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https://github.com/hpcaitech/ColossalAI.git
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* Add gradient accumulation, fix lr scheduler * fix FP16 optimizer and adapted torch amp with tensor parallel (#18) * fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes * fixed trainer * Revert "fixed trainer" This reverts commit2e0b0b7699. * improved consistency between trainer, engine and schedule (#23) Co-authored-by: 1SAA <c2h214748@gmail.com> * Split conv2d, class token, positional embedding in 2d, Fix random number in ddp Fix convergence in cifar10, Imagenet1000 * Integrate 1d tensor parallel in Colossal-AI (#39) * fixed 1D and 2D convergence (#38) * optimized 2D operations * fixed 1D ViT convergence problem * Feature/ddp (#49) * remove redundancy func in setup (#19) (#20) * use env to control the language of doc (#24) (#25) * Support TP-compatible Torch AMP and Update trainer API (#27) * Add gradient accumulation, fix lr scheduler * fix FP16 optimizer and adapted torch amp with tensor parallel (#18) * fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes * fixed trainer * Revert "fixed trainer" This reverts commit2e0b0b7699. * improved consistency between trainer, engine and schedule (#23) Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: ver217 <lhx0217@gmail.com> * add an example of ViT-B/16 and remove w_norm clipping in LAMB (#29) * add explanation for ViT example (#35) (#36) * support torch ddp * fix loss accumulation * add log for ddp * change seed * modify timing hook Co-authored-by: Frank Lee <somerlee.9@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> * Feature/pipeline (#40) * remove redundancy func in setup (#19) (#20) * use env to control the language of doc (#24) (#25) * Support TP-compatible Torch AMP and Update trainer API (#27) * Add gradient accumulation, fix lr scheduler * fix FP16 optimizer and adapted torch amp with tensor parallel (#18) * fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes * fixed trainer * Revert "fixed trainer" This reverts commit2e0b0b7699. * improved consistency between trainer, engine and schedule (#23) Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: ver217 <lhx0217@gmail.com> * add an example of ViT-B/16 and remove w_norm clipping in LAMB (#29) * add explanation for ViT example (#35) (#36) * optimize communication of pipeline parallel * fix grad clip for pipeline Co-authored-by: Frank Lee <somerlee.9@gmail.com> Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> * optimized 3d layer to fix slow computation ; tested imagenet performance with 3d; reworked lr_scheduler config definition; fixed launch args; fixed some printing issues; simplified apis of 3d layers (#51) * Update 2.5d layer code to get a similar accuracy on imagenet-1k dataset * update api for better usability (#58) update api for better usability Co-authored-by: 1SAA <c2h214748@gmail.com> Co-authored-by: ver217 <lhx0217@gmail.com> Co-authored-by: puck_WCR <46049915+WANG-CR@users.noreply.github.com> Co-authored-by: binmakeswell <binmakeswell@gmail.com> Co-authored-by: アマデウス <kurisusnowdeng@users.noreply.github.com> Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>
223 lines
7.1 KiB
Python
223 lines
7.1 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from colossalai.context import ParallelMode
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from colossalai.registry import HOOKS
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from colossalai.utils import is_no_pp_or_last_stage
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from ._base_hook import BaseHook
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from ..metric import Loss, Accuracy1D, Accuracy2D, Accuracy, Accuracy2p5D, Accuracy3D
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class MetricHook(BaseHook):
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"""Specialized hook classes for :class:`Metric`.
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Some help metric collectors initialize, reset and
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update their states. Others are used to display and
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record the metric.
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:param trainer: Trainer attached with current hook
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type trainer: Trainer
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:type priority: int
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"""
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def __init__(self,
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priority: int,
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):
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super().__init__(priority)
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self._is_stage_to_compute = is_no_pp_or_last_stage()
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def _check_metric_states_initialization(self, trainer):
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if 'metrics' not in trainer.states:
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self.init_runner_states(trainer, 'metrics', dict(train={}, test={}))
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@HOOKS.register_module
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class LossHook(MetricHook):
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"""Specialized hook class for :class:`Loss`.
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:param trainer: Trainer attached with current hook
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type trainer: Trainer
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:type priority: int, optional
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"""
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def __init__(self, priority: int = 0):
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super().__init__(priority)
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def after_hook_is_attached(self, trainer):
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self._check_metric_states_initialization(trainer)
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if self._is_stage_to_compute:
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self.train_loss = Loss(epoch_only=False)
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self.test_loss = Loss(epoch_only=True)
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# register the metric calculator
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trainer.states['metrics']['train'][
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self.train_loss.__class__.__name__] = self.train_loss
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trainer.states['metrics']['test'][
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self.test_loss.__class__.__name__] = self.test_loss
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def before_train_epoch(self, trainer):
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if self._is_stage_to_compute:
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self.train_loss.reset()
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def after_train_iter(self, trainer, logits, label, loss):
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if self._is_stage_to_compute:
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self.train_loss.update(loss)
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def before_test_epoch(self, trainer):
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if self._is_stage_to_compute:
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self.test_loss.reset()
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def after_test_iter(self, trainer, logits, label, loss):
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if self._is_stage_to_compute:
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self.test_loss.update(loss)
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@HOOKS.register_module
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class Accuracy1DHook(MetricHook):
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"""Specialized hook class for :class:`Accuracy1D`.
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It acts the same as :class:`AccuracyHook`.
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:param trainer: Trainer attached with current hook
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type trainer: Trainer
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:type priority: int, optional
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"""
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def __init__(self, priority: int = 10):
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super().__init__(priority)
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def after_hook_is_attached(self, trainer):
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self._check_metric_states_initialization(trainer)
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if self._is_stage_to_compute:
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self.metric = Accuracy1D(epoch_only=True)
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# register the metric
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trainer.states['metrics']['test'][
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self.metric.__class__.__name__] = self.metric
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def before_test(self, trainer):
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if self._is_stage_to_compute:
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self.metric.reset()
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def after_test_iter(self, trainer, logits, label, *args):
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if self._is_stage_to_compute:
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self.metric.update(logits, label)
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@HOOKS.register_module
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class Accuracy2DHook(MetricHook):
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"""Specialized hook class for :class:`Accuracy2D`.
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It acts the same as :class:`AccuracyHook`.
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:param trainer: Trainer attached with current hook
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type trainer: Trainer
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:type priority: int, optional
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"""
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def __init__(self, priority: int = 0):
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super().__init__(priority)
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def after_hook_is_attached(self, trainer):
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self._check_metric_states_initialization(trainer)
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if self._is_stage_to_compute:
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self.metric = Accuracy2D(epoch_only=True)
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# register the metric
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trainer.states['metrics']['test'][
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self.metric.__class__.__name__] = self.metric
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def before_test(self, trainer):
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if self._is_stage_to_compute:
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self.metric.reset()
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def after_test_iter(self, trainer, logits, label, *args):
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if self._is_stage_to_compute:
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self.metric.update(logits, label)
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@HOOKS.register_module
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class Accuracy2p5DHook(MetricHook):
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def __init__(self, priority: int = 0):
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super().__init__(priority)
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def after_hook_is_attached(self, trainer):
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self._check_metric_states_initialization(trainer)
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if self._is_stage_to_compute:
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self.metric = Accuracy2p5D(epoch_only=True)
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# register the metric
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trainer.states['metrics']['test'][
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self.metric.__class__.__name__] = self.metric
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def before_test(self, trainer):
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if self._is_stage_to_compute:
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self.metric.reset()
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def after_test_iter(self, trainer, logits, label, *args):
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if self._is_stage_to_compute:
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self.metric.update(logits, label)
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@HOOKS.register_module
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class Accuracy3DHook(MetricHook):
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"""Specialized hook class for :class:`Accuracy3D`.
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:param trainer: Trainer attached with current hook
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type trainer: Trainer
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:type priority: int
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"""
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def __init__(self,
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priority: int = 10):
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super().__init__(priority)
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def after_hook_is_attached(self, trainer):
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if self._is_stage_to_compute:
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self.metric = Accuracy3D(epoch_only=True)
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# register the metric
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trainer.states['metrics']['test'][
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self.metric.__class__.__name__] = self.metric
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def before_test(self, trainer):
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if self._is_stage_to_compute:
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self.metric.reset()
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def after_test_iter(self, trainer, logits, label, *args):
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if self._is_stage_to_compute:
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self.metric.update(logits, label)
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@HOOKS.register_module
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class AccuracyHook(MetricHook):
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"""Specialized hook class for :class:`Accuracy`.
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:param trainer: Trainer attached with current hook
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type trainer: Trainer
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:type priority: int
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"""
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def __init__(self, priority: int = 0):
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super().__init__(priority)
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def after_hook_is_attached(self, trainer):
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if self._is_stage_to_compute:
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self.metric = Accuracy(epoch_only=True)
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# register the metric
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trainer.states['metrics']['test'][
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self.metric.__class__.__name__] = self.metric
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def before_test(self, trainer):
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if self._is_stage_to_compute:
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self.metric.reset()
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def after_test_iter(self, trainer, logits, label, *args):
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if self._is_stage_to_compute:
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self.metric.update(logits, label)
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