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@alsrgv
Created August 28, 2019 01:35
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import requests
from io import BytesIO
from PIL import Image
import numpy as np
import timeit
import torch
from maskrcnn_benchmark.config import cfg
from predictor import COCODemo, to_image_list
config_file = "../configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml"
# update the config options with the config file
cfg.merge_from_file(config_file)
# manual override some options
#cfg.merge_from_list(["MODEL.DEVICE", "cpu"])
coco_demo = COCODemo(
cfg,
min_image_size=1024,
confidence_threshold=0.7,
)
def load(url):
"""
Given an url of an image, downloads the image and
returns a PIL image
"""
response = requests.get(url)
pil_image = Image.open(BytesIO(response.content)).convert("RGB")
# convert to BGR format
image = np.array(pil_image)[:, :, [2, 1, 0]]
return image
image = load("http://farm3.staticflickr.com/2469/3915380994_2e611b1779_z.jpg")
image_list = to_image_list(coco_demo.transforms(image), coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY).to('cuda')
def fun():
with torch.no_grad():
coco_demo.model(image_list)
for i in range(10):
fun()
print(timeit.timeit(fun, number=100))
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