Python卷积神经网络图片分类框架详解分析
程序员文章站
2022-06-23 19:39:54
【人工智能项目】卷积神经网络图片分类框架本次硬核分享当时做图片分类的工作，主要是整理了一个图片分类的框架，如果想换模型，引入新模型，在config中修改即可。那么走起来瓷！！！整体结构config在c...
【人工智能项目】卷积神经网络图片分类框架
本次硬核分享当时做图片分类的工作，主要是整理了一个图片分类的框架，如果想换模型，引入新模型，在config中修改即可。那么走起来瓷！！！
整体结构
config
在config文件夹下的config.py中主要定义数据集的位置，训练轮数，batch_size以及本次选用的模型。
# 定义训练集和测试集的路径 train_data_path = "./data/train/" train_anno_path = "./data/train.csv" test_data_path = "./data/test/" # 定义多线程 num_workers = 8 # 定义batch_size大小 batch_size = 8 # 定义训练轮数 epochs = 20 # 定义k折交叉验证 k = 5 # 定义模型选择 # inception_v3_google inceptionv4 # vgg16 # resnet50 resnet101 resnet152 resnext50_32x4d resnext101_32x8d wide_resnet50_2 wide_resnet101_2 # senet154 se_resnet50 se_resnet101 se_resnet152 se_resnext50_32x4d se_resnext101_32x4d # nasnetalarge pnasnet5large # densenet121 densenet161 densenet169 densenet201 # efficientnet-b0 efficientnet-b1 efficientnet-b2 efficientnet-b3 efficientnet-b4 efficientnet-b5 efficientnet-b6 efficientnet-b7 # xception # squeezenet1_0 squeezenet1_1 # mobilenet_v2 # mnasnet0_5 mnasnet0_75 mnasnet1_0 mnasnet1_3 # shufflenet_v2_x0_5 shufflenet_v2_x1_0 model_name = "vgg16" # 定义分类类别 num_classes = 102 # 定义图片尺寸 img_width = 320 img_height = 320
data
data文件夹存放了train和test图片信息。
在train.csv中的存放图片名称以及对应的标签
dataloader
dataloader里面主要有data.py和data_augmentation.py文件。其中一个用于读取数据，另外一个用于数据增强操作。
import torch
from pil import image
from torch.utils.data.dataset import dataset
import numpy as np
import pil
from torchvision import transforms
from config import config
import os
import cv2
# 定义dataset和transform
# 将df转换成标准的numpy array形式
def get_anno(path, images_path):
data = []
with open(path) as f:
for line in f:
idx, label = line.strip().split(',')
data.append((os.path.join(images_path, idx), int(label)))
return np.array(data)
# 定义读取traindata，读取df文件
# 通过df的idx，来获取image_path和label
class traindataset(dataset):
def __init__(self, data, transform=none):
self.data = data
self.transform = transform
def __getitem__(self, idx):
img_path, label = self.data[idx]
img = image.open(img_path).convert('rgb')
#img = cv2.imread(img_path)
#img = cv2.cvtcolor(img, cv2.color_bgr2rgb)
if self.transform is not none:
img = self.transform(img)
return img, int(label)
def __len__(self):
return len(self.data)
# 通过文件路径来读取测试图片
class testdataset(dataset):
def __init__(self, img_path, transform=none):
self.img_path = img_path
if transform is not none:
self.transform = transform
else:
self.transform = none
def __getitem__(self, index):
img = image.open(self.img_path[index]).convert('rgb')
# img = cv2.imread(self.img_path[index])
# img = cv2.cvtcolor(img, cv2.color_bgr2rgb)
if self.transform is not none:
img = self.transform(img)
return img
def __len__(self):
return len(self.img_path)
# train_transform = transforms.compose([
# transforms.resize([config.img_width, config.img_height]),
# transforms.randomrotation(10),
# transforms.colorjitter(brightness=0.3, contrast=0.2),
# transforms.randomhorizontalflip(),
# transforms.totensor(), # range [0, 255] -> [0.0,1.0]
# transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
# ])
train_transform = transforms.compose([
transforms.pad(4, padding_mode='reflect'),
transforms.randomrotation(10),
transforms.randomresizedcrop([config.img_width, config.img_height]),
transforms.randomhorizontalflip(),
transforms.totensor(),
transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
val_transform = transforms.compose([
transforms.randomresizedcrop([config.img_width, config.img_height]),
transforms.totensor(),
transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_transform = transforms.compose([
transforms.randomresizedcrop([config.img_width, config.img_height]),
transforms.totensor(),
transforms.normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
import random
from __future__ import division
import cv2
import numpy as np
from numpy import random
import math
from sklearn.utils import shuffle
# 固定角度随机旋转
class fixedrotation(object):
def __init__(self, angles):
self.angles = angles
def __call__(self, img):
return fixed_rotate(img, self.angles)
def fixed_rotate(img, angles):
angles = list(angles)
angles_num = len(angles)
index = random.randint(0, angles_num - 1)
return img.rotate(angles[index])
__all__ = ['compose','randomhflip', 'randomuppercrop', 'resize', 'uppercrop', 'randombottomcrop',"randomerasing",
'bottomcrop', 'normalize', 'randomswapchannels', 'randomrotate', 'randomhshift',"centercrop","randomvflip",
'expandborder', 'randomresizedcrop','randomdowncrop', 'downcrop', 'resizedcrop',"fixrandomrotate"]
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
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
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)
# print img_trans.max(), img_trans.min()
# print img_trans.dtype
#
# 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()
factory
factory里面主要定义了一些学习率，损失函数，优化器等之类的。
models
models中主要定义了常见的分类模型。
train.py
import os
from sklearn.model_selection import kfold
from torchvision import transforms
import torch.utils.data
from dataloader.data import traindataset,train_transform,val_transform,get_anno
from factory.loss import *
from models.model import model
from config import config
import numpy as np
from utils import utils
from factory.labelsmoothing import lsr
def train(model_type, prefix):
# df -> numpy.array()形式
data = get_anno(config.train_anno_path, config.train_data_path)
# 5折交叉验证
skf = kfold(n_splits=config.k, random_state=233, shuffle=true)
for flod_idx, (train_indices, val_indices) in enumerate(skf.split(data)):
train_loader = torch.utils.data.dataloader(
traindataset(data[train_indices],
train_transform),
batch_size=config.batch_size, shuffle=true, num_workers=config.num_workers, pin_memory=true
)
val_loader = torch.utils.data.dataloader(
traindataset(data[val_indices],
val_transform),
batch_size=config.batch_size, shuffle=false, num_workers=config.num_workers, pin_memory=true
)
#criterion = focalloss(0.5)
criterion = lsr()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model(model_type, config.num_classes, criterion, device=device, prefix=prefix, suffix=str(flod_idx))
for epoch in range(config.epochs):
print('epoch: ', epoch)
model.fit(train_loader)
model.validate(val_loader)
if __name__ == '__main__':
model_type_list = [config.model_name]
for model_type in model_type_list:
train(model_type, "resize")
小结
本次主要给出一个图片分类的框架，方便快速的切换模型。
那下回见！！！欢迎大家多多点赞评论呀！！！
到此这篇关于python卷积神经网络图片分类框架详解分析的文章就介绍到这了,更多相关python 卷积神经网络内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持！