remove not-use model

video_prediction_tools/model_modules/video_prediction/models/non_trainable_model.py

-# SPDX-FileCopyrightText: 2018, alexlee-gk
-#
-# SPDX-License-Identifier: MIT
-
-from tensorflow.python.util import nest
-from model_modules.video_prediction.utils.tf_utils import transpose_batch_time
-
-import tensorflow as tf
-
-from .base_model import BaseVideoPredictionModel
-
-
-class NonTrainableVideoPredictionModel(BaseVideoPredictionModel):
-    pass
-
-
-class GroundTruthVideoPredictionModel(NonTrainableVideoPredictionModel):
-    def build_graph(self, inputs):
-        super(GroundTruthVideoPredictionModel, self).build_graph(inputs)
-
-        self.outputs = OrderedDict()
-        self.outputs['gen_images'] = self.inputs['images'][:, 1:]
-        if 'pix_distribs' in self.inputs:
-            self.outputs['gen_pix_distribs'] = self.inputs['pix_distribs'][:, 1:]
-
-        inputs, outputs = nest.map_structure(transpose_batch_time, (self.inputs, self.outputs))
-        with tf.name_scope("metrics"):
-            metrics = self.metrics_fn(inputs, outputs)
-        with tf.name_scope("eval_outputs_and_metrics"):
-            eval_outputs, eval_metrics = self.eval_outputs_and_metrics_fn(inputs, outputs)
-        self.metrics, self.eval_outputs, self.eval_metrics = nest.map_structure(
-            transpose_batch_time, (metrics, eval_outputs, eval_metrics))
-
-
-class RepeatVideoPredictionModel(NonTrainableVideoPredictionModel):
-    def build_graph(self, inputs):
-        super(RepeatVideoPredictionModel, self).build_graph(inputs)
-
-        self.outputs = OrderedDict()
-        tile_pattern = [1, self.hparams.sequence_length - self.hparams.context_frames, 1, 1, 1]
-        last_context_images = self.inputs['images'][:, self.hparams.context_frames - 1]
-        self.outputs['gen_images'] = tf.concat([
-            self.inputs['images'][:, 1:self.hparams.context_frames - 1],
-            tf.tile(last_context_images[:, None], tile_pattern)], axis=-1)
-        if 'pix_distribs' in self.inputs:
-            last_context_pix_distrib = self.inputs['pix_distribs'][:, self.hparams.context_frames - 1]
-            self.outputs['gen_pix_distribs'] = tf.concat([
-                self.inputs['pix_distribs'][:, 1:self.hparams.context_frames - 1],
-                tf.tile(last_context_pix_distrib[:, None], tile_pattern)], axis=-1)
-
-        inputs, outputs = nest.map_structure(transpose_batch_time, (self.inputs, self.outputs))
-        with tf.name_scope("metrics"):
-            metrics = self.metrics_fn(inputs, outputs)
-        with tf.name_scope("eval_outputs_and_metrics"):
-            eval_outputs, eval_metrics = self.eval_outputs_and_metrics_fn(inputs, outputs)
-        self.metrics, self.eval_outputs, self.eval_metrics = nest.map_structure(
-            transpose_batch_time, (metrics, eval_outputs, eval_metrics))