Python 从tf.keras.utils.Sequence生成的自定义数据生成器不'；t使用tensorflow模型&x27；s-fit api

我根据中的指南实现了一个序列生成器对象

但是，它不适用于tensorflow模型的fit api。下面是我用来复制这个问题的简单模型架构

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv3D(10, input_shape=(TEMPORAL_LENGTH,HEIGHT,WIDTH,CHANNEL), kernel_size=(2,2,2), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
model.summary()

让我创建一个读者

r1=读卡器（x列、y列、20、10）

然后我调用model.fit api

train_history = model.fit(r1, epochs=3, steps_per_epoch=5, verbose=1)
### output ###
getitem 0
return item
(20, 16, 192, 256, 3)
WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to  
  ['...']
Train for 5 steps
Epoch 1/3

如果我不打断的话，它将永远像这样。出于好奇，我用从KerasAPI创建的模型尝试了这种方法，令我惊讶的是，它居然奏效了

model = ks.models.Sequential()
model.add(ks.layers.Conv3D(10, input_shape=(TEMPORAL_LENGTH,HEIGHT,WIDTH,CHANNEL), kernel_size=(2,2,2), strides=2))
model.add(ks.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(ks.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(ks.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(ks.layers.Flatten())
model.add(ks.layers.Dense(10))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
train_history = model.fit(r1, epochs=3, steps_per_epoch=5, verbose=1)
### output ###
Epoch 1/3
getitem 586
return item
(20, 16, 192, 256, 3)
getitem 169
1/5 [=====>........................] - ETA: 22s - loss: 11.0373 - accuracy: 0.0000e+00return item
(20, 16, 192, 256, 3)
getitem 601
2/5 [===========>..................] - ETA: 12s - loss: 7.9983 - accuracy: 0.0250     return item
(20, 16, 192, 256, 3)
getitem 426
3/5 [=================>............] - ETA: 8s - loss: 10.7049 - accuracy: 0.2500return item
(20, 16, 192, 256, 3)
getitem 243
4/5 [=======================>......] - ETA: 3s - loss: 8.5093 - accuracy: 0.1875

依赖关系

tensorflow gpu:2.1

keras gpu:2.3.1

---

老年人。我很抱歉反应太晚。我已找到解决此问题的方法。 我需要更改的只是将函数self中的数据_x转换为dtype='float32'。getitem（）。要复制该问题，只需将数据类型更改为“object”

除此之外，请允许我分享从教程中修改的ActionDataGenerator类

import tensorflow as tf
from sklearn.utils import shuffle
import cv2
from cv2 import imread, resize
from tensorflow.keras import utils
import math
import keras as ks
import pandas as pd
import numpy as np
import os
from collections import deque
import copy

class reader(tf.keras.utils.Sequence):

    def __init__(self, x, y, batch_size, n_class):
        self.x, self.y = x, y
        self.batch_size = batch_size
        self.n_class = n_class
        
    def __len__(self):
        return math.ceil(len(self.x) / self.batch_size)

    def __getitem__(self, idx):
        batch_x = self.x[idx * self.batch_size:(idx + 1) *
        self.batch_size]
        batch_y = self.y[idx * self.batch_size:(idx + 1) *
        self.batch_size]
        
        
        data_x = list()
        for batch in batch_x:
            tmp = list()
            for img_path in batch:
                try:
                    img = imread(img_path)
                    if img.shape != (192, 256, 3):
                        img = cv2.resize(img,(256, 192))
                    tmp.append(img)
                except Exception as e:
                    print(e)
                    print('failed to find path {}'.format(img_path))
            data_x.append(tmp)
        # 
        data_x = np.array(data_x, dtype='float32')
        data_y = np.array(batch_y)
        data_y = utils.to_categorical(data_y, self.n_class)
        return data_x, data_y
    
    def on_epoch_end(self):
        # option method to run some logic at the end of each epoch: e.g. reshuffling
        seed = np.random.randint()
        self.x = shuffle(self.x, random_state=seed)
        self.y = shuffle(self.y, random_state=seed)

class ActionDataGenerator(object):
    
    def __init__(self,root_data_path,temporal_stride=1,temporal_length=16,resize=224, max_sample=20):
        
        self.root_data_path = root_data_path
        self.temporal_length = temporal_length
        self.temporal_stride = temporal_stride
        self.resize=resize
        self.max_sample=max_sample

    def file_generator(self,data_path,data_files):
        '''
        data_files - list of csv files to be read.
        '''
        for f in data_files:       
            tmp_df = pd.read_csv(os.path.join(data_path,f))
            label_list = list(tmp_df['Label'])
            total_images = len(label_list) 
            if total_images>=self.temporal_length:
                num_samples = int((total_images-self.temporal_length)/self.temporal_stride)+1
                
                img_list = list(tmp_df['FileName'])
            else:
                print ('num of frames is less than temporal length; hence discarding this file-{}'.format(f))
                continue
            
            samples = deque()
            samp_count=0
            for img in img_list:
                if samp_count == self.max_sample:
                    break
                samples.append(img)
                if len(samples)==self.temporal_length:
                    samples_c=copy.deepcopy(samples)
                    samp_count+=1
                    for t in range(self.temporal_stride):
                        samples.popleft()
                    yield samples_c,label_list[0]

    def load_samples(self,data_cat='train', test_ratio=0.1):
        data_path = os.path.join(self.root_data_path,data_cat)
        csv_data_files = os.listdir(data_path)
        file_gen = self.file_generator(data_path,csv_data_files)
        iterator = True
        data_list = []
        while iterator:
            try:
                x,y = next(file_gen)
                x=list(x)
                data_list.append([x,y])
            except Exception as e:
                print ('the exception: ',e)
                iterator = False
                print ('end of data generator')
        # data_list = self.shuffle_data(data_list)
        return data_list
    
    def train_validation_split(self, data_list, target_column, val_size=0.1, ks_sequence=False):
        dataframe = pd.DataFrame(data_list)
        dataframe.columns = ['Feature', target_column]
        data_dict = dict()
        for i in range(len(np.unique(dataframe[target_column]))):
            data_dict[i] = dataframe[dataframe[target_column]==i]
        train, validation = pd.DataFrame(), pd.DataFrame()
        for df in data_dict.values():
            cut = int(df.shape[0] * val_size)
            val = df[:cut]
            rem = df[cut:]
            train = train.append(rem, ignore_index=True)
            validation = validation.append(val, ignore_index=True)
        if ks_sequence:
            return train['Feature'].values.tolist(), train['Label'].values.tolist(), \
                validation['Feature'].values.tolist(), validation['Label'].values.tolist() # without shuffle
        return train.values.tolist(), validation.values.tolist() # without shuffle

root_data_path = 'C:\\Users\\AI-lab\\Documents\\activity_file\\UCF101\\csv_files\\' # machine specific
CLASSES = 101
BATCH_SIZE = 10
EPOCHS = 1
TEMPORAL_STRIDE = 8
TEMPORAL_LENGTH = 16
MAX_SAMPLE = 20
HEIGHT = 192
WIDTH = 256
CHANNEL = 3

data_gen_obj = ActionDataGenerator(root_data_path, temporal_stride=TEMPORAL_STRIDE, \
                                  temporal_length=TEMPORAL_LENGTH, max_sample=MAX_SAMPLE)
train_data = data_gen_obj.load_samples(data_cat='train')
x_train, y_train, x_val, y_val = data_gen_obj.train_validation_split(train_data, 'Label', 0.1, True)
r1 = reader(x_train, y_train, BATCH_SIZE, CLASSES)
r2 = reader(x_val, y_val, BATCH_SIZE, CLASSES)
print(type(r1), type(r2))

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv3D(10, input_shape=(TEMPORAL_LENGTH,HEIGHT,WIDTH,CHANNEL), kernel_size=(2,2,2), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(101, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

train_history = model.fit(r1, epochs=3, steps_per_epoch=r1.__len__(), verbose=1)
score = model.evaluate(r2, steps=5)
print(score)

输出

例外情况：
数据生成器的结尾
模型：“顺序”
_________________________________________________________________
层（类型）输出形状参数
=================================================================
conv3d（conv3d）（无、8、96、128、10）250
_________________________________________________________________
conv3d_1（conv3d）（无、4、47、63、10）1810
_________________________________________________________________
conv3d_2（conv3d）（无、2、23、31、10）1810
_________________________________________________________________
conv3d_3（conv3d）（无、1、11、15、10）1810
_________________________________________________________________
展平（展平）（无，1650）0
_________________________________________________________________
密（密）（无，101）166751
=================================================================
总参数：172431
可培训参数：172431
不可训练参数：0
_________________________________________________________________
警告：tensorflow:样本重量模式是从
...
到
['...']
开往17562步的火车
纪元1/3
77/17562[预计到达时间：1:35:53-损失：67.0937-准确度：0.0156

您能否共享完整的代码来复制您的问题？这样我们可以试着帮助你。谢谢

import tensorflow as tf
from sklearn.utils import shuffle
import cv2
from cv2 import imread, resize
from tensorflow.keras import utils
import math
import keras as ks
import pandas as pd
import numpy as np
import os
from collections import deque
import copy

class reader(tf.keras.utils.Sequence):

    def __init__(self, x, y, batch_size, n_class):
        self.x, self.y = x, y
        self.batch_size = batch_size
        self.n_class = n_class
        
    def __len__(self):
        return math.ceil(len(self.x) / self.batch_size)

    def __getitem__(self, idx):
        batch_x = self.x[idx * self.batch_size:(idx + 1) *
        self.batch_size]
        batch_y = self.y[idx * self.batch_size:(idx + 1) *
        self.batch_size]
        
        
        data_x = list()
        for batch in batch_x:
            tmp = list()
            for img_path in batch:
                try:
                    img = imread(img_path)
                    if img.shape != (192, 256, 3):
                        img = cv2.resize(img,(256, 192))
                    tmp.append(img)
                except Exception as e:
                    print(e)
                    print('failed to find path {}'.format(img_path))
            data_x.append(tmp)
        # 
        data_x = np.array(data_x, dtype='float32')
        data_y = np.array(batch_y)
        data_y = utils.to_categorical(data_y, self.n_class)
        return data_x, data_y
    
    def on_epoch_end(self):
        # option method to run some logic at the end of each epoch: e.g. reshuffling
        seed = np.random.randint()
        self.x = shuffle(self.x, random_state=seed)
        self.y = shuffle(self.y, random_state=seed)

class ActionDataGenerator(object):
    
    def __init__(self,root_data_path,temporal_stride=1,temporal_length=16,resize=224, max_sample=20):
        
        self.root_data_path = root_data_path
        self.temporal_length = temporal_length
        self.temporal_stride = temporal_stride
        self.resize=resize
        self.max_sample=max_sample

    def file_generator(self,data_path,data_files):
        '''
        data_files - list of csv files to be read.
        '''
        for f in data_files:       
            tmp_df = pd.read_csv(os.path.join(data_path,f))
            label_list = list(tmp_df['Label'])
            total_images = len(label_list) 
            if total_images>=self.temporal_length:
                num_samples = int((total_images-self.temporal_length)/self.temporal_stride)+1
                
                img_list = list(tmp_df['FileName'])
            else:
                print ('num of frames is less than temporal length; hence discarding this file-{}'.format(f))
                continue
            
            samples = deque()
            samp_count=0
            for img in img_list:
                if samp_count == self.max_sample:
                    break
                samples.append(img)
                if len(samples)==self.temporal_length:
                    samples_c=copy.deepcopy(samples)
                    samp_count+=1
                    for t in range(self.temporal_stride):
                        samples.popleft()
                    yield samples_c,label_list[0]

    def load_samples(self,data_cat='train', test_ratio=0.1):
        data_path = os.path.join(self.root_data_path,data_cat)
        csv_data_files = os.listdir(data_path)
        file_gen = self.file_generator(data_path,csv_data_files)
        iterator = True
        data_list = []
        while iterator:
            try:
                x,y = next(file_gen)
                x=list(x)
                data_list.append([x,y])
            except Exception as e:
                print ('the exception: ',e)
                iterator = False
                print ('end of data generator')
        # data_list = self.shuffle_data(data_list)
        return data_list
    
    def train_validation_split(self, data_list, target_column, val_size=0.1, ks_sequence=False):
        dataframe = pd.DataFrame(data_list)
        dataframe.columns = ['Feature', target_column]
        data_dict = dict()
        for i in range(len(np.unique(dataframe[target_column]))):
            data_dict[i] = dataframe[dataframe[target_column]==i]
        train, validation = pd.DataFrame(), pd.DataFrame()
        for df in data_dict.values():
            cut = int(df.shape[0] * val_size)
            val = df[:cut]
            rem = df[cut:]
            train = train.append(rem, ignore_index=True)
            validation = validation.append(val, ignore_index=True)
        if ks_sequence:
            return train['Feature'].values.tolist(), train['Label'].values.tolist(), \
                validation['Feature'].values.tolist(), validation['Label'].values.tolist() # without shuffle
        return train.values.tolist(), validation.values.tolist() # without shuffle

root_data_path = 'C:\\Users\\AI-lab\\Documents\\activity_file\\UCF101\\csv_files\\' # machine specific
CLASSES = 101
BATCH_SIZE = 10
EPOCHS = 1
TEMPORAL_STRIDE = 8
TEMPORAL_LENGTH = 16
MAX_SAMPLE = 20
HEIGHT = 192
WIDTH = 256
CHANNEL = 3

data_gen_obj = ActionDataGenerator(root_data_path, temporal_stride=TEMPORAL_STRIDE, \
                                  temporal_length=TEMPORAL_LENGTH, max_sample=MAX_SAMPLE)
train_data = data_gen_obj.load_samples(data_cat='train')
x_train, y_train, x_val, y_val = data_gen_obj.train_validation_split(train_data, 'Label', 0.1, True)
r1 = reader(x_train, y_train, BATCH_SIZE, CLASSES)
r2 = reader(x_val, y_val, BATCH_SIZE, CLASSES)
print(type(r1), type(r2))

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv3D(10, input_shape=(TEMPORAL_LENGTH,HEIGHT,WIDTH,CHANNEL), kernel_size=(2,2,2), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Conv3D(10, kernel_size=(2,3,3), strides=2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(101, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

train_history = model.fit(r1, epochs=3, steps_per_epoch=r1.__len__(), verbose=1)
score = model.evaluate(r2, steps=5)
print(score)

the exception:  
end of data generator
<class '__main__.reader'> <class '__main__.reader'>
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv3d (Conv3D)              (None, 8, 96, 128, 10)    250       
_________________________________________________________________
conv3d_1 (Conv3D)            (None, 4, 47, 63, 10)     1810      
_________________________________________________________________
conv3d_2 (Conv3D)            (None, 2, 23, 31, 10)     1810      
_________________________________________________________________
conv3d_3 (Conv3D)            (None, 1, 11, 15, 10)     1810      
_________________________________________________________________
flatten (Flatten)            (None, 1650)              0         
_________________________________________________________________
dense (Dense)                (None, 101)               166751    
=================================================================
Total params: 172,431
Trainable params: 172,431
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to  
  ['...']
Train for 17562 steps
Epoch 1/3
   77/17562 [..............................] - ETA: 1:35:53 - loss: 67.0937 - accuracy: 0.0156