<path to the folder with training images> 举个例子
时间: 2023-11-27 15:49:19 浏览: 41
假设你的训练图像文件夹在桌面上的一个名为“image_folder”的文件夹中,那么你的路径可能是:
```
/Users/你的用户名/Desktop/image_folder/
```
请注意,这个路径将根据你的操作系统和文件夹的位置而有所不同。
相关问题
将下面代码简洁化:def split_dataset(img_path, target_folder_path, output_path): filename = [] total_imgs = os.listdir(img_path) #for root, dirs, files in os.walk(img_path): for img in total_imgs: filename.append(img) np.random.shuffle(filename) train = filename[:int(len(filename) * 0.9)] test = filename[int(len(filename) * 0.9):] out_images = os.path.join(output_path, 'imgs') if not os.path.exists(out_images): os.makedirs(out_images) out_images_train = os.path.join(out_images, 'training') if not os.path.exists(out_images_train): os.makedirs(out_images_train) out_images_test = os.path.join(out_images, 'test') if not os.path.exists(out_images_test): os.makedirs(out_images_test) out_annotations = os.path.join(output_path, 'annotations') if not os.path.exists(out_annotations): os.makedirs(out_annotations) out_annotations_train = os.path.join(out_annotations, 'training') if not os.path.exists(out_annotations_train): os.makedirs(out_annotations_train) out_annotations_test = os.path.join(out_annotations, 'test') if not os.path.exists(out_annotations_test): os.makedirs(out_annotations_test) for i in train: print(os.path.join(img_path, i)) print(os.path.join(out_images_train, i)) shutil.copyfile(os.path.join(img_path, i), os.path.join(out_images_train, i)) annotations_name = "gt_" + i[:-3] + 'txt' shutil.copyfile(os.path.join(target_folder_path, annotations_name), os.path.join(out_annotations_train, annotations_name)) for i in test: shutil.copyfile(os.path.join(img_path, i), os.path.join(out_images_test, i)) annotations_name = "gt_" + i[:-3] + 'txt' shutil.copyfile(os.path.join(target_folder_path, annotations_name), os.path.join(out_annotations_test, annotations_name))
def split_dataset(img_path, target_folder_path, output_path):
filename = os.listdir(img_path)
np.random.shuffle(filename)
train = filename[:int(len(filename) * 0.9)]
test = filename[int(len(filename) * 0.9):]
out_images = os.path.join(output_path, 'imgs')
os.makedirs(out_images, exist_ok=True)
out_images_train = os.path.join(out_images, 'training')
os.makedirs(out_images_train, exist_ok=True)
out_images_test = os.path.join(out_images, 'test')
os.makedirs(out_images_test, exist_ok=True)
out_annotations = os.path.join(output_path, 'annotations')
os.makedirs(out_annotations, exist_ok=True)
out_annotations_train = os.path.join(out_annotations, 'training')
os.makedirs(out_annotations_train, exist_ok=True)
out_annotations_test = os.path.join(out_annotations, 'test')
os.makedirs(out_annotations_test, exist_ok=True)
for i in train:
img_src = os.path.join(img_path, i)
img_dst = os.path.join(out_images_train, i)
shutil.copyfile(img_src, img_dst)
annotations_name = "gt_" + i[:-3] + 'txt'
annotations_src = os.path.join(target_folder_path, annotations_name)
annotations_dst = os.path.join(out_annotations_train, annotations_name)
shutil.copyfile(annotations_src, annotations_dst)
for i in test:
img_src = os.path.join(img_path, i)
img_dst = os.path.join(out_images_test, i)
shutil.copyfile(img_src, img_dst)
annotations_name = "gt_" + i[:-3] + 'txt'
annotations_src = os.path.join(target_folder_path, annotations_name)
annotations_dst = os.path.join(out_annotations_test, annotations_name)
shutil.copyfile(annotations_src, annotations_dst)
class args(): # training args epochs = 4 #"number of training epochs, default is 2" batch_size = 4 #"batch size for training, default is 4" dataset = "MSCOCO 2014 path" HEIGHT = 256 WIDTH = 256 save_model_dir = "models" #"path to folder where trained model will be saved." save_loss_dir = "models/loss" # "path to folder where trained model will be saved." image_size = 256 #"size of training images, default is 256 X 256" cuda = 1 #"set it to 1 for running on GPU, 0 for CPU" seed = 42 #"random seed for training" ssim_weight = [1,10,100,1000,10000] ssim_path = ['1e0', '1e1', '1e2', '1e3', '1e4'] lr = 1e-4 #"learning rate, default is 0.001" lr_light = 1e-4 # "learning rate, default is 0.001" log_interval = 5 #"number of images after which the training loss is logged, default is 500" resume = None resume_auto_en = None resume_auto_de = None resume_auto_fn = None # for test Final_cat_epoch_9_Wed_Jan__9_04_16_28_2019_1.0_1.0.model model_path_gray = "./models/densefuse_gray.model" model_path_rgb = "./models/densefuse_rgb.model"
这段代码定义了一个名为`args`的类,其中包含了许多训练和模型参数的设置。
下面是一些重要的参数:
- `epochs`: 训练的轮数,默认为4。
- `batch_size`: 训练时的批大小,默认为4。
- `dataset`: 数据集的路径,默认为"MSCOCO 2014 path"。
- `HEIGHT`和`WIDTH`: 训练图像的高度和宽度,默认为256。
- `save_model_dir`和`save_loss_dir`: 保存训练模型和损失的文件夹路径。
- `image_size`: 训练图像的大小,默认为256。
- `cuda`: 是否使用GPU进行训练,1表示使用GPU,0表示使用CPU。
- `seed`: 随机种子,默认为42。
- `ssim_weight`和`ssim_path`: SSIM权重和路径的设置。
- `lr`和`lr_light`: 学习率的设置。
- `log_interval`: 训练损失记录间隔,默认为5。
- `resume_*`: 恢复训练时的模型路径。
最后两个参数`model_path_gray`和`model_path_rgb`是用于测试的模型路径。
请注意,这段代码只是定义了这些参数的初始值,并没有实际使用它们。在实际使用时,你可以根据需要修改这些参数的值。
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