From e372046ff14d140e91e1d371532dcc5b08b63733 Mon Sep 17 00:00:00 2001 From: discountchubbs Date: Fri, 21 Aug 2026 22:02:02 -0700 Subject: [PATCH] dont reshape non 4 dim arrays --- openpilot/sunnypilot/modeld_v2/compile_modeld.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/openpilot/sunnypilot/modeld_v2/compile_modeld.py b/openpilot/sunnypilot/modeld_v2/compile_modeld.py index 5d8c837bcc..59056549f4 100755 --- a/openpilot/sunnypilot/modeld_v2/compile_modeld.py +++ b/openpilot/sunnypilot/modeld_v2/compile_modeld.py @@ -186,14 +186,14 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice, warped_dev = warped.to(Device.DEFAULT) Tensor.realize(packed_npy_inputs_dev, warped_dev) - img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn).realize() - big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn).realize() + img = shift_and_sample(img_q, warped_dev[0:1], sample_skip_fn) + big_img = shift_and_sample(big_img_q, warped_dev[1:2], sample_skip_fn) unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)] unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True)) desire_dev = unpacked_dict['desire'] - desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize() + desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn) inputs = {desire_key: desire_buf} for key, tensor_val in unpacked_dict.items(): @@ -202,22 +202,22 @@ def make_run_policy(vision_runner, policy_runners: list, features_slice: slice, if 'prev_feat' in unpacked_dict: prev_feat_dev = unpacked_dict['prev_feat'] - feat_buf = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize() - inputs['features_buffer'] = feat_buf.reshape(input_shapes['features_buffer']) + feat_buf = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn) + inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb) if vision_runner: vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize() if 'features_buffer' not in inputs: new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0) feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize() - inputs['features_buffer'] = feat_buf.reshape(input_shapes['features_buffer']) + inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb) policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners] return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0]) inputs.update({road_key: img, wide_key: big_img}) if 'features_buffer' not in inputs: feat_buf = sample_skip_fn(feat_q) - inputs['features_buffer'] = feat_buf.reshape(input_shapes['features_buffer']) + inputs['features_buffer'] = feat_buf if len(fb := input_shapes['features_buffer']) <= 3 else feat_buf.reshape(fb) policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize() if 'features_buffer' not in inputs and features_slice is not None: