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| author | Jules Laplace <julescarbon@gmail.com> | 2020-02-22 17:37:24 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-02-22 17:37:24 +0100 |
| commit | 0536d6034757083b7e59011b8105ef80e123e6fd (patch) | |
| tree | 5af8db7fac649e5af63d25d2ef24c8a103c4757a | |
| parent | ecc9df4237e09828cf387bf8dd1fda8617c4898d (diff) | |
incorporate layers into live script
| -rw-r--r-- | cli/app/search/live.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/cli/app/search/live.py b/cli/app/search/live.py index b5d547a..eb89202 100644 --- a/cli/app/search/live.py +++ b/cli/app/search/live.py @@ -316,9 +316,9 @@ class Interpolator: ] op_input_index = 1 - split_shape = [1, 128] + # split_shape = [1, 128] for op_name, layer_id in zip(layer_op_names, range(7)): - tensor_name = 'module_apply_{}/Generator_2/split:{}'.format(gen_signature, layer_id + 1) + tensor_name = 'module_apply_{}/linear_1/MatMul:0'.format(gen_signature) layer_tensor = tf.get_default_graph().get_tensor_by_name(tensor_name) op_name = 'module_apply_{}/{}'.format(gen_signature, op_name) @@ -326,8 +326,8 @@ class Interpolator: ## unlike the encoding, here we update the layer op directly, so we don't need a temporary constant to swap # raw_layer_placeholder = tf.constant(np.zeros(layer_tensor.shape, dtype=np.float32)) - layer_stored = LerpParam('layer_stored_{}'.format(layer_id), shape=split_shape, datatype="noise") - layer_stored_mix = LerpParam('layer_stored_mix_{}'.format(layer_id), a_in=layer_tensor, b_in=layer_stored.output, shape=split_shape, datatype="noise") + layer_stored = LerpParam('layer_stored_{}'.format(layer_id), shape=layer_tensor.shape, datatype="noise") + layer_stored_mix = LerpParam('layer_stored_mix_{}'.format(layer_id), a_in=layer_tensor, b_in=layer_stored.output, shape=layer_tensor.shape, datatype="noise") layer_op._update_input(op_input_index, layer_stored_mix.output) @@ -404,7 +404,7 @@ class Interpolator: layer_labels_stored = [] new_layer_labels = [] for i in range(layer_label_count): - layer_labels_stored[i] = self.lerp_params['layer_stored_mix_{}'.format(i)] + layer_labels_stored[i] = self.lerp_params['layer_stored_{}'.format(i)] new_layer_labels[i] = np.expand_dims(data['layer_labels'][i], axis=0) # if we're showing an encoding already, lerp to the next one |