Ji4chenLi
initialize demo
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import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
import kornia
import open_clip
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
from lvdm.common import autocast
from utils.utils import count_params
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class IdentityEncoder(AbstractEncoder):
def encode(self, x):
return x
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key="class", ucg_rate=0.1):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
self.n_classes = n_classes
self.ucg_rate = ucg_rate
def forward(self, batch, key=None, disable_dropout=False):
if key is None:
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
if self.ucg_rate > 0.0 and not disable_dropout:
mask = 1.0 - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
c = c.long()
c = self.embedding(c)
return c
def get_unconditional_conditioning(self, bs, device="cuda"):
uc_class = (
self.n_classes - 1
) # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
uc = torch.ones((bs,), device=device) * uc_class
uc = {self.key: uc}
return uc
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class FrozenT5Embedder(AbstractEncoder):
"""Uses the T5 transformer encoder for text"""
def __init__(
self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True
): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
super().__init__()
self.tokenizer = T5Tokenizer.from_pretrained(version)
self.transformer = T5EncoderModel.from_pretrained(version)
self.device = device
self.max_length = max_length # TODO: typical value?
if freeze:
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)
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = ["last", "pooled", "hidden"]
def __init__(
self,
version="openai/clip-vit-large-patch14",
device="cuda",
max_length=77,
freeze=True,
layer="last",
layer_idx=None,
): # clip-vit-base-patch32
super().__init__()
assert layer in self.LAYERS
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
self.layer_idx = layer_idx
if layer == "hidden":
assert layer_idx is not None
assert 0 <= abs(layer_idx) <= 12
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, output_hidden_states=self.layer == "hidden"
)
if self.layer == "last":
z = outputs.last_hidden_state
elif self.layer == "pooled":
z = outputs.pooler_output[:, None, :]
else:
z = outputs.hidden_states[self.layer_idx]
return z
def encode(self, text):
return self(text)
class ClipImageEmbedder(nn.Module):
def __init__(
self,
model,
jit=False,
device="cuda" if torch.cuda.is_available() else "cpu",
antialias=True,
ucg_rate=0.0,
):
super().__init__()
from clip import load as load_clip
self.model, _ = load_clip(name=model, device=device, jit=jit)
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
)
self.ucg_rate = ucg_rate
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(
x,
(224, 224),
interpolation="bicubic",
align_corners=True,
antialias=self.antialias,
)
x = (x + 1.0) / 2.0
# re-normalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def forward(self, x, no_dropout=False):
# x is assumed to be in range [-1,1]
out = self.model.encode_image(self.preprocess(x))
out = out.to(x.dtype)
if self.ucg_rate > 0.0 and not no_dropout:
out = (
torch.bernoulli(
(1.0 - self.ucg_rate) * torch.ones(out.shape[0], device=out.device)
)[:, None]
* out
)
return out
class FrozenOpenCLIPEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP transformer encoder for text
"""
LAYERS = [
# "pooled",
"last",
"penultimate",
]
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
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")
)
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):
self.device = self.model.positional_embedding.device
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
if (
self.model.transformer.grad_checkpointing
and not torch.jit.is_scripting()
):
x = checkpoint(r, x, attn_mask)
else:
x = r(x, attn_mask=attn_mask)
return x
def encode(self, text):
return self(text)
class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP vision transformer encoder for images
"""
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
device="cuda",
max_length=77,
freeze=True,
layer="pooled",
antialias=True,
ucg_rate=0.0,
):
super().__init__()
model, _, _ = open_clip.create_model_and_transforms(
arch,
device=torch.device("cpu"),
pretrained=version,
)
del model.transformer
self.model = model
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
if self.layer == "penultimate":
raise NotImplementedError()
self.layer_idx = 1
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
)
self.ucg_rate = ucg_rate
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(
x,
(224, 224),
interpolation="bicubic",
align_corners=True,
antialias=self.antialias,
)
x = (x + 1.0) / 2.0
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
@autocast
def forward(self, image, no_dropout=False):
z = self.encode_with_vision_transformer(image)
if self.ucg_rate > 0.0 and not no_dropout:
z = (
torch.bernoulli(
(1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
)[:, None]
* z
)
return z
def encode_with_vision_transformer(self, img):
img = self.preprocess(img)
x = self.model.visual(img)
return x
def encode(self, text):
return self(text)
class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder):
"""
Uses the OpenCLIP vision transformer encoder for images
"""
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
device="cuda",
freeze=True,
layer="pooled",
antialias=True,
):
super().__init__()
model, _, _ = open_clip.create_model_and_transforms(
arch,
device=torch.device("cpu"),
pretrained=version,
)
del model.transformer
self.model = model
self.device = device
if freeze:
self.freeze()
self.layer = layer
if self.layer == "penultimate":
raise NotImplementedError()
self.layer_idx = 1
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
)
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(
x,
(224, 224),
interpolation="bicubic",
align_corners=True,
antialias=self.antialias,
)
x = (x + 1.0) / 2.0
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def freeze(self):
self.model = self.model.eval()
for param in self.model.parameters():
param.requires_grad = False
def forward(self, image, no_dropout=False):
## image: b c h w
z = self.encode_with_vision_transformer(image)
return z
def encode_with_vision_transformer(self, x):
x = self.preprocess(x)
# to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
if self.model.visual.input_patchnorm:
# einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
x = x.reshape(
x.shape[0],
x.shape[1],
self.model.visual.grid_size[0],
self.model.visual.patch_size[0],
self.model.visual.grid_size[1],
self.model.visual.patch_size[1],
)
x = x.permute(0, 2, 4, 1, 3, 5)
x = x.reshape(
x.shape[0],
self.model.visual.grid_size[0] * self.model.visual.grid_size[1],
-1,
)
x = self.model.visual.patchnorm_pre_ln(x)
x = self.model.visual.conv1(x)
else:
x = self.model.visual.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
# class embeddings and positional embeddings
x = torch.cat(
[
self.model.visual.class_embedding.to(x.dtype)
+ torch.zeros(
x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
),
x,
],
dim=1,
) # shape = [*, grid ** 2 + 1, width]
x = x + self.model.visual.positional_embedding.to(x.dtype)
# a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
x = self.model.visual.patch_dropout(x)
x = self.model.visual.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.model.visual.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
return x
class FrozenCLIPT5Encoder(AbstractEncoder):
def __init__(
self,
clip_version="openai/clip-vit-large-patch14",
t5_version="google/t5-v1_1-xl",
device="cuda",
clip_max_length=77,
t5_max_length=77,
):
super().__init__()
self.clip_encoder = FrozenCLIPEmbedder(
clip_version, device, max_length=clip_max_length
)
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
print(
f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
)
def encode(self, text):
return self(text)
def forward(self, text):
clip_z = self.clip_encoder.encode(text)
t5_z = self.t5_encoder.encode(text)
return [clip_z, t5_z]