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misc.py
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misc.py
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"""
Misc Hook
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""
import glob
import os
import shutil
import sys
import time
from collections import OrderedDict
import torch
import torch.utils.data
if sys.version_info >= (3, 10):
from collections.abc import Sequence
else:
from collections import Sequence
import ponder.utils.comm as comm
from ponder.engines.test import TESTERS
from ponder.utils.cache import shared_dict
from ponder.utils.comm import get_world_size, is_main_process, synchronize
from ponder.utils.timer import Timer
from .builder import HOOKS
from .default import HookBase
@HOOKS.register_module()
class IterationTimer(HookBase):
def __init__(self, warmup_iter=1):
self._warmup_iter = warmup_iter
self._start_time = time.perf_counter()
self._iter_timer = Timer()
self._remain_iter = 0
def before_train(self):
self._start_time = time.perf_counter()
self._remain_iter = self.trainer.max_epoch * len(self.trainer.train_loader)
def before_epoch(self):
self._iter_timer.reset()
def before_step(self):
data_time = self._iter_timer.seconds()
self.trainer.storage.put_scalar("data_time", data_time)
def after_step(self):
batch_time = self._iter_timer.seconds()
self._iter_timer.reset()
self.trainer.storage.put_scalar("batch_time", batch_time)
self._remain_iter -= 1
remain_time = self._remain_iter * self.trainer.storage.history("batch_time").avg
t_m, t_s = divmod(remain_time, 60)
t_h, t_m = divmod(t_m, 60)
remain_time = "{:02d}:{:02d}:{:02d}".format(int(t_h), int(t_m), int(t_s))
if "iter_info" in self.trainer.comm_info.keys():
info = (
"Data {data_time_val:.3f} ({data_time_avg:.3f}) "
"Batch {batch_time_val:.3f} ({batch_time_avg:.3f}) "
"Remain {remain_time} ".format(
data_time_val=self.trainer.storage.history("data_time").val,
data_time_avg=self.trainer.storage.history("data_time").avg,
batch_time_val=self.trainer.storage.history("batch_time").val,
batch_time_avg=self.trainer.storage.history("batch_time").avg,
remain_time=remain_time,
)
)
self.trainer.comm_info["iter_info"] += info
if self.trainer.comm_info["iter"] <= self._warmup_iter:
self.trainer.storage.history("data_time").reset()
self.trainer.storage.history("batch_time").reset()
@HOOKS.register_module()
class InformationWriter(HookBase):
def __init__(self):
self.curr_iter = 0
self.model_output_keys = []
def before_train(self):
self.trainer.comm_info["iter_info"] = ""
self.curr_iter = self.trainer.start_epoch * len(self.trainer.train_loader)
def before_step(self):
self.curr_iter += 1
# MSC pretrain do not have offset information. Comment the code for support MSC
# info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] " \
# "Scan {batch_size} ({points_num}) ".format(
# epoch=self.trainer.epoch + 1, max_epoch=self.trainer.max_epoch,
# iter=self.trainer.comm_info["iter"], max_iter=len(self.trainer.train_loader),
# batch_size=len(self.trainer.comm_info["input_dict"]["offset"]),
# points_num=self.trainer.comm_info["input_dict"]["offset"][-1]
# )
info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] ".format(
epoch=self.trainer.epoch + 1,
max_epoch=self.trainer.max_epoch,
iter=self.trainer.comm_info["iter"] + 1,
max_iter=len(self.trainer.train_loader),
)
self.trainer.comm_info["iter_info"] += info
def after_step(self):
if "model_output_dict" in self.trainer.comm_info.keys():
model_output_dict = self.trainer.comm_info["model_output_dict"]
self.model_output_keys = model_output_dict.keys()
for key in self.model_output_keys:
self.trainer.storage.put_scalar(key, model_output_dict[key].item())
for key in self.model_output_keys:
self.trainer.comm_info["iter_info"] += "{key}: {value:.4f} ".format(
key=key, value=self.trainer.storage.history(key).val
)
lr = self.trainer.optimizer.state_dict()["param_groups"][0]["lr"]
self.trainer.comm_info["iter_info"] += "Lr: {lr:.5f}".format(lr=lr)
self.trainer.logger.info(self.trainer.comm_info["iter_info"])
self.trainer.comm_info["iter_info"] = "" # reset iter info
if self.trainer.writer is not None:
self.trainer.writer.add_scalar("lr", lr, self.curr_iter)
for key in self.model_output_keys:
self.trainer.writer.add_scalar(
"train_batch/" + key,
self.trainer.storage.history(key).val,
self.curr_iter,
)
def after_epoch(self):
epoch_info = "Train result: "
for key in self.model_output_keys:
epoch_info += "{key}: {value:.4f} ".format(
key=key, value=self.trainer.storage.history(key).avg
)
self.trainer.logger.info(epoch_info)
if self.trainer.writer is not None:
for key in self.model_output_keys:
self.trainer.writer.add_scalar(
"train/" + key,
self.trainer.storage.history(key).avg,
self.trainer.epoch + 1,
)
@HOOKS.register_module()
class CheckpointSaver(HookBase):
def __init__(self, save_freq=None):
self.save_freq = save_freq # None or int, None indicate only save model last
def after_epoch(self):
if is_main_process():
is_best = False
if self.trainer.cfg.evaluate:
current_metric_value = self.trainer.comm_info["current_metric_value"]
current_metric_name = self.trainer.comm_info["current_metric_name"]
if current_metric_value > self.trainer.best_metric_value:
self.trainer.best_metric_value = current_metric_value
is_best = True
self.trainer.logger.info(
"Best validation {} updated to: {:.4f}".format(
current_metric_name, current_metric_value
)
)
self.trainer.logger.info(
"Currently Best {}: {:.4f}".format(
current_metric_name, self.trainer.best_metric_value
)
)
filename = os.path.join(
self.trainer.cfg.save_path, "model", "model_last.pth"
)
self.trainer.logger.info("Saving checkpoint to: " + filename)
torch.save(
{
"epoch": self.trainer.epoch + 1,
"state_dict": self.trainer.model.state_dict(),
"optimizer": self.trainer.optimizer.state_dict(),
"scheduler": self.trainer.scheduler.state_dict(),
"scaler": (
self.trainer.scaler.state_dict()
if self.trainer.cfg.enable_amp
else None
),
"best_metric_value": self.trainer.best_metric_value,
},
filename + ".tmp",
)
os.replace(filename + ".tmp", filename)
if is_best:
shutil.copyfile(
filename,
os.path.join(self.trainer.cfg.save_path, "model", "model_best.pth"),
)
if self.save_freq and (self.trainer.epoch + 1) % self.save_freq == 0:
shutil.copyfile(
filename,
os.path.join(
self.trainer.cfg.save_path,
"model",
f"epoch_{self.trainer.epoch + 1}.pth",
),
)
@HOOKS.register_module()
class CheckpointLoader(HookBase):
def __init__(self, keywords="", replacement=None, strict=False):
self.keywords = keywords
self.replacement = replacement if replacement is not None else keywords
self.strict = strict
def before_train(self):
self.trainer.logger.info("=> Loading checkpoint & weight ...")
if self.trainer.cfg.weight and os.path.isfile(self.trainer.cfg.weight):
self.trainer.logger.info(f"Loading weight at: {self.trainer.cfg.weight}")
checkpoint = torch.load(
self.trainer.cfg.weight,
map_location=lambda storage, loc: storage.cuda(),
)
self.trainer.logger.info(
f"Loading layer weights with keyword: {self.keywords}, "
f"replace keyword with: {self.replacement}"
)
weight = OrderedDict()
for key, value in checkpoint["state_dict"].items():
if not key.startswith("module."):
if comm.get_world_size() > 1:
key = "module." + key # xxx.xxx -> module.xxx.xxx
# Now all keys contain "module." no matter DDP or not.
if self.keywords in key:
key = key.replace(self.keywords, self.replacement)
if comm.get_world_size() == 1:
key = key[7:] # module.xxx.xxx -> xxx.xxx
weight[key] = value
load_state_info = self.trainer.model.load_state_dict(
weight, strict=self.strict
)
self.trainer.logger.info(f"Missing keys: {load_state_info[0]}")
if self.trainer.cfg.resume:
self.trainer.logger.info(
f"Resuming train at eval epoch: {checkpoint['epoch']}"
)
self.trainer.start_epoch = checkpoint["epoch"]
self.trainer.best_metric_value = checkpoint["best_metric_value"]
self.trainer.optimizer.load_state_dict(checkpoint["optimizer"])
self.trainer.scheduler.load_state_dict(checkpoint["scheduler"])
if self.trainer.cfg.enable_amp:
self.trainer.scaler.load_state_dict(checkpoint["scaler"])
else:
self.trainer.logger.info(f"No weight found at: {self.trainer.cfg.weight}")
@HOOKS.register_module()
class PreciseEvaluator(HookBase):
def __init__(self, test_last=False):
self.test_last = test_last
def after_train(self):
self.trainer.logger.info(
">>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>>"
)
torch.cuda.empty_cache()
cfg = self.trainer.cfg
tester = TESTERS.build(
dict(type=cfg.test.type, cfg=cfg, model=self.trainer.model)
)
if self.test_last:
self.trainer.logger.info("=> Testing on model_last ...")
else:
self.trainer.logger.info("=> Testing on model_best ...")
best_path = os.path.join(
self.trainer.cfg.save_path, "model", "model_best.pth"
)
checkpoint = torch.load(best_path)
state_dict = checkpoint["state_dict"]
tester.model.load_state_dict(state_dict, strict=True)
tester.test()
@HOOKS.register_module()
class DataCacheOperator(HookBase):
def __init__(self, data_root, split):
self.data_root = data_root
self.split = split
self.data_list = self.get_data_list()
def get_data_list(self):
if isinstance(self.split, str):
data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
elif isinstance(self.split, Sequence):
data_list = []
for split in self.split:
data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
else:
raise NotImplementedError
return data_list
def get_cache_name(self, data_path):
data_name = data_path.replace(os.path.dirname(self.data_root), "").split(".")[0]
return "ponder" + data_name.replace(os.path.sep, "-")
def before_train(self):
self.trainer.logger.info(
f"=> Caching dataset: {self.data_root}, split: {self.split} ..."
)
if is_main_process():
for data_path in self.data_list:
cache_name = self.get_cache_name(data_path)
data = torch.load(data_path)
shared_dict(cache_name, data)
synchronize()
@HOOKS.register_module()
class RuntimeProfiler(HookBase):
def __init__(
self,
forward=True,
backward=True,
interrupt=False,
warm_up=2,
sort_by="cuda_time_total",
row_limit=30,
):
self.forward = forward
self.backward = backward
self.interrupt = interrupt
self.warm_up = warm_up
self.sort_by = sort_by
self.row_limit = row_limit
def before_train(self):
self.trainer.logger.info("Profiling runtime ...")
from torch.profiler import ProfilerActivity, profile, record_function
for i, input_dict in enumerate(self.trainer.train_loader):
if i == self.warm_up + 1:
break
for key in input_dict.keys():
if isinstance(input_dict[key], torch.Tensor):
input_dict[key] = input_dict[key].cuda(non_blocking=True)
if self.forward:
with profile(
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
record_shapes=True,
profile_memory=True,
with_stack=True,
) as forward_prof:
with record_function("model_inference"):
output_dict = self.trainer.model(input_dict)
else:
output_dict = self.trainer.model(input_dict)
loss = output_dict["loss"]
if self.backward:
with profile(
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
record_shapes=True,
profile_memory=True,
with_stack=True,
) as backward_prof:
with record_function("model_inference"):
loss.backward()
self.trainer.logger.info(f"Profile: [{i + 1}/{self.warm_up + 1}]")
if self.forward:
self.trainer.logger.info(
"Forward profile: \n"
+ str(
forward_prof.key_averages().table(
sort_by=self.sort_by, row_limit=self.row_limit
)
)
)
forward_prof.export_chrome_trace(
os.path.join(self.trainer.cfg.save_path, "forward_trace.json")
)
if self.backward:
self.trainer.logger.info(
"Backward profile: \n"
+ str(
backward_prof.key_averages().table(
sort_by=self.sort_by, row_limit=self.row_limit
)
)
)
backward_prof.export_chrome_trace(
os.path.join(self.trainer.cfg.save_path, "backward_trace.json")
)
if self.interrupt:
sys.exit(0)
@HOOKS.register_module()
class RuntimeProfilerV2(HookBase):
def __init__(
self,
interrupt=False,
wait=1,
warmup=1,
active=10,
repeat=1,
sort_by="cuda_time_total",
row_limit=30,
):
self.interrupt = interrupt
self.wait = wait
self.warmup = warmup
self.active = active
self.repeat = repeat
self.sort_by = sort_by
self.row_limit = row_limit
def before_train(self):
self.trainer.logger.info("Profiling runtime ...")
from torch.profiler import (
ProfilerActivity,
profile,
record_function,
schedule,
tensorboard_trace_handler,
)
prof = profile(
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
schedule=schedule(
wait=self.wait,
warmup=self.warmup,
active=self.active,
repeat=self.repeat,
),
on_trace_ready=tensorboard_trace_handler(self.trainer.cfg.save_path),
record_shapes=True,
profile_memory=True,
with_stack=True,
)
prof.start()
for i, input_dict in enumerate(self.trainer.train_loader):
if i >= (self.wait + self.warmup + self.active) * self.repeat:
break
for key in input_dict.keys():
if isinstance(input_dict[key], torch.Tensor):
input_dict[key] = input_dict[key].cuda(non_blocking=True)
with record_function("model_forward"):
output_dict = self.trainer.model(input_dict)
loss = output_dict["loss"]
with record_function("model_backward"):
loss.backward()
prof.step()
self.trainer.logger.info(
f"Profile: [{i + 1}/{(self.wait + self.warmup + self.active) * self.repeat}]"
)
self.trainer.logger.info(
"Profile: \n"
+ str(
prof.key_averages().table(
sort_by=self.sort_by, row_limit=self.row_limit
)
)
)
prof.stop()
if self.interrupt:
sys.exit(0)