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https://github.com/modelscope/modelscope.git
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ca1321f53f
1. Support trainer prediction 2. Fix bug in text classification metric 3. Move load checkpoint out of checkpointhook 4. Fix bug in train progressing (inner_iter variable not correct) Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11560269
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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from typing import Dict
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import numpy as np
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from sklearn.metrics import accuracy_score, f1_score
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from modelscope.metainfo import Metrics
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from modelscope.outputs import OutputKeys
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from modelscope.utils.registry import default_group
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from modelscope.utils.tensor_utils import (torch_nested_detach,
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torch_nested_numpify)
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from .base import Metric
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from .builder import METRICS, MetricKeys
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@METRICS.register_module(
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group_key=default_group, module_name=Metrics.seq_cls_metric)
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class SequenceClassificationMetric(Metric):
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"""The metric computation class for sequence classification tasks.
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This metric class calculates accuracy/F1 of all the input batches.
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Args:
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label_name: The key of label column in the 'inputs' arg.
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logit_name: The key of logits column in the 'inputs' arg.
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"""
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def __init__(self,
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label_name=OutputKeys.LABELS,
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logit_name=OutputKeys.LOGITS,
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*args,
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**kwargs):
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super().__init__(*args, **kwargs)
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self.preds = []
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self.labels = []
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self.label_name = label_name
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self.logit_name = logit_name
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def add(self, outputs: Dict, inputs: Dict):
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ground_truths = inputs[self.label_name]
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eval_results = outputs[self.logit_name]
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self.preds.append(
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torch_nested_numpify(torch_nested_detach(eval_results)))
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self.labels.append(
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torch_nested_numpify(torch_nested_detach(ground_truths)))
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def evaluate(self):
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preds = np.concatenate(self.preds, axis=0)
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labels = np.concatenate(self.labels, axis=0)
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assert len(preds.shape) == 2, 'Only support predictions with shape: (batch_size, num_labels),' \
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'multi-label classification is not supported in this metric class.'
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preds_max = np.argmax(preds, axis=1)
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if preds.shape[1] > 2:
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metrics = {
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MetricKeys.ACCURACY: accuracy_score(labels, preds_max),
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MetricKeys.Micro_F1:
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f1_score(labels, preds_max, average='micro'),
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MetricKeys.Macro_F1:
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f1_score(labels, preds_max, average='macro'),
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}
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metrics[MetricKeys.F1] = metrics[MetricKeys.Micro_F1]
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return metrics
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else:
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metrics = {
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MetricKeys.ACCURACY:
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accuracy_score(labels, preds_max),
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MetricKeys.Binary_F1:
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f1_score(labels, preds_max, average='binary'),
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}
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metrics[MetricKeys.F1] = metrics[MetricKeys.Binary_F1]
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return metrics
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def merge(self, other: 'SequenceClassificationMetric'):
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self.preds.extend(other.preds)
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self.labels.extend(other.labels)
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def __getstate__(self):
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return self.preds, self.labels, self.label_name, self.logit_name
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def __setstate__(self, state):
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self.__init__()
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self.preds, self.labels, self.label_name, self.logit_name = state
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