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105 lines
4.6 KiB
105 lines
4.6 KiB
import numpy as np
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from openpilot.selfdrive.modeld.constants import ModelConstants
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def safe_exp(x, out=None):
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# -11 is around 10**14, more causes float16 overflow
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return np.exp(np.clip(x, -np.inf, 11), out=out)
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def sigmoid(x):
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return 1. / (1. + safe_exp(-x))
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def softmax(x, axis=-1):
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x -= np.max(x, axis=axis, keepdims=True)
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if x.dtype == np.float32 or x.dtype == np.float64:
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safe_exp(x, out=x)
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else:
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x = safe_exp(x)
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x /= np.sum(x, axis=axis, keepdims=True)
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return x
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class Parser:
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def __init__(self, ignore_missing=False):
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self.ignore_missing = ignore_missing
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def check_missing(self, outs, name):
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if name not in outs and not self.ignore_missing:
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raise ValueError(f"Missing output {name}")
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return name not in outs
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def parse_categorical_crossentropy(self, name, outs, out_shape=None):
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if self.check_missing(outs, name):
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return
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raw = outs[name]
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if out_shape is not None:
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raw = raw.reshape((raw.shape[0],) + out_shape)
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outs[name] = softmax(raw, axis=-1)
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def parse_binary_crossentropy(self, name, outs):
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if self.check_missing(outs, name):
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return
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raw = outs[name]
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outs[name] = sigmoid(raw)
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def parse_mdn(self, name, outs, in_N=0, out_N=1, out_shape=None):
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if self.check_missing(outs, name):
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return
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raw = outs[name]
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raw = raw.reshape((raw.shape[0], max(in_N, 1), -1))
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n_values = (raw.shape[2] - out_N)//2
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pred_mu = raw[:,:,:n_values]
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pred_std = safe_exp(raw[:,:,n_values: 2*n_values])
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if in_N > 1:
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weights = np.zeros((raw.shape[0], in_N, out_N), dtype=raw.dtype)
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for i in range(out_N):
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weights[:,:,i - out_N] = softmax(raw[:,:,i - out_N], axis=-1)
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if out_N == 1:
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for fidx in range(weights.shape[0]):
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idxs = np.argsort(weights[fidx][:,0])[::-1]
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weights[fidx] = weights[fidx][idxs]
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pred_mu[fidx] = pred_mu[fidx][idxs]
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pred_std[fidx] = pred_std[fidx][idxs]
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full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
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outs[name + '_weights'] = weights
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outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
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outs[name + '_stds_hypotheses'] = pred_std.reshape(full_shape)
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pred_mu_final = np.zeros((raw.shape[0], out_N, n_values), dtype=raw.dtype)
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pred_std_final = np.zeros((raw.shape[0], out_N, n_values), dtype=raw.dtype)
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for fidx in range(weights.shape[0]):
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for hidx in range(out_N):
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idxs = np.argsort(weights[fidx,:,hidx])[::-1]
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pred_mu_final[fidx, hidx] = pred_mu[fidx, idxs[0]]
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pred_std_final[fidx, hidx] = pred_std[fidx, idxs[0]]
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else:
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pred_mu_final = pred_mu
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pred_std_final = pred_std
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if out_N > 1:
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final_shape = tuple([raw.shape[0], out_N] + list(out_shape))
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else:
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final_shape = tuple([raw.shape[0],] + list(out_shape))
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outs[name] = pred_mu_final.reshape(final_shape)
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outs[name + '_stds'] = pred_std_final.reshape(final_shape)
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def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
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self.parse_mdn('plan', outs, in_N=ModelConstants.PLAN_MHP_N, out_N=ModelConstants.PLAN_MHP_SELECTION,
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out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_WIDTH))
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self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
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self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
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self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
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self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
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self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
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self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
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out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
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if 'lat_planner_solution' in outs:
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self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N,ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
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if 'desired_curvature' in outs:
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self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
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for k in ['lead_prob', 'lane_lines_prob', 'meta']:
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self.parse_binary_crossentropy(k, outs)
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self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
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self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
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return outs
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