def main(_): # Caffe scope... caffemodel = caffe_scope.CaffeScope() caffemodel.load(FLAGS.caffemodel_path) tf.logging.set_verbosity(tf.logging.INFO) with tf.Graph().as_default(): global_step = slim.create_global_step() num_classes = int(FLAGS.num_classes) # Select the network. ssd_class = nets_factory.get_network(FLAGS.model_name) ssd_params = ssd_class.default_params._replace(num_classes=num_classes) ssd_net = ssd_class(ssd_params) ssd_shape = ssd_net.params.img_shape # Image placeholder and model. shape = (1, ssd_shape[0], ssd_shape[1], 3) img_input = tf.placeholder(shape=shape, dtype=tf.float32) # Create model. with slim.arg_scope(ssd_net.arg_scope_caffe(caffemodel)): ssd_net.net(img_input, is_training=False) init_op = tf.global_variables_initializer() with tf.Session() as session: # Run the init operation. session.run(init_op) # Save model in checkpoint. saver = tf.train.Saver() ckpt_path = FLAGS.caffemodel_path.replace('.caffemodel', '.ckpt') saver.save(session, ckpt_path, write_meta_graph=False)
def main(_): # Caffe scope... caffemodel = caffe_scope.CaffeScope() caffemodel.load(FLAGS.caffemodel_path) tf.logging.set_verbosity(tf.logging.INFO) with tf.Graph().as_default(): global_step = slim.create_global_step() ssd_net = ssd_vgg_640.SSDNet() ssd_shape = getter.EVAL_SIZE # Image placeholder and model. shape = (1, ssd_shape[0], ssd_shape[1], 3) img_input = tf.placeholder(shape=shape, dtype=tf.float32) # Create model. with slim.arg_scope(ssd_net.arg_scope_caffe(caffemodel)): ssd_net.net(img_input, is_training=False) init_op = tf.global_variables_initializer() with tf.Session() as session: # Run the init operation. session.run(init_op) # Save model in checkpoint. saver = tf.train.Saver() ckpt_path = FLAGS.caffemodel_path.replace('.caffemodel', '.ckpt') saver.save(session, ckpt_path, write_meta_graph=False)
layer = layers[59] layer = layers[1] print(layer.type) a = np.array(layer.blobs[0].data) s = layer.blobs[0].shape print(s, 38 * 38) # print(a) # In[ ]: from nets import caffe_scope # In[ ]: csc = caffe_scope.CaffeScope() # In[ ]: d = {} d[csc.conv_biases_init] = 0 d[csc.conv_biases_init] += 1 # In[ ]: # In[ ]: min_dim = 300 mbox_source_layers = [ 'conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2' ]