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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
import re, string
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from collections import Counter
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from tinygrad import Tensor
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def levenshtein(a, b):
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n, m = len(a), len(b)
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if n > m:
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a, b, n, m = b, a, m, n
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current = list(range(n + 1))
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for i in range(1, m + 1):
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previous, current = current, [i] + [0] * n
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for j in range(1, n + 1):
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add, delete = previous[j] + 1, current[j - 1] + 1
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change = previous[j - 1]
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if a[j - 1] != b[i - 1]:
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change = change + 1
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current[j] = min(add, delete, change)
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return current[n]
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def word_error_rate(x, y):
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scores = words = 0
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for h, r in zip(x, y):
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h_list = h.split()
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r_list = r.split()
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words += len(r_list)
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scores += levenshtein(h_list, r_list)
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return float(scores) / words, float(scores), words
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def one_hot(x):
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return x.one_hot(3).squeeze(1).permute(0, 4, 1, 2, 3)
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def dice_score(prediction, target, channel_axis=1, smooth_nr=1e-6, smooth_dr=1e-6, argmax=True, to_one_hot_x=True):
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channel_axis, reduce_axis = 1, tuple(range(2, len(prediction.shape)))
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if argmax: prediction = prediction.argmax(axis=channel_axis)
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else: prediction = prediction.softmax(axis=channel_axis)
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if to_one_hot_x: prediction = one_hot(prediction)
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target = one_hot(target)
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prediction, target = prediction[:, 1:], target[:, 1:]
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assert prediction.shape == target.shape, f"prediction ({prediction.shape}) and target ({target.shape}) shapes do not match"
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intersection = (prediction * target).sum(axis=reduce_axis)
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target_sum = target.sum(axis=reduce_axis)
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prediction_sum = prediction.sum(axis=reduce_axis)
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result = (2.0 * intersection + smooth_nr) / (target_sum + prediction_sum + smooth_dr)
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return result
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def normalize_string(s):
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s = "".join(c for c in s.lower() if c not in string.punctuation)
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s = re.sub(r'\b(a|an|the)\b', ' ', s)
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return " ".join(s.split())
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def f1_score(x, y):
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xt = normalize_string(x).split()
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yt = normalize_string(y).split()
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ct = Counter(xt) & Counter(yt)
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if (ns := sum(ct.values())) == 0:
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return 0.0
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p = ns / len(xt)
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r = ns / len(yt)
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return 2 * p * r / (p + r)
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def log_perplexity(logit:Tensor, target:Tensor, ignore_index:int|None=None):
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# logit has shape (n_samples, seq_len, vocab_size), target has shape (n_samples, seq_len)
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assert logit.ndim == 3, logit.ndim
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assert target.ndim == 2, target.ndim
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assert logit.shape[:2] == target.shape, f"{logit.shape[:2]=}, {target.shape=}"
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log_prob = logit.log_softmax(axis=-1)
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return log_prob.transpose(1, 2).nll_loss(target, ignore_index=ignore_index) |