Tensorflow_io: ValueError: Cannot infer argument `num` from shape (None, None, None)
Tensorflow_io: ValueError: Cannot infer argument `num` from shape (None, None, None)
我正在尝试在 tensorflow 中读取和解码 tiff 图像。我正在使用 tensrflow_io 包，如下所示，我收到了这个我无法弄清楚的错误。
import tensorflow as tf
import tensorflow_io as tfio
import os
def process_image(image):
image = tf.io.read_file(image)
image = tfio.experimental.image.decode_tiff(image)
image = tfio.experimental.color.rgba_to_rgb(image)
return image
path = os.path.join(os.curdir, '*.TIF')
files = tf.data.Dataset.list_files(path)
输出：
for file in files.take(5):
print(file)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s10_w1.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s04_w1.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s12_w1.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s04_w2.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s11_w1.TIF', shape=(), dtype=string)
现在如果我打电话：
dataset = files.map(process_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
for img in dataset.take(5):
print(img.shape)
ValueError: in user code:
File "<ipython-input-4-1d2deab36c6d>", line 5, in process_image *
image = tfio.experimental.color.rgba_to_rgb(image)
File "/usr/local/lib/python3.7/dist-packages/tensorflow_io/python/experimental/color_ops.py", line 80, in rgba_to_rgb *
rgba = tf.unstack(input, axis=-1)
ValueError: Cannot infer argument `num` from shape (None, None, None)
问题是 tfio.experimental.color.rgba_to_rgb 在后台使用 unstack，这在 graph mode. One solution would be to manually index the channels you want according to the source code 中无法工作 rgba_to_rgb。这是一个工作示例：
import numpy as np
from PIL import Image
import tensorflow as tf
import tensorflow_io as tfio
import os
# Create dummy data
data = np.random.randint(0, 255, (10,10)).astype(np.uint8)
im = Image.fromarray(data)
im.save('image1.tif')
im.save('image2.tif')
def process_image(image):
image = tf.io.read_file(image)
image = tfio.experimental.image.decode_tiff(image)
r, g, b = image[:, :, 0], image[:, :, 1], image[:, :, 2]
return tf.stack([r, g, b], axis=-1)
path = os.path.join(os.curdir, '*.tif')
files = tf.data.Dataset.list_files(path)
for file in files.take(5):
print(file)
dataset = files.map(process_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
for img in dataset.take(5):
print(img.shape)
tf.Tensor(b'./image2.tif', shape=(), dtype=string)
tf.Tensor(b'./image1.tif', shape=(), dtype=string)
(10, 10, 3)
(10, 10, 3)
如果你真的想使用tfio.experimental.color.rgba_to_rgb，它会脱离图形模式，例如使用tf.py_function。
我改了一点代码是因为参数没有按照你的预期更新，这种方式很容易理解。 ( 参数 0 )
[样本]:
import os
from os.path import exists
import tensorflow as tf
import tensorflow_io as tfio
import matplotlib.pyplot as plt
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = os.path.join('F:\datasets\downloads\Actors\train\Pikaploy', '*.tif')
PATH_2 = os.path.join('F:\datasets\downloads\Actors\train\Candidt Kibt', '*.tif')
files = tf.data.Dataset.list_files(PATH)
files_2 = tf.data.Dataset.list_files(PATH_2)
list_file = []
list_file_actual = []
list_label = []
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
for file in files.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(1)
for file in files_2.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(9)
checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\loggings.log"
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
log_dir = checkpoint_dir
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices((tf.constant(tf.cast(list_file, dtype=tf.int64), shape=(10, 1, 32, 32, 4), dtype=tf.int64),tf.constant(list_label, shape=(10, 1, 1), dtype=tf.int64)))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
tf.keras.layers.Normalization(mean=3., variance=2.),
tf.keras.layers.Normalization(mean=4., variance=6.),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Reshape((128, 225)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(192, activation='relu'),
tf.keras.layers.Dense(10),
])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Nadam'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50 )
model.save_weights(checkpoint_path)
plt.figure(figsize=(5,2))
plt.title("Actors recognitions")
for i in range(len(list_file)):
img = tf.keras.preprocessing.image.array_to_img(
list_file[i],
data_format=None,
scale=True
)
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
plt.subplot(5, 2, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(list_file_actual[i])
plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" + str(list_label_actual[tf.math.argmax(score)]))
plt.show()
input('...')
[输出]:
我正在尝试在 tensorflow 中读取和解码 tiff 图像。我正在使用 tensrflow_io 包，如下所示，我收到了这个我无法弄清楚的错误。
import tensorflow as tf
import tensorflow_io as tfio
import os
def process_image(image):
image = tf.io.read_file(image)
image = tfio.experimental.image.decode_tiff(image)
image = tfio.experimental.color.rgba_to_rgb(image)
return image
path = os.path.join(os.curdir, '*.TIF')
files = tf.data.Dataset.list_files(path)
输出：
for file in files.take(5):
print(file)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s10_w1.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s04_w1.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s12_w1.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s04_w2.TIF', shape=(), dtype=string)
tf.Tensor(b'./SIMCEPImages_A01_C1_F1_s11_w1.TIF', shape=(), dtype=string)
现在如果我打电话：
dataset = files.map(process_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
for img in dataset.take(5):
print(img.shape)
ValueError: in user code:
File "<ipython-input-4-1d2deab36c6d>", line 5, in process_image *
image = tfio.experimental.color.rgba_to_rgb(image)
File "/usr/local/lib/python3.7/dist-packages/tensorflow_io/python/experimental/color_ops.py", line 80, in rgba_to_rgb *
rgba = tf.unstack(input, axis=-1)
ValueError: Cannot infer argument `num` from shape (None, None, None)
问题是 tfio.experimental.color.rgba_to_rgb 在后台使用 unstack，这在 graph mode. One solution would be to manually index the channels you want according to the source code 中无法工作 rgba_to_rgb。这是一个工作示例：
import numpy as np
from PIL import Image
import tensorflow as tf
import tensorflow_io as tfio
import os
# Create dummy data
data = np.random.randint(0, 255, (10,10)).astype(np.uint8)
im = Image.fromarray(data)
im.save('image1.tif')
im.save('image2.tif')
def process_image(image):
image = tf.io.read_file(image)
image = tfio.experimental.image.decode_tiff(image)
r, g, b = image[:, :, 0], image[:, :, 1], image[:, :, 2]
return tf.stack([r, g, b], axis=-1)
path = os.path.join(os.curdir, '*.tif')
files = tf.data.Dataset.list_files(path)
for file in files.take(5):
print(file)
dataset = files.map(process_image, num_parallel_calls=tf.data.experimental.AUTOTUNE)
for img in dataset.take(5):
print(img.shape)
tf.Tensor(b'./image2.tif', shape=(), dtype=string)
tf.Tensor(b'./image1.tif', shape=(), dtype=string)
(10, 10, 3)
(10, 10, 3)
如果你真的想使用tfio.experimental.color.rgba_to_rgb，它会脱离图形模式，例如使用tf.py_function。
我改了一点代码是因为参数没有按照你的预期更新，这种方式很容易理解。 ( 参数 0 )
[样本]:
import os
from os.path import exists
import tensorflow as tf
import tensorflow_io as tfio
import matplotlib.pyplot as plt
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = os.path.join('F:\datasets\downloads\Actors\train\Pikaploy', '*.tif')
PATH_2 = os.path.join('F:\datasets\downloads\Actors\train\Candidt Kibt', '*.tif')
files = tf.data.Dataset.list_files(PATH)
files_2 = tf.data.Dataset.list_files(PATH_2)
list_file = []
list_file_actual = []
list_label = []
list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
for file in files.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(1)
for file in files_2.take(5):
image = tf.io.read_file( file )
image = tfio.experimental.image.decode_tiff(image, index=0)
list_file_actual.append(image)
image = tf.image.resize(image, [32,32], method='nearest')
list_file.append(image)
list_label.append(9)
checkpoint_path = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\models\checkpoint\" + os.path.basename(__file__).split('.')[0] + "\loggings.log"
if not exists(checkpoint_dir) :
os.mkdir(checkpoint_dir)
print("Create directory: " + checkpoint_dir)
log_dir = checkpoint_dir
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices((tf.constant(tf.cast(list_file, dtype=tf.int64), shape=(10, 1, 32, 32, 4), dtype=tf.int64),tf.constant(list_label, shape=(10, 1, 1), dtype=tf.int64)))
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
tf.keras.layers.Normalization(mean=3., variance=2.),
tf.keras.layers.Normalization(mean=4., variance=6.),
tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Reshape((128, 225)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(192, activation='relu'),
tf.keras.layers.Dense(10),
])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
name='Nadam'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=False,
reduction=tf.keras.losses.Reduction.AUTO,
name='sparse_categorical_crossentropy'
)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'])
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
model.load_weights(checkpoint_path)
print("model load: " + checkpoint_path)
input("Press Any Key!")
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50 )
model.save_weights(checkpoint_path)
plt.figure(figsize=(5,2))
plt.title("Actors recognitions")
for i in range(len(list_file)):
img = tf.keras.preprocessing.image.array_to_img(
list_file[i],
data_format=None,
scale=True
)
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
plt.subplot(5, 2, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(list_file_actual[i])
plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" + str(list_label_actual[tf.math.argmax(score)]))
plt.show()
input('...')
[输出]: