Update README.md

README.md

@@ -1,2 +1,76 @@
 # FineTune
-
+!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/26a2e3c3b2c50fe54e2fcab6e031a141_1607408726958.zip
+from paddlehub.dataset.base_cv_dataset import BaseCVDataset
+class DemoDataset(BaseCVDataset):
+   def __init__(self):
+       self.dataset_dir="car_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",
+           label_list_file="label_list.txt",
+           )
+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,
+    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,
+    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
+import os
+dirs=os.listdir('car_datasets/test')
+num=0
+for i in os.listdir('car_datasets/test'):
+    m='car_datasets/test/'+i
+    dirs[num]=m
+    num+=1
+s=0
+b=0
+a=os.listdir('car_datasets/test')
+for i in a:
+    b+=len(os.listdir('car_datasets/test/'+i))
+data=[]
+for i in range(b):
+    data.append('w')
+for i in dirs:
+    for j in os.listdir(i):
+        n=i+'/'+j
+        data[s]=n
+        s+=1
+label_map = dataset.label_dict()
+index = 0
+run_states = task.predict(data=data)
+results = [run_state.run_results for run_state in run_states]
+for batch_result in results:
+    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))