数据集简介：
中药材共5类 900张图片 分别是百合 枸杞 党参 槐花 金银花
部分代码：
def get_data_list(target_path,train_list_path,eval_list_path):
'''
生成数据列表
'''
#存放所有类别的信息
class_detail = []
#获取所有类别保存的文件夹名称
data_list_path=target_path+"Chinese Medicine/"
class_dirs = os.listdir(data_list_path)
#总的图像数量
all_class_images = 0
#存放类别标签
class_label=0
#存放类别数目
class_dim = 0
#存储要写进eval.txt和train.txt中的内容
trainer_list=[]
eval_list=[]
#读取每个类别，['river', 'lawn','church','ice','desert']
for class_dir in class_dirs:
if class_dir != ".DS_Store":
class_dim += 1
#每个类别的信息
class_detail_list = {}
eval_sum = 0
trainer_sum = 0
#统计每个类别有多少张图片
class_sum = 0
#获取类别路径
path = data_list_path + class_dir
# 获取所有图片
img_paths = os.listdir(path)
for img_path in img_paths: # 遍历文件夹下的每个图片
name_path = path + '/' + img_path # 每张图片的路径
if class_sum % 8 == 0: # 每8张图片取一个做验证数据
eval_sum += 1 # test_sum为测试数据的数目
eval_list.append(name_path + "\t%d" % class_label + "\n")
else:
trainer_sum += 1
trainer_list.append(name_path + "\t%d" % class_label + "\n")#trainer_sum测试数据的数目
class_sum += 1 #每类图片的数目
all_class_images += 1 #所有类图片的数目
# 说明的json文件的class_detail数据
class_detail_list['class_name'] = class_dir #类别名称
class_detail_list['class_label'] = class_label #类别标签
class_detail_list['class_eval_images'] = eval_sum #该类数据的测试集数目
class_detail_list['class_trainer_images'] = trainer_sum #该类数据的训练集数目
class_detail.append(class_detail_list)
#初始化标签列表
train_parameters['label_dict'][str(class_label)] = class_dir
class_label += 1
#初始化分类数
train_parameters['class_dim'] = class_dim
#乱序
random.shuffle(eval_list)
with open(eval_list_path, 'a') as f:
for eval_image in eval_list:
f.write(eval_image)
random.shuffle(trainer_list)
with open(train_list_path, 'a') as f2:
for train_image in trainer_list:
f2.write(train_image)
# 说明的json文件信息
readjson = {}
readjson['all_class_name'] = data_list_path #文件父目录
readjson['all_class_images'] = all_class_images
readjson['class_detail'] = class_detail
jsons = json.dumps(readjson, sort_keys=True, indent=4, separators=(',', ': '))
with open(train_parameters['readme_path'],'w') as f:
f.write(jsons)
print ('生成数据列表完成！')class dataset(Dataset):
def __init__(self, data_path, mode='train'):
"""
数据读取器
:param data_path: 数据集所在路径
:param mode: train or eval
"""
super().__init__()
self.data_path = data_path
self.img_paths = []
self.labels = []
if mode == 'train':
with open(os.path.join(self.data_path, "train.txt"), "r", encoding="utf-8") as f:
self.info = f.readlines()
for img_info in self.info:
img_path, label = img_info.strip().split('\t')
self.img_paths.append(img_path)
self.labels.append(int(label))
else:
with open(os.path.join(self.data_path, "eval.txt"), "r", encoding="utf-8") as f:
self.info = f.readlines()
for img_info in self.info:
img_path, label = img_info.strip().split('\t')
self.img_paths.append(img_path)
self.labels.append(int(label))
def __getitem__(self, index):
"""
获取一组数据
:param index: 文件索引号
:return:
"""
# 第一步打开图像文件并获取label值
img_path = self.img_paths[index]
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img = img.resize((224, 224), Image.BILINEAR)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1)) / 255
label = self.labels[index]
label = np.array([label], dtype="int64")
return img, label
def print_sample(self, index: int = 0):
print("文件名", self.img_paths[index], "\t标签值", self.labels[index])
def __len__(self):
return len(self.img_paths)
model = VGGNet()
model.train()
cross_entropy = paddle.nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(learning_rate=train_parameters['learning_strategy']['lr'],
parameters=model.parameters())
steps = 0
Iters, total_loss, total_acc = [], [], []
for epo in range(train_parameters['num_epochs']):
for _, data in enumerate(train_loader()):
steps += 1
x_data = data[0]
y_data = data[1]
predicts, acc = model(x_data, y_data)
loss = cross_entropy(predicts, y_data)
loss.backward()
optimizer.step()
optimizer.clear_grad()
if steps % train_parameters["skip_steps"] == 0:
Iters.append(steps)
total_loss.append(loss.numpy()[0])
total_acc.append(acc.numpy()[0])
#打印中间过程
print('epo: {}, step: {}, loss is: {}, acc is: {}'\
.format(epo, steps, loss.numpy(), acc.numpy()))
#保存模型参数
if steps % train_parameters["save_steps"] == 0:
save_path = train_parameters["checkpoints"]+"/"+"save_dir_" + str(steps) + '.pdparams'
print('save model to: ' + save_path)
paddle.save(model.state_dict(),save_path)
paddle.save(model.state_dict(),train_parameters["checkpoints"]+"/"+"save_dir_final.pdparams")
draw_process("trainning loss","red",Iters,total_loss,"trainning loss")
draw_process("trainning acc","green",Iters,total_acc,"trainning acc")
