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on
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Running
on
Zero
File size: 2,030 Bytes
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import torch
import open_clip
class FrozenOpenCLIPEmbedder(torch.nn.Module):
"""
Uses the OpenCLIP transformer encoder for text
"""
LAYERS = ['last', 'penultimate']
def __init__(self,
arch='ViT-H-14',
version='open_clip_pytorch_model.bin',
device='cuda',
max_length=77,
freeze=True,
layer='last'):
super().__init__()
assert layer in self.LAYERS
model, _, _ = open_clip.create_model_and_transforms(
arch, device=torch.device('cpu'), pretrained=version)
del model.visual
self.model = model
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
if self.layer == 'last':
self.layer_idx = 0
elif self.layer == 'penultimate':
self.layer_idx = 1
else:
raise NotImplementedError()
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
tokens = open_clip.tokenize(text)
z = self.encode_with_transformer(tokens.to(self.device))
return z
def encode_with_transformer(self, text):
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
x = x + self.model.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.model.ln_final(x)
return x
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
for i, r in enumerate(self.model.transformer.resblocks):
if i == len(self.model.transformer.resblocks) - self.layer_idx:
break
x = r(x, attn_mask=attn_mask)
return x
def encode(self, text):
return self(text) |