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evaluate_and_plot_shapenet3d.py
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evaluate_and_plot_shapenet3d.py
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# Copyright (c) 2022 Robert Bosch GmbH
# Author: Ning Gao
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import os
import torch
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from scipy.spatial.transform import Rotation as R
import argparse
import random
import imgaug
from trainer.losses import LossFunc
from dataset import ShapeNet3DData
from configs.config import Config
"""
Evaluate shapenet3d task with plotting the results
"""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def cal_angle_from_sincos(generated_angles):
angle_sin = generated_angles[..., 0]
angle_cos = generated_angles[..., 1]
a_acos = np.arccos(angle_cos)
angles = np.where(angle_sin < 0, np.rad2deg(-a_acos) % 360, np.rad2deg(a_acos))
return angles
def cal_angle_from_quatt(quaternions):
r = R.from_quat(quaternions)
eulers = r.as_euler('ZYX', degrees=True)
return eulers
def plot_image_and_angle(shot_images, gt_y, pr_y, image_height, image_width, output_path):
shot_num = shot_images.size(0)
shot_images = shot_images.cpu().numpy()
pr_y = pr_y.cpu().numpy()
gt_y = gt_y.cpu().numpy()
pr_y = cal_angle_from_quatt(pr_y)
gt_y = cal_angle_from_quatt(gt_y)
shot_images = shot_images.transpose(0, 2, 3, 1)
# plot context angles
for i in range(shot_num):
plt.rcParams["figure.figsize"] = (8, 8)
plt.rcParams['savefig.dpi'] = 64
plt.rcParams.update({'font.size': 35})
plt.axis('off')
fig = plt.figure()
ax = plt.subplot()
ax.axis('off')
# im = ax.imshow(shot_images[i].squeeze(), origin='upper', vmin=0, vmax=1.0)
im = ax.imshow(shot_images[i].squeeze(), origin='upper')
plt.text(0.01 * image_width, image_height + 4, f"gt: {gt_y[i][2].round(0), gt_y[i][0].round(0)}", color='green')
plt.text(0.01 * image_width, image_height + 10, f"pr: {pr_y[i][2].round(0), pr_y[i][0].round(0)}", color='blue')
patch = patches.Rectangle((0, 0), 128, 128, transform=ax.transData)
im.set_clip_path(patch)
plt.savefig(f"{output_path}/{i}", bbox_inches='tight')
plt.close()
def evaluate(device, config):
loss_func = LossFunc(loss_type=config.loss_type, task=config.task)
# load dataset
if config.task == 'shapenet_3d':
data = ShapeNet3DData(path='./data/ShapeNet3D_azi180ele30',
img_size=config.img_size,
train_fraction=0.8,
val_fraction=0.2,
num_instances_per_item=30,
seed=42,
aug=config.aug_list,
mode='eval')
else:
raise NameError("dataset doesn't exist, check dataset name!")
import importlib
module = importlib.import_module(f"networks.{config.method}")
np_class = getattr(module, config.method)
model = np_class(config)
model = model.to(config.device)
checkpoint = config.checkpoint
if checkpoint:
config.logger.info("load weights from " + checkpoint)
model.load_state_dict(torch.load(checkpoint))
test_iteration = 0
loss_all = []
latent_z_list = []
with torch.no_grad():
# data.gen_bg(config)
while test_iteration < config.val_iters:
source = 'test'
ctx_x, qry_x, ctx_y, qry_y = \
data.get_batch(source=source, tasks_per_batch=config.tasks_per_batch, shot=config.max_ctx_num)
ctx_x = ctx_x.to(config.device)
qry_x = qry_x.to(config.device)
ctx_y = ctx_y.to(config.device)
qry_y = qry_y.to(config.device)
pr_mu, pr_var, sample_z = model(ctx_x, ctx_y, qry_x)
latent_z_list.append(sample_z)
loss = loss_func.calc_loss(pr_mu, pr_var, qry_y, test=True)
loss_all.append(loss.item())
images_to_generate = qry_x
centers_to_generate = qry_y
path_save_image = os.path.join(config.save_path, "image")
if not os.path.exists(path_save_image):
os.makedirs(path_save_image)
output_path = os.path.join(path_save_image, 'output_{0:02d}'.format(test_iteration))
os.makedirs(output_path)
plot_image_and_angle(images_to_generate[0], centers_to_generate[0], pr_mu[0], data.get_image_height(), data.get_image_width(), output_path)
test_iteration += 1
with open(os.path.join(config.save_path, 'losses_all.txt'), 'w') as f:
np.savetxt(f, loss_all, delimiter=',', fmt='%.4f')
config.logger.info('Results have been saved to {}'.format(config.save_path))
config.logger.info('================= Evaluation finished =================\n')
return latent_z_list
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, help="path to config file")
args = parser.parse_args()
config = Config(args.config)
path = config.save_path
# for i in range(config.max_ctx_num, config.max_ctx_num + 1): # use loop for testing different number of contexts
i = 15
torch.backends.cudnn.deterministic = True
torch.manual_seed(config.seed)
random.seed(config.seed)
np.random.seed(config.seed)
imgaug.seed(config.seed)
config.max_ctx_num = i
config.save_path = path + f'/context_num_{i}'
latent_z_list = evaluate(device, config)