Python利用DNN实现宝石识别
作者：Livingbody
深度神经网络（Deep Neural Networks，简称DNN）是深度学习的基础，其结构为input、hidden（可有多层）、output，每层均为全连接。本文将利用DNN实现宝石识别，感兴趣的可以了解一下
任务描述
本次实践是一个多分类任务，需要将照片中的宝石分别进行识别，完成宝石的识别
实践平台：百度AI实训平台-AI Studio、PaddlePaddle1.8.0 动态图
深度神经网络(DNN)
深度神经网络（Deep Neural Networks，简称DNN）是深度学习的基础，其结构为input、hidden（可有多层）、output，每层均为全连接。
数据集介绍
- 数据集文件名为archive_train.zip,archive_test.zip。
- 该数据集包含25个类别不同宝石的图像。
- 这些类别已经分为训练和测试数据。
- 图像大小不一，格式为.jpeg。
# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原 # View dataset directory. This directory will be recovered automatically after resetting environment. !ls /home/aistudio/data
data55032 dataset
#导入需要的包 import os import zipfile import random import json import cv2 import numpy as np from PIL import Image import paddle import paddle.fluid as fluid from paddle.fluid.dygraph import Linear import matplotlib.pyplot as plt
1.数据准备
'''
参数配置
'''
train_parameters = {
"input_size": [3, 64, 64], #输入图片的shape
"class_dim": -1, #分类数
'augment_path' : '/home/aistudio/augment', #数据增强图片目录
"src_path":"data/data55032/archive_train.zip", #原始数据集路径
"target_path":"/home/aistudio/data/dataset", #要解压的路径
"train_list_path": "./train_data.txt", #train_data.txt路径
"eval_list_path": "./val_data.txt", #eval_data.txt路径
"label_dict":{}, #标签字典
"readme_path": "/home/aistudio/data/readme.json", #readme.json路径
"num_epochs": 20, #训练轮数
"train_batch_size": 64, #批次的大小
"learning_strategy": { #优化函数相关的配置
"lr": 0.001 #超参数学习率
}
}def unzip_data(src_path,target_path):
'''
解压原始数据集，将src_path路径下的zip包解压至data/dataset目录下
'''
if(not os.path.isdir(target_path)):
z = zipfile.ZipFile(src_path, 'r')
z.extractall(path=target_path)
z.close()
else:
print("文件已解压")
def get_data_list(target_path,train_list_path,eval_list_path, augment_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
class_dirs = os.listdir(data_list_path)
if '__MACOSX' in class_dirs:
class_dirs.remove('__MACOSX')
# #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
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 = os.path.join(data_list_path,class_dir)
# print(path)
# 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
if img_path =='.DS_Store':
continue
name_path = os.path.join(path,img_path) # 每张图片的路径
if class_sum % 15 == 0: # 每10张图片取一个做验证数据
eval_sum += 1 # eval_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
# ----------------------------------数据增强----------------------------------
aug_path = os.path.join(augment_path, class_dir)
for img_path in os.listdir(aug_path): # 遍历文件夹下的每个图片
name_path = os.path.join(aug_path,img_path) # 每张图片的路径
trainer_sum += 1
trainer_list.append(name_path + "\t%d" % class_label + "\n")#trainer_sum测试数据的数目
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
print(train_parameters)
#乱序
random.shuffle(eval_list)
with open(eval_list_path, 'a') as f:
for eval_image in eval_list:
f.write(eval_image)
#乱序
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！')def data_reader(file_list):
'''
自定义data_reader
'''
def reader():
with open(file_list, 'r') as f:
lines = [line.strip() for line in f]
for line in lines:
img_path, lab = line.strip().split('\t')
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img = img.resize((64, 64), Image.BILINEAR)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1)) # HWC to CHW
img = img/255 # 像素值归一化
yield img, int(lab)
return reader!pip install Augmentor
Looking in indexes: https://mirror.baidu.com/pypi/simple/ Requirement already satisfied: Augmentor in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (0.2.8) Requirement already satisfied: tqdm>=4.9.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Augmentor) (4.36.1) Requirement already satisfied: future>=0.16.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Augmentor) (0.18.0) Requirement already satisfied: numpy>=1.11.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Augmentor) (1.16.4) Requirement already satisfied: Pillow>=5.2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from Augmentor) (7.1.2)
''' 参数初始化 ''' src_path=train_parameters['src_path'] target_path=train_parameters['target_path'] train_list_path=train_parameters['train_list_path'] eval_list_path=train_parameters['eval_list_path'] batch_size=train_parameters['train_batch_size'] augment_path = train_parameters['augment_path'] ''' 解压原始数据到指定路径 ''' unzip_data(src_path,target_path)
文件已解压
def proc_img(src):
for root, dirs, files in os.walk(src):
if '__MACOSX' in root:continue
for file in files:
src=os.path.join(root,file)
img=Image.open(src)
if img.mode != 'RGB':
img = img.convert('RGB')
img.save(src)
if __name__=='__main__':
proc_img(r"data/dataset")
import os, Augmentor
import shutil, glob
if not os.path.exists(augment_path): # 控制不重复增强数据
for root, dirs, files in os.walk("data/dataset", topdown=False):
for name in dirs:
path_ = os.path.join(root, name)
if '__MACOSX' in path_:continue
print('数据增强：',os.path.join(root, name))
print('image：',os.path.join(root, name))
p = Augmentor.Pipeline(os.path.join(root, name),output_directory='output')
p.rotate(probability=0.6, max_left_rotation=2, max_right_rotation=2)
p.zoom(probability=0.6, min_factor=0.9, max_factor=1.1)
p.random_distortion(probability=0.4, grid_height=2, grid_width=2, magnitude=1)
count = 1000 - len(glob.glob(pathname=path_+'/*.jpg'))
p.sample(count, multi_threaded=False)
p.process()
print('将生成的图片拷贝到正确的目录')
for root, dirs, files in os.walk("data/dataset", topdown=False):
for name in files:
path_ = os.path.join(root, name)
if path_.rsplit('/',3)[2] == 'output':
type_ = path_.rsplit('/',3)[1]
dest_dir = os.path.join(augment_path ,type_)
if not os.path.exists(dest_dir):os.makedirs(dest_dir)
dest_path_ = os.path.join(augment_path ,type_, name)
shutil.move(path_, dest_path_)
print('删除所有output目录')
for root, dirs, files in os.walk("data/dataset", topdown=False):
for name in dirs:
if name == 'output':
path_ = os.path.join(root, name)
shutil.rmtree(path_)
print('完成数据增强')Processing kunzite_20.jpg: 1%| | 11/968 [00:00<00:14, 65.61 Samples/s] 数据增强： data/dataset/Kunzite image： data/dataset/Kunzite Initialised with 32 image(s) found. Output directory set to data/dataset/Kunzite/output. Processing kunzite_14.jpg: 2%|▏ | 24/968 [00:00<00:17, 54.43 Samples/s]Processing kunzite_15.jpg: 100%|██████████| 968/968 [00:15<00:00, 61.57 Samples/s] Processing <PIL.Image.Image image mode=RGB size=350x366 at 0x7F7060EB06D0>: 100%|██████████| 32/32 [00:00<00:00, 269.33 Samples/s] Processing almandine_5.jpg: 1%| | 6/969 [00:00<00:20, 45.91 Samples/s] 数据增强： data/dataset/Almandine image： data/dataset/Almandine Initialised with 31 image(s) found. Output directory set to data/dataset/Almandine/output. Processing almandine_2.jpg: 1%|▏ | 14/969 [00:00<00:27, 34.12 Samples/s] Processing almandine_25.jpg: 100%|██████████| 969/969 [00:22<00:00, 42.25 Samples/s] Processing <PIL.Image.Image image mode=RGB size=225x225 at 0x7F705E020C90>: 100%|██████████| 31/31 [00:00<00:00, 173.21 Samples/s] Processing emerald_2.jpg: 1%| | 10/964 [00:00<00:16, 58.72 Samples/s] 数据增强： data/dataset/Emerald image： data/dataset/Emerald Initialised with 36 image(s) found. Output directory set to data/dataset/Emerald/output. Processing emerald_36.jpg: 2%|▏ | 20/964 [00:00<00:17, 54.08 Samples/s]Processing emerald_15.jpg: 100%|██████████| 964/964 [00:26<00:00, 36.49 Samples/s] Processing <PIL.Image.Image image mode=RGB size=460x460 at 0x7F705DED0110>: 100%|██████████| 36/36 [00:00<00:00, 149.48 Samples/s] Processing sapphire blue_9.jpg: 1%| | 10/966 [00:00<00:13, 68.91 Samples/s] 数据增强： data/dataset/Sapphire Blue image： data/dataset/Sapphire Blue Initialised with 34 image(s) found. Output directory set to data/dataset/Sapphire Blue/output. Processing sapphire blue_16.jpg: 2%|▏ | 22/966 [00:00<00:16, 56.52 Samples/s]Processing sapphire blue_30.jpg: 100%|██████████| 966/966 [00:18<00:00, 53.08 Samples/s] Processing <PIL.Image.Image image mode=RGB size=450x450 at 0x7F706885B810>: 100%|██████████| 34/34 [00:00<00:00, 177.29 Samples/s] Processing malachite_2.jpg: 1%| | 10/972 [00:00<00:20, 47.64 Samples/s] 数据增强： data/dataset/Malachite image： data/dataset/Malachite Initialised with 28 image(s) found. Output directory set to data/dataset/Malachite/output. Processing malachite_16.jpg: 2%|▏ | 18/972 [00:00<00:20, 47.14 Samples/s]Processing malachite_22.jpg: 100%|██████████| 972/972 [00:18<00:00, 52.32 Samples/s] Processing <PIL.Image.Image image mode=RGB size=376x262 at 0x7F7060E93D10>: 100%|██████████| 28/28 [00:00<00:00, 173.34 Samples/s] Processing alexandrite_0.jpg: 1%| | 6/966 [00:00<00:24, 39.61 Samples/s] 数据增强： data/dataset/Alexandrite image： data/dataset/Alexandrite Initialised with 34 image(s) found. Output directory set to data/dataset/Alexandrite/output. Processing alexandrite_23.jpg: 2%|▏ | 18/966 [00:00<00:21, 44.52 Samples/s]Processing alexandrite_20.jpg: 100%|██████████| 966/966 [00:20<00:00, 48.06 Samples/s] Processing <PIL.Image.Image image mode=RGB size=500x500 at 0x7F705E025B10>: 100%|██████████| 34/34 [00:00<00:00, 129.49 Samples/s] Processing zircon_8.jpg: 1%| | 5/967 [00:00<00:33, 28.43 Samples/s] 数据增强： data/dataset/Zircon image： data/dataset/Zircon Initialised with 33 image(s) found. Output directory set to data/dataset/Zircon/output. Processing zircon_23.jpg: 1%| | 6/967 [00:00<00:33, 28.43 Samples/s]Processing zircon_24.jpg: 100%|██████████| 967/967 [00:24<00:00, 38.88 Samples/s] Processing <PIL.Image.Image image mode=RGB size=500x500 at 0x7F705DEAC3D0>: 100%|██████████| 33/33 [00:00<00:00, 134.76 Samples/s] Processing onyx black_16.jpg: 1%| | 8/972 [00:00<00:13, 69.17 Samples/s] 数据增强： data/dataset/Onyx Black image： data/dataset/Onyx Black Initialised with 28 image(s) found. Output directory set to data/dataset/Onyx Black/output. Processing onyx black_6.jpg: 2%|▏ | 18/972 [00:00<00:18, 51.84 Samples/s] Processing onyx black_2.jpg: 100%|██████████| 972/972 [00:18<00:00, 53.19 Samples/s] Processing <PIL.Image.Image image mode=RGB size=290x290 at 0x7F705DEE1910>: 100%|██████████| 28/28 [00:00<00:00, 131.50 Samples/s] Processing rhodochrosite_29.jpg: 1%| | 10/971 [00:00<00:18, 53.20 Samples/s] 数据增强： data/dataset/Rhodochrosite image： data/dataset/Rhodochrosite Initialised with 29 image(s) found. Output directory set to data/dataset/Rhodochrosite/output. Processing rhodochrosite_21.jpg: 2%|▏ | 21/971 [00:00<00:16, 58.01 Samples/s]Processing rhodochrosite_15.jpg: 100%|██████████| 971/971 [00:20<00:00, 46.42 Samples/s] Processing <PIL.Image.Image image mode=RGB size=373x356 at 0x7F705E011910>: 100%|██████████| 29/29 [00:00<00:00, 243.76 Samples/s] Processing diamond_16.jpg: 1%| | 5/969 [00:00<00:28, 34.31 Samples/s] 数据增强： data/dataset/Diamond image： data/dataset/Diamond Initialised with 31 image(s) found. Output directory set to data/dataset/Diamond/output. Processing diamond_6.jpg: 1%| | 11/969 [00:00<00:26, 35.79 Samples/s] Processing diamond_20.jpg: 100%|██████████| 969/969 [00:24<00:00, 40.22 Samples/s] Processing <PIL.Image.Image image mode=RGB size=400x400 at 0x7F705DE6CCD0>: 100%|██████████| 31/31 [00:00<00:00, 150.83 Samples/s] Processing benitoite_29.jpg: 1%| | 7/969 [00:00<00:15, 63.04 Samples/s] 数据增强： data/dataset/Benitoite image： data/dataset/Benitoite Initialised with 31 image(s) found. Output directory set to data/dataset/Benitoite/output. Processing benitoite_2.jpg: 2%|▏ | 24/969 [00:00<00:16, 57.15 Samples/s] Processing benitoite_12.jpg: 100%|██████████| 969/969 [00:17<00:00, 55.09 Samples/s] Processing <PIL.Image.Image image mode=RGB size=472x433 at 0x7F705DFE9290>: 100%|██████████| 31/31 [00:00<00:00, 178.70 Samples/s] Processing pearl_0.jpg: 1%| | 6/967 [00:00<00:25, 38.13 Samples/s] 数据增强： data/dataset/Pearl image： data/dataset/Pearl Initialised with 33 image(s) found. Output directory set to data/dataset/Pearl/output. Processing pearl_32.jpg: 2%|▏ | 21/967 [00:00<00:20, 47.09 Samples/s]Processing pearl_12.jpg: 100%|██████████| 967/967 [00:17<00:00, 54.49 Samples/s] Processing <PIL.Image.Image image mode=RGB size=301x301 at 0x7F705E020A50>: 100%|██████████| 33/33 [00:00<00:00, 205.47 Samples/s] Processing beryl golden_39.jpg: 1%| | 11/964 [00:00<00:12, 79.36 Samples/s] 数据增强： data/dataset/Beryl Golden image： data/dataset/Beryl Golden Initialised with 36 image(s) found. Output directory set to data/dataset/Beryl Golden/output. Processing beryl golden_29.jpg: 2%|▏ | 22/964 [00:00<00:14, 63.92 Samples/s]Processing beryl golden_2.jpg: 100%|██████████| 964/964 [00:16<00:00, 58.61 Samples/s] Processing <PIL.Image.Image image mode=RGB size=290x290 at 0x7F705DE6F910>: 100%|██████████| 36/36 [00:00<00:00, 273.71 Samples/s] Processing labradorite_16.jpg: 1%| | 9/960 [00:00<00:17, 55.49 Samples/s] 数据增强： data/dataset/Labradorite image： data/dataset/Labradorite Initialised with 40 image(s) found. Output directory set to data/dataset/Labradorite/output. Processing labradorite_17.jpg: 2%|▏ | 20/960 [00:00<00:18, 52.03 Samples/s]Processing labradorite_11.jpg: 100%|██████████| 960/960 [00:21<00:00, 45.63 Samples/s] Processing <PIL.Image.Image image mode=RGB size=400x400 at 0x7F705DE70F10>: 100%|██████████| 40/40 [00:00<00:00, 117.40 Samples/s] Processing fluorite_23.jpg: 1%| | 11/968 [00:00<00:14, 65.24 Samples/s] 数据增强： data/dataset/Fluorite image： data/dataset/Fluorite Initialised with 32 image(s) found. Output directory set to data/dataset/Fluorite/output. Processing fluorite_4.jpg: 1%|▏ | 14/968 [00:00<00:19, 49.03 Samples/s] Processing fluorite_4.jpg: 100%|██████████| 968/968 [00:21<00:00, 44.39 Samples/s] Processing <PIL.Image.Image image mode=RGB size=500x442 at 0x7F705DE87CD0>: 100%|██████████| 32/32 [00:00<00:00, 169.43 Samples/s] Processing iolite_2.jpg: 1%| | 7/968 [00:00<00:24, 39.15 Samples/s] 数据增强： data/dataset/Iolite image： data/dataset/Iolite Initialised with 32 image(s) found. Output directory set to data/dataset/Iolite/output. Processing iolite_35.jpg: 2%|▏ | 23/968 [00:00<00:18, 51.39 Samples/s]Processing iolite_23.jpg: 100%|██████████| 968/968 [00:16<00:00, 57.22 Samples/s] Processing <PIL.Image.Image image mode=RGB size=290x290 at 0x7F705DE764D0>: 100%|██████████| 32/32 [00:00<00:00, 373.16 Samples/s] Processing quartz beer_24.jpg: 1%| | 12/965 [00:00<00:16, 57.87 Samples/s] 数据增强： data/dataset/Quartz Beer image： data/dataset/Quartz Beer Initialised with 35 image(s) found. Output directory set to data/dataset/Quartz Beer/output. Processing quartz beer_28.jpg: 2%|▏ | 24/965 [00:00<00:14, 65.30 Samples/s]Processing quartz beer_30.jpg: 100%|██████████| 965/965 [00:16<00:00, 59.48 Samples/s] Processing <PIL.Image.Image image mode=RGB size=300x300 at 0x7F705DE82DD0>: 100%|██████████| 35/35 [00:00<00:00, 173.58 Samples/s] Processing garnet red_21.jpg: 1%| | 7/964 [00:00<00:34, 27.76 Samples/s] 数据增强： data/dataset/Garnet Red image： data/dataset/Garnet Red Initialised with 36 image(s) found. Output directory set to data/dataset/Garnet Red/output. Processing garnet red_2.jpg: 2%|▏ | 17/964 [00:00<00:28, 33.50 Samples/s] Processing garnet red_2.jpg: 100%|██████████| 964/964 [00:20<00:00, 46.97 Samples/s] Processing <PIL.Image.Image image mode=RGB size=301x301 at 0x7F705E020090>: 100%|██████████| 36/36 [00:00<00:00, 197.00 Samples/s] Processing danburite_35.jpg: 1%| | 8/968 [00:00<00:16, 58.65 Samples/s] 数据增强： data/dataset/Danburite image： data/dataset/Danburite Initialised with 32 image(s) found. Output directory set to data/dataset/Danburite/output. Processing danburite_32.jpg: 2%|▏ | 17/968 [00:00<00:19, 49.88 Samples/s]Processing danburite_23.jpg: 100%|██████████| 968/968 [00:19<00:00, 50.58 Samples/s] Processing <PIL.Image.Image image mode=RGB size=225x225 at 0x7F705DE78390>: 100%|██████████| 32/32 [00:00<00:00, 144.25 Samples/s] Processing cats eye_7.jpg: 1%| | 8/969 [00:00<00:24, 39.01 Samples/s] 数据增强： data/dataset/Cats Eye image： data/dataset/Cats Eye Initialised with 31 image(s) found. Output directory set to data/dataset/Cats Eye/output. Processing cats eye_26.jpg: 2%|▏ | 15/969 [00:00<00:23, 41.33 Samples/s]Processing cats eye_33.jpg: 100%|██████████| 969/969 [00:25<00:00, 38.19 Samples/s] Processing <PIL.Image.Image image mode=RGB size=401x401 at 0x7F706AF09510>: 100%|██████████| 31/31 [00:00<00:00, 214.03 Samples/s] Processing hessonite_1.jpg: 0%| | 3/970 [00:00<00:33, 28.84 Samples/s] 数据增强： data/dataset/Hessonite image： data/dataset/Hessonite Initialised with 30 image(s) found. Output directory set to data/dataset/Hessonite/output. Processing hessonite_19.jpg: 1%|▏ | 13/970 [00:00<00:31, 30.34 Samples/s]Processing hessonite_33.jpg: 100%|██████████| 970/970 [00:20<00:00, 47.73 Samples/s] Processing <PIL.Image.Image image mode=RGB size=301x301 at 0x7F705E020610>: 100%|██████████| 30/30 [00:00<00:00, 162.33 Samples/s] Processing carnelian_12.jpg: 1%| | 5/967 [00:00<00:28, 34.19 Samples/s] 数据增强： data/dataset/Carnelian image： data/dataset/Carnelian Initialised with 33 image(s) found. Output directory set to data/dataset/Carnelian/output. Processing carnelian_32.jpg: 1%| | 12/967 [00:00<00:29, 32.65 Samples/s]Processing carnelian_31.jpg: 100%|██████████| 967/967 [00:24<00:00, 39.93 Samples/s] Processing <PIL.Image.Image image mode=RGB size=425x425 at 0x7F705DE840D0>: 100%|██████████| 33/33 [00:00<00:00, 147.85 Samples/s] Processing jade_26.jpg: 1%| | 9/972 [00:00<00:25, 38.24 Samples/s] 数据增强： data/dataset/Jade image： data/dataset/Jade Initialised with 28 image(s) found. Output directory set to data/dataset/Jade/output. Processing jade_20.jpg: 2%|▏ | 22/972 [00:00<00:19, 47.93 Samples/s]Processing jade_18.jpg: 100%|██████████| 972/972 [00:18<00:00, 51.18 Samples/s] Processing <PIL.Image.Image image mode=RGB size=290x290 at 0x7F705DE8B050>: 100%|██████████| 28/28 [00:00<00:00, 331.02 Samples/s] Processing variscite_22.jpg: 1%| | 5/970 [00:00<00:25, 37.31 Samples/s] 数据增强： data/dataset/Variscite image： data/dataset/Variscite Initialised with 30 image(s) found. Output directory set to data/dataset/Variscite/output. Processing variscite_10.jpg: 1%|▏ | 13/970 [00:00<00:26, 35.70 Samples/s]Processing variscite_31.jpg: 100%|██████████| 970/970 [00:21<00:00, 45.58 Samples/s] Processing <PIL.Image.Image image mode=RGB size=225x225 at 0x7F705DE7BE50>: 100%|██████████| 30/30 [00:00<00:00, 157.22 Samples/s] Processing tanzanite_2.jpg: 1%| | 5/964 [00:00<00:31, 30.52 Samples/s] 数据增强： data/dataset/Tanzanite image： data/dataset/Tanzanite Initialised with 36 image(s) found. Output directory set to data/dataset/Tanzanite/output. Processing tanzanite_15.jpg: 2%|▏ | 15/964 [00:00<00:25, 36.60 Samples/s]Processing tanzanite_37.jpg: 100%|██████████| 964/964 [00:25<00:00, 38.41 Samples/s] Processing <PIL.Image.Image image mode=RGB size=225x225 at 0x7F705E00E4D0>: 100%|██████████| 36/36 [00:00<00:00, 144.18 Samples/s] 将生成的图片拷贝到正确的目录 删除所有output目录 完成数据增强
#每次生成数据列表前，首先清空train.txt和eval.txt
with open(train_list_path, 'w') as f:
f.seek(0)
f.truncate()
with open(eval_list_path, 'w') as f:
f.seek(0)
f.truncate()
#生成数据列表
get_data_list(target_path,train_list_path,eval_list_path,augment_path)
'''
构造数据提供器
'''
train_reader = paddle.batch(data_reader(train_list_path),
batch_size=batch_size,
drop_last=True)
eval_reader = paddle.batch(data_reader(eval_list_path),
batch_size=batch_size,
drop_last=True)
{'input_size': [3, 64, 64], 'class_dim': 25, 'augment_path': '/home/aistudio/augment', 'src_path': 'data/data55032/archive_train.zip', 'target_path': '/home/aistudio/data/dataset', 'train_list_path': './train_data.txt', 'eval_list_path': './val_data.txt', 'label_dict': {'0': 'Kunzite', '1': 'Almandine', '2': 'Emerald', '3': 'Sapphire Blue', '4': 'Malachite', '5': 'Alexandrite', '6': 'Zircon', '7': 'Onyx Black', '8': 'Rhodochrosite', '9': 'Diamond', '10': 'Benitoite', '11': 'Pearl', '12': 'Beryl Golden', '13': 'Labradorite', '14': 'Fluorite', '15': 'Iolite', '16': 'Quartz Beer', '17': 'Garnet Red', '18': 'Danburite', '19': 'Cats Eye', '20': 'Hessonite', '21': 'Carnelian', '22': 'Jade', '23': 'Variscite', '24': 'Tanzanite'}, 'readme_path': '/home/aistudio/data/readme.json', 'num_epochs': 20, 'train_batch_size': 64, 'learning_strategy': {'lr': 0.001}}
生成数据列表完成！Batch=0
Batchs=[]
all_train_accs=[]
def draw_train_acc(Batchs, train_accs):
title="training accs"
plt.title(title, fontsize=24)
plt.xlabel("batch", fontsize=14)
plt.ylabel("acc", fontsize=14)
plt.plot(Batchs, train_accs, color='green', label='training accs')
plt.legend()
plt.grid()
plt.show()
all_train_loss=[]
def draw_train_loss(Batchs, train_loss):
title="training loss"
plt.title(title, fontsize=24)
plt.xlabel("batch", fontsize=14)
plt.ylabel("loss", fontsize=14)
plt.plot(Batchs, train_loss, color='red', label='training loss')
plt.legend()
plt.grid()
plt.show()2.定义模型
###在以下cell中完成DNN网络的定义###
#定义网络
class MyDNN(fluid.dygraph.Layer):
'''
卷积神经网络
'''
def __init__(self):
super(MyDNN,self).__init__()
self.hidden1=fluid.dygraph.Linear(3*64*64,1000, act='relu')
self.hidden2=fluid.dygraph.Linear(1000,500, act='relu')
self.hidden3=fluid.dygraph.Linear(500,100, act='relu')
self.out = fluid.dygraph.Linear(input_dim=100, output_dim=25, act='softmax')
def forward(self,input):
x = fluid.layers.reshape(input,shape=[-1,3*64*64])
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x3.训练模型
with fluid.dygraph.guard(place = fluid.CUDAPlace(0)):
print(train_parameters['class_dim'])
print(train_parameters['label_dict'])
model=MyDNN() #模型实例化
model.train() #训练模式
opt=fluid.optimizer.SGDOptimizer(learning_rate=train_parameters['learning_strategy']['lr'], parameter_list=model.parameters())#优化器选用SGD随机梯度下降，学习率为0.001.
epochs_num=train_parameters['num_epochs'] #迭代次数
for pass_num in range(epochs_num):
for batch_id,data in enumerate(train_reader()):
images = np.array([x[0] for x in data]).astype('float32').reshape(-1, 3,64,64)
labels = np.array([x[1] for x in data]).astype('int64')
labels = labels[:, np.newaxis]
image=fluid.dygraph.to_variable(images)
label=fluid.dygraph.to_variable(labels)
predict=model(image) #数据传入model
loss=fluid.layers.cross_entropy(predict,label)
avg_loss=fluid.layers.mean(loss)#获取loss值
acc=fluid.layers.accuracy(predict,label)#计算精度
if batch_id!=0 and batch_id%5==0:
Batch = Batch+5
Batchs.append(Batch)
all_train_loss.append(avg_loss.numpy()[0])
all_train_accs.append(acc.numpy()[0])
print("train_pass:{},batch_id:{},train_loss:{},train_acc:{}".format(pass_num,batch_id,avg_loss.numpy(),acc.numpy()))
avg_loss.backward()
opt.minimize(avg_loss) #优化器对象的minimize方法对参数进行更新
model.clear_gradients() #model.clear_gradients()来重置梯度
fluid.save_dygraph(model.state_dict(),'MyDNN')#保存模型
draw_train_acc(Batchs,all_train_accs)
draw_train_loss(Batchs,all_train_loss)train_pass:19,batch_id:400,train_loss:[0.24890603],train_acc:[0.96875]
4.模型评估
#模型评估
with fluid.dygraph.guard():
accs = []
model_dict, _ = fluid.load_dygraph('MyDNN')
model = MyDNN()
model.load_dict(model_dict) #加载模型参数
model.eval() #训练模式
for batch_id,data in enumerate(eval_reader()):#测试集
images = np.array([x[0] for x in data]).astype('float32').reshape(-1, 3,64,64)
labels = np.array([x[1] for x in data]).astype('int64')
labels = labels[:, np.newaxis]
image=fluid.dygraph.to_variable(images)
label=fluid.dygraph.to_variable(labels)
predict=model(image)
acc=fluid.layers.accuracy(predict,label)
accs.append(acc.numpy()[0])
avg_acc = np.mean(accs)
print(avg_acc)0.96875
5.模型预测
import os
import zipfile
def unzip_infer_data(src_path,target_path):
'''
解压预测数据集
'''
if(not os.path.isdir(target_path)):
z = zipfile.ZipFile(src_path, 'r')
z.extractall(path=target_path)
z.close()
def load_image(img_path):
'''
预测图片预处理
'''
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img = img.resize((64, 64), Image.BILINEAR)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1)) # HWC to CHW
img = img/255 # 像素值归一化
return img
infer_src_path = '/home/aistudio/data/data55032/archive_test.zip'
infer_dst_path = '/home/aistudio/data/archive_test'
unzip_infer_data(infer_src_path,infer_dst_path)label_dic = train_parameters['label_dict']
'''
模型预测
'''
with fluid.dygraph.guard():
model_dict, _ = fluid.load_dygraph('MyDNN')
model = MyDNN()
model.load_dict(model_dict) #加载模型参数
model.eval() #训练模式
#展示预测图片
infer_path='data/archive_test/alexandrite_3.jpg'
img = Image.open(infer_path)
plt.imshow(img) #根据数组绘制图像
plt.show() #显示图像
#对预测图片进行预处理
infer_imgs = []
infer_imgs.append(load_image(infer_path))
infer_imgs = np.array(infer_imgs)
for i in range(len(infer_imgs)):
data = infer_imgs[i]
dy_x_data = np.array(data).astype('float32')
dy_x_data=dy_x_data[np.newaxis,:, : ,:]
img = fluid.dygraph.to_variable(dy_x_data)
out = model(img)
lab = np.argmax(out.numpy()) #argmax():返回最大数的索引
print("第{}个样本,被预测为：{},真实标签为：{}".format(i+1,label_dic[str(lab)],infer_path.split('/')[-1].split("_")[0]))
print("结束")
第1个样本,被预测为：Malachite,真实标签为：alexandrite 结束
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