From 0e7d17bae1211d8019428ba4cd59e0af2a7ab074 Mon Sep 17 00:00:00 2001
From: Francois Fleuret <francois@fleuret.org>
Date: Fri, 14 Dec 2018 10:07:10 +0100
Subject: [PATCH] Update.

---
 mine_mnist.py | 101 +++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 99 insertions(+), 2 deletions(-)

diff --git a/mine_mnist.py b/mine_mnist.py
index 7845d81..c22d7fe 100755
--- a/mine_mnist.py
+++ b/mine_mnist.py
@@ -1,15 +1,18 @@
 #!/usr/bin/env python
 
 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():
     device = torch.device('cuda')
+    torch.backends.cudnn.benchmark = True
 else:
     device = torch.device('cpu')
 
@@ -73,7 +76,7 @@ test_input.sub_(mu).div_(std)
 # c is a 1d long tensor real classes
 
 def create_image_pairs(train = False):
-    ua, ub = [], []
+    ua, ub, uc = [], [], []
 
     if train:
         input, target = train_input, train_target
@@ -135,6 +138,52 @@ def create_image_values_pairs(train = False):
 
 ######################################################################
 
+def create_sequences_pairs(train = False):
+    nb, length = 10000, 1024
+    noise_level = 1e-2
+
+    nb_classes = 4
+    ha = torch.randint(nb_classes, (nb, ), device = device) + 1
+    # hb = torch.randint(nb_classes, (nb, ), device = device)
+    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 + nb_classes)
+    noise = a.new(a.size()).normal_(0, noise_level)
+    a = a + noise
+
+    pos = torch.empty(nb, device = device).uniform_(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)
+    pos = pos + hb.float() / (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)
+
+    b = b1 + b2
+    noise = b.new(b.size()).normal_(0, noise_level)
+    b = b + noise
+
+    ######################################################################
+    # for k in range(10):
+        # file = open(f'/tmp/dat{k:02d}', 'w')
+        # for i in range(a.size(1)):
+            # file.write(f'{a[k, i]:f} {b[k,i]:f}\n')
+        # file.close()
+    # exit(0)
+    ######################################################################
+
+    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__()
@@ -196,12 +245,60 @@ class NetForImageValuesPair(nn.Module):
 
 ######################################################################
 
+class NetForSequencePair(nn.Module):
+
+    def feature_model(self):
+        return  nn.Sequential(
+            nn.Conv1d(1, self.nc, kernel_size = 5),
+            nn.MaxPool1d(2), nn.ReLU(),
+            nn.Conv1d(self.nc, self.nc, kernel_size = 5),
+            nn.MaxPool1d(2), nn.ReLU(),
+            nn.Conv1d(self.nc, self.nc, kernel_size = 5),
+            nn.MaxPool1d(2), nn.ReLU(),
+            nn.Conv1d(self.nc, self.nc, kernel_size = 5),
+            nn.MaxPool1d(2), nn.ReLU(),
+            nn.Conv1d(self.nc, self.nc, kernel_size = 5),
+            nn.MaxPool1d(2), nn.ReLU(),
+        )
+
+    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()
 else:
     raise Exception('Unknown data ' + args.data)
 
@@ -235,7 +332,7 @@ for e in range(nb_epochs):
 
     acc_mi /= (input_a.size(0) // batch_size)
 
-    print('%d %.04f %.04f' % (e, acc_mi / math.log(2), entropy(classes) / math.log(2)))
+    print('%d %.04f %.04f' % (e + 1, acc_mi / math.log(2), entropy(classes) / math.log(2)))
 
     sys.stdout.flush()
 
-- 
2.39.5