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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import contextlib
import io
import itertools
import json
import tempfile
import time
from collections import ChainMap, defaultdict
from loguru import logger
from tabulate import tabulate
from tqdm import tqdm
import numpy as np
import torch
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import (
gather,
is_main_process,
postprocess,
synchronize,
time_synchronized,
xyxy2xywh
)
def per_class_AR_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "AR"], colums=6):
per_class_AR = {}
recalls = coco_eval.eval["recall"]
# dimension of recalls: [TxKxAxM]
# recall has dims (iou, cls, area range, max dets)
assert len(class_names) == recalls.shape[1]
for idx, name in enumerate(class_names):
recall = recalls[:, idx, 0, -1]
recall = recall[recall > -1]
ar = np.mean(recall) if recall.size else float("nan")
per_class_AR[name] = float(ar * 100)
num_cols = min(colums, len(per_class_AR) * len(headers))
result_pair = [x for pair in per_class_AR.items() for x in pair]
row_pair = itertools.zip_longest(*[result_pair[i::num_cols] for i in range(num_cols)])
table_headers = headers * (num_cols // len(headers))
table = tabulate(
row_pair, tablefmt="pipe", floatfmt=".3f", headers=table_headers, numalign="left",
)
return table
def per_class_AP_table(coco_eval, class_names=COCO_CLASSES, headers=["class", "AP"], colums=6):
per_class_AP = {}
precisions = coco_eval.eval["precision"]
# dimension of precisions: [TxRxKxAxM]
# precision has dims (iou, recall, cls, area range, max dets)
assert len(class_names) == precisions.shape[2]
for idx, name in enumerate(class_names):
# area range index 0: all area ranges
# max dets index -1: typically 100 per image
precision = precisions[:, :, idx, 0, -1]
precision = precision[precision > -1]
ap = np.mean(precision) if precision.size else float("nan")
per_class_AP[name] = float(ap * 100)
num_cols = min(colums, len(per_class_AP) * len(headers))
result_pair = [x for pair in per_class_AP.items() for x in pair]
row_pair = itertools.zip_longest(*[result_pair[i::num_cols] for i in range(num_cols)])
table_headers = headers * (num_cols // len(headers))
table = tabulate(
row_pair, tablefmt="pipe", floatfmt=".3f", headers=table_headers, numalign="left",
)
return table
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: int,
confthre: float,
nmsthre: float,
num_classes: int,
testdev: bool = False,
per_class_AP: bool = True,
per_class_AR: bool = True,
):
"""
Args:
dataloader (Dataloader): evaluate dataloader.
img_size: image size after preprocess. images are resized
to squares whose shape is (img_size, img_size).
confthre: confidence threshold ranging from 0 to 1, which
is defined in the config file.
nmsthre: IoU threshold of non-max supression ranging from 0 to 1.
per_class_AP: Show per class AP during evalution or not. Default to True.
per_class_AR: Show per class AR during evalution or not. Default to True.
"""
self.dataloader = dataloader
self.img_size = img_size
self.confthre = confthre
self.nmsthre = nmsthre
self.num_classes = num_classes
self.testdev = testdev
self.per_class_AP = per_class_AP
self.per_class_AR = per_class_AR
def evaluate(
self, model, distributed=False, half=False, trt_file=None,
decoder=None, test_size=None, return_outputs=False
):
"""
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 = []
output_data = defaultdict()
progress_bar = tqdm if is_main_process() else iter
inference_time = 0
nms_time = 0
n_samples = max(len(self.dataloader) - 1, 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 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_elem, image_wise_data = self.convert_to_coco_format(
outputs, info_imgs, ids, return_outputs=True)
data_list.extend(data_list_elem)
output_data.update(image_wise_data)
statistics = torch.cuda.FloatTensor([inference_time, nms_time, n_samples])
if distributed:
# different process/device might have different speed,
# to make sure the process will not be stucked, sync func is used here.
synchronize()
data_list = gather(data_list, dst=0)
output_data = gather(output_data, dst=0)
data_list = list(itertools.chain(*data_list))
output_data = dict(ChainMap(*output_data))
torch.distributed.reduce(statistics, dst=0)
eval_results = self.evaluate_prediction(data_list, statistics)
synchronize()
if return_outputs:
return eval_results, output_data
return eval_results
def convert_to_coco_format(self, outputs, info_imgs, ids, return_outputs=False):
data_list = []
image_wise_data = defaultdict(dict)
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
cls = output[:, 6]
scores = output[:, 4] * output[:, 5]
image_wise_data.update({
int(img_id): {
"bboxes": [box.numpy().tolist() for box in bboxes],
"scores": [score.numpy().item() for score in scores],
"categories": [
self.dataloader.dataset.class_ids[int(cls[ind])]
for ind in range(bboxes.shape[0])
],
}
})
bboxes = xyxy2xywh(bboxes)
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)
if return_outputs:
return data_list, image_wise_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.cocoeval import COCOeval
logger.warning("Use standard 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()
cat_ids = list(cocoGt.cats.keys())
cat_names = [cocoGt.cats[catId]['name'] for catId in sorted(cat_ids)]
if self.per_class_AP:
AP_table = per_class_AP_table(cocoEval, class_names=cat_names)
info += "per class AP:\n" + AP_table + "\n"
if self.per_class_AR:
AR_table = per_class_AR_table(cocoEval, class_names=cat_names)
info += "per class AR:\n" + AR_table + "\n"
return cocoEval.stats[0], cocoEval.stats[1], info
else:
return 0, 0, info
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