小编给大家分享一下Python卷积神经网络图片分类框架的示例分析，希望大家阅读完这篇文章之后都有所收获，下面让我们一起去探讨吧！
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 = 320data
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")看完了这篇文章，相信你对“Python卷积神经网络图片分类框架的示例分析”有了一定的了解，如果想了解更多相关知识，欢迎关注编程网行业资讯频道，感谢各位的阅读！
