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eval_willow.py
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eval_willow.py
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import torch
import torch.nn.functional as F
import numpy as np
import os
import random
from custom_dataset import PF_WILLOW
from model import SFNet
#import matplotlib.pyplot as plt
import argparse
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('--test_csv_path', type=str, default='./data/PF_WILLOW/test_pairs_pf.csv', help='directory of test csv file')
parser.add_argument('--test_image_path', type=str, default='./data/PF_WILLOW/', 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)')
args = parser.parse_args()
# os.environ["CUDA_VISIBLE_DEVICES"]="0"
# Data Loader
print("Instantiate dataloader")
test_dataset = PF_WILLOW(args.test_csv_path, args.test_image_path, args.feature_h, args.feature_w, args.eval_type)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=1,
shuffle=False, num_workers = args.num_workers)
# Instantiate model
print("Instantiate model")
net = SFNet(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("./weights/best_checkpoint.pt")
adap3_dict = best_weights['state_dict1']
adap4_dict = best_weights['state_dict2']
net.adap_layer_feat3.load_state_dict(adap3_dict, strict=False)
net.adap_layer_feat4.load_state_dict(adap4_dict, strict=False)
# PCK metric from 'https://github.com/ignacio-rocco/weakalign/blob/master/util/eval_util.py'
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
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'][:,1:-1,1:-1,:]
small_grid[:,:,:,0] = small_grid[:,:,:,0] * (args.feature_w//2)/(args.feature_w//2 - 1)
small_grid[:,:,:,1] = small_grid[:,:,:,1] * (args.feature_h//2)/(args.feature_h//2 - 1)
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]
total_correct_points += correct_keypoints(batch['image1_points'], torch.FloatTensor(est_image1_points).unsqueeze(0), batch['L_pck'], alpha=0.1)
PCK = total_correct_points / len(test_dataset)
print('PCK: %5f' % PCK)