vqrkuco2fs

!pip install paddlehub==1.8.1 -i https://pypi.tuna.tsinghua.edu.cn/simple import paddlehub as hub module = hub.Module(name="mobilenet_v2_imagenet") !unzip -o /data/shixunfiles/f4670c416ae11690ac449e419a3f04f0_1604373961269.zip from paddlehub.dataset.base_cv_dataset import BaseCVDataset

class DemoDataset(BaseCVDataset): def init(self): # 数据集存放位置 self.dataset_dir = "datasets" super(DemoDataset, self).init( base_path=self.dataset_dir, train_list_file="train_list.txt", validate_list_file="validate_list.txt", test_list_file="test_list.txt", # predict_file="predict_list.txt", label_list_file="label_list.txt", # label_list=["bus","suv","truck"] ) dataset = DemoDataset() 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) config = hub.RunConfig( use_cuda=False, num_epoch=10, # checkpoint_dir="cv_finetune_turtorial_demo", batch_size=32, eval_interval=50, strategy=hub.finetune.strategy.DefaultFinetuneStrategy()) input_dict, output_dict, program = module.context(trainable=True)

img = input_dict["image"]

feature_map = output_dict["feature_map"]

feed_list = [img.name]

task = hub.ImageClassifierTask( data_reader=data_reader, feed_list=feed_list, feature=feature_map, num_classes=dataset.num_labels, # num_classes=3, config=config) run_states = task.finetune_and_eval() import numpy as np import matplotlib.pyplot as plt import pandas as pd from pandas import Series,DataFrame %matplotlib inline

data = ["/data/shixunfiles/8cb6ea434be671fab6b37394c9259cd7_1604389830291.jpg"] 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("/data/shixunfiles/8cb6ea434be671fab6b37394c9259cd7_1604389830291.jpg") plt.imshow(d)