def __init__(self, project_name="deeplodocus_project", main_path: Optional[str] = None, force_overwrite: bool = False):
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Initialize a instance ready to create a new Deeplodocus project
PARAMETERS:
-----------
:param project_name(str, optional): The name of the project
:param main_path(str, optional): The path of the working directory
:param force_overwrite(bool, optional): Whether we want to overwrite an existing project or not
RETURN:
-------
None
"""
self.project_name = project_name
self.force_overwrite = force_overwrite
if main_path is None:
self.main_path = get_main_path()
else:
self.main_path = main_path
def __add_log(message: str) -> None:
"""
AUTHORS:
--------
:author: Alix Leroy
:author: Samuel Westlake
DESCRIPTION:
------------
Add a message to the logs.
PARAMETERS:
-----------
:param message (str): The message to save in the logs.
RETURN:
-------
:return: None
"""
Logs(log_type=DEEP_LOG_NOTIFICATION,
directory=get_main_path(),
extension=DEEP_EXT_LOGS).add(message)
import deeplodocus.app.metrics as deep_metrics
import deeplodocus.data.load as deep_sources
import torchvision.datasets as tv_data
DEEP_MODULE_OPTIMIZERS = {
"pytorch": {
"path": torch.optim.__path__,
"prefix": torch.optim.__name__
},
"deeplodocus": {
"path": deep_optim.__path__,
"prefix": deep_optim.__name__
},
"custom": {
"path": [get_main_path() + "/modules/optimizers"],
"prefix": "modules.optimizers"
}
}
DEEP_MODULE_SOURCES = {
"deeplodocus": {
"path": deep_sources.__path__,
"prefix": deep_sources.__name__
},
"custom": {
"path": ["%s/modules/sources" % get_main_path()],
"prefix": "modules.sources"
}
}
from deeplodocus.flags.ext import DEEP_EXT_CSV, DEEP_EXT_LOGS
from deeplodocus.utils import get_main_path
DEEP_LOG_NOTIFICATION = "notification"
DEEP_LOG_HISTORY_TRAIN_BATCHES = "history_train_batches"
DEEP_LOG_HISTORY_TRAIN_EPOCHS = "history_train_epochs"
DEEP_LOG_HISTORY_VALIDATION = "history_validation"
DEEP_LOGS = {
DEEP_LOG_NOTIFICATION: [get_main_path(), DEEP_EXT_LOGS],
DEEP_LOG_HISTORY_TRAIN_BATCHES: [get_main_path(), DEEP_EXT_CSV],
DEEP_LOG_HISTORY_TRAIN_EPOCHS: [get_main_path(), DEEP_EXT_CSV],
DEEP_LOG_HISTORY_VALIDATION: [get_main_path(), DEEP_EXT_CSV]
}
DEEP_LOG_RESULT_DIRECTORIES = ["logs", "weights", "history"]
from deeplodocus.utils import get_main_path DEEP_PATH_NOTIFICATION = r"%s/logs" % get_main_path() DEEP_PATH_HISTORY = r"%s/results/history" % get_main_path() DEEP_PATH_SAVE_MODEL = r"%s/results/models" % get_main_path()
from deeplodocus.utils import get_main_path
from deeplodocus.utils.flag import Flag
#
# ENTRIES
#
DEEP_ENTRY_INPUT = 0
DEEP_ENTRY_LABEL = 1
DEEP_ENTRY_OUTPUT = 2
DEEP_ENTRY_ADDITIONAL_DATA = 3
DEEP_ENTRY_INPUT = Flag(name="Input",
description="Input entry",
names=["input", "inputs", "inp", "x"])
DEEP_ENTRY_LABEL = Flag(name="Label",
description="Label entry",
names=[
"label", "labels", "lab", "expected output",
"expected_output", "y_expected", "y_hat"
])
DEEP_ENTRY_OUTPUT = Flag(name="Output",
description="Output entry",
names=["output", "outputs", "y"])
DEEP_ENTRY_ADDITIONAL_DATA = Flag(name="Additional data",
description="Additional Data entry",
names=["additional_data", "additional data"])
DEEP_ENTRY_BASE_FILE_NAME = get_main_path(
) + "/data/auto-generated_dataset_%s_entry_%s_%i_source_%i.dat"
import torch
import torch.nn.functional
from deeplodocus.utils import get_main_path
import deeplodocus.data.transforms as tfm
DEEP_MODULE_OPTIMIZERS = {
"pytorch": {
"path": torch.optim.__path__,
"prefix": torch.optim.__name__
},
"custom": {
"path": [get_main_path() + "/modules/optimizers"],
"prefix": "modules.optimizers"
}
}
DEEP_MODULE_MODELS = {
"custom": {
"path": [get_main_path() + "/modules/models"],
"prefix": "modules.models"
}
}
DEEP_MODULE_LOSSES = {
"pytorch": {
"path": torch.nn.__path__,
"prefix": torch.nn.__name__
},
"custom": {
"path": [get_main_path() + "/modules/losses"],
#
DEEP_ENTRY_INPUT = 0
DEEP_ENTRY_LABEL = 1
DEEP_ENTRY_OUTPUT = 2
DEEP_ENTRY_ADDITIONAL_DATA = 3
DEEP_ENTRY_INPUT = Flag(
name="Input",
description="Input entry",
names=["input", "inputs", "inp", "x"]
)
DEEP_ENTRY_LABEL = Flag(
name="Label",
description="Label entry",
names=["label", "labels", "lab", "expected output", "expected_output", "y_expected", "y_hat"]
)
DEEP_ENTRY_OUTPUT = Flag(
name="Output",
description="Output entry",
names=["output", "outputs", "y"]
)
DEEP_ENTRY_ADDITIONAL_DATA = Flag(
name="Additional data",
description="Additional Data entry",
names=["additional_data", "additional data"]
)
DEEP_ENTRY_BASE_FILE_NAME = get_main_path() + "/data/auto-generated_dataset_source_folder_%i.dat"
