+++ /dev/null
-#!/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 <http://www.gnu.org/licenses/>. #
-# #
-# Written by and Copyright (C) Francois Fleuret #
-# Contact <francois.fleuret@idiap.ch> 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}')
-
-######################################################################
projects / pytorch.git / blobdiff