Shulou(Shulou.com)06/02 Report--

Editor to share with you the example analysis of the Python convolution neural network picture classification framework. I hope you will get something after reading this article. Let's discuss it together.

Whole structure

Config

The config.py under the config folder mainly defines the location of the dataset, the number of training wheels, batch_size and the selected model.

# define the path to the training set and test set train_data_path = ". / data/train/" train_anno_path = ". / data/train.csv" test_data_path = ". / data/test/" # define multithreaded num_workers = define batch_size size batch_size = define the number of training wheels epochs = 2 define k-fold cross-validation k = define model selection # 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" # define category num_classes = 10cm define picture size img_width = 320img_height = 320data

The data folder holds train and test image information.

The name of the image stored in train.csv and the corresponding tag

Dataloader

There are mainly data.py and data_augmentation.py files in dataloader. One of them is used to read data, and the other is used for data enhancement operations.

Import torchfrom PIL import Imagefrom torch.utils.data.dataset import Datasetimport numpy as npimport PILfrom torchvision import transformsfrom configimport configimport osimport cv2# defines DataSet and Transform# to convert df into the standard numpy array form 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) # defines the read trainData Read df file # idx through df To get image_path and 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) # read the test image class testDataset (Dataset): def _ init__ (self, img_path) through the file 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]-> [0.0je 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# fixed angle random rotation 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 (anglesindex) _ 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 = 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 (0.0255.0) 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]-= 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 = (hmurw) / / 2 pad2 = hmurw-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) ((0Jing 0), (pad1, pad2), (0B0)), 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: 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

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