9c7833faf9
* Vegetarian Filet o Fish model * fix.. atc.. * test cluster_speed_limit * fix.. cluster_speed_limit.. 2 * fix.. clusterspeedlimit3 * cruise speed to roadlimit speed * fix.. * fix.. eng * deltaUp/Down for lanechange * fix.. atc desire... * fix.. * ff * ff * fix.. * fix.. eng * fix engsound * Update desire_helper.py * fix.. connect... * fix curve_min speed * Revert "fix curve_min speed" This reverts commit fcc9c2eb14eb3504abef3e420db93e8882e56f37. * Reapply "fix curve_min speed" This reverts commit 2d2bba476c58a7b4e13bac3c3ad0e4694c95515d. * fix.. auto speed up.. roadlimit * fix.. atc auto lanechange... * Update desire_helper.py * Update cruise.py * debug atc... * fix.. waze alert offset.. * fix.. * test atc.. * fix.. * fix.. atc * atc test.. * fix.. atc * fix.. atc2 * fix.. atc3 * KerryGold Model. latsmooth_sec = 0.0 * lat smooth seconds 0.13 * fix comment * fix.. auto cruise, and speed unit * change lanemode switching. * erase mazda lkas button.
65 lines
3.1 KiB
Python
65 lines
3.1 KiB
Python
from tinygrad import Tensor
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from tinygrad.tensor import _to_np_dtype
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from tinygrad.frontend.onnx import OnnxRunner, onnx_load
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from extra.onnx import OnnxValue
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import numpy as np
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import onnxruntime as ort
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def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
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def _get_shape(onnx_shape: tuple[str|int]):
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shape = []
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for onnx_dim in onnx_shape:
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match onnx_dim:
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case int(): shape.append(onnx_dim)
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case "width" | "height":
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size = config.get("size", {})
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shape.append(size) if isinstance(size, int) else shape.append(size.get(onnx_dim, 224))
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case "sequence" | "sequence_length" | "decoder_sequence_length": shape.append(64)
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case "encoder_sequence_length": shape.append(config.get("nb_max_frames", 64))
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case "past_decoder_sequence_length" | "encoder_sequence_length_out": shape.append(64)
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case "encoder_sequence_length / 2": shape.append(32)
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case "batch_size": shape.append(1)
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case "num_channels": shape.append(config.get("in_channels", 3))
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case "num_channels_latent": shape.append(config.get("latent_channels", 4))
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case "height_latent" | "width_latent": shape.append(config.get("sample_size", 1024) // 8)
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case "feature_size": shape.append(config.get("num_mel_bins", 128))
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case _: shape.append(1)
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return shape
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def _get_value(name, shape, dtype):
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match name:
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case "input_ids":
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vocab_size = config.get("text_config", {}).get("vocab_size") or config.get("vocab_size", 32)
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val = np.random.randint(0, vocab_size-1, shape)
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case "attention_mask": val = np.random.randint(0, 2, size=shape)
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case "token_type_ids": val = np.random.randint(0, config.get("type_vocab_size", 2), shape)
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case "image_tensor": val = np.random.randint(0, 256, shape)
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case "task_id": return Tensor(0, dtype=dtype)
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case _: val = np.random.uniform(size=shape) * 8
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return Tensor(val.astype(_to_np_dtype(dtype))).realize()
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ret: dict[str, Tensor] = {}
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for name, spec in graph_inputs.items():
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assert not spec.is_optional and not spec.is_sequence, "only allow tensor input for now"
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shape = _get_shape(spec.shape)
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value = _get_value(name, shape, spec.dtype)
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ret.update({name:value})
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return ret
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def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
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run_onnx = OnnxRunner(onnx_load(onnx_file))
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ort_options = ort.SessionOptions()
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ort_options.log_severity_level = 3
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ort_sess = ort.InferenceSession(onnx_file, ort_options, ["CPUExecutionProvider"])
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np_inputs = {k:v.numpy() if isinstance(v, Tensor) else v for k,v in inputs.items()}
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out_names = list(run_onnx.graph_outputs)
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out_values = ort_sess.run(out_names, np_inputs)
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ort_out = dict(zip(out_names, out_values))
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tinygrad_out = run_onnx(inputs)
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assert tinygrad_out.keys() == ort_out.keys()
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for k in tinygrad_out.keys():
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tiny_v, onnx_v = tinygrad_out[k], ort_out[k]
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if tiny_v is None: assert onnx_v is None, f"{k}: {tiny_v=}, {onnx_v=}"
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else: np.testing.assert_allclose(tiny_v.numpy(), onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}") |