File size: 4,678 Bytes
cc9c7ee |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import os
import json
import torch
from torch.autograd import Variable
from torch.utils.tensorboard import SummaryWriter
# from torchvision.utils import make_grid
# from torchviz import make_dot
class Logger:
""""""
def __init__(self, model, trainer, log_dir, comment=None):
"""Initializer for Logger Class
Args:
"""
self.model_path = os.path.join(log_dir, model, trainer)
self.writer = SummaryWriter(log_dir=self.model_path, comment=comment)
try:
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not (os.path.exists(self.model_path)):
os.makedirs(self.model_path)
else:
pass
# print("Directory Already Exists.")
except Exception as e:
print(e)
print("Failed to Create Log Directory.")
def save_params(
self,
param_list,
param_name_list,
epoch=None,
batch_size=None,
batch=None,
combine=False,
combine_name=None,
global_step=None,
):
if combine == False:
for i in range(len(param_list)):
if isinstance(param_list[i], Variable):
param_list[i] = param_list[i].data.cpu().numpy()
if global_step is None:
self.writer.add_scalar(
param_name_list[i],
param_list[i],
Logger._global_step(epoch, batch_size, batch),
)
else:
self.writer.add_scalar(
param_name_list[i], param_list[i], global_step
)
else:
scalar_dict = dict(zip(param_name_list, param_list))
if global_step is None:
self.writer.add_scalars(
combine_name,
scalar_dict,
Logger._global_step(epoch, batch_size, batch),
)
else:
self.writer.add_scalars(combine_name, scalar_dict, global_step)
def save_batch_images(
self, image_name, image_batch, epoch, batch_size, batch=None, dataformats="CHW"
):
self.writer.add_images(
image_name,
image_batch,
Logger._global_step(epoch, batch_size, batch),
dataformats=dataformats,
)
def save_prcurve(self, labels, preds, epoch, batch_size, batch=None):
self.writer.add_pr_curve(
"pr_curve", labels, preds, Logger._global_step(epoch, batch_size, batch)
)
def save_hyperparams(
self, hparam_list, hparam_name_list, metric_list, metric_name_list
):
for i in range(len(hparam_list)):
if isinstance(hparam_list[i], list):
hparam_list[i] = ",".join(list(map(str, hparam_list[i])))
if isinstance(hparam_list[i], dict):
hparam_list[i] = json.dumps(hparam_list[i])
if hparam_list[i] is None:
hparam_list[i] = "None"
print(hparam_list, hparam_name_list, metric_list, metric_name_list)
self.writer.add_hparams(
dict(zip(hparam_name_list, hparam_list)),
dict(zip(metric_name_list, metric_list)),
)
def save_models(self, model_list, model_names_list, epoch):
for model_name, model in zip(model_names_list, model_list):
torch.save(model.state_dict(), os.path.join(self.model_path, model_name))
def save_fig(self, fig, fig_name, epoch, batch_size, batch=None):
self.writer.add_figure(
fig_name, fig, Logger._global_step(epoch, batch_size, batch)
)
# def display_params(self,
# params_list, params_name_list, epoch, num_epochs, batch_size, batch
# ):
# for i in range(len(params_list)):
# if isinstance(params_list[i], Variable):
# params_list[i] = params_list[i].data.cpu().numpy()
# print("Epoch: {}/{}, Batch: {}/{}".format(epoch, num_epochs, batch, batch_size))
# for i in range(len(params_list)):
# print("{}:{}".format(params_name_list[i], params_list[i]))
#
# def draw_model_architecture(self,model, output, input, input_name, save_name):
# make_dot(
# output, params=dict(list(model.named_parameters())) + [(input_name, input)]
# )
def close(self):
self.writer.close()
@staticmethod
def _global_step(epoch, batch_size, batch):
if batch:
return epoch * batch_size + batch
else:
return epoch
|