def config(model_def, pretrained_model, gpu, image_dim, image_mean_file):
global IMAGE_DIM, CROPPED_DIM, IMAGE_CENTER, IMAGE_MEAN, CROPPED_IMAGE_MEAN
global NET, BATCH_SIZE, NUM_OUTPUT
# Initialize network by loading model definition and weights.
t = time.time()
print("Loading Caffe model.")
NET = caffe.CaffeNet(model_def, pretrained_model)
NET.set_phase_test()
if gpu:
NET.set_mode_gpu()
print("Caffe model loaded in {:.3f} s".format(time.time() - t))
# Configure for input/output data
IMAGE_DIM = image_dim
CROPPED_DIM = NET.blobs()[0].width
IMAGE_CENTER = int((IMAGE_DIM - CROPPED_DIM) / 2)
# Load the data set mean file
IMAGE_MEAN = np.load(image_mean_file)
CROPPED_IMAGE_MEAN = IMAGE_MEAN[IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM,
IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM,
:]
BATCH_SIZE = NET.blobs()[0].num # network batch size
NUM_OUTPUT = NET.blobs()[-1].channels # number of output classes
def __init__(self, model_def_file, pretrained_model, center_only = False,
num_output=1000):
if center_only:
num = 1
else:
num = 10
self.caffenet = caffe.CaffeNet(model_def_file, pretrained_model)
self._output_blobs = [np.empty((num, num_output, 1, 1), dtype=np.float32)]
self._center_only = center_only
FLAGS(sys.argv)
## Load list of image filenames and assemble into batches.
t = time.time()
print('Assembling batches...')
with open(FLAGS.images_file) as f:
image_fnames = [_.strip() for _ in f.readlines()]
image_batches = assemble_batches(image_fnames, FLAGS.crop_mode,
FLAGS.batch_size)
print('{} batches assembled in {:.3f} s'.format(len(image_batches),
time.time() - t))
# Initialize network by loading model definition and weights.
t = time.time()
print("Loading Caffe model.")
caffenet = caffe.CaffeNet(FLAGS.model_def, FLAGS.pretrained_model)
caffenet.set_phase_test()
if FLAGS.gpu:
caffenet.set_mode_gpu()
print("Caffe model loaded in {:.3f} s".format(time.time() - t))
# Process the batches.
t = time.time()
print 'Processing {} files in {} batches'.format(len(image_fnames),
len(image_batches))
dfs_with_feats = []
for i in range(len(image_batches)):
if i % 10 == 0:
print('Batch {}/{}, elapsed time: {:.3f} s'.format(
i, len(image_batches),
time.time() - t))