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main_eval.py
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main_eval.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""overall code framework is adapped from https://github.com/weigq/3d_pose_baseline_pytorch"""
from __future__ import print_function, absolute_import, division
import os
import time
import torch
import torch.nn as nn
import torch.optim
from torch.utils.data import DataLoader
from torch.autograd import Variable
import numpy as np
from progress.bar import Bar
import pandas as pd
from utils import loss_funcs, utils as utils
from utils.opt import Options
from utils.h36motion import H36motion
import utils.model as nnmodel
import utils.data_utils as data_utils
def main(opt):
start_epoch = 0
err_best = 10000
lr_now = opt.lr
is_cuda = torch.cuda.is_available()
opt.is_load = True
# define log csv file
script_name = os.path.basename(__file__).split('.')[0]
script_name = script_name + "_in{:d}_out{:d}_dctn{:d}".format(opt.input_n, opt.output_n, opt.dct_n)
# create model
print(">>> creating model")
input_n = opt.input_n
output_n = opt.output_n
dct_n = opt.dct_n
sample_rate = opt.sample_rate
# 48 nodes for angle prediction
model = nnmodel.GCN(input_feature=dct_n, hidden_feature=opt.linear_size, p_dropout=opt.dropout,
num_stage=opt.num_stage, node_n=48)
if is_cuda:
model.cuda()
print(">>> total params: {:.2f}M".format(sum(p.numel() for p in model.parameters()) / 1000000.0))
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
# continue from checkpoint
if opt.is_load:
model_path_len = 'checkpoint/pretrained/h36m_in10_out25_dctn35.pth.tar'
print(">>> loading ckpt len from '{}'".format(model_path_len))
if is_cuda:
ckpt = torch.load(model_path_len)
else:
ckpt = torch.load(model_path_len, map_location='cpu')
start_epoch = ckpt['epoch']
err_best = ckpt['err']
lr_now = ckpt['lr']
model.load_state_dict(ckpt['state_dict'])
optimizer.load_state_dict(ckpt['optimizer'])
print(">>> ckpt len loaded (epoch: {} | err: {})".format(start_epoch, err_best))
# data loading
print(">>> loading data")
# train_dataset = H36motion(path_to_data=opt.data_dir, actions='all', input_n=input_n, output_n=output_n,
# split=0, sample_rate=sample_rate, dct_n=dct_n)
# data_std = train_dataset.data_std
# data_mean = train_dataset.data_mean
dim_used = [6, 7, 8, 9, 12, 13, 14, 15, 21, 22, 23, 24, 27, 28, 29, 30, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 51, 52, 53, 54, 55, 56, 57, 60, 61, 62, 75, 76, 77, 78, 79, 80, 81, 84, 85, 86]
# val_dataset = H36motion(path_to_data=opt.data_dir, actions='all', input_n=input_n, output_n=output_n,
# split=2, sample_rate=sample_rate, data_mean=data_mean, data_std=data_std, dct_n=dct_n)
# # load dadasets for training
# train_loader = DataLoader(
# dataset=train_dataset,
# batch_size=opt.train_batch,
# shuffle=True,
# num_workers=opt.job,
# pin_memory=True)
# val_loader = DataLoader(
# dataset=val_dataset,
# batch_size=opt.test_batch,
# shuffle=False,
# num_workers=opt.job,
# pin_memory=True)
acts = data_utils.define_actions('all')
test_data = dict()
for act in acts:
test_dataset = H36motion(path_to_data=opt.data_dir, actions=act, input_n=input_n, output_n=output_n, split=1,
sample_rate=sample_rate, dct_n=dct_n)
test_data[act] = DataLoader(
dataset=test_dataset,
batch_size=opt.test_batch,
shuffle=False,
num_workers=opt.job,
pin_memory=True)
print(">>> data loaded !")
# print(">>> train data {}".format(train_dataset.__len__()))
# print(">>> validation data {}".format(val_dataset.__len__()))
# for epoch in range(start_epoch, opt.epochs):
#
# if (epoch + 1) % opt.lr_decay == 0:
# lr_now = utils.lr_decay(optimizer, lr_now, opt.lr_gamma)
# print('==========================')
# print('>>> epoch: {} | lr: {:.5f}'.format(epoch + 1, lr_now))
ret_log = np.array([start_epoch])
head = np.array(['epoch'])
# per epoch
# lr_now, t_l, t_e, t_3d = train(train_loader, model, optimizer, input_n=input_n,
# lr_now=lr_now, max_norm=opt.max_norm, is_cuda=is_cuda,
# dim_used=train_dataset.dim_used, dct_n=dct_n)
# ret_log = np.append(ret_log, [lr_now, t_l, t_e, t_3d])
# head = np.append(head, ['lr', 't_l', 't_e', 't_3d'])
#
# v_e, v_3d = val(val_loader, model, input_n=input_n, is_cuda=is_cuda, dim_used=train_dataset.dim_used,
# dct_n=dct_n)
#
# ret_log = np.append(ret_log, [v_e, v_3d])
# head = np.append(head, ['v_e', 'v_3d'])
test_3d_temp = np.array([])
test_3d_head = np.array([])
for act in acts:
test_e, test_3d = test(test_data[act], model, input_n=input_n, output_n=output_n, is_cuda=is_cuda,
dim_used=dim_used, dct_n=dct_n)
ret_log = np.append(ret_log, test_e)
test_3d_temp = np.append(test_3d_temp, test_3d)
test_3d_head = np.append(test_3d_head,
[act + '3d80', act + '3d160', act + '3d320', act + '3d400'])
head = np.append(head, [act + '80', act + '160', act + '320', act + '400'])
if output_n > 10:
head = np.append(head, [act + '560', act + '1000'])
test_3d_head = np.append(test_3d_head,
[act + '3d560', act + '3d1000'])
ret_log = np.append(ret_log, test_3d_temp)
head = np.append(head, test_3d_head)
# update log file and save checkpoint
df = pd.DataFrame(np.expand_dims(ret_log, axis=0))
# if epoch == start_epoch:
df.to_csv(opt.ckpt + '/' + script_name + '.csv', header=head, index=False)
# else:
# with open(opt.ckpt + '/' + script_name + '.csv', 'a') as f:
# df.to_csv(f, header=False, index=False)
# if not np.isnan(v_e):
# is_best = v_e < err_best
# err_best = min(v_e, err_best)
# else:
# is_best = False
# file_name = ['ckpt_' + script_name + '_best.pth.tar', 'ckpt_' + script_name + '_last.pth.tar']
# utils.save_ckpt({'epoch': epoch + 1,
# 'lr': lr_now,
# 'err': test_e[0],
# 'state_dict': model.state_dict(),
# 'optimizer': optimizer.state_dict()},
# ckpt_path=opt.ckpt,
# is_best=is_best,
# file_name=file_name)
def train(train_loader, model, optimizer, input_n=20, dct_n=20, lr_now=None, max_norm=True, is_cuda=False, dim_used=[]):
t_l = utils.AccumLoss()
t_e = utils.AccumLoss()
t_3d = utils.AccumLoss()
model.train()
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
# skip the last batch if only have one sample for batch_norm layers
batch_size = inputs.shape[0]
if batch_size == 1:
continue
bt = time.time()
if is_cuda:
inputs = Variable(inputs.cuda()).float()
# targets = Variable(targets.cuda(async=True)).float()
all_seq = Variable(all_seq.cuda(async=True)).float()
outputs = model(inputs)
n = outputs.shape[0]
outputs = outputs.view(n, -1)
# targets = targets.view(n, -1)
loss = loss_funcs.sen_loss(outputs, all_seq, dim_used, dct_n)
# calculate loss and backward
optimizer.zero_grad()
loss.backward()
if max_norm:
nn.utils.clip_grad_norm(model.parameters(), max_norm=1)
optimizer.step()
n, _, _ = all_seq.data.shape
# 3d error
m_err = loss_funcs.mpjpe_error(outputs, all_seq, input_n, dim_used, dct_n)
# angle space error
e_err = loss_funcs.euler_error(outputs, all_seq, input_n, dim_used, dct_n)
# update the training loss
t_l.update(loss.cpu().data.numpy()[0] * n, n)
t_e.update(e_err.cpu().data.numpy()[0] * n, n)
t_3d.update(m_err.cpu().data.numpy()[0] * n, n)
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(i + 1, len(train_loader), time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return lr_now, t_l.avg, t_e.avg, t_3d.avg
def test(train_loader, model, input_n=20, output_n=50, dct_n=20, is_cuda=False, dim_used=[]):
N = 0
# t_l = 0
if output_n >= 25:
eval_frame = [1, 3, 7, 9, 13, 24]
elif output_n == 10:
eval_frame = [1, 3, 7, 9]
t_e = np.zeros(len(eval_frame))
t_3d = np.zeros(len(eval_frame))
model.eval()
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
bt = time.time()
if is_cuda:
inputs = Variable(inputs.cuda()).float()
# targets = Variable(targets.cuda(async=True)).float()
all_seq = Variable(all_seq.cuda(async=True)).float()
outputs = model(inputs)
n = outputs.shape[0]
# outputs = outputs.view(n, -1)
# targets = targets.view(n, -1)
# loss = loss_funcs.sen_loss(outputs, all_seq, dim_used)
n, seq_len, dim_full_len = all_seq.data.shape
dim_used_len = len(dim_used)
# inverse dct transformation
_, idct_m = data_utils.get_dct_matrix(seq_len)
idct_m = Variable(torch.from_numpy(idct_m)).float().cuda()
outputs_t = outputs.view(-1, dct_n).transpose(0, 1)
outputs_exp = torch.matmul(idct_m[:, :dct_n], outputs_t).transpose(0, 1).contiguous().view(-1, dim_used_len,
seq_len).transpose(1,
2)
pred_expmap = all_seq.clone()
dim_used = np.array(dim_used)
pred_expmap[:, :, dim_used] = outputs_exp
pred_expmap = pred_expmap[:, input_n:, :].contiguous().view(-1, dim_full_len)
targ_expmap = all_seq[:, input_n:, :].clone().contiguous().view(-1, dim_full_len)
pred_expmap[:, 0:6] = 0
targ_expmap[:, 0:6] = 0
pred_expmap = pred_expmap.view(-1, 3)
targ_expmap = targ_expmap.view(-1, 3)
# get euler angles from expmap
pred_eul = data_utils.rotmat2euler_torch(data_utils.expmap2rotmat_torch(pred_expmap))
pred_eul = pred_eul.view(-1, dim_full_len).view(-1, output_n, dim_full_len)
targ_eul = data_utils.rotmat2euler_torch(data_utils.expmap2rotmat_torch(targ_expmap))
targ_eul = targ_eul.view(-1, dim_full_len).view(-1, output_n, dim_full_len)
# get 3d coordinates
targ_p3d = data_utils.expmap2xyz_torch(targ_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
pred_p3d = data_utils.expmap2xyz_torch(pred_expmap.view(-1, dim_full_len)).view(n, output_n, -1, 3)
# update loss and testing errors
for k in np.arange(0, len(eval_frame)):
j = eval_frame[k]
t_e[k] += torch.mean(torch.norm(pred_eul[:, j, :] - targ_eul[:, j, :], 2, 1)).cpu().data.numpy()[0] * n
t_3d[k] += torch.mean(torch.norm(
targ_p3d[:, j, :, :].contiguous().view(-1, 3) - pred_p3d[:, j, :, :].contiguous().view(-1, 3), 2,
1)).cpu().data.numpy()[0] * n
# t_l += loss.cpu().data.numpy()[0] * n
N += n
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(i + 1, len(train_loader), time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return t_e / N, t_3d / N
def val(train_loader, model, input_n=20, dct_n=20, is_cuda=False, dim_used=[]):
# t_l = utils.AccumLoss()
t_e = utils.AccumLoss()
t_3d = utils.AccumLoss()
model.eval()
st = time.time()
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, (inputs, targets, all_seq) in enumerate(train_loader):
bt = time.time()
if is_cuda:
inputs = Variable(inputs.cuda()).float()
# targets = Variable(targets.cuda(async=True)).float()
all_seq = Variable(all_seq.cuda(async=True)).float()
outputs = model(inputs)
n = outputs.shape[0]
outputs = outputs.view(n, -1)
# targets = targets.view(n, -1)
# loss = loss_funcs.sen_loss(outputs, all_seq, dim_used)
n, _, _ = all_seq.data.shape
m_err = loss_funcs.mpjpe_error(outputs, all_seq, input_n, dim_used, dct_n)
e_err = loss_funcs.euler_error(outputs, all_seq, input_n, dim_used, dct_n)
# t_l.update(loss.cpu().data.numpy()[0] * n, n)
t_e.update(e_err.cpu().data.numpy()[0] * n, n)
t_3d.update(m_err.cpu().data.numpy()[0] * n, n)
bar.suffix = '{}/{}|batch time {:.4f}s|total time{:.2f}s'.format(i + 1, len(train_loader), time.time() - bt,
time.time() - st)
bar.next()
bar.finish()
return t_e.avg, t_3d.avg
if __name__ == "__main__":
option = Options().parse()
main(option)