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eval_mocap.py
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eval_mocap.py
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import argparse
import torch
import torch.utils.data
from motion.dataset import MotionDataset
from model.eghn import EGHN
import os
from torch import nn, optim
import json
import random
import numpy as np
parser = argparse.ArgumentParser(description='VAE MNIST Example')
parser.add_argument('--exp_name', type=str, default='exp_1', metavar='N', help='experiment_name')
parser.add_argument('--batch_size', type=int, default=100, metavar='N',
help='input batch size for training (default: 128)')
parser.add_argument('--epochs', type=int, default=10000, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log_interval', type=int, default=1, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--test_interval', type=int, default=5, metavar='N',
help='how many epochs to wait before logging test')
parser.add_argument('--outf', type=str, default='exp_results', metavar='N',
help='folder to output the json log file')
parser.add_argument('--lr', type=float, default=5e-4, metavar='N',
help='learning rate')
parser.add_argument('--nf', type=int, default=64, metavar='N',
help='hidden dim')
parser.add_argument('--model', type=str, default='hier', metavar='N')
parser.add_argument('--attention', type=int, default=0, metavar='N',
help='attention in the ae model')
parser.add_argument('--n_layers', type=int, default=4, metavar='N',
help='number of layers for the autoencoder')
parser.add_argument('--max_training_samples', type=int, default=3000, metavar='N',
help='maximum amount of training samples')
parser.add_argument('--dataset', type=str, default="nbody_small", metavar='N',
help='nbody_small, nbody')
parser.add_argument('--weight_decay', type=float, default=1e-12, metavar='N',
help='timing experiment')
parser.add_argument('--delta_frame', type=int, default=50,
help='Number of frames delta.')
parser.add_argument('--data_dir', type=str, default='spatial_graph/md17',
help='Data directory.')
parser.add_argument('--dropout', type=float, default=0.5,
help='Dropout rate (1 - keep probability).')
parser.add_argument("--config_by_file", default=False, action="store_true", )
parser.add_argument('--lambda_link', type=float, default=1,
help='The weight of the linkage loss.')
parser.add_argument('--n_cluster', type=int, default=3,
help='The number of clusters.')
parser.add_argument('--flat', action='store_true', default=False,
help='flat MLP')
parser.add_argument('--interaction_layer', type=int, default=3,
help='The number of interaction layers per block.')
parser.add_argument('--pooling_layer', type=int, default=3,
help='The number of pooling layers in EGPN.')
parser.add_argument('--decoder_layer', type=int, default=1,
help='The number of decoder layers.')
parser.add_argument('--case', type=str, default='walk',
help='The case, walk or run.')
time_exp_dic = {'time': 0, 'counter': 0}
args = parser.parse_args()
# Place the checkpoint file here
ckpt_file = args.outf + '/' + args.exp_name + '/' + 'saved_model.pth'
if args.config_by_file:
job_param_path = './job_param.json'
with open(job_param_path, 'r') as f:
hyper_params = json.load(f)
args.exp_name = hyper_params["exp_name"]
args.batch_size = hyper_params["batch_size"]
args.epochs = hyper_params["epochs"]
args.seed = hyper_params["seed"]
args.lr = hyper_params["lr"]
args.nf = hyper_params["nf"]
args.model = hyper_params["model"]
args.n_layers = hyper_params["n_layers"]
args.max_training_samples = hyper_params["max_training_samples"]
# Do not necessary in practice.
args.data_dir = hyper_params["data_dir"]
args.weight_decay = hyper_params["weight_decay"]
args.dropout = hyper_params["dropout"]
args.lambda_link = hyper_params["lambda_link"]
args.n_cluster = hyper_params["n_cluster"]
args.flat = hyper_params["flat"]
args.interaction_layer = hyper_params["interaction_layer"]
args.pooling_layer = hyper_params["pooling_layer"]
args.decoder_layer = hyper_params["decoder_layer"]
args.case = hyper_params["case"]
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
loss_mse = nn.MSELoss()
print(args)
def main():
# fix seed
seed = args.seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
dataset_train = MotionDataset(partition='train', max_samples=args.max_training_samples, data_dir=args.data_dir,
delta_frame=args.delta_frame, case=args.case)
loader_train = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle=True, drop_last=False,
num_workers=8)
dataset_val = MotionDataset(partition='val', max_samples=600, data_dir=args.data_dir,
delta_frame=args.delta_frame, case=args.case)
loader_val = torch.utils.data.DataLoader(dataset_val, batch_size=args.batch_size, shuffle=False, drop_last=False,
num_workers=8)
dataset_test = MotionDataset(partition='test', max_samples=600, data_dir=args.data_dir,
delta_frame=args.delta_frame, case=args.case)
loader_test = torch.utils.data.DataLoader(dataset_test, batch_size=args.batch_size, shuffle=False, drop_last=False,
num_workers=8)
if args.model == 'hier':
model = EGHN(in_node_nf=2, in_edge_nf=2, hidden_nf=args.nf, device=device,
n_cluster=args.n_cluster, flat=args.flat, layer_per_block=args.interaction_layer,
layer_pooling=args.pooling_layer, activation=nn.SiLU(),
layer_decoder=args.decoder_layer)
model.load_state_dict(torch.load(ckpt_file))
print('loaded from ', ckpt_file)
else:
raise Exception("Wrong model specified")
print(model)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
model.eval()
save_name = args.outf + '/' + args.exp_name + '/' + 'eval_train.pkl'
train_loss = train(model, optimizer, 0, loader_train, backprop=False, save_name=save_name)
save_name = args.outf + '/' + args.exp_name + '/' + 'eval_test.pkl'
test_loss = train(model, optimizer, 0, loader_test, backprop=False, save_name=save_name)
exit(0)
return best_train_loss, best_val_loss, best_test_loss, best_epoch
def train(model, optimizer, epoch, loader, backprop=True, save_name=None):
all_loc, all_loc_pred, all_loc_end = None, None, None
all_pooling_plan = None
if backprop:
model.train()
else:
model.eval()
res = {'epoch': epoch, 'loss': 0, 'counter': 0}
for batch_idx, data in enumerate(loader):
batch_size, n_nodes, _ = data[0].size()
data = [d.to(device) for d in data]
# data = [d.view(-1, d.size(2)) for d in data] # construct mini-batch graphs
loc, vel, edges, edge_attr, local_edges, local_edge_fea, Z, loc_end, vel_end = data
# convert into graph minibatch
loc = loc.view(-1, loc.size(2))
vel = vel.view(-1, vel.size(2))
offset = (torch.arange(batch_size) * n_nodes).unsqueeze(-1).unsqueeze(-1).to(edges.device)
edges = torch.cat(list(edges + offset), dim=-1) # [2, BM]
edge_attr = torch.cat(list(edge_attr), dim=0) # [BM, ]
local_edge_index = torch.cat(list(local_edges + offset), dim=-1) # [2, BM]
local_edge_fea = torch.cat(list(local_edge_fea), dim=0) # [BM, ]
# local_edge_mask = torch.cat(list(local_edge_mask), dim=0) # [BM, ]
Z = Z.view(-1, Z.size(2))
loc_end = loc_end.view(-1, loc_end.size(2))
vel_end = vel_end.view(-1, vel_end.size(2))
if all_loc is None:
all_loc = loc
else:
all_loc = torch.cat((all_loc, loc), dim=0)
optimizer.zero_grad()
if args.model == 'hier':
nodes = torch.sqrt(torch.sum(vel ** 2, dim=1)).unsqueeze(1).detach()
nodes = torch.cat((nodes, Z / Z.max()), dim=-1)
rows, cols = edges
loc_dist = torch.sum((loc[rows] - loc[cols])**2, 1).unsqueeze(1) # relative distances among locations
edge_attr = torch.cat([edge_attr, loc_dist], 1).detach() # concatenate all edge properties
loc_dist1 = torch.sum((loc[local_edge_index[0]] - loc[local_edge_index[1]])**2, 1).unsqueeze(1)
local_edge_fea = torch.cat([local_edge_fea, loc_dist1], 1).detach() # concatenate all edge properties
loc_pred, vel_pred, _ = model(loc, nodes, edges, edge_attr, local_edge_index, local_edge_fea,
n_node=n_nodes, v=vel, node_mask=None, node_nums=None)
else:
raise Exception("Wrong model")
if all_loc_pred is None:
all_loc_pred = loc_pred
else:
all_loc_pred = torch.cat((all_loc_pred, loc_pred), dim=0)
if all_loc_end is None:
all_loc_end = loc_end
else:
all_loc_end = torch.cat((all_loc_end, loc_end), dim=0)
cur_pooling_plan = model.current_pooling_plan
if all_pooling_plan is None:
all_pooling_plan = cur_pooling_plan
else:
all_pooling_plan = torch.cat((all_pooling_plan, cur_pooling_plan), dim=0)
loss = loss_mse(loc_pred, loc_end)
if backprop:
loss.backward()
optimizer.step()
pass
res['loss'] += loss.item()*batch_size
res['counter'] += batch_size
import pickle as pkl
with open(save_name, 'wb') as f:
pkl.dump((all_loc.detach().cpu().numpy(),
all_loc_end.detach().cpu().numpy(),
all_loc_pred.detach().cpu().numpy(),
all_pooling_plan.detach().cpu().numpy()
), f)
print('Saved to ', save_name)
if not backprop:
prefix = "==> "
else:
prefix = ""
print('%s epoch %d avg loss: %.5f'
% (prefix+loader.dataset.partition, epoch, res['loss'] / res['counter']))
return res['loss'] / res['counter']
if __name__ == "__main__":
best_train_loss, best_val_loss, best_test_loss, best_epoch = main()
print("best_train = %.6f" % best_train_loss)
print("best_val = %.6f" % best_val_loss)
print("best_test = %.6f" % best_test_loss)
print("best_epoch = %d" % best_epoch)
print("best_train = %.6f, best_val = %.6f, best_test = %.6f, best_epoch = %d" % (best_train_loss, best_val_loss, best_test_loss, best_epoch))