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#!/usr/bin/env python3
# Copyright (c) Megvii Inc. All rights reserved.
import ast
import pprint
from abc import ABCMeta, abstractmethod
from typing import Dict, List, Tuple
from tabulate import tabulate
import torch
from torch.nn import Module
from yolox.utils import LRScheduler
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
self.dataset = None
@abstractmethod
def get_model(self) -> Module:
pass
@abstractmethod
def get_dataset(self, cache: bool = False, cache_type: str = "ram"):
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, f"length must be even, check value here: {cfg_list}"
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)
# pre-process input if source type is list or tuple
if isinstance(src_value, (List, Tuple)):
v = v.strip("[]()")
v = [t.strip() for t in v.split(",")]
# find type of tuple
if len(src_value) > 0:
src_item_type = type(src_value[0])
v = [src_item_type(t) for t in v]
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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