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eval_spair.py
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eval_spair.py
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import torch
import torch.nn.functional as F
import numpy as np
import os
import random
import cv2
from dataset import SPair
from model_noise import PMDNet
# import matplotlib.pyplot as plt
import argparse
import scipy.io as sio
parser = argparse.ArgumentParser(description="SFNet evaluation")
parser.add_argument('--num_workers', type=int, default=4, help='number of workers for data loader')
parser.add_argument('--feature_h', type=int, default=20, help='height of feature volume')
parser.add_argument('--feature_w', type=int, default=20, help='width of feature volume')
parser.add_argument('--spair_path', type=str, default='data/SPair/', help='directory of test data')
parser.add_argument('--beta', type=float, default=50, help='inverse temperature of softmax @ kernel soft argmax')
parser.add_argument('--kernel_sigma', type=float, default=5,
help='standard deviation of Gaussian kerenl @ kernel soft argmax')
parser.add_argument('--eval_type', type=str, default='bounding_box', choices=('bounding_box', 'image_size'),
help='evaluation type for PCK threshold (bounding box | image size)')
parser.add_argument('--gpu', type=str, default='4', help='number of workers for data loader')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
# Data Loader
print("Instantiate dataloader")
test_dataset = SPair(args.spair_path, args.feature_h, args.feature_w, 'test', args.eval_type)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=1,
shuffle=True, num_workers=args.num_workers)
# Instantiate model
print("Instantiate model")
net = PMDNet(args.feature_h, args.feature_w, beta=args.beta, kernel_sigma=args.kernel_sigma)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
# Load weights
print("Load pre-trained weights")
best_weights = torch.load("./models/spair_strongly.pt")
adap3_dict = best_weights['state_dict1']
adap4_dict = best_weights['state_dict2']
chn4_dict = best_weights['chn4_dict']
net.adap_layer_feat3.load_state_dict(adap3_dict)
net.adap_layer_feat4.load_state_dict(adap4_dict)
net.chn4.load_state_dict(chn4_dict)
def correct_keypoints(source_points, warped_points, L_pck, alpha=0.1):
# compute correct keypoints
p_src = source_points[0, :]
p_wrp = warped_points[0, :]
N_pts = torch.sum(torch.ne(p_src[0, :], -1) * torch.ne(p_src[1, :], -1))
point_distance = torch.pow(torch.sum(torch.pow(p_src[:, :N_pts] - p_wrp[:, :N_pts], 2), 0), 0.5)
L_pck_mat = L_pck[0].expand_as(point_distance)
correct_points = torch.le(point_distance, L_pck_mat * alpha)
pck = torch.mean(correct_points.float())
return pck
class_names = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow',
'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
per_class_pck = np.zeros(20)
num_instances = np.zeros(20)
tot_num = len(test_loader)
all_pck = []
kps=[]
cls=[]
with torch.no_grad():
print('Computing PCK@Test set...')
net.eval()
total_correct_points = 0
total_points = 0
for i, batch in enumerate(test_loader):
src_image = batch['image1'].to(device)
tgt_image = batch['image2'].to(device)
output = net(src_image, tgt_image, train=False)
small_grid = output['grid_T2S']
src_image_H = int(batch['image1_size'][0][0])
src_image_W = int(batch['image1_size'][0][1])
tgt_image_H = int(batch['image2_size'][0][0])
tgt_image_W = int(batch['image2_size'][0][1])
small_grid = small_grid.permute(0, 3, 1, 2)
grid = F.interpolate(small_grid, size=(tgt_image_H, tgt_image_W), mode='bilinear', align_corners=True)
grid = grid.permute(0, 2, 3, 1)
grid_np = grid.cpu().data.numpy()
image1_points = batch['image1_points'][0]
image2_points = batch['image2_points'][0]
est_image1_points = np.zeros((2, image1_points.size(1)))
for j in range(image2_points.size(1)):
point_x = int(np.round(image2_points[0, j]))
point_y = int(np.round(image2_points[1, j]))
if point_x == -1 and point_y == -1:
continue
if point_x == tgt_image_W:
point_x = point_x - 1
if point_y == tgt_image_H:
point_y = point_y - 1
est_y = (grid_np[0, point_y, point_x, 1] + 1) * (src_image_H - 1) / 2
est_x = (grid_np[0, point_y, point_x, 0] + 1) * (src_image_W - 1) / 2
est_image1_points[:, j] = [est_x, est_y]
correct_points = correct_keypoints(batch['image1_points'],
torch.FloatTensor(est_image1_points).unsqueeze(0), batch['L_pck'],
alpha=0.1)
kps.append(correct_points)
cls.append(batch['class_num'])
total_correct_points += correct_points
per_class_pck[batch['class_num']] += correct_points
num_instances[batch['class_num']] += 1
all_pck.append(correct_points)
print('%d/%d'%(i,tot_num))
PCK = total_correct_points / len(test_dataset)
print('PCK: %5f' % PCK)
per_class_pck = per_class_pck / num_instances
for i in range(per_class_pck.shape[0]):
print('%-12s' % class_names[i], ': %5f' % per_class_pck[i])