# -*- coding: utf-8 -*-
"""
Function that learns feature model + 3layer pose models x 12 object categories
in an end-to-end manner by minimizing the mean squared error for axis-angle representation
"""
import torch
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torch.optim import Optimizer
from featureModels import resnet_model
from axisAngle import get_error2, geodesic_loss
# from quaternion import get_error2, geodesic_loss
from poseModels import model_3layer
from helperFunctions import classes
from dataGenerators import ImagesAll, TestImages, my_collate
import numpy as np
import scipy.io as spio
import gc
import os
import progressbar
import argparse
from tensorboardX import SummaryWriter
import time
parser = argparse.ArgumentParser(description='Pure Regression Models')
parser.add_argument('--gpu_id', type=str, default='0')
parser.add_argument('--db_type', type=str, default='clean')
parser.add_argument('--save_str', type=str)
parser.add_argument('--ydata_type', type=str, default='axis_angle')
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--nonlinearity', type=str, default='valid')
parser.add_argument('--num_epochs', type=int, default=9)
args = parser.parse_args()
print(args)
# assign GPU
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
model_file = os.path.join('models', args.save_str + '.tar')
results_dir = os.path.join('results', args.save_str + '_' + args.db_type)
plots_file = os.path.join('plots', args.save_str + '_' + args.db_type)
log_dir = os.path.join('logs', args.save_str + '_' + args.db_type)
if not os.path.exists(results_dir):
os.mkdir(results_dir)
def myProj(x):
angle = torch.norm(x, 2, 1, True)
axis = F.normalize(x)
angle = torch.fmod(angle, 2*np.pi)
return angle*axis
# my model for pose estimation: feature model + 1layer pose model x 12
class my_model(nn.Module):
def __init__(self):
super().__init__()
self.num_classes = num_classes
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.pose_models = nn.ModuleList([model_3layer(N0, N1, N2, ndim) for i in range(self.num_classes)]).cuda()
def forward(self, x, label):
x = self.feature_model(x)
x = torch.stack([self.pose_models[i](x) for i in range(self.num_classes)]).permute(1, 2, 0)
label = torch.zeros(label.size(0), self.num_classes).scatter_(1, label.data.cpu(), 1.0)
label = Variable(label.unsqueeze(2).cuda())
y = torch.squeeze(torch.bmm(x, label), 2)
if args.nonlinearity == 'valid':
y = np.pi*F.tanh(y)
elif args.nonlinearity == 'correct':
y = myProj(y)
# y = F.normalize(y)
else:
pass
del x, label
return y
# Implements variation of SGD (optionally with momentum)
class mySGD(Optimizer):
def __init__(self, params, c, alpha1=1e-6, alpha2=1e-8, momentum=0, dampening=0, weight_decay=0, nesterov=False):
defaults = dict(alpha1=alpha1, alpha2=alpha2, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov)
super(mySGD, self).__init__(params, defaults)
self.c = c
def __setstate__(self, state):
super(mySGD, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['step'] += 1
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)
buf.mul_(momentum).add_(d_p)
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
# cyclical learning rate
t = (np.fmod(state['step']-1, self.c)+1)/self.c
if t <= 0.5:
step_size = (1-2*t)*group['alpha1'] + 2*t*group['alpha2']
else:
step_size = 2*(1-t)*group['alpha2'] + (2*t-1)*group['alpha1']
writer.add_scalar('lr', step_size, state['step'])
p.data.add_(-step_size, d_p)
return loss
if args.db_type == 'clean':
db_path = 'data/flipped_new'
else:
db_path = 'data/flipped_all'
num_classes = len(classes)
train_path = os.path.join(db_path, 'train')
test_path = os.path.join(db_path, 'test')
render_path = 'data/renderforcnn/'
# DATA
real_data = ImagesAll(train_path, 'real', args.ydata_type)
render_data = ImagesAll(render_path, 'render', args.ydata_type)
test_data = TestImages(test_path, args.ydata_type)
real_loader = DataLoader(real_data, batch_size=args.num_workers, shuffle=True, num_workers=args.num_workers, pin_memory=True, collate_fn=my_collate)
render_loader = DataLoader(render_data, batch_size=args.num_workers, shuffle=True, num_workers=args.num_workers, pin_memory=True, collate_fn=my_collate)
test_loader = DataLoader(test_data, batch_size=32)
print('Real: {0} \t Render: {1} \t Test: {2}'.format(len(real_loader), len(render_loader), len(test_loader)))
# MODEL
N0, N1, N2 = 2048, 1000, 500
if args.ydata_type == 'axis_angle':
ndim = 3
else:
ndim = 4
model = my_model()
model.load_state_dict(torch.load(model_file))
# print(model)
criterion = geodesic_loss().cuda()
# criterion = nn.MSELoss().cuda()
optimizer = mySGD(model.parameters(), c=2*len(real_loader))
# store stuff
writer = SummaryWriter(log_dir)
count = 0
val_loss = []
num_ensemble = 0
def training():
global count, val_loss, num_ensemble
model.train()
bar = progressbar.ProgressBar(max_value=len(real_loader))
for i, (sample_real, sample_render) in enumerate(zip(real_loader, render_loader)):
# forward steps
xdata_real = Variable(sample_real['xdata'].cuda())
label_real = Variable(sample_real['label'].cuda())
ydata_real = Variable(sample_real['ydata'].cuda())
output_real = model(xdata_real, label_real)
loss_real = criterion(output_real, ydata_real)
xdata_render = Variable(sample_render['xdata'].cuda())
label_render = Variable(sample_render['label'].cuda())
ydata_render = Variable(sample_render['ydata'].cuda())
output_render = model(xdata_render, label_render)
loss_render = criterion(output_render, ydata_render)
loss = loss_real + loss_render
optimizer.zero_grad()
loss.backward()
optimizer.step()
# store
writer.add_scalar('train_loss', loss.item(), count)
if i % 500 == 0:
ytest, yhat_test, test_labels = testing()
tmp_val_loss = get_error2(ytest, yhat_test, test_labels, num_classes)
writer.add_scalar('val_loss', tmp_val_loss, count)
val_loss.append(tmp_val_loss)
count += 1
if count % optimizer.c == optimizer.c/2:
ytest, yhat_test, test_labels = testing()
num_ensemble += 1
results_file = os.path.join(results_dir, 'num'+str(num_ensemble))
spio.savemat(results_file, {'ytest': ytest, 'yhat_test': yhat_test, 'test_labels': test_labels})
# cleanup
del xdata_real, xdata_render, label_real, label_render, ydata_real, ydata_render
del output_real, output_render, loss_real, loss_render, sample_real, sample_render, loss
bar.update(i)
render_loader.dataset.shuffle_images()
real_loader.dataset.shuffle_images()
def testing():
model.eval()
bar = progressbar.ProgressBar(max_value=len(test_loader))
ypred = []
ytrue = []
labels = []
for i, sample in enumerate(test_loader):
xdata = Variable(sample['xdata'].cuda())
label = Variable(sample['label'].cuda())
output = model(xdata, label)
ypred.append(output.data.cpu().numpy())
ytrue.append(sample['ydata'].numpy())
labels.append(sample['label'].numpy())
bar.update(i)
del xdata, label, output, sample
gc.collect()
ypred = np.concatenate(ypred)
ytrue = np.concatenate(ytrue)
labels = np.concatenate(labels)
model.train()
return ytrue, ypred, labels
ytest, yhat_test, test_labels = testing()
print('\nMedErr: {0}'.format(get_error2(ytest, yhat_test, test_labels, num_classes)))
results_file = os.path.join(results_dir, 'num'+str(num_ensemble))
spio.savemat(results_file, {'ytest': ytest, 'yhat_test': yhat_test, 'test_labels': test_labels})
for epoch in range(args.num_epochs):
tic = time.time()
# training step
training()
# validation
ytest, yhat_test, test_labels = testing()
tmp_val_loss = get_error2(ytest, yhat_test, test_labels, num_classes)
print('\nMedErr: {0}'.format(tmp_val_loss))
writer.add_scalar('val_loss', tmp_val_loss, count)
val_loss.append(tmp_val_loss)
# time and output
toc = time.time() - tic
print('Epoch: {0} done in time {1}s'.format(epoch, toc))
# cleanup
gc.collect()
writer.close()
val_loss = np.stack(val_loss)
spio.savemat(plots_file, {'val_loss': val_loss})
