Spaces:
Sleeping
Sleeping
File size: 6,258 Bytes
2875fe6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
import os
import sys
import yaml
import json
import torch
import random
import warnings
import importlib
import numpy as np
def load_yaml_config(path):
with open(path) as f:
config = yaml.full_load(f)
return config
def save_config_to_yaml(config, path):
assert path.endswith(".yaml")
with open(path, "w") as f:
f.write(yaml.dump(config))
f.close()
def save_dict_to_json(d, path, indent=None):
json.dump(d, open(path, "w"), indent=indent)
def load_dict_from_json(path):
return json.load(open(path, "r"))
def write_args(args, path):
args_dict = dict(
(name, getattr(args, name)) for name in dir(args) if not name.startswith("_")
)
with open(path, "a") as args_file:
args_file.write("==> torch version: {}\n".format(torch.__version__))
args_file.write(
"==> cudnn version: {}\n".format(torch.backends.cudnn.version())
)
args_file.write("==> Cmd:\n")
args_file.write(str(sys.argv))
args_file.write("\n==> args:\n")
for k, v in sorted(args_dict.items()):
args_file.write(" %s: %s\n" % (str(k), str(v)))
args_file.close()
def seed_everything(seed, cudnn_deterministic=False):
"""
Function that sets seed for pseudo-random number generators in:
pytorch, numpy, python.random
Args:
seed: the integer value seed for global random state
"""
if seed is not None:
print(f"Global seed set to {seed}")
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = False
if cudnn_deterministic:
torch.backends.cudnn.deterministic = True
warnings.warn(
"You have chosen to seed training. "
"This will turn on the CUDNN deterministic setting, "
"which can slow down your training considerably! "
"You may see unexpected behavior when restarting "
"from checkpoints."
)
def merge_opts_to_config(config, opts):
def modify_dict(c, nl, v):
if len(nl) == 1:
c[nl[0]] = type(c[nl[0]])(v)
else:
# print(nl)
c[nl[0]] = modify_dict(c[nl[0]], nl[1:], v)
return c
if opts is not None and len(opts) > 0:
assert (
len(opts) % 2 == 0
), "each opts should be given by the name and values! The length shall be even number!"
for i in range(len(opts) // 2):
name = opts[2 * i]
value = opts[2 * i + 1]
config = modify_dict(config, name.split("."), value)
return config
def modify_config_for_debug(config):
config["dataloader"]["num_workers"] = 0
config["dataloader"]["batch_size"] = 1
return config
def get_model_parameters_info(model):
# for mn, m in model.named_modules():
parameters = {"overall": {"trainable": 0, "non_trainable": 0, "total": 0}}
for child_name, child_module in model.named_children():
parameters[child_name] = {"trainable": 0, "non_trainable": 0}
for pn, p in child_module.named_parameters():
if p.requires_grad:
parameters[child_name]["trainable"] += p.numel()
else:
parameters[child_name]["non_trainable"] += p.numel()
parameters[child_name]["total"] = (
parameters[child_name]["trainable"]
+ parameters[child_name]["non_trainable"]
)
parameters["overall"]["trainable"] += parameters[child_name]["trainable"]
parameters["overall"]["non_trainable"] += parameters[child_name][
"non_trainable"
]
parameters["overall"]["total"] += parameters[child_name]["total"]
# format the numbers
def format_number(num):
K = 2**10
M = 2**20
G = 2**30
if num > G: # K
uint = "G"
num = round(float(num) / G, 2)
elif num > M:
uint = "M"
num = round(float(num) / M, 2)
elif num > K:
uint = "K"
num = round(float(num) / K, 2)
else:
uint = ""
return "{}{}".format(num, uint)
def format_dict(d):
for k, v in d.items():
if isinstance(v, dict):
format_dict(v)
else:
d[k] = format_number(v)
format_dict(parameters)
return parameters
def format_seconds(seconds):
h = int(seconds // 3600)
m = int(seconds // 60 - h * 60)
s = int(seconds % 60)
d = int(h // 24)
h = h - d * 24
if d == 0:
if h == 0:
if m == 0:
ft = "{:02d}s".format(s)
else:
ft = "{:02d}m:{:02d}s".format(m, s)
else:
ft = "{:02d}h:{:02d}m:{:02d}s".format(h, m, s)
else:
ft = "{:d}d:{:02d}h:{:02d}m:{:02d}s".format(d, h, m, s)
return ft
def instantiate_from_config(config):
if config is None:
return None
if not "target" in config:
raise KeyError("Expected key `target` to instantiate.")
module, cls = config["target"].rsplit(".", 1)
cls = getattr(importlib.import_module(module, package=None), cls)
return cls(**config.get("params", dict()))
def class_from_string(class_name):
module, cls = class_name.rsplit(".", 1)
cls = getattr(importlib.import_module(module, package=None), cls)
return cls
def get_all_file(dir, end_with=".h5"):
if isinstance(end_with, str):
end_with = [end_with]
filenames = []
for root, dirs, files in os.walk(dir):
for f in files:
for ew in end_with:
if f.endswith(ew):
filenames.append(os.path.join(root, f))
break
return filenames
def get_sub_dirs(dir, abs=True):
sub_dirs = os.listdir(dir)
if abs:
sub_dirs = [os.path.join(dir, s) for s in sub_dirs]
return sub_dirs
def get_model_buffer(model):
state_dict = model.state_dict()
buffers_ = {}
params_ = {n: p for n, p in model.named_parameters()}
for k in state_dict:
if k not in params_:
buffers_[k] = state_dict[k]
return buffers_
|