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# coding=utf-8
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'''
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Input:(Device, ImagePath, ResultVisualationPath)
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DclNumberToSpeciesPath:we need to convert number to related species,so need to find a train.txt to relieve this operation
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DclNumberToSpeciesPath:../data_MPKFNet/data/DCL/datasets/HCData/anno/train.txt
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ImagePath:../data_MPKFNet/data/DCL/datasets/HCData/data
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ResultVisualationPath:../data_MPKFNet/result/Dcl_FineGrainedRecognition
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How to use:
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Dcl = DclInference(devices, args.ImagePath, args.ResultVisualation)
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Dcl.NumbwerToSpecies()
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Dcl.parse_args()
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Species, ConfidenceLevel= Dcl.DclInference()
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'''
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import os
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import json
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import time
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import cv2
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import sys
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import csv
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import pickle
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import shutil
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import argparse
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import pandas as pd
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import numpy as np
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from math import ceil
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from tqdm import tqdm
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import PIL.Image as Image
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import matplotlib.pyplot as plt
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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from torch.nn import CrossEntropyLoss
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from torchvision import datasets, models
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import torch.backends.cudnn as cudnn
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# Add the absolute path to find the package
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#sys.path.append('/data1/Hatcher/model/MPKFNet/Dcl')
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from Dcl.transforms import transforms
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from Dcl.models.LoadModel import MainModel
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from Dcl.DclConfig import LoadConfig, load_data_transformers
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from Dcl.util.test_tool import set_text, save_multi_img, cls_base_acc
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os.environ['CUDA_VISIBLE_DEVICES'] = '6'
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devices = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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class DclInference:
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def __init__(self, device, DclImagePath, DclResultVisualationPath):
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self.device = device
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self.ImagesPath = []
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self.DclOutputSpecies = []
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self.DclOutputConfidenceLevel = []
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self.DclImagePath = DclImagePath
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self.DataSpeciesClassier = dict()
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self.DclResultVisualationPath = DclResultVisualationPath
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self.DclNumberToSpeciesPath = '../data_MPKFNet/data/DCL/datasets/HCData/anno/train.txt'
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self.DclPreWeightPath = '../data_MPKFNet/weigth/FinedGrainedRecognition/Test/weights_170_999_0.8298_0.9289.pth'
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def DclSoftmax(DcloutputsMatrix, DclPredictedValue):
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InputMax, InputIdx = DcloutputsMatrix.max(dim=1) # row max
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InputExp = torch.exp(DclPredictedValue - InputMax)
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ConfidenceLevel = InputExp / torch.sum(torch.exp(DcloutputsMatrix - InputMax))
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ConfidenceLevel = ConfidenceLevel.item()
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return ConfidenceLevel
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def NumbwerToSpecies(self):
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Number_last = 0
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with open(self.DclNumberToSpeciesPath) as file:
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Data = file.readlines()
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for line in Data:
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line = line.strip('\n')
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species = line.split(' ')[0]
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Number = line.split(' ')[1]
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# Judge Number
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if Number != Number_last:
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self.DataSpeciesClassier[Number] = str(species.split('_')[0]) + '_' + str(species.split('_')[1])
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Number_last = Number
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def parse_args(self):
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parser = argparse.ArgumentParser(description='dcl parameters')
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parser.add_argument('--data', dest='dataset', default='HCData', type=str)
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parser.add_argument('--backbone', dest='backbone', default='resnet50', type=str)
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parser.add_argument('--b', dest='batch_size', default=1, type=int)
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parser.add_argument('--nw', dest='num_workers', default=8, type=int)
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parser.add_argument('--ver', dest='version', default='val', type=str)
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parser.add_argument('--size', dest='resize_resolution', default=512, type=int)
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parser.add_argument('--crop', dest='crop_resolution', default=448, type=int)
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parser.add_argument('--ss', dest='save_suffix', default=None, type=str)
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parser.add_argument('--acc_report', dest='acc_report', action='store_true')
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parser.add_argument('--swap_num', default=[7, 7], nargs=2, metavar=('swap1', 'swap2'), type=int, help='specify a range')
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self.args = parser.parse_args()
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def DclInference(self):
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# Traverse Images
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if os.path.exists(self.DclImagePath):
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for File in os.listdir(self.DclImagePath):
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if File.endswith('jpg'):
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self.ImagesPath.append(os.path.join(self.DclImagePath, File))
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print('Load data is ok')
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# Data Pretreated
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self.Config = LoadConfig(self.args, 's')
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transformers = load_data_transformers(self.args.resize_resolution, self.args.crop_resolution, self.args.swap_num)
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# load model
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self.model = MainModel(self.Config)
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model_dict = self.model.state_dict()
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pretrained_dict = torch.load(self.DclPreWeightPath)
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model_dict.update(pretrained_dict)
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self.model.load_state_dict(model_dict)
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print("load model weight is ok")
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self.model.to(self.device)
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self.model.train(False)
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count_bar = tqdm(total = len(self.ImagesPath), desc='Dcl Inference')
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with torch.no_grad():
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for ImgPath in self.ImagesPath:
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count_bar.update(1)
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Img = cv2.imread(ImgPath)
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Img = Image.fromarray(cv2.cvtColor(Img, cv2.COLOR_BGR2RGB))
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Img = transformers['test_totensor'](Img)
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Img = Img.unsqueeze(0)
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inputs = Variable(Img.cuda())
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outputs = self.model(inputs)
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t1 = time.time()
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top3_val, top3_pos = torch.topk(outputs[0], 1) # topk返回的是一个元组，第一个元素是每一行最大的三个值（默认是按行取最大值），第二个元素是每一行最大的值的排序
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Species = top3_pos.squeeze()
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ConfidenceLevel = DclInference.DclSoftmax(outputs[0], top3_val[0][0])
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t2 = time.time()
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self.DclOutputSpecies.append(Species)
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self.DclOutputConfidenceLevel.append(ConfidenceLevel)
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print('Post Process Time:{:.2f} ConfidenceLevel:{:.2f}'.format((t2 - t1) * 1000, ConfidenceLevel))
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return self.DclOutputSpecies, self.DclOutputConfidenceLevel
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def parse_args():
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parser = argparse.ArgumentParser(description='dcl parameters')
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parser.add_argument('--save', dest='resume',
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default='../data_MPKFNet/weigth/FinedGrainedRecognition/Test/weights_170_999_0.8298_0.9289.pth', type=str)
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parser.add_argument('--ResultVisualation',
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default='../data_MPKFNet/result/Dcl_FineGrainedRecognition', type=str)
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parser.add_argument('--ImagePath',
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default='../data_MPKFNet/data/DCL/datasets/HCData/data', type=str)
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parser.add_argument('--NumberToSpecies',
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default='../data_MPKFNet/data/DCL/datasets/HCData/anno/train.txt', type=str)
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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Dcl = DclInference(devices, args.ImagePath, args.ResultVisualation)
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Dcl.NumbwerToSpecies()
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Dcl.parse_args()
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Species, ConfidenceLevel = Dcl.DclInference()
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print(Species)
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