Delete 'tryClsGarbage.py'

tryClsGarbage.py

@@ -1,138 +0,0 @@
-#coding:utf-8
-# 环境（均为CPU）
-# paddlehub           1.8.0
-# paddlepaddle        2.3.0
-
-
-import argparse
-import os
-import ast
-import paddle.fluid as fluid
-import paddlehub as hub
-import paddle
-import numpy as np
-import matplotlib
-matplotlib.rcParams['font.family']='simHei' #显示汉字
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-from paddlehub.dataset.base_cv_dataset import BaseCVDataset   
-
-# yapf: disable
-parser = argparse.ArgumentParser(__doc__)
-parser.add_argument("--num_epoch",          type=int,               default=1,                          help="Number of epoches for fine-tuning.")
-parser.add_argument("--use_gpu",            type=ast.literal_eval,  default=False,                      help="Whether use GPU for fine-tuning.")
-parser.add_argument("--checkpoint_dir",     type=str,               default="paddlehub_finetune_ckpt",  help="Path to save log data.")
-parser.add_argument("--batch_size",         type=int,               default=16,                         help="Total examples' number in batch for training.")
-parser.add_argument("--module",             type=str,               default="resnet50",                 help="Module used as feature extractor.")
-parser.add_argument("--dataset",            type=str,               default="flowers",                  help="Dataset to fine-tune.")
-parser.add_argument("--use_data_parallel",  type=ast.literal_eval,  default=True,                      help="Whether use data parallel.")
-# yapf: enable.
-
-module_map = {
-    "resnet50": "resnet_v2_50_imagenet",
-    "resnet101": "resnet_v2_101_imagenet",
-    "resnet152": "resnet_v2_152_imagenet",
-    "mobilenet": "mobilenet_v2_imagenet",
-    "nasnet": "nasnet_imagenet",
-    "pnasnet": "pnasnet_imagenet"
-}
-
-class DemoDataset(BaseCVDataset):	
-   def __init__(self):	
-       # 数据集存放位置（换成自己电脑数据集的位置）
-       self.dataset_dir = "D:\clsGarbageCode\garbage"       
-       super(DemoDataset, self).__init__(
-           base_path=self.dataset_dir,
-           train_list_file="train_list.txt",
-           validate_list_file="val_list.txt",
-           test_list_file="test_list.txt",
-         #   predict_file="predict_list.txt",
-           label_list_file="label_list.txt"
-        )
-
-def finetune(args):
-    # Load Paddlehub  pretrained model
-    paddle.enable_static()
-    module = hub.Module(name=args.module)
-    input_dict, output_dict, program = module.context(trainable=True)
-
-    dataset = DemoDataset() 
-
-    # Use ImageClassificationReader to read dataset
-    data_reader = hub.reader.ImageClassificationReader(
-        image_width=module.get_expected_image_width(),
-        image_height=module.get_expected_image_height(),
-        images_mean=module.get_pretrained_images_mean(),
-        images_std=module.get_pretrained_images_std(),
-        dataset=dataset)
-
-    feature_map = output_dict["feature_map"]
-
-    # Setup feed list for data feeder
-    feed_list = [input_dict["image"].name]
-
-    # Setup RunConfig for PaddleHub Fine-tune API
-    config = hub.RunConfig(
-        use_data_parallel=args.use_data_parallel,
-        use_cuda=False,
-        # use_cuda=args.use_gpu,
-        num_epoch=args.num_epoch,
-        batch_size=args.batch_size,
-        checkpoint_dir=args.checkpoint_dir,
-        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
-
-    # Define a image classification task by PaddleHub Fine-tune API
-    task = hub.ImageClassifierTask(
-        data_reader=data_reader,
-        feed_list=feed_list,
-        feature=feature_map,
-        num_classes=dataset.num_labels,
-        config=config)
-
-    # Fine-tune by PaddleHub's API
-   # task.finetune_and_eval()
-    return task
-
-#暂时注释的代码
-'''def testShow(task):
-    # "任选一张图片路径/xx.jpg"
-    data = ["/media/lsz/lll/DLcode/paddle/flower_photos/dandelion/7355522_b66e5d3078_m.jpg"]
-    dataset = DemoDataset()
-    label_map = dataset.label_dict()
-    index = 0
-    # get classification result
-    run_states = task.predict(data=data)
-    results = [run_state.run_results for run_state in run_states]
-    for batch_result in results:
-        # get predict index
-        batch_result = np.argmax(batch_result, axis=2)[0]
-        for result in batch_result:
-            index += 1
-            result = label_map[result]
-            print("input %i is %s, and the predict result is ( %s )" %
-                (index, data[index - 1], result))
-    d=plt.imread("/media/lsz/lll/DLcode/paddle/flower_photos/dandelion/7355522_b66e5d3078_m.jpg")
-    plt.imshow(d)'''
-
-def testShow(task):
-    # "任选一张图片路径/xx.jpg"
-    data = ["D:\clsGarbageCode\garbage\你是什么垃圾.(jpg)\下载.jpg"]
-    #print('图片类型是',end='')
-    #print(task.predict(data=data,return_result=True))
-    matplotlib.use('TkAgg')
-    d=mpimg.imread(data[0])
-    #plt.xlabel('垃圾类型为')
-    plt.title(task.predict(data=data,return_result=True))
-    plt.imshow(d)
-    plt.show()
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-    if not args.module in module_map:
-        hub.logger.error("module should in %s" % module_map.keys())
-        exit(1)
-    args.module = module_map[args.module]
-
-    task = finetune(args)  # 微调模型
-    testShow(task)  # 传入微调模型，使用任意图片进行测试
-