本文将利用 TorchVision Faster R-CNN 预训练模型，于 Kaggle: 寰球小麦检测 ???? 上实际迁徙学习中的一种罕用技术：微调（fine tuning）。
本文相干的 Kaggle Notebooks 可见：
- TorchVision Faster R-CNN Finetuning
- TorchVision Faster R-CNN Inference
如果你没有 GPU ，也可于 Kaggle 上在线训练。应用介绍：
- Use Kaggle Notebooks
那么，咱们开始吧 ????
筹备数据
import osimport numpy as npimport pandas as pdimport seaborn as snsfrom matplotlib import pyplot as pltfrom PIL import Image下载数据
Kaggle: 寰球小麦检测 Data 页下载数据，内容如下：
- train.csv - the training data
- sample_submission.csv - a sample submission file in the correct format
- train.zip - training images
- test.zip - test images
DIR_INPUT = 'global-wheat-detection'DIR_TRAIN = f'{DIR_INPUT}/train'DIR_TEST = f'{DIR_INPUT}/test'读取数据
读取 train.csv 内容：
train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')train_df.head()- image_id - the unique image ID
- width, height - the width and height of the images
- bbox - a bounding box, formatted as a Python-style list of [xmin, ymin, width, height]
- etc.
把 bbox 替换成 x y w h：
train_df[['x','y','w','h']] = 0train_df[['x','y','w','h']] = np.stack(train_df['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=','))).astype(np.float)train_df.drop(columns=['bbox'], inplace=True)train_df.head()剖析数据
训练数据大小：
train_df.shape(147793, 8)
惟一 image_id 数量：
train_df['image_id'].nunique()3373
train 目录下图片数量：
len(os.listdir(DIR_TRAIN))3423
阐明有 3422-3373=49 张图片没有标注。
训练数据，图片大小：
train_df['width'].unique(), train_df['height'].unique()(array([1024]), array([1024]))
都是 1024x1024 的。
查看标注数量的散布状况：
counts = train_df['image_id'].value_counts()print(f'number of boxes, range [{min(counts)}, {max(counts)}]')sns.displot(counts, kde=False)plt.xlabel('boxes')plt.ylabel('images')plt.title('boxes vs. images')plt.show()number of boxes, range [1, 116]
一张图最多的有 116 个标注。
查看标注坐标和宽高的散布状况：
train_df['cx'] = train_df['x'] + train_df['w'] / 2train_df['cy'] = train_df['y'] + train_df['h'] / 2ax = plt.subplots(1, 4, figsize=(16, 4), tight_layout=True)[1].ravel()ax[0].set_title('x vs. y')ax[0].set_xlim(0, 1024)ax[0].set_ylim(0, 1024)ax[1].set_title('cx vs. cy')ax[1].set_xlim(0, 1024)ax[1].set_ylim(0, 1024)ax[2].set_title('w vs. h')ax[3].set_title('area size')sns.histplot(data=train_df, x='x', y='y', ax=ax[0], bins=50, pmax=0.9)sns.histplot(data=train_df, x='cx', y='cy', ax=ax[1], bins=50, pmax=0.9)sns.histplot(data=train_df, x='w', y='h', ax=ax[2], bins=50, pmax=0.9)sns.histplot(train_df['w'] * train_df['h'], ax=ax[3], bins=50, kde=False)plt.show()把数据集分为训练集和验证集，比例 8:2：
image_ids = train_df['image_id'].unique()split_len = round(len(image_ids)*0.8)train_ids = image_ids[:split_len]valid_ids = image_ids[split_len:]train = train_df[train_df['image_id'].isin(train_ids)]valid = train_df[train_df['image_id'].isin(valid_ids)]train.shape, valid.shape((122577, 10), (25216, 10))
预览数据
定义下辅助函数：
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): figsize = (num_cols * scale, num_rows * scale) _, axes = plt.subplots(num_rows, num_cols, figsize=figsize) axes = axes.flatten() for i, (ax, img) in enumerate(zip(axes, imgs)): ax.imshow(img) ax.axes.get_xaxis().set_visible(False) ax.axes.get_yaxis().set_visible(False) if titles and len(titles) > i: ax.set_title(titles[i]) return axesdef show_bboxes(axes, bboxes, labels=None, colors=None): def _make_list(obj, default_values=None): if obj is None: obj = default_values elif not isinstance(obj, (list, tuple)): obj = [obj] return obj labels = _make_list(labels) colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c']) for i, bbox in enumerate(bboxes): color = colors[i % len(colors)] rect = plt.Rectangle( xy=(bbox[0], bbox[1]), width=bbox[2] - bbox[0], height=bbox[3] - bbox[1], fill=False, edgecolor=color, linewidth=2) axes.add_patch(rect) if labels and len(labels) > i: text_color = 'k' if color == 'w' else 'w' axes.text(rect.xy[0], rect.xy[1], labels[i], va='center', ha='center', fontsize=9, color=text_color, bbox=dict(facecolor=color, lw=0))# https://github.com/d2l-ai/d2l-en/blob/master/d2l/torch.py预览图片，不加标注：
num_rows, num_cols = 2, 4imgs = [plt.imread(f'{DIR_TRAIN}/{n}.jpg') for n in train_df['image_id'].unique()[:num_rows*num_cols]]show_images(imgs, num_rows, num_cols, scale=4)plt.show()预览图片，加上标注：
num_rows, num_cols = 1, 2ids = train_df['image_id'].unique()[:num_rows*num_cols]imgs = [plt.imread(f'{DIR_TRAIN}/{n}.jpg') for n in ids]axes = show_images(imgs, num_rows, num_cols, scale=8)for ax, id in zip(axes, ids): datas = train_df[train_df['image_id'] == id] bboxes = [(d['x'], d['y'], d['x']+d['w'], d['y']+d['h']) for _, d in datas.iterrows()] show_bboxes(ax, bboxes, labels=None, colors=['w'])plt.show()创立 Dataset
继承 torch.utils.data.Dataset 抽象类，实现 __len__ __getitem__ 。且 __getitem__ 返回数据为：
- image: a
numpy.ndarrayimage target: a dict containing the following fields
boxes(FloatTensor[N, 4]): the coordinates of theNbounding boxes in[x0, y0, x1, y1]format, ranging from0toWand0toHlabels(Int64Tensor[N]): the label for each bounding boximage_id(Int64Tensor[1]): an image identifier. It should be unique between all the images in the dataset, and is used during evaluationarea(Tensor[N]): The area of the bounding box. This is used during evaluation with the COCO metric, to separate the metric scores between small, medium and large boxes.iscrowd(UInt8Tensor[N]): instances withiscrowd=Truewill be ignored during evaluation.
import cv2 as cvimport numpy as npimport albumentations as Afrom albumentations.pytorch.transforms import ToTensorV2import torchfrom torch.utils.data import Datasetclass Wheat(Dataset): def __init__(self, dataframe, image_dir, transforms=None): super().__init__() self.image_ids = dataframe['image_id'].unique() self.df = dataframe self.image_dir = image_dir self.transforms = transforms def __getitem__(self, idx: int): image_id = self.image_ids[idx] records = self.df[self.df['image_id'] == image_id] image = cv.imread(f'{self.image_dir}/{image_id}.jpg', cv.IMREAD_COLOR) image = cv.cvtColor(image, cv.COLOR_BGR2RGB).astype(np.float32) image /= 255.0 boxes = records[['x', 'y', 'w', 'h']].values area = boxes[:, 2] * boxes[:, 3] area = torch.as_tensor(area, dtype=torch.float32) boxes[:, 2] = boxes[:, 0] + boxes[:, 2] boxes[:, 3] = boxes[:, 1] + boxes[:, 3] # there is only one class labels = torch.ones((records.shape[0],), dtype=torch.int64) # suppose all instances are not crowd iscrowd = torch.zeros((records.shape[0],), dtype=torch.uint8) target = {} target['boxes'] = boxes target['labels'] = labels target['image_id'] = torch.tensor([idx]) target['area'] = area target['iscrowd'] = iscrowd if self.transforms: sample = { 'image': image, 'bboxes': target['boxes'], 'labels': labels, } sample = self.transforms(**sample) image = sample['image'] target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0) return image, target, image_id def __len__(self) -> int: return len(self.image_ids) # albumentations # https://github.com/albumentations-team/albumentations @staticmethod def get_train_transform(): return A.Compose([ A.Flip(0.5), ToTensorV2(p=1.0) ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']}) @staticmethod def get_valid_transform(): return A.Compose([ ToTensorV2(p=1.0) ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})albumentations 是一个优良的图像增强的库，用它定义了 train valid 的转换方法。
当初创立 train valid 数据集：
train_dataset = Wheat(train, DIR_TRAIN, Wheat.get_train_transform())valid_dataset = Wheat(valid, DIR_TRAIN, Wheat.get_valid_transform())预览下数据集里的图片：
datas = [train_dataset[i] for i in range(2)]imgs = [d[0].permute(1, 2, 0).numpy() for d in datas]axes = show_images(imgs, 1, 2, scale=8)for ax, (image, target, image_id) in zip(axes, datas): show_bboxes(ax, target['boxes'], labels=None, colors=['w'])plt.show()筹备模型
import torchvisionfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictorfrom torchvision.models.detection import FasterRCNNfrom torchvision.models.detection.rpn import AnchorGenerator创立模型
创立一个 Faster R-CNN 预训练模型：
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)微调模型
输入模型最初一层：
print(model.roi_heads.box_predictor)FastRCNNPredictor( (cls_score): Linear(in_features=1024, out_features=91, bias=True) (bbox_pred): Linear(in_features=1024, out_features=364, bias=True))
替换该层，指明输入特色大小为 2：
num_classes = 2 # wheat or not(background)# get number of input features for the classifierin_features = model.roi_heads.box_predictor.cls_score.in_features# replace the pre-trained model's head with a new onemodel.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)再次输入模型最初一层：
print(model.roi_heads.box_predictor)FastRCNNPredictor( (cls_score): Linear(in_features=1024, out_features=2, bias=True) (bbox_pred): Linear(in_features=1024, out_features=8, bias=True))
训练模型
创立 DataLoader
from torch.utils.data import DataLoaderdef collate_fn(batch): return tuple(zip(*batch))train_data_loader = DataLoader( train_dataset, batch_size=4, shuffle=False, num_workers=4, collate_fn=collate_fn)valid_data_loader = DataLoader( valid_dataset, batch_size=4, shuffle=False, num_workers=4, collate_fn=collate_fn)创立参数
# train on the GPU or on the CPU, if a GPU is not availabledevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')# move model to the right devicemodel.to(device)# create an optimizerparams = [p for p in model.parameters() if p.requires_grad]optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)# create a learning rate scheduler# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)lr_scheduler = None# train it for 10 epochsnum_epochs = 10开始训练
import timefrom tqdm import tqdm#from tqdm.notebook import tqdm as tqdmitr = 1total_train_loss = []total_valid_loss = []losses_value = 0for epoch in range(num_epochs): start_time = time.time() # train ------------------------------ model.train() train_loss = [] pbar = tqdm(train_data_loader, desc='let\'s train') for images, targets, image_ids in pbar: images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] loss_dict = model(images, targets) losses = sum(loss for loss in loss_dict.values()) losses_value = losses.item() train_loss.append(losses_value) optimizer.zero_grad() losses.backward() optimizer.step() pbar.set_description(f"Epoch: {epoch+1}, Batch: {itr}, Loss: {losses_value}") itr += 1 epoch_train_loss = np.mean(train_loss) total_train_loss.append(epoch_train_loss) # update the learning rate if lr_scheduler is not None: lr_scheduler.step() # valid ------------------------------ with torch.no_grad(): valid_loss = [] for images, targets, image_ids in valid_data_loader: images = list(image.to(device) for image in images) targets = [{k: v.to(device) for k, v in t.items()} for t in targets] loss_dict = model(images, targets) losses = sum(loss for loss in loss_dict.values()) loss_value = losses.item() valid_loss.append(loss_value) epoch_valid_loss = np.mean(valid_loss) total_valid_loss.append(epoch_valid_loss) # print ------------------------------ print(f"Epoch Completed: {epoch+1}/{num_epochs}, Time: {time.time()-start_time}, " f"Train Loss: {epoch_train_loss}, Valid Loss: {epoch_valid_loss}")Epoch: 1, Batch: 675, Loss: 0.8600306952323752: 100%|██████████| 675/675 [08:26<00:00, 1.33it/s]Epoch Completed: 1/2, Time: 566.7130048274994, Train Loss: 0.909390335455138, Valid Loss: 0.8790632066434829Epoch: 2, Batch: 1350, Loss: 0.8195391336080114: 100%|██████████| 675/675 [08:27<00:00, 1.33it/s]Epoch Completed: 2/2, Time: 569.5055477619171, Train Loss: 0.8158109236268262, Valid Loss: 0.8743081629490502...
绘制 Loss
plt.figure(figsize=(8, 5))sns.set_style(style="whitegrid")sns.lineplot(x=range(1, len(total_train_loss)+1), y=total_train_loss, label="Train Loss")sns.lineplot(x=range(1, len(total_train_loss)+1), y=total_valid_loss, label="Valid Loss")plt.xlabel("Epochs")plt.ylabel("Loss")plt.show()保留模型
torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')模型预测
这里咱们从头筹备数据，再载入模型，进行预测。
筹备数据
import osimport numpy as npimport pandas as pdimport seaborn as snsfrom matplotlib import pyplot as pltfrom PIL import ImageDIR_INPUT = 'global-wheat-detection'DIR_TRAIN = f'{DIR_INPUT}/train'DIR_TEST = f'{DIR_INPUT}/test'WEIGHTS_FILE = 'fasterrcnn_resnet50_fpn.pth'Submission 文件
用于提交后果的文件。一行内容，示意一个图片的预测后果。如下：
ce4833752,0.5 0 0 100 100
image_id ce4833752 的图片，预测出 x y w h 0 0 100 100 处是小麦，置信度 0.5。如果有多个预测框，能空格分隔。
test_df = pd.read_csv(f'{DIR_INPUT}/sample_submission.csv')test_df.tail()创立 Dataset
import cv2 as cvimport numpy as npimport albumentations as Afrom albumentations.pytorch.transforms import ToTensorV2import torchfrom torch.utils.data import Datasetclass WheatTest(Dataset): def __init__(self, image_ids, image_dir, transforms=None): super().__init__() self.image_ids = image_ids self.image_dir = image_dir self.transforms = transforms def __getitem__(self, idx: int): image_id = self.image_ids[idx] image = cv.imread(f'{self.image_dir}/{image_id}.jpg', cv.IMREAD_COLOR) image = cv.cvtColor(image, cv.COLOR_BGR2RGB).astype(np.float32) image /= 255.0 if self.transforms: sample = { 'image': image, } sample = self.transforms(**sample) image = sample['image'] return image, image_id def __len__(self) -> int: return len(self.image_ids) @staticmethod def get_test_transform(): return A.Compose([ ToTensorV2(p=1.0) ])实例化测试数据集：
def get_image_ids(p): import glob image_ids = [] for p in glob.glob(f'{p}/*.jpg'): n, _ = os.path.splitext(os.path.basename(p)) image_ids.append(n) return image_ids# try more images for submission#test_dataset = WheatTest(get_image_ids(DIR_TRAIN), DIR_TRAIN, WheatTest.get_test_transform())test_dataset = WheatTest(test_df["image_id"].unique(), DIR_TEST, WheatTest.get_test_transform())创立 DataLoader
from torch.utils.data import DataLoaderdef collate_fn(batch): return tuple(zip(*batch))test_data_loader = DataLoader( test_dataset, batch_size=2, shuffle=False, num_workers=4, drop_last=False, collate_fn=collate_fn)载入模型
import torchvisionfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictorfrom torchvision.models.detection import FasterRCNNfrom torchvision.models.detection.rpn import AnchorGeneratordevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')# create a Faster R-CNN model without pre-trainedmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)num_classes = 2 # wheat or not(background)# get number of input features for the classifierin_features = model.roi_heads.box_predictor.cls_score.in_features# replace the pre-trained model's head with a new onemodel.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)# load the trained weightsmodel.load_state_dict(torch.load(WEIGHTS_FILE, map_location=device))model.eval()# move model to the right device_ = model.to(device)开始预测
score_threshold = 0.7image_outputs = []for images, image_ids in test_data_loader: images = list(image.to(device) for image in images) outputs = model(images) for image_id, output in zip(image_ids, outputs): boxes = output['boxes'].data.cpu().numpy() scores = output['scores'].data.cpu().numpy() mask = scores >= score_threshold boxes = boxes[mask].astype(np.int32) scores = scores[mask] image_outputs.append((image_id, boxes, scores))预览后果
num_rows, num_cols = 1, 2scale = 16figsize = (num_rows * scale, num_cols * scale)_, axes = plt.subplots(num_rows, num_cols, figsize=figsize)axes = axes.flatten()fig_n = num_rows * num_colsfig_i = 0for i in range(1, 1+fig_n): image, image_id = test_dataset[i] _, boxes, scores = image_outputs[i] sample = image.permute(1, 2, 0).cpu().numpy() for box in boxes: cv.rectangle(sample, (box[0], box[1]), (box[2], box[3]), (220, 0, 0), 2) axes[fig_i].imshow(sample) fig_i += 1保留后果
def format_prediction_string(boxes, scores): pred_strings = [] for score, box in zip(scores, boxes): pred_strings.append(round(score, 4)) pred_strings.extend(box) return ' '.join(map(str, pred_strings))results = []for image_id, boxes, scores in image_outputs: #boxes = boxes_.copy() boxes[:, 2] = boxes[:, 2] - boxes[:, 0] boxes[:, 3] = boxes[:, 3] - boxes[:, 1] result = { 'image_id': image_id, 'PredictionString': format_prediction_string(boxes, scores) } results.append(result)results[0]{'image_id': 'aac893a91',
'PredictionString': '0.9928 72 2 96 166 0.9925 553 528 123 203 0.9912 613 921 85 102 0.9862 691 392 125 193 0.9855 819 708 105 204 0.9842 356 531 100 88 0.982 586 781 100 119 0.9795 739 768 82 122 0.9779 324 662 126 160 0.9764 27 454 102 156 0.9763 545 76 145 182 0.9736 450 858 90 95 0.9626 241 91 137 146 0.9406 306 0 75 68 0.9404 89 618 128 80 0.9366 177 576 114 182 0.9363 234 845 144 91 0.9265 64 857 115 69 0.824 822 630 90 124 0.7516 815 921 134 100'}
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])test_dftest_df.to_csv('submission.csv', index=False)提交后果
这就是 baseline 了，能够试着持续调优 ????
参考
- TorchVision Instance Segmentation Finetuning Tutorial
Kaggle: Global Wheat Detection
- Pytorch Starter - FasterRCNN Train
- Global Wheat Detection: Starter EDA
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