fixed encoder

dnnlib/tflib/network.py

@@ -389,6 +389,7 @@ class Network:
             minibatch_size: int = None,
             num_gpus: int = 1,
             assume_frozen: bool = False,
+            custom_inputs: Any = None,
             **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]:
         """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
 
@@ -404,6 +405,7 @@ class Network:
             minibatch_size:     Maximum minibatch size to use, None = disable batching.
             num_gpus:           Number of GPUs to use.
             assume_frozen:      Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls.
+            custom_inputs:      Allow to use another tensor as input instead of default placeholders.
             dynamic_kwargs:     Additional keyword arguments to be passed into the network build function.
         """
         assert len(in_arrays) == self.num_inputs
@@ -428,6 +430,11 @@ class Network:
         # Build graph.
         if key not in self._run_cache:
            with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None):
+                if custom_inputs is not None:
+                    with tf.device("/gpu:0"):
+                        in_expr = [input_builder(name) for input_builder, name in zip(custom_inputs, self.input_names)]
+                        in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr]))
+                else:
                     with tf.device("/cpu:0"):
                         in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
                         in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr]))

encode_images.py

@@ -10,6 +10,7 @@ import dnnlib.tflib as tflib
 import pretrained_networks
 from encoder.generator_model import Generator
 from encoder.perceptual_model import PerceptualModel
+from encoder.perceptual_model import load_images
 from keras.models import load_model
 from keras.applications.resnet50 import preprocess_input