649 lines
22 KiB
Python
649 lines
22 KiB
Python
import torch
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import torch.nn as nn
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import kornia
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import open_clip
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import math
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from torch.utils.checkpoint import checkpoint
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from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
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from unifolm_wma.utils.common import autocast
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from unifolm_wma.utils.utils import count_params
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from unifolm_wma.modules.encoders.resampler import reshape_tensor
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class AbstractEncoder(nn.Module):
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def __init__(self):
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super().__init__()
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def encode(self, *args, **kwargs):
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raise NotImplementedError
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class IdentityEncoder(AbstractEncoder):
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def encode(self, x):
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return x
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class ClassEmbedder(nn.Module):
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def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
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super().__init__()
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self.key = key
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self.embedding = nn.Embedding(n_classes, embed_dim)
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self.n_classes = n_classes
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self.ucg_rate = ucg_rate
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def forward(self, batch, key=None, disable_dropout=False):
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if key is None:
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key = self.key
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# this is for use in crossattn
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c = batch[key][:, None]
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if self.ucg_rate > 0. and not disable_dropout:
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mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
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c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes -
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1)
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c = c.long()
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c = self.embedding(c)
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return c
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def get_unconditional_conditioning(self, bs, device="cuda"):
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uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
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uc = torch.ones((bs, ), device=device) * uc_class
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uc = {self.key: uc}
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return uc
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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class FrozenT5Embedder(AbstractEncoder):
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"""Uses the T5 transformer encoder for text"""
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def __init__(self,
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version="google/t5-v1_1-xxl",
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device="cuda",
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max_length=77,
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freeze=True
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): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
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super().__init__()
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self.tokenizer = T5Tokenizer.from_pretrained(version)
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self.transformer = T5EncoderModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length # TODO: typical value?
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if freeze:
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self.freeze()
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def freeze(self):
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self.transformer = self.transformer.eval()
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# self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt")
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens)
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z = outputs.last_hidden_state
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return z
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def encode(self, text):
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return self(text)
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class FrozenCLIPEmbedder(AbstractEncoder):
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"""Uses the CLIP transformer encoder for text (from huggingface)"""
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LAYERS = ["last", "pooled", "hidden"]
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def __init__(self,
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version="openai/clip-vit-large-patch14",
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device="cuda",
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max_length=77,
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freeze=True,
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layer="last",
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layer_idx=None): # clip-vit-base-patch32
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super().__init__()
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assert layer in self.LAYERS
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self.tokenizer = CLIPTokenizer.from_pretrained(version)
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self.transformer = CLIPTextModel.from_pretrained(version)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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self.layer_idx = layer_idx
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if layer == "hidden":
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assert layer_idx is not None
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assert 0 <= abs(layer_idx) <= 12
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def freeze(self):
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self.transformer = self.transformer.eval()
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# self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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batch_encoding = self.tokenizer(text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt")
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tokens = batch_encoding["input_ids"].to(self.device)
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outputs = self.transformer(input_ids=tokens,
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output_hidden_states=self.layer == "hidden")
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if self.layer == "last":
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z = outputs.last_hidden_state
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elif self.layer == "pooled":
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z = outputs.pooler_output[:, None, :]
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else:
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z = outputs.hidden_states[self.layer_idx]
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return z
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def encode(self, text):
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return self(text)
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class ClipImageEmbedder(nn.Module):
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def __init__(self,
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model,
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jit=False,
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device='cuda' if torch.cuda.is_available() else 'cpu',
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antialias=True,
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ucg_rate=0.):
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super().__init__()
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from clip import load as load_clip
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self.model, _ = load_clip(name=model, device=device, jit=jit)
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self.antialias = antialias
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self.register_buffer('mean',
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torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
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persistent=False)
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self.register_buffer('std',
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torch.Tensor([0.26862954, 0.26130258,
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0.27577711]),
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persistent=False)
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self.ucg_rate = ucg_rate
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def preprocess(self, x):
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# normalize to [0,1]
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x = kornia.geometry.resize(x, (224, 224),
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interpolation='bicubic',
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align_corners=True,
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antialias=self.antialias)
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x = (x + 1.) / 2.
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# re-normalize according to clip
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x = kornia.enhance.normalize(x, self.mean, self.std)
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return x
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def forward(self, x, no_dropout=False):
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# x is assumed to be in range [-1,1]
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out = self.model.encode_image(self.preprocess(x))
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out = out.to(x.dtype)
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if self.ucg_rate > 0. and not no_dropout:
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out = torch.bernoulli(
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(1. - self.ucg_rate) *
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torch.ones(out.shape[0], device=out.device))[:, None] * out
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return out
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class FrozenOpenCLIPEmbedder(AbstractEncoder):
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"""
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Uses the OpenCLIP transformer encoder for text
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"""
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LAYERS = [
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# "pooled",
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"last",
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"penultimate"
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]
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def __init__(self,
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arch="ViT-H-14",
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version="laion2b_s32b_b79k",
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device="cuda",
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max_length=77,
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freeze=True,
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layer="last"):
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super().__init__()
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assert layer in self.LAYERS
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model, _, _ = open_clip.create_model_and_transforms(
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arch, device=torch.device('cpu'), pretrained=version)
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del model.visual
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self.model = model
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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if self.layer == "last":
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self.layer_idx = 0
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elif self.layer == "penultimate":
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self.layer_idx = 1
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else:
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raise NotImplementedError()
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def freeze(self):
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self.model = self.model.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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tokens = open_clip.tokenize(
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text) ## all clip models use 77 as context length
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z = self.encode_with_transformer(tokens.to(self.device))
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return z
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def encode_with_transformer(self, text):
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x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
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x = x + self.model.positional_embedding
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.model.ln_final(x)
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return x
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def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
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for i, r in enumerate(self.model.transformer.resblocks):
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if i == len(self.model.transformer.resblocks) - self.layer_idx:
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break
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if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(
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):
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x = checkpoint(r, x, attn_mask)
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else:
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x = r(x, attn_mask=attn_mask)
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return x
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def encode(self, text):
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return self(text)
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class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
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"""
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Uses the OpenCLIP vision transformer encoder for images
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"""
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def __init__(self,
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arch="ViT-H-14",
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version="laion2b_s32b_b79k",
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device="cuda",
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max_length=77,
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freeze=True,
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layer="pooled",
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antialias=True,
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ucg_rate=0.):
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super().__init__()
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model, _, _ = open_clip.create_model_and_transforms(
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arch,
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device=torch.device('cpu'),
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pretrained=version,
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)
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del model.transformer
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self.model = model
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# self.mapper = torch.nn.Linear(1280, 1024)
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self.device = device
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self.max_length = max_length
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if freeze:
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self.freeze()
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self.layer = layer
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if self.layer == "penultimate":
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raise NotImplementedError()
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self.layer_idx = 1
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self.antialias = antialias
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self.register_buffer('mean',
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torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
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persistent=False)
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self.register_buffer('std',
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torch.Tensor([0.26862954, 0.26130258,
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0.27577711]),
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persistent=False)
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self.ucg_rate = ucg_rate
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def preprocess(self, x):
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# normalize to [0,1]
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x = kornia.geometry.resize(x, (224, 224),
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interpolation='bicubic',
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align_corners=True,
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antialias=self.antialias)
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x = (x + 1.) / 2.
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# renormalize according to clip
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x = kornia.enhance.normalize(x, self.mean, self.std)
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return x
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def freeze(self):
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self.model = self.model.eval()
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for param in self.model.parameters():
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param.requires_grad = False
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@autocast
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def forward(self, image, no_dropout=False):
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if not hasattr(self, "_printed_autocast_info"):
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print(
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">>> 图像编码 autocast:",
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torch.is_autocast_enabled(),
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torch.get_autocast_gpu_dtype(),
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"输入dtype:",
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image.dtype,
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)
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self._printed_autocast_info = True
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z = self.encode_with_vision_transformer(image)
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if self.ucg_rate > 0. and not no_dropout:
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z = torch.bernoulli(
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(1. - self.ucg_rate) *
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torch.ones(z.shape[0], device=z.device))[:, None] * z
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return z
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def encode_with_vision_transformer(self, img):
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img = self.preprocess(img)
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x = self.model.visual(img)
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return x
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def encode(self, text):
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return self(text)
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class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder):
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"""
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Uses the OpenCLIP vision transformer encoder for images
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"""
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def __init__(self,
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arch="ViT-H-14",
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version="laion2b_s32b_b79k",
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device="cuda",
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freeze=True,
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layer="pooled",
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antialias=True):
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super().__init__()
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model, _, _ = open_clip.create_model_and_transforms(
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arch,
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device=torch.device('cpu'),
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pretrained=version,
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)
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del model.transformer
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self.model = model
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self.device = device
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if freeze:
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self.freeze()
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self.layer = layer
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if self.layer == "penultimate":
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raise NotImplementedError()
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self.layer_idx = 1
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self.antialias = antialias
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self.register_buffer('mean',
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torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
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persistent=False)
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self.register_buffer('std',
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torch.Tensor([0.26862954, 0.26130258,
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0.27577711]),
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persistent=False)
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def preprocess(self, x):
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# normalize to [0,1]
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x = kornia.geometry.resize(x, (224, 224),
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interpolation='bicubic',
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align_corners=True,
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antialias=self.antialias)
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x = (x + 1.) / 2.
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# renormalize according to clip
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x = kornia.enhance.normalize(x, self.mean, self.std)
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return x
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def freeze(self):
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self.model = self.model.eval()
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for param in self.model.parameters():
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param.requires_grad = False
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def forward(self, image, no_dropout=False):
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## image: b c h w
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if not hasattr(self, "_printed_autocast_info"):
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print(
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">>> 图像编码V2 autocast:",
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torch.is_autocast_enabled(),
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torch.get_autocast_gpu_dtype(),
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"输入dtype:",
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image.dtype,
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)
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self._printed_autocast_info = True
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z = self.encode_with_vision_transformer(image)
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return z
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def encode_with_vision_transformer(self, x):
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x = self.preprocess(x)
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# to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
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if self.model.visual.input_patchnorm:
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# einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
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x = x.reshape(x.shape[0], x.shape[1],
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self.model.visual.grid_size[0],
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self.model.visual.patch_size[0],
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self.model.visual.grid_size[1],
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self.model.visual.patch_size[1])
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x = x.permute(0, 2, 4, 1, 3, 5)
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x = x.reshape(
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x.shape[0], self.model.visual.grid_size[0] *
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self.model.visual.grid_size[1], -1)
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x = self.model.visual.patchnorm_pre_ln(x)
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x = self.model.visual.conv1(x)
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else:
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x = self.model.visual.conv1(x) # shape = [*, width, grid, grid]
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x = x.reshape(x.shape[0], x.shape[1],
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-1) # shape = [*, width, grid ** 2]
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x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
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# class embeddings and positional embeddings
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x = torch.cat([
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self.model.visual.class_embedding.to(x.dtype) + torch.zeros(
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x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x
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],
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dim=1) # shape = [*, grid ** 2 + 1, width]
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x = x + self.model.visual.positional_embedding.to(x.dtype)
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# a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
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x = self.model.visual.patch_dropout(x)
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x = self.model.visual.ln_pre(x)
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.model.visual.transformer(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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return x
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class FrozenCLIPT5Encoder(AbstractEncoder):
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def __init__(self,
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clip_version="openai/clip-vit-large-patch14",
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t5_version="google/t5-v1_1-xl",
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device="cuda",
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clip_max_length=77,
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t5_max_length=77):
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super().__init__()
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self.clip_encoder = FrozenCLIPEmbedder(clip_version,
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device,
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max_length=clip_max_length)
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self.t5_encoder = FrozenT5Embedder(t5_version,
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device,
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max_length=t5_max_length)
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print(
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f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
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f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
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)
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def encode(self, text):
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return self(text)
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def forward(self, text):
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clip_z = self.clip_encoder.encode(text)
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t5_z = self.t5_encoder.encode(text)
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return [clip_z, t5_z]
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class LinearProjector(nn.Module):
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def __init__(self, input_dim: int, output_dim: int) -> None:
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super().__init__()
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self.projector = nn.Linear(input_dim, output_dim, bias=True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.projector(x)
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|
|
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class MLPProjector(nn.Module):
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|
|
|
def __init__(self,
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input_dim: int,
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output_dim: int,
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mlp_type: str = "gelu-mlp") -> None:
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super().__init__()
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|
if mlp_type == "gelu-mlp":
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self.projector = nn.Sequential(
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nn.Linear(input_dim, output_dim, bias=True),
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nn.GELU(approximate='tanh'),
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nn.Linear(output_dim, output_dim, bias=True),
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)
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elif mlp_type == "silu-mlp":
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|
self.projector = nn.Sequential(
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nn.Linear(input_dim, output_dim, bias=True),
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|
nn.SiLU(),
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|
nn.Linear(output_dim, output_dim, bias=True),
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|
)
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|
else:
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raise ValueError(
|
|
f"Projector with `{mlp_type = }` is not supported!")
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|
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|
def forward(self, x: torch.Tensor) -> torch.Tensor:
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|
return self.projector(x)
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|
|
|
|
|
class PerceiverAttention(nn.Module):
|
|
|
|
def __init__(self, *, dim, dim_head=64, heads=8):
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|
super().__init__()
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|
self.scale = dim_head**-0.5
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|
self.dim_head = dim_head
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|
self.heads = heads
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|
inner_dim = dim_head * heads
|
|
|
|
self.norm1 = nn.LayerNorm(dim)
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|
self.norm2 = nn.LayerNorm(dim)
|
|
|
|
self.to_q = nn.Linear(dim, inner_dim, bias=False)
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|
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
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|
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
|
|
|
def forward(self, x, latents):
|
|
"""
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|
Args:
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|
x (torch.Tensor): image features
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|
shape (b, n1, D)
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|
latent (torch.Tensor): latent features
|
|
shape (b, n2, D)
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|
"""
|
|
|
|
x = self.norm1(x)
|
|
latents = self.norm2(latents)
|
|
|
|
b, l, _ = latents.shape
|
|
|
|
q = self.to_q(latents)
|
|
kv_input = torch.cat((x, latents), dim=-2)
|
|
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
|
|
|
q = reshape_tensor(q, self.heads)
|
|
k = reshape_tensor(k, self.heads)
|
|
v = reshape_tensor(v, self.heads)
|
|
|
|
# attention
|
|
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
|
|
weight = (q * scale) @ (k * scale).transpose(
|
|
-2, -1) # More stable with f16 than dividing afterwards
|
|
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
|
|
out = weight @ v
|
|
|
|
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
|
|
|
|
return self.to_out(out)
|
|
|
|
|
|
def FeedForward(dim, mult=4, ffd_type="gelu-ffd"):
|
|
inner_dim = int(dim * mult)
|
|
if ffd_type == "gelu-ffd":
|
|
return nn.Sequential(
|
|
nn.LayerNorm(dim),
|
|
nn.Linear(dim, inner_dim, bias=False),
|
|
nn.GELU(approximate='tanh'),
|
|
nn.Linear(inner_dim, dim, bias=False),
|
|
)
|
|
elif ffd_type == "silu-ffd":
|
|
return nn.Sequential(
|
|
nn.LayerNorm(dim),
|
|
nn.Linear(dim, inner_dim, bias=False),
|
|
nn.SiLU(),
|
|
nn.Linear(inner_dim, dim, bias=False),
|
|
)
|
|
else:
|
|
raise ValueError(f"Projector with `{mlp_type = }` is not supported!")
|
|
|
|
|
|
class SATokenProjector(nn.Module):
|
|
|
|
def __init__(self,
|
|
dim=1024,
|
|
depth=1,
|
|
dim_head=64,
|
|
heads=16,
|
|
num_queries=16,
|
|
output_dim=1024,
|
|
ff_mult=4,
|
|
chunk_size=None):
|
|
super().__init__()
|
|
self.num_queries = num_queries
|
|
self.chunk_size = chunk_size
|
|
|
|
if chunk_size is not None:
|
|
num_queries = num_queries * chunk_size
|
|
|
|
self.latents = nn.Parameter(
|
|
torch.randn(1, num_queries, dim) / dim**0.5)
|
|
self.proj_out = nn.Linear(dim, output_dim)
|
|
self.norm_out = nn.LayerNorm(dim)
|
|
|
|
self.layers = nn.ModuleList([])
|
|
for _ in range(depth):
|
|
self.layers.append(
|
|
nn.ModuleList([
|
|
PerceiverAttention(dim=dim, dim_head=dim_head,
|
|
heads=heads),
|
|
FeedForward(dim=dim, mult=ff_mult),
|
|
]))
|
|
|
|
def forward(self, x):
|
|
latents = self.latents.repeat(x.size(0), 1, 1)
|
|
for attn, ff in self.layers:
|
|
latents = attn(x, latents) + latents
|
|
latents = ff(latents) + latents
|
|
latents = self.proj_out(latents)
|
|
latents = self.norm_out(latents)
|
|
return latents
|