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fix ofa new transformers compatible issue

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14317517
* fix ofa new transformers compatible issue

* fix timm.layers to timm.models.layers compatible issue
This commit is contained in:
mulin.lyh
2023-10-16 22:12:31 +08:00
parent 049bde9ddf
commit c67b2cfc34
4 changed files with 12 additions and 12 deletions
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4
modelscope/models/cv/shop_segmentation/head_fpn.py
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@@ -9,8 +9,8 @@ import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from timm.layers.drop import drop_path
from timm.layers.weight_init import trunc_normal_
from timm.models.layers.drop import drop_path
from timm.models.layers.weight_init import trunc_normal_
from .common import Upsample, resize

4
modelscope/models/cv/shop_segmentation/models.py
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@@ -11,8 +11,8 @@ from collections import OrderedDict
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.layers.drop import drop_path
from timm.layers.weight_init import trunc_normal_
from timm.models.layers.drop import drop_path
from timm.models.layers.weight_init import trunc_normal_
from torch import nn

4
modelscope/models/cv/shop_segmentation/neck_fpn.py
View File

@@ -8,8 +8,8 @@
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from timm.layers.drop import drop_path
from timm.layers.weight_init import trunc_normal_
from timm.models.layers.drop import drop_path
from timm.models.layers.weight_init import trunc_normal_
from .common import resize

12
modelscope/models/multi_modal/ofa/tokenization_ofa.py
View File

@@ -183,6 +183,12 @@ class OFATokenizerZH(PreTrainedTokenizer):
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs):
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
'model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'
)
self.vocab = load_vocab(vocab_file)
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
@@ -199,12 +205,6 @@ class OFATokenizerZH(PreTrainedTokenizer):
**kwargs,
)
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
'model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([
(ids, tok) for tok, ids in self.vocab.items()
])