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basic structure for testing is added
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#!/usr/bin/env python | ||
# -*- coding: utf-8 -*- | ||
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from __future__ import print_function | ||
import os | ||
import gc | ||
import argparse | ||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
from model import PCRNet | ||
from util import transform_point_cloud, npmat2euler | ||
import numpy as np | ||
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# Use this function to read the data. | ||
def read_data(): | ||
# Output -> | ||
# source: Torch tensor on CPU [Nx3] | ||
# template: Torch tensor on CPU [Nx3] | ||
# rotation_ab: Torch tensor on CPU [Nx3] | ||
pass | ||
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def test_one_pair(args, net): | ||
source, template, rotation_ab, translation_ab = read_data() | ||
net.eval() | ||
src = source.to(args.device) | ||
target = template.to(args.device) | ||
batch_size = src.size(0) | ||
rotation_ab_pred, translation_ab_pred, rotation_ba_pred, translation_ba_pred = net(src, target) | ||
transformed_src = transform_point_cloud(src, rotation_ab_pred, translation_ab_pred) | ||
transformed_target = transform_point_cloud(target, rotation_ba_pred, translation_ba_pred) | ||
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return source, template, transformed_src.cpu().numpy(), rotations_ab_pred, translation_ab_pred | ||
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def main(): | ||
parser = argparse.ArgumentParser(description='Point Cloud Registration') | ||
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# Settings for network. | ||
parser.add_argument('--exp_name', type=str, default='exp', metavar='N', | ||
help='Name of the experiment') | ||
parser.add_argument('--model', type=str, default='pcrnet', metavar='N', | ||
choices=['dcp'], | ||
help='Model to use, [dcp]') | ||
parser.add_argument('--emb_nn', type=str, default='pointnet', metavar='N', | ||
choices=['pointnet', 'dgcnn'], | ||
help='Embedding nn to use, [pointnet, dgcnn]') | ||
parser.add_argument('--pointer', type=str, default='identity', metavar='N', | ||
choices=['identity', 'transformer'], | ||
help='Attention-based pointer generator to use, [identity, transformer]') | ||
parser.add_argument('--head', type=str, default='mlp', metavar='N', | ||
choices=['mlp', 'svd', ], | ||
help='Head to use, [mlp, svd]') | ||
parser.add_argument('--iterations', type=int, default=1, help='[No of iterations for PCRNet]') | ||
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# Settings for training | ||
parser.add_argument('--emb_dims', type=int, default=1024, metavar='N', | ||
help='Dimension of embeddings') | ||
parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size', | ||
help='Size of batch)') | ||
parser.add_argument('--test_batch_size', type=int, default=10, metavar='batch_size', | ||
help='Size of batch)') | ||
parser.add_argument('--epochs', type=int, default=250, metavar='N', | ||
help='number of episode to train ') | ||
parser.add_argument('--device', action='store_true', default=False, | ||
help='enables CUDA training') | ||
parser.add_argument('--seed', type=int, default=1234, metavar='S', | ||
help='random seed (default: 1)') | ||
parser.add_argument('--eval', action='store_true', default=False, | ||
help='evaluate the model') | ||
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# Settings for attention | ||
parser.add_argument('--n_blocks', type=int, default=1, metavar='N', | ||
help='Num of blocks of encoder&decoder') | ||
parser.add_argument('--n_heads', type=int, default=4, metavar='N', | ||
help='Num of heads in multiheadedattention') | ||
parser.add_argument('--ff_dims', type=int, default=1024, metavar='N', | ||
help='Num of dimensions of fc in transformer') | ||
parser.add_argument('--dropout', type=float, default=0.0, metavar='N', | ||
help='Dropout ratio in transformer') | ||
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parser.add_argument('--cycle', type=bool, default=False, metavar='N', | ||
help='Whether to use cycle consistency') | ||
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# Settings for dataset | ||
parser.add_argument('--gaussian_noise', type=bool, default=False, metavar='N', | ||
help='Wheter to add gaussian noise') | ||
parser.add_argument('--unseen', type=bool, default=False, metavar='N', | ||
help='Wheter to test on unseen category') | ||
parser.add_argument('--num_points', type=int, default=1024, metavar='N', | ||
help='Num of points to use') | ||
parser.add_argument('--dataset', type=str, default='modelnet40', choices=['modelnet40'], metavar='N', | ||
help='dataset to use') | ||
parser.add_argument('--factor', type=float, default=4, metavar='N', | ||
help='Divided factor for rotations') | ||
parser.add_argument('--model_path', type=str, default='', metavar='N', | ||
help='Pretrained model path') | ||
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args = parser.parse_args() | ||
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use_cuda = torch.cuda.is_available() | ||
args.cuda = torch.device("cuda" if use_cuda else "cpu") | ||
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torch.backends.cudnn.deterministic = True | ||
torch.manual_seed(args.seed) | ||
torch.cuda.manual_seed_all(args.seed) | ||
np.random.seed(args.seed) | ||
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if args.model == 'pcrnet': | ||
net = PCRNet(args).to(args.device) | ||
model_path = args.model_path | ||
net.load_state_dict(torch.load(model_path), strict=False) | ||
else: | ||
raise Exception('Not implemented') | ||
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source, template, transformed_src, _, _ = test_one_pair(args, net) | ||
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import helper | ||
helper.display_three_clouds(template, source, transformed_src, 'Results') | ||
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print('FINISH') | ||
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if __name__ == '__main__': | ||
main() |