Python misc.resnet() Examples

The following are 6 code examples of misc.resnet(). 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 misc , or try the search function .
Example #1
Source File: prepro_feats.py    From VSUA-Captioning with MIT License 5 votes vote down vote up 
def main(params):
  net = getattr(resnet, params['model'])()
  net.load_state_dict(torch.load(os.path.join(params['model_root'],params['model']+'.pth')))
  my_resnet = myResnet(net)
  my_resnet.cuda()
  my_resnet.eval()

  imgs = json.load(open(params['input_json'], 'r'))
  imgs = imgs['images']
  N = len(imgs)

  seed(123) # make reproducible

  dir_fc = params['output_dir']+'_fc'
  dir_att = params['output_dir']+'_att'
  if not os.path.isdir(dir_fc):
    os.mkdir(dir_fc)
  if not os.path.isdir(dir_att):
    os.mkdir(dir_att)

  for i,img in enumerate(imgs):
    # load the image
    I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
    # handle grayscale input images
    if len(I.shape) == 2:
      I = I[:,:,np.newaxis]
      I = np.concatenate((I,I,I), axis=2)

    I = I.astype('float32')/255.0
    I = torch.from_numpy(I.transpose([2,0,1])).cuda()
    I = preprocess(I)
    with torch.no_grad():
      tmp_fc, tmp_att = my_resnet(I, params['att_size'])
    # write to pkl
    np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy())
    np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy())

    if i % 1000 == 0:
      print('processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N))
  print('wrote ', params['output_dir'])
Example #2
Source File: prepro_feats.py    From AAT with MIT License 5 votes vote down vote up 
def main(params):
  net = getattr(resnet, params['model'])()
  net.load_state_dict(torch.load(os.path.join(params['model_root'],params['model']+'.pth')))
  my_resnet = myResnet(net)
  my_resnet.cuda()
  my_resnet.eval()

  imgs = json.load(open(params['input_json'], 'r'))
  imgs = imgs['images']
  N = len(imgs)

  seed(123) # make reproducible

  dir_fc = params['output_dir']+'_fc'
  dir_att = params['output_dir']+'_att'
  if not os.path.isdir(dir_fc):
    os.mkdir(dir_fc)
  if not os.path.isdir(dir_att):
    os.mkdir(dir_att)

  for i,img in enumerate(imgs):
    # load the image
    I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
    # handle grayscale input images
    if len(I.shape) == 2:
      I = I[:,:,np.newaxis]
      I = np.concatenate((I,I,I), axis=2)

    I = I.astype('float32')/255.0
    I = torch.from_numpy(I.transpose([2,0,1])).cuda()
    I = preprocess(I)
    with torch.no_grad():
      tmp_fc, tmp_att = my_resnet(I, params['att_size'])
    # write to pkl
    np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy())
    np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy())

    if i % 1000 == 0:
      print('processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N))
  print('wrote ', params['output_dir'])
Example #3
Source File: prepro_feats.py    From AoANet with MIT License 5 votes vote down vote up 
def main(params):
  net = getattr(resnet, params['model'])()
  net.load_state_dict(torch.load(os.path.join(params['model_root'],params['model']+'.pth')))
  my_resnet = myResnet(net)
  my_resnet.cuda()
  my_resnet.eval()

  imgs = json.load(open(params['input_json'], 'r'))
  imgs = imgs['images']
  N = len(imgs)

  seed(123) # make reproducible

  dir_fc = params['output_dir']+'_fc'
  dir_att = params['output_dir']+'_att'
  if not os.path.isdir(dir_fc):
    os.mkdir(dir_fc)
  if not os.path.isdir(dir_att):
    os.mkdir(dir_att)

  for i,img in enumerate(imgs):
    # load the image
    I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
    # handle grayscale input images
    if len(I.shape) == 2:
      I = I[:,:,np.newaxis]
      I = np.concatenate((I,I,I), axis=2)

    I = I.astype('float32')/255.0
    I = torch.from_numpy(I.transpose([2,0,1])).cuda()
    I = preprocess(I)
    with torch.no_grad():
      tmp_fc, tmp_att = my_resnet(I, params['att_size'])
    # write to pkl
    np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy())
    np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy())

    if i % 1000 == 0:
      print('processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N))
  print('wrote ', params['output_dir'])
Example #4
Source File: prepro_feats.py    From GoogleConceptualCaptioning with MIT License 5 votes vote down vote up 
def main(params):
  net = getattr(resnet, params['model'])()
  net.load_state_dict(torch.load(os.path.join(params['model_root'],params['model']+'.pth')))
  my_resnet = myResnet(net)
  my_resnet.cuda()
  my_resnet.eval()

  imgs = json.load(open(params['input_json'], 'r'))
  imgs = imgs['images']
  N = len(imgs)

  seed(123) # make reproducible

  dir_fc = params['output_dir']+'_fc'
  dir_att = params['output_dir']+'_att'
  if not os.path.isdir(dir_fc):
    os.mkdir(dir_fc)
  if not os.path.isdir(dir_att):
    os.mkdir(dir_att)

  for i,img in enumerate(imgs):
    # load the image
    I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
    # handle grayscale input images
    if len(I.shape) == 2:
      I = I[:,:,np.newaxis]
      I = np.concatenate((I,I,I), axis=2)

    I = I.astype('float32')/255.0
    I = torch.from_numpy(I.transpose([2,0,1])).cuda()
    I = preprocess(I)
    with torch.no_grad():
      tmp_fc, tmp_att = my_resnet(I, params['att_size'])
    # write to pkl
    np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy())
    np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy())

    if i % 1000 == 0:
      print('processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N))
  print('wrote ', params['output_dir'])
Example #5
Source File: prepro_feats.py    From PriorImageCaption with MIT License 5 votes vote down vote up 
def main(params):
  net = getattr(resnet, params['model'])()
  net.load_state_dict(torch.load(os.path.join(params['model_root'],params['model']+'.pth')))
  my_resnet = myResnet(net)
  my_resnet.cuda()
  my_resnet.eval()

  imgs = json.load(open(params['input_json'], 'r'))
  imgs = imgs['images']
  N = len(imgs)

  seed(123) # make reproducible

  dir_fc = params['output_dir']+'_fc'
  dir_att = params['output_dir']+'_att'
  if not os.path.isdir(dir_fc):
    os.mkdir(dir_fc)
  if not os.path.isdir(dir_att):
    os.mkdir(dir_att)

  for i,img in enumerate(imgs):
    # load the image
    I = skimage.io.imread(os.path.join(params['images_root'], img['filepath'], img['filename']))
    # handle grayscale input images
    if len(I.shape) == 2:
      I = I[:,:,np.newaxis]
      I = np.concatenate((I,I,I), axis=2)

    I = I.astype('float32')/255.0
    I = torch.from_numpy(I.transpose([2,0,1])).cuda()
    I = Variable(preprocess(I), volatile=True)
    tmp_fc, tmp_att = my_resnet(I, params['att_size'])
    # write to pkl
    np.save(os.path.join(dir_fc, str(img['cocoid'])), tmp_fc.data.cpu().float().numpy())
    np.savez_compressed(os.path.join(dir_att, str(img['cocoid'])), feat=tmp_att.data.cpu().float().numpy())

    if i % 1000 == 0:
      print('processing %d/%d (%.2f%% done)' % (i, N, i*100.0/N))
  print('wrote ', params['output_dir'])
Example #6
Source File: extract_features.py    From AREL with MIT License 4 votes vote down vote up 
def main(params):
    assert params['feature_type'] in ['fc', 'conv', 'both']
    compute_fc = params['feature_type'] == 'fc' or params['feature_type'] == 'both'
    compute_conv = params['feature_type'] == 'conv' or params['feature_type'] == 'both'

    net = getattr(resnet, params['model'])()
    net.load_state_dict(torch.load(os.path.join(params['model_root'], params['model'] + '.pth')))
    my_resnet = myResnet(net)
    my_resnet.cuda()
    my_resnet.eval()

    if compute_fc:
        dir_fc = os.path.join(params['out_dir'], 'fc')
        if not os.path.exists(dir_fc):
            os.makedirs(dir_fc)
    if compute_conv:
        dir_conv = os.path.join(params['out_dir'], 'conv')
        if not os.path.exists(dir_conv):
            os.makedirs(dir_conv)

    for split in ['train', 'val', 'test']:
        count = 0
        if compute_fc and not os.path.exists(os.path.join(dir_fc, split)):
            os.makedirs(os.path.join(dir_fc, split))
        if compute_conv and not os.path.exists(os.path.join(dir_conv, split)):
            os.makedirs(os.path.join(dir_conv, split))

        files = glob.glob("{}/{}/*.jpg".format(params['img_dir'], split))
        start = time.time()
        for file in files:
            count += 1
            basename = os.path.basename(file)
            img_id = splitext(basename)[0]
            try:
                I = imread(file)
            except:
                I = np.zeros((224, 224, 3), 'float32')

            # handle grayscale input frames
            if len(I.shape) == 2:
                I = I[:, :, np.newaxis]
                I = np.concatenate((I, I, I), axis=2)

            I = I.astype('float32') / 255.0
            I = torch.from_numpy(I.transpose([2, 0, 1])).cuda()
            I = Variable(preprocess(I), volatile=True)
            tmp_fc, tmp_conv = my_resnet(I, params['att_size'])

            # write to pkl
            if compute_fc:
                np.save(os.path.join(dir_fc, split, img_id), tmp_fc.data.cpu().float().numpy())
            if compute_conv:
                np.savez_compressed(os.path.join(dir_conv, split, img_id), tmp_conv.data.cpu().float().numpy())

            if count % 100 == 0:
                print('processing {} set -- {}/{} {:.3}%, time used: {}s'.format(split, count, len(files),
                                                                                 count * 100.0 / len(files),
                                                                                 time.time() - start))
                start = time.time()