wehub-resource-sync
e06fe8e8c6
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Has been cancelled
New model PR merged notification / Notify new model (push) Has been cancelled
Update Transformers metadata / build_and_package (push) Has been cancelled
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
"""
|
|
Here, because clip is not consistent with the use of the "Text" and "Vision" prefixes, we cannot simply use
|
|
```
|
|
class Multimodal2VisionModel(CLIPVisionModel):
|
|
pass
|
|
```
|
|
with the hope that all dependencies will be renamed as `Multimodal2VisionClass`. For this reason, if we want consistency and
|
|
use the "Vision" part everywhere, we need to overwrite the intermediate classes and add the prefix everytime.
|
|
This adds noise to the modular, but is unfortunately unavoidable.
|
|
"""
|
|
|
|
from torch import nn
|
|
|
|
from transformers.models.clip.modeling_clip import (
|
|
CLIPMLP,
|
|
CLIPAttention,
|
|
CLIPEncoder,
|
|
CLIPEncoderLayer,
|
|
CLIPPreTrainedModel,
|
|
CLIPVisionModel,
|
|
)
|
|
|
|
|
|
class Multimodal2VisionAttention(CLIPAttention):
|
|
pass
|
|
|
|
|
|
class Multimodal2VisionMLP(CLIPMLP):
|
|
pass
|
|
|
|
|
|
class Multimodal2VisionEncoderLayer(CLIPEncoderLayer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.mlp = Multimodal2VisionMLP(config)
|
|
self.self_attn = Multimodal2VisionAttention(config)
|
|
|
|
|
|
class Multimodal2VisionEncoder(CLIPEncoder):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.layers = nn.ModuleList([Multimodal2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
|
|
|
|
|
|
class Multimodal2VisionPreTrainedModel(CLIPPreTrainedModel):
|
|
_can_record_outputs = {
|
|
"hidden_states": Multimodal2VisionEncoderLayer,
|
|
"attentions": Multimodal2VisionAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
if isinstance(module, Multimodal2VisionMLP):
|
|
pass
|
|
|
|
|
|
# `CLIPVisionModel` inherits from `CLIPPreTrainedModel`. We need to add the 2nd base here to add the `Vision` part
|
|
class Multimodal2VisionModel(CLIPVisionModel, Multimodal2VisionPreTrainedModel):
|
|
_no_split_modules = ["Multimodal2VisionEncoderLayer"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.encoder = Multimodal2VisionEncoder(config)
|