#!/usr/bin/env python import os, sys import setlog conf_file = os.environ['DEV'] + 'dl_management/.log/logging.yaml' save_file = os.path.abspath(sys.argv[0])[:-len(sys.argv[0])] + 'log/' setlog.reconfigure(conf_file, save_file) import system.PoseRegression as System if __name__ == '__main__': scene = 'indoor/apt1-kitchen' machine = System.MultNet( root=os.path.abspath(sys.argv[0])[:-len(sys.argv[0])], #trainer_file= '../relative_pnp.yaml', #trainer_file='../self_multiscale_depth_trainer.yaml', trainer_file='../nn_index.yaml', #trainer_file='../pnp-5-images.yaml', #trainer_file='../pnp-1-image.yaml', dataset_file='../../../../datasets/' + scene + '.yaml', cnn_type='../multiscale_cnn.yaml') action = input( 'Exec:\n[t]\ttrain\n[e]\ttest\n[p]\tprint (console)\n[P]\tprint (full)\n[ ]\ttrain+test\n' ) if action == 't': machine.train() elif action == 'e': machine.test() machine.plot(print_loss=False, print_val=False) elif action == 'ef': machine.test_on_final()
import setlog file = '.log/logging.yaml' root = '/home/nathan/Dev/Code/dl_management/' setlog.reconfigure(file, root) import torchvision import matplotlib.pyplot as plt import torch.utils.data as data import datasets.multmodtf as tf import datasets.SevenScene logger = setlog.get_logger(__name__) def show_batch(sample_batched): """Show image with landmarks for a batch of samples.""" grid = torchvision.utils.make_grid(sample_batched['rgb']) plt.imshow(grid.numpy().transpose((1, 2, 0))) def show_batch_mono(sample_batched): """Show image with landmarks for a batch of samples.""" depth = sample_batched['depth'] # /torch.max(sample_batched['depth']) grid = torchvision.utils.make_grid(depth) plt.imshow(grid.numpy().transpose((1, 2, 0))) if __name__ == '__main__': logger.debug('Beginning main')