| |
| import logging |
| import os |
| from datetime import datetime |
| from types import MethodType |
|
|
| import torch |
| import torch.nn.functional as F |
| from mmcv.utils import get_logger |
| from package_utils.utils import make_dir |
|
|
| LOG_DIR = "logs/{}".format(datetime.today().strftime("%d-%m-%Y")) |
| make_dir(LOG_DIR) |
|
|
|
|
| class Logger: |
| def __init__(self, task="training", workdir=LOG_DIR): |
| super().__init__() |
| self.logger = logging.getLogger("") |
| self.logger.setLevel(logging.INFO) |
|
|
| file_handler = logging.FileHandler("{}/{}.log".format(workdir, task)) |
| stream_handler = logging.StreamHandler() |
| self.logger.addHandler(file_handler) |
| self.logger.addHandler(stream_handler) |
| self.logger.epochInfor = MethodType(self.epochInfor, self.logger) |
| self.info = self.logger.info |
|
|
| def epochInfor( |
| self, |
| epoch, |
| idx, |
| length, |
| batch_time, |
| speed, |
| data_time, |
| losses, |
| acc, |
| loss_cls=None, |
| **kwargs, |
| ): |
| msg = ( |
| "Epoch: [{0}][{1}/{2}]\t" |
| "Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t" |
| "Speed {speed:.1f} samples/s\t" |
| "Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t" |
| "Loss {loss.val:.5f} ({loss.avg:.5f})\t" |
| "Accuracy {acc.val:.3f} ({acc.avg:.3f})".format( |
| epoch, |
| idx, |
| length, |
| batch_time=batch_time, |
| speed=speed, |
| data_time=data_time, |
| loss=losses, |
| acc=acc, |
| ) |
| ) |
| if loss_cls is not None: |
| msg += "\t Cls Loss: {loss_cls:.5f}".format(loss_cls=loss_cls) |
|
|
| for k, v in kwargs.items(): |
| if v is not None: |
| msg += f"\t {k}: {v:.5f}" |
|
|
| self.logger.info(msg) |
|
|
|
|
| def board_writing(writer, loss, acc, iterations, dataset="Train"): |
| writer.add_scalar("{}/loss".format(dataset), loss, iterations) |
| writer.add_scalar("{}/acc".format(dataset), acc, iterations) |
|
|
|
|
| def debug_writing(writer, outputs, labels, inputs, iterations): |
| tmp_tar = torch.unsqueeze(labels.cpu().data[0], dim=1) |
| |
|
|
| tmp_inp = inputs.cpu().data[0] |
| tmp_inp[0] += 0.406 |
| tmp_inp[1] += 0.457 |
| tmp_inp[2] += 0.480 |
|
|
| tmp_inp[0] += torch.sum( |
| F.interpolate(tmp_tar, scale_factor=4, mode="bilinear"), dim=0 |
| )[0] |
| tmp_inp.clamp_(0, 1) |
|
|
| writer.add_image("Data/input", tmp_inp, iterations) |
|
|
|
|
| def get_root_logger(log_file=None, log_level=logging.INFO): |
| """Use `get_logger` method in mmcv to get the root logger. |
| |
| The logger will be initialized if it has not been initialized. By default a |
| StreamHandler will be added. If `log_file` is specified, a FileHandler will |
| also be added. The name of the root logger is the top-level package name, |
| e.g., "mmpose". |
| |
| Args: |
| log_file (str | None): The log filename. If specified, a FileHandler |
| will be added to the root logger. |
| log_level (int): The root logger level. Note that only the process of |
| rank 0 is affected, while other processes will set the level to |
| "Error" and be silent most of the time. |
| |
| Returns: |
| logging.Logger: The root logger. |
| """ |
| return get_logger(LOG_DIR, log_file, log_level) |
|
|
