coursera 深層学習専門の事前トレーニング済み YOLO モデルを使用するために、転移学習を試みています。YOLO モデルは画像の検出と認識を行います。そのため、検出されたオブジェクトの性別を識別するために、このモデルに同じレイヤーを追加したいと考えました。
したがって、m 個の画像があり、それらを既存の YOLO モデルに渡して出力を取得し、これらの出力を新しく追加されたレイヤーのトレーニング セットとして使用しようとしました。ここで私の問題が発生します: m 例を 1 行のコードで渡そうとすると: エラーが発生しました...
実行したすべての手順と得られた出力を指定します。
ライブラリのインポート
import argparse
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
import scipy.io
import scipy.misc
import numpy as np
import pandas as pd
import PIL
import tensorflow as tf
from keras import backend as K
from keras.preprocessing import image
from keras.layers import Input, Lambda, Conv2D
from keras.models import load_model, Model
from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes
from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body
from keras.models import Sequential
from scipy.misc import imread
get_ipython().magic('matplotlib inline')
import matplotlib.pyplot as plt
import numpy as np
import keras
from keras.layers import Dense
import pandas as pd
%matplotlib inline
データセットのインポート:
形状の 2155 枚の画像 (608,608,3)
train=pd.read_csv("datset.csv",sep=';')
train_img=[]
for i in range(len(train)):
(img, train_img_data)=preprocess_image('path_dataset'+train['ImageURL'][i],model_image_size = (608, 608))
train_img.append(train_img_data)
train_img= np.array(train_img)
train_img=train_img.reshape(2155,608,608,3)
データセットの次元の確認
print('shape of train_img: ',train_img.shape)
print("shape of first element in train_img: ",train_img[0].shape)
print("reshaping first element in tran_img: ",train_img[0].reshape(1,608,608,3).shape)
データセット ディメンションの出力
shape of train_img: (2155, 608, 608, 3)
shape of first element in train_img: (608, 608, 3)
reshaping first element in tran_img: (1, 608, 608, 3)
Yolo モデルのインポート
yolo_model = load_model("model_data/yolo.h5")
YOLO モデルに train_img を供給して、追加されたレイヤーのトレーニング セットとして使用される出力を取得します。
sess = K.get_session()
output=sess.run([yolo_model.output], feed_dict={yolo_model.input: train_img , K.learning_phase(): 0})
私が得るエラー：
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1360 try:
-> 1361 return fn(*args)
1362 except errors.OpError as e:
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1339 return tf_session.TF_Run(session, options, feed_dict, fetch_list,
-> 1340 target_list, status, run_metadata)
1341
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
515 compat.as_text(c_api.TF_Message(self.status.status)),
--> 516 c_api.TF_GetCode(self.status.status))
517 # Delete the underlying status object from memory otherwise it stays alive
ResourceExhaustedError: OOM when allocating tensor with shape[2155,608,608,32] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
[[Node: conv2d_1/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_input_1_0_1, conv2d_1/kernel/read)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
During handling of the above exception, another exception occurred:
ResourceExhaustedError Traceback (most recent call last)
<ipython-input-14-067537a70066> in <module>()
1 sess = K.get_session()
----> 2 output=sess.run([yolo_model.output], feed_dict={yolo_model.input: train_img , K.learning_phase(): 0})
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
903 try:
904 result = self._run(None, fetches, feed_dict, options_ptr,
--> 905 run_metadata_ptr)
906 if run_metadata:
907 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1135 if final_fetches or final_targets or (handle and feed_dict_tensor):
1136 results = self._do_run(handle, final_targets, final_fetches,
-> 1137 feed_dict_tensor, options, run_metadata)
1138 else:
1139 results = []
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1353 if handle is None:
1354 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1355 options, run_metadata)
1356 else:
1357 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1372 except KeyError:
1373 pass
-> 1374 raise type(e)(node_def, op, message)
1375
1376 def _extend_graph(self):
ResourceExhaustedError: OOM when allocating tensor with shape[2155,608,608,32] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
[[Node: conv2d_1/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_input_1_0_1, conv2d_1/kernel/read)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Caused by op 'conv2d_1/convolution', defined at:
File "C:\ProgramData\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\ProgramData\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "C:\ProgramData\Anaconda3\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
app.start()
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 478, in start
self.io_loop.start()
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2728, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2850, in run_ast_nodes
if self.run_code(code, result):
File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2910, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-11-c868ea7b7486>", line 7, in <module>
yolo_model = load_model("model_data/yolo.h5")
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\models.py", line 243, in load_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\models.py", line 317, in model_from_config
return layer_module.deserialize(config, custom_objects=custom_objects)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\__init__.py", line 55, in deserialize
printable_module_name='layer')
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\generic_utils.py", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\topology.py", line 2524, in from_config
process_node(layer, node_data)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\topology.py", line 2481, in process_node
layer(input_tensors[0], **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\topology.py", line 619, in __call__
output = self.call(inputs, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\layers\convolutional.py", line 168, in call
dilation_rate=self.dilation_rate)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 3335, in conv2d
data_format=tf_data_format)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 781, in convolution
return op(input, filter)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 869, in __call__
return self.conv_op(inp, filter)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 521, in __call__
return self.call(inp, filter)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 205, in __call__
name=self.name)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 717, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3271, in create_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1650, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[2155,608,608,32] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
[[Node: conv2d_1/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_input_1_0_1, conv2d_1/kernel/read)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
入力のプレースホルダーのサイズは(None,608,608,3)であることに注意してください。したがって、サイズ(2155,608,608,3)のデータセットを送信しても問題はありません (それが理解できませんでした)。これに加えて、サイズ(1,608,608,3)の例をネットワークにフィードすると、エラーは発生しません! data_set 内のすべての要素を繰り返し処理し、ネットワークに 2155 回 ( (1,608,608,3) をフィードするたびに) フィードすることもできますが、これは時間がかかり、最善の方法ではありません。
ちなみにプレースホルダのNoneを使ってm個の訓練例を同時に送れると思っていました。
出力によると、エラーが何であるかを本当に理解できませんでした。これを理解するためにあなたの助けを待っています。