def get_activations()

in evaluations/th_evaluator.py [0:0]

• 38 lines of code
• 7 McCabe index (conditional complexity)

    def get_activations(self, data, num_samples, global_batch_size, pr_samples=50000):
        if self.is_root:
            preds = []
            spatial_preds = []
            clip_preds = []
            pr_images = []

        for _ in tqdm(range(0, int(np.ceil(num_samples / global_batch_size)))):
            batch, cond, _ = next(data)
            batch, cond = batch.to(dist_util.dev()), {
                k: v.to(dist_util.dev()) for k, v in cond.items()
            }
            pred, spatial_pred, clip_pred, _, np_batch = self.get_preds(batch)
            pred, spatial_pred, clip_pred = (
                all_gather(pred).cpu().numpy(),
                all_gather(spatial_pred).cpu().numpy(),
                all_gather(clip_pred).cpu().numpy(),
            )
            if self.is_root:
                preds.append(pred)
                spatial_preds.append(spatial_pred)
                clip_preds.append(clip_pred)
                if len(pr_images) * np_batch.shape[0] < pr_samples:
                    pr_images.append(np_batch)

        if self.is_root:
            preds, spatial_preds, clip_preds, pr_images = (
                np.concatenate(preds, axis=0),
                np.concatenate(spatial_preds, axis=0),
                np.concatenate(clip_preds, axis=0),
                np.concatenate(pr_images, axis=0),
            )
            # assert len(pr_images) >= pr_samples
            return (
                preds[:num_samples],
                spatial_preds[:num_samples],
                clip_preds[:num_samples],
                pr_images[:pr_samples],
            )
        else:
            return [], [], [], []