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test_and_generate_H5.py
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test_and_generate_H5.py
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# %%
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020-05-27 15:00
# @Author : Xiaoke Huang
# @Email : [email protected]
from modeling.vectornet import HGNN
import torch.nn.functional as F
import torch.optim as optim
import torch
import numpy as np
import pandas as pd
from utils.viz_utils import show_predict_result
import matplotlib.pyplot as plt
import numpy as np
import pdb
import os
from dataset import GraphDataset
from torch_geometric.data import DataLoader
from utils.eval import get_eval_metric_results
from tqdm import tqdm
import time
from typing import List
import pickle
# %%
TRAIN_DIR = os.path.join('interm_data', 'train_intermediate')
VAL_DIR = os.path.join('interm_data', 'val_intermediate')
TEST_DIR = os.path.join('interm_data', 'test_intermediate')
SEED = 13
epochs = 50
device = torch.device('cuda:2' if torch.cuda.is_available() else 'cpu')
batch_size = 4096
decay_lr_factor = 0.3
decay_lr_every = 10
lr = 0.001
in_channels, out_channels = 8, 60
show_every = 10
val_every = 5
small_dataset = False
end_epoch = 0
save_dir = 'trained_params'
best_minade = float('inf')
date = f"200630.epochs{epochs}.lr_decay{decay_lr_factor}.decay_every{decay_lr_every}.lr{lr}"
global_step = 0
FILENAME = 'epoch_9.valminade_2.500.200707.epochs50.lr_decay0.3.decay_every15.lr0.0003.xkhuang.pth'
checkpoint_dir = os.path.join('trained_params', FILENAME)
# checkpoint_dir = None
NORM_CENTERS_DICT_DIR = os.path.join('interm_data', 'test-norm_center_dict.pkl')
output_path = 'competition_files/'
# eval related
max_n_guesses = 1
horizon = 30
miss_threshold = 2.0
#%%
#%%
def save_checkpoint(checkpoint_dir, model, optimizer, end_epoch, val_minade, date):
# state_dict: a Python dictionary object that:
# - for a model, maps each layer to its parameter tensor;
# - for an optimizer, contains info about the optimizer’s states and hyperparameters used.
os.makedirs(checkpoint_dir, exist_ok=True)
state = {
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict(),
'end_epoch' : end_epoch,
'val_minade': val_minade
}
checkpoint_path = os.path.join(checkpoint_dir, f'epoch_{end_epoch}.valminade_{val_minade:.3f}.{date}.{"xkhuang"}.pth')
torch.save(state, checkpoint_path)
print('model saved to %s' % checkpoint_path)
def load_checkpoint(checkpoint_path, model):
state = torch.load(checkpoint_path, map_location='cpu')
model.load_state_dict(state['state_dict'])
# optimizer.load_state_dict(state['optimizer'])
print('model loaded from %s' % checkpoint_path)
#%%
if __name__ == "__main__":
np.random.seed(SEED)
torch.manual_seed(SEED)
# hyper parameters
# train_data = GraphDataset(TRAIN_DIR)
# val_data = GraphDataset(VAL_DIR)
# if small_dataset:
# train_loader = DataLoader(train_data[:1000], batch_size=batch_size)
# val_loader = DataLoader(val_data[:200], batch_size=batch_size)
# else:
# train_loader = DataLoader(train_data, batch_size=batch_size)
# val_loader = DataLoader(val_data, batch_size=batch_size)
test_data = GraphDataset(TEST_DIR)
test_loader = DataLoader(test_data, batch_size=batch_size)
model = HGNN(in_channels, out_channels)
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = optim.lr_scheduler.StepLR(
optimizer, step_size=decay_lr_every, gamma=decay_lr_factor)
if checkpoint_dir:
load_checkpoint(checkpoint_dir, model)
model = model.to(device)
norm_centers_dict = None
with open(NORM_CENTERS_DICT_DIR, 'rb') as f:
norm_centers_dict = pickle.load(f)
norm_centers_ls = sorted(norm_centers_dict.items())
forecasted_trajectories = {}
seq_index = 0
model.eval()
with torch.no_grad():
for data in test_loader:
data = data.to(device)
out = model(data)
for i in range(out.size(0)):
seq_id = int(norm_centers_ls[seq_index][0])
norm_center = norm_centers_ls[seq_index][1]
pred_y = out[i].view((-1, 2)).cumsum(axis=0).cpu().numpy()
pred_y += norm_center.reshape(-1, 2)
pred_y = np.array([pred_y])
assert(pred_y.shape == (1, 30, 2))
# y = gt[i].view((-1, 2)).cumsum(axis=0).cpu().numpy()
forecasted_trajectories[seq_id] = pred_y
seq_index += 1
from argoverse.evaluation.competition_util import generate_forecasting_h5
generate_forecasting_h5(forecasted_trajectories, output_path, filename=FILENAME)
#%%
#this might take awhile
# metric_results = get_displacement_errors_and_miss_rate(
# forecasted_trajectories, gt_trajectories, max_n_guesses, horizon, miss_threshold
# )
# return metric_results
# overfit the small dataset
# model.train()
# for epoch in range(epochs):
# print(f"start training at epoch:{epoch}")
# acc_loss = .0
# num_samples = 1
# start_tic = time.time()
# for data in train_loader:
# if epoch < end_epoch: break
# if isinstance(data, List):
# y = torch.cat([i.y for i in data], 0).view(-1, out_channels).to(device)
# else:
# data = data.to(device)
# y = data.y.view(-1, out_channels)
# optimizer.zero_grad()
# out = model(data)
# loss = F.mse_loss(out, y)
# loss.backward()
# acc_loss += batch_size * loss.item()
# num_samples += y.shape[0]
# optimizer.step()
# global_step += 1
# if (global_step + 1) % show_every == 0:
# print( f"epoch {epoch} step {global_step}： loss:{loss.item():3f}, lr:{optimizer.state_dict()['param_groups'][0]['lr']: .6f}, time:{time.time() - start_tic: 4f}sec")
# scheduler.step()
# print(
# f"finished epoch {epoch}: loss:{acc_loss / num_samples:.3f}, lr:{optimizer.state_dict()['param_groups'][0]['lr']: .6f}, time:{time.time() - start_tic: 4f}sec")
# if (epoch+1) % val_every == 0 and (not epoch < end_epoch):
# print("eval as epoch:{epoch}")
# metrics = get_eval_metric_results(model, val_loader, device, out_channels, max_n_guesses, horizon, miss_threshold)
# curr_minade = metrics["minADE"]
# print(f"minADE:{metrics['minADE']:3f}, minFDE:{metrics['minFDE']:3f}, MissRate:{metrics['MR']:3f}")
# if curr_minade < best_minade:
# best_minade = curr_minade
# save_checkpoint(save_dir, model, optimizer, epoch, best_minade, date)
# # eval result on the identity dataset
# metrics = get_eval_metric_results(model, val_loader, device, out_channels, max_n_guesses, horizon, miss_threshold)
# curr_minade = metrics["minADE"]
# if curr_minade < best_minade:
# best_minade = curr_minade
# save_checkpoint(save_dir, model, optimizer, -1, best_minade, date)
# %%