无法从 'utils' 导入名称 'INPUT_SHAPE'
cannot import name 'INPUT_SHAPE' from 'utils'
我要运行代码
from utils import INPUT_SHAPE, batch_generator
首先，报错
<No module named utils>
当我解决这个问题时，现在运行宁该代码行给出了这个错误：
ImportError: cannot import name 'INPUT_SHAPE' from 'utils'
(C:\Users\Lenovo\anaconda3\lib\site-packages\utils\__init__.py)
我要实现的全部代码是：
import pandas as pd # data analysis toolkit - create, read, update, delete datasets
import numpy as np #matrix math
from sklearn.model_selection import train_test_split #to split out training and testing data
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten
#helper class to define input shape and generate training images given image paths & steering angles
from utils import INPUT_SHAPE, batch_generator
import argparse
import os
#for debugging, allows for reproducible (deterministic) results
np.random.seed(0)
def load_data(args):
"""
Load training data and split it into training and validation set
"""
#reads CSV file into a single dataframe variable
data_df = pd.read_csv(os.path.join(os.getcwd(), args.data_dir, 'driving_log.csv'), names=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])
#yay dataframes, we can select rows and columns by their names
#we'll store the camera images as our input data
X = data_df[['center', 'left', 'right']].values
#and our steering commands as our output data
y = data_df['steering'].values
#now we can split the data into a training (80), testing(20), and validation set
#thanks scikit learn
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0)
return X_train, X_valid, y_train, y_valid
def build_model(args):
"""
NVIDIA model used
Image normalization to avoid saturation and make gradients work better.
Convolution: 5x5, filter: 24, strides: 2x2, activation: ELU
Convolution: 5x5, filter: 36, strides: 2x2, activation: ELU
Convolution: 5x5, filter: 48, strides: 2x2, activation: ELU
Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
Drop out (0.5)
Fully connected: neurons: 100, activation: ELU
Fully connected: neurons: 50, activation: ELU
Fully connected: neurons: 10, activation: ELU
Fully connected: neurons: 1 (output)
# the convolution layers are meant to handle feature engineering
the fully connected layer for predicting the steering angle.
dropout avoids overfitting
ELU(Exponential linear unit) function takes care of the Vanishing gradient problem.
"""
model = Sequential()
model.add(Lambda(lambda x: x/127.5-1.0, input_shape=INPUT_SHAPE))
model.add(Conv2D(24, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(36, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(48, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(64, 3, 3, activation='elu'))
model.add(Conv2D(64, 3, 3, activation='elu'))
model.add(Dropout(args.keep_prob))
model.add(Flatten())
model.add(Dense(100, activation='elu'))
model.add(Dense(50, activation='elu'))
model.add(Dense(10, activation='elu'))
model.add(Dense(1))
model.summary()
return model
def train_model(model, args, X_train, X_valid, y_train, y_valid):
"""
Train the model
"""
#Saves the model after every epoch.
#quantity to monitor, verbosity i.e logging mode (0 or 1),
#if save_best_only is true the latest best model according to the quantity monitored will not be overwritten.
#mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is
# made based on either the maximization or the minimization of the monitored quantity. For val_acc,
#this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically
# inferred from the name of the monitored quantity.
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
monitor='val_loss',
verbose=0,
save_best_only=args.save_best_only,
mode='auto')
#calculate the difference between expected steering angle and actual steering angle
#square the difference
#add up all those differences for as many data points as we have
#divide by the number of them
#that value is our mean squared error! this is what we want to minimize via
#gradient descent
model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate))
#Fits the model on data generated batch-by-batch by a Python generator.
#The generator is run in parallel to the model, for efficiency.
#For instance, this allows you to do real-time data augmentation on images on CPU in
#parallel to training your model on GPU.
#so we reshape our data into their appropriate batches and train our model simulatenously
model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),
args.samples_per_epoch,
args.nb_epoch,
max_q_size=1,
validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False),
nb_val_samples=len(X_valid),
callbacks=[checkpoint],
verbose=1)
#for command line args
def s2b(s):
"""
Converts a string to boolean value
"""
s = s.lower()
return s == 'true' or s == 'yes' or s == 'y' or s == '1'
def main():
"""
Load train/validation data set and train the model
"""
parser = argparse.ArgumentParser(description='Behavioral Cloning Training Program')
parser.add_argument('-d', help='data directory', dest='data_dir', type=str, default='data')
parser.add_argument('-t', help='test size fraction', dest='test_size', type=float, default=0.2)
parser.add_argument('-k', help='drop out probability', dest='keep_prob', type=float, default=0.5)
parser.add_argument('-n', help='number of epochs', dest='nb_epoch', type=int, default=10)
parser.add_argument('-s', help='samples per epoch', dest='samples_per_epoch', type=int, default=20000)
parser.add_argument('-b', help='batch size', dest='batch_size', type=int, default=40)
parser.add_argument('-o', help='save best models only', dest='save_best_only', type=s2b, default='true')
parser.add_argument('-l', help='learning rate', dest='learning_rate', type=float, default=1.0e-4)
args = parser.parse_args()
#print parameters
print('-' * 30)
print('Parameters')
print('-' * 30)
for key, value in vars(args).items():
print('{:<20} := {}'.format(key, value))
print('-' * 30)
#load data
data = load_data(args)
#build model
model = build_model(args)
#train model on data, it saves as model.h5
train_model(model, args, *data)
if __name__ == '__main__':
main()
编辑
我检查了 anaconda3 文件夹中的 utils.py 个文件，发现了其中的 9 个。我还在每个 python 文件中检查了 INPUT_SHAPE，但找不到其中任何一个。 9 files of utils.py
[在此处输入图片描述][2]
我在文件 model.py 的项目 How_to_simulate_a_self_driving_car 中找到了您的代码。该项目还有文件 utils.py 和 INPUT_SHAPE。
您似乎复制了文件 model.py，但您忘记了复制文件 utils.py。
您应该将这两个文件放在同一个文件夹中 - 即。 C:\Users\Lenovo - 并从此文件夹 C:\Users\Lenovo
开始 python model.py
因此将 utils.py 从项目 How_to_simulate_a_self_driving_car 复制到您的项目。
但最好从项目 How_to_simulate_a_self_driving_car 中复制所有文件，因为它可能需要此项目中的其他文件。
我要运行代码
from utils import INPUT_SHAPE, batch_generator
首先，报错
<No module named utils>
当我解决这个问题时，现在运行宁该代码行给出了这个错误：
ImportError: cannot import name 'INPUT_SHAPE' from 'utils'
(C:\Users\Lenovo\anaconda3\lib\site-packages\utils\__init__.py)
我要实现的全部代码是：
import pandas as pd # data analysis toolkit - create, read, update, delete datasets
import numpy as np #matrix math
from sklearn.model_selection import train_test_split #to split out training and testing data
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten
#helper class to define input shape and generate training images given image paths & steering angles
from utils import INPUT_SHAPE, batch_generator
import argparse
import os
#for debugging, allows for reproducible (deterministic) results
np.random.seed(0)
def load_data(args):
"""
Load training data and split it into training and validation set
"""
#reads CSV file into a single dataframe variable
data_df = pd.read_csv(os.path.join(os.getcwd(), args.data_dir, 'driving_log.csv'), names=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])
#yay dataframes, we can select rows and columns by their names
#we'll store the camera images as our input data
X = data_df[['center', 'left', 'right']].values
#and our steering commands as our output data
y = data_df['steering'].values
#now we can split the data into a training (80), testing(20), and validation set
#thanks scikit learn
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0)
return X_train, X_valid, y_train, y_valid
def build_model(args):
"""
NVIDIA model used
Image normalization to avoid saturation and make gradients work better.
Convolution: 5x5, filter: 24, strides: 2x2, activation: ELU
Convolution: 5x5, filter: 36, strides: 2x2, activation: ELU
Convolution: 5x5, filter: 48, strides: 2x2, activation: ELU
Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU
Drop out (0.5)
Fully connected: neurons: 100, activation: ELU
Fully connected: neurons: 50, activation: ELU
Fully connected: neurons: 10, activation: ELU
Fully connected: neurons: 1 (output)
# the convolution layers are meant to handle feature engineering
the fully connected layer for predicting the steering angle.
dropout avoids overfitting
ELU(Exponential linear unit) function takes care of the Vanishing gradient problem.
"""
model = Sequential()
model.add(Lambda(lambda x: x/127.5-1.0, input_shape=INPUT_SHAPE))
model.add(Conv2D(24, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(36, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(48, 5, 5, activation='elu', subsample=(2, 2)))
model.add(Conv2D(64, 3, 3, activation='elu'))
model.add(Conv2D(64, 3, 3, activation='elu'))
model.add(Dropout(args.keep_prob))
model.add(Flatten())
model.add(Dense(100, activation='elu'))
model.add(Dense(50, activation='elu'))
model.add(Dense(10, activation='elu'))
model.add(Dense(1))
model.summary()
return model
def train_model(model, args, X_train, X_valid, y_train, y_valid):
"""
Train the model
"""
#Saves the model after every epoch.
#quantity to monitor, verbosity i.e logging mode (0 or 1),
#if save_best_only is true the latest best model according to the quantity monitored will not be overwritten.
#mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is
# made based on either the maximization or the minimization of the monitored quantity. For val_acc,
#this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically
# inferred from the name of the monitored quantity.
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
monitor='val_loss',
verbose=0,
save_best_only=args.save_best_only,
mode='auto')
#calculate the difference between expected steering angle and actual steering angle
#square the difference
#add up all those differences for as many data points as we have
#divide by the number of them
#that value is our mean squared error! this is what we want to minimize via
#gradient descent
model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate))
#Fits the model on data generated batch-by-batch by a Python generator.
#The generator is run in parallel to the model, for efficiency.
#For instance, this allows you to do real-time data augmentation on images on CPU in
#parallel to training your model on GPU.
#so we reshape our data into their appropriate batches and train our model simulatenously
model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),
args.samples_per_epoch,
args.nb_epoch,
max_q_size=1,
validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False),
nb_val_samples=len(X_valid),
callbacks=[checkpoint],
verbose=1)
#for command line args
def s2b(s):
"""
Converts a string to boolean value
"""
s = s.lower()
return s == 'true' or s == 'yes' or s == 'y' or s == '1'
def main():
"""
Load train/validation data set and train the model
"""
parser = argparse.ArgumentParser(description='Behavioral Cloning Training Program')
parser.add_argument('-d', help='data directory', dest='data_dir', type=str, default='data')
parser.add_argument('-t', help='test size fraction', dest='test_size', type=float, default=0.2)
parser.add_argument('-k', help='drop out probability', dest='keep_prob', type=float, default=0.5)
parser.add_argument('-n', help='number of epochs', dest='nb_epoch', type=int, default=10)
parser.add_argument('-s', help='samples per epoch', dest='samples_per_epoch', type=int, default=20000)
parser.add_argument('-b', help='batch size', dest='batch_size', type=int, default=40)
parser.add_argument('-o', help='save best models only', dest='save_best_only', type=s2b, default='true')
parser.add_argument('-l', help='learning rate', dest='learning_rate', type=float, default=1.0e-4)
args = parser.parse_args()
#print parameters
print('-' * 30)
print('Parameters')
print('-' * 30)
for key, value in vars(args).items():
print('{:<20} := {}'.format(key, value))
print('-' * 30)
#load data
data = load_data(args)
#build model
model = build_model(args)
#train model on data, it saves as model.h5
train_model(model, args, *data)
if __name__ == '__main__':
main()
编辑
我检查了 anaconda3 文件夹中的 utils.py 个文件，发现了其中的 9 个。我还在每个 python 文件中检查了 INPUT_SHAPE，但找不到其中任何一个。 9 files of utils.py
[在此处输入图片描述][2]
我在文件 model.py 的项目 How_to_simulate_a_self_driving_car 中找到了您的代码。该项目还有文件 utils.py 和 INPUT_SHAPE。
您似乎复制了文件 model.py，但您忘记了复制文件 utils.py。
您应该将这两个文件放在同一个文件夹中 - 即。 C:\Users\Lenovo - 并从此文件夹 C:\Users\Lenovo
python model.py
因此将 utils.py 从项目 How_to_simulate_a_self_driving_car 复制到您的项目。
但最好从项目 How_to_simulate_a_self_driving_car 中复制所有文件，因为它可能需要此项目中的其他文件。