X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mine_mnist.py;fp=mine_mnist.py;h=0000000000000000000000000000000000000000;hb=236238fdfe7d65612b58fbbb5bb29cff4ec45d54;hp=f8b859dd6fc07dc69f2b8fecfa2c30f4d1eeace3;hpb=f07dc15e422fd58a38c5a2ea3b260cd2b44e21af;p=pytorch.git diff --git a/mine_mnist.py b/mine_mnist.py deleted file mode 100755 index f8b859d..0000000 --- a/mine_mnist.py +++ /dev/null @@ -1,426 +0,0 @@ -#!/usr/bin/env python - -######################################################################### -# This program is free software: you can redistribute it and/or modify # -# it under the terms of the version 3 of the GNU General Public License # -# as published by the Free Software Foundation. # -# # -# 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 # -# General Public License for more details. # -# # -# You should have received a copy of the GNU General Public License # -# along with this program. If not, see . # -# # -# Written by and Copyright (C) Francois Fleuret # -# Contact for comments & bug reports # -######################################################################### - -import argparse, math, sys -from copy import deepcopy - -import torch, torchvision - -from torch import nn -import torch.nn.functional as F - -###################################################################### - -if torch.cuda.is_available(): - torch.backends.cudnn.benchmark = True - device = torch.device('cuda') -else: - device = torch.device('cpu') - -###################################################################### - -parser = argparse.ArgumentParser( - description = '''An implementation of a Mutual Information estimator with a deep model - -Three different toy data-sets are implemented: - - (1) Two MNIST images of same class. The "true" MI is the log of the - number of used MNIST classes. - - (2) One MNIST image and a pair of real numbers whose difference is - the class of the image. The "true" MI is the log of the number of - used MNIST classes. - - (3) Two 1d sequences, the first with a single peak, the second with - two peaks, and the height of the peak in the first is the - difference of timing of the peaks in the second. The "true" MI is - the log of the number of possible peak heights.''', - - formatter_class = argparse.ArgumentDefaultsHelpFormatter -) - -parser.add_argument('--data', - type = str, default = 'image_pair', - help = 'What data: image_pair, image_values_pair, sequence_pair') - -parser.add_argument('--seed', - type = int, default = 0, - help = 'Random seed (default 0, < 0 is no seeding)') - -parser.add_argument('--mnist_classes', - type = str, default = '0, 1, 3, 5, 6, 7, 8, 9', - help = 'What MNIST classes to use') - -parser.add_argument('--nb_classes', - type = int, default = 2, - help = 'How many classes for sequences') - -parser.add_argument('--nb_epochs', - type = int, default = 50, - help = 'How many epochs') - -parser.add_argument('--batch_size', - type = int, default = 100, - help = 'Batch size') - -parser.add_argument('--learning_rate', - type = float, default = 1e-3, - help = 'Batch size') - -parser.add_argument('--independent', action = 'store_true', - help = 'Should the pair components be independent') - -###################################################################### - -args = parser.parse_args() - -if args.seed >= 0: - torch.manual_seed(args.seed) - -used_MNIST_classes = torch.tensor(eval('[' + args.mnist_classes + ']'), device = device) - -###################################################################### - -def entropy(target): - probas = [] - for k in range(target.max() + 1): - n = (target == k).sum().item() - if n > 0: probas.append(n) - probas = torch.tensor(probas).float() - probas /= probas.sum() - return - (probas * probas.log()).sum().item() - -###################################################################### - -train_set = torchvision.datasets.MNIST('./data/mnist/', train = True, download = True) -train_input = train_set.train_data.view(-1, 1, 28, 28).to(device).float() -train_target = train_set.train_labels.to(device) - -test_set = torchvision.datasets.MNIST('./data/mnist/', train = False, download = True) -test_input = test_set.test_data.view(-1, 1, 28, 28).to(device).float() -test_target = test_set.test_labels.to(device) - -mu, std = train_input.mean(), train_input.std() -train_input.sub_(mu).div_(std) -test_input.sub_(mu).div_(std) - -###################################################################### - -# Returns a triplet of tensors (a, b, c), where a and b contain each -# half of the samples, with a[i] and b[i] of same class for any i, and -# c is a 1d long tensor real classes - -def create_image_pairs(train = False): - ua, ub, uc = [], [], [] - - if train: - input, target = train_input, train_target - else: - input, target = test_input, test_target - - for i in used_MNIST_classes: - used_indices = torch.arange(input.size(0), device = target.device)\ - .masked_select(target == i.item()) - x = input[used_indices] - x = x[torch.randperm(x.size(0))] - hs = x.size(0)//2 - ua.append(x.narrow(0, 0, hs)) - ub.append(x.narrow(0, hs, hs)) - uc.append(target[used_indices]) - - a = torch.cat(ua, 0) - b = torch.cat(ub, 0) - c = torch.cat(uc, 0) - perm = torch.randperm(a.size(0)) - a = a[perm].contiguous() - - if args.independent: - perm = torch.randperm(a.size(0)) - b = b[perm].contiguous() - - return a, b, c - -###################################################################### - -# Returns a triplet a, b, c where a are the standard MNIST images, c -# the classes, and b is a Nx2 tensor, with for every n: -# -# b[n, 0] ~ Uniform(0, 10) -# b[n, 1] ~ b[n, 0] + Uniform(0, 0.5) + c[n] - -def create_image_values_pairs(train = False): - ua, ub = [], [] - - if train: - input, target = train_input, train_target - else: - input, target = test_input, test_target - - m = torch.zeros(used_MNIST_classes.max() + 1, dtype = torch.uint8, device = target.device) - m[used_MNIST_classes] = 1 - m = m[target] - used_indices = torch.arange(input.size(0), device = target.device).masked_select(m) - - input = input[used_indices].contiguous() - target = target[used_indices].contiguous() - - a = input - c = target - - b = a.new(a.size(0), 2) - b[:, 0].uniform_(0.0, 10.0) - b[:, 1].uniform_(0.0, 0.5) - - if args.independent: - b[:, 1] += b[:, 0] + \ - used_MNIST_classes[torch.randint(len(used_MNIST_classes), target.size())] - else: - b[:, 1] += b[:, 0] + target.float() - - return a, b, c - -###################################################################### - -def create_sequences_pairs(train = False): - nb, length = 10000, 1024 - noise_level = 2e-2 - - ha = torch.randint(args.nb_classes, (nb, ), device = device) + 1 - if args.independent: - hb = torch.randint(args.nb_classes, (nb, ), device = device) - else: - hb = ha - - pos = torch.empty(nb, device = device).uniform_(0.0, 0.9) - a = torch.linspace(0, 1, length, device = device).view(1, -1).expand(nb, -1) - a = a - pos.view(nb, 1) - a = (a >= 0).float() * torch.exp(-a * math.log(2) / 0.1) - a = a * ha.float().view(-1, 1).expand_as(a) / (1 + args.nb_classes) - noise = a.new(a.size()).normal_(0, noise_level) - a = a + noise - - pos = torch.empty(nb, device = device).uniform_(0.0, 0.5) - b1 = torch.linspace(0, 1, length, device = device).view(1, -1).expand(nb, -1) - b1 = b1 - pos.view(nb, 1) - b1 = (b1 >= 0).float() * torch.exp(-b1 * math.log(2) / 0.1) * 0.25 - pos = pos + hb.float() / (args.nb_classes + 1) * 0.5 - # pos += pos.new(hb.size()).uniform_(0.0, 0.01) - b2 = torch.linspace(0, 1, length, device = device).view(1, -1).expand(nb, -1) - b2 = b2 - pos.view(nb, 1) - b2 = (b2 >= 0).float() * torch.exp(-b2 * math.log(2) / 0.1) * 0.25 - - b = b1 + b2 - noise = b.new(b.size()).normal_(0, noise_level) - b = b + noise - - # a = (a - a.mean()) / a.std() - # b = (b - b.mean()) / b.std() - - return a, b, ha - -###################################################################### - -class NetForImagePair(nn.Module): - def __init__(self): - super(NetForImagePair, self).__init__() - self.features_a = nn.Sequential( - nn.Conv2d(1, 16, kernel_size = 5), - nn.MaxPool2d(3), nn.ReLU(), - nn.Conv2d(16, 32, kernel_size = 5), - nn.MaxPool2d(2), nn.ReLU(), - ) - - self.features_b = nn.Sequential( - nn.Conv2d(1, 16, kernel_size = 5), - nn.MaxPool2d(3), nn.ReLU(), - nn.Conv2d(16, 32, kernel_size = 5), - nn.MaxPool2d(2), nn.ReLU(), - ) - - self.fully_connected = nn.Sequential( - nn.Linear(256, 200), - nn.ReLU(), - nn.Linear(200, 1) - ) - - def forward(self, a, b): - a = self.features_a(a).view(a.size(0), -1) - b = self.features_b(b).view(b.size(0), -1) - x = torch.cat((a, b), 1) - return self.fully_connected(x) - -###################################################################### - -class NetForImageValuesPair(nn.Module): - def __init__(self): - super(NetForImageValuesPair, self).__init__() - self.features_a = nn.Sequential( - nn.Conv2d(1, 16, kernel_size = 5), - nn.MaxPool2d(3), nn.ReLU(), - nn.Conv2d(16, 32, kernel_size = 5), - nn.MaxPool2d(2), nn.ReLU(), - ) - - self.features_b = nn.Sequential( - nn.Linear(2, 32), nn.ReLU(), - nn.Linear(32, 32), nn.ReLU(), - nn.Linear(32, 128), nn.ReLU(), - ) - - self.fully_connected = nn.Sequential( - nn.Linear(256, 200), - nn.ReLU(), - nn.Linear(200, 1) - ) - - def forward(self, a, b): - a = self.features_a(a).view(a.size(0), -1) - b = self.features_b(b).view(b.size(0), -1) - x = torch.cat((a, b), 1) - return self.fully_connected(x) - -###################################################################### - -class NetForSequencePair(nn.Module): - - def feature_model(self): - kernel_size = 11 - pooling_size = 4 - return nn.Sequential( - nn.Conv1d( 1, self.nc, kernel_size = kernel_size), - nn.AvgPool1d(pooling_size), - nn.LeakyReLU(), - nn.Conv1d(self.nc, self.nc, kernel_size = kernel_size), - nn.AvgPool1d(pooling_size), - nn.LeakyReLU(), - nn.Conv1d(self.nc, self.nc, kernel_size = kernel_size), - nn.AvgPool1d(pooling_size), - nn.LeakyReLU(), - nn.Conv1d(self.nc, self.nc, kernel_size = kernel_size), - nn.AvgPool1d(pooling_size), - nn.LeakyReLU(), - ) - - def __init__(self): - super(NetForSequencePair, self).__init__() - - self.nc = 32 - self.nh = 256 - - self.features_a = self.feature_model() - self.features_b = self.feature_model() - - self.fully_connected = nn.Sequential( - nn.Linear(2 * self.nc, self.nh), - nn.ReLU(), - nn.Linear(self.nh, 1) - ) - - def forward(self, a, b): - a = a.view(a.size(0), 1, a.size(1)) - a = self.features_a(a) - a = F.avg_pool1d(a, a.size(2)) - - b = b.view(b.size(0), 1, b.size(1)) - b = self.features_b(b) - b = F.avg_pool1d(b, b.size(2)) - - x = torch.cat((a.view(a.size(0), -1), b.view(b.size(0), -1)), 1) - return self.fully_connected(x) - -###################################################################### - -if args.data == 'image_pair': - create_pairs = create_image_pairs - model = NetForImagePair() - -elif args.data == 'image_values_pair': - create_pairs = create_image_values_pairs - model = NetForImageValuesPair() - -elif args.data == 'sequence_pair': - create_pairs = create_sequences_pairs - model = NetForSequencePair() - - ###################### - ## Save for figures - a, b, c = create_pairs() - for k in range(10): - file = open(f'train_{k:02d}.dat', 'w') - for i in range(a.size(1)): - file.write(f'{a[k, i]:f} {b[k,i]:f}\n') - file.close() - ###################### - -else: - raise Exception('Unknown data ' + args.data) - -###################################################################### -# Train - -print(f'nb_parameters {sum(x.numel() for x in model.parameters())}') - -model.to(device) - -input_a, input_b, classes = create_pairs(train = True) - -for e in range(args.nb_epochs): - - optimizer = torch.optim.Adam(model.parameters(), lr = args.learning_rate) - - input_br = input_b[torch.randperm(input_b.size(0))] - - acc_mi = 0.0 - - for batch_a, batch_b, batch_br in zip(input_a.split(args.batch_size), - input_b.split(args.batch_size), - input_br.split(args.batch_size)): - mi = model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log() - acc_mi += mi.item() - loss = - mi - optimizer.zero_grad() - loss.backward() - optimizer.step() - - acc_mi /= (input_a.size(0) // args.batch_size) - - print(f'{e+1} {acc_mi / math.log(2):.04f} {entropy(classes) / math.log(2):.04f}') - - sys.stdout.flush() - -###################################################################### -# Test - -input_a, input_b, classes = create_pairs(train = False) - -input_br = input_b[torch.randperm(input_b.size(0))] - -acc_mi = 0.0 - -for batch_a, batch_b, batch_br in zip(input_a.split(args.batch_size), - input_b.split(args.batch_size), - input_br.split(args.batch_size)): - mi = model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log() - acc_mi += mi.item() - -acc_mi /= (input_a.size(0) // args.batch_size) - -print(f'test {acc_mi / math.log(2):.04f} {entropy(classes) / math.log(2):.04f}') - -######################################################################