93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
from icraft.xir import *
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from icraft.xrt import *
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from icraft.host_backend import *
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from icraft.buyibackend import *
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import numpy as np
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import pandas as pd
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from numpy.linalg import norm
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from typing import List
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import platform
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import cv2
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import json
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import os
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from tqdm import tqdm
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import torch
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from torch.nn import functional as F
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def Normalization(image):
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in_ = image[:, :, ::-1]
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in_ -= np.array((123.675, 116.28, 103.53))
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in_ /= np.array((58.395, 57.12, 57.375))
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return in_
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def load_image(im, image_size=320):
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in_ = np.array(im, dtype=np.float32)
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in_ = cv2.resize(in_, (image_size, image_size))
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in_ = Normalization(in_)
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in_ = in_.transpose((2, 0, 1))
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return in_
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def sim(network :Network, input: List[Tensor]) -> List[Tensor]:
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session = Session.Create([ HostBackend ], network.view(0), [ HostDevice.Default() ])
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session.enableTimeProfile(True)
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session.apply()
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output_tensors = session.forward( input )
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return output_tensors
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def run(network: Network, input: List[Tensor]) -> List[Tensor]:
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URL_PATH=""
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if platform.machine() == 'aarch64':
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URL_PATH = R"axi://ql100aiu?npu=0x40000000&dma=0x80000000";
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else:
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URL_PATH = Rf"socket://ql100aiu@192.168.125.{ip_octet}:9981?npu=0x40000000&dma=0x80000000"
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# Buyi_device = Device.Open(URL_PATH)
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session = Session.Create([ HostBackend], network.view(0), [HostDevice.Default()])
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# session = Session.Create([ BuyiBackend, HostBackend], network.view(0), [ Buyi_device,HostDevice.Default()])
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session.enableTimeProfile(True) #打开计时功能
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session.apply()
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output_tensors = session.forward( input ) #前向
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# 计算时间
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result = session.timeProfileResults() #获取时间，[总时间，传输时间, 硬件时间，余下时间]
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# save_time(network, result, result_table_name)
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time = np.array(list(result.values()))
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total_softtime = np.sum(time[:,1])
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total_hardtime = np.sum(time[:,2])
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# print("total_softtime:{},total_hardtime:{}(ms)",total_softtime,total_hardtime)
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return output_tensors
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ip_octet = 93
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GENERATED_JSON_FILE = "../2_compile/imodel/JLDCF/JLDCF_BY.json"
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GENERATED_RAW_FILE = "../2_compile/imodel/JLDCF/JLDCF_BY.raw"
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generated_network = Network.CreateFromJsonFile(GENERATED_JSON_FILE)
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generated_network.loadParamsFromFile(GENERATED_RAW_FILE)
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img_path = '../0_JL-DCF-pytorch-master/dataset/test/LFSD/RGB/1.jpg'
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dep_path = '../0_JL-DCF-pytorch-master/dataset/test/LFSD/depth/1.png'
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images = cv2.imread(img_path)
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shape = images.shape[:2]
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depth = cv2.imread(dep_path)
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images = np.ascontiguousarray(np.expand_dims(load_image(images).transpose(1, 2, 0), axis=0))
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depth = np.ascontiguousarray(np.expand_dims(load_image(depth).transpose(1, 2, 0), axis=0))
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generated_output = run(generated_network, [Tensor(images, Layout("NHWC")), Tensor(depth, Layout("NHWC"))])
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output = np.array(generated_output[0]).transpose(0, 3, 1, 2)
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# output.tofile('output.bin')
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# output = np.fromfile('output.bin', dtype=np.float32).reshape(1, 320, 320, 1).transpose(0, 3, 1, 2)
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preds = torch.from_numpy(output)
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preds = F.interpolate(preds, shape, mode='bilinear', align_corners=True)
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pred = np.squeeze(torch.sigmoid(preds)).cpu().data.numpy()
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pred = (pred - pred.min()) / (pred.max() - pred.min() + 1e-8)
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multi_fuse = 255 * pred
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cv2.imwrite('./test1.png', multi_fuse) |