Question

我开发了一个工具，我想在另一个应用程序中使用它。现在，我只是在新应用程序中复制我的工具。文件架构如下所示。

.
├── inner
│   ├── a
│   │   ├── a.py
│   │   └── __init__.py
│   ├── b
│   │   ├── b.py
│   │   └── __init__.py
│   ├── __init__.py
│   └── inner.py
└── outer.py

a.py

class A(object):

    def output(self):
        print('A')

b.py

from a.a import A


class B(object):

    def output(self, a):
        print('B')
        print(isinstance(a, A))

inner.py

from a.a import A
from b.b import B

a = A()
b = B()
a.output()
b.output(a)

B.output将检查第二个参数a是否是A类的实例。在文件夹inner.py下运行inner给出输出

A
B
True

但是，当我在新的应用程序文件夹下运行几乎相同的代码outer.py时，不会产生输出。

A
B
False

outer.py

import sys

sys.path.append('inner')


if __name__ == '__main__':
    from inner.a.a import A
    from inner.b.b import B
    a = A()
    b = B()
    a.output()
    b.output(a)

当我在outer.py中添加print(a)时，我得到<inner.a.a.A object at 0x7f45e179f2d0>，而不是a.a.A object。

我想知道，如何整合内部应用程序以使isinstance返回正确的结果？我应该将inner下的所有文件夹添加到sys.path吗？如果是这样，我将选择删除类型检测。

Answer 1

这确实会不时被问到，但我找不到合适的副本。

不要在软件包中运行 scripts ，而是将其安装到您将安装的可安装Python软件包中，并使用from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import tempfile from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf FLAGS = None def deepnn(x): """deepnn builds the graph for a deep net for classifying digits. Args: x: an input tensor with the dimensions (N_examples, 784), where 784 is the number of pixels in a standard MNIST image. Returns: A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values equal to the logits of classifying the digit into one of 10 classes (the digits 0-9). keep_prob is a scalar placeholder for the probability of dropout. """ # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is only one here, since images are # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. with tf.name_scope('reshape'): x_image = tf.reshape(x, [-1, 28, 28, 1]) # First convolutional layer - maps one grayscale image to 32 feature maps. with tf.name_scope('conv1'): W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # Pooling layer - downsamples by 2X. with tf.name_scope('pool1'): h_pool1 = max_pool_2x2(h_conv1) # Second convolutional layer -- maps 32 feature maps to 64. with tf.name_scope('conv2'): W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # Second pooling layer. with tf.name_scope('pool2'): h_pool2 = max_pool_2x2(h_conv2) # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image # is down to 7x7x64 feature maps -- maps this to 1024 features. with tf.name_scope('fc1'): W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # Dropout - controls the complexity of the model, prevents co-adaptation of # features. with tf.name_scope('dropout'): keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # Map the 1024 features to 10 classes, one for each digit with tf.name_scope('fc2'): W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 return y_conv, keep_prob def conv2d(x, W): """conv2d returns a 2d convolution layer with full stride.""" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): """max_pool_2x2 downsamples a feature map by 2X.""" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def weight_variable(shape): """weight_variable generates a weight variable of a given shape.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """bias_variable generates a bias variable of a given shape.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) # Import data mnist = input_data.read_data_sets("./MNIST_data", one_hot=True) # Create the model x = tf.placeholder(tf.float32, [None, 784]) # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) # Build the graph for the deep net y_conv, keep_prob = deepnn(x) with tf.name_scope('loss'): cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv) cross_entropy = tf.reduce_mean(cross_entropy) with tf.name_scope('adam_optimizer'): train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) with tf.name_scope('accuracy'): correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) correct_prediction = tf.cast(correct_prediction, tf.float32) accuracy = tf.reduce_mean(correct_prediction) graph_location = tempfile.mkdtemp() print('Saving graph to: %s' % graph_location) train_writer = tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph()) builder = tf.saved_model.builder.SavedModelBuilder("./model") with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(20000): batch = mnist.train.next_batch(50) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={ x: batch[0], y_: batch[1], keep_prob: 1.0}) print('step %d, training accuracy %g' % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) builder.add_meta_graph_and_variables(sess,"CNN4mnist") print('test accuracy %g' % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) builder.save()将给定模块作为{运行{1}}。

isinstance返回嵌套应用程序

1 个答案: