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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import torch
from torch.nn import Module
from yolox.utils import LRScheduler
import ast
import pprint
from abc import ABCMeta, abstractmethod
from tabulate import tabulate
from typing import Dict
class BaseExp(metaclass=ABCMeta):
"""Basic class for any experiment."""
def __init__(self):
self.seed = None
self.output_dir = "./YOLOX_outputs"
self.print_interval = 100
self.eval_interval = 10
@abstractmethod
def get_model(self) -> Module:
pass
@abstractmethod
def get_data_loader(
self, batch_size: int, is_distributed: bool
) -> Dict[str, torch.utils.data.DataLoader]:
pass
@abstractmethod
def get_optimizer(self, batch_size: int) -> torch.optim.Optimizer:
pass
@abstractmethod
def get_lr_scheduler(
self, lr: float, iters_per_epoch: int, **kwargs
) -> LRScheduler:
pass
@abstractmethod
def get_evaluator(self):
pass
@abstractmethod
def eval(self, model, evaluator, weights):
pass
def __repr__(self):
table_header = ["keys", "values"]
exp_table = [
(str(k), pprint.pformat(v))
for k, v in vars(self).items()
if not k.startswith("_")
]
return tabulate(exp_table, headers=table_header, tablefmt="fancy_grid")
def merge(self, cfg_list):
assert len(cfg_list) % 2 == 0
for k, v in zip(cfg_list[0::2], cfg_list[1::2]):
# only update value with same key
if hasattr(self, k):
src_value = getattr(self, k)
src_type = type(src_value)
if src_value is not None and src_type != type(v):
try:
v = src_type(v)
except Exception:
v = ast.literal_eval(v)
setattr(self, k, v)
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