我编写以下代码，以从具有深CNN usinf张量流的两个图像中提取特征：
# -*- coding: utf-8 -*-
# Implementation of Wang et al 2017: Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks. https://arxiv.org/abs/1709.00382
# Author: Guotai Wang
# Copyright (c) 2017-2018 University College London, United Kingdom. All rights reserved.
# http://cmictig.cs.ucl.ac.uk
#
# Distributed under the BSD-3 licence. Please see the file licence.txt
# This software is not certified for clinical use.
#
from __future__ import absolute_import, print_function
import numpy as np
from scipy import ndimage
import time
import os
import sys
import pickle
import tensorflow as tf
from tensorflow.contrib.data import Iterator
from util.data_loader import *
from util.data_process import *
from util.train_test_func import *
from util.parse_config import parse_config
from train import NetFactory
print("import finished")
def test(config_file):
# 1, load configure file
config = parse_config(config_file)
config_data = config['data']
config_net1 = config.get('network1', None)
config_net2 = config.get('network2', None)
config_net3 = config.get('network3', None)
config_test = config['testing']
batch_size = config_test.get('batch_size', 5)
print("configure file loaded")
# 2.1, network for whole tumor
if(config_net1):
net_type1 = config_net1['net_type']
net_name1 = config_net1['net_name']
data_shape1 = config_net1['data_shape']
label_shape1 = config_net1['label_shape']
class_num1 = config_net1['class_num']
print("configure file of whole tumor is loaded")
# construct graph for 1st network
full_data_shape1 = [batch_size] + data_shape1
x1 = tf.placeholder(tf.float32, shape = full_data_shape1)
net_class1 = NetFactory.create(net_type1)
net1 = net_class1(num_classes = class_num1,w_regularizer = None,
b_regularizer = None, name = net_name1)
net1.set_params(config_net1)
predicty1, caty1 = net1(x1, is_training = True)
proby1 = tf.nn.softmax(predicty1)
else:
config_net1ax = config['network1ax']
config_net1sg = config['network1sg']
config_net1cr = config['network1cr']
print("configure files of whole tumor in three planes are loaded")
# construct graph for 1st network axial
net_type1ax = config_net1ax['net_type']
net_name1ax = config_net1ax['net_name']
data_shape1ax = config_net1ax['data_shape']
label_shape1ax = config_net1ax['label_shape']
class_num1ax = config_net1ax['class_num']
full_data_shape1ax = [batch_size] + data_shape1ax
x1ax = tf.placeholder(tf.float32, shape = full_data_shape1ax)
net_class1ax = NetFactory.create(net_type1ax)
net1ax = net_class1ax(num_classes = class_num1ax,w_regularizer = None,
b_regularizer = None, name = net_name1ax)
net1ax.set_params(config_net1ax)
predicty1ax, caty1ax = net1ax(x1ax, is_training = True)
proby1ax = tf.nn.softmax(predicty1ax)
print("graph for 1st network1ax is constructed")
# construct graph for 1st network sagittal
net_type1sg = config_net1sg['net_type']
net_name1sg = config_net1sg['net_name']
data_shape1sg = config_net1sg['data_shape']
label_shape1sg = config_net1sg['label_shape']
class_num1sg = config_net1sg['class_num']
full_data_shape1sg = [batch_size] + data_shape1sg
x1sg = tf.placeholder(tf.float32, shape = full_data_shape1sg)
net_class1sg = NetFactory.create(net_type1sg)
net1sg = net_class1sg(num_classes = class_num1sg,w_regularizer = None,
b_regularizer = None, name = net_name1sg)
net1sg.set_params(config_net1sg)
predicty1sg, caty1sg = net1sg(x1sg, is_training = True)
proby1sg = tf.nn.softmax(predicty1sg)
print("graph for 1st network1sg is constructed")
# construct graph for 1st network coronal
net_type1cr = config_net1cr['net_type']
net_name1cr = config_net1cr['net_name']
data_shape1cr = config_net1cr['data_shape']
label_shape1cr = config_net1cr['label_shape']
class_num1cr = config_net1cr['class_num']
full_data_shape1cr = [batch_size] + data_shape1cr
x1cr = tf.placeholder(tf.float32, shape = full_data_shape1cr)
net_class1cr = NetFactory.create(net_type1cr)
net1cr = net_class1cr(num_classes = class_num1cr,w_regularizer = None,
b_regularizer = None, name = net_name1cr)
net1cr.set_params(config_net1cr)
predicty1cr, caty1cr = net1cr(x1cr, is_training = True)
proby1cr = tf.nn.softmax(predicty1cr)
print("graph for 1st network1cr is constructed")
# 3, create session and load trained models
all_vars = tf.global_variables()
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
if(config_net1):
net1_vars = [x for x in all_vars if x.name[0:len(net_name1) + 1]==net_name1 + '/']
saver1 = tf.train.Saver(net1_vars)
saver1.restore(sess, config_net1['model_file'])
else:
net1ax_vars = [x for x in all_vars if x.name[0:len(net_name1ax) + 1]==net_name1ax + '/']
saver1ax = tf.train.Saver(net1ax_vars)
saver1ax.restore(sess, config_net1ax['model_file'])
net1sg_vars = [x for x in all_vars if x.name[0:len(net_name1sg) + 1]==net_name1sg + '/']
saver1sg = tf.train.Saver(net1sg_vars)
saver1sg.restore(sess, config_net1sg['model_file'])
net1cr_vars = [x for x in all_vars if x.name[0:len(net_name1cr) + 1]==net_name1cr + '/']
saver1cr = tf.train.Saver(net1cr_vars)
saver1cr.restore(sess, config_net1cr['model_file'])
print("all variables of net1 is saved")
# 4, load test images
dataloader = DataLoader(config_data)
dataloader.load_data()
image_num = dataloader.get_total_image_number()
# 5, start to test
test_slice_direction = config_test.get('test_slice_direction', 'all')
save_folder = config_data['save_folder']
test_time = []
struct = ndimage.generate_binary_structure(3, 2)
margin = config_test.get('roi_patch_margin', 5)
x=['x1','x2']
paddings=tf.constant([[0,0],[0,0],[10,10],[0,0],[0,0]])
for i in range(image_num):
[temp_imgs, temp_weight, temp_name, img_names, temp_bbox, temp_size] = dataloader.get_image_data_with_name(i)
t0 = time.time()
# 5.1, test of 1st network
if(config_net1):
data_shapes = [ data_shape1[:-1], data_shape1[:-1], data_shape1[:-1]]
label_shapes = [label_shape1[:-1], label_shape1[:-1], label_shape1[:-1]]
nets = [net1, net1, net1]
outputs = [proby1, proby1, proby1]
inputs = [x1, x1, x1]
class_num = class_num1
else:
data_shapes = [ data_shape1ax[:-1], data_shape1sg[:-1], data_shape1cr[:-1]]
label_shapes = [label_shape1ax[:-1], label_shape1sg[:-1], label_shape1cr[:-1]]
nets = [net1ax, net1sg, net1cr]
outputs = [proby1ax, proby1sg, proby1cr]
inputs = [x1ax, x1sg, x1cr]
class_num = class_num1ax
predi=tf.concat([predicty1ax,tf.reshape(predicty1sg,[5,11,180,160,2]),tf.pad(predicty1cr,paddings,"CONSTANT")],0)
cati=tf.concat([caty1ax,tf.reshape(caty1sg,[5,11,180,160,14]),tf.pad(caty1cr,paddings,"CONSTANT")],0)
prob1 = test_one_image_three_nets_adaptive_shape(temp_imgs, data_shapes, label_shapes, data_shape1ax[-1], class_num,
batch_size, sess, nets, outputs, inputs, shape_mode = 0)
pred1 = np.asarray(np.argmax(prob1, axis = 3), np.uint16)
pred1 = pred1 * temp_weight
print("net1 is tested")
globals()[x[i]]=predi
test_time.append(time.time() - t0)
print(temp_name)
test_time = np.asarray(test_time)
print('test time', test_time.mean())
np.savetxt(save_folder + '/test_time.txt', test_time)
if __name__ == '__main__':
if(len(sys.argv) != 2):
print('Number of arguments should be 2. e.g.')
print(' python test.py config17/test_all_class.txt')
exit()
config_file = str(sys.argv[1])
assert(os.path.isfile(config_file))
test(config_file)
y=tf.stack([x1,x2],0)
z=tf.Session().run(y)
输出为tensor(y)，我想使用tf.Session().run()将其转换为numpy数组，但出现此错误：
InvalidArgumentError（请参见上面的回溯）：您必须使用dtype float和shape [5,19,180,160,4]输入占位符张量“ Placeholder”的值 [[节点：占位符= Placeholderdtype = DT_FLOAT，形状= [5,19,180,160,4]，_ device =“ / job：localhost /副本：0 / task：0 / device：GPU：0”]]
答案 0 :(得分：0)
请注意，此答案基于对水晶球的深入了解，可以预测似乎已被分类的代码-至少没有写在问题本身中。
看看错误消息：
InvalidArgumentError（请参阅上面的回溯）：您必须输入占位符张量的值
这正是您的代码出了什么问题。整理一下，您的代码本质上就是（存在很多问题）：
import tensorflow as tf
x1 = tf.placeholder(tf.float32, [None, 3])
y = tf.layers.dense(x1, 2)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
print(tf.Session().run(y))
在不知道y的值的情况下无法评估输出张量x1 ，因为它取决于该值。
import tensorflow as tf
x1 = tf.placeholder(tf.float32, [None, 3], name='my_input')
y = tf.layers.dense(x1, 2, name='fc1')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
print(tf.Session().run(y))
现在，错误消息变得更加清晰
tensorflow.python.framework.errors_impl.InvalidArgumentError：必须输入占位符张量' my_input '的值，其类型为dtype float和形状[？，3]
feed_dict 要让TensorFlow知道y的计算应基于哪个值，您需要将其输入到图中：
import tensorflow as tf
x1 = tf.placeholder(tf.float32, [None, 3], name='my_input')
y = tf.layers.dense(x1, 2, name='fc1')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
np_result = tf.Session().run(y, feed_dict={x1: [[42, 43, 44]]})
现在，这揭示了代码的第二个问题。您有 2 个会话：
sess = tf.InteractiveSession()（session_a）tf.Session()中的tf.Session().run()（session_b）现在， session_a 获取所有已初始化的变量，因为您的代码包含
sess.run(tf.global_variables_initializer())
但是，在tf.Session().run(...)期间创建了另一个会话，并留下了新的错误消息：
FailedPreconditionError（请参阅上面的回溯）：尝试使用未初始化的值...
import tensorflow as tf
x1 = tf.placeholder(tf.float32, [None, 3], name='my_input')
y = tf.layers.dense(x1, 2, name='fc1')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
np_result = sess.run(y, feed_dict={x1: [[42, 43, 44]]})
并提供最佳的解决方案：
import tensorflow as tf
# construct graph somewhere
x1 = tf.placeholder(tf.float32, [None, 3], name='my_input')
y = tf.layers.dense(x1, 2, name='fc1')
with tf.Session() as sess:
# init variables / or load them
sess.run(tf.global_variables_initializer())
# make sure, that no operations willl be added to the graph
sess.graph.finalize()
# fetch result as numpy array
np_result = sess.run(y, feed_dict={x1: [[42, 43, 44]]})
您自己编写或从某个地方复制的代码是“如何不在张量流中编写”的最佳演示。
TensorFlow会强制您创建干净的结构。这个很重要。遵循这种结构应该成为一种习惯。一段时间后，您会立即看到这些部分，闻起来像是错误的代码。
如果您使用整个网络，只需将tf.layers.dense替换为my_network_definition，然后
def my_network_definition(x1):
output = ...
return output
在pytorch中，您可以使用问题中提供的任意样式进行编写。不说，你应该这样做。但是有可能。因此，请尝试遵循TensorFlow期望的结构。
尊敬的pytorch用户，我期待您的反馈。