Hi, I’m training faster RCNN with ResNet 50, I’m getting a question that whether my code completely utilize the GPU or not. When I monitored the task manager I see the GPU utilization is minimal or my understanding is wrong.
Can some help me to utilize the GPU maximum.
when I tried to add the num_workers I’m getting bottleneck error.
gpu_utilisation1920×1080 270 KB


gpu968×656 21.3 KB

System configuration:
windows 10, GEFORCE RTX 2060, python 3.7.11, torch = 1.8.1
please find my code below
from torch_snippets import *
from PIL import Image
import glob, numpy as np, cv2, warnings,random, albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
warnings.filterwarnings('ignore')
 
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
    
seed_everything(42)
 
IMAGE_ROOT = 'images'
DF_RAW = pd.read_csv('train_labels.csv')
DF_RAW['image_id'] = DF_RAW['filename'].apply(lambda x: x.split('.')[0])
DF_RAW['labels'] = DF_RAW['class'].apply(lambda x: 1 if x=='car' else 0)
 
 
label2target = {l:t+1 for t,l in enumerate(DF_RAW['class'].unique())}
label2target['background'] = 0
target2label = {t:l for l,t in label2target.items()}
background_class = label2target['background']
num_classes = len(label2target)
 
def preprocess_image(img):
    img = torch.tensor(img).permute(2,0,1)
    return img.to(device).float()
 
import torch
class OpenDataset(torch.utils.data.Dataset):
    def __init__(self, df, image_folder=IMAGE_ROOT,transforms=None):
        self.root = image_folder
        self.df = df
        self.unique_images = df['image_id'].unique()
        self.transforms = transforms
    def __len__(self): return len(self.unique_images)
    def __getitem__(self, ix):
        image_id = self.unique_images[ix]
        image_id_ = torch.tensor([ix])
        image_path = f'{image_id}.jpg'
        img = Image.open('images/'+image_path).convert("RGB")
        img = np.array(img)/255
        df = self.df.copy()
        df = df[df['image_id'] == image_id]
        boxes = df[['xmin','ymin','xmax','ymax']].values
        classes = df['class'].values
 
        boxes = torch.as_tensor(boxes, dtype=torch.float32)
        # area of the bounding boxes
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
        # no crowd instances
        iscrowd = torch.zeros((boxes.shape[0],), dtype=torch.int64)
        # labels to tensor
        labels = torch.as_tensor([label2target[i] for i in classes],dtype=torch.int64)
        # prepare the final `target` dictionary
        target = {}
        target["boxes"] = boxes
        target["labels"] = labels
        target["area"] = area
        target["iscrowd"] = iscrowd
        image_id = torch.tensor([image_id_])
        target["image_id"] = image_id
        # apply the image transforms
        if self.transforms:
            sample = self.transforms(image = img,
                                     bboxes = target['boxes'],
                                     labels = labels)
            image_resized = sample['image']
            target['boxes'] = torch.Tensor(sample['bboxes'])
        img = preprocess_image(img)
        return img, target
    def collate_fn(self, batch):
        return tuple(zip(*batch)) 
 
 
import albumentations as A
from albumentations.pytorch import ToTensorV2
def get_train_transform():
    return A.Compose([
        A.Flip(0.5),
        A.RandomRotate90(0.5),
        A.MotionBlur(p=0.2),
        A.Blur(blur_limit=3, p=0.1),
        ToTensorV2(p=1.0),
    ], bbox_params={
        'format': 'pascal_voc',
        'label_fields': ['labels']
    })
# define the validation transforms
def get_valid_transform():
    return A.Compose([
        ToTensorV2(p=1.0),
    ], bbox_params={
        'format': 'pascal_voc', 
        'label_fields': ['labels']
    })
 
 
from sklearn.model_selection import train_test_split
trn_ids, val_ids = train_test_split(DF_RAW['image_id'].unique(), test_size=0.1, random_state=99)
trn_df, val_df = DF_RAW[DF_RAW['image_id'].isin(trn_ids)], DF_RAW[DF_RAW['image_id'].isin(val_ids)]
print(len(trn_df), len(val_df))
 
train_ds = OpenDataset(trn_df,transforms = get_train_transform())
test_ds = OpenDataset(val_df,transforms = get_valid_transform())
 
train_loader = DataLoader(train_ds, batch_size=2, collate_fn=train_ds.collate_fn, drop_last=False,shuffle=True)
#train_test_loader = DataLoader(train_ds, batch_size=1, collate_fn=train_ds.collate_fn, drop_last=False,shuffle=True)
test_loader = DataLoader(test_ds, batch_size=1, collate_fn=test_ds.collate_fn, drop_last=False,shuffle=False)
 

import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 
device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
def get_model():
    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
    return model
 
# Defining training and validation functions for a single batch
def train_batch(inputs, model, optimizer):
    model.train()
    input_, targets = inputs
    input_ = list(image.to(device) for image in input_)
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
    optimizer.zero_grad()
    losses = model(input_, targets)
    loss = sum(loss for loss in losses.values())
    loss.backward()
    optimizer.step()
    return loss, losses
 
@torch.no_grad() # this will disable gradient computation in the function below
def validate_batch(inputs, model):
    model.train() # to obtain the losses, model needs to be in train mode only. # #Note that here we are not defining the model's forward method 
#and hence need to work per the way the model class is defined
    input_, targets = inputs
    input_ = list(image.to(device) for image in input_)
    targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
 
    optimizer.zero_grad()
    losses = model(input_, targets)
    loss = sum(loss for loss in losses.values())
    return loss, losses

model = get_model().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.005,
                            momentum=0.9, weight_decay=0.0005)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[3,8], gamma=0.1)
n_epochs = 10
log = Report(n_epochs)
 

#from torch.utils.tensorboard import SummaryWriter
from engine import train_one_epoch, evaluate
#writer = SummaryWriter('runs/object_detection_1')
monitor_val_loss = float('inf')
for epoch in range(n_epochs):
    _n = len(train_loader)
    for ix, inputs in enumerate(train_loader):
        loss, losses = train_batch(inputs, model, optimizer)
        loc_loss, regr_loss, loss_objectness, loss_rpn_box_reg = \
            [losses[k] for k in ['loss_classifier','loss_box_reg','loss_objectness','loss_rpn_box_reg']]
        pos = (epoch + (ix+1)/_n)
        log.record(pos, trn_loss=loss.item(), trn_loc_loss=loc_loss.item(), 
                   trn_regr_loss=regr_loss.item(), trn_objectness_loss=loss_objectness.item(),
                   trn_rpn_box_reg_loss=loss_rpn_box_reg.item(), end='\r')
    # update the learning rate
    lr_scheduler.step()
 
    _n = len(test_loader)
    for ix,inputs in enumerate(test_loader):
        loss, losses = validate_batch(inputs, model)
        loc_loss, regr_loss, loss_objectness, loss_rpn_box_reg = \
          [losses[k] for k in ['loss_classifier','loss_box_reg','loss_objectness','loss_rpn_box_reg']]
        pos = (epoch + (ix+1)/_n)
        log.record(pos, val_loss=loss.item(), val_loc_loss=loc_loss.item(), 
                  val_regr_loss=regr_loss.item(), val_objectness_loss=loss_objectness.item(),
                  val_rpn_box_reg_loss=loss_rpn_box_reg.item(), end='\r')
    if (epoch+1)%(n_epochs//10)==0: log.report_avgs(epoch+1)
 
    monitor_val = log.report_avgs(epoch+1,return_avgs=True)
 
    #writer.add_scalar('training loss',monitor_val['trn_loss'],epoch + 1)
    #writer.add_scalar("validation loss", monitor_val['val_loss'], epoch + 1)
 
    if monitor_val['val_loss'] < monitor_val_loss:
        monitor_val_loss = monitor_val['val_loss']
        print('loss reducing...', monitor_val_loss)
        #torch.save(model.state_dict(), f'fasterrcnn_custom_test_{epoch+1}_loss_{monitor_val_loss}.pth')
        #torch.save(model.state_dict(), 'best_weights.pth')
    evaluate(model, test_loader, device=device)

can anyone guide me to utilize maximum GPU,please?