示例#1
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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)
示例#2
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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
示例#3
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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)
示例#4
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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
示例#5
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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')]
示例#6
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    '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__), '..'))
示例#7
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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)
示例#8
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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)