def cfg_from_file(file_name, target=__C):
""" Load a config file and merge it into the default options.
"""
import yaml
with open(file_name, 'r') as f:
print('Loading YAML config file from %s' % f)
yaml_cfg = edict(yaml.load(f))
_merge_two_config(yaml_cfg, target)
import numpy as np import os import yaml import logging from collections import OrderedDict from nowcasting.helpers.ordered_easydict import OrderedEasyDict as edict C = edict() cfg = C # type: edict() # Random seed C.MOVINGMNIST = edict() C.MOVINGMNIST.DIGIT_NUM = 3
def save_movingmnist_cfg(dir_path):
tmp_cfg = edict()
tmp_cfg.MOVINGMNIST = cfg.MOVINGMNIST
tmp_cfg.MODEL = cfg.MODEL
save_cfg(dir_path=dir_path, source=tmp_cfg)
from nowcasting.helpers.ordered_easydict import OrderedEasyDict as edict
import numpy as np
import os
import torch
__C = edict()
cfg = __C
__C.GLOBAL = edict()
__C.GLOBAL.DEVICE = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
__C.GLOBAL.BATCH_SZIE = 2
for dirs in ['/home/hzzone/save', '/Users/hzzone/Downloads']:
if os.path.exists(dirs):
__C.GLOBAL.MODEL_SAVE_DIR = dirs
assert __C.GLOBAL.MODEL_SAVE_DIR is not None
__C.ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
__C.HKO_DATA_BASE_PATH = os.path.join(__C.ROOT_DIR, 'hko_data')
for dirs in [
'/Users/hzzone/Downloads/HKO-7_data/radarPNG',
'/home/hzzone/HKO-7/radarPNG'
]:
if os.path.exists(dirs):
__C.HKO_PNG_PATH = dirs
for dirs in [
'/Users/hzzone/Downloads/HKO-7_data/radarPNG_mask',
'/home/hzzone/HKO-7/radarPNG_mask'
]:
if os.path.exists(dirs):
__C.HKO_MASK_PATH = dirs
import numpy as np
import os
import yaml
import logging
from collections import OrderedDict
from nowcasting.helpers.ordered_easydict import OrderedEasyDict as edict
__C = edict()
cfg = __C # type: edict()
# Random seed
__C.SEED = None
# Dataset name
# Used by symbols factories who need to adjust for different
# inputs based on dataset used. Should be set by the script.
__C.DATASET = None
# Project directory, since config.py is supposed to be in $ROOT_DIR/nowcasting
__C.ROOT_DIR = os.path.abspath('/content/lstmnowcast')
__C.MNIST_PATH = os.path.join(__C.ROOT_DIR, 'mnist_data')
if not os.path.exists(__C.MNIST_PATH):
os.makedirs(__C.MNIST_PATH)
__C.HKO_DATA_BASE_PATH = os.path.join(__C.ROOT_DIR, 'hko_data')
# Append your path to the possible paths
# possible_hko_png_paths = [os.path.join('E:\\datasets\\HKO-data\\radarPNG\\radarPNG'),
# os.path.join(__C.HKO_DATA_BASE_PATH, 'radarPNG')]
# possible_hko_mask_paths = [os.path.join('E:\\datasets\\HKO-data\\radarPNG\\radarPNG_mask'),
# os.path.join(__C.HKO_DATA_BASE_PATH, 'radarPNG_mask')]
'memory.free',
'memory.used',
'utilization.gpu',
'utilization.memory'
)
def get_gpu_info(nvidia_smi_path='nvidia-smi', keys=DEFAULT_ATTRIBUTES, no_units=True):
nu_opt = '' if not no_units else ',nounits'
cmd = '%s --query-gpu=%s --format=csv,noheader%s' % (nvidia_smi_path, ','.join(keys), nu_opt)
output = subprocess.check_output(cmd, shell=True)
lines = output.decode().split('\n')
lines = [ line.strip() for line in lines if line.strip() != '' ]
return [ { k: v for k, v in zip(keys, line.split(', ')) } for line in lines ]
__C = edict()
cfg = __C
__C.GLOBAL = edict()
__C.GLOBAL.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if __C.GLOBAL.DEVICE.type == 'cuda':
gpu_info = get_gpu_info()
memory_use = np.array(list(map(int, [info['utilization.gpu'] for info in gpu_info])))
__C.GLOBAL.DEVICE = torch.device("cuda:%i" % (memory_use.argmin()) if torch.cuda.is_available() else "cpu")
__C.GLOBAL.BATCH_SZIE = 2
# for dirs in ['/home/hzzone/save', '/Users/hzzone/Downloads']:
# if os.path.exists(dirs):
# __C.GLOBAL.MODEL_SAVE_DIR = dirs
# assert __C.GLOBAL.MODEL_SAVE_DIR is not None
__C.ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
def save_icdm_cfg(dir_path):
tmp_cfg = edict()
tmp_cfg.ICDM = cfg.ICDM
tmp_cfg.MODEL = cfg.MODEL
save_cfg(dir_path=dir_path, source=tmp_cfg)
from nowcasting.helpers.ordered_easydict import OrderedEasyDict as edict
import numpy as np
import os
import torch
__C = edict()
cfg = __C
__C.GLOBAL = edict()
__C.GLOBAL.DEVICE = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
__C.GLOBAL.BATCH_SZIE = 10
for dirs in ['/home/s1818503/dissertation/save', '/Users/hzzone/Downloads']:
if os.path.exists(dirs):
__C.GLOBAL.MODEL_SAVE_DIR = dirs
assert __C.GLOBAL.MODEL_SAVE_DIR is not None
__C.ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
# __C.HKO_DATA_BASE_PATH = os.path.join(__C.ROOT_DIR, 'hko_data')
# for dirs in ['/Users/hzzone/Downloads/HKO-7_data/radarPNG', '/home/hzzone/HKO-7/radarPNG']:
# if os.path.exists(dirs):
# __C.HKO_PNG_PATH = dirs
# for dirs in ['/Users/hzzone/Downloads/HKO-7_data/radarPNG_mask', '/home/hzzone/HKO-7/radarPNG_mask']:
# if os.path.exists(dirs):
# __C.HKO_MASK_PATH = dirs
__C.HKO = edict()
__C.HKO.EVALUATION = edict()
__C.HKO.EVALUATION.THRESHOLDS = np.array([0.5, 2, 5, 10, 30])
__C.HKO.EVALUATION.CENTRAL_REGION = (120, 120, 360, 360)