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Aug 19, 2022
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Code check and base model improvement of unstructured lifelong learning framework #350

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185 changes: 130 additions & 55 deletions examples/lifelong_learning/RFNet/basemodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,20 +3,20 @@
import torch
from PIL import Image
import argparse
from tqdm import tqdm

from torchvision import transforms
from torch.utils.data import DataLoader
from sedna.common.config import Context
from sedna.common.file_ops import FileOps
from sedna.common.log import LOGGER

from utils.metrics import Evaluator
from train import Trainer
from eval import Validator
from tqdm import tqdm
from eval import load_my_state_dict
from utils.metrics import Evaluator
from dataloaders import make_data_loader
from dataloaders import custom_transforms as tr
from torchvision import transforms
from sedna.common.class_factory import ClassType, ClassFactory
from sedna.common.config import Context
from sedna.datasources import TxtDataParse
from torch.utils.data import DataLoader
from sedna.common.file_ops import FileOps
from utils.lr_scheduler import LR_Scheduler

def preprocess(image_urls):
transformed_images = []
Expand All @@ -34,7 +34,10 @@ def preprocess(image_urls):
composed_transforms = transforms.Compose([
# tr.CropBlackArea(),
# tr.FixedResize(size=self.args.crop_size),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.Normalize(
mean=(
0.485, 0.456, 0.406), std=(
0.229, 0.224, 0.225)),
tr.ToTensor()])

transformed_images.append((composed_transforms(sample), img_path))
Expand All @@ -52,78 +55,113 @@ def __init__(self, **kwargs):
self.train_args.no_val = kwargs.get("no_val", True)
# self.train_args.resume = Context.get_parameters("PRETRAINED_MODEL_URL", None)
self.trainer = None

label_save_dir = Context.get_parameters("INFERENCE_RESULT_DIR", "./inference_results")
self.val_args.color_label_save_path = os.path.join(label_save_dir, "color")
self.val_args.merge_label_save_path = os.path.join(label_save_dir, "merge")
self.train_model_url = None

label_save_dir = Context.get_parameters(
"INFERENCE_RESULT_DIR", "./inference_results")
self.val_args.color_label_save_path = os.path.join(
label_save_dir, "color")
self.val_args.merge_label_save_path = os.path.join(
label_save_dir, "merge")
self.val_args.label_save_path = os.path.join(label_save_dir, "label")
self.val_args.save_predicted_image = kwargs.get(
"save_predicted_image", "true").lower()
self.validator = Validator(self.val_args)

def train(self, train_data, valid_data=None, **kwargs):
def train(self, train_data, valid_data=None, **kwargs):
self.trainer = Trainer(self.train_args, train_data=train_data)
print("Total epoches:", self.trainer.args.epochs)
for epoch in range(self.trainer.args.start_epoch, self.trainer.args.epochs):
for epoch in range(
self.trainer.args.start_epoch,
self.trainer.args.epochs):
if epoch == 0 and self.trainer.val_loader:
self.trainer.validation(epoch)
self.trainer.training(epoch)

if self.trainer.args.no_val and \
(epoch % self.trainer.args.eval_interval == (self.trainer.args.eval_interval - 1)
or epoch == self.trainer.args.epochs - 1):
# save checkpoint when it meets eval_interval or the training finished
if self.trainer.args.no_val and (
epoch %
self.trainer.args.eval_interval == (
self.trainer.args.eval_interval -
1) or epoch == self.trainer.args.epochs -
1):
# save checkpoint when it meets eval_interval or the training
# finished
is_best = False
checkpoint_path = self.trainer.saver.save_checkpoint({
self.train_model_url = self.trainer.saver.save_checkpoint({
'epoch': epoch + 1,
'state_dict': self.trainer.model.state_dict(),
'optimizer': self.trainer.optimizer.state_dict(),
'best_pred': self.trainer.best_pred,
}, is_best)

# if not self.trainer.args.no_val and \
# epoch % self.train_args.eval_interval == (self.train_args.eval_interval - 1) \
# and self.trainer.val_loader:
# self.trainer.validation(epoch)

self.trainer.writer.close()

return checkpoint_path
return self.train_model_url

def predict(self, data, **kwargs):
if not isinstance(data[0][0], dict):
data = preprocess(data)

if type(data) is np.ndarray:
if isinstance(data, np.ndarray):
data = data.tolist()

self.validator.test_loader = DataLoader(data, batch_size=self.val_args.test_batch_size, shuffle=False,
pin_memory=True)
self.validator.test_loader = DataLoader(
data,
batch_size=self.val_args.test_batch_size,
shuffle=False,
pin_memory=True)
return self.validator.validate()

def evaluate(self, data, **kwargs):
self.val_args.save_predicted_image = kwargs.get("save_predicted_image", True)
samples = preprocess(data.x)
predictions = self.predict(samples)
return accuracy(data.y, predictions)

def load(self, model_url, **kwargs):
if model_url:
self.validator.new_state_dict = torch.load(model_url, map_location=torch.device("cpu"))
self.validator.new_state_dict = torch.load(
model_url, map_location=torch.device("cpu"))
self.validator.model = load_my_state_dict(
self.validator.model, self.validator.new_state_dict['state_dict'])

self.train_args.resume = model_url
else:
raise Exception("model url does not exist.")
self.validator.model = load_my_state_dict(self.validator.model, self.validator.new_state_dict['state_dict'])

def save(self, model_path=None):
# TODO: how to save unstructured data model
pass
# TODO: save unstructured data model
if not model_path:
LOGGER.warning(f"Not specify model path.")
return self.train_model_url

return FileOps.upload(self.train_model_url, model_path)


def train_args():
parser = argparse.ArgumentParser(description="PyTorch RFNet Training")
parser.add_argument('--depth', action="store_true", default=False,
help='training with depth image or not (default: False)')
parser.add_argument('--dataset', type=str, default='cityscapes',
choices=['citylostfound', 'cityscapes', 'cityrand', 'target', 'xrlab', 'e1', 'mapillary'],
help='dataset name (default: cityscapes)')
parser.add_argument(
'--depth',
action="store_true",
default=False,
help='training with depth image or not (default: False)')
parser.add_argument(
'--dataset',
type=str,
default='cityscapes',
choices=[
'citylostfound',
'cityscapes',
'cityrand',
'target',
'xrlab',
'e1',
'mapillary'],
help='dataset name (default: cityscapes)')
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='dataloader threads')
parser.add_argument('--base-size', type=int, default=1024,
Expand All @@ -149,8 +187,11 @@ def train_args():
parser.add_argument('--test-batch-size', type=int, default=1,
metavar='N', help='input batch size for \
testing (default: auto)')
parser.add_argument('--use-balanced-weights', action='store_true', default=False,
help='whether to use balanced weights (default: True)')
parser.add_argument(
'--use-balanced-weights',
action='store_true',
default=False,
help='whether to use balanced weights (default: True)')
parser.add_argument('--num-class', type=int, default=24,
help='number of training classes (default: 24')
# optimizer params
Expand All @@ -164,8 +205,11 @@ def train_args():
parser.add_argument('--weight-decay', type=float, default=2.5e-5,
metavar='M', help='w-decay (default: 5e-4)')
# cuda, seed and logging
parser.add_argument('--no-cuda', action='store_true', default=
False, help='disables CUDA training')
parser.add_argument(
'--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--gpu-ids', type=str, default='0',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
Expand Down Expand Up @@ -193,7 +237,8 @@ def train_args():
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
raise ValueError(
'Argument --gpu_ids must be a comma-separated list of integers only')

if args.epochs is None:
epoches = {
Expand All @@ -214,7 +259,8 @@ def train_args():
'citylostfound': 0.0001,
'cityrand': 0.0001
}
args.lr = lrs[args.dataset.lower()] / (4 * len(args.gpu_ids)) * args.batch_size
args.lr = lrs[args.dataset.lower()] / \
(4 * len(args.gpu_ids)) * args.batch_size

if args.checkname is None:
args.checkname = 'RFNet'
Expand All @@ -223,11 +269,19 @@ def train_args():

return args


def val_args():
parser = argparse.ArgumentParser(description="PyTorch RFNet validation")
parser.add_argument('--dataset', type=str, default='cityscapes',
choices=['citylostfound', 'cityscapes', 'xrlab', 'mapillary'],
help='dataset name (default: cityscapes)')
parser.add_argument(
'--dataset',
type=str,
default='cityscapes',
choices=[
'citylostfound',
'cityscapes',
'xrlab',
'mapillary'],
help='dataset name (default: cityscapes)')
parser.add_argument('--workers', type=int, default=4,
metavar='N', help='dataloader threads')
parser.add_argument('--base-size', type=int, default=1024,
Expand All @@ -243,17 +297,27 @@ def val_args():
metavar='N', help='input batch size for \
testing (default: auto)')
parser.add_argument('--num-class', type=int, default=24,
help='number of training classes (default: 24')
parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training')
help='number of training classes (default: 24)')
parser.add_argument(
'--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--gpu-ids', type=str, default='0',
help='use which gpu to train, must be a \
comma-separated list of integers only (default=0)')
parser.add_argument('--checkname', type=str, default=None,
help='set the checkpoint name')
parser.add_argument('--weight-path', type=str, default="./models/530_exp3_2.pth",
help='enter your path of the weight')
parser.add_argument('--save-predicted-image', action='store_true', default=False,
help='save predicted images')
parser.add_argument(
'--weight-path',
type=str,
default="./models/530_exp3_2.pth",
help='enter your path of the weight')
parser.add_argument(
'--save-predicted-image',
action='store_true',
default=False,
help='save predicted images')
parser.add_argument('--color-label-save-path', type=str,
default='./test/color/',
help='path to save label')
Expand All @@ -262,19 +326,29 @@ def val_args():
help='path to save merged label')
parser.add_argument('--label-save-path', type=str, default='./test/label/',
help='path to save merged label')
parser.add_argument('--merge', action='store_true', default=True, help='merge image and label')
parser.add_argument('--depth', action='store_true', default=False, help='add depth image or not')
parser.add_argument(
'--merge',
action='store_true',
default=False,
help='merge image and label')
parser.add_argument(
'--depth',
action='store_true',
default=False,
help='add depth image or not')

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
try:
args.gpu_ids = [int(s) for s in args.gpu_ids.split(',')]
except ValueError:
raise ValueError('Argument --gpu_ids must be a comma-separated list of integers only')
raise ValueError(
'Argument --gpu_ids must be a comma-separated list of integers only')

return args


def accuracy(y_true, y_pred, **kwargs):
args = val_args()
_, _, test_loader, num_class = make_data_loader(args, test_data=y_true)
Expand All @@ -291,7 +365,7 @@ def accuracy(y_true, y_pred, **kwargs):
if args.depth:
depth = depth.cuda()

target[target > evaluator.num_class-1] = 255
target[target > evaluator.num_class - 1] = 255
target = target.cpu().numpy()
# Add batch sample into evaluator
evaluator.add_batch(target, y_pred[i])
Expand All @@ -305,6 +379,7 @@ def accuracy(y_true, y_pred, **kwargs):
print("CPA:{}, mIoU:{}, fwIoU: {}".format(CPA, mIoU, FWIoU))
return CPA


if __name__ == '__main__':
model_path = "/tmp/RFNet/"
if not os.path.exists(model_path):
Expand Down
2 changes: 1 addition & 1 deletion examples/lifelong_learning/RFNet/dataloaders/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def decode_segmap(label_mask, dataset, plot=False):
n_classes = 21
label_colours = get_pascal_labels()
elif dataset == 'cityscapes':
n_classes = 19
n_classes = 24
label_colours = get_cityscapes_labels()
elif dataset == 'target':
n_classes = 24
Expand Down
5 changes: 2 additions & 3 deletions examples/lifelong_learning/RFNet/eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,8 +60,7 @@ def validate(self):
if self.args.depth:
image, depth, target = sample['image'], sample['depth'], sample['label']
else:
# spec = time.time()
image, target = sample['image'], sample['label']
image, target = sample['image'], sample['label']

if self.args.cuda:
image = image.cuda()
Expand All @@ -82,7 +81,7 @@ def validate(self):
pred = np.argmax(pred, axis=1)
predictions.append(pred)

if not self.args.save_predicted_image:
if self.args.save_predicted_image != "true":
continue

pre_colors = Colorize()(torch.max(output, 1)[1].detach().cpu().byte())
Expand Down
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