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evaluate_retrieval.py
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evaluate_retrieval.py
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from tools.test_dataloader import TestDataloader
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.models as models
import argparse
import torch.optim as optim
import time
import torchvision.models as models
from models.meshnet import MeshNet
from models.dgcnn import get_graph_feature
import numpy as np
import copy
from collections import defaultdict
import sys
import warnings
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from matplotlib import pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
import sklearn.metrics as metrics
from sklearn.metrics.pairwise import cosine_distances, euclidean_distances
from sklearn.preprocessing import normalize
import scipy
def extract(args):
img_net = torch.load('./checkpoints/%s/%d-img_net.pkl'%(args.model_folder, args.iterations), map_location=lambda storage, loc: storage)
img_net = img_net.eval()
dgcnn = torch.load('./checkpoints/%s/%d-pt_net.pkl'%(args.model_folder, args.iterations), map_location=lambda storage, loc: storage)
dgcnn = dgcnn.eval()
mesh_net= torch.load('./checkpoints/%s/%d-mesh_net.pkl'%(args.model_folder, args.iterations), map_location=lambda storage, loc: storage)
mesh_net = mesh_net.eval()
torch.cuda.empty_cache()
#################################
test_set = TestDataloader(dataset=args.dataset, num_points = 1024 , dataset_dir = args.dataset_dir, partition= 'test')
data_loader_loader = torch.utils.data.DataLoader(test_set, batch_size=1,shuffle=False, num_workers=8)
print('length of the dataset: ', len(test_set))
#################################
img_feat_1 = np.zeros((len(test_set),512))
img_feat_2 = np.zeros((len(test_set),512))
img_feat_4 = np.zeros((len(test_set),512))
pt_feat = np.zeros((len(test_set), 512))
mesh_feat = np.zeros((len(test_set), 512))
label = np.zeros((len(test_set)))
#################################
iteration = 0
for data in data_loader_loader:
print(iteration)
pt, img_list, centers, corners, normals, neighbor_index, target = data
#################################
img_v1, img_v2, img_v3 , img_v4= img_list
img_v1 = Variable(img_v1).to('cuda')
img_v2 = Variable(img_v2).to('cuda')
img_v3 = Variable(img_v3).to('cuda')
img_v4 = Variable(img_v4).to('cuda')
#################################
target = target[:,0]
target = Variable(target).to('cuda')
pt = Variable(pt).to('cuda')
pt = pt.permute(0,2,1)
centers = Variable(torch.cuda.FloatTensor(centers.cuda()))
corners = Variable(torch.cuda.FloatTensor(corners.cuda()))
normals = Variable(torch.cuda.FloatTensor(normals.cuda()))
neighbor_index = Variable(torch.cuda.LongTensor(neighbor_index.cuda()))
##########################################
img_net = img_net.to('cuda')
feat_1_view = img_net(img_v1, img_v1)
feat_2_views = img_net(img_v1, img_v2)
feat_4_views = 0.5*(img_net(img_v1, img_v2)+img_net(img_v3, img_v4))
dgcnn = dgcnn.to('cuda')
cloud_feat = dgcnn(pt)
mesh_net = mesh_net.to('cuda')
M_feat = mesh_net(centers, corners, normals, neighbor_index)
########################################
img_feat_1[iteration,:] = img_feat_1[iteration,:] + feat_1_view.data.cpu().numpy()
img_feat_2[iteration,:] = img_feat_2[iteration,:] + feat_2_views.data.cpu().numpy()
img_feat_4[iteration,:] = img_feat_4[iteration,:] + feat_4_views.data.cpu().numpy()
pt_feat[iteration,:] = cloud_feat.data.cpu().numpy()
mesh_feat[iteration,:] = M_feat.data.cpu().numpy()
label[iteration] = target.data.cpu().numpy()
iteration = iteration + 1
np.save(args.save+'/img_feat_{}'.format(1),img_feat_1)
np.save(args.save+'/img_feat_{}'.format(2),img_feat_2)
np.save(args.save+'/img_feat_{}'.format(4),img_feat_4)
np.save(args.save+'/pt_feat',pt_feat)
np.save(args.save+'/mesh_feat',mesh_feat)
np.save(args.save+'/label',label)
def fx_calc_map_label(view_1, view_2, label_test):
dist = scipy.spatial.distance.cdist(view_1, view_2, 'cosine') #rows view_1 , columns view 2
ord = dist.argsort()
numcases = dist.shape[0]
res = []
for i in range(numcases):
order = ord[i]
p = 0.0
r = 0.0
for j in range(numcases):
if label_test[i] == label_test[order[j]]:
r += 1
p += (r / (j + 1))
if r > 0:
res += [p / r]
else:
res += [0]
return np.mean(res)
def eval_func(img_pairs):
print('number of img views: ',img_pairs)
img_feat = np.load(args.save+'/img_feat_{}.npy'.format(img_pairs))
pt_feat = np.load(args.save+'/pt_feat.npy')
mesh_feat = np.load(args.save+'/mesh_feat.npy')
label = np.load(args.save+'/label.npy')
########################################
img_test = normalize(img_feat, norm='l1', axis=1)
cloud_test = normalize(pt_feat, norm='l1', axis=1)
mesh_test = normalize(mesh_feat, norm='l1', axis=1)
########################################
par_list = [
(img_test,img_test,'Image2Image'),
(img_test,mesh_test,'Image2Mesh'),
(img_test,cloud_test,'Image2Point'),
(mesh_test,mesh_test,'Mesh2Mesh'),
(mesh_test,img_test,'Mesh2Image'),
(mesh_test,cloud_test,'Mesh2Point'),
(cloud_test,cloud_test,'Point2Point'),
(cloud_test,img_test,'Point2Image'),
(cloud_test,mesh_test,'Point2Mesh')]
########################################
for index in range(9):
view_1,view_2,name = par_list[index]
print(name+ '---------------------------')
acc = fx_calc_map_label(view_1,view_2 , label)
acc_round = round(acc*100,2)
print(str(acc_round))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Cross Modal Retrieval for Point Cloud, Mesh, and Image Models')
parser.add_argument('--dataset', type=str, default='ModelNet40', metavar='dataset',help='ModelNet10 or ModelNet40')
parser.add_argument('--dataset_dir', type=str, default='./dataset/',
metavar='dataset_dir',help='dataset_dir')
parser.add_argument('--model_folder', type=str, default='ModelNet40',help='path to load model')
parser.add_argument('--iterations', type=int, default=55000,help='iteration to load the model')
parser.add_argument('--gpu_id', type=str, default='0,1,2,3',help='GPU used to train the network')
parser.add_argument('--save', type=str, default='extracted_features/ModelNet40',help='save features')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
torch.backends.cudnn.enabled = False
if not os.path.exists(args.save):
os.mkdir(args.save)
extract(args)
eval_func(1)
eval_func(2)
eval_func(4)