在Google Colab上训练模型时，我遇到以下错误，亚当依赖项/优化器可能存在问题。
ValueError：张量转换请求具有dtype float32的Tensor的dtype float32_ref：
有什么办法可以解决此问题？
训练脚本的代码：
import os
import sys
import argparse
def get_parent_dir(n=1):
""" returns the n-th parent dicrectory of the current
working directory """
current_path = os.path.dirname(os.path.abspath(__file__))
for k in range(n):
current_path = os.path.dirname(current_path)
return current_path
src_path = os.path.join(get_parent_dir(0),'src')
sys.path.append(src_path)
utils_path = os.path.join(get_parent_dir(1),'Utils')
sys.path.append(utils_path)
import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras_yolo3.yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from keras_yolo3.yolo3.utils import get_random_data
from PIL import Image
from time import time
import pickle
from Train_Utils import get_classes, get_anchors, create_model, create_tiny_model, data_generator, data_generator_wrapper, ChangeToOtherMachine
keras_path = os.path.join(src_path,'keras_yolo3')
Data_Folder = os.path.join(get_parent_dir(1),'Data')
Image_Folder = os.path.join(Data_Folder,'Source_Images','Training_Images')
VoTT_Folder = os.path.join(Image_Folder,'vott-csv-export')
YOLO_filename = os.path.join(VoTT_Folder,'data_train.txt')
Model_Folder = os.path.join(Data_Folder,'Model_Weights')
YOLO_classname = os.path.join(Model_Folder,'data_classes.txt')
log_dir = Model_Folder
anchors_path = os.path.join(keras_path,'model_data','yolo_anchors.txt')
weights_path = os.path.join(keras_path,'yolo.h5')
FLAGS = None
if __name__ == '__main__':
# Delete all default flags
parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS)
'''
Command line options
'''
parser.add_argument(
"--annotation_file", type=str, default=YOLO_filename,
help = "Path to annotation file for Yolo. Default is "+ YOLO_filename
)
parser.add_argument(
"--classes_file", type=str, default=YOLO_classname,
help = "Path to YOLO classnames. Default is "+ YOLO_classname
)
parser.add_argument(
"--log_dir", type=str, default=log_dir,
help = "Folder to save training logs and trained weights to. Default is "+ log_dir
)
parser.add_argument(
"--anchors_path", type=str, default=anchors_path,
help = "Path to YOLO anchors. Default is "+ anchors_path
)
parser.add_argument(
"--weights_path", type=str, default=weights_path,
help = "Path to pre-trained YOLO weights. Default is "+ weights_path
)
parser.add_argument(
"--val_split", type=float, default=0.1,
help = "Percentage of training set to be used for validation. Default is 10%."
)
parser.add_argument(
"--is_tiny", default=False, action="store_true",
help = "Use the tiny Yolo version for better performance and less accuracy. Default is False."
)
parser.add_argument(
"--random_seed", type=float, default=None,
help = "Random seed value to make script deterministic. Default is 'None', i.e. non-deterministic."
)
parser.add_argument(
"--epochs", type=float, default=51,
help = "Number of epochs for training last layers and number of epochs for fine-tuning layers. Default is 51."
)
FLAGS = parser.parse_args()
np.random.seed(FLAGS.random_seed)
log_dir = FLAGS.log_dir
class_names = get_classes(FLAGS.classes_file)
num_classes = len(class_names)
anchors = get_anchors(FLAGS.anchors_path)
weights_path = FLAGS.weights_path
input_shape = (416, 416) # multiple of 32, height, width
epoch1, epoch2 = FLAGS.epochs, FLAGS.epochs
is_tiny_version = (len(anchors)==6) # default setting
if FLAGS.is_tiny:
model = create_tiny_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path = weights_path)
else:
model = create_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path = weights_path) # make sure you know what you freeze
log_dir_time = os.path.join(log_dir,'{}'.format(int(time())))
logging = TensorBoard(log_dir=log_dir_time)
checkpoint = ModelCheckpoint(os.path.join(log_dir,'checkpoint.h5'),
monitor='val_loss', save_weights_only=True, save_best_only=True, period=5)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
val_split = FLAGS.val_split
with open(FLAGS.annotation_file) as f:
lines = f.readlines()
# This step makes sure that the path names correspond to the local machine
# This is important if annotation and training are done on different machines (e.g. training on AWS)
lines = ChangeToOtherMachine(lines,remote_machine = '')
np.random.shuffle(lines)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a decent model.
if True:
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
batch_size = 32
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
history = model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=epoch1,
initial_epoch=0,
callbacks=[logging, checkpoint])
model.save_weights(os.path.join(log_dir,'trained_weights_stage_1.h5'))
step1_train_loss = history.history['loss']
file = open(os.path.join(log_dir_time,'step1_loss.npy'), "w")
with open(os.path.join(log_dir_time,'step1_loss.npy'), 'w') as f:
for item in step1_train_loss:
f.write("%s\n" % item)
file.close()
step1_val_loss = np.array(history.history['val_loss'])
file = open(os.path.join(log_dir_time,'step1_val_loss.npy'), "w")
with open(os.path.join(log_dir_time,'step1_val_loss.npy'), 'w') as f:
for item in step1_val_loss:
f.write("%s\n" % item)
file.close()
# Unfreeze and continue training, to fine-tune.
# Train longer if the result is unsatisfactory.
if True:
for i in range(len(model.layers)):
model.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
print('Unfreeze all layers.')
batch_size = 4 # note that more GPU memory is required after unfreezing the body
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
history=model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=epoch1+epoch2,
initial_epoch=epoch1,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
model.save_weights(os.path.join(log_dir,'trained_weights_final.h5'))
step2_train_loss = history.history['loss']
file = open(os.path.join(log_dir_time,'step2_loss.npy'), "w")
with open(os.path.join(log_dir_time,'step2_loss.npy'), 'w') as f:
for item in step2_train_loss:
f.write("%s\n" % item)
file.close()
step2_val_loss = np.array(history.history['val_loss'])
file = open(os.path.join(log_dir_time,'step2_val_loss.npy'), "w")
with open(os.path.join(log_dir_time,'step2_val_loss.npy'), 'w') as f:
for item in step2_val_loss:
f.write("%s\n" % item)
file.close()