g_file_test
时间: 2023-12-09 18:02:41 浏览: 41
`g_file_test`是GLib库中的一个函数,用于检查给定的文件路径是否存在,并根据需要执行其他检查。该函数有以下几个参数:
- `filename`:需要检查的文件路径。
- `test`:一个标志,用于指定要执行的检查类型,可以是以下值之一:
- `G_FILE_TEST_IS_REGULAR`:检查文件是否是常规文件。
- `G_FILE_TEST_IS_SYMLINK`:检查文件是否是符号链接。
- `G_FILE_TEST_IS_DIR`:检查文件是否是目录。
- `G_FILE_TEST_IS_EXECUTABLE`:检查文件是否可执行。
- `G_FILE_TEST_EXISTS`:检查文件是否存在。
- `G_FILE_TEST_IS_HIDDEN`:检查文件是否隐藏。
- `error`:用于存储任何错误的GError指针。如果不需要错误信息,则可以传递NULL。
该函数将返回TRUE或FALSE,具体取决于执行的检查。
相关问题
def test(checkpoint_dir, style_name, test_dir, if_adjust_brightness, img_size=[256,256]): # tf.reset_default_graph() result_dir = 'results/'+style_name check_folder(result_dir) test_files = glob('{}/*.*'.format(test_dir)) test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test') with tf.variable_scope("generator", reuse=False): test_generated = generator.G_net(test_real).fake saver = tf.train.Saver() gpu_options = tf.GPUOptions(allow_growth=True) with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)) as sess: # tf.global_variables_initializer().run() # load model ckpt = tf.train.get_checkpoint_state(checkpoint_dir) # checkpoint file information if ckpt and ckpt.model_checkpoint_path: ckpt_name = os.path.basename(ckpt.model_checkpoint_path) # first line saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name)) print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name))) else: print(" [*] Failed to find a checkpoint") return # stats_graph(tf.get_default_graph()) begin = time.time() for sample_file in tqdm(test_files) : # print('Processing image: ' + sample_file) sample_image = np.asarray(load_test_data(sample_file, img_size)) image_path = os.path.join(result_dir,'{0}'.format(os.path.basename(sample_file))) fake_img = sess.run(test_generated, feed_dict = {test_real : sample_image}) if if_adjust_brightness: save_images(fake_img, image_path, sample_file) else: save_images(fake_img, image_path, None) end = time.time() print(f'test-time: {end-begin} s') print(f'one image test time : {(end-begin)/len(test_files)} s'什么意思
这是一个 Python 函数,该函数接受五个参数:`checkpoint_dir`,`style_name`,`test_dir`,`if_adjust_brightness` 和 `img_size`。该函数使用 TensorFlow 实现了一个测试函数,用于测试训练好的 AnimeGAN V2 模型生成的效果。在函数中,首先使用 `glob` 函数获取测试图片的路径列表,然后使用 TensorFlow 定义了一个占位符 `test_real`,用于输入测试图片。接着,使用 AnimeGAN V2 模型生成器 `generator.G_net` 生成虚假图像 `test_generated`。随后,使用 TensorFlow 定义了一个 Saver 对象,用于从检查点文件中恢复模型。最后,使用 TensorFlow Session 对象运行生成器模型,生成对应的虚假图像,并将其保存到指定的目录中。
其中,如果 `if_adjust_brightness` 为真,则调用 `save_images` 函数保存虚假图像,并调用 `load_test_data` 函数加载测试图片并调整亮度;否则,调用 `save_images` 函数保存虚假图像,但不会调用 `load_test_data` 函数调整亮度。函数还输出了测试的总时间和每张图片测试所需的时间。
帮我为下面的代码加上注释:class SimpleDeepForest: def __init__(self, n_layers): self.n_layers = n_layers self.forest_layers = [] def fit(self, X, y): X_train = X for _ in range(self.n_layers): clf = RandomForestClassifier() clf.fit(X_train, y) self.forest_layers.append(clf) X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1) return self def predict(self, X): X_test = X for i in range(self.n_layers): X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1) return self.forest_layers[-1].predict(X_test[:, :-2]) # 1. 提取序列特征(如:GC-content、序列长度等) def extract_features(fasta_file): features = [] for record in SeqIO.parse(fasta_file, "fasta"): seq = record.seq gc_content = (seq.count("G") + seq.count("C")) / len(seq) seq_len = len(seq) features.append([gc_content, seq_len]) return np.array(features) # 2. 读取相互作用数据并创建数据集 def create_dataset(rna_features, protein_features, label_file): labels = pd.read_csv(label_file, index_col=0) X = [] y = [] for i in range(labels.shape[0]): for j in range(labels.shape[1]): X.append(np.concatenate([rna_features[i], protein_features[j]])) y.append(labels.iloc[i, j]) return np.array(X), np.array(y) # 3. 调用SimpleDeepForest分类器 def optimize_deepforest(X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = SimpleDeepForest(n_layers=3) model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred)) # 4. 主函数 def main(): rna_fasta = "RNA.fasta" protein_fasta = "pro.fasta" label_file = "label.csv" rna_features = extract_features(rna_fasta) protein_features = extract_features(protein_fasta) X, y = create_dataset(rna_features, protein_features, label_file) optimize_deepforest(X, y) if __name__ == "__main__": main()
# Define a class named 'SimpleDeepForest'
class SimpleDeepForest:
# Initialize the class with 'n_layers' parameter
def __init__(self, n_layers):
self.n_layers = n_layers
self.forest_layers = []
# Define a method named 'fit' to fit the dataset into the classifier
def fit(self, X, y):
X_train = X
# Use the forest classifier to fit the dataset for 'n_layers' times
for _ in range(self.n_layers):
clf = RandomForestClassifier()
clf.fit(X_train, y)
# Append the classifier to the list of forest layers
self.forest_layers.append(clf)
# Concatenate the training data with the predicted probability of the last layer
X_train = np.concatenate((X_train, clf.predict_proba(X_train)), axis=1)
# Return the classifier
return self
# Define a method named 'predict' to make predictions on the test set
def predict(self, X):
X_test = X
# Concatenate the test data with the predicted probability of each layer
for i in range(self.n_layers):
X_test = np.concatenate((X_test, self.forest_layers[i].predict_proba(X_test)), axis=1)
# Return the predictions of the last layer
return self.forest_layers[-1].predict(X_test[:, :-2])
# Define a function named 'extract_features' to extract sequence features
def extract_features(fasta_file):
features = []
# Parse the fasta file to extract sequence features
for record in SeqIO.parse(fasta_file, "fasta"):
seq = record.seq
gc_content = (seq.count("G") + seq.count("C")) / len(seq)
seq_len = len(seq)
features.append([gc_content, seq_len])
# Return the array of features
return np.array(features)
# Define a function named 'create_dataset' to create the dataset
def create_dataset(rna_features, protein_features, label_file):
labels = pd.read_csv(label_file, index_col=0)
X = []
y = []
# Create the dataset by concatenating the RNA and protein features
for i in range(labels.shape[0]):
for j in range(labels.shape[1]):
X.append(np.concatenate([rna_features[i], protein_features[j]]))
y.append(labels.iloc[i, j])
# Return the array of features and the array of labels
return np.array(X), np.array(y)
# Define a function named 'optimize_deepforest' to optimize the deep forest classifier
def optimize_deepforest(X, y):
# Split the dataset into training set and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create an instance of the SimpleDeepForest classifier with 3 layers
model = SimpleDeepForest(n_layers=3)
# Fit the training set into the classifier
model.fit(X_train, y_train)
# Make predictions on the testing set
y_pred = model.predict(X_test)
# Print the classification report
print(classification_report(y_test, y_pred))
# Define the main function to run the program
def main():
rna_fasta = "RNA.fasta"
protein_fasta = "pro.fasta"
label_file = "label.csv"
# Extract the RNA and protein features
rna_features = extract_features(rna_fasta)
protein_features = extract_features(protein_fasta)
# Create the dataset
X, y = create_dataset(rna_features, protein_features, label_file)
# Optimize the DeepForest classifier
optimize_deepforest(X, y)
# Check if the program is being run as the main program
if __name__ == "__main__":
main()