.43580785, 0.02744378]],
[[ 0.9003515 , 1.0893728 ],
[ 0.93065804, 1.0662789 ],
[ 0.96096456, 1.0431851 ],
...,
[ 0.43580785, 0.02744378],
[ 0.43580785, 0.02744378],
[ 0.43580785, 0.02744378]]], dtype=float32), array([[[ 0.49922907, 0.08599953],
[ 0.5034714 , 0.1123561 ],
[ 0.5077137 , 0.13871266],
...,
[-0.3719127 , 0.1080336 ],
[-0.3719127 , 0.1080336 ],
[-0.3719127 , 0.1080336 ]],
[[ 0.49763823, 0.11536887],
[ 0.4972613 , 0.13717887],
[ 0.49688435, 0.15898886],
...,
[-0.36932352, 0.11556612],
[-0.36932352, 0.11556612],
[-0.36932352, 0.11556612]],
[[ 0.49604735, 0.14473821],
[ 0.4910512 , 0.16200164],
[ 0.48605505, 0.17926508],
...,
[-0.36673436, 0.12309864],
[-0.36673436, 0.12309864],
[-0.36673436, 0.12309864]],
...,
[[ 0.46260613, -0.47470346],
[ 0.46841043, -0.46383834],
[ 0.47421476, -0.4529732 ],
...,
[-0.29265293, -0.4021799 ],
[-0.29265293, -0.4021799 ],
[-0.29265293, -0.4021799 ]],
[[ 0.46260613, -0.47470346],
[ 0.46841043, -0.46383834],
[ 0.47421476, -0.4529732 ],
...,
[-0.29265293, -0.4021799 ],
[-0.29265293, -0.4021799 ],
[-0.29265293, -0.4021799 ]],
[[ 0.46260613, -0.47470346],
[ 0.46841043, -0.46383834],
[ 0.47421476, -0.4529732 ],
...,
[-0.29265293, -0.4021799 ],
[-0.29265293, -0.4021799 ],
[-0.29265293, -0.4021799 ]]], dtype=float32)] - [2, 436, 1024, 2], but wanted [2]
如果我将批处理大小减小到一个，则会出现此错误
ValueError：参数必须为密集的张量：-形状为[1，436，1024，2]，但需要[1]。
要获得最小的可复制示例，它需要满足以下条件：-
Python 3.7.3 Tensorflow 1.13.1（最新稳定版） 另外，您需要下载并复制以下脚本并将其粘贴到现有脚本上
还有一个模型可以从https://drive.google.com/drive/folders/1iRJ8SFF6fyoICShRVWuzr192m_DgzSYp下载pwcnet-lg-6-2-multisteps-chairsthingsmix
"""
pwcnet_predict_from_img_pairs.py
Run inference on a list of images pairs.
Written by Phil Ferriere
Licensed under the MIT License (see LICENSE for details)
"""
from __future__ import absolute_import, division, print_function
from voxel_flow_geo_layer_utils import bilinear_interp
from voxel_flow_geo_layer_utils import meshgrid
from copy import deepcopy
from skimage.io import imread
from model_pwcnet import ModelPWCNet, _DEFAULT_PWCNET_TEST_OPTIONS
#from visualize import display_img_pairs_w_flows
import visualize
import numpy as np
import tensorflow as tf
# TODO: Set device to use for inference
# Here, we're using a GPU (use '/device:CPU:0' to run inference on the CPU)
gpu_devices = ['/device:GPU:0']
controller = '/device:GPU:0'
# TODO: Set the path to the trained model (make sure you've downloaded it first https://drive.google.com/drive/folders/1iRJ8SFF6fyoICShRVWuzr192m_DgzSYp)
ckpt_path = './models/pwcnet-lg-6-2-multisteps-chairsthingsmix/pwcnet.ckpt-595000'
# Build a list of image pairs to process (in this case it's just one image pair)
img_pairs = []
image_path1 = f'./samples/mpisintel_test_clean_ambush_1_frame_0001.png'
image_path2 = f'./samples/mpisintel_test_clean_ambush_1_frame_0002.png'
image1, image2 = imread(image_path1), imread(image_path2)
img_pairs.append((image1, image2))
# Configure the model for inference, starting with the default options
nn_opts = deepcopy(_DEFAULT_PWCNET_TEST_OPTIONS)
nn_opts['verbose'] = True
nn_opts['ckpt_path'] = ckpt_path
nn_opts['batch_size'] = 1
nn_opts['gpu_devices'] = gpu_devices
nn_opts['controller'] = controller
# We're running the PWC-Net-large model in quarter-resolution mode
# That is, with a 6 level pyramid, and upsampling of level 2 by 4 in each dimension as the final flow prediction
nn_opts['use_dense_cx'] = True
nn_opts['use_res_cx'] = True
nn_opts['pyr_lvls'] = 6
nn_opts['flow_pred_lvl'] = 2
# The size of the images in this dataset are not multiples of 64, while the model generates flows padded to multiples
# of 64. Hence, we need to crop the predicted flows to their original size
nn_opts['adapt_info'] = (1, 8, 8, 2)
# Instantiate the model in inference mode and display the model configuration
nn = ModelPWCNet(mode='test', options=nn_opts)
nn.print_config()
# Generate the predictions and display them
pred_labels = nn.predict_from_img_pair # pred_labels shape is [436, 1024,2]
# array has len 1 when there is only one image pair.
pred_labels_tensor = tf.convert_to_tensor(pred_labels)