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evaluateLaplacianBDModel.py
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evaluateLaplacianBDModel.py
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# -*- coding: utf-8 -*-
"""
Laplacian Bin and Delta model for the axis-angle representation
"""
import torch
from torch import nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from dataGenerators import TestImages, my_collate
from binDeltaGenerators import GBDGenerator
from axisAngle import get_error2
from binDeltaModels import OneBinDeltaModel, OneDeltaPerBinModel
from helperFunctions import classes, mySGD
import numpy as np
import math
import scipy.io as spio
import gc
import os
import time
import progressbar
import pickle
import argparse
from tensorboardX import SummaryWriter
parser = argparse.ArgumentParser(description='Laplacian Bin & Delta Model')
parser.add_argument('--gpu_id', type=str, default='0')
parser.add_argument('--save_str', type=str)
parser.add_argument('--dict_size', type=int, default=200)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--feature_network', type=str, default='resnet')
parser.add_argument('--num_epochs', type=int, default=9)
parser.add_argument('--multires', type=bool, default=False)
parser.add_argument('--db_type', type=str, default='clean')
args = parser.parse_args()
print(args)
# assign GPU
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
# save stuff here
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)
# kmeans data
kmeans_file = 'data/kmeans_dictionary_axis_angle_' + str(args.dict_size) + '.pkl'
kmeans = pickle.load(open(kmeans_file, 'rb'))
kmeans_dict = kmeans.cluster_centers_
cluster_centers_ = Variable(torch.from_numpy(kmeans_dict).float()).cuda()
num_clusters = kmeans.n_clusters
# relevant variables
ndim = 3
num_classes = len(classes)
N0, N1, N2, N3 = 2048, 1000, 500, 100
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/'
mse_loss = nn.MSELoss().cuda()
ce_loss = nn.CrossEntropyLoss().cuda()
l1_loss = nn.L1Loss().cuda()
# DATA
# datasets
real_data = GBDGenerator(train_path, 'real', kmeans_file)
render_data = GBDGenerator(render_path, 'render', kmeans_file)
test_data = TestImages(test_path)
# setup data loaders
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)))
max_iterations = len(real_loader)
# my_model
if not args.multires:
model = OneBinDeltaModel(args.feature_network, num_classes, num_clusters, N0, N1, N2, ndim)
else:
model = OneDeltaPerBinModel(args.feature_network, num_classes, num_clusters, N0, N1, N2, N3, ndim)
model.load_state_dict(torch.load(model_file))
# print(model)
# loss and optimizer
optimizer = mySGD(model.parameters(), c=2*len(real_loader))
# store stuff
writer = SummaryWriter(log_dir)
count = 0
val_loss = []
s = 0
num_ensemble = 0
# OPTIMIZATION functions
def training():
global count, val_loss, s, num_ensemble
model.train()
bar = progressbar.ProgressBar(max_value=max_iterations)
for i, (sample_real, sample_render) in enumerate(zip(real_loader, render_loader)):
# forward steps
# output
xdata_real = Variable(sample_real['xdata'].cuda())
label_real = Variable(sample_real['label'].cuda())
ydata_real = [Variable(sample_real['ydata_bin'].cuda()), Variable(sample_real['ydata'].cuda())]
output_real = model(xdata_real, label_real)
xdata_render = Variable(sample_render['xdata'].cuda())
label_render = Variable(sample_render['label'].cuda())
ydata_render = [Variable(sample_render['ydata_bin'].cuda()), Variable(sample_render['ydata'].cuda())]
output_render = model(xdata_render, label_render)
# loss
ydata_bin = torch.cat((ydata_real[0], ydata_render[0]))
ydata = torch.cat((ydata_real[1], ydata_render[1]))
output_bin = torch.cat((output_real[0], output_render[0]))
_, ind = torch.max(output_bin, dim=1)
y = torch.index_select(cluster_centers_, 0, ind)
output = y + torch.cat((output_real[1], output_render[1]))
Lc = ce_loss(output_bin, ydata_bin)
Lr = l1_loss(output, ydata)
loss = Lc + math.exp(-s)*Lr + s
# parameter updates
optimizer.zero_grad()
loss.backward()
optimizer.step()
s = math.log(Lr)
# store
writer.add_scalar('train_loss', loss.item(), count)
writer.add_scalar('alpha', math.exp(-s), 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 ydata_bin, ydata, output_bin, output
del output_real, output_render, sample_real, sample_render, loss
bar.update(i)
# stop
if i == max_iterations:
break
render_loader.dataset.shuffle_images()
real_loader.dataset.shuffle_images()
def testing():
model.eval()
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_bin = np.argmax(output[0].data.cpu().numpy(), axis=1)
ypred_res = output[1].data.cpu().numpy()
ypred.append(kmeans_dict[ypred_bin, :] + ypred_res)
ytrue.append(sample['ydata'].numpy())
labels.append(sample['label'].numpy())
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})