83af0d9266d3650c0260faf8c6d9a4b97389d75b,deepplantphenomics/classification_model.py,ClassificationModel,forward_pass_with_file_inputs,#ClassificationModel#Any#,261

Before Change

        with self._graph.as_default():
            total_outputs = np.empty([1, self._last_layer().output_size])

            num_batches = len(x) // self._batch_size
            remainder = len(x) % self._batch_size

            if remainder != 0:
                num_batches += 1
                remainder = self._batch_size - remainder

            // self.load_images_from_list(x) no longer calls following 2 lines so we needed to force them here
            images = x
            self._parse_images(images)

            x_test = tf.train.batch([self._all_images], batch_size=self._batch_size, num_threads=self._num_threads)
            x_test = tf.reshape(x_test, shape=[-1, self._image_height, self._image_width, self._image_depth])

            if self._load_from_saved:
                self.load_state()
            self._initialize_queue_runners()
            // Run model on them
            x_pred = self.forward_pass(x_test, deterministic=True)

            for i in range(int(num_batches)):
                xx = self._session.run(x_pred)
                for img in np.array_split(xx, self._batch_size):
                    total_outputs = np.append(total_outputs, img, axis=0)

            // delete weird first row
            total_outputs = np.delete(total_outputs, 0, 0)

            // delete any outputs which are overruns from the last batch
            if remainder != 0:
                for i in range(remainder):
                    total_outputs = np.delete(total_outputs, -1, 0)

        return total_outputs

    def forward_pass_with_interpreted_outputs(self, x):
        // Perform forward pass of the network to get raw outputs and apply a softmax

After Change

            self._parse_images(images)
            // x_test = tf.train.batch([self._all_images], batch_size=self._batch_size, num_threads=self._num_threads)
            // x_test = tf.reshape(x_test, shape=[-1, self._image_height, self._image_width, self._image_depth])
            im_data = self._all_images.batch(self._batch_size).prefetch(1)
            x_test = im_data.make_one_shot_iterator().get_next()

            if self._load_from_saved:
                self.load_state()
            // self._initialize_queue_runners()

Instances