#!/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 #
-#########################################################################
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
import argparse, math, sys
from copy import deepcopy
if torch.cuda.is_available():
torch.backends.cudnn.benchmark = True
- device = torch.device('cuda')
+ device = torch.device("cuda")
else:
- device = torch.device('cpu')
+ 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:
+ description="""An implementation of a Mutual Information estimator with a deep model
- (1) Two MNIST images of same class. The "true" MI is the log of the
- number of used MNIST classes.
+ Three different toy data-sets are implemented, each consists of
+ pairs of samples, that may be from different spaces:
- (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.
+ (1) Two MNIST images of same class. 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.''',
+ (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.
- formatter_class = argparse.ArgumentDefaultsHelpFormatter
+ (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(
+ "--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(
+ "--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(
+ "--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_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("--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("--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("--learning_rate", type=float, default=1e-3, help="Batch size")
-parser.add_argument('--independent', action = 'store_true',
- help = 'Should the pair components be independent')
+parser.add_argument(
+ "--independent",
+ action="store_true",
+ help="Should the pair components be independent",
+)
######################################################################
if args.seed >= 0:
torch.manual_seed(args.seed)
-used_MNIST_classes = torch.tensor(eval('[' + args.mnist_classes + ']'), device = device)
+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)
+ if n > 0:
+ probas.append(n)
probas = torch.tensor(probas).float()
probas /= probas.sum()
- return - (probas * probas.log()).sum().item()
+ 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_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_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)
# 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):
+
+def create_image_pairs(train=False):
ua, ub, uc = [], [], []
if train:
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())
+ 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
+ hs = x.size(0) // 2
ua.append(x.narrow(0, 0, hs))
ub.append(x.narrow(0, hs, hs))
uc.append(target[used_indices])
return a, b, c
+
######################################################################
# Returns a triplet a, b, c where a are the standard MNIST images, c
# 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):
+
+def create_image_values_pairs(train=False):
ua, ub = [], []
if train:
else:
input, target = test_input, test_target
- m = torch.zeros(used_MNIST_classes.max() + 1, dtype = torch.uint8, device = target.device)
+ 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)
+ used_indices = torch.arange(input.size(0), device=target.device).masked_select(m)
input = input[used_indices].contiguous()
target = target[used_indices].contiguous()
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())]
+ 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):
+#
+
+
+def create_sequences_pairs(train=False):
nb, length = 10000, 1024
noise_level = 2e-2
- ha = torch.randint(args.nb_classes, (nb, ), device = device) + 1
+ ha = torch.randint(args.nb_classes, (nb,), device=device) + 1
if args.independent:
- hb = torch.randint(args.nb_classes, (nb, ), device = device)
+ 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)
+ 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)
+ 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 = 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
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__()
+ super().__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(),
+ 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(),
+ 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)
+ nn.Linear(256, 200), nn.ReLU(), nn.Linear(200, 1)
)
def forward(self, a, b):
x = torch.cat((a, b), 1)
return self.fully_connected(x)
+
######################################################################
+
class NetForImageValuesPair(nn.Module):
def __init__(self):
- super(NetForImageValuesPair, self).__init__()
+ super().__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(),
+ 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(),
+ 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)
+ nn.Linear(256, 200), nn.ReLU(), nn.Linear(200, 1)
)
def forward(self, a, b):
x = torch.cat((a, b), 1)
return self.fully_connected(x)
+
######################################################################
-class NetForSequencePair(nn.Module):
+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),
+ 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.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.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.Conv1d(self.nc, self.nc, kernel_size=kernel_size),
nn.AvgPool1d(pooling_size),
nn.LeakyReLU(),
)
def __init__(self):
- super(NetForSequencePair, self).__init__()
+ super().__init__()
self.nc = 32
self.nh = 256
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)
+ nn.Linear(2 * self.nc, self.nh), nn.ReLU(), nn.Linear(self.nh, 1)
)
def forward(self, a, b):
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':
+if args.data == "image_pair":
create_pairs = create_image_pairs
model = NetForImagePair()
-elif args.data == 'image_values_pair':
+elif args.data == "image_values_pair":
create_pairs = create_image_values_pairs
model = NetForImageValuesPair()
-elif args.data == 'sequence_pair':
+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')
+ 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.write(f"{a[k, i]:f} {b[k,i]:f}\n")
file.close()
######################
else:
- raise Exception('Unknown data ' + args.data)
+ raise Exception("Unknown data " + args.data)
######################################################################
# Train
-print(f'nb_parameters {sum(x.numel() for x in model.parameters())}')
+print(f"nb_parameters {sum(x.numel() for x in model.parameters())}")
model.to(device)
-input_a, input_b, classes = create_pairs(train = True)
+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)
+ 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()
+ 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
+ loss = -mi
optimizer.zero_grad()
loss.backward()
optimizer.step()
- acc_mi /= (input_a.size(0) // args.batch_size)
+ 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}')
+ 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_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)):
+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)
+acc_mi /= input_a.size(0) // args.batch_size
-print(f'test {acc_mi / math.log(2):.04f} {entropy(classes) / math.log(2):.04f}')
+print(f"test {acc_mi / math.log(2):.04f} {entropy(classes) / math.log(2):.04f}")
######################################################################
projects / pytorch.git / blobdiff