From 6c83bf23d43bdbf2a8cae2df4654b26d46d53046 Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Thu, 15 Jun 2017 22:59:13 +0200 Subject: [PATCH] Added classes VignetteSet and CompressedVignetteSet to abstract the data-sets and allow lossless compression of vignettes. --- cnn-svrt.py | 132 +++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 95 insertions(+), 37 deletions(-) diff --git a/cnn-svrt.py b/cnn-svrt.py index ab1b363..bbce4c9 100755 --- a/cnn-svrt.py +++ b/cnn-svrt.py @@ -23,6 +23,7 @@ import time import argparse +import math from colorama import Fore, Back, Style @@ -64,6 +65,10 @@ parser.add_argument('--log_file', type = str, default = 'cnn-svrt.log', help = 'Log file name') +parser.add_argument('--compress_vignettes', + action='store_true', default = False, + help = 'Should we use lossless compression of vignette to reduce the memory footprint') + args = parser.parse_args() ###################################################################### @@ -80,14 +85,70 @@ def log_string(s): ###################################################################### -def generate_set(p, n): - target = torch.LongTensor(n).bernoulli_(0.5) - t = time.time() - input = svrt.generate_vignettes(p, target) - t = time.time() - t - log_string('data_set_generation {:.02f} sample/s'.format(n / t)) - input = input.view(input.size(0), 1, input.size(1), input.size(2)).float() - return Variable(input), Variable(target) +class VignetteSet: + def __init__(self, problem_number, nb_batches): + self.batch_size = args.batch_size + self.problem_number = problem_number + self.nb_batches = nb_batches + self.nb_samples = self.nb_batches * self.batch_size + self.targets = [] + self.inputs = [] + + acc = 0.0 + acc_sq = 0.0 + + for k in range(0, self.nb_batches): + target = torch.LongTensor(self.batch_size).bernoulli_(0.5) + input = svrt.generate_vignettes(problem_number, target) + input = input.float().view(input.size(0), 1, input.size(1), input.size(2)) + if torch.cuda.is_available(): + input = input.cuda() + target = target.cuda() + acc += input.float().sum() / input.numel() + acc_sq += input.float().pow(2).sum() / input.numel() + self.targets.append(target) + self.inputs.append(input) + + mean = acc / self.nb_batches + std = math.sqrt(acc_sq / self.nb_batches - mean * mean) + for k in range(0, self.nb_batches): + self.inputs[k].sub_(mean).div_(std) + + def get_batch(self, b): + return self.inputs[b], self.targets[b] + +class CompressedVignetteSet: + def __init__(self, problem_number, nb_batches): + self.batch_size = args.batch_size + self.problem_number = problem_number + self.nb_batches = nb_batches + self.nb_samples = self.nb_batches * self.batch_size + self.targets = [] + self.input_storages = [] + + acc = 0.0 + acc_sq = 0.0 + for k in range(0, self.nb_batches): + target = torch.LongTensor(self.batch_size).bernoulli_(0.5) + input = svrt.generate_vignettes(problem_number, target) + acc += input.float().sum() / input.numel() + acc_sq += input.float().pow(2).sum() / input.numel() + self.targets.append(target) + self.input_storages.append(svrt.compress(input.storage())) + + self.mean = acc / self.nb_batches + self.std = math.sqrt(acc_sq / self.nb_batches - self.mean * self.mean) + + def get_batch(self, b): + input = torch.ByteTensor(svrt.uncompress(self.input_storages[b])).float() + input = input.view(self.batch_size, 1, 128, 128).sub_(self.mean).div_(self.std) + target = self.targets[b] + + if torch.cuda.is_available(): + input = input.cuda() + target = target.cuda() + + return input, target ###################################################################### @@ -123,8 +184,8 @@ class AfrozeShallowNet(nn.Module): x = self.fc2(x) return x -def train_model(model, train_input, train_target): - bs = args.batch_size +def train_model(model, train_set): + batch_size = args.batch_size criterion = nn.CrossEntropyLoss() if torch.cuda.is_available(): @@ -134,9 +195,10 @@ def train_model(model, train_input, train_target): for k in range(0, args.nb_epochs): acc_loss = 0.0 - for b in range(0, train_input.size(0), bs): - output = model.forward(train_input.narrow(0, b, bs)) - loss = criterion(output, train_target.narrow(0, b, bs)) + for b in range(0, train_set.nb_batches): + input, target = train_set.get_batch(b) + output = model.forward(Variable(input)) + loss = criterion(output, Variable(target)) acc_loss = acc_loss + loss.data[0] model.zero_grad() loss.backward() @@ -147,16 +209,15 @@ def train_model(model, train_input, train_target): ###################################################################### -def nb_errors(model, data_input, data_target): - bs = args.batch_size - +def nb_errors(model, data_set): ne = 0 - for b in range(0, data_input.size(0), bs): - output = model.forward(data_input.narrow(0, b, bs)) + for b in range(0, data_set.nb_batches): + input, target = data_set.get_batch(b) + output = model.forward(Variable(input)) wta_prediction = output.data.max(1)[1].view(-1) - for i in range(0, bs): - if wta_prediction[i] != data_target.narrow(0, b, bs).data[i]: + for i in range(0, data_set.batch_size): + if wta_prediction[i] != target[i]: ne = ne + 1 return ne @@ -167,21 +228,18 @@ for arg in vars(args): log_string('argument ' + str(arg) + ' ' + str(getattr(args, arg))) for problem_number in range(1, 24): - train_input, train_target = generate_set(problem_number, - args.nb_train_batches * args.batch_size) - test_input, test_target = generate_set(problem_number, - args.nb_test_batches * args.batch_size) + if args.compress_vignettes: + train_set = CompressedVignetteSet(problem_number, args.nb_train_batches) + test_set = CompressedVignetteSet(problem_number, args.nb_test_batches) + else: + train_set = VignetteSet(problem_number, args.nb_train_batches) + test_set = VignetteSet(problem_number, args.nb_test_batches) + model = AfrozeShallowNet() if torch.cuda.is_available(): - train_input, train_target = train_input.cuda(), train_target.cuda() - test_input, test_target = test_input.cuda(), test_target.cuda() model.cuda() - mu, std = train_input.data.mean(), train_input.data.std() - train_input.data.sub_(mu).div_(std) - test_input.data.sub_(mu).div_(std) - nb_parameters = 0 for p in model.parameters(): nb_parameters += p.numel() @@ -194,26 +252,26 @@ for problem_number in range(1, 24): log_string('loaded_model ' + model_filename) except: log_string('training_model') - train_model(model, train_input, train_target) + train_model(model, train_set) torch.save(model.state_dict(), model_filename) log_string('saved_model ' + model_filename) - nb_train_errors = nb_errors(model, train_input, train_target) + nb_train_errors = nb_errors(model, train_set) log_string('train_error {:d} {:.02f}% {:d} {:d}'.format( problem_number, - 100 * nb_train_errors / train_input.size(0), + 100 * nb_train_errors / train_set.nb_samples, nb_train_errors, - train_input.size(0)) + train_set.nb_samples) ) - nb_test_errors = nb_errors(model, test_input, test_target) + nb_test_errors = nb_errors(model, test_set) log_string('test_error {:d} {:.02f}% {:d} {:d}'.format( problem_number, - 100 * nb_test_errors / test_input.size(0), + 100 * nb_test_errors / test_set.nb_samples, nb_test_errors, - test_input.size(0)) + test_set.nb_samples) ) ###################################################################### -- 2.20.1