File size: 3,413 Bytes
9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | # -*- coding: utf-8 -*-
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_out = torch.unsqueeze(outputs.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)
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