efficientdet_test.py

# Author: Zylo117

"""
Simple Inference Script of EfficientDet-Pytorch
"""
import time

import torch
from torch.backends import cudnn

from backbone import EfficientDetBackbone
import cv2
import numpy as np

from efficientdet.utils import BBoxTransform, ClipBoxes
from utils.utils import preprocess, invert_affine, postprocess

compound_coef = 2
force_input_size = None  # set None to use default size
img_path = "test/knife.jpg"

# replace this part with your project's anchor config
anchor_ratios = [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]
anchor_scales = [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]

threshold = 0.6
iou_threshold = 0.6

use_cuda = True
use_float16 = False
cudnn.fastest = True
cudnn.benchmark = True

obj_list = ["short_gun","long_gun","knife"]

# tf bilinear interpolation is different from any other's, just make do
input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536]
input_size = input_sizes[compound_coef] if force_input_size is None else force_input_size
ori_imgs, framed_imgs, framed_metas = preprocess(img_path, max_size=input_size)

if use_cuda:
    x = torch.stack([torch.from_numpy(fi).cuda() for fi in framed_imgs], 0)
else:
    x = torch.stack([torch.from_numpy(fi) for fi in framed_imgs], 0)

x = x.to(torch.float32 if not use_float16 else torch.float16).permute(0, 3, 1, 2)

model = EfficientDetBackbone(compound_coef=compound_coef, num_classes=len(obj_list),
                             ratios=anchor_ratios, scales=anchor_scales)
model.load_state_dict(torch.load("weights/efficientdet-d2.pth")) # 模型地址
model.requires_grad_(False)
model.eval()

if use_cuda:
    model = model.cuda()
if use_float16:
    model = model.half()

with torch.no_grad():
    features, regression, classification, anchors = model(x)

    regressBoxes = BBoxTransform()
    clipBoxes = ClipBoxes()

    out = postprocess(x,
                      anchors, regression, classification,
                      regressBoxes, clipBoxes,
                      threshold, iou_threshold)


def display(preds, imgs, imshow=True, imwrite=False):
    for i in range(len(imgs)):
        if len(preds[i]['rois']) == 0:
            continue

        for j in range(len(preds[i]['rois'])):
            (x1, y1, x2, y2) = preds[i]['rois'][j].astype(np.int)
            cv2.rectangle(imgs[i], (x1, y1), (x2, y2), (255, 255, 0), 2)
            obj = obj_list[preds[i]['class_ids'][j]]
            score = float(preds[i]['scores'][j])

            cv2.putText(imgs[i], '{}, {:.3f}'.format(obj, score),
                        (x1, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                        (255, 255, 0), 1)

        if imshow:
            cv2.imshow('img', imgs[i])
            cv2.waitKey(0)

        if imwrite:
            cv2.imwrite('test/img_inferred_d{}_this_repo_{}.jpg'.format(compound_coef,i), imgs[i])


out = invert_affine(framed_metas, out)
display(out, ori_imgs, imshow=False, imwrite=True)

print('running speed test...')
with torch.no_grad():
    print('test1: model inferring and postprocessing')
    print('inferring image for 10 times...')
    t1 = time.time()
    for _ in range(10):
        _, regression, classification, anchors = model(x)

        out = postprocess(x,
                          anchors, regression, classification,
                          regressBoxes, clipBoxes,
                          threshold, iou_threshold)
        out = invert_affine(framed_metas, out)

    t2 = time.time()
    tact_time = (t2 - t1) / 10
    print('{} seconds, {} FPS, @batch_size 1'.format(tact_time,1 / tact_time))