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evaluate_CCVPE_on_VIGOR.py
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import os
import argparse
from torch.utils.data import DataLoader, Subset
from torchvision import transforms
import torch
import torch.nn as nn
import numpy as np
import math
from dataloader import VIGORDataset, transform_grd, transform_sat
from models import CVM_VIGOR, CVM_VIGOR_ori_prior
from losses import infoNCELoss, cross_entropy_loss
import scipy.io as scio
torch.manual_seed(17)
np.random.seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
"The device is: {}".format(device)
parser = argparse.ArgumentParser()
parser.add_argument('--inference_on', choices=('train', 'val', 'test'), default='test')
parser.add_argument('-b', '--batch_size', type=int, help='batch size', default=16)
parser.add_argument('--known_ori', choices=('True','False'), default='True')
parser.add_argument('--model', choices=('teacher', 'auxiliary_student', 'final_student'), default=None)
args = vars(parser.parse_args())
if args['inference_on']=='train':
label = args['model'] + '_prediction_on_trainingset'
batch_size = args['batch_size']
inference_on = args['inference_on']
known_ori = args['known_ori'] == 'True'
selected_model = args['model']
model_path = '/home/ziminxia/Work/experiments/Adapting_CVL/models/CCVPE/'+selected_model+'/model.pt'
# model_path = '/home/ziminxia/Work/experiments/Weakly_supervised_learning/main_experiment_results/Gaussian_sig4_lr_0.0001_from_pretrainedmodel_infoNCE12/1/model.pt'
# model_path = '/home/ziminxia/Work/experiments/Adapting_CVL/models/CCVPE/final_student/1/model.pt'
dataset_root = '/home/ziminxia/Work/datasets/VIGOR'
vigor = VIGORDataset(root=dataset_root, transform=(transform_grd, transform_sat), known_ori=known_ori)
with open('shuffled_crossarea_index_list.npy', 'rb') as f:
index_list = np.load(f)
with open('predefined_random_rot.npy', 'rb') as f:
predefined_random_rot = np.load(f)
train_indices = index_list[0: int(len(index_list)*0.7)]
val_indices = index_list[int(len(index_list)*0.7):int(len(index_list)*0.8)]
test_indices = index_list[int(len(index_list)*0.8):]
train_set = Subset(vigor, train_indices)
val_set = Subset(vigor, val_indices)
test_set = Subset(vigor, test_indices)
if inference_on == 'train':
dataloader = DataLoader(train_set, batch_size=batch_size, shuffle=False)
elif inference_on == 'val':
dataloader = DataLoader(val_set, batch_size=batch_size, shuffle=False)
elif inference_on == 'test':
dataloader = DataLoader(test_set, batch_size=batch_size, shuffle=False)
vigor.predefined_random_rot = predefined_random_rot
torch.cuda.empty_cache()
if known_ori:
CVM_model = CVM_VIGOR_ori_prior(device)
else:
CVM_model = CVM_VIGOR(device)
CVM_model.load_state_dict(torch.load(model_path))
CVM_model.to(device)
CVM_model.eval()
distance_in_meters = []
pred_row_offsets = []
pred_col_offsets = []
for i, data in enumerate(dataloader, 0):
with torch.no_grad():
print(i)
grd, sat, gt, gt_with_ori, gt_orientation, city = data
grd = grd.to(device)
sat = sat.to(device)
gt_with_ori = gt_with_ori.to(device)
gt_flattened = torch.flatten(gt, start_dim=1)
gt_flattened = gt_flattened / torch.sum(gt_flattened, dim=1, keepdim=True)
logits_flattened, heatmap, ori, matching_score_stacked, matching_score_stacked2, matching_score_stacked3, matching_score_stacked4, matching_score_stacked5, matching_score_stacked6 = CVM_model(grd, sat)
gt = gt.cpu().detach().numpy()
gt_with_ori = gt_with_ori.cpu().detach().numpy()
gt_orientation = gt_orientation.cpu().detach().numpy()
heatmap = heatmap.cpu().detach().numpy()
for batch_idx in range(gt.shape[0]):
if city[batch_idx] == 'None':
pass
else:
current_gt = gt[batch_idx, :, :, :]
loc_gt = np.unravel_index(current_gt.argmax(), current_gt.shape)
current_pred = heatmap[batch_idx, :, :, :]
loc_pred = np.unravel_index(current_pred.argmax(), current_pred.shape)
pred_row_offsets.append((loc_pred[1] - 256) / 512 * 640)
pred_col_offsets.append((256 - loc_pred[2]) / 512 * 640)
pixel_distance = np.sqrt((loc_gt[1]-loc_pred[1])**2+(loc_gt[2]-loc_pred[2])**2)
if city[batch_idx] == 'NewYork':
meter_distance = pixel_distance * 0.113248 / 512 * 640
elif city[batch_idx] == 'Seattle':
meter_distance = pixel_distance * 0.100817 / 512 * 640
elif city[batch_idx] == 'SanFrancisco':
meter_distance = pixel_distance * 0.118141 / 512 * 640
elif city[batch_idx] == 'Chicago':
meter_distance = pixel_distance * 0.111262 / 512 * 640
distance_in_meters.append(meter_distance)
print(np.mean(distance_in_meters))
print(np.median(distance_in_meters))
if inference_on == 'train':
scio.savemat(label+'.mat', {'pred_row_offsets': np.array(pred_row_offsets), 'pred_col_offsets': np.array(pred_col_offsets)})