Python model.NetworkImageNet() Examples

The following are 7 code examples of model.NetworkImageNet(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module model , or try the search function .
Example #1
Source File: convert.py    From PNASNet.pytorch with Apache License 2.0 5 votes vote down vote up 
def write_pytorch_weight(self):
    model = NetworkImageNet(216, 1001, 12, False, PNASNet)
    model.drop_path_prob = 0
    model.eval()

    self.used_keys = []
    self.convert_conv(model.conv0, 'conv0/weights')
    self.convert_bn(model.conv0_bn, 'conv0_bn/gamma', 'conv0_bn/beta',
        'conv0_bn/moving_mean', 'conv0_bn/moving_variance')
    self.convert_cell(model.stem1, 'cell_stem_0/')
    self.convert_cell(model.stem2, 'cell_stem_1/')

    for i in range(12):
      self.convert_cell(model.cells[i], 'cell_{}/'.format(i))
    
    self.convert_fc(model.classifier, 'final_layer/FC/weights',
        'final_layer/FC/biases')

    print('Conversion complete!')
    print('Check 1: whether all TF variables are used...')
    assert len(self.weight_dict) == len(self.used_keys)
    print('Pass!')

    model = model.cuda()
    image = self.tf_image_proc.transpose((2, 0, 1))
    image = Variable(self.Tensor(image)).cuda()
    logits, _ = model(image.unsqueeze(0))
    self.pytorch_logits = logits.data.cpu().numpy()

    print('Check 2: whether logits have small diff...')
    assert np.max(np.abs(self.tf_logits - self.pytorch_logits)) < 1e-5
    print('Pass!')

    model_path = 'data/PNASNet-5_Large.pth'
    torch.save(model.state_dict(), model_path)
    print('PyTorch model saved to {}'.format(model_path))
Example #2
Source File: main.py    From PNASNet.pytorch with Apache License 2.0 5 votes vote down vote up 
def main():
  args = parser.parse_args()
  assert torch.cuda.is_available()

  image_ph = tf.placeholder(tf.uint8, (None, None, 3))
  image_proc = preprocess_for_eval(image_ph, args.image_size, args.image_size)
  config = tf.ConfigProto()
  config.gpu_options.allow_growth = True
  sess = tf.Session(config=config)

  model = NetworkImageNet(args.num_conv_filters, args.num_classes,
                          args.num_cells, False, PNASNet)
  model.drop_path_prob = 0
  model.eval()
  model.load_state_dict(torch.load('data/PNASNet-5_Large.pth'))
  model = model.cuda()

  c1, c5 = 0, 0
  val_dataset = datasets.ImageFolder(args.valdir)
  for i, (image, label) in enumerate(val_dataset):
    tf_image_proc = sess.run(image_proc, feed_dict={image_ph: image})
    image = torch.from_numpy(tf_image_proc.transpose((2, 0, 1)))
    image = Variable(image).cuda()
    logits, _ = model(image.unsqueeze(0))
    top5 = logits.data.cpu().numpy().squeeze().argsort()[::-1][:5]
    top1 = top5[0]
    if label + 1 == top1:
      c1 += 1
    if label + 1 in top5:
      c5 += 1
    print('Test: [{0}/{1}]\t'
          'Prec@1 {2:.3f}\t'
          'Prec@5 {3:.3f}\t'.format(
          i + 1, len(val_dataset), c1 / (i + 1.), c5 / (i + 1.)))
Example #3
Source File: test_imagenet.py    From sgas with MIT License 5 votes vote down vote up 
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)
  cudnn.enabled=True
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  model = nn.DataParallel(model)
  model = model.cuda()
  model.load_state_dict(torch.load(args.model_path)['state_dict'])

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  validdir = os.path.join(args.data, 'val')
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  valid_data = dset.ImageFolder(
    validdir,
    transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      normalize,
    ]))

  valid_queue = torch.utils.data.DataLoader(
    valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=False, num_workers=4)

  model.module.drop_path_prob = 0.0
  valid_acc_top1, valid_acc_top5, valid_obj = infer(valid_queue, model, criterion)
  logging.info('Valid_acc_top1 %f', valid_acc_top1)
  logging.info('Valid_acc_top5 %f', valid_acc_top5)
Example #4
Source File: test_imagenet.py    From eval-nas with MIT License 4 votes vote down vote up 
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  model.load_state_dict(torch.load(args.model_path)['state_dict'])

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  validdir = os.path.join(args.data, 'val')
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  valid_data = dset.ImageFolder(
    validdir,
    transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      normalize,
    ]))

  valid_queue = torch.utils.data.DataLoader(
    valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)

  model.drop_path_prob = args.drop_path_prob
  valid_acc_top1, valid_acc_top5, valid_obj = infer(valid_queue, model, criterion)
  logging.info('valid_acc_top1 %f', valid_acc_top1)
  logging.info('valid_acc_top5 %f', valid_acc_top5)
Example #5
Source File: test_imagenet.py    From darts with Apache License 2.0 4 votes vote down vote up 
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  model.load_state_dict(torch.load(args.model_path)['state_dict'])

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  validdir = os.path.join(args.data, 'val')
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  valid_data = dset.ImageFolder(
    validdir,
    transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      normalize,
    ]))

  valid_queue = torch.utils.data.DataLoader(
    valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)

  model.drop_path_prob = args.drop_path_prob
  valid_acc_top1, valid_acc_top5, valid_obj = infer(valid_queue, model, criterion)
  logging.info('valid_acc_top1 %f', valid_acc_top1)
  logging.info('valid_acc_top5 %f', valid_acc_top5)
Example #6
Source File: test.py    From NAS-Benchmark with GNU General Public License v3.0 4 votes vote down vote up 
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  torch.cuda.set_device(args.gpu)
  cudnn.enabled=True
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  if args.dataset in LARGE_DATASETS:
    model = NetworkLarge(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  else:
    model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  utils.load(model, args.model_path)

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  _, test_transform = utils.data_transforms(args.dataset,args.cutout,args.cutout_length)
  if args.dataset=="CIFAR100":
    test_data = dset.CIFAR100(root=args.data, train=False, download=True, transform=test_transform)
  elif args.dataset=="CIFAR10":
    test_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=test_transform)
  elif args.dataset=="sport8":
    dset_cls = dset.ImageFolder
    val_path = '%s/Sport8/test' %args.data
    test_data = dset_cls(root=val_path, transform=test_transform)
  elif args.dataset=="mit67":
    dset_cls = dset.ImageFolder
    val_path = '%s/MIT67/test' %args.data
    test_data = dset_cls(root=val_path, transform=test_transform)
  elif args.dataset == "flowers102":
    dset_cls = dset.ImageFolder
    val_path = '%s/flowers102/test' % args.tmp_data_dir
    test_data = dset_cls(root=val_path, transform=test_transform)
  test_queue = torch.utils.data.DataLoader(
      test_data, batch_size=args.batch_size, shuffle=False, pin_memory=False, num_workers=2)

  model.drop_path_prob = 0.0
  test_acc, test_obj = infer(test_queue, model, criterion)
  logging.info('Test_acc %f', test_acc)
Example #7
Source File: test_imagenet.py    From NAS-Benchmark with GNU General Public License v3.0 4 votes vote down vote up 
def main():
  if not torch.cuda.is_available():
    logging.info('no gpu device available')
    sys.exit(1)

  np.random.seed(args.seed)
  torch.cuda.set_device(args.gpu)
  cudnn.benchmark = True
  torch.manual_seed(args.seed)
  cudnn.enabled=True
  torch.cuda.manual_seed(args.seed)
  logging.info('gpu device = %d' % args.gpu)
  logging.info("args = %s", args)

  genotype = eval("genotypes.%s" % args.arch)
  model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype)
  model = model.cuda()
  model.load_state_dict(torch.load(args.model_path)['state_dict'])

  logging.info("param size = %fMB", utils.count_parameters_in_MB(model))

  criterion = nn.CrossEntropyLoss()
  criterion = criterion.cuda()

  validdir = os.path.join(args.data, 'val')
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  valid_data = dset.ImageFolder(
    validdir,
    transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      normalize,
    ]))

  valid_queue = torch.utils.data.DataLoader(
    valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)

  model.drop_path_prob = args.drop_path_prob
  valid_acc_top1, valid_acc_top5, valid_obj = infer(valid_queue, model, criterion)
  logging.info('valid_acc_top1 %f', valid_acc_top1)
  logging.info('valid_acc_top5 %f', valid_acc_top5)