在 tensorflow 1.14 中使用混合精度训练时，Tensor' 对象在 keras vgg16 中没有属性 'is_initialized'
Tensor' object has no attribute 'is_initialized' in keras vgg16 when using it in tensorflow 1.14 with mixed precision training
让我从头说起。我在 tensorflow 1.14 中实现了一个基于 not official Keras implementation 的图像修复的部分卷积层（我已经对其进行了测试并且它适用于我的数据集）。
此架构使用预训练 (imagenet) VGG16 来计算一些损失项。遗憾的是，在 tensorflow 中实现的 VGG 没有工作（我尝试使用 this one), as the one in keras application. Therefore, I used this class 将 keras 应用程序 VGG16 合并到我的 tensorflow 1.14 代码中。
一切正常，但后来我将混合精度训练 (documentation) 合并到我的代码中，VGG16 部分出现以下错误：
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized_3:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv,
data_mask) File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line
17, in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized_2:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line 17,
in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized_1:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line 17,
in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line 17,
in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
Traceback (most recent call last):
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt)
File "/workspace/vgg.py", line 17, in __init__
self._build_graph(input_tensor)
File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img)
File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line 1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in <listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193, in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 3083, in is_variable_initialized
return state_ops.is_variable_initialized(variable)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/state_ops.py", line 133, in is_variable_initialized
return ref.is_initialized(name=name)
AttributeError: 'Tensor' object has no attribute 'is_initialized'
对于混合精度训练，我使用的是 nvidia docker。
这里使用的是VGG16来获取3张图片的feature map:
def build_vgg(gt, y_pred, mask):
vgg_layer = ['block1_pool', 'block2_pool', 'block3_pool']
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt)
psi_gt = {}
psi_gt[vgg_layer[0]] = tf.identity(vgg[vgg_layer[0]], name='gt_vgg0')
psi_gt[vgg_layer[1]] = tf.identity(vgg[vgg_layer[1]], name='gt_vgg1')
psi_gt[vgg_layer[2]] = tf.identity(vgg[vgg_layer[2]], name='gt_vgg2')
vgg = vgg16.VGG16(image_shape=y_pred.shape, input_tensor=y_pred)
psi_out = {}
psi_out[vgg_layer[0]] = tf.identity(vgg[vgg_layer[0]], name='out_vgg0')
psi_out[vgg_layer[1]] = tf.identity(vgg[vgg_layer[1]], name='out_vgg1')
psi_out[vgg_layer[2]] = tf.identity(vgg[vgg_layer[2]], name='out_vgg2')
I_comp = (mask * gt) + ((1-mask) * y_pred)
vgg = vgg16.VGG16(image_shape=I_comp.shape, input_tensor=I_comp)
psi_comp = {}
psi_comp[vgg_layer[0]] = tf.identity(vgg[vgg_layer[0]], name='comp_vgg0')
psi_comp[vgg_layer[1]] = tf.identity(vgg[vgg_layer[1]], name='comp_vgg1')
psi_comp[vgg_layer[2]] = tf.identity(vgg[vgg_layer[2]], name='comp_vgg2')
return psi_gt, psi_out, psi_comp, I_comp, vgg_layer
主脚本中使用的前一个函数：
import tensorflow as tf
import PConv
import model
import layers
import math
import os
import data
import utils
import numpy as np
import datetime
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Mixed precision training variable storage
def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
initializer=None, regularizer=None,
trainable=True, *args, **kwargs):
storage_dtype = tf.float32 if trainable else dtype
variable = getter(name, shape, dtype=storage_dtype,
initializer=initializer, regularizer=regularizer,
trainable=trainable, *args, **kwargs)
if trainable and dtype != tf.float32:
variable = tf.cast(variable, dtype)
return variable
# ==============================================================================
# SETTINGS
# ==============================================================================
path_ =''
batch_size = 16
best_val = math.inf
best_val_epoch = 0
patience = 0
stop = 300
epochs = 2000
steps_train = 25
steps_val = 8
template = '{}, Epoch {}, train_loss: {:.4f} - val_loss: {:.4f}'
path = path_ + 'tmp/'
if not os.path.isdir(path):
os.mkdir(path)
# ==============================================================================
# DATA
# ==============================================================================
X_train, m_train, y_train = data.get_filenames()
X_val, m_val, y_val = data.get_filenames(train=False)
# ==============================================================================
# DATASET
# ==============================================================================
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, m_train, y_train))#(images, mask, gt))
train_dataset = train_dataset.map(data.load, num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_dataset = train_dataset.batch(batch_size)
train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
val_dataset = tf.data.Dataset.from_tensor_slices((X_val, m_val, y_val))#(images, mask, gt))
val_dataset = val_dataset.map(data.load, num_parallel_calls=tf.data.experimental.AUTOTUNE)
val_dataset = val_dataset.batch(batch_size)
val_dataset = val_dataset.prefetch(buffer_size=1)
iterator = tf.data.Iterator.from_structure(train_dataset.output_types,
train_dataset.output_shapes)
data_im, data_mask, data_gt = iterator.get_next()
# create the initialization operations
train_init_op = iterator.make_initializer(train_dataset)
val_init_op = iterator.make_initializer(val_dataset)
# ==============================================================================
# MODEL
# ==============================================================================
data_im = tf.cast(data_im, tf.float16)
data_mask = tf.cast(data_mask, tf.float16)
with tf.variable_scope('fp32_vars', custom_getter=float32_variable_storage_getter):
unet_pconv = model.pconv_unet(data_im, data_mask)
unet_pconv = tf.cast(unet_pconv, tf.float32)
data_mask = tf.cast(data_mask, tf.float32)
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
I_comp = tf.cast(I_comp, tf.float32)
# # ==============================================================================
# # LOSS
# # ==============================================================================
loss = utils.get_total_loss(unet_pconv, data_gt, data_mask, psi_gt, psi_out, psi_comp, I_comp, layers)
lr = 0.0002
optimizer = utils.optimize(loss, lr)
saver = tf.train.Saver()
# # ==============================================================================
# # TRAINING
# # ==============================================================================
output_summary = tf.summary.image(name='output', tensor=unet_pconv)
merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('graphs',sess.graph)
train_loss_, val_loss_ = [], []
for epoch in range(epochs):
pred_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
tl, vl = [], []
#Initialize iterator with training data
sess.run(train_init_op)
try:
for step in range (steps_train):
_, train_loss, summ = sess.run([optimizer, loss, merged])
writer.add_summary(summ, epoch)
tl.append(train_loss)
mean_train = utils.list_mean(tl)
train_loss_.append(mean_train)
except tf.errors.OutOfRangeError:
pass
if (epoch+1) % 1 == 0:
sess.run(val_init_op)
try:
for step in range (steps_val):
val_loss = sess.run([loss])
vl.append(val_loss)
mean_val = utils.list_mean(vl)
val_loss_.append(mean_val)
except tf.errors.OutOfRangeError:
pass
print(template.format(pred_time, epoch, mean_train, mean_val))
# early stopping
if mean_val < best_val:
print('Saving on epoch {0}'.format(epoch))
best_val = mean_val
patience = 0
best_val_epoch = epoch
saver.save(sess, path+'best_model')
else:
patience += 1
if patience == stop:
print('Early stopping at epoch: {}'.format(best_val_epoch))
break
# # ==============================================================================
# # SAVE CURVES
# # ==============================================================================
np.save(path_+'loss.npy', train_loss_)
np.save(path_+'val_loss.npy', val_loss_)
正在做的优化如下：
def optimize(loss, learning_rate=1e-4):
U_vars = [var for var in tf.trainable_variables() if 'UNET' in var.name]
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt, loss_scale=128.0)
train_opt = opt.minimize(loss, var_list=U_vars)
return train_opt
我已经尝试解决这个问题一段时间了，但仍然不明白为什么在我实施混合精度训练时它不起作用。欢迎询问更多详情。
如果你能伸出援手就太好了！提前谢谢你。
我尝试了很多方法，最后的想法是预训练的 keras 模型不兼容。我将其更改为 tensorflow VGG16 模型，它运行速度较慢，但至少可以运行。
让我从头说起。我在 tensorflow 1.14 中实现了一个基于 not official Keras implementation 的图像修复的部分卷积层（我已经对其进行了测试并且它适用于我的数据集）。
此架构使用预训练 (imagenet) VGG16 来计算一些损失项。遗憾的是，在 tensorflow 中实现的 VGG 没有工作（我尝试使用 this one), as the one in keras application. Therefore, I used this class 将 keras 应用程序 VGG16 合并到我的 tensorflow 1.14 代码中。
一切正常，但后来我将混合精度训练 (documentation) 合并到我的代码中，VGG16 部分出现以下错误：
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized_3:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv,
data_mask) File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line
17, in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized_2:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line 17,
in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized_1:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line 17,
in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'VGG16/model/IsVariableInitialized:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt) File "/workspace/vgg.py", line 17,
in __init__
self._build_graph(input_tensor) File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs) File "/usr/local/lib/python3.6/dist-
packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line
1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in
load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session) File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in
<listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars]) File
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193,
in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
Traceback (most recent call last):
File "main.py", line 131, in <module>
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
File "/workspace/model.py", line 52, in build_vgg
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt)
File "/workspace/vgg.py", line 17, in __init__
self._build_graph(input_tensor)
File "/workspace/vgg.py", line 35, in _build_graph
self.vgg16 = tf.keras.applications.VGG16(weights='imagenet', include_top=False,
input_tensor=img)
File "/usr/local/lib/python3.6/dist-
packages/tensorflow/python/keras/applications/__init__.py", line 70, in wrapper
return base_fun(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/applications/vgg16.py", line 32, in VGG16
return vgg16.VGG16(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/keras_applications/vgg16.py", line 210, in VGG16
model.load_weights(weights_path)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 162, in load_weights
return super(Model, self).load_weights(filepath, by_name)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py", line 1424, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/hdf5_format.py", line 759, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 3071, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 462, in get_session
_initialize_variables(session)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py", line 879, in <listcomp>
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/tf_should_use.py", line 193, in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py", line 3083, in is_variable_initialized
return state_ops.is_variable_initialized(variable)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/state_ops.py", line 133, in is_variable_initialized
return ref.is_initialized(name=name)
AttributeError: 'Tensor' object has no attribute 'is_initialized'
对于混合精度训练，我使用的是 nvidia docker。
这里使用的是VGG16来获取3张图片的feature map:
def build_vgg(gt, y_pred, mask):
vgg_layer = ['block1_pool', 'block2_pool', 'block3_pool']
vgg = vgg16.VGG16(image_shape=gt.shape, input_tensor=gt)
psi_gt = {}
psi_gt[vgg_layer[0]] = tf.identity(vgg[vgg_layer[0]], name='gt_vgg0')
psi_gt[vgg_layer[1]] = tf.identity(vgg[vgg_layer[1]], name='gt_vgg1')
psi_gt[vgg_layer[2]] = tf.identity(vgg[vgg_layer[2]], name='gt_vgg2')
vgg = vgg16.VGG16(image_shape=y_pred.shape, input_tensor=y_pred)
psi_out = {}
psi_out[vgg_layer[0]] = tf.identity(vgg[vgg_layer[0]], name='out_vgg0')
psi_out[vgg_layer[1]] = tf.identity(vgg[vgg_layer[1]], name='out_vgg1')
psi_out[vgg_layer[2]] = tf.identity(vgg[vgg_layer[2]], name='out_vgg2')
I_comp = (mask * gt) + ((1-mask) * y_pred)
vgg = vgg16.VGG16(image_shape=I_comp.shape, input_tensor=I_comp)
psi_comp = {}
psi_comp[vgg_layer[0]] = tf.identity(vgg[vgg_layer[0]], name='comp_vgg0')
psi_comp[vgg_layer[1]] = tf.identity(vgg[vgg_layer[1]], name='comp_vgg1')
psi_comp[vgg_layer[2]] = tf.identity(vgg[vgg_layer[2]], name='comp_vgg2')
return psi_gt, psi_out, psi_comp, I_comp, vgg_layer
主脚本中使用的前一个函数：
import tensorflow as tf
import PConv
import model
import layers
import math
import os
import data
import utils
import numpy as np
import datetime
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Mixed precision training variable storage
def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
initializer=None, regularizer=None,
trainable=True, *args, **kwargs):
storage_dtype = tf.float32 if trainable else dtype
variable = getter(name, shape, dtype=storage_dtype,
initializer=initializer, regularizer=regularizer,
trainable=trainable, *args, **kwargs)
if trainable and dtype != tf.float32:
variable = tf.cast(variable, dtype)
return variable
# ==============================================================================
# SETTINGS
# ==============================================================================
path_ =''
batch_size = 16
best_val = math.inf
best_val_epoch = 0
patience = 0
stop = 300
epochs = 2000
steps_train = 25
steps_val = 8
template = '{}, Epoch {}, train_loss: {:.4f} - val_loss: {:.4f}'
path = path_ + 'tmp/'
if not os.path.isdir(path):
os.mkdir(path)
# ==============================================================================
# DATA
# ==============================================================================
X_train, m_train, y_train = data.get_filenames()
X_val, m_val, y_val = data.get_filenames(train=False)
# ==============================================================================
# DATASET
# ==============================================================================
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, m_train, y_train))#(images, mask, gt))
train_dataset = train_dataset.map(data.load, num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_dataset = train_dataset.batch(batch_size)
train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
val_dataset = tf.data.Dataset.from_tensor_slices((X_val, m_val, y_val))#(images, mask, gt))
val_dataset = val_dataset.map(data.load, num_parallel_calls=tf.data.experimental.AUTOTUNE)
val_dataset = val_dataset.batch(batch_size)
val_dataset = val_dataset.prefetch(buffer_size=1)
iterator = tf.data.Iterator.from_structure(train_dataset.output_types,
train_dataset.output_shapes)
data_im, data_mask, data_gt = iterator.get_next()
# create the initialization operations
train_init_op = iterator.make_initializer(train_dataset)
val_init_op = iterator.make_initializer(val_dataset)
# ==============================================================================
# MODEL
# ==============================================================================
data_im = tf.cast(data_im, tf.float16)
data_mask = tf.cast(data_mask, tf.float16)
with tf.variable_scope('fp32_vars', custom_getter=float32_variable_storage_getter):
unet_pconv = model.pconv_unet(data_im, data_mask)
unet_pconv = tf.cast(unet_pconv, tf.float32)
data_mask = tf.cast(data_mask, tf.float32)
psi_gt, psi_out, psi_comp, I_comp, layers = model.build_vgg(data_gt, unet_pconv, data_mask)
I_comp = tf.cast(I_comp, tf.float32)
# # ==============================================================================
# # LOSS
# # ==============================================================================
loss = utils.get_total_loss(unet_pconv, data_gt, data_mask, psi_gt, psi_out, psi_comp, I_comp, layers)
lr = 0.0002
optimizer = utils.optimize(loss, lr)
saver = tf.train.Saver()
# # ==============================================================================
# # TRAINING
# # ==============================================================================
output_summary = tf.summary.image(name='output', tensor=unet_pconv)
merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('graphs',sess.graph)
train_loss_, val_loss_ = [], []
for epoch in range(epochs):
pred_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
tl, vl = [], []
#Initialize iterator with training data
sess.run(train_init_op)
try:
for step in range (steps_train):
_, train_loss, summ = sess.run([optimizer, loss, merged])
writer.add_summary(summ, epoch)
tl.append(train_loss)
mean_train = utils.list_mean(tl)
train_loss_.append(mean_train)
except tf.errors.OutOfRangeError:
pass
if (epoch+1) % 1 == 0:
sess.run(val_init_op)
try:
for step in range (steps_val):
val_loss = sess.run([loss])
vl.append(val_loss)
mean_val = utils.list_mean(vl)
val_loss_.append(mean_val)
except tf.errors.OutOfRangeError:
pass
print(template.format(pred_time, epoch, mean_train, mean_val))
# early stopping
if mean_val < best_val:
print('Saving on epoch {0}'.format(epoch))
best_val = mean_val
patience = 0
best_val_epoch = epoch
saver.save(sess, path+'best_model')
else:
patience += 1
if patience == stop:
print('Early stopping at epoch: {}'.format(best_val_epoch))
break
# # ==============================================================================
# # SAVE CURVES
# # ==============================================================================
np.save(path_+'loss.npy', train_loss_)
np.save(path_+'val_loss.npy', val_loss_)
正在做的优化如下：
def optimize(loss, learning_rate=1e-4):
U_vars = [var for var in tf.trainable_variables() if 'UNET' in var.name]
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt, loss_scale=128.0)
train_opt = opt.minimize(loss, var_list=U_vars)
return train_opt
我已经尝试解决这个问题一段时间了，但仍然不明白为什么在我实施混合精度训练时它不起作用。欢迎询问更多详情。
如果你能伸出援手就太好了！提前谢谢你。
我尝试了很多方法，最后的想法是预训练的 keras 模型不兼容。我将其更改为 tensorflow VGG16 模型，它运行速度较慢，但至少可以运行。