如何使用datagenerator在数据框列中按其路径加载图像以进行双输入 [英] how to load images by their paths in dataframe columns for dual input using datagenerator
本文介绍了如何使用datagenerator在数据框列中按其路径加载图像以进行双输入的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
由于图像数量过多，我发生了内存错误，当我直接将所有图像从给定路径中的所有图像加载到数据帧中时会发生这种情况. 训练集的
dataframe(df_train_data)格式如下:
class_id ID uu vv
Abnormal 1001 1001_05.png 1001_06.png
Abnormal 1002 1002_05.png 1002_06.png
Abnormal 1003 1003_05.png 1003_06.png
Normal 1554 1554_05.png 1554_06.png
Normal 1555 1555_05.png 1555_06.png
Normal 1556 1556_05.png 1556_06.png
...
请注意，Normal类实例排在所有Abnormal类实例之后，它们都以这种方式排序.
我正在阅读以下格式的图像及其ID:
X_uu_train = read_imgs(df_train_data.uu.values, img_height, img_width, channels)
X_vv_train = read_imgs(df_train_data.vv.values, img_height, img_width, channels)
train_labels = df_train_data.ID.values
其中read_imgs返回numpy数组中的所有图像.
Memory错误恰好发生在X_uu_train = read_imgs(df_train_data.uu.values, img_height, img_width, channels)处.
我已经看到了一些建议使用ImageDataGenerator批量加载图像的解决方案.但是，我没有从大多数网站中所示的目录中加载图像.原来有一种加载方式来自数据帧的图像，其格式类似于.flow_from_dataframe.
这里是训练阶段:
hist = base_model.fit([X_uu_train, X_vv_train], train_labels,
batch_size=batch_size, epochs=epochs, verbose=1,
validation_data=([X_uu_val, X_vv_val], val_labels), shuffle=True)
preds = base_model.predict([X_uu_val, X_vv_val])
问题是它仅使用单输入就可以完成，但是我的生成器应该为双输入带来图像批处理.
有人可以帮助我构造一个ImageDataGenerator，以便我希望加载图像而不会遇到MemoryError
从uu和vv列加载时，应将图像及其对应的对以乱序排列的顺序输入到网络中.
P.S.如有必要，我可能会提供更多信息
谢谢.
<BatchDataset shapes: (((None, 224, 224, 3), (None, 224, 224, 3)), (None,)), types: ((tf.float32, tf.float32), tf.int32)>
EDIT-2:
AttributeError Traceback (most recent call last)
<ipython-input-18-4ae4c12b2b76> in <module>
43
44 base_model = combined_net()
---> 45 hist = base_model.fit(ds_train, epochs=epochs, verbose=1, validation_data=ds_val, shuffle=True)
46
47 preds = base_model.predict(ds_val)
~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
1152 sample_weight=sample_weight,
1153 class_weight=class_weight,
-> 1154 batch_size=batch_size)
1155
1156 # Prepare validation data.
~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
577 feed_input_shapes,
578 check_batch_axis=False, # Don't enforce the batch size.
--> 579 exception_prefix='input')
580
581 if y is not None:
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
97 data = data.values if data.__class__.__name__ == 'DataFrame' else data
98 data = [data]
---> 99 data = [standardize_single_array(x) for x in data]
100
101 if len(data) != len(names):
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in <listcomp>(.0)
97 data = data.values if data.__class__.__name__ == 'DataFrame' else data
98 data = [data]
---> 99 data = [standardize_single_array(x) for x in data]
100
101 if len(data) != len(names):
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_single_array(x)
32 'Got tensor with shape: %s' % str(shape))
33 return x
---> 34 elif x.ndim == 1:
35 x = np.expand_dims(x, 1)
36 return x
AttributeError: 'BatchDataset' object has no attribute 'ndim'
解决方案
ImageDataGenerator创建一个tf.data.Dataset对象，因此您可以直接使用该对象以提高灵活性.您可以传递文件名列表，它只会迭代加载它们.
import pandas as pd
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
df = pd.read_clipboard()
x = df.uu
y = df.vv
z = df.class_id
def load(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, size=(100, 100))
return img
ds = tf.data.Dataset.from_tensor_slices((x, y, z)).\
map(lambda xx, yy, zz: (load(xx), load(yy), zz)).\
batch(4)
next(iter(ds))
这是一个完整的示例，从文件列表开始(如果有数据框，这很容易)，并且一路建模培训.
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import numpy as np
import cv2
from skimage import data
import tensorflow as tf
coffee = data.coffee()
cat = data.chelsea()
for image, name in zip([coffee, cat], ['coffee', 'cat']):
for i in range(5):
cv2.imwrite(f'{name}_{i}.png', image)
cat_files = list(filter(lambda x: x.startswith('cat'), os.listdir()))
coffee_files = list(filter(lambda x: x.startswith('coffee'), os.listdir()))
def load(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, size=(100, 100))
return img
def label(string):
return tf.cast(tf.equal(string, 'abnormal'), tf.int32)
x = cat_files
y = coffee_files
z = np.random.choice(['normal', 'abnormal'], 5)
inputs = tf.data.Dataset.from_tensor_slices((x, y)).map(lambda x, y: (load(x), load(y)))
labels = tf.data.Dataset.from_tensor_slices(z).map(lambda x: label(x))
ds = tf.data.Dataset.zip((inputs, labels)).batch(4)
next(iter(ds))
inputs1 = tf.keras.layers.Input(shape=(100, 100, 3), name='input1')
inputs2 = tf.keras.layers.Input(shape=(100, 100, 3), name='input2')
xx = tf.keras.layers.Flatten()(inputs1)
yy = tf.keras.layers.Flatten()(inputs2)
x = tf.keras.layers.Concatenate()([xx, yy])
x = tf.keras.layers.Dense(32, activation='relu')(x)
output = tf.keras.layers.Dense(1, activation='sigmoid')(x)
model = tf.keras.Model(inputs=[inputs1, inputs2], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam')
history = model.fit(ds)
Train for 2 steps
1/2 [==============>...............] - ETA: 0s - loss: 0.7527
2/2 [==============================] - 1s 251ms/step - loss: 5.5188
那么您还可以预测:
model.predict(ds)
array([[4.7391814e-26],
[4.7391814e-26],
[4.7391814e-26],
[4.7391814e-26],
[4.7390730e-26]], dtype=float32)
I have got a memory error due to a huge amount of images, that happens when I directly load all the images from their given paths in a dataframe.
dataframe(df_train_data)'s format for training set is like below:
class_id ID uu vv
Abnormal 1001 1001_05.png 1001_06.png
Abnormal 1002 1002_05.png 1002_06.png
Abnormal 1003 1003_05.png 1003_06.png
Normal 1554 1554_05.png 1554_06.png
Normal 1555 1555_05.png 1555_06.png
Normal 1556 1556_05.png 1556_06.png
...
Note that Normal class instances come after all Abnormal class instances, they are all ordered in that way.
I am reading the images and their IDs in the following form:
X_uu_train = read_imgs(df_train_data.uu.values, img_height, img_width, channels)
X_vv_train = read_imgs(df_train_data.vv.values, img_height, img_width, channels)
train_labels = df_train_data.ID.values
where read_imgs returns all of the images in numpy array.
The Memory error happens right at the X_uu_train = read_imgs(df_train_data.uu.values, img_height, img_width, channels).
I have seen some solutions where it is recommended to use ImageDataGenerator to load images as batches. However I am not loading images from directory as shown in most sites. Turns out that there is a way to load images from dataframes that goes like .flow_from_dataframe.
Here is the training stage:
hist = base_model.fit([X_uu_train, X_vv_train], train_labels,
batch_size=batch_size, epochs=epochs, verbose=1,
validation_data=([X_uu_val, X_vv_val], val_labels), shuffle=True)
preds = base_model.predict([X_uu_val, X_vv_val])
The thing is it does it only with single input, but my generator should bring image batches for dual input.
Could someone help me construct an ImageDataGenerator so that I can hopefully load images without running into MemoryError
While loading from uu and vv columns, images should be input into the network with their corresponding pairs in a shuffled order.
P.S. I may provide more info if necessary
Thank you.
EDIT:
<BatchDataset shapes: (((None, 224, 224, 3), (None, 224, 224, 3)), (None,)), types: ((tf.float32, tf.float32), tf.int32)>
EDIT-2:
AttributeError Traceback (most recent call last)
<ipython-input-18-4ae4c12b2b76> in <module>
43
44 base_model = combined_net()
---> 45 hist = base_model.fit(ds_train, epochs=epochs, verbose=1, validation_data=ds_val, shuffle=True)
46
47 preds = base_model.predict(ds_val)
~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
1152 sample_weight=sample_weight,
1153 class_weight=class_weight,
-> 1154 batch_size=batch_size)
1155
1156 # Prepare validation data.
~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
577 feed_input_shapes,
578 check_batch_axis=False, # Don't enforce the batch size.
--> 579 exception_prefix='input')
580
581 if y is not None:
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
97 data = data.values if data.__class__.__name__ == 'DataFrame' else data
98 data = [data]
---> 99 data = [standardize_single_array(x) for x in data]
100
101 if len(data) != len(names):
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in <listcomp>(.0)
97 data = data.values if data.__class__.__name__ == 'DataFrame' else data
98 data = [data]
---> 99 data = [standardize_single_array(x) for x in data]
100
101 if len(data) != len(names):
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_single_array(x)
32 'Got tensor with shape: %s' % str(shape))
33 return x
---> 34 elif x.ndim == 1:
35 x = np.expand_dims(x, 1)
36 return x
AttributeError: 'BatchDataset' object has no attribute 'ndim'
解决方案
ImageDataGenerator creates a tf.data.Dataset object, so you can use that directly for more flexiblity. You can pass a list of filenames and it will only load them iteratively.
import pandas as pd
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
df = pd.read_clipboard()
x = df.uu
y = df.vv
z = df.class_id
def load(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, size=(100, 100))
return img
ds = tf.data.Dataset.from_tensor_slices((x, y, z)).\
map(lambda xx, yy, zz: (load(xx), load(yy), zz)).\
batch(4)
next(iter(ds))
Here's a complete example starting from a list of files (it's easy when you have a dataframe), all the way to model training.
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import numpy as np
import cv2
from skimage import data
import tensorflow as tf
coffee = data.coffee()
cat = data.chelsea()
for image, name in zip([coffee, cat], ['coffee', 'cat']):
for i in range(5):
cv2.imwrite(f'{name}_{i}.png', image)
cat_files = list(filter(lambda x: x.startswith('cat'), os.listdir()))
coffee_files = list(filter(lambda x: x.startswith('coffee'), os.listdir()))
def load(file_path):
img = tf.io.read_file(file_path)
img = tf.image.decode_png(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, size=(100, 100))
return img
def label(string):
return tf.cast(tf.equal(string, 'abnormal'), tf.int32)
x = cat_files
y = coffee_files
z = np.random.choice(['normal', 'abnormal'], 5)
inputs = tf.data.Dataset.from_tensor_slices((x, y)).map(lambda x, y: (load(x), load(y)))
labels = tf.data.Dataset.from_tensor_slices(z).map(lambda x: label(x))
ds = tf.data.Dataset.zip((inputs, labels)).batch(4)
next(iter(ds))
inputs1 = tf.keras.layers.Input(shape=(100, 100, 3), name='input1')
inputs2 = tf.keras.layers.Input(shape=(100, 100, 3), name='input2')
xx = tf.keras.layers.Flatten()(inputs1)
yy = tf.keras.layers.Flatten()(inputs2)
x = tf.keras.layers.Concatenate()([xx, yy])
x = tf.keras.layers.Dense(32, activation='relu')(x)
output = tf.keras.layers.Dense(1, activation='sigmoid')(x)
model = tf.keras.Model(inputs=[inputs1, inputs2], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam')
history = model.fit(ds)
Train for 2 steps
1/2 [==============>...............] - ETA: 0s - loss: 0.7527
2/2 [==============================] - 1s 251ms/step - loss: 5.5188
Then you can also predict:
model.predict(ds)
array([[4.7391814e-26],
[4.7391814e-26],
[4.7391814e-26],
[4.7391814e-26],
[4.7390730e-26]], dtype=float32)
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