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import torch
import torch.nn as nn
import numpy as np
from functools import partial
from lib.model_zoo.common.get_model import register
version = '0'
symbol = 'clip'
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
from transformers import CLIPTokenizer, CLIPTextModel
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
@register('clip_text_frozen', version)
class FrozenCLIPTextEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): # clip-vit-base-patch32
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length # TODO: typical value?
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
#self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
from transformers import CLIPProcessor, CLIPVisionModel
@register('clip_vision_frozen', version)
class FrozenCLIPVisionEmbedder(AbstractEncoder):
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): # clip-vit-base-patch32
super().__init__()
self.processor = CLIPProcessor.from_pretrained(version)
self.transformer = CLIPVisionModel.from_pretrained(version)
self.device = device
self.max_length = max_length # TODO: typical value?
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
#self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def forward(self, images):
inputs = self.processor(images=images, return_tensors="pt")
pixels = inputs['pixel_values'].to(self.device)
outputs = self.transformer(pixel_values=pixels)
z = outputs.last_hidden_state
return z
def encode(self, image):
return self(image)
from transformers import CLIPModel
@register('clip_frozen', version)
class FrozenCLIP(AbstractEncoder):
def __init__(self,
version="openai/clip-vit-large-patch14",
max_length=77,
encode_type='encode_text',): # clip-vit-base-patch32
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.processor = CLIPProcessor.from_pretrained(version)
self.model = CLIPModel.from_pretrained(version)
self.max_length = max_length # TODO: typical value?
self.encode_type = encode_type
self.pinv_text_projection = None
self.freeze()
def get_device(self):
# A trick to get device
return self.model.text_projection.weight.device
def freeze(self):
self.model = self.model.eval()
#self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def encode_text_pooled(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.get_device())
return self.model.get_text_features(input_ids=tokens)
def encode_vision_pooled(self, images):
inputs = self.processor(images=images, return_tensors="pt")
pixels = inputs['pixel_values'].to(self.get_device())
return self.model.get_image_features(pixel_values=pixels)
def encode_text_noproj(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.get_device())
outputs = self.model.text_model(input_ids=tokens)
return outputs.last_hidden_state
def encode_vision_noproj(self, images):
inputs = self.processor(images=images, return_tensors="pt")
pixels = inputs['pixel_values'].to(self.get_device())
outputs = self.model.vision_model(pixel_values=pixels)
return outputs.last_hidden_state
def encode_text_bug(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.get_device())
outputs = self.model.text_model(input_ids=tokens)
z = outputs.last_hidden_state
z_pooled = outputs.pooler_output
z = z / torch.norm(z_pooled.unsqueeze(1), dim=-1, keepdim=True)
return self.model.text_projection(z)
def encode_text(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.get_device())
outputs = self.model.text_model(input_ids=tokens)
z = self.model.text_projection(outputs.last_hidden_state)
z_pooled = self.model.text_projection(outputs.pooler_output)
z = z / torch.norm(z_pooled.unsqueeze(1), dim=-1, keepdim=True)
return z
def encode_vision(self, images):
z = self.encode_vision_noproj(images)
z = self.model.vision_model.post_layernorm(z)
z = self.model.visual_projection(z)
z_pooled = z[:, 0:1]
# z_pooled_normed = z_pooled / z_pooled.norm(dim=-1, keepdim=True)
z = z / torch.norm(z_pooled, dim=-1, keepdim=True)
return z
def encode_vision_pinvtext(self, images):
blank_text_encode_norm_avg = 28.9096
z = self.encode_vision(images)
if self.pinv_text_projection is None:
self.pinv_text_projection = torch.linalg.pinv(self.model.text_projection.weight).T
z = torch.matmul(z, self.pinv_text_projection)
# z = z / torch.norm(z[:, 0:1], dim=-1, keepdim=True)
z = z / torch.norm(z, dim=-1, keepdim=True)
z = z*blank_text_encode_norm_avg
# return z[:, 1:2].repeat(1, 77, 1)
z2 = self.encode_text_noproj('')
# z2[:, 1:77] = z[:, 0:76]
return torch.flip(z, dims=(1,))[:, 0:77]
def encode(self, *args, **kwargs):
return getattr(self, self.encode_type)(*args, **kwargs)
#############################
# copyed from justin's code #
#############################
@register('clip_vision_frozen_justin', version)
class FrozenCLIPVisionEmbedder_Justin(AbstractEncoder):
"""
Uses the CLIP image encoder.
"""
def __init__(
self,
model='ViT-L/14',
jit=False,
device='cuda' if torch.cuda.is_available() else 'cpu',
antialias=False,
):
super().__init__()
from . import clip_justin
self.model, _ = clip_justin.load(name=model, device=device, jit=jit)
self.device = device
self.antialias = antialias
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
# I didn't call this originally, but seems like it was frozen anyway
self.freeze()
def freeze(self):
self.transformer = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def preprocess(self, x):
import kornia
# Expects inputs in the range -1, 1
x = kornia.geometry.resize(x, (224, 224),
interpolation='bicubic',align_corners=True,
antialias=self.antialias)
x = (x + 1.) / 2.
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def forward(self, x):
# x is assumed to be in range [-1,1]
return self.model.encode_image(self.preprocess(x)).float()
def encode(self, im):
return self(im).unsqueeze(1)
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