Active learning building

Author: nducthang

AL_yolov5.py

@@ -0,0 +1,263 @@
+from activelearning.base_model import BaseModel
+from detect import detect
+import argparse
+import logging
+import os
+import random
+import time
+from pathlib import Path
+from train import train
+from warnings import warn
+import numpy as np
+import torch.distributed as dist
+import torch.utils.data
+import yaml
+from torch.utils.tensorboard import SummaryWriter
+from utils.general import increment_path, fitness, get_latest_run, check_file, check_git_status, print_mutation, set_logging, strip_optimizer
+from utils.plots import plot_evolution
+from utils.torch_utils import select_device
+
+logger = logging.getLogger(__name__)
+
+try:
+ import wandb
+except ImportError:
+ wandb = None
+ logger.info(
+ "Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")
+
+
+class Yolov5(BaseModel):
+ model_type = 'Yolo version 5'
+
+ def train(self):
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', type=str, default='activelearning/yolov5s.pt', help='initial weights path')
+ parser.add_argument('--cfg', type=str, default='models/yolo_gun.yaml', help='models/yolo_gun.yaml path')
+ parser.add_argument('--data', type=str, default='data/gun.yaml', help='data.yaml path')
+ parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
+ parser.add_argument('--epochs', type=int, default=1)
+ parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')
+ parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
+ parser.add_argument('--rect', action='store_true', help='rectangular training')
+ parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
+ parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
+ parser.add_argument('--notest', action='store_true', help='only test final epoch')
+ parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
+ parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
+ parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
+ parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
+ parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
+ parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') # using GPU 0
+ parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
+ parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
+ # parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
+ parser.add_argument('--adam', type=int, default=1, help='use torch.optim.Adam() optimizer') # using Adam optimizer
+ parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
+ parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
+ parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
+ parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model')
+ parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
+ parser.add_argument('--project', default='runs/train', help='save to project/name')
+ parser.add_argument('--name', default='gun', help='save to project/name')
+ # parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+ parser.add_argument('--exist-ok', type=int, default=1, help='existing project/name ok, do not increment')
+ opt = parser.parse_args()
+
+ # Set DDP variables
+ opt.total_batch_size = opt.batch_size
+ opt.world_size = int(
+ os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
+ opt.global_rank = int(
+ os.environ['RANK']) if 'RANK' in os.environ else -1
+ set_logging(opt.global_rank)
+ if opt.global_rank in [-1, 0]:
+ check_git_status()
+
+ # Resume
+ if opt.resume: # resume an interrupted run
+ # specified or most recent path
+ ckpt = opt.resume if isinstance(
+ opt.resume, str) else get_latest_run()
+ assert os.path.isfile(
+ ckpt), 'ERROR: --resume checkpoint does not exist'
+ with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
+ opt = argparse.Namespace(
+ **yaml.load(f, Loader=yaml.FullLoader)) # replace
+ opt.cfg, opt.weights, opt.resume = '', ckpt, True
+ logger.info('Resuming training from %s' % ckpt)
+ else:
+ # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
+ opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(
+ opt.cfg), check_file(opt.hyp) # check files
+ assert len(opt.cfg) or len(
+ opt.weights), 'either --cfg or --weights must be specified'
+ # extend to 2 sizes (train, test)
+ opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))
+ opt.name = 'evolve' if opt.evolve else opt.name
+ opt.save_dir = increment_path(Path(
+ opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run
+
+ # DDP mode
+ device = select_device(opt.device, batch_size=opt.batch_size)
+ if opt.local_rank != -1:
+ assert torch.cuda.device_count() > opt.local_rank
+ torch.cuda.set_device(opt.local_rank)
+ device = torch.device('cuda', opt.local_rank)
+ # distributed backend
+ dist.init_process_group(backend='nccl', init_method='env://')
+ assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
+ opt.batch_size = opt.total_batch_size // opt.world_size
+
+ # Hyperparameters
+ with open(opt.hyp) as f:
+ hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps
+ if 'box' not in hyp:
+ warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' %
+ (opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120'))
+ hyp['box'] = hyp.pop('giou')
+
+ # Train
+ logger.info(opt)
+ if not opt.evolve:
+ tb_writer = None # init loggers
+ if opt.global_rank in [-1, 0]:
+ logger.info(
+ f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')
+ tb_writer = SummaryWriter(opt.save_dir) # Tensorboard
+ train(hyp, opt, device, tb_writer, wandb)
+
+ # Evolve hyperparameters (optional)
+ else:
+ # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
+ meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
+ # final OneCycleLR learning rate (lr0 * lrf)
+ 'lrf': (1, 0.01, 1.0),
+ 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
+ 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
+ # warmup epochs (fractions ok)
+ 'warmup_epochs': (1, 0.0, 5.0),
+ # warmup initial momentum
+ 'warmup_momentum': (1, 0.0, 0.95),
+ 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
+ 'box': (1, 0.02, 0.2), # box loss gain
+ 'cls': (1, 0.2, 4.0), # cls loss gain
+ 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
+ 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
+ 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
+ 'iou_t': (0, 0.1, 0.7), # IoU training threshold
+ 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
+ # anchors per output grid (0 to ignore)
+ 'anchors': (2, 2.0, 10.0),
+ # focal loss gamma (efficientDet default gamma=1.5)
+ 'fl_gamma': (0, 0.0, 2.0),
+ # image HSV-Hue augmentation (fraction)
+ 'hsv_h': (1, 0.0, 0.1),
+ # image HSV-Saturation augmentation (fraction)
+ 'hsv_s': (1, 0.0, 0.9),
+ # image HSV-Value augmentation (fraction)
+ 'hsv_v': (1, 0.0, 0.9),
+ 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
+ # image translation (+/- fraction)
+ 'translate': (1, 0.0, 0.9),
+ 'scale': (1, 0.0, 0.9), # image scale (+/- gain)
+ 'shear': (1, 0.0, 10.0), # image shear (+/- deg)
+ # image perspective (+/- fraction), range 0-0.001
+ 'perspective': (0, 0.0, 0.001),
+ # image flip up-down (probability)
+ 'flipud': (1, 0.0, 1.0),
+ # image flip left-right (probability)
+ 'fliplr': (0, 0.0, 1.0),
+ 'mosaic': (1, 0.0, 1.0), # image mixup (probability)
+ 'mixup': (1, 0.0, 1.0)} # image mixup (probability)
+
+ assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
+ opt.notest, opt.nosave = True, True # only test/save final epoch
+ # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
+ yaml_file = Path(opt.save_dir) / \
+ 'hyp_evolved.yaml' # save best result here
+ if opt.bucket:
+ os.system('gsutil cp gs://%s/evolve.txt .' %
+ opt.bucket) # download evolve.txt if exists
+
+ for _ in range(300): # generations to evolve
+ if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
+ # Select parent(s)
+ parent = 'single' # parent selection method: 'single' or 'weighted'
+ x = np.loadtxt('evolve.txt', ndmin=2)
+ # number of previous results to consider
+ n = min(5, len(x))
+ x = x[np.argsort(-fitness(x))][:n] # top n mutations
+ w = fitness(x) - fitness(x).min() # weights
+ if parent == 'single' or len(x) == 1:
+ # x = x[random.randint(0, n - 1)] # random selection
+ x = x[random.choices(range(n), weights=w)[
+ 0]] # weighted selection
+ elif parent == 'weighted':
+ x = (x * w.reshape(n, 1)).sum(0) / \
+ w.sum() # weighted combination
+
+ # Mutate
+ mp, s = 0.8, 0.2 # mutation probability, sigma
+ npr = np.random
+ npr.seed(int(time.time()))
+ g = np.array([x[0] for x in meta.values()]) # gains 0-1
+ ng = len(meta)
+ v = np.ones(ng)
+ while all(v == 1): # mutate until a change occurs (prevent duplicates)
+ v = (g * (npr.random(ng) < mp) * npr.randn(ng)
+ * npr.random() * s + 1).clip(0.3, 3.0)
+ for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
+ hyp[k] = float(x[i + 7] * v[i]) # mutate
+
+ # Constrain to limits
+ for k, v in meta.items():
+ hyp[k] = max(hyp[k], v[1]) # lower limit
+ hyp[k] = min(hyp[k], v[2]) # upper limit
+ hyp[k] = round(hyp[k], 5) # significant digits
+
+ # Train mutation
+ results = train(hyp.copy(), opt, device, wandb=wandb)
+
+ # Write mutation results
+ print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
+
+ # Plot results
+ plot_evolution(yaml_file)
+ print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
+ f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
+
+ def detect(self):
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', nargs='+', type=str, default='activelearning/yolov5s.pt', help='model.pt path(s)')
+ parser.add_argument('--source', type=str, default='data/test', help='source') # file/folder, 0 for webcam
+ parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
+ parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
+ parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
+ parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ parser.add_argument('--view-img', action='store_true', help='display results')
+ # parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+ parser.add_argument('--save-txt', type=int, default=1, help='save results to *.txt')
+ parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+ parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
+ parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
+ parser.add_argument('--augment', action='store_true', help='augmented inference')
+ parser.add_argument('--update', action='store_true', help='update all models')
+ parser.add_argument('--project', default='runs/detect', help='save results to project/name')
+ parser.add_argument('--name', default='gun', help='save results to project/name')
+ # parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+ parser.add_argument('--exist-ok', type=int, default=1, help='existing project/name ok, do not increment')
+ opt = parser.parse_args()
+ print(opt)
+
+ with torch.no_grad():
+ if opt.update: # update all models (to fix SourceChangeWarning)
+ for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
+ detect(opt)
+ strip_optimizer(opt.weights)
+ else:
+ detect(opt)
+
+if __name__ == '__main__':
+ model = Yolov5()
+ model.train()

__pycache__/detect.cpython-36.pyc

@@ -0,0 +1,28 @@
+""" THUẬT TOÁN ACTIVE LEARNING
+Require:
+ - Tập các mẫu đã gán nhãn L
+ - Tạp các mẫu chưa gán nhãn U
+ - Model khởi tạo f0
+ - Active learning metric v
+Algorithm:
+1. Chia U thành các batches
+2. f <- f0
+3. Nếu U vẫn trống hoặc chưa đạt điều kiện dừng thì:
+ - Tính scores cho tất cả batches của U sử dụng f
+ - U_best <- Batches điểm cao nhất trong U theo v
+ - Y_best <- Gán nhãn cho U_best (người)
+ - Train f sử dụng L và (U_best, Y_best)
+ - U = U\U_best
+ - L = L + (U_best, y_best)
+"""
+
+class ActiveLearning(object):
+ def __init__(self, model, select_function):
+ self.model = model
+ self.select_function = select_function
+
+ def run(self, X_train, y_train, X_test, y_test):
+ pass
+
+if __name__ == '__main__':
+ pass

__pycache__/train.cpython-36.pyc

@@ -0,0 +1,9 @@
+class BaseModel(object):
+ def __init__(self):
+ pass
+
+ def train(self):
+ pass
+
+ def detect(self):
+ pass

activelearning/__pycache__/base_model.cpython-36.pyc

@@ -0,0 +1 @@
+max_queried = 500

activelearning/active_learning.py

@@ -0,0 +1,22 @@
+class BaseSelectFunction(object):
+ def __init__(self):
+ pass
+ 
+ def select(self):
+ pass
+
+
+class RandomSelect(BaseSelectFunction):
+ @staticmethod
+ def select(probas_val):
+ pass
+
+class EntropySelect(BaseSelectFunction):
+ @staticmethod
+ def select(probas_val):
+ pass
+
+class MarginSamplingSelect(BaseSelectFunction):
+ @staticmethod
+ def select(probas_val):
+ pass