期末專題問題

2020/02/22 00:31 下午

電腦視覺深度學習討論版

塔米

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cvdl

cfdl-49

cvdl-50

老師您好，

我用colab跑以下這一段程式碼：(此段由D41-Sample程式碼複製過來，僅修改annotation path)

annotation_path = 'train.txt' # 轉換好格式的標註檔案
log_dir = 'logs/000/' # 訓練好的模型儲存的路徑
classes_path = 'model_data/voc_classes.txt'
anchors_path = 'model_data/yolo_anchors.txt'
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)

input_shape = (416,416) # multiple of 32, hw

is_tiny_version = len(anchors)==6 # default setting
if is_tiny_version:
    model = create_tiny_model(input_shape, anchors, num_classes,
        freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
else:
    model = create_model(input_shape, anchors, num_classes,
        freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze

logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
    monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
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)

# 分為 training 以及 validation
val_split = 0.1
with open(annotation_path) as f:
    lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
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 not bad model.
# 一開始先 freeze YOLO 除了 output layer 以外的 darknet53 backbone 來 train
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 = 16
    print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
    # 模型利用 generator 產生的資料做訓練，強烈建議大家去閱讀及理解 data_generator_wrapper 在 train.py 中的實現
    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=50,
            initial_epoch=0,
            callbacks=[logging, checkpoint])
    model.save_weights(log_dir + 'trained_weights_stage_1.h5')

# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if True:
    # 把所有 layer 都改為 trainable
    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 of the layers.')

    batch_size = 16 # 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))
    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=100,
        initial_epoch=50,
        callbacks=[logging, checkpoint, reduce_lr, early_stopping])
    model.save_weights(log_dir + 'trained_weights_final.h5')

annotation_path = 'train.txt' # 轉換好格式的標註檔案
log_dir = 'logs/000/' # 訓練好的模型儲存的路徑
classes_path = 'model_data/voc_classes.txt'
anchors_path = 'model_data/yolo_anchors.txt'
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)

input_shape = (416,416) # multiple of 32, hw

is_tiny_version = len(anchors)==6 # default setting
if is_tiny_version:
    model = create_tiny_model(input_shape, anchors, num_classes,
        freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
else:
    model = create_model(input_shape, anchors, num_classes,
        freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze

logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
    monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
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)

# 分為 training 以及 validation
val_split = 0.1
with open(annotation_path) as f:
    lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
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 not bad model.
# 一開始先 freeze YOLO 除了 output layer 以外的 darknet53 backbone 來 train
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 = 16
    print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
    # 模型利用 generator 產生的資料做訓練，強烈建議大家去閱讀及理解 data_generator_wrapper 在 train.py 中的實現
    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=50,
            initial_epoch=0,
            callbacks=[logging, checkpoint])
    model.save_weights(log_dir + 'trained_weights_stage_1.h5')

# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if True:
    # 把所有 layer 都改為 trainable
    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 of the layers.')

    batch_size = 16 # 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))
    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=100,
        initial_epoch=50,
        callbacks=[logging, checkpoint, reduce_lr, early_stopping])
    model.save_weights(log_dir + 'trained_weights_final.h5')

但出現一個問題顯示:`FileNotFoundError: [Errno 2] No such file or directory: '/content/gdrive/My'`

完整錯誤資訊如下：

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.

Create YOLOv3 model with 9 anchors and 20 classes.
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_59 due to mismatch in shape ((1, 1, 1024, 75) vs (255, 1024, 1, 1)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_59 due to mismatch in shape ((75,) vs (255,)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_67 due to mismatch in shape ((1, 1, 512, 75) vs (255, 512, 1, 1)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_67 due to mismatch in shape ((75,) vs (255,)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_75 due to mismatch in shape ((1, 1, 256, 75) vs (255, 256, 1, 1)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_75 due to mismatch in shape ((75,) vs (255,)).
  weight_values[i].shape))
Load weights model_data/yolo_weights.h5.
Freeze the first 249 layers of total 252 layers.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1521: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3080: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

Train on 328 samples, val on 36 samples, with batch size 16.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:850: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:853: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

Epoch 1/50
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-11-30305ac0cad2> in <module>()
     50             epochs=50,
     51             initial_epoch=0,
---> 52             callbacks=[logging, checkpoint])
     53     model.save_weights(log_dir + 'trained_weights_stage_1.h5')
     54 

11 frames
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output, '__len__'):

/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py in get(self)
    707                     "`use_multiprocessing=False, workers > 1`."
    708                     "For more information see issue #1638.")
--> 709             six.reraise(*sys.exc_info())

/usr/local/lib/python3.6/dist-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py in get(self)
    683         try:
    684             while self.is_running():
--> 685                 inputs = self.queue.get(block=True).get()
    686                 self.queue.task_done()
    687                 if inputs is not None:

/usr/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self, i, obj):

/usr/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    117         job, i, func, args, kwds = task
    118         try:
--> 119             result = (True, func(*args, **kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py in next_sample(uid)
    624         The next value of generator `uid`.
    625     """
--> 626     return six.next(_SHARED_SEQUENCES[uid])
    627 
    628 

/content/gdrive/My Drive/keras-yolo3/train.py in data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
    173             if i==0:
    174                 np.random.shuffle(annotation_lines)
--> 175             image, box = get_random_data(annotation_lines[i], input_shape, random=True)
    176             image_data.append(image)
    177             box_data.append(box)

/content/gdrive/My Drive/keras-yolo3/yolo3/utils.py in get_random_data(annotation_line, input_shape, random, max_boxes, jitter, hue, sat, val, proc_img)
     37     '''random preprocessing for real-time data augmentation'''
     38     line = annotation_line.split()
---> 39     image = Image.open(line[0])
     40     iw, ih = image.size
     41     h, w = input_shape

/usr/local/lib/python3.6/dist-packages/PIL/Image.py in open(fp, mode)
   2764 
   2765     if filename:
-> 2766         fp = builtins.open(filename, "rb")
   2767         exclusive_fp = True
   2768 

FileNotFoundError: [Errno 2] No such file or directory: '/content/gdrive/My'

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:186: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.

Create YOLOv3 model with 9 anchors and 20 classes.
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_59 due to mismatch in shape ((1, 1, 1024, 75) vs (255, 1024, 1, 1)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_59 due to mismatch in shape ((75,) vs (255,)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_67 due to mismatch in shape ((1, 1, 512, 75) vs (255, 512, 1, 1)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_67 due to mismatch in shape ((75,) vs (255,)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_75 due to mismatch in shape ((1, 1, 256, 75) vs (255, 256, 1, 1)).
  weight_values[i].shape))
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:1140: UserWarning: Skipping loading of weights for layer conv2d_75 due to mismatch in shape ((75,) vs (255,)).
  weight_values[i].shape))
Load weights model_data/yolo_weights.h5.
Freeze the first 249 layers of total 252 layers.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1521: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3080: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

Train on 328 samples, val on 36 samples, with batch size 16.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:850: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:853: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

Epoch 1/50
---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-11-30305ac0cad2> in <module>()
     50             epochs=50,
     51             initial_epoch=0,
---> 52             callbacks=[logging, checkpoint])
     53     model.save_weights(log_dir + 'trained_weights_stage_1.h5')
     54 

11 frames
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,
   1417             shuffle=shuffle,
-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output, '__len__'):

/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py in get(self)
    707                     "`use_multiprocessing=False, workers > 1`."
    708                     "For more information see issue #1638.")
--> 709             six.reraise(*sys.exc_info())

/usr/local/lib/python3.6/dist-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py in get(self)
    683         try:
    684             while self.is_running():
--> 685                 inputs = self.queue.get(block=True).get()
    686                 self.queue.task_done()
    687                 if inputs is not None:

/usr/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self, i, obj):

/usr/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    117         job, i, func, args, kwds = task
    118         try:
--> 119             result = (True, func(*args, **kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

/usr/local/lib/python3.6/dist-packages/keras/utils/data_utils.py in next_sample(uid)
    624         The next value of generator `uid`.
    625     """
--> 626     return six.next(_SHARED_SEQUENCES[uid])
    627 
    628 

/content/gdrive/My Drive/keras-yolo3/train.py in data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
    173             if i==0:
    174                 np.random.shuffle(annotation_lines)
--> 175             image, box = get_random_data(annotation_lines[i], input_shape, random=True)
    176             image_data.append(image)
    177             box_data.append(box)

/content/gdrive/My Drive/keras-yolo3/yolo3/utils.py in get_random_data(annotation_line, input_shape, random, max_boxes, jitter, hue, sat, val, proc_img)
     37     '''random preprocessing for real-time data augmentation'''
     38     line = annotation_line.split()
---> 39     image = Image.open(line[0])
     40     iw, ih = image.size
     41     h, w = input_shape

/usr/local/lib/python3.6/dist-packages/PIL/Image.py in open(fp, mode)
   2764 
   2765     if filename:
-> 2766         fp = builtins.open(filename, "rb")
   2767         exclusive_fp = True
   2768 

FileNotFoundError: [Errno 2] No such file or directory: '/content/gdrive/My'

我在這邊卡很久，找了很多資料還是無解。

請老師多指教，謝謝!