Python卷积神经网络图片分类框架的示例分析

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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_0model_name = "vgg16"# 定义分类类别num_classes = 102# 定义图片尺寸img_width = 320img_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 torchfrom PIL import Imagefrom torch.utils.data.dataset import Datasetimport numpy as npimport PILfrom torchvision import transformsfrom config import configimport  osimport 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labelclass 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 randomfrom __future__ import divisionimport cv2import numpy as npfrom numpy import randomimport mathfrom 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 rotateddef 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 outclass 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 imgdef 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 outdef 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 outdef 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 rotatedclass Compose(object):    def __init__(self, transforms):        self.transforms = transforms    def __call__(self, img):        for t in self.transforms:            img = t(img)        return imgclass 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 imgclass 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 imageclass 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 imageclass 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 imageclass 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 imageclass 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 imageclass RandomSwapChannels(object):    def __call__(self, img):        if np.random.randint(2):            order = np.random.permutation(3)            return img[:,:,order]        return imgclass 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 outclass 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_idxclass 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_idxclass RandomHflip(object):    def __call__(self, image):        if random.randint(2):            return cv2.flip(image, 1)        else:            return imageclass RandomVflip(object):    def __call__(self, image):        if random.randint(2):            return cv2.flip(image, 0)        else:            return imageclass Hflip(object):    def __init__(self,doHflip):        self.doHflip = doHflip    def __call__(self, image):        if self.doHflip:            return cv2.flip(image, 1)        else:            return imageclass 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_idxclass 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_idxclass 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_idxclass 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_idxclass 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_idxclass 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 imageclass AstypeToInt():    def __call__(self, image, attr_idx):        return image.clip(0,255.0).astype(np.uint8), attr_idxclass AstypeToFloat():    def __call__(self, image, attr_idx):        return image.astype(np.float32), attr_idximport matplotlib.pyplot as pltclass 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.stdclass 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 osfrom sklearn.model_selection import KFoldfrom torchvision import transformsimport torch.utils.datafrom dataloader.data import trainDataset,train_transform,val_transform,get_annofrom factory.loss import *from models.model import Modelfrom config import configimport numpy as npfrom utils import utilsfrom factory.LabelSmoothing import LSRdef 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")

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