bloom1123 commented 2 years ago

I use efficientdet_test.py and get the result. It is so slow, but I don't know the reason. the code is here. I don't change too much code. I test the most time spent on the backbonenet to extract the features. """ Simple Inference Script of EfficientDet-Pytorch """ import time import torch from torch.backends import cudnn from matplotlib import colors

from backbone import EfficientDetBackbone import cv2 import numpy as np

from efficientdet.utils import BBoxTransform, ClipBoxes from utils.utils import preprocess, invert_affine, postprocess, STANDARD_COLORS, standard_to_bgr, get_index_label, plot_one_box import glob

compound_coef = 0 force_input_size =None # set None to use default size img_path = 'test/img.png'

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 = 0.5 iou_threshold = 0.5

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

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

    imgs[i] = imgs[i].copy()

    for j in range(len(preds[i]['rois'])):
        x1, y1, x2, y2 = preds[i]['rois'][j].astype(np.int)
        obj = obj_list[preds[i]['class_ids'][j]]
        score = float(preds[i]['scores'][j])
        plot_one_box(imgs[i], [x1, y1, x2, y2], label=obj,score=score,color=color_list[get_index_label(obj, obj_list)])

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

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

obj_list = ['circle']

color_list = standard_to_bgr(STANDARD_COLORS)

tf bilinear interpolation is different from any other's, just make do

input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536, 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)

i=0 for fi in framed_imgs: if use_cuda:

    x = torch.stack([torch.from_numpy(fi).cuda()], 0)
else:
    x = torch.stack([torch.from_numpy(fi)], 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(f'weights/efficientdet-d{compound_coef}.pth', map_location='cpu'))
model.load_state_dict(torch.load(f'logs/coco/efficientdet-d0_99_44000.pth'),strict=False)

model.requires_grad_(False)
model.eval()

if use_cuda:
    model = model.cuda(0)
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)

out = invert_affine(framed_metas, out)
# display(out, ori_imgs, imshow=False, imwrite=True)
cv2.imwrite(f'test_results/img_inferred_d{compound_coef}_this_repo_{i}.jpg', ori_imgs[i])

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(f'picture {i},{tact_time} seconds, {1 / tact_time} FPS, @batch_size 1')

zylo117 commented 2 years ago

no idea. don't know what code you changed. maybe it has something to do with torch/cuda optimization