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test.py
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test.py
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import os
import math
import sys
import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import pickle
import argparse
import glob
import torch.distributions.multivariate_normal as torchdist
from utils import *
from metrics import *
from model import social_stgcnn
import copy
def test(KSTEPS=20):
global loader_test, model
model.eval()
ade_bigls = []
fde_bigls = []
raw_data_dict = {}
step = 0
for batch in loader_test:
step += 1
# Get data
batch = [tensor.cuda() for tensor in batch]
obs_traj, pred_traj_gt, obs_traj_rel, pred_traj_gt_rel, obs_flow, pred_flow, \
obs_img, pred_img, non_linear_ped, loss_mask, V_obs, A_obs, V_tr, A_tr = batch
num_of_objs = obs_traj_rel.shape[1]
# Forward
# V_obs = batch,seq,node,feat
# V_obs_tmp = batch,feat,seq,node
V_obs_tmp = V_obs.permute(0, 3, 1, 2)
V_pred, _ = model(V_obs_tmp, A_obs.squeeze(), obs_flow, obs_img)
# print(V_pred.shape)
# torch.Size([1, 5, 12, 2])
# torch.Size([12, 2, 5])
V_pred = V_pred.permute(0, 2, 3, 1)
# torch.Size([1, 12, 2, 5])>>seq,node,feat
# V_pred= torch.rand_like(V_tr).cuda()
V_tr = V_tr.squeeze()
A_tr = A_tr.squeeze()
V_pred = V_pred.squeeze()
num_of_objs = obs_traj_rel.shape[1]
V_pred, V_tr = V_pred[:, :num_of_objs, :], V_tr[:, :num_of_objs, :]
# print(V_pred.shape)
# For now I have my bi-variate parameters
# normx = V_pred[:,:,0:1]
# normy = V_pred[:,:,1:2]
sx = torch.exp(V_pred[:, :, 2]) # sx
sy = torch.exp(V_pred[:, :, 3]) # sy
corr = torch.tanh(V_pred[:, :, 4]) # corr
cov = torch.zeros(V_pred.shape[0], V_pred.shape[1], 2, 2).cuda()
cov[:, :, 0, 0] = sx * sx
cov[:, :, 0, 1] = corr * sx * sy
cov[:, :, 1, 0] = corr * sx * sy
cov[:, :, 1, 1] = sy * sy
mean = V_pred[:, :, 0:2]
mvnormal = torchdist.MultivariateNormal(mean, cov)
# Now sample 20 samples
ade_ls = {}
fde_ls = {}
V_x = seq_to_nodes(obs_traj.data.cpu().numpy().copy())
V_x_rel_to_abs = nodes_rel_to_nodes_abs(V_obs.data.cpu().numpy().squeeze().copy(),
V_x[0, :, :].copy())
V_y = seq_to_nodes(pred_traj_gt.data.cpu().numpy().copy())
V_y_rel_to_abs = nodes_rel_to_nodes_abs(V_tr.data.cpu().numpy().squeeze().copy(),
V_x[-1, :, :].copy())
raw_data_dict[step] = {}
raw_data_dict[step]['obs'] = copy.deepcopy(V_x_rel_to_abs)
raw_data_dict[step]['trgt'] = copy.deepcopy(V_y_rel_to_abs)
raw_data_dict[step]['pred'] = []
for n in range(num_of_objs):
ade_ls[n] = []
fde_ls[n] = []
for k in range(KSTEPS):
V_pred = mvnormal.sample()
# V_pred = seq_to_nodes(pred_traj_gt.data.numpy().copy())
V_pred_rel_to_abs = nodes_rel_to_nodes_abs(V_pred.data.cpu().numpy().squeeze().copy(),
V_x[-1, :, :].copy())
raw_data_dict[step]['pred'].append(copy.deepcopy(V_pred_rel_to_abs))
# print(V_pred_rel_to_abs.shape) #(12, 3, 2) = seq, ped, location
for n in range(num_of_objs):
pred = []
target = []
obsrvs = []
number_of = []
pred.append(V_pred_rel_to_abs[:, n:n + 1, :])
target.append(V_y_rel_to_abs[:, n:n + 1, :])
obsrvs.append(V_x_rel_to_abs[:, n:n + 1, :])
number_of.append(1)
ade_ls[n].append(ade(pred, target, number_of))
fde_ls[n].append(fde(pred, target, number_of))
for n in range(num_of_objs):
ade_bigls.append(min(ade_ls[n]))
fde_bigls.append(min(fde_ls[n]))
with open('checkpoint/your-experiment-name/ade_ls.pickle', 'wb') as f:
pickle.dump(ade_bigls, f)
with open('checkpoint/your-experiment-name/fde_ls.pickle', 'wb') as f:
pickle.dump(fde_bigls, f)
ade_ = sum(ade_bigls) / len(ade_bigls)
fde_ = sum(fde_bigls) / len(fde_bigls)
return ade_, fde_, raw_data_dict
paths = ['./checkpoint/your-experiment-name']
KSTEPS = 20
print("*" * 50)
print('Number of samples:', KSTEPS)
print("*" * 50)
for feta in range(len(paths)):
ade_ls = []
fde_ls = []
path = paths[feta]
exps = glob.glob(path)
print('Model being tested are:', exps)
for exp_path in exps:
print("*" * 50)
print("Evaluating model:", exp_path)
model_path = exp_path + '/val_best.pth'
args_path = exp_path + '/args.pkl'
with open(args_path, 'rb') as f:
args = pickle.load(f)
stats = exp_path + '/constant_metrics.pkl'
with open(stats, 'rb') as f:
cm = pickle.load(f)
print("Stats:", cm)
# Data prep
obs_seq_len = args.obs_seq_len
pred_seq_len = args.pred_seq_len
data_set = './datasets/' + args.dataset + '/'
dset_test = TrajectoryDataset(
data_set + 'test/',
data_set + 'flow_test/',
data_set + 'image_test/',
obs_len=obs_seq_len,
pred_len=pred_seq_len,
skip=1, norm_lap_matr=True)
loader_test = DataLoader(
dset_test,
batch_size=1, # This is irrelative to the args batch size parameter
shuffle=False,
num_workers=0)
# Defining the model
model = social_stgcnn(n_stgcnn=args.n_stgcnn, n_txpcnn=args.n_txpcnn,
output_feat=args.output_size, seq_len=args.obs_seq_len,
kernel_size=args.kernel_size, pred_seq_len=args.pred_seq_len, use_image=args.use_image,
use_flow=args.use_flow).cuda()
model.load_state_dict(torch.load(model_path))
ade_ = 999999
fde_ = 999999
print("Testing ....")
ad, fd, raw_data_dic_ = test()
ade_ = min(ade_, ad)
fde_ = min(fde_, fd)
ade_ls.append(ade_)
fde_ls.append(fde_)
print("ADE:", ade_, " FDE:", fde_)
with open('checkpoint/your-experiment-name/raw_data_dic.pickle', 'wb') as f:
pickle.dump(raw_data_dic_, f)
print("*" * 50)
print("Avg ADE:", sum(ade_ls) / 5)
print("Avg FDE:", sum(fde_ls) / 5)