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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
from loguru import logger
from tqdm import tqdm
import torch
from yolox.utils import (
gather,
is_main_process,
postprocess,
synchronize,
time_synchronized,
xyxy2xywh
)
import contextlib
import io
import itertools
import json
import tempfile
import time
class COCOEvaluator:
"""
COCO AP Evaluation class. All the data in the val2017 dataset are processed
and evaluated by COCO API.
"""
def __init__(
self, dataloader, img_size, confthre, nmsthre, num_classes, testdev=False
):
"""
Args:
dataloader (Dataloader): evaluate dataloader.
img_size (int): image size after preprocess. images are resized
to squares whose shape is (img_size, img_size).
confthre (float): confidence threshold ranging from 0 to 1, which
is defined in the config file.
nmsthre (float): IoU threshold of non-max supression ranging from 0 to 1.
"""
self.dataloader = dataloader
self.img_size = img_size
self.confthre = confthre
self.nmsthre = nmsthre
self.num_classes = num_classes
self.testdev = testdev
def evaluate(
self,
model,
distributed=False,
half=False,
trt_file=None,
decoder=None,
test_size=None,
):
"""
COCO average precision (AP) Evaluation. Iterate inference on the test dataset
and the results are evaluated by COCO API.
NOTE: This function will change training mode to False, please save states if needed.
Args:
model : model to evaluate.
Returns:
ap50_95 (float) : COCO AP of IoU=50:95
ap50 (float) : COCO AP of IoU=50
summary (sr): summary info of evaluation.
"""
# TODO half to amp_test
tensor_type = torch.cuda.HalfTensor if half else torch.cuda.FloatTensor
model = model.eval()
if half:
model = model.half()
ids = []
data_list = []
progress_bar = tqdm if is_main_process() else iter
inference_time = 0
nms_time = 0
n_samples = len(self.dataloader) - 1
if trt_file is not None:
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(trt_file))
x = torch.ones(1, 3, test_size[0], test_size[1]).cuda()
model(x)
model = model_trt
for cur_iter, (imgs, _, info_imgs, ids) in enumerate(
progress_bar(self.dataloader)
):
with torch.no_grad():
imgs = imgs.type(tensor_type)
# skip the the last iters since batchsize might be not enough for batch inference
is_time_record = cur_iter < len(self.dataloader) - 1
if is_time_record:
start = time.time()
outputs = model(imgs)
if decoder is not None:
outputs = decoder(outputs, dtype=outputs.type())
if is_time_record:
infer_end = time_synchronized()
inference_time += infer_end - start
outputs = postprocess(
outputs, self.num_classes, self.confthre, self.nmsthre
)
if is_time_record:
nms_end = time_synchronized()
nms_time += nms_end - infer_end
data_list.extend(self.convert_to_coco_format(outputs, info_imgs, ids))
statistics = torch.cuda.FloatTensor([inference_time, nms_time, n_samples])
if distributed:
data_list = gather(data_list, dst=0)
data_list = list(itertools.chain(*data_list))
torch.distributed.reduce(statistics, dst=0)
eval_results = self.evaluate_prediction(data_list, statistics)
synchronize()
return eval_results
def convert_to_coco_format(self, outputs, info_imgs, ids):
data_list = []
for (output, img_h, img_w, img_id) in zip(
outputs, info_imgs[0], info_imgs[1], ids
):
if output is None:
continue
output = output.cpu()
bboxes = output[:, 0:4]
# preprocessing: resize
scale = min(
self.img_size[0] / float(img_h), self.img_size[1] / float(img_w)
)
bboxes /= scale
bboxes = xyxy2xywh(bboxes)
cls = output[:, 6]
scores = output[:, 4] * output[:, 5]
for ind in range(bboxes.shape[0]):
label = self.dataloader.dataset.class_ids[int(cls[ind])]
pred_data = {
"image_id": int(img_id),
"category_id": label,
"bbox": bboxes[ind].numpy().tolist(),
"score": scores[ind].numpy().item(),
"segmentation": [],
} # COCO json format
data_list.append(pred_data)
return data_list
def evaluate_prediction(self, data_dict, statistics):
if not is_main_process():
return 0, 0, None
logger.info("Evaluate in main process...")
annType = ["segm", "bbox", "keypoints"]
inference_time = statistics[0].item()
nms_time = statistics[1].item()
n_samples = statistics[2].item()
a_infer_time = 1000 * inference_time / (n_samples * self.dataloader.batch_size)
a_nms_time = 1000 * nms_time / (n_samples * self.dataloader.batch_size)
time_info = ", ".join(
[
"Average {} time: {:.2f} ms".format(k, v)
for k, v in zip(
["forward", "NMS", "inference"],
[a_infer_time, a_nms_time, (a_infer_time + a_nms_time)],
)
]
)
info = time_info + "\n"
# Evaluate the Dt (detection) json comparing with the ground truth
if len(data_dict) > 0:
cocoGt = self.dataloader.dataset.coco
# TODO: since pycocotools can't process dict in py36, write data to json file.
if self.testdev:
json.dump(data_dict, open("./yolox_testdev_2017.json", "w"))
cocoDt = cocoGt.loadRes("./yolox_testdev_2017.json")
else:
_, tmp = tempfile.mkstemp()
json.dump(data_dict, open(tmp, "w"))
cocoDt = cocoGt.loadRes(tmp)
'''
try:
from yolox.layers import COCOeval_opt as COCOeval
except ImportError:
from pycocotools import cocoeval as COCOeval
logger.warning("Use standard COCOeval.")
'''
#from pycocotools.cocoeval import COCOeval
from yolox.layers import COCOeval_opt as COCOeval
cocoEval = COCOeval(cocoGt, cocoDt, annType[1])
cocoEval.evaluate()
cocoEval.accumulate()
redirect_string = io.StringIO()
with contextlib.redirect_stdout(redirect_string):
cocoEval.summarize()
info += redirect_string.getvalue()
return cocoEval.stats[0], cocoEval.stats[1], info
else:
return 0, 0, info
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