generated_music = generator.predict(noise)[0] NameError: name 'generator' is not defined
时间: 2023-11-03 20:58:04 浏览: 45
这个错误通常意味着你在使用变量`generator`之前没有定义它。请检查你的代码,确认你在使用`generator`之前已经定义了它。如果你使用的是一个包含`generator`的函数或类,那么你需要先实例化它或者调用它的构造函数,然后才能使用它。
如果你已经定义了`generator`变量,但仍然收到这个错误,请确保你没有在变量名或其他地方拼写错误。如果问题仍然存在,请尝试在使用`generator`之前显式地定义它,或者检查你的代码中是否存在其他名称与`generator`冲突的变量或函数。
相关问题
def train_step(real_ecg, dim): noise = tf.random.normal(dim) for i in range(disc_steps): with tf.GradientTape() as disc_tape: generated_ecg = generator(noise, training=True) real_output = discriminator(real_ecg, training=True) fake_output = discriminator(generated_ecg, training=True) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) ### for tensorboard ### disc_losses.update_state(disc_loss) fake_disc_accuracy.update_state(tf.zeros_like(fake_output), fake_output) real_disc_accuracy.update_state(tf.ones_like(real_output), real_output) ####################### with tf.GradientTape() as gen_tape: generated_ecg = generator(noise, training=True) fake_output = discriminator(generated_ecg, training=True) gen_loss = generator_loss(fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) ### for tensorboard ### gen_losses.update_state(gen_loss) ####################### def train(dataset, epochs, dim): for epoch in tqdm(range(epochs)): for batch in dataset: train_step(batch, dim) disc_losses_list.append(disc_losses.result().numpy()) gen_losses_list.append(gen_losses.result().numpy()) fake_disc_accuracy_list.append(fake_disc_accuracy.result().numpy()) real_disc_accuracy_list.append(real_disc_accuracy.result().numpy()) ### for tensorboard ### # with disc_summary_writer.as_default(): # tf.summary.scalar('loss', disc_losses.result(), step=epoch) # tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch) # tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch) # with gen_summary_writer.as_default(): # tf.summary.scalar('loss', gen_losses.result(), step=epoch) disc_losses.reset_states() gen_losses.reset_states() fake_disc_accuracy.reset_states() real_disc_accuracy.reset_states() ####################### # Save the model every 5 epochs # if (epoch + 1) % 5 == 0: # generate_and_save_ecg(generator, epochs, seed, False) # checkpoint.save(file_prefix = checkpoint_prefix) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_ecg(generator, epochs, seed, False)
下面是将上述代码转换为PyTorch代码的示例:
```python
def train_step(real_ecg, dim):
noise = torch.randn(dim)
for i in range(disc_steps):
generated_ecg = generator(noise, training=True)
real_output = discriminator(real_ecg, training=True)
fake_output = discriminator(generated_ecg, training=True)
disc_loss = discriminator_loss(real_output, fake_output)
discriminator.zero_grad()
disc_loss.backward()
discriminator_optimizer.step()
### for tensorboard ###
disc_losses.update(disc_loss)
fake_disc_accuracy.update(torch.zeros_like(fake_output), fake_output)
real_disc_accuracy.update(torch.ones_like(real_output), real_output)
#######################
for i in range(gen_steps):
generated_ecg = generator(noise, training=True)
fake_output = discriminator(generated_ecg, training=True)
gen_loss = generator_loss(fake_output)
generator.zero_grad()
gen_loss.backward()
generator_optimizer.step()
### for tensorboard ###
gen_losses.update(gen_loss)
#######################
def train(dataset, epochs, dim):
for epoch in tqdm(range(epochs)):
for batch in dataset:
train_step(batch, dim)
disc_losses_list.append(disc_losses.avg)
gen_losses_list.append(gen_losses.avg)
fake_disc_accuracy_list.append(fake_disc_accuracy.avg)
real_disc_accuracy_list.append(real_disc_accuracy.avg)
### for tensorboard ###
# with disc_summary_writer.as_default():
# tf.summary.scalar('loss', disc_losses.result(), step=epoch)
# tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch)
# tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch)
# with gen_summary_writer.as_default():
# tf.summary.scalar('loss', gen_losses.result(), step=epoch)
#######################
disc_losses.reset()
gen_losses.reset()
fake_disc_accuracy.reset()
real_disc_accuracy.reset()
#######################
# Save the model every 5 epochs
# if (epoch + 1) % 5 == 0:
# generate_and_save_ecg(generator, epochs, seed, False)
# checkpoint.save(file_prefix = checkpoint_prefix)
# Generate after the final epoch
# display.clear_output(wait=True)
# generate_and_save_ecg(generator, epochs, seed, False)
```
注意:上述代码仅作为示例,可能需要根据实际情况进行调整和修改。
def test(checkpoint_dir, style_name, test_dir, if_adjust_brightness, img_size=[256,256]): # tf.reset_default_graph() result_dir = 'results/'+style_name check_folder(result_dir) test_files = glob('{}/*.*'.format(test_dir)) test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test') with tf.variable_scope("generator", reuse=False): test_generated = generator.G_net(test_real).fake saver = tf.train.Saver() gpu_options = tf.GPUOptions(allow_growth=True) with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)) as sess: # tf.global_variables_initializer().run() # load model ckpt = tf.train.get_checkpoint_state(checkpoint_dir) # checkpoint file information if ckpt and ckpt.model_checkpoint_path: ckpt_name = os.path.basename(ckpt.model_checkpoint_path) # first line saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name)) print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name))) else: print(" [*] Failed to find a checkpoint") return # stats_graph(tf.get_default_graph()) begin = time.time() for sample_file in tqdm(test_files) : # print('Processing image: ' + sample_file) sample_image = np.asarray(load_test_data(sample_file, img_size)) image_path = os.path.join(result_dir,'{0}'.format(os.path.basename(sample_file))) fake_img = sess.run(test_generated, feed_dict = {test_real : sample_image}) if if_adjust_brightness: save_images(fake_img, image_path, sample_file) else: save_images(fake_img, image_path, None) end = time.time() print(f'test-time: {end-begin} s') print(f'one image test time : {(end-begin)/len(test_files)} s'什么意思
这是一个 Python 函数,该函数接受五个参数:`checkpoint_dir`,`style_name`,`test_dir`,`if_adjust_brightness` 和 `img_size`。该函数使用 TensorFlow 实现了一个测试函数,用于测试训练好的 AnimeGAN V2 模型生成的效果。在函数中,首先使用 `glob` 函数获取测试图片的路径列表,然后使用 TensorFlow 定义了一个占位符 `test_real`,用于输入测试图片。接着,使用 AnimeGAN V2 模型生成器 `generator.G_net` 生成虚假图像 `test_generated`。随后,使用 TensorFlow 定义了一个 Saver 对象,用于从检查点文件中恢复模型。最后,使用 TensorFlow Session 对象运行生成器模型,生成对应的虚假图像,并将其保存到指定的目录中。
其中,如果 `if_adjust_brightness` 为真,则调用 `save_images` 函数保存虚假图像,并调用 `load_test_data` 函数加载测试图片并调整亮度;否则,调用 `save_images` 函数保存虚假图像,但不会调用 `load_test_data` 函数调整亮度。函数还输出了测试的总时间和每张图片测试所需的时间。