1820 lines
72 KiB
Python
1820 lines
72 KiB
Python
#
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#
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# 0=================================0
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# | Kernel Point Convolutions |
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# 0=================================0
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#
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Class handling the visualization
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Hugues THOMAS - 11/06/2018
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Imports and global variables
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# \**********************************/
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#
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# Basic libs
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import torch
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import numpy as np
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from sklearn.neighbors import KDTree
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from os import makedirs, remove, rename, listdir
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from os.path import exists, join
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import time
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from mayavi import mlab
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import sys
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from models.blocks import KPConv
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# PLY reader
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from utils.ply import write_ply, read_ply
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# Configuration class
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from utils.config import Config, bcolors
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Trainer Class
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# \*******************/
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#
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class ModelVisualizer:
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# Initialization methods
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# ------------------------------------------------------------------------------------------------------------------
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def __init__(self, net, config, chkp_path, on_gpu=True):
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"""
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Initialize training parameters and reload previous model for restore/finetune
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:param net: network object
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:param config: configuration object
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:param chkp_path: path to the checkpoint that needs to be loaded (None for new training)
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:param finetune: finetune from checkpoint (True) or restore training from checkpoint (False)
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:param on_gpu: Train on GPU or CPU
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"""
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############
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# Parameters
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############
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# Choose to train on CPU or GPU
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if on_gpu and torch.cuda.is_available():
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self.device = torch.device("cuda:0")
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else:
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self.device = torch.device("cpu")
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net.to(self.device)
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##########################
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# Load previous checkpoint
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##########################
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checkpoint = torch.load(chkp_path)
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new_dict = {}
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for k, v in checkpoint['model_state_dict'].items():
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if 'blocs' in k:
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k = k.replace('blocs', 'blocks')
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new_dict[k] = v
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net.load_state_dict(new_dict)
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self.epoch = checkpoint['epoch']
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net.eval()
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print("\nModel state restored from {:s}.".format(chkp_path))
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return
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# Main visualization methods
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# ------------------------------------------------------------------------------------------------------------------
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def top_relu_activations(self, model, dataset, relu_idx=0, top_num=5):
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"""
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Test the model on test dataset to see which points activate the most each neurons in a relu layer
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:param model: model used at training
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:param dataset: dataset used at training
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:param relu_idx: which features are to be visualized
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:param top_num: how many top candidates are kept per features
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"""
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#####################################
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# First choose the visualized feature
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#####################################
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# List all relu ops
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all_ops = [op for op in tf.get_default_graph().get_operations() if op.name.startswith('KernelPointNetwork')
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and op.name.endswith('LeakyRelu')]
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# List all possible Relu indices
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print('\nPossible Relu indices:')
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for i, t in enumerate(all_ops):
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print(i, ': ', t.name)
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# Print the chosen one
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if relu_idx is not None:
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features_tensor = all_ops[relu_idx].outputs[0]
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else:
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relu_idx = int(input('Choose a Relu index: '))
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features_tensor = all_ops[relu_idx].outputs[0]
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# Get parameters
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layer_idx = int(features_tensor.name.split('/')[1][6:])
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if 'strided' in all_ops[relu_idx].name and not ('strided' in all_ops[relu_idx+1].name):
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layer_idx += 1
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features_dim = int(features_tensor.shape[1])
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radius = model.config.first_subsampling_dl * model.config.density_parameter * (2 ** layer_idx)
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print('You chose to compute the output of operation named:\n' + all_ops[relu_idx].name)
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print('\nIt contains {:d} features.'.format(int(features_tensor.shape[1])))
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print('\n****************************************************************************')
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#######################
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# Initialize containers
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#######################
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# Initialize containers
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self.top_features = -np.ones((top_num, features_dim))
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self.top_classes = -np.ones((top_num, features_dim), dtype=np.int32)
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self.saving = model.config.saving
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# Testing parameters
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num_votes = 3
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# Create visu folder
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self.visu_path = None
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self.fmt_str = None
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if model.config.saving:
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self.visu_path = join('visu',
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'visu_' + model.saving_path.split('/')[-1],
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'top_activations',
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'Relu{:02d}'.format(relu_idx))
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self.fmt_str = 'f{:04d}_top{:02d}.ply'
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if not exists(self.visu_path):
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makedirs(self.visu_path)
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# *******************
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# Network predictions
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# *******************
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mean_dt = np.zeros(2)
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last_display = time.time()
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for v in range(num_votes):
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# Run model on all test examples
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# ******************************
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# Initialise iterator with test data
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if model.config.dataset.startswith('S3DIS'):
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self.sess.run(dataset.val_init_op)
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else:
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self.sess.run(dataset.test_init_op)
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count = 0
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while True:
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try:
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if model.config.dataset.startswith('ShapeNetPart'):
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if model.config.dataset.split('_')[1] == 'multi':
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label_op = model.inputs['super_labels']
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else:
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label_op = model.inputs['point_labels']
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elif model.config.dataset.startswith('S3DIS'):
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label_op = model.inputs['point_labels']
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elif model.config.dataset.startswith('Scannet'):
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label_op = model.inputs['point_labels']
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elif model.config.dataset.startswith('ModelNet40'):
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label_op = model.inputs['labels']
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else:
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raise ValueError('Unsupported dataset')
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# Run one step of the model
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t = [time.time()]
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ops = (all_ops[-1].outputs[0],
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features_tensor,
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label_op,
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model.inputs['points'],
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model.inputs['pools'],
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model.inputs['in_batches'])
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_, stacked_features, labels, all_points, all_pools, in_batches = self.sess.run(ops, {model.dropout_prob: 1.0})
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t += [time.time()]
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count += in_batches.shape[0]
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# Stack all batches
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max_ind = np.max(in_batches)
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stacked_batches = []
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for b_i, b in enumerate(in_batches):
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stacked_batches += [b[b < max_ind - 0.5]*0+b_i]
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stacked_batches = np.hstack(stacked_batches)
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# Find batches at wanted layer
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for l in range(model.config.num_layers - 1):
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if l >= layer_idx:
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break
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stacked_batches = stacked_batches[all_pools[l][:, 0]]
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# Get each example and update top_activations
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for b_i, b in enumerate(in_batches):
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b = b[b < max_ind - 0.5]
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in_points = all_points[0][b]
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features = stacked_features[stacked_batches == b_i]
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points = all_points[layer_idx][stacked_batches == b_i]
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if model.config.dataset in ['ShapeNetPart_multi', 'ModelNet40_classif']:
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l = labels[b_i]
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else:
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l = np.argmax(np.bincount(labels[b]))
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self.update_top_activations(features, labels[b_i], points, in_points, radius)
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# Average timing
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t += [time.time()]
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mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
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# Display
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if (t[-1] - last_display) > 1.0:
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last_display = t[-1]
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if model.config.dataset.startswith('S3DIS'):
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completed = count / (model.config.validation_size * model.config.batch_num)
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else:
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completed = count / dataset.num_test
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message = 'Vote {:d} : {:.1f}% (timings : {:4.2f} {:4.2f})'
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print(message.format(v,
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100 * completed,
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1000 * (mean_dt[0]),
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1000 * (mean_dt[1])))
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#class_names = np.array([dataset.label_to_names[i] for i in range(dataset.num_classes)])
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#print(class_names[self.top_classes[:, :20]].T)
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except tf.errors.OutOfRangeError:
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break
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return relu_idx
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def update_top_activations(self, features, label, l_points, input_points, radius, max_computed=60):
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top_num = self.top_features.shape[0]
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# Compute top indice for each feature
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max_indices = np.argmax(features, axis=0)
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# get top_point neighborhoods
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for features_i, idx in enumerate(max_indices[:max_computed]):
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if features[idx, features_i] <= self.top_features[-1, features_i]:
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continue
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if label in self.top_classes[:, features_i]:
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ind0 = np.where(self.top_classes[:, features_i] == label)[0][0]
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if features[idx, features_i] <= self.top_features[ind0, features_i]:
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continue
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elif ind0 < top_num - 1:
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self.top_features[ind0:-1, features_i] = self.top_features[ind0+1:, features_i]
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self.top_classes[ind0:-1, features_i] = self.top_classes[ind0+1:, features_i]
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for next_i in range(ind0 + 1, top_num):
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old_f = join(self.visu_path, self.fmt_str.format(features_i, next_i + 1))
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new_f = join(self.visu_path, self.fmt_str.format(features_i, next_i))
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if exists(old_f):
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if exists(new_f):
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remove(new_f)
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rename(old_f, new_f)
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# Find indice where new top should be placed
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top_i = np.where(features[idx, features_i] > self.top_features[:, features_i])[0][0]
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# Update top features
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if top_i < top_num - 1:
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self.top_features[top_i + 1:, features_i] = self.top_features[top_i:-1, features_i]
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self.top_features[top_i, features_i] = features[idx, features_i]
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self.top_classes[top_i + 1:, features_i] = self.top_classes[top_i:-1, features_i]
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self.top_classes[top_i, features_i] = label
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# Find in which batch lays the point
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if self.saving:
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# Get inputs
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l_features = features[:, features_i]
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point = l_points[idx, :]
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dist = np.linalg.norm(input_points - point, axis=1)
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influence = (radius - dist) / radius
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# Project response on input cloud
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if l_points.shape[0] == input_points.shape[0]:
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responses = l_features
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else:
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tree = KDTree(l_points, leaf_size=50)
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nn_k = min(l_points.shape[0], 10)
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interp_dists, interp_inds = tree.query(input_points, nn_k, return_distance=True)
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tukeys = np.square(1 - np.square(interp_dists / radius))
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tukeys[interp_dists > radius] = 0
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responses = np.sum(l_features[interp_inds] * tukeys, axis=1)
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# Handle last examples
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for next_i in range(top_num - 1, top_i, -1):
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old_f = join(self.visu_path, self.fmt_str.format(features_i, next_i))
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new_f = join(self.visu_path, self.fmt_str.format(features_i, next_i + 1))
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if exists(old_f):
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if exists(new_f):
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remove(new_f)
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rename(old_f, new_f)
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# Save
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filename = join(self.visu_path, self.fmt_str.format(features_i, top_i + 1))
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write_ply(filename,
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[input_points, influence, responses],
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['x', 'y', 'z', 'influence', 'responses'])
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def show_deformable_kernels_old(self, model, dataset, deform_idx=0):
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##########################################
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# First choose the visualized deformations
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##########################################
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# List all deformation ops
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all_ops = [op for op in tf.get_default_graph().get_operations() if op.name.startswith('KernelPointNetwork')
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and op.name.endswith('deformed_KP')]
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print('\nPossible deformed indices:')
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for i, t in enumerate(all_ops):
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print(i, ': ', t.name)
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# Chosen deformations
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deformed_KP_tensor = all_ops[deform_idx].outputs[0]
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# Layer index
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layer_idx = int(all_ops[deform_idx].name.split('/')[1].split('_')[-1])
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# Original kernel point positions
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KP_vars = [v for v in tf.global_variables() if 'kernel_points' in v.name]
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tmp = np.array(all_ops[deform_idx].name.split('/'))
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test = []
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for v in KP_vars:
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cmp = np.array(v.name.split('/'))
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l = min(len(cmp), len(tmp))
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cmp = cmp[:l]
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tmp = tmp[:l]
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test += [np.sum(cmp == tmp)]
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chosen_KP = np.argmax(test)
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print('You chose to visualize the output of operation named: ' + all_ops[deform_idx].name)
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print('\n****************************************************************************')
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# Run model on all test examples
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# ******************************
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# Initialise iterator with test data
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if model.config.dataset.startswith('S3DIS'):
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self.sess.run(dataset.val_init_op)
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else:
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self.sess.run(dataset.test_init_op)
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count = 0
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while True:
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try:
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# Run one step of the model
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t = [time.time()]
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ops = (deformed_KP_tensor,
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model.inputs['points'],
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model.inputs['features'],
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model.inputs['pools'],
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model.inputs['in_batches'],
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KP_vars)
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stacked_deformed_KP, \
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all_points, \
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all_colors, \
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all_pools, \
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in_batches, \
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original_KPs = self.sess.run(ops, {model.dropout_prob: 1.0})
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t += [time.time()]
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count += in_batches.shape[0]
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# Stack all batches
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max_ind = np.max(in_batches)
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stacked_batches = []
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for b_i, b in enumerate(in_batches):
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stacked_batches += [b[b < max_ind - 0.5] * 0 + b_i]
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stacked_batches = np.hstack(stacked_batches)
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# Find batches at wanted layer
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for l in range(model.config.num_layers - 1):
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if l >= layer_idx:
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break
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stacked_batches = stacked_batches[all_pools[l][:, 0]]
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# Get each example and update top_activations
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in_points = []
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in_colors = []
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deformed_KP = []
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points = []
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lookuptrees = []
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for b_i, b in enumerate(in_batches):
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b = b[b < max_ind - 0.5]
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in_points += [all_points[0][b]]
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deformed_KP += [stacked_deformed_KP[stacked_batches == b_i]]
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points += [all_points[layer_idx][stacked_batches == b_i]]
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lookuptrees += [KDTree(points[-1])]
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if all_colors.shape[1] == 4:
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in_colors += [all_colors[b, 1:]]
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else:
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in_colors += [None]
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print('New batch size : ', len(in_batches))
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###########################
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# Interactive visualization
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###########################
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# Create figure for features
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fig1 = mlab.figure('Features', bgcolor=(1.0, 1.0, 1.0), size=(1280, 920))
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fig1.scene.parallel_projection = False
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# Indices
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global obj_i, point_i, plots, offsets, p_scale, show_in_p, aim_point
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p_scale = 0.03
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obj_i = 0
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point_i = 0
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plots = {}
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offsets = False
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show_in_p = 2
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aim_point = np.zeros((1, 3))
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def picker_callback(picker):
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""" Picker callback: this get called when on pick events.
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"""
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global plots, aim_point
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if 'in_points' in plots:
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if plots['in_points'].actor.actor._vtk_obj in [o._vtk_obj for o in picker.actors]:
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point_rez = plots['in_points'].glyph.glyph_source.glyph_source.output.points.to_array().shape[0]
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new_point_i = int(np.floor(picker.point_id / point_rez))
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if new_point_i < len(plots['in_points'].mlab_source.points):
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# Get closest point in the layer we are interested in
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aim_point = plots['in_points'].mlab_source.points[new_point_i:new_point_i + 1]
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update_scene()
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if 'points' in plots:
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if plots['points'].actor.actor._vtk_obj in [o._vtk_obj for o in picker.actors]:
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point_rez = plots['points'].glyph.glyph_source.glyph_source.output.points.to_array().shape[0]
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new_point_i = int(np.floor(picker.point_id / point_rez))
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if new_point_i < len(plots['points'].mlab_source.points):
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# Get closest point in the layer we are interested in
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aim_point = plots['points'].mlab_source.points[new_point_i:new_point_i + 1]
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update_scene()
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def update_scene():
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global plots, offsets, p_scale, show_in_p, aim_point, point_i
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# Get the current view
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v = mlab.view()
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roll = mlab.roll()
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# clear figure
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for key in plots.keys():
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plots[key].remove()
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plots = {}
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# Plot new data feature
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p = points[obj_i]
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# Rescale points for visu
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p = (p * 1.5 / model.config.in_radius)
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# Show point cloud
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if show_in_p <= 1:
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plots['points'] = mlab.points3d(p[:, 0],
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p[:, 1],
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p[:, 2],
|
|
resolution=8,
|
|
scale_factor=p_scale,
|
|
scale_mode='none',
|
|
color=(0, 1, 1),
|
|
figure=fig1)
|
|
|
|
if show_in_p >= 1:
|
|
|
|
# Get points and colors
|
|
in_p = in_points[obj_i]
|
|
in_p = (in_p * 1.5 / model.config.in_radius)
|
|
|
|
# Color point cloud if possible
|
|
in_c = in_colors[obj_i]
|
|
if in_c is not None:
|
|
|
|
# Primitives
|
|
scalars = np.arange(len(in_p)) # Key point: set an integer for each point
|
|
|
|
# Define color table (including alpha), which must be uint8 and [0,255]
|
|
colors = np.hstack((in_c, np.ones_like(in_c[:, :1])))
|
|
colors = (colors * 255).astype(np.uint8)
|
|
|
|
plots['in_points'] = mlab.points3d(in_p[:, 0],
|
|
in_p[:, 1],
|
|
in_p[:, 2],
|
|
scalars,
|
|
resolution=8,
|
|
scale_factor=p_scale*0.8,
|
|
scale_mode='none',
|
|
figure=fig1)
|
|
plots['in_points'].module_manager.scalar_lut_manager.lut.table = colors
|
|
|
|
else:
|
|
|
|
plots['in_points'] = mlab.points3d(in_p[:, 0],
|
|
in_p[:, 1],
|
|
in_p[:, 2],
|
|
resolution=8,
|
|
scale_factor=p_scale*0.8,
|
|
scale_mode='none',
|
|
figure=fig1)
|
|
|
|
|
|
# Get KP locations
|
|
rescaled_aim_point = aim_point * model.config.in_radius / 1.5
|
|
point_i = lookuptrees[obj_i].query(rescaled_aim_point, return_distance=False)[0][0]
|
|
if offsets:
|
|
KP = points[obj_i][point_i] + deformed_KP[obj_i][point_i]
|
|
scals = np.ones_like(KP[:, 0])
|
|
else:
|
|
KP = points[obj_i][point_i] + original_KPs[chosen_KP]
|
|
scals = np.zeros_like(KP[:, 0])
|
|
|
|
KP = (KP * 1.5 / model.config.in_radius)
|
|
|
|
plots['KP'] = mlab.points3d(KP[:, 0],
|
|
KP[:, 1],
|
|
KP[:, 2],
|
|
scals,
|
|
colormap='autumn',
|
|
resolution=8,
|
|
scale_factor=1.2*p_scale,
|
|
scale_mode='none',
|
|
vmin=0,
|
|
vmax=1,
|
|
figure=fig1)
|
|
|
|
|
|
if True:
|
|
plots['center'] = mlab.points3d(p[point_i, 0],
|
|
p[point_i, 1],
|
|
p[point_i, 2],
|
|
scale_factor=1.1*p_scale,
|
|
scale_mode='none',
|
|
color=(0, 1, 0),
|
|
figure=fig1)
|
|
|
|
# New title
|
|
plots['title'] = mlab.title(str(obj_i), color=(0, 0, 0), size=0.3, height=0.01)
|
|
text = '<--- (press g for previous)' + 50 * ' ' + '(press h for next) --->'
|
|
plots['text'] = mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
|
|
plots['orient'] = mlab.orientation_axes()
|
|
|
|
# Set the saved view
|
|
mlab.view(*v)
|
|
mlab.roll(roll)
|
|
|
|
return
|
|
|
|
def animate_kernel():
|
|
global plots, offsets, p_scale, show_in_p
|
|
|
|
# Get KP locations
|
|
|
|
KP_def = points[obj_i][point_i] + deformed_KP[obj_i][point_i]
|
|
KP_def = (KP_def * 1.5 / model.config.in_radius)
|
|
KP_def_color = (1, 0, 0)
|
|
|
|
KP_rigid = points[obj_i][point_i] + original_KPs[chosen_KP]
|
|
KP_rigid = (KP_rigid * 1.5 / model.config.in_radius)
|
|
KP_rigid_color = (1, 0.7, 0)
|
|
|
|
if offsets:
|
|
t_list = np.linspace(0, 1, 150, dtype=np.float32)
|
|
else:
|
|
t_list = np.linspace(1, 0, 150, dtype=np.float32)
|
|
|
|
@mlab.animate(delay=10)
|
|
def anim():
|
|
for t in t_list:
|
|
plots['KP'].mlab_source.set(x=t * KP_def[:, 0] + (1 - t) * KP_rigid[:, 0],
|
|
y=t * KP_def[:, 1] + (1 - t) * KP_rigid[:, 1],
|
|
z=t * KP_def[:, 2] + (1 - t) * KP_rigid[:, 2],
|
|
scalars=t * np.ones_like(KP_def[:, 0]))
|
|
|
|
yield
|
|
|
|
anim()
|
|
|
|
return
|
|
|
|
def keyboard_callback(vtk_obj, event):
|
|
global obj_i, point_i, offsets, p_scale, show_in_p
|
|
|
|
if vtk_obj.GetKeyCode() in ['b', 'B']:
|
|
p_scale /= 1.5
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['n', 'N']:
|
|
p_scale *= 1.5
|
|
update_scene()
|
|
|
|
if vtk_obj.GetKeyCode() in ['g', 'G']:
|
|
obj_i = (obj_i - 1) % len(deformed_KP)
|
|
point_i = 0
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['h', 'H']:
|
|
obj_i = (obj_i + 1) % len(deformed_KP)
|
|
point_i = 0
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['k', 'K']:
|
|
offsets = not offsets
|
|
animate_kernel()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['z', 'Z']:
|
|
show_in_p = (show_in_p + 1) % 3
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['0']:
|
|
|
|
print('Saving')
|
|
|
|
# Find a new name
|
|
file_i = 0
|
|
file_name = 'KP_{:03d}.ply'.format(file_i)
|
|
files = [f for f in listdir('KP_clouds') if f.endswith('.ply')]
|
|
while file_name in files:
|
|
file_i += 1
|
|
file_name = 'KP_{:03d}.ply'.format(file_i)
|
|
|
|
KP_deform = points[obj_i][point_i] + deformed_KP[obj_i][point_i]
|
|
KP_normal = points[obj_i][point_i] + original_KPs[chosen_KP]
|
|
|
|
# Save
|
|
write_ply(join('KP_clouds', file_name),
|
|
[in_points[obj_i], in_colors[obj_i]],
|
|
['x', 'y', 'z', 'red', 'green', 'blue'])
|
|
write_ply(join('KP_clouds', 'KP_{:03d}_deform.ply'.format(file_i)),
|
|
[KP_deform],
|
|
['x', 'y', 'z'])
|
|
write_ply(join('KP_clouds', 'KP_{:03d}_normal.ply'.format(file_i)),
|
|
[KP_normal],
|
|
['x', 'y', 'z'])
|
|
print('OK')
|
|
|
|
return
|
|
|
|
# Draw a first plot
|
|
pick_func = fig1.on_mouse_pick(picker_callback)
|
|
pick_func.tolerance = 0.01
|
|
update_scene()
|
|
fig1.scene.interactor.add_observer('KeyPressEvent', keyboard_callback)
|
|
mlab.show()
|
|
|
|
|
|
|
|
|
|
except tf.errors.OutOfRangeError:
|
|
break
|
|
|
|
def show_effective_recep_field(self, net, loader, config, f_idx=0):
|
|
|
|
##########################################
|
|
# First choose the visualized deformations
|
|
##########################################
|
|
|
|
blocks = {}
|
|
|
|
named_blocks = [(m_name, m) for m_name, m in net.named_modules()
|
|
if len(m_name.split('.')) == 2 and m_name.split('.')[0].endswith('_blocks')]
|
|
chosen_block = named_blocks[-1][0]
|
|
|
|
for mi, (m_name, m) in enumerate(named_blocks):
|
|
|
|
|
|
c1 = bcolors.OKBLUE
|
|
c2 = bcolors.BOLD
|
|
ce = bcolors.ENDC
|
|
print('{:}{:}{:s}{:}{:} {:s}'.format(c1, c2, m_name, ce, ce, m.__repr__()))
|
|
blocks[m_name] = m
|
|
|
|
if mi == f_idx:
|
|
chosen_block = m_name
|
|
|
|
print('\nChoose which block output you want to visualize by entering the block name in blue')
|
|
override_block = input('Block name: ')
|
|
|
|
if len(override_block) > 0:
|
|
chosen_block = override_block
|
|
print('{:}{:}{:s}{:}{:} {:s}'.format(c1, c2, chosen_block, ce, ce, blocks[chosen_block].__repr__()))
|
|
features_dim = blocks[chosen_block].out_dim
|
|
|
|
# Fix all the trainable variables in the network (is it needed in eval mode?)
|
|
print('\n*************************************\n')
|
|
for p_name, param in net.named_parameters():
|
|
if param.requires_grad:
|
|
param.requires_grad = False
|
|
print('\n*************************************\n')
|
|
|
|
# Create modulation variable that requires grad
|
|
input_modulations = torch.nn.Parameter(torch.zeros((200000, 1),
|
|
dtype=torch.float32),
|
|
requires_grad=True)
|
|
|
|
print('\n*************************************\n')
|
|
for p_name, param in net.named_parameters():
|
|
if param.requires_grad:
|
|
print(p_name, param.shape)
|
|
print('\n*************************************\n')
|
|
|
|
# Create ERF loss
|
|
|
|
# Create ERF optimizer
|
|
|
|
|
|
|
|
|
|
|
|
global plots, p_scale, show_in_p, remove_h, aim_point
|
|
aim_point = np.zeros((1, 3), dtype=np.float32)
|
|
remove_h = 1.05
|
|
p_scale = 0.1
|
|
plots = {}
|
|
show_in_p = False
|
|
|
|
global points, in_points, grad_values, chosen_point, in_colors
|
|
points = None
|
|
in_points = np.zeros((0, 3))
|
|
grad_values = None
|
|
chosen_point = None
|
|
in_colors = None
|
|
|
|
###########################
|
|
# Interactive visualization
|
|
###########################
|
|
|
|
# Create figure for features
|
|
fig1 = mlab.figure('Features', bgcolor=(0.5, 0.5, 0.5), size=(640, 480))
|
|
fig1.scene.parallel_projection = False
|
|
|
|
# Indices
|
|
|
|
def update_ERF(only_points=False):
|
|
global points, in_points, grad_values, chosen_point, aim_point, in_colors
|
|
|
|
# Generate clouds until we effectively changed
|
|
batch = None
|
|
if only_points:
|
|
# get a new batch (index does not matter given our input pipeline)
|
|
for batch in loader:
|
|
if batch.points[0].shape[0] != in_points.shape[0]:
|
|
break
|
|
|
|
sum_grads = 0
|
|
if only_points:
|
|
num_tries = 1
|
|
else:
|
|
num_tries = 10
|
|
|
|
#################################################
|
|
# Apply ERF optim to the same batch several times
|
|
#################################################
|
|
|
|
if 'cuda' in self.device.type:
|
|
batch.to(self.device)
|
|
|
|
|
|
|
|
for test_i in range(num_tries):
|
|
|
|
print('Updating ERF {:.0f}%'.format((test_i + 1) * 100 / num_tries))
|
|
rand_f_i = np.random.randint(features_dim)
|
|
|
|
# Reset input modulation variable
|
|
torch.nn.init.zeros_(input_modulations)
|
|
|
|
reset_op = input_modulations_var.assign(tf.zeros_like(input_modulations_var))
|
|
self.sess.run(reset_op)
|
|
|
|
# zero the parameter gradients
|
|
ERF_optimizer.zero_grad()
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
|
|
loss = net.ERF_loss(outputs)
|
|
|
|
# Backward
|
|
loss.backward()
|
|
|
|
# Get result from hook here?
|
|
|
|
ERF_optimizer.step()
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
original_KP = deform_convs[deform_idx].kernel_points.cpu().detach().numpy()
|
|
stacked_deformed_KP = deform_convs[deform_idx].deformed_KP.cpu().detach().numpy()
|
|
count += batch.lengths[0].shape[0]
|
|
|
|
if 'cuda' in self.device.type:
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Reset input modulation variable
|
|
reset_op = input_modulations_var.assign(tf.zeros_like(input_modulations_var))
|
|
self.sess.run(reset_op)
|
|
|
|
# Apply gradient to input modulations
|
|
t = [time.time()]
|
|
ops = (ERF_train_op,
|
|
chosen_i_tf,
|
|
input_modulations_var,
|
|
model.inputs['points'],
|
|
model.inputs['features'],
|
|
model.inputs['pools'],
|
|
model.inputs['in_batches'])
|
|
feed_dict = {aimed_coordinates: aim_point,
|
|
chosen_f_tf: rand_f_i,
|
|
model.dropout_prob: 1.0}
|
|
_, chosen_i, new_mods, all_points, all_colors, all_pools, in_batches = self.sess.run(ops, feed_dict)
|
|
t += [time.time()]
|
|
|
|
# Get the new value of the modulations
|
|
sum_grads += np.abs(self.sess.run(input_modulations_var))
|
|
|
|
grad = sum_grads / num_tries
|
|
|
|
# Stack all batches
|
|
max_ind = np.max(in_batches)
|
|
stacked_batches = []
|
|
for b_i, b in enumerate(in_batches):
|
|
stacked_batches += [b[b < max_ind - 0.5] * 0 + b_i]
|
|
stacked_batches = np.hstack(stacked_batches)
|
|
|
|
# Find batches at wanted layer
|
|
for l in range(model.config.num_layers - 1):
|
|
if l >= layer_idx:
|
|
break
|
|
stacked_batches = stacked_batches[all_pools[l][:, 0]]
|
|
|
|
# Get each example and update top_activations
|
|
for b_i, b in enumerate(in_batches):
|
|
b = b[b < max_ind - 0.5]
|
|
in_points = all_points[0][b]
|
|
in_colors = all_colors[b, 1:]
|
|
points = all_points[layer_idx][stacked_batches == b_i]
|
|
grad_values = grad[b]
|
|
|
|
chosen_point = all_points[layer_idx][chosen_i]
|
|
|
|
def update_scene():
|
|
global plots, p_scale, show_in_p, remove_h
|
|
global points, in_points, grad_values, chosen_point
|
|
|
|
# Get the current view
|
|
v = mlab.view()
|
|
roll = mlab.roll()
|
|
|
|
# clear figure
|
|
for key in plots.keys():
|
|
plots[key].remove()
|
|
|
|
plots = {}
|
|
|
|
# Plot new data feature
|
|
in_p = in_points
|
|
p = points
|
|
p0 = chosen_point
|
|
responses = 100 * np.abs(np.ravel(grad_values))
|
|
#xresponses = responses ** (1/2)
|
|
|
|
# Remove roof
|
|
if 0.0 < remove_h < 1.0:
|
|
floor_h = np.min(in_p[:, 2])
|
|
ceil_h = np.max(in_p[:, 2])
|
|
threshold = floor_h + (ceil_h - floor_h) * remove_h
|
|
responses = responses[in_p[:, 2] < threshold]
|
|
in_p = in_p[in_p[:, 2] < threshold]
|
|
p = p[p[:, 2] < threshold]
|
|
|
|
# Rescale responses
|
|
min_response, max_response = np.min(responses), np.max(responses)
|
|
|
|
# Show point cloud
|
|
if show_in_p:
|
|
plots['points'] = mlab.points3d(p[:, 0],
|
|
p[:, 1],
|
|
p[:, 2],
|
|
resolution=8,
|
|
scale_factor=p_scale,
|
|
scale_mode='none',
|
|
color=(0, 1, 1),
|
|
figure=fig1)
|
|
|
|
plots['in_points'] = mlab.points3d(in_p[:, 0],
|
|
in_p[:, 1],
|
|
in_p[:, 2],
|
|
responses,
|
|
resolution=8,
|
|
scale_factor=p_scale * 0.8,
|
|
scale_mode='none',
|
|
vmin=0.1,
|
|
vmax=1.5,
|
|
figure=fig1)
|
|
|
|
plots['center'] = mlab.points3d(p0[0],
|
|
p0[1],
|
|
p0[2],
|
|
scale_factor=1.5 * p_scale,
|
|
scale_mode='none',
|
|
color=(0, 0, 0),
|
|
figure=fig1)
|
|
|
|
# New title
|
|
plots['title'] = mlab.title(str(int(100*remove_h)) + '%', color=(0, 0, 0), size=0.3, height=0.01)
|
|
text = '<--- (press g to remove ceiling)' + 50 * ' ' + '(press h to add ceiling) --->'
|
|
plots['text'] = mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
|
|
plots['orient'] = mlab.orientation_axes()
|
|
|
|
# Set the saved view
|
|
mlab.view(*v)
|
|
mlab.roll(roll)
|
|
|
|
return
|
|
|
|
def picker_callback(picker):
|
|
""" Picker callback: this get called when on pick events.
|
|
"""
|
|
global plots, aim_point, in_points
|
|
|
|
if plots['in_points'].actor.actor._vtk_obj in [o._vtk_obj for o in picker.actors]:
|
|
point_rez = plots['in_points'].glyph.glyph_source.glyph_source.output.points.to_array().shape[0]
|
|
new_point_i = int(np.floor(picker.point_id / point_rez))
|
|
if new_point_i < len(plots['in_points'].mlab_source.points):
|
|
|
|
# Get closest point in the layer we are interested in
|
|
aim_point = plots['in_points'].mlab_source.points[new_point_i:new_point_i + 1]
|
|
update_ERF()
|
|
update_scene()
|
|
|
|
def keyboard_callback(vtk_obj, event):
|
|
global remove_h, p_scale, show_in_p
|
|
global in_points, grad_values, chosen_point, in_colors
|
|
|
|
print(vtk_obj.GetKeyCode())
|
|
|
|
|
|
if vtk_obj.GetKeyCode() in ['b', 'B']:
|
|
p_scale /= 1.5
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['n', 'N']:
|
|
p_scale *= 1.5
|
|
update_scene()
|
|
|
|
if vtk_obj.GetKeyCode() in ['g', 'G']:
|
|
if remove_h > 0.0:
|
|
remove_h -= 0.1
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['h', 'H']:
|
|
if remove_h < 1.0:
|
|
remove_h += 0.1
|
|
update_ERF()
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['z', 'Z']:
|
|
show_in_p = not show_in_p
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['x', 'X']:
|
|
# Reset potentials
|
|
dataset.potentials['ERF'] = []
|
|
dataset.min_potentials['ERF'] = []
|
|
for i, tree in enumerate(dataset.input_trees['test']):
|
|
dataset.potentials['ERF'] += [np.random.rand(tree.data.shape[0]) * 1e-3]
|
|
dataset.min_potentials['ERF'] += [float(np.min(dataset.potentials['ERF'][-1]))]
|
|
|
|
# Update figure
|
|
update_ERF(only_points=True)
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['0']:
|
|
|
|
print('Saving')
|
|
|
|
# Find a new name
|
|
file_i = 0
|
|
file_name = 'ERF_{:03d}.ply'.format(file_i)
|
|
files = [f for f in listdir('ERF_clouds') if f.endswith('.ply')]
|
|
while file_name in files:
|
|
file_i += 1
|
|
file_name = 'ERF_{:03d}.ply'.format(file_i)
|
|
|
|
# Save
|
|
responses = 100 * np.abs(np.ravel(grad_values))
|
|
write_ply(join('ERF_clouds', file_name),
|
|
[in_points, in_colors, responses],
|
|
['x', 'y', 'z', 'red', 'green', 'blue', 'erf'])
|
|
write_ply(join('ERF_clouds', 'ERF_{:03d}_center.ply'.format(file_i)),
|
|
[chosen_point.reshape([1, -1])],
|
|
['x', 'y', 'z'])
|
|
print('OK')
|
|
|
|
return
|
|
|
|
# Draw a first plot
|
|
pick_func = fig1.on_mouse_pick(picker_callback)
|
|
pick_func.tolerance = 0.01
|
|
update_ERF(only_points=True)
|
|
update_scene()
|
|
fig1.scene.interactor.add_observer('KeyPressEvent', keyboard_callback)
|
|
mlab.show()
|
|
|
|
return
|
|
|
|
def show_deformable_kernels(self, net, loader, config, deform_idx=0):
|
|
"""
|
|
Show some inference with deformable kernels
|
|
"""
|
|
|
|
##########################################
|
|
# First choose the visualized deformations
|
|
##########################################
|
|
|
|
print('\nList of the deformable convolution available (chosen one highlighted in green)')
|
|
fmt_str = ' {:}{:2d} > KPConv(r={:.3f}, Din={:d}, Dout={:d}){:}'
|
|
deform_convs = []
|
|
for m in net.modules():
|
|
if isinstance(m, KPConv) and m.deformable:
|
|
if len(deform_convs) == deform_idx:
|
|
color = bcolors.OKGREEN
|
|
else:
|
|
color = bcolors.FAIL
|
|
print(fmt_str.format(color, len(deform_convs), m.radius, m.in_channels, m.out_channels, bcolors.ENDC))
|
|
deform_convs.append(m)
|
|
|
|
################
|
|
# Initialization
|
|
################
|
|
|
|
print('\n****************************************************\n')
|
|
|
|
# Loop variables
|
|
t0 = time.time()
|
|
t = [time.time()]
|
|
last_display = time.time()
|
|
mean_dt = np.zeros(1)
|
|
count = 0
|
|
|
|
# Start training loop
|
|
for epoch in range(config.max_epoch):
|
|
|
|
for batch in loader:
|
|
|
|
##################
|
|
# Processing batch
|
|
##################
|
|
|
|
# New time
|
|
t = t[-1:]
|
|
t += [time.time()]
|
|
|
|
if 'cuda' in self.device.type:
|
|
batch.to(self.device)
|
|
|
|
# Forward pass
|
|
outputs = net(batch, config)
|
|
original_KP = deform_convs[deform_idx].kernel_points.cpu().detach().numpy()
|
|
stacked_deformed_KP = deform_convs[deform_idx].deformed_KP.cpu().detach().numpy()
|
|
count += batch.lengths[0].shape[0]
|
|
|
|
if 'cuda' in self.device.type:
|
|
torch.cuda.synchronize(self.device)
|
|
|
|
# Find layer
|
|
l = None
|
|
for i, p in enumerate(batch.points):
|
|
if p.shape[0] == stacked_deformed_KP.shape[0]:
|
|
l = i
|
|
|
|
t += [time.time()]
|
|
|
|
# Get data
|
|
in_points = []
|
|
in_colors = []
|
|
deformed_KP = []
|
|
points = []
|
|
lookuptrees = []
|
|
i0 = 0
|
|
for b_i, length in enumerate(batch.lengths[0]):
|
|
in_points.append(batch.points[0][i0:i0 + length].cpu().detach().numpy())
|
|
if batch.features.shape[1] == 4:
|
|
in_colors.append(batch.features[i0:i0 + length, 1:].cpu().detach().numpy())
|
|
else:
|
|
in_colors.append(None)
|
|
i0 += length
|
|
|
|
i0 = 0
|
|
for b_i, length in enumerate(batch.lengths[l]):
|
|
points.append(batch.points[l][i0:i0 + length].cpu().detach().numpy())
|
|
deformed_KP.append(stacked_deformed_KP[i0:i0 + length])
|
|
lookuptrees.append(KDTree(points[-1]))
|
|
i0 += length
|
|
|
|
###########################
|
|
# Interactive visualization
|
|
###########################
|
|
|
|
# Create figure for features
|
|
fig1 = mlab.figure('Deformations', bgcolor=(1.0, 1.0, 1.0), size=(1280, 920))
|
|
fig1.scene.parallel_projection = False
|
|
|
|
# Indices
|
|
global obj_i, point_i, plots, offsets, p_scale, show_in_p, aim_point
|
|
p_scale = 0.03
|
|
obj_i = 0
|
|
point_i = 0
|
|
plots = {}
|
|
offsets = False
|
|
show_in_p = 2
|
|
aim_point = np.zeros((1, 3))
|
|
|
|
def picker_callback(picker):
|
|
""" Picker callback: this get called when on pick events.
|
|
"""
|
|
global plots, aim_point
|
|
|
|
if 'in_points' in plots:
|
|
if plots['in_points'].actor.actor._vtk_obj in [o._vtk_obj for o in picker.actors]:
|
|
point_rez = plots['in_points'].glyph.glyph_source.glyph_source.output.points.to_array().shape[0]
|
|
new_point_i = int(np.floor(picker.point_id / point_rez))
|
|
if new_point_i < len(plots['in_points'].mlab_source.points):
|
|
# Get closest point in the layer we are interested in
|
|
aim_point = plots['in_points'].mlab_source.points[new_point_i:new_point_i + 1]
|
|
update_scene()
|
|
|
|
if 'points' in plots:
|
|
if plots['points'].actor.actor._vtk_obj in [o._vtk_obj for o in picker.actors]:
|
|
point_rez = plots['points'].glyph.glyph_source.glyph_source.output.points.to_array().shape[0]
|
|
new_point_i = int(np.floor(picker.point_id / point_rez))
|
|
if new_point_i < len(plots['points'].mlab_source.points):
|
|
# Get closest point in the layer we are interested in
|
|
aim_point = plots['points'].mlab_source.points[new_point_i:new_point_i + 1]
|
|
update_scene()
|
|
|
|
def update_scene():
|
|
global plots, offsets, p_scale, show_in_p, aim_point, point_i
|
|
|
|
# Get the current view
|
|
v = mlab.view()
|
|
roll = mlab.roll()
|
|
|
|
# clear figure
|
|
for key in plots.keys():
|
|
plots[key].remove()
|
|
|
|
plots = {}
|
|
|
|
# Plot new data feature
|
|
p = points[obj_i]
|
|
|
|
# Rescale points for visu
|
|
p = (p * 1.5 / config.in_radius)
|
|
|
|
|
|
# Show point cloud
|
|
if show_in_p <= 1:
|
|
plots['points'] = mlab.points3d(p[:, 0],
|
|
p[:, 1],
|
|
p[:, 2],
|
|
resolution=8,
|
|
scale_factor=p_scale,
|
|
scale_mode='none',
|
|
color=(0, 1, 1),
|
|
figure=fig1)
|
|
|
|
if show_in_p >= 1:
|
|
|
|
# Get points and colors
|
|
in_p = in_points[obj_i]
|
|
in_p = (in_p * 1.5 / config.in_radius)
|
|
|
|
# Color point cloud if possible
|
|
in_c = in_colors[obj_i]
|
|
if in_c is not None:
|
|
|
|
# Primitives
|
|
scalars = np.arange(len(in_p)) # Key point: set an integer for each point
|
|
|
|
# Define color table (including alpha), which must be uint8 and [0,255]
|
|
colors = np.hstack((in_c, np.ones_like(in_c[:, :1])))
|
|
colors = (colors * 255).astype(np.uint8)
|
|
|
|
plots['in_points'] = mlab.points3d(in_p[:, 0],
|
|
in_p[:, 1],
|
|
in_p[:, 2],
|
|
scalars,
|
|
resolution=8,
|
|
scale_factor=p_scale*0.8,
|
|
scale_mode='none',
|
|
figure=fig1)
|
|
plots['in_points'].module_manager.scalar_lut_manager.lut.table = colors
|
|
|
|
else:
|
|
|
|
plots['in_points'] = mlab.points3d(in_p[:, 0],
|
|
in_p[:, 1],
|
|
in_p[:, 2],
|
|
resolution=8,
|
|
scale_factor=p_scale*0.8,
|
|
scale_mode='none',
|
|
figure=fig1)
|
|
|
|
|
|
# Get KP locations
|
|
rescaled_aim_point = aim_point * config.in_radius / 1.5
|
|
point_i = lookuptrees[obj_i].query(rescaled_aim_point, return_distance=False)[0][0]
|
|
if offsets:
|
|
KP = points[obj_i][point_i] + deformed_KP[obj_i][point_i]
|
|
scals = np.ones_like(KP[:, 0])
|
|
else:
|
|
KP = points[obj_i][point_i] + original_KP
|
|
scals = np.zeros_like(KP[:, 0])
|
|
|
|
KP = (KP * 1.5 / config.in_radius)
|
|
|
|
plots['KP'] = mlab.points3d(KP[:, 0],
|
|
KP[:, 1],
|
|
KP[:, 2],
|
|
scals,
|
|
colormap='autumn',
|
|
resolution=8,
|
|
scale_factor=1.2*p_scale,
|
|
scale_mode='none',
|
|
vmin=0,
|
|
vmax=1,
|
|
figure=fig1)
|
|
|
|
|
|
if True:
|
|
plots['center'] = mlab.points3d(p[point_i, 0],
|
|
p[point_i, 1],
|
|
p[point_i, 2],
|
|
scale_factor=1.1*p_scale,
|
|
scale_mode='none',
|
|
color=(0, 1, 0),
|
|
figure=fig1)
|
|
|
|
# New title
|
|
plots['title'] = mlab.title(str(obj_i), color=(0, 0, 0), size=0.3, height=0.01)
|
|
text = '<--- (press g for previous)' + 50 * ' ' + '(press h for next) --->'
|
|
plots['text'] = mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
|
|
plots['orient'] = mlab.orientation_axes()
|
|
|
|
# Set the saved view
|
|
mlab.view(*v)
|
|
mlab.roll(roll)
|
|
|
|
return
|
|
|
|
def animate_kernel():
|
|
global plots, offsets, p_scale, show_in_p
|
|
|
|
# Get KP locations
|
|
|
|
KP_def = points[obj_i][point_i] + deformed_KP[obj_i][point_i]
|
|
KP_def = (KP_def * 1.5 / config.in_radius)
|
|
KP_def_color = (1, 0, 0)
|
|
|
|
KP_rigid = points[obj_i][point_i] + original_KP
|
|
KP_rigid = (KP_rigid * 1.5 / config.in_radius)
|
|
KP_rigid_color = (1, 0.7, 0)
|
|
|
|
if offsets:
|
|
t_list = np.linspace(0, 1, 150, dtype=np.float32)
|
|
else:
|
|
t_list = np.linspace(1, 0, 150, dtype=np.float32)
|
|
|
|
@mlab.animate(delay=10)
|
|
def anim():
|
|
for t in t_list:
|
|
plots['KP'].mlab_source.set(x=t * KP_def[:, 0] + (1 - t) * KP_rigid[:, 0],
|
|
y=t * KP_def[:, 1] + (1 - t) * KP_rigid[:, 1],
|
|
z=t * KP_def[:, 2] + (1 - t) * KP_rigid[:, 2],
|
|
scalars=t * np.ones_like(KP_def[:, 0]))
|
|
|
|
yield
|
|
|
|
anim()
|
|
|
|
return
|
|
|
|
def keyboard_callback(vtk_obj, event):
|
|
global obj_i, point_i, offsets, p_scale, show_in_p
|
|
|
|
if vtk_obj.GetKeyCode() in ['b', 'B']:
|
|
p_scale /= 1.5
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['n', 'N']:
|
|
p_scale *= 1.5
|
|
update_scene()
|
|
|
|
if vtk_obj.GetKeyCode() in ['g', 'G']:
|
|
obj_i = (obj_i - 1) % len(deformed_KP)
|
|
point_i = 0
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['h', 'H']:
|
|
obj_i = (obj_i + 1) % len(deformed_KP)
|
|
point_i = 0
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['k', 'K']:
|
|
offsets = not offsets
|
|
animate_kernel()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['z', 'Z']:
|
|
show_in_p = (show_in_p + 1) % 3
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['0']:
|
|
|
|
print('Saving')
|
|
|
|
# Find a new name
|
|
file_i = 0
|
|
file_name = 'KP_{:03d}.ply'.format(file_i)
|
|
files = [f for f in listdir('KP_clouds') if f.endswith('.ply')]
|
|
while file_name in files:
|
|
file_i += 1
|
|
file_name = 'KP_{:03d}.ply'.format(file_i)
|
|
|
|
KP_deform = points[obj_i][point_i] + deformed_KP[obj_i][point_i]
|
|
KP_normal = points[obj_i][point_i] + original_KP
|
|
|
|
# Save
|
|
write_ply(join('KP_clouds', file_name),
|
|
[in_points[obj_i], in_colors[obj_i]],
|
|
['x', 'y', 'z', 'red', 'green', 'blue'])
|
|
write_ply(join('KP_clouds', 'KP_{:03d}_deform.ply'.format(file_i)),
|
|
[KP_deform],
|
|
['x', 'y', 'z'])
|
|
write_ply(join('KP_clouds', 'KP_{:03d}_normal.ply'.format(file_i)),
|
|
[KP_normal],
|
|
['x', 'y', 'z'])
|
|
print('OK')
|
|
|
|
return
|
|
|
|
# Draw a first plot
|
|
pick_func = fig1.on_mouse_pick(picker_callback)
|
|
pick_func.tolerance = 0.01
|
|
update_scene()
|
|
fig1.scene.interactor.add_observer('KeyPressEvent', keyboard_callback)
|
|
mlab.show()
|
|
|
|
return
|
|
|
|
@staticmethod
|
|
def show_activation(path, relu_idx=0, save_video=False):
|
|
"""
|
|
This function show the saved input point clouds maximizing the activations. You can also directly load the files
|
|
in a visualization software like CloudCompare.
|
|
In the case of relu_idx = 0 and if gaussian mode, the associated filter is also shown. This function can only
|
|
show the filters for the last saved epoch.
|
|
"""
|
|
|
|
################
|
|
# Find the files
|
|
################
|
|
|
|
# Check visu folder
|
|
visu_path = join('visu',
|
|
'visu_' + path.split('/')[-1],
|
|
'top_activations',
|
|
'Relu{:02d}'.format(relu_idx))
|
|
if not exists(visu_path):
|
|
message = 'Relu {:d} activations of the model {:s} not found.'
|
|
raise ValueError(message.format(relu_idx, path.split('/')[-1]))
|
|
|
|
# Get the list of files
|
|
feature_files = np.sort([f for f in listdir(visu_path) if f.endswith('.ply')])
|
|
if len(feature_files) == 0:
|
|
message = 'Relu {:d} activations of the model {:s} not found.'
|
|
raise ValueError(message.format(relu_idx, path.split('/')[-1]))
|
|
|
|
# Load mode
|
|
config = Config()
|
|
config.load(path)
|
|
mode = config.convolution_mode
|
|
|
|
#################
|
|
# Get activations
|
|
#################
|
|
|
|
all_points = []
|
|
all_responses = []
|
|
|
|
for file in feature_files:
|
|
|
|
# Load points
|
|
data = read_ply(join(visu_path, file))
|
|
all_points += [np.vstack((data['x'], data['y'], data['z'])).T]
|
|
all_responses += [data['responses']]
|
|
|
|
###########################
|
|
# Interactive visualization
|
|
###########################
|
|
|
|
# Create figure for features
|
|
fig1 = mlab.figure('Features', bgcolor=(0.5, 0.5, 0.5), size=(640, 480))
|
|
fig1.scene.parallel_projection = False
|
|
|
|
# Indices
|
|
global file_i
|
|
file_i = 0
|
|
|
|
def update_scene():
|
|
|
|
# clear figure
|
|
mlab.clf(fig1)
|
|
|
|
# Plot new data feature
|
|
points = all_points[file_i]
|
|
responses = all_responses[file_i]
|
|
min_response, max_response = np.min(responses), np.max(responses)
|
|
responses = (responses - min_response) / (max_response - min_response)
|
|
|
|
# Rescale points for visu
|
|
points = (points * 1.5 / config.in_radius + np.array([1.0, 1.0, 1.0])) * 50.0
|
|
|
|
# Show point clouds colorized with activations
|
|
activations = mlab.points3d(points[:, 0],
|
|
points[:, 1],
|
|
points[:, 2],
|
|
responses,
|
|
scale_factor=3.0,
|
|
scale_mode='none',
|
|
vmin=0.1,
|
|
vmax=0.9,
|
|
figure=fig1)
|
|
|
|
# New title
|
|
mlab.title(feature_files[file_i], color=(0, 0, 0), size=0.3, height=0.01)
|
|
text = '<--- (press g for previous)' + 50*' ' + '(press h for next) --->'
|
|
mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
|
|
mlab.orientation_axes()
|
|
|
|
return
|
|
|
|
def keyboard_callback(vtk_obj, event):
|
|
global file_i
|
|
|
|
if vtk_obj.GetKeyCode() in ['g', 'G']:
|
|
|
|
file_i = (file_i - 1) % len(all_responses)
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['h', 'H']:
|
|
|
|
file_i = (file_i + 1) % len(all_responses)
|
|
update_scene()
|
|
|
|
return
|
|
|
|
# Draw a first plot
|
|
update_scene()
|
|
fig1.scene.interactor.add_observer('KeyPressEvent', keyboard_callback)
|
|
mlab.show()
|
|
|
|
return
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# Utilities
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# ------------------------------------------------------------------------------------------------------------------
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@staticmethod
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def load_last_kernels(path):
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# Directories of validation error
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kernel_dirs = np.array([f for f in listdir(join(path, 'kernel_points')) if f.startswith('epoch')])
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|
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# Find last epoch folder
|
|
epochs = np.array([int(f[5:]) for f in kernel_dirs])
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last_dir = kernel_dirs[np.argmax(epochs)]
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|
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# Find saved files for the first layer
|
|
kernel_file = join(path, 'kernel_points', last_dir, 'layer_0_simple_0.ply')
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weights_file = join(path, 'kernel_points', last_dir, 'layer_0_simple_0.npy')
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|
|
# Read kernel file
|
|
data = read_ply(kernel_file)
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points = np.vstack((data['x'], data['y'], data['z'])).T
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extents = data['sigma'].astype(np.float32)
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|
|
# Read weight file
|
|
w = np.load(weights_file)
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return points, extents, w
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@staticmethod
|
|
def apply_weights(points, kernel, weights, extents):
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|
|
# Get all difference matrices [n_points, n_kpoints, dim]
|
|
points = np.expand_dims(points, 1)
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|
points = np.tile(points, [1, kernel.shape[0], 1])
|
|
differences = points - kernel
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|
|
# Compute distance matrices [n_points, n_kpoints]
|
|
sq_distances = np.sum(np.square(differences), axis=-1)
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|
|
# Compute gaussians [n_points, n_kpoints]
|
|
gaussian_values = np.exp(-sq_distances / (2 * np.square(extents)))
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|
|
|
# Apply weights
|
|
return np.matmul(gaussian_values, np.squeeze(weights))
|
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|
|
|
|
def top_relu_activations_old(self, model, dataset, relu_idx=0, top_num=5):
|
|
"""
|
|
Test the model on test dataset to see which points activate the most each neurons in a relu layer
|
|
:param model: model used at training
|
|
:param dataset: dataset used at training
|
|
:param relu_idx: which features are to be visualized
|
|
:param top_num: how many top candidates are kept per features
|
|
"""
|
|
|
|
#####################################
|
|
# First choose the visualized feature
|
|
#####################################
|
|
|
|
# List all relu ops
|
|
all_ops = [op for op in tf.get_default_graph().get_operations() if op.name.startswith('KernelPointNetwork')
|
|
and op.name.endswith('LeakyRelu')]
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|
|
|
# Non relu ops in case we want the first KPConv features
|
|
KPConv_0 = [op for op in tf.get_default_graph().get_operations() if op.name.endswith('layer_0/simple_0/Sum_1')]
|
|
|
|
# Print the chosen one
|
|
if relu_idx == 0:
|
|
features_tensor = KPConv_0[relu_idx].outputs[0]
|
|
else:
|
|
features_tensor = all_ops[relu_idx].outputs[0]
|
|
|
|
# Get parameters
|
|
layer_idx = int(features_tensor.name.split('/')[1][6:])
|
|
if 'strided' in all_ops[relu_idx].name and not ('strided' in all_ops[relu_idx+1].name):
|
|
layer_idx += 1
|
|
features_dim = int(features_tensor.shape[1])
|
|
radius = model.config.first_subsampling_dl * model.config.density_parameter * (2 ** layer_idx)
|
|
|
|
if relu_idx == 0 :
|
|
print('SPECIAL CASE : relu_idx = 0 => visualization of the fist KPConv before relu')
|
|
print('You chose to visualize the output of operation named: ' + KPConv_0[0].name)
|
|
print('It contains {:d} features.'.format(int(features_tensor.shape[1])))
|
|
else :
|
|
print('You chose to visualize the output of operation named: ' + all_ops[relu_idx].name)
|
|
print('It contains {:d} features.'.format(int(features_tensor.shape[1])))
|
|
|
|
print('\nPossible Relu indices:')
|
|
for i, t in enumerate(all_ops):
|
|
print(i, ': ', t.name)
|
|
|
|
print('\n****************************************************************************')
|
|
|
|
#####################
|
|
# Initialize containers
|
|
#####################
|
|
|
|
# Initialize containers
|
|
self.top_features = -np.ones((top_num, features_dim))
|
|
self.top_classes = -np.ones((top_num, features_dim), dtype=np.int32)
|
|
self.saving = model.config.saving
|
|
|
|
# Testing parameters
|
|
num_votes = 3
|
|
|
|
# Create visu folder
|
|
self.visu_path = None
|
|
self.fmt_str = None
|
|
if model.config.saving:
|
|
self.visu_path = join('visu',
|
|
'visu_' + model.saving_path.split('/')[-1],
|
|
'top_activations',
|
|
'Relu{:02d}'.format(relu_idx))
|
|
self.fmt_str = 'f{:04d}_top{:02d}.ply'
|
|
if not exists(self.visu_path):
|
|
makedirs(self.visu_path)
|
|
|
|
# *******************
|
|
# Network predictions
|
|
# *******************
|
|
|
|
mean_dt = np.zeros(2)
|
|
last_display = time.time()
|
|
for v in range(num_votes):
|
|
|
|
# Run model on all test examples
|
|
# ******************************
|
|
|
|
# Initialise iterator with test data
|
|
if model.config.dataset.startswith('S3DIS'):
|
|
self.sess.run(dataset.val_init_op)
|
|
else:
|
|
self.sess.run(dataset.test_init_op)
|
|
count = 0
|
|
|
|
while True:
|
|
try:
|
|
|
|
if model.config.dataset.startswith('ShapeNetPart'):
|
|
if model.config.dataset.split('_')[1] == 'multi':
|
|
label_op = model.inputs['super_labels']
|
|
else:
|
|
label_op = model.inputs['point_labels']
|
|
elif model.config.dataset.startswith('S3DIS'):
|
|
label_op = model.inputs['point_labels']
|
|
elif model.config.dataset.startswith('Scannet'):
|
|
label_op = model.inputs['point_labels']
|
|
elif model.config.dataset.startswith('ModelNet40'):
|
|
label_op = model.inputs['labels']
|
|
else:
|
|
raise ValueError('Unsupported dataset')
|
|
|
|
# Run one step of the model
|
|
t = [time.time()]
|
|
ops = (all_ops[-1].outputs[0],
|
|
features_tensor,
|
|
label_op,
|
|
model.inputs['points'],
|
|
model.inputs['pools'],
|
|
model.inputs['in_batches'])
|
|
_, stacked_features, labels, all_points, all_pools, in_batches = self.sess.run(ops, {model.dropout_prob: 1.0})
|
|
t += [time.time()]
|
|
count += in_batches.shape[0]
|
|
|
|
|
|
# Stack all batches
|
|
max_ind = np.max(in_batches)
|
|
stacked_batches = []
|
|
for b_i, b in enumerate(in_batches):
|
|
stacked_batches += [b[b < max_ind - 0.5]*0+b_i]
|
|
stacked_batches = np.hstack(stacked_batches)
|
|
|
|
# Find batches at wanted layer
|
|
for l in range(model.config.num_layers - 1):
|
|
if l >= layer_idx:
|
|
break
|
|
stacked_batches = stacked_batches[all_pools[l][:, 0]]
|
|
|
|
# Get each example and update top_activations
|
|
for b_i, b in enumerate(in_batches):
|
|
b = b[b < max_ind - 0.5]
|
|
in_points = all_points[0][b]
|
|
features = stacked_features[stacked_batches == b_i]
|
|
points = all_points[layer_idx][stacked_batches == b_i]
|
|
if model.config.dataset in ['ShapeNetPart_multi', 'ModelNet40_classif']:
|
|
l = labels[b_i]
|
|
else:
|
|
l = np.argmax(np.bincount(labels[b]))
|
|
|
|
self.update_top_activations(features, labels[b_i], points, in_points, radius)
|
|
|
|
# Average timing
|
|
t += [time.time()]
|
|
mean_dt = 0.95 * mean_dt + 0.05 * (np.array(t[1:]) - np.array(t[:-1]))
|
|
|
|
# Display
|
|
if (t[-1] - last_display) > 1.0:
|
|
last_display = t[-1]
|
|
if model.config.dataset.startswith('S3DIS'):
|
|
completed = count / (model.config.validation_size * model.config.batch_num)
|
|
else:
|
|
completed = count / dataset.num_test
|
|
message = 'Vote {:d} : {:.1f}% (timings : {:4.2f} {:4.2f})'
|
|
print(message.format(v,
|
|
100 * completed,
|
|
1000 * (mean_dt[0]),
|
|
1000 * (mean_dt[1])))
|
|
#class_names = np.array([dataset.label_to_names[i] for i in range(dataset.num_classes)])
|
|
#print(class_names[self.top_classes[:, :20]].T)
|
|
|
|
except tf.errors.OutOfRangeError:
|
|
break
|
|
|
|
return
|
|
|
|
|
|
|
|
|
|
def show_ModelNet_models(all_points):
|
|
|
|
###########################
|
|
# Interactive visualization
|
|
###########################
|
|
|
|
# Create figure for features
|
|
fig1 = mlab.figure('Models', bgcolor=(1, 1, 1), size=(1000, 800))
|
|
fig1.scene.parallel_projection = False
|
|
|
|
# Indices
|
|
global file_i
|
|
file_i = 0
|
|
|
|
def update_scene():
|
|
|
|
# clear figure
|
|
mlab.clf(fig1)
|
|
|
|
# Plot new data feature
|
|
points = all_points[file_i]
|
|
|
|
# Rescale points for visu
|
|
points = (points * 1.5 + np.array([1.0, 1.0, 1.0])) * 50.0
|
|
|
|
# Show point clouds colorized with activations
|
|
activations = mlab.points3d(points[:, 0],
|
|
points[:, 1],
|
|
points[:, 2],
|
|
points[:, 2],
|
|
scale_factor=3.0,
|
|
scale_mode='none',
|
|
figure=fig1)
|
|
|
|
# New title
|
|
mlab.title(str(file_i), color=(0, 0, 0), size=0.3, height=0.01)
|
|
text = '<--- (press g for previous)' + 50 * ' ' + '(press h for next) --->'
|
|
mlab.text(0.01, 0.01, text, color=(0, 0, 0), width=0.98)
|
|
mlab.orientation_axes()
|
|
|
|
return
|
|
|
|
def keyboard_callback(vtk_obj, event):
|
|
global file_i
|
|
|
|
if vtk_obj.GetKeyCode() in ['g', 'G']:
|
|
|
|
file_i = (file_i - 1) % len(all_points)
|
|
update_scene()
|
|
|
|
elif vtk_obj.GetKeyCode() in ['h', 'H']:
|
|
|
|
file_i = (file_i + 1) % len(all_points)
|
|
update_scene()
|
|
|
|
return
|
|
|
|
# Draw a first plot
|
|
update_scene()
|
|
fig1.scene.interactor.add_observer('KeyPressEvent', keyboard_callback)
|
|
mlab.show()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|