X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pytorch.git;a=blobdiff_plain;f=mine_mnist.py;h=389544b780f7b5d051460d914a25285f4251cc5d;hp=f573b894634e439134e64ac46f019472bb8aab41;hb=4469498b31c1fb90cb2b1202dbaf86be0f2d18b0;hpb=5e18633709b82b0739e62d105b2685793be1d014

diff --git a/mine_mnist.py b/mine_mnist.py
index f573b89..389544b 100755
--- a/mine_mnist.py
+++ b/mine_mnist.py
@@ -1,105 +1,388 @@
 #!/usr/bin/env python
 
-# @XREMOTE_HOST: elk.fleuret.org
-# @XREMOTE_EXEC: ~/conda/bin/python
-# @XREMOTE_PRE: ln -s ~/data/pytorch ./data
-# @XREMOTE_PRE: killall -q -9 python || echo "Nothing killed"
+import argparse, math, sys
+from copy import deepcopy
 
-import math, sys, torch, torchvision
+import torch, torchvision
 
 from torch import nn
-from torch.nn import functional as F
+import torch.nn.functional as F
 
 ######################################################################
 
-# Returns a pair of tensors (x, c), where x is a Nx2x28x28 containing
-# pairs of images of same classes (one per channel), and p is a 1d
-# long tensor with the count of pairs per class used
+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 Mutual Information estimator with a deep model',
+    formatter_class = argparse.ArgumentDefaultsHelpFormatter
+)
+
+parser.add_argument('--data',
+                    type = str, default = 'image_pair',
+                    help = 'What data')
+
+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('--independent', action = 'store_true',
+                    help = 'Should the pair components be independent')
+
+######################################################################
+
+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()
+
+def robust_log_mean_exp(x):
+    # a = x.max()
+    # return (x-a).exp().mean().log() + a
+    # a = x.max()
+    return x.exp().mean().log()
+
+######################################################################
+
+args = parser.parse_args()
+
+if args.seed >= 0:
+    torch.manual_seed(args.seed)
+
+used_MNIST_classes = torch.tensor(eval('[' + args.mnist_classes + ']'), device = device)
+
+######################################################################
+
+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 = [], [], []
 
-def create_pair_set(used_classes, input, target):
-    u = []
+    if train:
+        input, target = train_input, train_target
+    else:
+        input, target = test_input, test_target
 
-    for i in used_classes:
+    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))]
-        # Careful with odd numbers of samples in a class
-        x = x[0:2 * (x.size(0) // 2)].reshape(-1, 2, 28, 28)
-        u.append(x)
+        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, eith 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()
 
-    x = torch.cat(u, 0).contiguous()
-    c = torch.tensor([x.size(0) for x in u])
+    a = input
+    c = target
 
-    return x, c
+    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
+    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 Net(nn.Module):
+class NetForImagePair(nn.Module):
     def __init__(self):
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(2, 32, kernel_size = 5)
-        self.conv2 = nn.Conv2d(32, 64, kernel_size = 5)
-        self.fc1 = nn.Linear(256, 200)
-        self.fc2 = nn.Linear(200, 1)
-
-    def forward(self, x):
-        x = F.relu(F.max_pool2d(self.conv1(x), kernel_size = 3))
-        x = F.relu(F.max_pool2d(self.conv2(x), kernel_size = 2))
-        x = x.view(x.size(0), -1)
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
+        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)
 
 ######################################################################
 
-train_set = torchvision.datasets.MNIST('./data/mnist/', train = True, download = True)
-train_input  = train_set.train_data.view(-1, 1, 28, 28).float()
-train_target = train_set.train_labels
+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(),
+        )
 
-mu, std = train_input.mean(), train_input.std()
-train_input.sub_(mu).div_(std)
+        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)
 
 ######################################################################
 
-# The information bound is the log of the number of classes in there
+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()
+    ######################################################################
+    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()
+    # exit(0)
+    ######################################################################
+else:
+    raise Exception('Unknown data ' + args.data)
 
-# used_classes = torch.tensor([ 0, 1, 3, 5, 6, 7, 8, 9])
-used_classes = torch.tensor([ 3, 4, 7, 0 ])
+######################################################################
 
-nb_epochs, batch_size = 50, 100
+print('nb_parameters %d' % sum(x.numel() for x in model.parameters()))
 
-model = Net()
-optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3)
+model.to(device)
 
-if torch.cuda.is_available():
-    model.cuda()
-    train_input, train_target = train_input.cuda(), train_target.cuda()
+for e in range(args.nb_epochs):
 
-for e in range(nb_epochs):
-    input, count = create_pair_set(used_classes, train_input, train_target)
+    input_a, input_b, classes = create_pairs(train = True)
 
-    class_proba = count.float()
-    class_proba /= class_proba.sum()
-    class_entropy = - (class_proba.log() * class_proba).sum().item()
+    input_br = input_b[torch.randperm(input_b.size(0))]
 
-    input = input[torch.randperm(input.size(0))]
-    indep_input = input.clone()
-    indep_input[:, 1] = input[torch.randperm(input.size(0)), 1]
+    acc_mi = 0.0
 
-    mi = 0.0
+    optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4)
 
-    for batch, indep_batch in zip(input.split(batch_size), indep_input.split(batch_size)):
-        loss = - (model(batch).mean() - model(indep_batch).exp().mean().log())
-        mi -= loss.item()
+    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()
 
-    mi /= (input.size(0) // batch_size)
+    acc_mi /= (input_a.size(0) // args.batch_size)
 
-    print('%d %.04f %.04f'%(e, mi / math.log(2), class_entropy / math.log(2)))
+    print('%d %.04f %.04f' % (e + 1, acc_mi / math.log(2), entropy(classes) / math.log(2)))
 
     sys.stdout.flush()
 
 ######################################################################
+
+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('test %.04f %.04f'%(acc_mi / math.log(2), entropy(classes) / math.log(2)))
+
+######################################################################