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gen_gap_predictor.py
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gen_gap_predictor.py
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#!/usr/bin/env python
from models.utils import get_model
import torch
import torch.nn as nn
from loaders import *
from graph import *
from utils import save_dipha
from passers import Passer
from config import SAVE_PATH, MAX_EPSILON, UPPER_DIM, NPROC
from pathlib import Path
from bettis import read_pd
import numpy as np
class GenGapPredictor:
def __init__(self, net, dataloader):
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.net = net
self.dataloader = dataloader
self.net.to(self.device) # move net to gpu if necessary
self.net = torch.nn.DataParallel(net) # allow usage of multiple gpus
self.criterion = nn.CrossEntropyLoss()
def _construct_functional_graph(self, epoch, checkpoint):
print("="*50)
print("Construction functional graph for model:")
print(net)
print("="*50)
print("==> Loading checkpoint for epoch {} ...".format(epoch))
self.net.load_state_dict(checkpoint['net'])
passer_test = Passer(self.net, self.dataloader, self.criterion, self.device)
print()
passer_test.run()
activations = passer_test.get_function()
activations = signal_concat(activations)
adj = adjacency(activations)
print('The dimension of the adjacency matrix is {}.'.format(adj.shape))
print('Adj mean {}, min {}, max {}'.format(np.mean(adj), np.min(adj), np.max(adj)))
return adj
def export_functional_graph_dipha(self, adj, save_dir, epoch):
# save functional graph to binary file to load it later with DIPHA library.
graphfile_path = save_dir + 'adj_epc{}_trl0.bin'.format(epoch)
print("Saving functional graph to '"+graphfile_path+"' ...")
save_dipha(graphfile_path, 1-adj)
return graphfile_path
def compute_topology(self, graph_file_path):
print(graphfile_path)
# get epoch from path string
epoch = int(graphfile_path.split('/')[-1].split('_')[1].replace('epc',' '))
# construct path to store vietoris-rips filtration in
filtration_path = os.path.join(Path(graphfile_path).parent , 'adj_epc{}_trl0_{}.bin'.format(
epoch, MAX_EPSILON))
# construct path to store results in
persistence_diag_path = filtration_path + ".out"
# calculate vietoris-rips filtration of function graph and
# store it in 'out_path'
os.system("./dipha/build/full_to_sparse_distance_matrix "
+ str(MAX_EPSILON) + " " + graphfile_path + " "+ filtration_path)
# calculate persistence diagram
os.system("mpiexec -n " +str(NPROC) + " ./dipha/build/dipha --upper_dim "
+ str(UPPER_DIM) + " --benchmark --dual " + filtration_path + " "
+ persistence_diag_path)
return persistence_diag_path
def calculate_summary(self, persistence_diag_path, dim=1, persistence=0.02):
birth, death = np.array(
read_pd(persistence_diag_path, dimension=dim, persistence=persistence)
)
avg_life = np.mean(death - birth)
midlife = np.mean((birth + death)/2)
print("Average Life: {}".format(avg_life))
print("Midlife: {}".format(midlife))
return avg_life, midlife
if __name__ == '__main__':
model = 'lenet'
dataset = 'mnist'
epoch = 0
save_dir = os.path.join(SAVE_PATH, model + '_' + dataset + '/')
checkpoint = torch.load(
'./checkpoint/'+ model + '_' + dataset + '/ckpt_trial_0_epoch_' + str(epoch)+'.t7'
)
dataloader = loader(dataset+'_test', batch_size=100, subset=list(range(0,1000)))
net = get_model(model, dataset)
predictor = GenGapPredictor(net, dataloader)
funcgraph = predictor._construct_functional_graph(epoch, checkpoint)
graphfile_path = predictor.export_functional_graph_dipha(funcgraph, save_dir, epoch)
persistence_diag_path = predictor.compute_topology(graphfile_path)
avg_life, midlife = predictor.calculate_summary(persistence_diag_path)