From: Francois Fleuret Date: Sun, 2 Dec 2018 23:16:41 +0000 (-0500) Subject: Update. X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pytorch.git;a=commitdiff_plain;h=e7b065a38122910f512b87ac9551b3ac535361a9 Update. --- diff --git a/mine_mnist.py b/mine_mnist.py index 5ab427f..412c624 100755 --- a/mine_mnist.py +++ b/mine_mnist.py @@ -1,5 +1,7 @@ #!/usr/bin/env python +import argparse + import math, sys, torch, torchvision from torch import nn @@ -7,6 +9,34 @@ from torch.nn import functional as F ###################################################################### +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') + +###################################################################### + +args = parser.parse_args() + +if args.seed >= 0: + torch.manual_seed(args.seed) + +used_MNIST_classes = torch.tensor(eval('[' + args.mnist_classes + ']')) + +###################################################################### + 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 @@ -15,21 +45,22 @@ test_set = torchvision.datasets.MNIST('./data/mnist/', train = False, download = test_input = test_set.test_data.view(-1, 1, 28, 28).float() test_target = test_set.test_labels +if torch.cuda.is_available(): + used_MNIST_classes = used_MNIST_classes.cuda() + train_input, train_target = train_input.cuda(), train_target.cuda() + test_input, test_target = test_input.cuda(), test_target.cuda() + mu, std = train_input.mean(), train_input.std() train_input.sub_(mu).div_(std) test_input.sub_(mu).div_(std) -used_MNIST_classes = torch.tensor([ 0, 1, 3, 5, 6, 7, 8, 9]) -# used_MNIST_classes = torch.tensor([ 0, 9, 7 ]) -# used_MNIST_classes = torch.tensor([ 3, 4, 7, 0 ]) - ###################################################################### # 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 with the count of pairs per class used. -def create_MNIST_pair_set(train = False): +def create_image_pairs(train = False): ua, ub = [], [] if train: @@ -57,29 +88,108 @@ def create_MNIST_pair_set(train = False): ###################################################################### -class Net(nn.Module): +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 + + b = a.new(a.size(0), 2) + b[:, 0].uniform_(10) + b[:, 1].uniform_(0.5) + b[:, 1] += b[:, 0] + target.float() + + c = torch.tensor([(target == k).sum().item() for k in used_MNIST_classes]) + + return a, b, c + +###################################################################### + +class NetImagePair(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) + super(NetImagePair, 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): - # Make the two images a single two-channel image + 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) - 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 + return self.fully_connected(x) ###################################################################### -nb_epochs, batch_size = 50, 100 +class NetImageValuesPair(nn.Module): + def __init__(self): + super(NetImageValuesPair, 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) + ) -model = Net() + 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) + +###################################################################### + +if args.data == 'image_pair': + create_pairs = create_image_pairs + model = NetImagePair() +elif args.data == 'image_values_pair': + create_pairs = create_image_values_pairs + model = NetImageValuesPair() +else: + raise Exception('Unknown data ' + args.data) + +###################################################################### + +nb_epochs, batch_size = 50, 100 print('nb_parameters %d' % sum(x.numel() for x in model.parameters())) @@ -87,12 +197,10 @@ optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3) if torch.cuda.is_available(): model.cuda() - train_input, train_target = train_input.cuda(), train_target.cuda() - test_input, test_target = test_input.cuda(), test_target.cuda() for e in range(nb_epochs): - input_a, input_b, count = create_MNIST_pair_set(train = True) + input_a, input_b, count = create_pairs(train = True) # The information bound is the entropy of the class distribution class_proba = count.float() @@ -121,7 +229,7 @@ for e in range(nb_epochs): ###################################################################### -input_a, input_b, count = create_MNIST_pair_set(train = False) +input_a, input_b, count = create_pairs(train = False) for e in range(nb_epochs): class_proba = count.float()