import os
import shutil
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from deepsense import neptune
from torch.optim.lr_scheduler import ExponentialLR
from minerva.backend.models.pytorch.utils import overlay_box, overlay_keypoints, Averager, save_model
from minerva.backend.models.pytorch.validation import score_model_multi_output, predict_on_batch_multi_output
from minerva.utils import get_logger
logger = get_logger()
class Callback:
def __init__(self):
self.epoch_id = None
self.batch_id = None
self.model = None
self.optimizer = None
self.loss_function = None
self.validation_datagen = None
self.lr_scheduler = None
def set_params(self, transformer, validation_datagen):
self.model = transformer.model
self.optimizer = transformer.optimizer
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import importlib
import click
from minerva.utils import init_logger, setup_torch_multiprocessing, get_logger, SUBPROBLEM_INFERENCE, \
get_available_problems
logging = get_logger()
PROBLEMS_CHOICE = click.Choice(get_available_problems())
@click.group()
def action():
pass
@action.command()
@click.option('-p',
'--problem',
type=PROBLEMS_CHOICE,
help='problem to choose',
required=True)
@click.option('-d', '--dev_mode', help='dev mode on', is_flag=True)
def dry_train(problem, dev_mode):
dry_run(problem, train_mode=True, dev_mode=dev_mode)
@action.command()
