Keras/HDF5 TypeError: PointSelection __getitem__ only works with bool arrays
Keras/HDF5 TypeError: PointSelection __getitem__ only works with bool arrays
当我尝试使用 fit_generator 和生成器函数从 HDF5 文件加载训练数据时，我得到一个 ValueError，它是由 HDF5 的 PointSelectionError 引起的：
Epoch 1/10
Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 810, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 763, in run
self.__target(*self.__args, **self.__kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 429, in data_generator_task
generator_output = next(self._generator)
File "osr.py", line 108, in generate_training_sequences
X = training_save_file["X"][batch_idxs]
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2684)
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2642)
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/dataset.py", line 462, in __getitem__
selection = sel.select(self.shape, args, dsid=self.id)
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/selections.py", line 72, in select
sel[arg]
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/selections.py", line 210, in __getitem__
raise TypeError("PointSelection __getitem__ only works with bool arrays")
TypeError: PointSelection __getitem__ only works with bool arrays
Traceback (most recent call last):
File "osr.py", line 359, in <module>
osr.train_osr_model()
File "osr.py", line 89, in train_osr_model
nb_worker=1)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1532, in fit_generator
str(generator_output))
ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
我研究了这个错误，有人提到它可能是由于重复索引造成的，但在我的情况下似乎并非如此。以下是访问的行索引：
[581 305 67 510 631 832 340 663 689 801 579 701 831 879 382 844 15 798
342 329 118 657 503 129 602 2 528 157 341 299 731 539]
这里是训练和生成函数：
def train_osr_model(self):
""" Train the optical speech recognizer
"""
print "\nTraining OSR"
validation_ratio = 0.3
batch_size = 32
with h5py.File(self.training_save_fn, "r") as training_save_file:
sample_count = int(training_save_file.attrs["sample_count"])
sample_idxs = range(0, sample_count)
sample_idxs = np.random.permutation(sample_idxs)
training_sample_idxs = sample_idxs[0:int((1-validation_ratio)*sample_count)]
validation_sample_idxs = sample_idxs[int((1-validation_ratio)*sample_count):]
training_sequence_generator = self.generate_training_sequences(batch_size=batch_size,
training_save_file=training_save_file,
training_sample_idxs=training_sample_idxs)
validation_sequence_generator = self.generate_validation_sequences(batch_size=batch_size,
training_save_file=training_save_file,
validation_sample_idxs=validation_sample_idxs)
print "Sample Idxs: {0}\n".format(sample_idxs) # FOR DEBUG ONLY
print "Training Idxs: {0}\n".format(training_sample_idxs) # FOR DEBUG ONLY
print "Validation Idxs: {0}\n".format(validation_sample_idxs) # FOR DEBUG ONLY
pbi = ProgressDisplay()
self.osr.fit_generator(generator=training_sequence_generator,
validation_data=validation_sequence_generator,
samples_per_epoch=len(training_sample_idxs),
nb_val_samples=len(validation_sample_idxs),
nb_epoch=10,
max_q_size=1,
verbose=2,
callbacks=[pbi],
class_weight=None,
nb_worker=1)
def generate_training_sequences(self, batch_size, training_save_file, training_sample_idxs):
""" Generates training sequences from HDF5 file on demand
"""
while True:
# generate sequences for training
training_sample_count = len(training_sample_idxs)
batches = int(training_sample_count/batch_size)
remainder_samples = training_sample_count%batch_size
if remainder_samples:
batches = batches + 1
# generate batches of samples
for idx in xrange(0, batches):
if idx == batches - 1:
batch_idxs = training_sample_idxs[idx*batch_size:]
else:
batch_idxs = training_sample_idxs[idx*batch_size:idx*batch_size+batch_size]
print batch_idxs # FOR DEBUG ONLY
X = training_save_file["X"][batch_idxs]
Y = training_save_file["Y"][batch_idxs]
yield (np.array(X), np.array(Y))
def generate_validation_sequences(self, batch_size, training_save_file, validation_sample_idxs):
while True:
# generate sequences for validation
validation_sample_count = len(validation_sample_idxs)
batches = int(validation_sample_count/batch_size)
remainder_samples = validation_sample_count%batch_size
if remainder_samples:
batches = batches + 1
# generate batches of samples
for idx in xrange(0, batches):
if idx == batches - 1:
batch_idxs = validation_sample_idxs[idx*batch_size:]
else:
batch_idxs = validation_sample_idxs[idx*batch_size:idx*batch_size+batch_size]
print batch_idxs # FOR DEBUG ONLY
X = training_save_file["X"][batch_idxs]
Y = training_save_file["Y"][batch_idxs]
yield (np.array(X), np.array(Y))
以下是预处理训练数据并将其保存到 HDF5 文件中的函数：
def process_training_data(self):
""" Preprocesses training data and saves them into an HDF5 file
"""
# load training metadata from config file
training_metadata = {}
training_classes = []
with open(self.config_file) as training_config:
training_metadata = json.load(training_config)
training_classes = sorted(list(training_metadata.keys()))
print "".join(["\n",
"Found {0} training classes!\n".format(len(training_classes)),
"-"*40])
for class_label, training_class in enumerate(training_classes):
print "{0:<4d} {1:<10s} {2:<30s}".format(class_label, training_class, training_metadata[training_class])
print ""
# count number of samples
sample_count = 0
sample_count_by_class = [0]*len(training_classes)
for class_label, training_class in enumerate(training_classes):
# get training class sequeunce paths
training_class_data_path = training_metadata[training_class]
training_class_sequence_paths = [os.path.join(training_class_data_path, file_name)
for file_name in os.listdir(training_class_data_path)
if (os.path.isfile(os.path.join(training_class_data_path, file_name))
and ".mov" in file_name)]
# update sample count
sample_count += len(training_class_sequence_paths)
sample_count_by_class[class_label] = len(training_class_sequence_paths)
print "".join(["\n",
"Found {0} training samples!\n".format(sample_count),
"-"*40])
for class_label, training_class in enumerate(training_classes):
print "{0:<4d} {1:<10s} {2:<6d}".format(class_label, training_class, sample_count_by_class[class_label])
print ""
# initialize HDF5 save file, but clear older duplicate first if it exists
try:
print "Saved file \"{0}\" already exists! Overwriting previous saved file.\n".format(self.training_save_fn)
os.remove(self.training_save_fn)
except OSError:
pass
# process and save training data into HDF5 file
print "Generating {0} samples from {1} samples via data augmentation\n".format(sample_count*self.samples_generated_per_sample,
sample_count)
sample_count = sample_count*self.samples_generated_per_sample
with h5py.File(self.training_save_fn, "w") as training_save_file:
training_save_file.attrs["training_classes"] = np.string_(",".join(training_classes))
training_save_file.attrs["sample_count"] = sample_count
x_training_dataset = training_save_file.create_dataset("X",
shape=(sample_count, self.frames_per_sequence, 3, self.rows, self.columns),
dtype="f")
y_training_dataset = training_save_file.create_dataset("Y",
shape=(sample_count, len(training_classes)),
dtype="i")
# iterate through each class data
sample_idx = 0
for class_label, training_class in enumerate(training_classes):
# get training class sequeunce paths
training_class_data_path = training_metadata[training_class]
training_class_sequence_paths = [os.path.join(training_class_data_path, file_name)
for file_name in os.listdir(training_class_data_path)
if (os.path.isfile(os.path.join(training_class_data_path, file_name))
and ".mov" in file_name)]
# iterate through each sequence
for idx, training_class_sequence_path in enumerate(training_class_sequence_paths):
sys.stdout.write("Processing training data for class \"{0}\": {1}/{2} sequences\r"
.format(training_class, idx+1, len(training_class_sequence_paths)))
sys.stdout.flush()
# accumulate samples and labels
samples_batch = self.process_frames(training_class_sequence_path)
label = [0]*len(training_classes)
label[class_label] = 1
label = np.array(label).astype("int32")
for sample in samples_batch:
x_training_dataset[sample_idx] = sample
y_training_dataset[sample_idx] = label
# update sample index
sample_idx += 1
print "\n"
training_save_file.close()
print "Training data processed and saved to {0}".format(self.training_save_fn)
def process_frames(self, video_file_path):
""" Preprocesses sequence frames
"""
# haar cascades for localizing oral region
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
mouth_cascade = cv2.CascadeClassifier('haarcascade_mcs_mouth.xml')
video = cv2.VideoCapture(video_file_path)
success, frame = video.read()
frames = []
success = True
# convert to grayscale, localize oral region, equalize frame dimensions, and accumulate valid frames
while success:
success, frame = video.read()
if success:
# convert to grayscale
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# localize single facial region
faces_coords = face_cascade.detectMultiScale(frame, 1.3, 5)
if len(faces_coords) == 1:
face_x, face_y, face_w, face_h = faces_coords[0]
frame = frame[face_y:face_y + face_h, face_x:face_x + face_w]
# localize oral region
mouth_coords = mouth_cascade.detectMultiScale(frame, 1.3, 5)
threshold = 0
for (mouth_x, mouth_y, mouth_w, mouth_h) in mouth_coords:
if (mouth_y > threshold):
threshold = mouth_y
valid_mouth_coords = (mouth_x, mouth_y, mouth_w, mouth_h)
else:
pass
mouth_x, mouth_y, mouth_w, mouth_h = valid_mouth_coords
frame = frame[mouth_y:mouth_y + mouth_h, mouth_x:mouth_x + mouth_w]
# equalize frame dimensions
frame = cv2.resize(frame, (self.columns, self.rows)).astype('float32')
# accumulate frames
frames.append(frame)
# ignore multiple facial region detections
else:
pass
# equalize sequence lengths
if len(frames) < self.frames_per_sequence:
frames = [frames[0]]*(self.frames_per_sequence - len(frames)) + frames
frames = np.array(frames[-self.frames_per_sequence:])
# function to normalize and add channel dimension to each frame
proc_frame = lambda frame: np.array([frame / 255.0]*3)
samples_batch = [np.array(map(proc_frame, frames))]
# random transformations for data augmentation
for _ in xrange(0, self.samples_generated_per_sample-1):
rotated_frames = random_rotation(frames, rg=4.5)
shifted_frames = random_shift(rotated_frames, wrg=0.05, hrg=0.05)
sheared_frames = random_shear(shifted_frames, intensity=0.08)
zoomed_frames = random_zoom(sheared_frames, zoom_range=(1.05, 1.05))
samples_batch.append(np.array(map(proc_frame, zoomed_frames)))
return samples_batch
错误来自两件事：
您正在阅读的是因为 batch_idxs 是一个数组，而不是列表。 h5py 对象接受列表索引。但即使你改变
X = training_save_file["X"][list(batch_idxs)]
你仍然会得到一个错误。这来自对列表索引的一些限制。这就把我们带到了第二点。
如果你读了doc you sent me，这是这样写的：
The following restrictions exist:
- List selections may not be empty
- Selection coordinates must be given in increasing order
- Duplicate selections are ignored
- Very long lists (> 1000 elements) may produce poor performance
第二个项目符号是我们的问题：您在创建 training_sample_idxs 时进行的随机改组使索引顺序随机，并且数据集期望它们按递增顺序排列。这是您必须处理的一个限制，但它并不太受限制，因为批次中的顺序无关紧要，模型无论如何都会在整个批次上进行优化。
有帮助吗？
当我尝试使用 fit_generator 和生成器函数从 HDF5 文件加载训练数据时，我得到一个 ValueError，它是由 HDF5 的 PointSelectionError 引起的：
Epoch 1/10
Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 810, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 763, in run
self.__target(*self.__args, **self.__kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 429, in data_generator_task
generator_output = next(self._generator)
File "osr.py", line 108, in generate_training_sequences
X = training_save_file["X"][batch_idxs]
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2684)
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2642)
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/dataset.py", line 462, in __getitem__
selection = sel.select(self.shape, args, dsid=self.id)
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/selections.py", line 72, in select
sel[arg]
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/selections.py", line 210, in __getitem__
raise TypeError("PointSelection __getitem__ only works with bool arrays")
TypeError: PointSelection __getitem__ only works with bool arrays
Traceback (most recent call last):
File "osr.py", line 359, in <module>
osr.train_osr_model()
File "osr.py", line 89, in train_osr_model
nb_worker=1)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1532, in fit_generator
str(generator_output))
ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
我研究了这个错误，有人提到它可能是由于重复索引造成的，但在我的情况下似乎并非如此。以下是访问的行索引：
[581 305 67 510 631 832 340 663 689 801 579 701 831 879 382 844 15 798
342 329 118 657 503 129 602 2 528 157 341 299 731 539]
这里是训练和生成函数：
def train_osr_model(self):
""" Train the optical speech recognizer
"""
print "\nTraining OSR"
validation_ratio = 0.3
batch_size = 32
with h5py.File(self.training_save_fn, "r") as training_save_file:
sample_count = int(training_save_file.attrs["sample_count"])
sample_idxs = range(0, sample_count)
sample_idxs = np.random.permutation(sample_idxs)
training_sample_idxs = sample_idxs[0:int((1-validation_ratio)*sample_count)]
validation_sample_idxs = sample_idxs[int((1-validation_ratio)*sample_count):]
training_sequence_generator = self.generate_training_sequences(batch_size=batch_size,
training_save_file=training_save_file,
training_sample_idxs=training_sample_idxs)
validation_sequence_generator = self.generate_validation_sequences(batch_size=batch_size,
training_save_file=training_save_file,
validation_sample_idxs=validation_sample_idxs)
print "Sample Idxs: {0}\n".format(sample_idxs) # FOR DEBUG ONLY
print "Training Idxs: {0}\n".format(training_sample_idxs) # FOR DEBUG ONLY
print "Validation Idxs: {0}\n".format(validation_sample_idxs) # FOR DEBUG ONLY
pbi = ProgressDisplay()
self.osr.fit_generator(generator=training_sequence_generator,
validation_data=validation_sequence_generator,
samples_per_epoch=len(training_sample_idxs),
nb_val_samples=len(validation_sample_idxs),
nb_epoch=10,
max_q_size=1,
verbose=2,
callbacks=[pbi],
class_weight=None,
nb_worker=1)
def generate_training_sequences(self, batch_size, training_save_file, training_sample_idxs):
""" Generates training sequences from HDF5 file on demand
"""
while True:
# generate sequences for training
training_sample_count = len(training_sample_idxs)
batches = int(training_sample_count/batch_size)
remainder_samples = training_sample_count%batch_size
if remainder_samples:
batches = batches + 1
# generate batches of samples
for idx in xrange(0, batches):
if idx == batches - 1:
batch_idxs = training_sample_idxs[idx*batch_size:]
else:
batch_idxs = training_sample_idxs[idx*batch_size:idx*batch_size+batch_size]
print batch_idxs # FOR DEBUG ONLY
X = training_save_file["X"][batch_idxs]
Y = training_save_file["Y"][batch_idxs]
yield (np.array(X), np.array(Y))
def generate_validation_sequences(self, batch_size, training_save_file, validation_sample_idxs):
while True:
# generate sequences for validation
validation_sample_count = len(validation_sample_idxs)
batches = int(validation_sample_count/batch_size)
remainder_samples = validation_sample_count%batch_size
if remainder_samples:
batches = batches + 1
# generate batches of samples
for idx in xrange(0, batches):
if idx == batches - 1:
batch_idxs = validation_sample_idxs[idx*batch_size:]
else:
batch_idxs = validation_sample_idxs[idx*batch_size:idx*batch_size+batch_size]
print batch_idxs # FOR DEBUG ONLY
X = training_save_file["X"][batch_idxs]
Y = training_save_file["Y"][batch_idxs]
yield (np.array(X), np.array(Y))
以下是预处理训练数据并将其保存到 HDF5 文件中的函数：
def process_training_data(self):
""" Preprocesses training data and saves them into an HDF5 file
"""
# load training metadata from config file
training_metadata = {}
training_classes = []
with open(self.config_file) as training_config:
training_metadata = json.load(training_config)
training_classes = sorted(list(training_metadata.keys()))
print "".join(["\n",
"Found {0} training classes!\n".format(len(training_classes)),
"-"*40])
for class_label, training_class in enumerate(training_classes):
print "{0:<4d} {1:<10s} {2:<30s}".format(class_label, training_class, training_metadata[training_class])
print ""
# count number of samples
sample_count = 0
sample_count_by_class = [0]*len(training_classes)
for class_label, training_class in enumerate(training_classes):
# get training class sequeunce paths
training_class_data_path = training_metadata[training_class]
training_class_sequence_paths = [os.path.join(training_class_data_path, file_name)
for file_name in os.listdir(training_class_data_path)
if (os.path.isfile(os.path.join(training_class_data_path, file_name))
and ".mov" in file_name)]
# update sample count
sample_count += len(training_class_sequence_paths)
sample_count_by_class[class_label] = len(training_class_sequence_paths)
print "".join(["\n",
"Found {0} training samples!\n".format(sample_count),
"-"*40])
for class_label, training_class in enumerate(training_classes):
print "{0:<4d} {1:<10s} {2:<6d}".format(class_label, training_class, sample_count_by_class[class_label])
print ""
# initialize HDF5 save file, but clear older duplicate first if it exists
try:
print "Saved file \"{0}\" already exists! Overwriting previous saved file.\n".format(self.training_save_fn)
os.remove(self.training_save_fn)
except OSError:
pass
# process and save training data into HDF5 file
print "Generating {0} samples from {1} samples via data augmentation\n".format(sample_count*self.samples_generated_per_sample,
sample_count)
sample_count = sample_count*self.samples_generated_per_sample
with h5py.File(self.training_save_fn, "w") as training_save_file:
training_save_file.attrs["training_classes"] = np.string_(",".join(training_classes))
training_save_file.attrs["sample_count"] = sample_count
x_training_dataset = training_save_file.create_dataset("X",
shape=(sample_count, self.frames_per_sequence, 3, self.rows, self.columns),
dtype="f")
y_training_dataset = training_save_file.create_dataset("Y",
shape=(sample_count, len(training_classes)),
dtype="i")
# iterate through each class data
sample_idx = 0
for class_label, training_class in enumerate(training_classes):
# get training class sequeunce paths
training_class_data_path = training_metadata[training_class]
training_class_sequence_paths = [os.path.join(training_class_data_path, file_name)
for file_name in os.listdir(training_class_data_path)
if (os.path.isfile(os.path.join(training_class_data_path, file_name))
and ".mov" in file_name)]
# iterate through each sequence
for idx, training_class_sequence_path in enumerate(training_class_sequence_paths):
sys.stdout.write("Processing training data for class \"{0}\": {1}/{2} sequences\r"
.format(training_class, idx+1, len(training_class_sequence_paths)))
sys.stdout.flush()
# accumulate samples and labels
samples_batch = self.process_frames(training_class_sequence_path)
label = [0]*len(training_classes)
label[class_label] = 1
label = np.array(label).astype("int32")
for sample in samples_batch:
x_training_dataset[sample_idx] = sample
y_training_dataset[sample_idx] = label
# update sample index
sample_idx += 1
print "\n"
training_save_file.close()
print "Training data processed and saved to {0}".format(self.training_save_fn)
def process_frames(self, video_file_path):
""" Preprocesses sequence frames
"""
# haar cascades for localizing oral region
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
mouth_cascade = cv2.CascadeClassifier('haarcascade_mcs_mouth.xml')
video = cv2.VideoCapture(video_file_path)
success, frame = video.read()
frames = []
success = True
# convert to grayscale, localize oral region, equalize frame dimensions, and accumulate valid frames
while success:
success, frame = video.read()
if success:
# convert to grayscale
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# localize single facial region
faces_coords = face_cascade.detectMultiScale(frame, 1.3, 5)
if len(faces_coords) == 1:
face_x, face_y, face_w, face_h = faces_coords[0]
frame = frame[face_y:face_y + face_h, face_x:face_x + face_w]
# localize oral region
mouth_coords = mouth_cascade.detectMultiScale(frame, 1.3, 5)
threshold = 0
for (mouth_x, mouth_y, mouth_w, mouth_h) in mouth_coords:
if (mouth_y > threshold):
threshold = mouth_y
valid_mouth_coords = (mouth_x, mouth_y, mouth_w, mouth_h)
else:
pass
mouth_x, mouth_y, mouth_w, mouth_h = valid_mouth_coords
frame = frame[mouth_y:mouth_y + mouth_h, mouth_x:mouth_x + mouth_w]
# equalize frame dimensions
frame = cv2.resize(frame, (self.columns, self.rows)).astype('float32')
# accumulate frames
frames.append(frame)
# ignore multiple facial region detections
else:
pass
# equalize sequence lengths
if len(frames) < self.frames_per_sequence:
frames = [frames[0]]*(self.frames_per_sequence - len(frames)) + frames
frames = np.array(frames[-self.frames_per_sequence:])
# function to normalize and add channel dimension to each frame
proc_frame = lambda frame: np.array([frame / 255.0]*3)
samples_batch = [np.array(map(proc_frame, frames))]
# random transformations for data augmentation
for _ in xrange(0, self.samples_generated_per_sample-1):
rotated_frames = random_rotation(frames, rg=4.5)
shifted_frames = random_shift(rotated_frames, wrg=0.05, hrg=0.05)
sheared_frames = random_shear(shifted_frames, intensity=0.08)
zoomed_frames = random_zoom(sheared_frames, zoom_range=(1.05, 1.05))
samples_batch.append(np.array(map(proc_frame, zoomed_frames)))
return samples_batch
错误来自两件事：
您正在阅读的是因为
batch_idxs是一个数组，而不是列表。 h5py 对象接受列表索引。但即使你改变X = training_save_file["X"][list(batch_idxs)]你仍然会得到一个错误。这来自对列表索引的一些限制。这就把我们带到了第二点。
如果你读了doc you sent me，这是这样写的：
The following restrictions exist:
- List selections may not be empty
- Selection coordinates must be given in increasing order
- Duplicate selections are ignored
- Very long lists (> 1000 elements) may produce poor performance
第二个项目符号是我们的问题：您在创建
training_sample_idxs时进行的随机改组使索引顺序随机，并且数据集期望它们按递增顺序排列。这是您必须处理的一个限制，但它并不太受限制，因为批次中的顺序无关紧要，模型无论如何都会在整个批次上进行优化。
有帮助吗？