ignore ipu

mmpretrain/configs/vision_transformer/vit_base_p16_4xb544_ipu_in1k.py

@@ -1,125 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# This is a BETA new format config file, and the usage may change recently.
-from mmcv.transforms import (CenterCrop, ImageToTensor, Normalize, Resize,
-                             ToTensor)
-from mmengine.config import read_base
-from mmengine.model import PretrainedInit
-from mmengine.optim import CosineAnnealingLR, LinearLR
-from mmengine.runner import CheckpointHook, IterBasedRunner
-from torch.optim import SGD
-
-from mmpretrain.datasets import Collect
-
-with read_base():
-    from .._base_.datasets.imagenet_bs64_pil_resize_autoaug import *
-    from .._base_.default_runtime import *
-    from .._base_.models.vit_base_p16 import *
-
-# specific to vit pretrain
-paramwise_cfg = dict(custom_keys={
-    '.cls_token': dict(decay_mult=0.0),
-    '.pos_embed': dict(decay_mult=0.0)
-})
-
-pretrained = 'https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p16_3rdparty_pt-64xb64_in1k-224_20210928-02284250.pth'  # noqa
-
-model = dict(
-    head=dict(
-        loss=dict(type=CrossEntropyLoss, loss_weight=1.0, _delete_=True), ),
-    backbone=dict(
-        img_size=224,
-        init_cfg=dict(
-            type=PretrainedInit,
-            checkpoint=pretrained,
-            _delete_=True,
-            prefix='backbone')))
-
-img_norm_cfg = dict(
-    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
-
-train_pipeline = [
-    dict(type=LoadImageFromFile),
-    dict(type=RandomResizedCrop, scale=224, backend='pillow'),
-    dict(type=RandomFlip, prob=0.5, direction='horizontal'),
-    dict(type=Normalize, **img_norm_cfg),
-    dict(type=ImageToTensor, keys=['img']),
-    dict(type=ToTensor, keys=['gt_label']),
-    dict(type=ToHalf, keys=['img']),
-    dict(type=Collect, keys=['img', 'gt_label'])
-]
-
-test_pipeline = [
-    dict(type=LoadImageFromFile),
-    dict(type=Resize, scale=(224, -1), keep_ratio=True, backend='pillow'),
-    dict(type=CenterCrop, crop_size=224),
-    dict(type=Normalize, **img_norm_cfg),
-    dict(type=ImageToTensor, keys=['img']),
-    dict(type=ToHalf, keys=['img']),
-    dict(type=Collect, keys=['img'])
-]
-
-# change batch size
-data = dict(
-    samples_per_gpu=17,
-    workers_per_gpu=16,
-    drop_last=True,
-    train=dict(pipeline=train_pipeline),
-    train_dataloader=dict(mode='async'),
-    val=dict(pipeline=test_pipeline, ),
-    val_dataloader=dict(samples_per_gpu=4, workers_per_gpu=1),
-    test=dict(pipeline=test_pipeline),
-    test_dataloader=dict(samples_per_gpu=4, workers_per_gpu=1))
-
-# optimizer
-optimizer = dict(
-    type=SGD,
-    lr=0.08,
-    weight_decay=1e-5,
-    momentum=0.9,
-    paramwise_cfg=paramwise_cfg,
-)
-
-# learning policy
-param_scheduler = [
-    dict(type=LinearLR, start_factor=0.02, by_epoch=False, begin=0, end=800),
-    dict(
-        type=CosineAnnealingLR,
-        T_max=4200,
-        by_epoch=False,
-        begin=800,
-        end=5000)
-]
-
-# ipu cfg
-# model partition config
-ipu_model_cfg = dict(
-    train_split_edges=[
-        dict(layer_to_call='backbone.patch_embed', ipu_id=0),
-        dict(layer_to_call='backbone.layers.3', ipu_id=1),
-        dict(layer_to_call='backbone.layers.6', ipu_id=2),
-        dict(layer_to_call='backbone.layers.9', ipu_id=3)
-    ],
-    train_ckpt_nodes=['backbone.layers.{}'.format(i) for i in range(12)])
-
-# device config
-options_cfg = dict(
-    randomSeed=42,
-    partialsType='half',
-    train_cfg=dict(
-        executionStrategy='SameAsIpu',
-        Training=dict(gradientAccumulation=32),
-        availableMemoryProportion=[0.3, 0.3, 0.3, 0.3],
-    ),
-    eval_cfg=dict(deviceIterations=1, ),
-)
-
-# add model partition config and device config to runner
-runner = dict(
-    type=IterBasedRunner,
-    ipu_model_cfg=ipu_model_cfg,
-    options_cfg=options_cfg,
-    max_iters=5000)
-
-default_hooks = dict(checkpoint=dict(type=CheckpointHook, interval=1000))
-
-fp16 = dict(loss_scale=256.0, velocity_accum_type='half', accum_type='half')