【转载】使用Pytorch进行图像分类，AI challenger 农作物病害分类竞赛源码解读
1.首先对给的数据进行划分，类型为每个类单独放在一个文件夹中
import json
import shutil
import os
from glob import glob
from tqdm import tqdm
# 此文件的作用是创建每个类的文件夹，以及根据给出来的Json中已经做好的分类，对数据进行对号入座划分。
# 加载json文件得出一个字典，然后根据Key值来提取每个文件到相应的文件夹中，（注意去除了不合理数据）
try:
for i in range(0,59):
os.mkdir("./data/train/" + str(i))
except:
pass
file_train = json.load(open("./data/temp/labels/AgriculturalDisease_train_annotations.json","r",encoding="utf-8"))
file_val = json.load(open("./data/temp/labels/AgriculturalDisease_validation_annotations.json","r",encoding="utf-8"))
file_list = file_train + file_val
for file in tqdm(file_list):
filename = file["image_id"]
origin_path = "./data/temp/images/" + filename
ids = file["disease_class"]
if ids == 44:
continue
if ids == 45:
continue
if ids > 45:
ids = ids -2
save_path = "./data/train/" + str(ids) + "/"
shutil.copy(origin_path,save_path)2.获取增强数据集类的定义
# 数据增强的多种方式，使用自定义的方法。调用只需在dataloader.py文件中的get_item函数中调用类自身参数
# transforms，transforms中集合了compose，compose中列出详细所使用的增强方式。
from __future__ import division
import cv2
import numpy as np
from numpy import random
import math
from sklearn.utils import shuffle
# 常用的增强方式几乎都在这里，只需在compose中列出类名即可
__all__ = ['Compose','RandomHflip', 'RandomUpperCrop', 'Resize', 'UpperCrop', 'RandomBottomCrop',
"RandomErasing",'BottomCrop', 'Normalize', 'RandomSwapChannels', 'RandomRotate',
'RandomHShift',"CenterCrop","RandomVflip",'ExpandBorder', 'RandomResizedCrop',
'RandomDownCrop', 'DownCrop', 'ResizedCrop',"FixRandomRotate"]
# 组合
# “随机翻转”，“随机顶部切割”，“调整大小”，“上切割”，“随机底部切割”、
# “随机擦除”，“底部切割”，“正则化”，“随机交换频道”，“随机旋转”，
# “随机HShift”，“中央切割”，“随机Vflip”，“扩展边界”，“随机调整切割”，
# “随机下降”，“下降切割”， “调整切割”，“固定随机化”。
# 每个增强方式类需要调用普通方法描述如下：
def rotate_nobound(image, angle, center=None, scale=1.):
(h, w) = image.shape[:2]
# if the center is None, initialize it as the center of
# the image
if center is None:
center = (w // 2, h // 2)
# perform the rotation
M = cv2.getRotationMatrix2D(center, angle, scale)
rotated = cv2.warpAffine(image, M, (w, h))
return rotated
def scale_down(src_size, size):
w, h = size
sw, sh = src_size
if sh < h:
w, h = float(w * sh) / h, sh
if sw < w:
w, h = sw, float(h * sw) / w
return int(w), int(h)
def fixed_crop(src, x0, y0, w, h, size=None):
out = src[y0:y0 + h, x0:x0 + w]
if size is not None and (w, h) != size:
out = cv2.resize(out, (size[0], size[1]), interpolation=cv2.INTER_CUBIC)
return out
# 固定随机旋转
class FixRandomRotate(object):
def __init__(self, angles=[0,90,180,270], bound=False):
self.angles = angles
self.bound = bound
def __call__(self,img):
do_rotate = random.randint(0, 4)
angle=self.angles[do_rotate]
if self.bound:
img = rotate_bound(img, angle)
else:
img = rotate_nobound(img, angle)
return img
def center_crop(src, size):
h, w = src.shape[0:2]
new_w, new_h = scale_down((w, h), size)
x0 = int((w - new_w) / 2)
y0 = int((h - new_h) / 2)
out = fixed_crop(src, x0, y0, new_w, new_h, size)
return out
def bottom_crop(src, size):
h, w = src.shape[0:2]
new_w, new_h = scale_down((w, h), size)
x0 = int((w - new_w) / 2)
y0 = int((h - new_h) * 0.75)
out = fixed_crop(src, x0, y0, new_w, new_h, size)
return out
def rotate_bound(image, angle):
# grab the dimensions of the image and then determine the
# center
h, w = image.shape[:2]
(cX, cY) = (w // 2, h // 2)
M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
rotated = cv2.warpAffine(image, M, (nW, nH))
return rotated
# 常用增强方式，以类的方式体现：
# 将多个transform组合起来使用
crop切割 filp旋转
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
class RandomRotate(object):
def __init__(self, angles, bound=False):
self.angles = angles
self.bound = bound
def __call__(self,img):
do_rotate = random.randint(0, 2)
if do_rotate:
angle = np.random.uniform(self.angles[0], self.angles[1])
if self.bound:
img = rotate_bound(img, angle)
else:
img = rotate_nobound(img, angle)
return img
class RandomBrightness(object):
def __init__(self, delta=10):
assert delta >= 0
assert delta <= 255
self.delta = delta
def __call__(self, image):
if random.randint(2):
delta = random.uniform(-self.delta, self.delta)
image = (image + delta).clip(0.0, 255.0)
# print('RandomBrightness,delta ',delta)
return image
class RandomContrast(object):
def __init__(self, lower=0.9, upper=1.05):
self.lower = lower
self.upper = upper
assert self.upper >= self.lower, "contrast upper must be >= lower."
assert self.lower >= 0, "contrast lower must be non-negative."
# expects float image
def __call__(self, image):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
# print('contrast:', alpha)
image = (image * alpha).clip(0.0,255.0)
return image
class RandomSaturation(object):
def __init__(self, lower=0.8, upper=1.2):
self.lower = lower
self.upper = upper
assert self.upper >= self.lower, "contrast upper must be >= lower."
assert self.lower >= 0, "contrast lower must be non-negative."
def __call__(self, image):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
image[:, :, 1] *= alpha
# print('RandomSaturation,alpha',alpha)
return image
class RandomHue(object):
def __init__(self, delta=18.0):
assert delta >= 0.0 and delta <= 360.0
self.delta = delta
def __call__(self, image):
if random.randint(2):
alpha = random.uniform(-self.delta, self.delta)
image[:, :, 0] += alpha
image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
# print('RandomHue,alpha:', alpha)
return image
class ConvertColor(object):
def __init__(self, current='BGR', transform='HSV'):
self.transform = transform
self.current = current
def __call__(self, image):
if self.current == 'BGR' and self.transform == 'HSV':
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
elif self.current == 'HSV' and self.transform == 'BGR':
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
else:
raise NotImplementedError
return image
class RandomSwapChannels(object):
def __call__(self, img):
if np.random.randint(2):
order = np.random.permutation(3)
return img[:,:,order]
return img
class RandomCrop(object):
def __init__(self, size):
self.size = size
def __call__(self, image):
h, w, _ = image.shape
new_w, new_h = scale_down((w, h), self.size)
if w == new_w:
x0 = 0
else:
x0 = random.randint(0, w - new_w)
if h == new_h:
y0 = 0
else:
y0 = random.randint(0, h - new_h)
out = fixed_crop(image, x0, y0, new_w, new_h, self.size)
return out
class RandomResizedCrop(object):
def __init__(self, size,scale=(0.49, 1.0), ratio=(1., 1.)):
self.size = size
self.scale = scale
self.ratio = ratio
def __call__(self,img):
if random.random() < 0.2:
return cv2.resize(img,self.size)
h, w, _ = img.shape
area = h * w
d=1
for attempt in range(10):
target_area = random.uniform(self.scale[0], self.scale[1]) * area
aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
new_w = int(round(math.sqrt(target_area * aspect_ratio)))
new_h = int(round(math.sqrt(target_area / aspect_ratio)))
if random.random() < 0.5:
new_h, new_w = new_w, new_h
if new_w < w and new_h < h:
x0 = random.randint(0, w - new_w)
y0 = (random.randint(0, h - new_h))//d
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
return out
# Fallback
return center_crop(img, self.size)
class DownCrop():
def __init__(self, size, select, scale=(0.36,0.81)):
self.size = size
self.scale = scale
self.select = select
def __call__(self,img, attr_idx):
if attr_idx not in self.select:
return img, attr_idx
if attr_idx == 0:
self.scale=(0.64,1.0)
h, w, _ = img.shape
area = h * w
s = (self.scale[0]+self.scale[1])/2.0
target_area = s * area
new_w = int(round(math.sqrt(target_area)))
new_h = int(round(math.sqrt(target_area)))
if new_w < w and new_h < h:
dw = w-new_w
x0 = int(0.5*dw)
y0 = h-new_h
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
return out, attr_idx
# Fallback
return center_crop(img, self.size), attr_idx
class ResizedCrop(object):
def __init__(self, size, select,scale=(0.64, 1.0), ratio=(3. / 4., 4. / 3.)):
self.size = size
self.scale = scale
self.ratio = ratio
self.select = select
def __call__(self,img, attr_idx):
if attr_idx not in self.select:
return img, attr_idx
h, w, _ = img.shape
area = h * w
d=1
if attr_idx == 2:
self.scale=(0.36,0.81)
d=2
if attr_idx == 0:
self.scale=(0.81,1.0)
target_area = (self.scale[0]+self.scale[1])/2.0 * area
# aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
new_w = int(round(math.sqrt(target_area)))
new_h = int(round(math.sqrt(target_area)))
# if random.random() < 0.5:
# new_h, new_w = new_w, new_h
if new_w < w and new_h < h:
x0 = (w - new_w)//2
y0 = (h - new_h)//d//2
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
# cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img)
# cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out)
#
# cv2.waitKey(0)
return out, attr_idx
# Fallback
return center_crop(img, self.size), attr_idx
class RandomHflip(object):
def __call__(self, image):
if random.randint(2):
return cv2.flip(image, 1)
else:
return image
class RandomVflip(object):
def __call__(self, image):
if random.randint(2):
return cv2.flip(image, 0)
else:
return image
class Hflip(object):
def __init__(self,doHflip):
self.doHflip = doHflip
def __call__(self, image):
if self.doHflip:
return cv2.flip(image, 1)
else:
return image
class CenterCrop(object):
def __init__(self, size):
self.size = size
def __call__(self, image):
return center_crop(image, self.size)
class UpperCrop():
def __init__(self, size, scale=(0.09, 0.64)):
self.size = size
self.scale = scale
def __call__(self,img):
h, w, _ = img.shape
area = h * w
s = (self.scale[0]+self.scale[1])/2.0
target_area = s * area
new_w = int(round(math.sqrt(target_area)))
new_h = int(round(math.sqrt(target_area)))
if new_w < w and new_h < h:
dw = w-new_w
x0 = int(0.5*dw)
y0 = 0
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
return out
# Fallback
return center_crop(img, self.size)
class RandomUpperCrop(object):
def __init__(self, size, select, scale=(0.09, 0.64), ratio=(3. / 4., 4. / 3.)):
self.size = size
self.scale = scale
self.ratio = ratio
self.select = select
def __call__(self,img, attr_idx):
if random.random() < 0.2:
return img, attr_idx
if attr_idx not in self.select:
return img, attr_idx
h, w, _ = img.shape
area = h * w
for attempt in range(10):
s = random.uniform(self.scale[0], self.scale[1])
d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])
target_area = s * area
aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
new_w = int(round(math.sqrt(target_area * aspect_ratio)))
new_h = int(round(math.sqrt(target_area / aspect_ratio)))
# new_w = int(round(math.sqrt(target_area)))
# new_h = int(round(math.sqrt(target_area)))
if new_w < w and new_h < h:
dw = w-new_w
x0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1)
y0 = (random.randint(0, h - new_h))//10
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
return out, attr_idx
# Fallback
return center_crop(img, self.size), attr_idx
class RandomDownCrop(object):
def __init__(self, size, select, scale=(0.36, 0.81), ratio=(3. / 4., 4. / 3.)):
self.size = size
self.scale = scale
self.ratio = ratio
self.select = select
def __call__(self,img, attr_idx):
if random.random() < 0.2:
return img, attr_idx
if attr_idx not in self.select:
return img, attr_idx
if attr_idx == 0:
self.scale=(0.64,1.0)
h, w, _ = img.shape
area = h * w
for attempt in range(10):
s = random.uniform(self.scale[0], self.scale[1])
d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])
target_area = s * area
aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
new_w = int(round(math.sqrt(target_area * aspect_ratio)))
new_h = int(round(math.sqrt(target_area / aspect_ratio)))
#
# new_w = int(round(math.sqrt(target_area)))
# new_h = int(round(math.sqrt(target_area)))
if new_w < w and new_h < h:
dw = w-new_w
x0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1)
y0 = (random.randint((h - new_h)*9//10, h - new_h))
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
# cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img)
# cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out)
#
# cv2.waitKey(0)
return out, attr_idx
# Fallback
return center_crop(img, self.size), attr_idx
class RandomHShift(object):
def __init__(self, select, scale=(0.0, 0.2)):
self.scale = scale
self.select = select
def __call__(self,img, attr_idx):
if attr_idx not in self.select:
return img, attr_idx
do_shift_crop = random.randint(0, 2)
if do_shift_crop:
h, w, _ = img.shape
min_shift = int(w*self.scale[0])
max_shift = int(w*self.scale[1])
shift_idx = random.randint(min_shift, max_shift)
direction = random.randint(0,2)
if direction:
right_part = img[:, -shift_idx:, :]
left_part = img[:, :-shift_idx, :]
else:
left_part = img[:, :shift_idx, :]
right_part = img[:, shift_idx:, :]
img = np.concatenate((right_part, left_part), axis=1)
# Fallback
return img, attr_idx
class RandomBottomCrop(object):
def __init__(self, size, select, scale=(0.4, 0.8)):
self.size = size
self.scale = scale
self.select = select
def __call__(self,img, attr_idx):
if attr_idx not in self.select:
return img, attr_idx
h, w, _ = img.shape
area = h * w
for attempt in range(10):
s = random.uniform(self.scale[0], self.scale[1])
d = 0.25 + (0.45 - 0.25) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])
target_area = s * area
new_w = int(round(math.sqrt(target_area)))
new_h = int(round(math.sqrt(target_area)))
if new_w < w and new_h < h:
dw = w-new_w
dh = h - new_h
x0 = random.randint(int((0.5-d)*dw), min(int((0.5+d)*dw)+1,dw))
y0 = (random.randint(max(0,int(0.8*dh)-1), dh))
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
return out, attr_idx
# Fallback
return bottom_crop(img, self.size), attr_idx
class BottomCrop():
def __init__(self, size, select, scale=(0.4, 0.8)):
self.size = size
self.scale = scale
self.select = select
def __call__(self,img, attr_idx):
if attr_idx not in self.select:
return img, attr_idx
h, w, _ = img.shape
area = h * w
s = (self.scale[0]+self.scale[1])/3.*2.
target_area = s * area
new_w = int(round(math.sqrt(target_area)))
new_h = int(round(math.sqrt(target_area)))
if new_w < w and new_h < h:
dw = w-new_w
dh = h-new_h
x0 = int(0.5*dw)
y0 = int(0.9*dh)
out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
return out, attr_idx
# Fallback
return bottom_crop(img, self.size), attr_idx
class Resize(object):
def __init__(self, size, inter=cv2.INTER_CUBIC):
self.size = size
self.inter = inter
def __call__(self, image):
return cv2.resize(image, (self.size[0], self.size[0]), interpolation=self.inter)
class ExpandBorder(object):
def __init__(self, mode='constant', value=255, size=(336,336), resize=False):
self.mode = mode
self.value = value
self.resize = resize
self.size = size
def __call__(self, image):
h, w, _ = image.shape
if h > w:
pad1 = (h-w)//2
pad2 = h - w - pad1
if self.mode == 'constant':
image = np.pad(image, ((0, 0), (pad1, pad2), (0, 0)),
self.mode, constant_values=self.value)
else:
image = np.pad(image,((0,0), (pad1, pad2),(0,0)), self.mode)
elif h < w:
pad1 = (w-h)//2
pad2 = w-h - pad1
if self.mode == 'constant':
image = np.pad(image, ((pad1, pad2),(0, 0), (0, 0)),
self.mode,constant_values=self.value)
else:
image = np.pad(image, ((pad1, pad2), (0, 0), (0, 0)),self.mode)
if self.resize:
image = cv2.resize(image, (self.size[0], self.size[0]),interpolation=cv2.INTER_LINEAR)
return image
class AstypeToInt():
def __call__(self, image, attr_idx):
return image.clip(0,255.0).astype(np.uint8), attr_idx
class AstypeToFloat():
def __call__(self, image, attr_idx):
return image.astype(np.float32), attr_idx
import matplotlib.pyplot as plt
class Normalize(object):
def __init__(self,mean, std):
'''
:param mean: RGB order
:param std: RGB order
'''
self.mean = np.array(mean).reshape(3,1,1)
self.std = np.array(std).reshape(3,1,1)
def __call__(self, image):
'''
:param image: (H,W,3) RGB
:return:
'''
# plt.figure(1)
# plt.imshow(image)
# plt.show()
return (image.transpose((2, 0, 1)) / 255. - self.mean) / self.std
class RandomErasing(object):
def __init__(self, select,EPSILON=0.5,sl=0.02, sh=0.09, r1=0.3, mean=[0.485, 0.456, 0.406]):
self.EPSILON = EPSILON
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1
self.select = select
def __call__(self, img,attr_idx):
if attr_idx not in self.select:
return img,attr_idx
if random.uniform(0, 1) > self.EPSILON:
return img,attr_idx
for attempt in range(100):
area = img.shape[1] * img.shape[2]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w <= img.shape[2] and h <= img.shape[1]:
x1 = random.randint(0, img.shape[1] - h)
y1 = random.randint(0, img.shape[2] - w)
if img.shape[0] == 3:
# img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
# img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
# img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
# img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w))
else:
img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
# img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w))
return img,attr_idx
return img,attr_idx
if __name__ == '__main__':
import matplotlib.pyplot as plt
class FSAug(object):
def __init__(self):
self.augment = Compose([
AstypeToFloat(),
# RandomHShift(scale=(0.,0.2),select=range(8)),
# RandomRotate(angles=(-20., 20.), bound=True),
ExpandBorder(select=range(8), mode='symmetric'),# symmetric
# Resize(size=(336, 336), select=[ 2, 7]),
AstypeToInt()
])
def __call__(self, spct,attr_idx):
return self.augment(spct,attr_idx)
trans = FSAug()
img_path = '/media/gserver/data/FashionAI/round2/train/Images/coat_length_labels/0b6b4a2146fc8616a19fcf2026d61d50.jpg'
img = cv2.cvtColor(cv2.imread(img_path),cv2.COLOR_BGR2RGB)
img_trans,_ = trans(img,5)
# img_trans2,_ = trans(img,6)
plt.figure()
plt.subplot(221)
plt.imshow(img)
plt.subplot(222)
plt.imshow(img_trans)
# plt.subplot(223)
# plt.imshow(img_trans2)
# plt.imshow(img_trans2)
plt.show()方式二： 用于线下增强数据，采用的方法是
- 高斯噪声
- 亮度变化
- 左右翻转
- 上下翻转
- 色彩抖动
- 对化
- 锐度变化
from PIL import Image,ImageEnhance,ImageFilter,ImageOps import os import shutil import numpy as np import cv2 import random from skimage.util import random_noise from skimage import exposure image_number = 0 raw_path = "./data/train/" new_path = "./aug/train/" # 加高斯噪声 def addNoise(img): ''' 注意：输出的像素是[0,1]之间,所以乘以5得到[0,255]之间 ''' return random_noise(img, mode='gaussian', seed=13, clip=True)*255 def changeLight(img): rate = random.uniform(0.5, 1.5) # print(rate) img = exposure.adjust_gamma(img, rate) #大于1为调暗，小于1为调亮;1.05 return img try: for i in range(59): os.makedirs(new_path + os.sep + str(i)) except: pass for raw_dir_name in range(59): raw_dir_name = str(raw_dir_name) saved_image_path = new_path + raw_dir_name+"/" raw_image_path = raw_path + raw_dir_name+"/" if not os.path.exists(saved_image_path): os.mkdir(saved_image_path) raw_image_file_name = os.listdir(raw_image_path) raw_image_file_path = [] for i in raw_image_file_name: raw_image_file_path.append(raw_image_path+i) for x in raw_image_file_path: img = Image.open(x) cv_image = cv2.imread(x) # 高斯噪声 gau_image = addNoise(cv_image) # 随机改变 light = changeLight(cv_image) light_and_gau = addNoise(light) cv2.imwrite(saved_image_path + "gau_" + os.path.basename(x),gau_image) cv2.imwrite(saved_image_path + "light_" + os.path.basename(x),light) cv2.imwrite(saved_image_path + "gau_light" + os.path.basename(x),light_and_gau) #img = img.resize((800,600)) #1.翻转 img_flip_left_right = img.transpose(Image.FLIP_LEFT_RIGHT) img_flip_top_bottom = img.transpose(Image.FLIP_TOP_BOTTOM) #2.旋转 #img_rotate_90 = img.transpose(Image.ROTATE_90) #img_rotate_180 = img.transpose(Image.ROTATE_180) #img_rotate_270 = img.transpose(Image.ROTATE_270) #img_rotate_90_left = img_flip_left_right.transpose(Image.ROTATE_90) #img_rotate_270_left = img_flip_left_right.transpose(Image.ROTATE_270) #3.亮度 #enh_bri = ImageEnhance.Brightness(img) #brightness = 1.5 #image_brightened = enh_bri.enhance(brightness) #4.色彩 #enh_col = ImageEnhance.Color(img) #color = 1.5 #image_colored = enh_col.enhance(color) #5.对比度 enh_con = ImageEnhance.Contrast(img) contrast = 1.5 image_contrasted = enh_con.enhance(contrast) #6.锐度 #enh_sha = ImageEnhance.Sharpness(img) #sharpness = 3.0 #image_sharped = enh_sha.enhance(sharpness) #保存 img.save(saved_image_path + os.path.basename(x)) img_flip_left_right.save(saved_image_path + "left_right_" + os.path.basename(x)) img_flip_top_bottom.save(saved_image_path + "top_bottom_" + os.path.basename(x)) #img_rotate_90.save(saved_image_path + "rotate_90_" + os.path.basename(x)) #img_rotate_180.save(saved_image_path + "rotate_180_" + os.path.basename(x)) #img_rotate_270.save(saved_image_path + "rotate_270_" + os.path.basename(x)) #img_rotate_90_left.save(saved_image_path + "rotate_90_left_" + os.path.basename(x)) #img_rotate_270_left.save(saved_image_path + "rotate_270_left_" + os.path.basename(x)) #image_brightened.save(saved_image_path + "brighted_" + os.path.basename(x)) #image_colored.save(saved_image_path + "colored_" + os.path.basename(x)) image_contrasted.save(saved_image_path + "contrasted_" + os.path.basename(x)) #image_sharped.save(saved_image_path + "sharped_" + os.path.basename(x)) image_number += 1 print("convert pictur" "es :%s size:%s mode:%s" % (image_number, img.size, img.mode))加载数据的类（自定义继承）
- 与pytorch中的加载数据类差不多，只是多了自己的某些功能。
-
from torch.utils.data import Dataset from torchvision import transforms as T from config import config from PIL import Image from itertools import chain from glob import glob from tqdm import tqdm import random import numpy as np import pandas as pd import os import cv2 import torch #1.set random seed random.seed(config.seed) np.random.seed(config.seed) torch.manual_seed(config.seed) torch.cuda.manual_seed_all(config.seed) #2.define dataset class ZiyiDataset(Dataset): def __init__(self,label_list,transforms=None,train=True,test=False): self.test = test self.train = train imgs = [] if self.test: for index,row in label_list.iterrows(): imgs.append((row["filename"])) self.imgs = imgs else: for index,row in label_list.iterrows(): imgs.append((row["filename"],row["label"])) self.imgs = imgs if transforms is None: if self.test or not train: self.transforms = T.Compose([ T.Resize((config.img_weight,config.img_height)), T.ToTensor(), T.Normalize(mean = [0.485,0.456,0.406], std = [0.229,0.224,0.225])]) else: self.transforms = T.Compose([ T.Resize((config.img_weight,config.img_height)), T.RandomRotation(30), T.RandomHorizontalFlip(), T.RandomVerticalFlip(), T.RandomAffine(45), T.ToTensor(), T.Normalize(mean = [0.485,0.456,0.406], std = [0.229,0.224,0.225])]) else: self.transforms = transforms def __getitem__(self,index): if self.test: filename = self.imgs[index] img = Image.open(filename) img = self.transforms(img) return img,filename else: filename,label = self.imgs[index] img = Image.open(filename) img = self.transforms(img) return img,label def __len__(self): return len(self.imgs) def collate_fn(batch): imgs = [] label = [] for sample in batch: imgs.append(sample[0]) label.append(sample[1]) return torch.stack(imgs, 0), \ label def get_files(root,mode): #for test if mode == "test": files = [] for img in os.listdir(root): files.append(root + img) files = pd.DataFrame({"filename":files}) return files elif mode != "test": #for train and val all_data_path,labels = [],[] image_folders = list(map(lambda x:root+x,os.listdir(root))) jpg_image_1 = list(map(lambda x:glob(x+"/*.jpg"),image_folders)) jpg_image_2 = list(map(lambda x:glob(x+"/*.JPG"),image_folders)) all_images = list(chain.from_iterable(jpg_image_1 + jpg_image_2)) print("loading train dataset") for file in tqdm(all_images): all_data_path.append(file) labels.append(int(file.split("/")[-2])) all_files = pd.DataFrame({"filename":all_data_path,"label":labels}) return all_files else: print("check the mode please!")3.获取模型
- 获取模型较为简单，单一模型采取pytorch中的预训练模型，添加所需要的层，进行微调然后迁移学习新数据。
import torchvision import torch.nn.functional as F from torch import nn from config import config def generate_model(): class DenseModel(nn.Module): def __init__(self, pretrained_model): super(DenseModel, self).__init__() self.classifier = nn.Linear(pretrained_model.classifier.in_features, config.num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal(m.weight) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.bias.data.zero_() self.features = pretrained_model.features self.layer1 = pretrained_model.features._modules['denseblock1'] self.layer2 = pretrained_model.features._modules['denseblock2'] self.layer3 = pretrained_model.features._modules['denseblock3'] self.layer4 = pretrained_model.features._modules['denseblock4'] def forward(self, x): features = self.features(x) out = F.relu(features, inplace=True) out = F.avg_pool2d(out, kernel_size=8).view(features.size(0), -1) out = F.sigmoid(self.classifier(out)) return out return DenseModel(torchvision.models.densenet169(pretrained=True)) def get_net(): #return MyModel(torchvision.models.resnet101(pretrained = True)) model = torchvision.models.resnet50(pretrained = True) #for param in model.parameters(): # param.requires_grad = False # pytorch添加层的方式直接在Model.层名=层具体形式 model.avgpool = nn.AdaptiveAvgPool2d(1) model.fc = nn.Linear(2048,config.num_classes) #添加全连接层以作分类任务，num_classes为分类个数 return model4.开始训练
-
import os import random import time import json import torch import torchvision import numpy as np import pandas as pd import warnings from datetime import datetime from torch import nn,optim from config import config from collections import OrderedDict from torch.autograd import Variable from torch.utils.data import DataLoader from dataset.dataloader import * from sklearn.model_selection import train_test_split,StratifiedKFold from timeit import default_timer as timer from models.model import * from utils import * #1. 设置随机种子 and cudnn performance random.seed(config.seed) np.random.seed(config.seed) torch.manual_seed(config.seed) torch.cuda.manual_seed_all(config.seed) os.environ["CUDA_VISIBLE_DEVICES"] = config.gpus torch.backends.cudnn.benchmark = True warnings.filterwarnings('ignore') #2. 评估函数，通过Losses，topk的不断更新来评估模型 def evaluate(val_loader,model,criterion): #2.1 AverageMeter类是Computes and stores the average and current value # 创建三个其对象，以用于评估 losses = AverageMeter() top1 = AverageMeter() top2 = AverageMeter() #2.2 开启评估模式 and confirm model has been transfered to cuda model.cuda() model.eval() with torch.no_grad(): for i,(input,target) in enumerate(val_loader): input = Variable(input).cuda() target = Variable(torch.from_numpy(np.array(target)).long()).cuda() #target = Variable(target).cuda() #2.2.1 compute output output = model(input) loss = criterion(output,target) #2.2.2 measure accuracy and record loss precision1,precision2 = accuracy(output,target,topk=(1,2)) losses.update(loss.item(),input.size(0)) top1.update(precision1[0],input.size(0)) top2.update(precision2[0],input.size(0)) return [losses.avg,top1.avg,top2.avg] #3. test model on public dataset and save the probability matrix def test(test_loader,model,folds): #3.1 confirm the model converted to cuda # 得出的结果是概率，再用softmax得出最终分类结果 csv_map = OrderedDict({"filename":[],"probability":[]}) model.cuda() model.eval() with open("./submit/baseline.json","w",encoding="utf-8") as f : submit_results = [] for i,(input,filepath) in enumerate(tqdm(test_loader)): # filepath?????? # 通过模型得到输出概率结果，再用softmax得出预测结果，写入文件。 #3.2 change everything to cuda and get only basename filepath = [os.path.basename(x) for x in filepath] with torch.no_grad(): image_var = Variable(input).cuda() #3.3.output #print(filepath) #print(input,input.shape) y_pred = model(image_var) #print(y_pred.shape) smax = nn.Softmax(1) smax_out = smax(y_pred) #3.4 save probability to csv files csv_map["filename"].extend(filepath) for output in smax_out: prob = ";".join([str(i) for i in output.data.tolist()]) csv_map["probability"].append(prob) result = pd.DataFrame(csv_map) result["probability"] = result["probability"].map(lambda x : [float(i) for i in x.split(";")]) for index, row in result.iterrows(): # 因为44，45类删除，所以预测结果加2 pred_label = np.argmax(row['probability']) if pred_label > 43: pred_label = pred_label + 2 submit_results.append({"image_id":row['filename'],"disease_class":pred_label}) json.dump(submit_results,f,ensure_ascii=False,cls = MyEncoder) #4. more details to build main function def main(): fold = 0 #4.1 mkdirs if not os.path.exists(config.submit): os.mkdir(config.submit) if not os.path.exists(config.weights): os.mkdir(config.weights) if not os.path.exists(config.best_models): os.mkdir(config.best_models) if not os.path.exists(config.logs): os.mkdir(config.logs) if not os.path.exists(config.weights + config.model_name + os.sep +str(fold) + os.sep): os.makedirs(config.weights + config.model_name + os.sep +str(fold) + os.sep) if not os.path.exists(config.best_models + config.model_name + os.sep +str(fold) + os.sep): os.makedirs(config.best_models + config.model_name + os.sep +str(fold) + os.sep) #4.2 get model and optimizer model = get_net() #model = torch.nn.DataParallel(model) model.cuda() #optimizer = optim.SGD(model.parameters(),lr = config.lr,momentum=0.9,weight_decay=config.weight_decay) optimizer = optim.Adam(model.parameters(),lr = config.lr,amsgrad=True,weight_decay=config.weight_decay) criterion = nn.CrossEntropyLoss().cuda() #criterion = FocalLoss().cuda() log = Logger() log.open(config.logs + "log_train.txt",mode="a") log.write("\n----------------------------------------------- [START %s] %s\n\n" % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), '-' * 51)) #4.3 some parameters for K-fold and restart model start_epoch = 0 best_precision1 = 0 best_precision_save = 0 resume = False #4.4 restart the training process if resume: checkpoint = torch.load(config.best_models + str(fold) + "/model_best.pth.tar") start_epoch = checkpoint["epoch"] fold = checkpoint["fold"] best_precision1 = checkpoint["best_precision1"] model.load_state_dict(checkpoint["state_dict"]) optimizer.load_state_dict(checkpoint["optimizer"]) #4.5 get files and split for K-fold dataset #4.5.1 read files train_ = get_files(config.train_data,"train") #val_data_list = get_files(config.val_data,"val") test_files = get_files(config.test_data,"test") """ #4.5.2 split split_fold = StratifiedKFold(n_splits=3) folds_indexes = split_fold.split(X=origin_files["filename"],y=origin_files["label"]) folds_indexes = np.array(list(folds_indexes)) fold_index = folds_indexes[fold] #4.5.3 using fold index to split for train data and val data train_data_list = pd.concat([origin_files["filename"][fold_index[0]],origin_files["label"][fold_index[0]]],axis=1) val_data_list = pd.concat([origin_files["filename"][fold_index[1]],origin_files["label"][fold_index[1]]],axis=1) """ train_data_list,val_data_list = train_test_split(train_,test_size = 0.15,stratify=train_["label"]) #4.5.4 load dataset train_dataloader = DataLoader(ZiyiDataset(train_data_list),batch_size=config.batch_size,shuffle=True,collate_fn=collate_fn,pin_memory=True) val_dataloader = DataLoader(ZiyiDataset(val_data_list,train=False),batch_size=config.batch_size,shuffle=True,collate_fn=collate_fn,pin_memory=False) test_dataloader = DataLoader(ZiyiDataset(test_files,test=True),batch_size=1,shuffle=False,pin_memory=False) #scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,"max",verbose=1,patience=3) scheduler = optim.lr_scheduler.StepLR(optimizer,step_size = 10,gamma=0.1) # optim.lr_scheduler 提供了基于多种epoch数目调整学习率的方法 # step_size(整数类型): 调整学习率的步长,每过step_size次,更新一次学习率 # gamma(float 类型):学习率下降的乘数因子 #4.5.5.1 define metrics train_losses = AverageMeter() train_top1 = AverageMeter() train_top2 = AverageMeter() valid_loss = [np.inf,0,0] model.train() #logs log.write('** start training here! **\n') log.write(' |------------ VALID -------------|----------- TRAIN -------------|------Accuracy------|------------|\n') log.write('lr iter epoch | loss top-1 top-2 | loss top-1 top-2 | Current Best | time |\n') log.write('-------------------------------------------------------------------------------------------------------------------------------\n') #4.5.5 train start = timer() for epoch in range(start_epoch,config.epochs): # 一个epoch为所有数据迭代一次进入模型拟合的过程，其中又分为batch_size来分批次进行 scheduler.step(epoch) # train #global iter for iter,(input,target) in enumerate(train_dataloader): #4.5.5 switch to continue train process model.train() input = Variable(input).cuda() target = Variable(torch.from_numpy(np.array(target)).long()).cuda() #target = Variable(target).cuda() output = model(input) loss = criterion(output,target) precision1_train,precision2_train = accuracy(output,target,topk=(1,2)) train_losses.update(loss.item(),input.size(0)) train_top1.update(precision1_train[0],input.size(0)) train_top2.update(precision2_train[0],input.size(0)) #backward optimizer.zero_grad() loss.backward() optimizer.step() lr = get_learning_rate(optimizer) print('\r',end='',flush=True) print('%0.4f %5.1f %6.1f | %0.3f %0.3f %0.3f | %0.3f %0.3f %0.3f | %s | %s' % (\ lr, iter/len(train_dataloader) + epoch, epoch, valid_loss[0], valid_loss[1], valid_loss[2], train_losses.avg, train_top1.avg, train_top2.avg,str(best_precision_save), time_to_str((timer() - start),'min')) , end='',flush=True) #evaluate lr = get_learning_rate(optimizer) #evaluate every half epoch valid_loss = evaluate(val_dataloader,model,criterion) is_best = valid_loss[1] > best_precision1 best_precision1 = max(valid_loss[1],best_precision1) try: best_precision_save = best_precision1.cpu().data.numpy() except: pass save_checkpoint({ "epoch":epoch + 1, "model_name":config.model_name, "state_dict":model.state_dict(), "best_precision1":best_precision1, "optimizer":optimizer.state_dict(), "fold":fold, "valid_loss":valid_loss, },is_best,fold) #adjust learning rate #scheduler.step(valid_loss[1]) print("\r",end="",flush=True) log.write('%0.4f %5.1f %6.1f | %0.3f %0.3f %0.3f | %0.3f %0.3f %0.3f | %s | %s' % (\ lr, 0 + epoch, epoch, valid_loss[0], valid_loss[1], valid_loss[2], train_losses.avg, train_top1.avg, train_top2.avg, str(best_precision_save), time_to_str((timer() - start),'min')) ) log.write('\n') time.sleep(0.01) best_model = torch.load(config.best_models + os.sep+config.model_name+os.sep+ str(fold) +os.sep+ 'model_best.pth.tar') model.load_state_dict(best_model["state_dict"]) test(test_dataloader,model,fold) if __name__ =="__main__": main()