New CSV Logger (#4148)
* New CSV Logger * cleanup * move batch plots into Logger * rename comment * Remove total loss from progress bar * mloss :-1 bug fix * Update plot_results() * Update plot_results() * plot_results bug fixpull/4162/head
Glenn Jocher
GitHub
parent
3764277f95
commit
96e36a7c91
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@ -31,6 +31,7 @@ data/*
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!data/*.sh
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results*.txt
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results*.csv
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# Datasets -------------------------------------------------------------------------------------------------------------
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coco/
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40
train.py
40
train.py
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@ -12,7 +12,6 @@ import sys
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import time
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from copy import deepcopy
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from pathlib import Path
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from threading import Thread
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import math
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import numpy as np
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@ -38,7 +37,7 @@ from utils.general import labels_to_class_weights, increment_path, labels_to_ima
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check_requirements, print_mutation, set_logging, one_cycle, colorstr
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from utils.google_utils import attempt_download
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from utils.loss import ComputeLoss
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from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
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from utils.plots import plot_labels, plot_evolution
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from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel
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from utils.loggers.wandb.wandb_utils import check_wandb_resume
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from utils.metrics import fitness
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@ -61,7 +60,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# Directories
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w = save_dir / 'weights' # weights dir
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w.mkdir(parents=True, exist_ok=True) # make dir
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last, best, results_file = w / 'last.pt', w / 'best.pt', save_dir / 'results.txt'
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last, best = w / 'last.pt', w / 'best.pt'
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# Hyperparameters
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if isinstance(hyp, str):
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@ -88,7 +87,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# Loggers
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if RANK in [-1, 0]:
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loggers = Loggers(save_dir, results_file, weights, opt, hyp, data_dict, LOGGER).start() # loggers dict
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loggers = Loggers(save_dir, weights, opt, hyp, data_dict, LOGGER).start() # loggers dict
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if loggers.wandb and resume:
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weights, epochs, hyp, data_dict = opt.weights, opt.epochs, opt.hyp, loggers.wandb.data_dict
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@ -167,10 +166,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
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ema.updates = ckpt['updates']
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# Results
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if ckpt.get('training_results') is not None:
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results_file.write_text(ckpt['training_results']) # write results.txt
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# Epochs
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start_epoch = ckpt['epoch'] + 1
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if resume:
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@ -275,11 +270,11 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
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# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
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mloss = torch.zeros(4, device=device) # mean losses
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mloss = torch.zeros(3, device=device) # mean losses
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if RANK != -1:
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train_loader.sampler.set_epoch(epoch)
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pbar = enumerate(train_loader)
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LOGGER.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
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LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
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if RANK in [-1, 0]:
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pbar = tqdm(pbar, total=nb) # progress bar
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optimizer.zero_grad()
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@ -327,20 +322,13 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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ema.update(model)
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last_opt_step = ni
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# Print
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# Log
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if RANK in [-1, 0]:
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
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s = ('%10s' * 2 + '%10.4g' * 6) % (
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f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])
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pbar.set_description(s)
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# Plot
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if plots:
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if ni < 3:
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f = save_dir / f'train_batch{ni}.jpg' # filename
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Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
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loggers.on_train_batch_end(ni, model, imgs)
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pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
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f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
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loggers.on_train_batch_end(ni, model, imgs, targets, paths, plots)
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# end batch ------------------------------------------------------------------------------------------------
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@ -371,13 +359,12 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
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if fi > best_fitness:
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best_fitness = fi
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loggers.on_train_val_end(mloss, results, lr, epoch, s, best_fitness, fi)
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loggers.on_train_val_end(mloss, results, lr, epoch, best_fitness, fi)
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# Save model
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if (not nosave) or (final_epoch and not evolve): # if save
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ckpt = {'epoch': epoch,
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'best_fitness': best_fitness,
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'training_results': results_file.read_text(),
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'model': deepcopy(de_parallel(model)).half(),
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'ema': deepcopy(ema.ema).half(),
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'updates': ema.updates,
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@ -395,9 +382,6 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# end training -----------------------------------------------------------------------------------------------------
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if RANK in [-1, 0]:
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LOGGER.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
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if plots:
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plot_results(save_dir=save_dir) # save as results.png
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if not evolve:
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if is_coco: # COCO dataset
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for m in [last, best] if best.exists() else [last]: # speed, mAP tests
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@ -411,13 +395,11 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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save_dir=save_dir,
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save_json=True,
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plots=False)
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# Strip optimizers
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for f in last, best:
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if f.exists():
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strip_optimizer(f) # strip optimizers
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loggers.on_train_end(last, best)
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loggers.on_train_end(last, best, plots)
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torch.cuda.empty_cache()
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return results
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@ -1,15 +1,17 @@
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# YOLOv5 experiment logging utils
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import warnings
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from threading import Thread
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from utils.general import colorstr, emojis
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from utils.loggers.wandb.wandb_utils import WandbLogger
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from utils.plots import plot_images, plot_results
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from utils.torch_utils import de_parallel
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LOGGERS = ('txt', 'tb', 'wandb') # text-file, TensorBoard, Weights & Biases
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LOGGERS = ('csv', 'tb', 'wandb') # text-file, TensorBoard, Weights & Biases
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try:
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import wandb
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@ -21,10 +23,8 @@ except (ImportError, AssertionError):
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class Loggers():
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# YOLOv5 Loggers class
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def __init__(self, save_dir=None, results_file=None, weights=None, opt=None, hyp=None,
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data_dict=None, logger=None, include=LOGGERS):
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def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, data_dict=None, logger=None, include=LOGGERS):
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self.save_dir = save_dir
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self.results_file = results_file
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self.weights = weights
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self.opt = opt
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self.hyp = hyp
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@ -35,7 +35,7 @@ class Loggers():
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setattr(self, k, None) # init empty logger dictionary
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def start(self):
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self.txt = True # always log to txt
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self.csv = True # always log to csv
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# Message
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try:
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@ -63,15 +63,19 @@ class Loggers():
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return self
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def on_train_batch_end(self, ni, model, imgs):
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def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
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# Callback runs on train batch end
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if ni == 0:
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with warnings.catch_warnings():
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warnings.simplefilter('ignore') # suppress jit trace warning
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self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
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if self.wandb and ni == 10:
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files = sorted(self.save_dir.glob('train*.jpg'))
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self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
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if plots:
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if ni == 0:
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with warnings.catch_warnings():
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warnings.simplefilter('ignore') # suppress jit trace warning
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self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
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if ni < 3:
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f = self.save_dir / f'train_batch{ni}.jpg' # filename
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Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
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if self.wandb and ni == 10:
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files = sorted(self.save_dir.glob('train*.jpg'))
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self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
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def on_train_epoch_end(self, epoch):
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# Callback runs on train epoch end
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@ -89,21 +93,28 @@ class Loggers():
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files = sorted(self.save_dir.glob('val*.jpg'))
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self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
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def on_train_val_end(self, mloss, results, lr, epoch, s, best_fitness, fi):
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# Callback runs on validation end during training
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vals = list(mloss[:-1]) + list(results) + lr
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tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
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'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
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def on_train_val_end(self, mloss, results, lr, epoch, best_fitness, fi):
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# Callback runs on val end during training
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vals = list(mloss) + list(results) + lr
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keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
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'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', # metrics
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'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
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'x/lr0', 'x/lr1', 'x/lr2'] # params
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if self.txt:
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with open(self.results_file, 'a') as f:
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f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
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x = {k: v for k, v in zip(keys, vals)} # dict
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if self.csv:
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file = self.save_dir / 'results.csv'
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n = len(x) + 1 # number of cols
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s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # add header
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with open(file, 'a') as f:
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f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
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if self.tb:
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for x, tag in zip(vals, tags):
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self.tb.add_scalar(tag, x, epoch) # TensorBoard
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for k, v in x.items():
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self.tb.add_scalar(k, v, epoch) # TensorBoard
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if self.wandb:
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self.wandb.log({k: v for k, v in zip(tags, vals)})
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self.wandb.log(x)
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self.wandb.end_epoch(best_result=best_fitness == fi)
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def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
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@ -112,8 +123,10 @@ class Loggers():
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if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
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self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
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def on_train_end(self, last, best):
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def on_train_end(self, last, best, plots):
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# Callback runs on training end
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if plots:
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plot_results(dir=self.save_dir) # save results.png
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files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
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files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
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if self.wandb:
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@ -162,8 +162,7 @@ class ComputeLoss:
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lcls *= self.hyp['cls']
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bs = tobj.shape[0] # batch size
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loss = lbox + lobj + lcls
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return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
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return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
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def build_targets(self, p, targets):
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# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
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@ -1,7 +1,5 @@
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# Plotting utils
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import glob
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import os
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from copy import copy
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from pathlib import Path
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@ -387,63 +385,29 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
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plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
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def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay()
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# Plot training 'results*.txt', overlaying train and val losses
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s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends
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t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
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for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
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results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
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n = results.shape[1] # number of rows
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x = range(start, min(stop, n) if stop else n)
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fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
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ax = ax.ravel()
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for i in range(5):
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for j in [i, i + 5]:
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y = results[j, x]
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ax[i].plot(x, y, marker='.', label=s[j])
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# y_smooth = butter_lowpass_filtfilt(y)
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# ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
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ax[i].set_title(t[i])
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ax[i].legend()
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ax[i].set_ylabel(f) if i == 0 else None # add filename
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fig.savefig(f.replace('.txt', '.png'), dpi=200)
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def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
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# Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
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def plot_results(file='', dir=''):
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# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
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save_dir = Path(file).parent if file else Path(dir)
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fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
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ax = ax.ravel()
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s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
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'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
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if bucket:
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# files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
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files = ['results%g.txt' % x for x in id]
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c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
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os.system(c)
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else:
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files = list(Path(save_dir).glob('results*.txt'))
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assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
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files = list(save_dir.glob('results*.csv'))
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assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
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for fi, f in enumerate(files):
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try:
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results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
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n = results.shape[1] # number of rows
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x = range(start, min(stop, n) if stop else n)
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for i in range(10):
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y = results[i, x]
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if i in [0, 1, 2, 5, 6, 7]:
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y[y == 0] = np.nan # don't show zero loss values
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# y /= y[0] # normalize
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label = labels[fi] if len(labels) else f.stem
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ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
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ax[i].set_title(s[i])
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# if i in [5, 6, 7]: # share train and val loss y axes
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data = pd.read_csv(f)
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s = [x.strip() for x in data.columns]
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x = data.values[:, 0]
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for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
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y = data.values[:, j]
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# y[y == 0] = np.nan # don't show zero values
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ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
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ax[i].set_title(s[j], fontsize=12)
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# if j in [8, 9, 10]: # share train and val loss y axes
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# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
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except Exception as e:
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print('Warning: Plotting error for %s; %s' % (f, e))
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print(f'Warning: Plotting error for {f}: {e}')
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ax[1].legend()
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fig.savefig(Path(save_dir) / 'results.png', dpi=200)
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fig.savefig(save_dir / 'results.png', dpi=200)
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def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
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|
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2
val.py
2
val.py
|
|
@ -171,7 +171,7 @@ def run(data,
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# Compute loss
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if compute_loss:
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loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls
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loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls
|
||||
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||||
# Run NMS
|
||||
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
|
||||
|
|
|
|||
Loading…
Reference in New Issue