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Running
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Zero
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from datetime import datetime
from pathlib import Path
import ignite.distributed as idist
import torch
from ignite.contrib.engines import common
from ignite.engine import Engine, Events
from ignite.utils import manual_seed, setup_logger
from torch.cuda.amp import autocast
from scenedino.common.logging import log_basic_info
from scenedino.common.array_operations import to
# from ignite.contrib.handlers.tensorboard_logger import *
from ignite.contrib.handlers import TensorboardLogger
from scenedino.common.metrics import DictMeanMetric, SegmentationMetric, ConcatenateMetric
from scenedino.training.handlers import VisualizationHandler
from scenedino.visualization.vis_2d import tb_visualize
from .wrapper import make_eval_fn
def base_evaluation(
local_rank,
config,
get_dataflow,
initialize,
):
rank = idist.get_rank()
if "eval_seed" in config:
manual_seed(config["eval_seed"] + rank)
else:
manual_seed(config["seed"] + rank)
device = idist.device()
model_name = config["name"]
logger = setup_logger(
name=model_name, format="%(levelname)s: %(message)s"
) ## default
output_path = config["output"]["path"]
if rank == 0:
unique_id = config["output"].get(
"unique_id", datetime.now().strftime("%Y%m%d-%H%M%S")
)
folder_name = unique_id
output_path = Path(output_path) / folder_name
if not output_path.exists():
output_path.mkdir(parents=True)
config["output"]["path"] = output_path.as_posix()
logger.info(f"Output path: {config['output']['path']}")
if "cuda" in device.type:
config["cuda device name"] = torch.cuda.get_device_name(local_rank)
tb_logger = TensorboardLogger(log_dir=output_path)
log_basic_info(logger, config)
# Setup dataflow, model, optimizer, criterion
test_loader = get_dataflow(config) ## default
if hasattr(test_loader, "dataset"):
logger.info(f"Dataset length: Test: {len(test_loader.dataset)}")
config["dataset"]["steps_per_epoch"] = len(test_loader)
# ===================================================== MODEL =====================================================
model = initialize(config)
cp_path = config.get("checkpoint", None)
if cp_path is not None:
if not cp_path.endswith(".pt"):
cp_path = Path(cp_path)
cp_path = next(cp_path.glob("training*.pt"))
checkpoint = torch.load(cp_path, map_location=device)
logger.info(f"Loading checkpoint from path: {cp_path}")
if "model" in checkpoint:
model.load_state_dict(checkpoint["model"], strict=False)
else:
model.load_state_dict(checkpoint, strict=False)
else:
logger.warning("Careful, no model is loaded")
model.to(device)
logger.info(f"Model parameters: {sum(p.numel() for p in model.parameters())}")
logger.info(f"Trainable model parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
# Let's now setup evaluator engine to perform model's validation and compute metrics
evaluator = create_evaluator(model, config=config, logger=logger, vis_logger=tb_logger)
# evaluator.add_event_handler(
# Events.ITERATION_COMPLETED(every=config["log_every"]),
# log_metrics_current(logger, metrics),
# )
try:
state = evaluator.run(test_loader, max_epochs=1)
log_metrics(logger, state.times["COMPLETED"], "Test", state.metrics)
logger.info(f"Checkpoint: {str(cp_path)}")
except Exception as e:
logger.exception("")
raise e
# def log_metrics_current(logger, metrics):
# def f(engine):
# out_str = "\n" + "\t".join(
# [
# f"{v.compute():.3f}".ljust(8)
# for v in metrics.values()
# if v._num_examples != 0
# ]
# )
# out_str += "\n" + "\t".join([f"{k}".ljust(8) for k in metrics.keys()])
# logger.info(out_str)
# return f
def log_metrics(logger, elapsed, tag, metrics):
metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()])
logger.info(
f"\nEvaluation time (seconds): {elapsed:.2f} - {tag} metrics:\n {metrics_output}"
)
# def create_evaluator(model, metrics, config, tag="val"):
def create_evaluator(model, config, logger=None, vis_logger=None, tag="val"):
with_amp = config["with_amp"]
device = idist.device()
metrics = {}
for eval_config in config["evaluations"]:
agg_type = eval_config.get("agg_type", None)
if agg_type == "unsup_seg":
metrics[eval_config["type"]] = SegmentationMetric(
eval_config["type"], make_eval_fn(model, eval_config), assign_pseudo=True
)
elif agg_type == "sup_seg":
metrics[eval_config["type"]] = SegmentationMetric(
eval_config["type"], make_eval_fn(model, eval_config), assign_pseudo=False
)
elif agg_type == "concat":
metrics[eval_config["type"]] = ConcatenateMetric(
eval_config["type"], make_eval_fn(model, eval_config)
)
else:
metrics[eval_config["type"]] = DictMeanMetric(
eval_config["type"], make_eval_fn(model, eval_config)
)
@torch.no_grad()
def evaluate_step(engine: Engine, data):
# if not engine.state_dict["iteration"] % 10 == 0: ## to prevent iterating whole testset for viz purpose
model.eval()
if "t__get_item__" in data:
timing = {"t__get_item__": torch.mean(data["t__get_item__"]).item()}
else:
timing = {}
data = to(data, device)
with autocast(enabled=with_amp):
data = model(data) ## ! This is where the occupancy prediction is made.
loss_metrics = {}
return {
"output": data,
"loss_dict": loss_metrics,
"timings_dict": timing,
"metrics_dict": {},
}
evaluator = Engine(evaluate_step)
evaluator.logger = logger ##
for name, metric in metrics.items():
metric.attach(evaluator, name)
eval_visualize = config.get("eval_visualize", [])
if eval_visualize and vis_logger is not None:
for name, vis_config in config["validation"].items():
if "visualize" in vis_config:
visualize = tb_visualize(
(model.renderer.net if hasattr(model, "renderer") else model.module.renderer.net),
None,
vis_config["visualize"],
)
def vis_wrapper(*args, **kwargs):
with autocast(enabled=with_amp):
return visualize(*args, **kwargs)
def custom_vis_filter(engine, event):
return engine.state.iteration-1 in eval_visualize
vis_logger.attach(
evaluator,
VisualizationHandler(
tag=tag,
visualizer=vis_wrapper,
),
Events.ITERATION_COMPLETED(event_filter=custom_vis_filter),
)
if idist.get_rank() == 0 and (not config.get("with_clearml", False)):
common.ProgressBar(desc=f"Evaluation ({tag})", persist=False).attach(evaluator)
return evaluator
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