一、[VinBigData]-Train-EfficientNet-2-classes

转自：https://www.kaggle.com/philyyong/vinbigdata-train-efficientnet-2-classes/edit

1、添加环境，这就可以直接访问包而不用安装

package_paths = [
    '../input/pytorch-image-models/pytorch-image-models-master', #'../input/efficientnet-pytorch-07/efficientnet_pytorch-0.7.0'
    '../input/image-fmix/FMix-master'
]
import sys; 

for pth in package_paths:
    sys.path.append(pth)
    
from fmix import sample_mask, make_low_freq_image, binarise_mask

2、导入需要的模块

from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as np
from tqdm import tqdm

import matplotlib.pyplot as plt
from torch.utils.data import Dataset,DataLoader
from torch.utils.data.sampler import SequentialSampler, RandomSampler
from torch.cuda.amp import autocast, GradScaler
from torch.nn.modules.loss import _WeightedLoss
import torch.nn.functional as F

import timm

import sklearn
import warnings
import joblib
from sklearn.metrics import roc_auc_score, log_loss
from sklearn import metrics
import warnings
import cv2
import pydicom
#from efficientnet_pytorch import EfficientNet
from scipy.ndimage.interpolation import zoom

3、配置文件

CFG = {
    'fold_num': 5,
    'seed': 2021,
    'model_arch': 'tf_efficientnet_b6_ns',
    'img_size': 512,
    'epochs': 1,
    'train_bs': 8,
    'valid_bs': 8,
    'T_0': 10,
    'lr': 1e-4,
    'min_lr': 1e-6,
    'weight_decay':1e-6,
    'num_workers': 4,
    'accum_iter': 2, # suppoprt to do batch accumulation for backprop with effectively larger batch size
    'verbose_step': 1,
    'device': 'cuda:0',
    'path_to_train_data' : '../input/vinbigdata-512-image-dataset/vinbigdata/train/'
}

4、查看数据集形式

train_ori = pd.read_csv('../input/vinbigdata-512-image-dataset/vinbigdata/train.csv')
train_ori.head() #将df第一行看作表头，显示出前五行数据

img_names = list(train_ori['image_id'].unique())
train = pd.DataFrame({'image_id' : img_names})

#making the labels
for img in img_names:
    sub_df = train_ori.loc[train_ori['image_id'] == img]
    no_finding_classes = sub_df[sub_df['class_name'] == 'No finding']
    
    if (no_finding_classes.shape[0] > (sub_df.shape[0]/ 2)): #this line can be modified if you want
        train.loc[train['image_id'] == img, 'target'] = 1
    else:
        train.loc[train['image_id'] == img, 'target'] = 0

train.target.value_counts()

5、Helper Functions

def seed_everything(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True
    
def get_img(path):
    im_bgr = cv2.imread(path)
    im_rgb = im_bgr[:, :, ::-1]
    #print(im_rgb)
    return im_rgb

img = get_img('../input/vinbigdata-512-image-dataset/vinbigdata/train/0005e8e3701dfb1dd93d53e2ff537b6e.png')
plt.imshow(img)
plt.show()

6、Define Train\Validation Image Augmentations

from albumentations import (
    HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
    IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
    IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose, Normalize, Cutout, CoarseDropout, ShiftScaleRotate, CenterCrop, Resize
)

from albumentations.pytorch import ToTensorV2

def get_train_transforms():
    return Compose([
            RandomResizedCrop(CFG['img_size'], CFG['img_size']),
            Transpose(p=0.5),
            HorizontalFlip(p=0.5),
            #VerticalFlip(p=0.5),
            ShiftScaleRotate(p=0.5),
            HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),
            RandomBrightnessContrast(brightness_limit=(-0.1,0.1), contrast_limit=(-0.1, 0.1), p=0.5),
            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),
            #CoarseDropout(p=0.5),
            #Cutout(p=0.5),
            ToTensorV2(p=1.0),
        ], p=1.)
  
        
def get_valid_transforms():
    return Compose([
            CenterCrop(CFG['img_size'], CFG['img_size'], p=1.),
            Resize(CFG['img_size'], CFG['img_size']),
            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),
            ToTensorV2(p=1.0),
        ], p=1.)

7、Dataset

def rand_bbox(size, lam):
    W = size[0]
    H = size[1]
    cut_rat = np.sqrt(1. - lam)
    cut_w = np.int(W * cut_rat)
    cut_h = np.int(H * cut_rat)

    # uniform
    cx = np.random.randint(W)
    cy = np.random.randint(H)

    bbx1 = np.clip(cx - cut_w // 2, 0, W)
    bby1 = np.clip(cy - cut_h // 2, 0, H)
    bbx2 = np.clip(cx + cut_w // 2, 0, W)
    bby2 = np.clip(cy + cut_h // 2, 0, H)
    return bbx1, bby1, bbx2, bby2


class ChestXRayDataset(Dataset):
    def __init__(self, df, data_root, 
                 transforms=None, 
                 output_label=True, 
                 one_hot_label=False,
                 do_fmix=False, 
                 fmix_params={
                     'alpha': 1., 
                     'decay_power': 3., 
                     'shape': (CFG['img_size'], CFG['img_size']),
                     'max_soft': True, 
                     'reformulate': False
                 },
                 do_cutmix=False,
                 cutmix_params={
                     'alpha': 1,
                 }
                ):
        
        super().__init__()
        self.df = df.reset_index(drop=True).copy()
        self.transforms = transforms
        self.data_root = data_root
        self.do_fmix = do_fmix
        self.fmix_params = fmix_params
        self.do_cutmix = do_cutmix
        self.cutmix_params = cutmix_params
        
        self.output_label = output_label
        self.one_hot_label = one_hot_label
        
        if output_label == True:
            self.labels = self.df['target'].values
            #print(self.labels)
            
            if one_hot_label is True:
                self.labels = np.eye(self.df['target'].max()+1)[self.labels]
                #print(self.labels)
            
    def __len__(self):
        return self.df.shape[0]
    
    def __getitem__(self, index: int):
        
        # get labels
        if self.output_label:
            target = self.labels[index]
          
        img  = get_img("{}/{}.png".format(self.data_root, self.df.loc[index]['image_id']))

        if self.transforms:
            img = self.transforms(image=img)['image']
        
        if self.do_fmix and np.random.uniform(0., 1., size=1)[0] > 0.5:
            with torch.no_grad():
                #lam, mask = sample_mask(**self.fmix_params)
                
                lam = np.clip(np.random.beta(self.fmix_params['alpha'], self.fmix_params['alpha']),0.6,0.7)
                
                # Make mask, get mean / std
                mask = make_low_freq_image(self.fmix_params['decay_power'], self.fmix_params['shape'])
                mask = binarise_mask(mask, lam, self.fmix_params['shape'], self.fmix_params['max_soft'])
    
                fmix_ix = np.random.choice(self.df.index, size=1)[0]
                fmix_img  = get_img("{}/{}".format(self.data_root, self.df.iloc[fmix_ix]['image_id']))

                if self.transforms:
                    fmix_img = self.transforms(image=fmix_img)['image']

                mask_torch = torch.from_numpy(mask)
                
                # mix image
                img = mask_torch*img+(1.-mask_torch)*fmix_img

                #print(mask.shape)

                #assert self.output_label==True and self.one_hot_label==True

                # mix target
                rate = mask.sum()/CFG['img_size']/CFG['img_size']
                target = rate*target + (1.-rate)*self.labels[fmix_ix]
                #print(target, mask, img)
                #assert False
        
        if self.do_cutmix and np.random.uniform(0., 1., size=1)[0] > 0.5:
            #print(img.sum(), img.shape)
            with torch.no_grad():
                cmix_ix = np.random.choice(self.df.index, size=1)[0]
                cmix_img  = get_img("{}/{}".format(self.data_root, self.df.iloc[cmix_ix]['image_id']))
                if self.transforms:
                    cmix_img = self.transforms(image=cmix_img)['image']
                    
                lam = np.clip(np.random.beta(self.cutmix_params['alpha'], self.cutmix_params['alpha']),0.3,0.4)
                bbx1, bby1, bbx2, bby2 = rand_bbox((CFG['img_size'], CFG['img_size']), lam)

                img[:, bbx1:bbx2, bby1:bby2] = cmix_img[:, bbx1:bbx2, bby1:bby2]

                rate = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (CFG['img_size'] * CFG['img_size']))
                target = rate*target + (1.-rate)*self.labels[cmix_ix]
                
            #print('-', img.sum())
            #print(target)
            #assert False
                            
        # do label smoothing
        #print(type(img), type(target))
        if self.output_label == True:
            return img, target
        else:
            return img

8、Model

class ChestXrayImgClassifier(nn.Module):
    def __init__(self, model_arch, n_class, pretrained=False):
        super().__init__()
        self.model = timm.create_model(model_arch, pretrained=pretrained)
        n_features = self.model.classifier.in_features
        self.model.classifier = nn.Linear(n_features, n_class)
        '''
        self.model.classifier = nn.Sequential(
            nn.Dropout(0.3),
            #nn.Linear(n_features, hidden_size,bias=True), nn.ELU(),
            nn.Linear(n_features, n_class, bias=True)
        )
        '''
    def forward(self, x):
        x = self.model(x)
        return x

9、Training APIs

def prepare_dataloader(df, trn_idx, val_idx, data_root='../input/cassava-leaf-disease-classification/train_images/'):
    
    from catalyst.data.sampler import BalanceClassSampler
    
    train_ = df.loc[trn_idx,:].reset_index(drop=True)
    valid_ = df.loc[val_idx,:].reset_index(drop=True)
        
    train_ds = ChestXRayDataset(train_, data_root, transforms=get_train_transforms(), output_label=True, one_hot_label=False, do_fmix=False, do_cutmix=False)
    valid_ds = ChestXRayDataset(valid_, data_root, transforms=get_valid_transforms(), output_label=True)
    
    train_loader = torch.utils.data.DataLoader(
        train_ds,
        batch_size=CFG['train_bs'],
        pin_memory=False,
        drop_last=False,
        shuffle=True,        
        num_workers=CFG['num_workers'],
        #sampler=BalanceClassSampler(labels=train_['label'].values, mode="downsampling")
    )
    val_loader = torch.utils.data.DataLoader(
        valid_ds, 
        batch_size=CFG['valid_bs'],
        num_workers=CFG['num_workers'],
        shuffle=False,
        pin_memory=False,
    )
    return train_loader, val_loader

def train_one_epoch(epoch, model, loss_fn, optimizer, train_loader, device, scheduler=None, schd_batch_update=False):
    model.train()

    t = time.time()
    running_loss = None

    pbar = tqdm(enumerate(train_loader), total=len(train_loader))
    for step, (imgs, image_labels) in pbar:
        imgs = imgs.to(device).float()
        image_labels = image_labels.to(device).long()

        #print(image_labels.shape, exam_label.shape)
        with autocast():
            image_preds = model(imgs)   #output = model(input)
            #print(image_preds.shape, exam_pred.shape)

            loss = loss_fn(image_preds, image_labels)
            
            scaler.scale(loss).backward()

            if running_loss is None:
                running_loss = loss.item()
            else:
                running_loss = running_loss * .99 + loss.item() * .01

            if ((step + 1) %  CFG['accum_iter'] == 0) or ((step + 1) == len(train_loader)):
                # may unscale_ here if desired (e.g., to allow clipping unscaled gradients)

                scaler.step(optimizer)
                scaler.update()
                optimizer.zero_grad() 
                
                if scheduler is not None and schd_batch_update:
                    scheduler.step()

            if ((step + 1) % CFG['verbose_step'] == 0) or ((step + 1) == len(train_loader)):
                description = f'epoch {epoch} loss: {running_loss:.4f}'
                
                pbar.set_description(description)
                
    if scheduler is not None and not schd_batch_update:
        scheduler.step()
        
def valid_one_epoch(epoch, model, loss_fn, val_loader, device, best_val, fold, scheduler=None, schd_loss_update=False):
    model.eval()

    t = time.time()
    loss_sum = 0
    sample_num = 0
    image_preds_all = []
    image_targets_all = []
    
    pbar = tqdm(enumerate(val_loader), total=len(val_loader))
    for step, (imgs, image_labels) in pbar:
        imgs = imgs.to(device).float()
        image_labels = image_labels.to(device).long()
        
        image_preds = model(imgs)   #output = model(input)
        #print(image_preds.shape, exam_pred.shape)
        image_preds_all += [torch.argmax(image_preds, 1).detach().cpu().numpy()]
        image_targets_all += [image_labels.detach().cpu().numpy()]
        
        loss = loss_fn(image_preds, image_labels)
        
        loss_sum += loss.item()*image_labels.shape[0]
        sample_num += image_labels.shape[0]  

        if ((step + 1) % CFG['verbose_step'] == 0) or ((step + 1) == len(val_loader)):
            description = f'epoch {epoch} loss: {loss_sum/sample_num:.4f}'
            pbar.set_description(description)
    
    image_preds_all = np.concatenate(image_preds_all)
    image_targets_all = np.concatenate(image_targets_all)
    valid_acc = (image_preds_all==image_targets_all).mean()
    if (valid_acc > best_val):
        best_val = valid_acc
        torch.save(model.state_dict(), f'./best_model_fold{fold}.pt')
        
    print('validation multi-class accuracy = {:.4f}'.format(valid_acc))
    
    if scheduler is not None:
        if schd_loss_update:
            scheduler.step(loss_sum/sample_num)
        else:
            scheduler.step()
    
    return best_val

# reference: https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/173733
class MyCrossEntropyLoss(_WeightedLoss):
    def __init__(self, weight=None, reduction='mean'):
        super().__init__(weight=weight, reduction=reduction)
        self.weight = weight
        self.reduction = reduction

    def forward(self, inputs, targets):
        lsm = F.log_softmax(inputs, -1)

        if self.weight is not None:
            lsm = lsm * self.weight.unsqueeze(0)

        loss = -(targets * lsm).sum(-1)

        if  self.reduction == 'sum':
            loss = loss.sum()
        elif  self.reduction == 'mean':
            loss = loss.mean()

        return loss

10 、Main Loop

if __name__ == '__main__':
     # for training only, need nightly build pytorch

    seed_everything(CFG['seed'])
    
    #mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)
    
    folds = StratifiedKFold(n_splits=CFG['fold_num'], shuffle=True, random_state=CFG['seed']).split(np.arange(train.shape[0]), train.target.values)
    
    for fold, (trn_idx, val_idx) in enumerate(folds):
        # we'll train fold 0 first
        if fold > 0:
            break 

        print('Training with {} started'.format(fold))

        print(len(trn_idx), len(val_idx))
        train_loader, val_loader = prepare_dataloader(train, trn_idx, val_idx, data_root=CFG['path_to_train_data'])

        device = torch.device(CFG['device'])
        
        model = ChestXrayImgClassifier(CFG['model_arch'], train.target.nunique(), pretrained=True).to(device)
        scaler = GradScaler()   
        optimizer = torch.optim.Adam(model.parameters(), lr=CFG['lr'], weight_decay=CFG['weight_decay'])
        #scheduler = torch.optim.lr_scheduler.StepLR(optimizer, gamma=0.1, step_size=CFG['epochs']-1)
        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=CFG['T_0'], T_mult=1, eta_min=CFG['min_lr'], last_epoch=-1)
        #scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, pct_start=0.1, div_factor=25, 
        #                                                max_lr=CFG['lr'], epochs=CFG['epochs'], steps_per_epoch=len(train_loader))
        
        loss_tr = nn.CrossEntropyLoss().to(device) #MyCrossEntropyLoss().to(device)
        loss_fn = nn.CrossEntropyLoss().to(device)
        
        best_val = 0
        
        for epoch in range(CFG['epochs']):
            train_one_epoch(epoch, model, loss_tr, optimizer, train_loader, device, scheduler=scheduler, schd_batch_update=False)

            with torch.no_grad():
                best_val = valid_one_epoch(epoch, model, loss_fn, val_loader, device, best_val, fold, scheduler=None, schd_loss_update=False)

            torch.save(model.state_dict(),'{}_fold_{}_{}'.format(CFG['model_arch'], fold, epoch))
            
        #torch.save(model.cnn_model.state_dict(),'{}/cnn_model_fold_{}_{}'.format(CFG['model_path'], fold, CFG['tag']))
        del model, optimizer, train_loader, val_loader, scaler, scheduler
        torch.cuda.empty_cache()

二、Pytorch Starter - FasterRCNN Train

转自：https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train

FasterRCNN from torchvision
Use Resnet50 backbone
Albumentation enabled (simple flip for now)

import pandas as pd
import numpy as np
import cv2
import os
import re

from PIL import Image

import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2

import torch
import torchvision

from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

from torch.utils.data import DataLoader, Dataset
from torch.utils.data.sampler import SequentialSampler

from matplotlib import pyplot as plt

DIR_INPUT = '/kaggle/input/global-wheat-detection'
DIR_TRAIN = f'{DIR_INPUT}/train'
DIR_TEST = f'{DIR_INPUT}/test'

train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')
train_df.shape

train_df['x'] = -1
train_df['y'] = -1
train_df['w'] = -1
train_df['h'] = -1

def expand_bbox(x):
    r = np.array(re.findall("([0-9]+[.]?[0-9]*)", x))
    if len(r) == 0:
        r = [-1, -1, -1, -1]
    return r

train_df[['x', 'y', 'w', 'h']] = np.stack(train_df['bbox'].apply(lambda x: expand_bbox(x)))
train_df.drop(columns=['bbox'], inplace=True)
train_df['x'] = train_df['x'].astype(np.float)
train_df['y'] = train_df['y'].astype(np.float)
train_df['w'] = train_df['w'].astype(np.float)
train_df['h'] = train_df['h'].astype(np.float)

image_ids = train_df['image_id'].unique()
valid_ids = image_ids[-665:]
train_ids = image_ids[:-665]

valid_df = train_df[train_df['image_id'].isin(valid_ids)]
train_df = train_df[train_df['image_id'].isin(train_ids)]

valid_df.shape, train_df.shape

class WheatDataset(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, index: int):

        image_id = self.image_ids[index]
        records = self.df[self.df['image_id'] == image_id]

        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
        image /= 255.0

        boxes = records[['x', 'y', 'w', 'h']].values
        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
        
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
        area = torch.as_tensor(area, dtype=torch.float32)

        # 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.int64)
        
        target = {}
        target['boxes'] = boxes
        target['labels'] = labels
        # target['masks'] = None
        target['image_id'] = torch.tensor([index])
        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 self.image_ids.shape[0]

# Albumentations
def get_train_transform():
    return A.Compose([
        A.Flip(0.5),
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

def get_valid_transform():
    return A.Compose([
        ToTensorV2(p=1.0)
    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})

1、Create the model

# load a model; pre-trained on COCO
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)

num_classes = 2  # 1 class (wheat) + background

# get number of input features for the classifier
in_features = model.roi_heads.box_predictor.cls_score.in_features

# replace the pre-trained head with a new one
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)

class Averager:
    def __init__(self):
        self.current_total = 0.0
        self.iterations = 0.0

    def send(self, value):
        self.current_total += value
        self.iterations += 1

    @property
    def value(self):
        if self.iterations == 0:
            return 0
        else:
            return 1.0 * self.current_total / self.iterations

    def reset(self):
        self.current_total = 0.0
        self.iterations = 0.0

def collate_fn(batch):
    return tuple(zip(*batch))

train_dataset = WheatDataset(train_df, DIR_TRAIN, get_train_transform())
valid_dataset = WheatDataset(valid_df, DIR_TRAIN, get_valid_transform())


# split the dataset in train and test set
indices = torch.randperm(len(train_dataset)).tolist()

train_data_loader = DataLoader(
    train_dataset,
    batch_size=16,
    shuffle=False,
    num_workers=4,
    collate_fn=collate_fn
)

valid_data_loader = DataLoader(
    valid_dataset,
    batch_size=8,
    shuffle=False,
    num_workers=4,
    collate_fn=collate_fn
)

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

2、Sample

images, targets, image_ids = next(iter(train_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]

boxes = targets[2]['boxes'].cpu().numpy().astype(np.int32)
sample = images[2].permute(1,2,0).cpu().numpy()

fig, ax = plt.subplots(1, 1, figsize=(16, 8))

for box in boxes:
    cv2.rectangle(sample,
                  (box[0], box[1]),
                  (box[2], box[3]),
                  (220, 0, 0), 3)
    
ax.set_axis_off()
ax.imshow(sample)

3、Train

model.to(device)
params = [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)
# lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
lr_scheduler = None

num_epochs = 2

loss_hist = Averager()
itr = 1

for epoch in range(num_epochs):
    loss_hist.reset()
    
    for images, targets, image_ids in train_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()

        loss_hist.send(loss_value)

        optimizer.zero_grad()
        losses.backward()
        optimizer.step()

        if itr % 50 == 0:
            print(f"Iteration #{itr} loss: {loss_value}")

        itr += 1
    
    # update the learning rate
    if lr_scheduler is not None:
        lr_scheduler.step()

    print(f"Epoch #{epoch} loss: {loss_hist.value}")  

images, targets, image_ids = next(iter(valid_data_loader))

images = list(img.to(device) for img in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]

boxes = targets[1]['boxes'].cpu().numpy().astype(np.int32)
sample = images[1].permute(1,2,0).cpu().numpy()

model.eval()
cpu_device = torch.device("cpu")

outputs = model(images)
outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]

fig, ax = plt.subplots(1, 1, figsize=(16, 8))

for box in boxes:
    cv2.rectangle(sample,
                  (box[0], box[1]),
                  (box[2], box[3]),
                  (220, 0, 0), 3)
    
ax.set_axis_off()
ax.imshow(sample)

torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')

三、Barrier Reef YOLOv5 [Training]

转自：https://www.kaggle.com/ammarnassanalhajali/barrier-reef-yolov5-training

版权声明：本文为CSDN博主「phily123」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/phily123/article/details/121642889