test.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import argparse
import importlib
import json
import os
import random
import sys
from multiprocessing import Process, Queue

import cv2
import megengine as mge
import numpy as np
from megengine import jit
from megengine.data import DataLoader, SequentialSampler
from megengine.data.dataset import COCO as COCODataset
import megengine.data.transform as T
from tqdm import tqdm

from official.vision.keypoints.dataset import COCOJoints
from official.vision.keypoints.transforms import RandomBoxAffine, ExtendBoxes
from official.vision.keypoints.config import Config as cfg
import official.vision.keypoints.models as M


logger = mge.get_logger(__name__)


def build_dataloader(rank, world_size, data_root, ann_file):
    val_dataset = COCOJoints(
        data_root, ann_file, image_set="val2017", order=("image", "boxes", "info")
    )
    val_sampler = SequentialSampler(val_dataset, 1, world_size=world_size, rank=rank)
    val_dataloader = DataLoader(
        val_dataset,
        sampler=val_sampler,
        num_workers=4,
        transform=T.Compose(
            transforms=[
                T.Normalize(mean=cfg.img_mean, std=cfg.img_std),
                ExtendBoxes(
                    cfg.test_x_ext,
                    cfg.test_y_ext,
                    cfg.input_shape[1] / cfg.input_shape[0],
                    random_extend_prob=0,
                ),
                RandomBoxAffine(
                    degrees=(0, 0),
                    scale=(1, 1),
                    output_shape=cfg.input_shape,
                    rotate_prob=0,
                    scale_prob=0,
                ),
                T.ToMode(),
            ],
            order=("image", "boxes", "info"),
        ),
    )
    return val_dataloader


def find_keypoints(pred, bbox):

    heat_prob = pred.copy()
    heat_prob = heat_prob / cfg.heat_range + 1

    border = cfg.test_aug_border
    pred_aug = np.zeros(
        (pred.shape[0], pred.shape[1] + 2 * border, pred.shape[2] + 2 * border),
        dtype=np.float32,
    )
    pred_aug[:, border:-border, border:-border] = pred.copy()
    for i in range(pred_aug.shape[0]):
        pred_aug[i] = cv2.GaussianBlur(
            pred_aug[i], (cfg.test_gaussian_kernel, cfg.test_gaussian_kernel), 0
        )

    results = np.zeros((pred_aug.shape[0], 3), dtype=np.float32)
    for i in range(pred_aug.shape[0]):
        lb = pred_aug[i].argmax()
        y, x = np.unravel_index(lb, pred_aug[i].shape)
        if cfg.second_value_aug:
            y -= border
            x -= border

            pred_aug[i, y, x] = 0
            lb = pred_aug[i].argmax()
            py, px = np.unravel_index(lb, pred_aug[i].shape)
            pred_aug[i, py, px] = 0

            py -= border + y
            px -= border + x
            ln = (px ** 2 + py ** 2) ** 0.5
            delta = 0.35
            if ln > 1e-3:
                x += delta * px / ln
                y += delta * py / ln

            lb = pred_aug[i].argmax()
            py, px = np.unravel_index(lb, pred_aug[i].shape)
            pred_aug[i, py, px] = 0

            py -= border + y
            px -= border + x
            ln = (px ** 2 + py ** 2) ** 0.5
            delta = 0.15
            if ln > 1e-3:
                x += delta * px / ln
                y += delta * py / ln

            lb = pred_aug[i].argmax()
            py, px = np.unravel_index(lb, pred_aug[i].shape)
            pred_aug[i, py, px] = 0

            py -= border + y
            px -= border + x
            ln = (px ** 2 + py ** 2) ** 0.5
            delta = 0.05
            if ln > 1e-3:
                x += delta * px / ln
                y += delta * py / ln
        else:
            y -= border
            x -= border
        x = max(0, min(x, cfg.output_shape[1] - 1))
        y = max(0, min(y, cfg.output_shape[0] - 1))
        skeleton_score = heat_prob[i, int(round(y)), int(round(x))]

        stride = cfg.input_shape[1] / cfg.output_shape[1]

        x = (x + 0.5) * stride - 0.5
        y = (y + 0.5) * stride - 0.5

        bbox_top_leftx, bbox_top_lefty, bbox_bottom_rightx, bbox_bottom_righty = bbox
        x = (
            x / cfg.input_shape[1] * (bbox_bottom_rightx - bbox_top_leftx)
            + bbox_top_leftx
        )
        y = (
            y / cfg.input_shape[0] * (bbox_bottom_righty - bbox_top_lefty)
            + bbox_top_lefty
        )

        results[i, 0] = x
        results[i, 1] = y
        results[i, 2] = skeleton_score

    return results


def find_results(func, img, bbox, info):
    outs = func()
    outs = outs.numpy()
    pred = outs[0]
    fliped_pred = outs[1][cfg.keypoint_flip_order][:, :, ::-1]
    pred = (pred + fliped_pred) / 2

    results = find_keypoints(pred, bbox)

    final_score = float(results[:, -1].mean() * info[-1])
    image_id = int(info[-2])
    keypoints = results.copy()
    keypoints[:, -1] = 1
    keypoints = keypoints.reshape(-1,).tolist()
    instance = {
        "image_id": image_id,
        "category_id": 1,
        "score": final_score,
        "keypoints": keypoints,
    }
    return instance


def worker(
    arch, model_file, data_root, ann_file, worker_id, total_worker, result_queue,
):
    """
    :param net_file: network description file
    :param model_file: file of dump weights
    :param data_dir: the dataset directory
    :param worker_id: the index of the worker
    :param total_worker: number of gpu for evaluation
    :param result_queue: processing queue
    """
    os.environ["CUDA_VISIBLE_DEVICES"] = str(worker_id)

    @jit.trace(symbolic=True, opt_level=2)
    def val_func():
        pred = model.predict()
        return pred

    model = getattr(M, arch)()
    model.eval()
    model.load_state_dict(mge.load(model_file)["state_dict"])

    loader = build_dataloader(worker_id, total_worker, data_root, ann_file)
    for data_dict in loader:
        img, bbox, info = data_dict
        fliped_img = img[:, :, :, ::-1] - np.zeros_like(img)
        data = np.concatenate([img, fliped_img], 0)
        model.inputs["image"].set_value(np.ascontiguousarray(data).astype(np.float32))
        instance = find_results(val_func, img, bbox[0, 0], info)

        result_queue.put_nowait(instance)


def make_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument("-n", "--ngpus", default=8, type=int)
    parser.add_argument(
        "-dt",
        "--dt_file",
        default="COCO_val2017_detections_AP_H_56_person.json",
        type=str,
    )
    parser.add_argument("-se", "--start_epoch", default=-1, type=int)
    parser.add_argument("-ee", "--end_epoch", default=-1, type=int)
    parser.add_argument(
        "-a",
        "--arch",
        default="simplebaseline_res50",
        type=str,
        choices=[
            "simplebaseline_res50",
            "Simplebaseline_res101",
            "Simplebaseline_res152",
            "mspn_4stage",
        ],
    )
    parser.add_argument(
        "-m",
        "--model",
        default="/data/models/simplebaseline_res50_256x192/epoch_199.pkl",
        type=str,
    )
    return parser


def main():
    from pycocotools.coco import COCO
    from pycocotools.cocoeval import COCOeval

    parser = make_parser()
    args = parser.parse_args()

    dt_path = os.path.join(cfg.data_root, "person_detection_results", args.dt_file)
    dets = json.load(open(dt_path, "r"))

    gt_path = os.path.join(
        cfg.data_root, "annotations", "person_keypoints_val2017.json"
    )
    eval_gt = COCO(gt_path)
    gt = eval_gt.dataset

    dets = [
        i for i in dets if (i["image_id"] in eval_gt.imgs and i["category_id"] == 1)
    ]
    ann_file = {"images": gt["images"], "annotations": dets}

    if args.end_epoch == -1:
        args.end_epoch = args.start_epoch

    for epoch_num in range(args.start_epoch, args.end_epoch + 1):
        if args.model:
            model_file = args.model
        else:
            model_file = "log-of-{}/epoch_{}.pkl".format(
                os.path.basename(args.file).split(".")[0], epoch_num
            )
        logger.info("Load Model : %s completed", model_file)

        all_results = list()
        result_queue = Queue(2000)
        procs = []
        for i in range(args.ngpus):
            proc = Process(
                target=worker,
                args=(
                    args.arch,
                    model_file,
                    cfg.data_root,
                    ann_file,
                    i,
                    args.ngpus,
                    result_queue,
                ),
            )
            proc.start()
            procs.append(proc)

        for _ in tqdm(range(len(dets))):
            all_results.append(result_queue.get())
        for p in procs:
            p.join()

        json_path = "log-of-{}_epoch_{}.json".format(args.arch, epoch_num)
        all_results = json.dumps(all_results)
        with open(json_path, "w") as fo:
            fo.write(all_results)
        logger.info("Save to %s finished, start evaluation!", json_path)

        eval_dt = eval_gt.loadRes(json_path)
        cocoEval = COCOeval(eval_gt, eval_dt, iouType="keypoints")
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()
        metrics = [
            "AP",
            "AP@0.5",
            "AP@0.75",
            "APm",
            "APl",
            "AR",
            "AR@0.5",
            "AR@0.75",
            "ARm",
            "ARl",
        ]
        logger.info("mmAP".center(32, "-"))
        for i, m in enumerate(metrics):
            logger.info("|\t%s\t|\t%.03f\t|", m, cocoEval.stats[i])
        logger.info("-" * 32)


if __name__ == "__main__":
    main()