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modules.py
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modules.py
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import torch
from torch import Tensor, nn
import math
from typing import Tuple, Type
from .common import MLPBlock
from torch import nn
# from functools import partial
from einops.layers.torch import Rearrange, Reduce
import numpy as np
import torch.nn.functional as F
pair = lambda x: x if isinstance(x, tuple) else (x, x)
def gaussian_kernel(size, mean, std):
"""Generates a 2D Gaussian kernel."""
d = torch.distributions.Normal(mean, std)
vals = d.log_prob(torch.arange(size).float())
grid = torch.exp(vals[:, None] + vals[None, :])
grid /= grid.sum()
return grid
class GaussianConv2d(nn.Module):
def __init__(self, in_channels = 1, out_channels = 1, kernel_size = 3, stride=1, padding=1, mean=0.0, std=1.0):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.mean = nn.Parameter(torch.tensor(mean), requires_grad=True)
self.std = nn.Parameter(torch.tensor(std), requires_grad=True)
self.weights = nn.Parameter(gaussian_kernel(kernel_size, self.mean, self.std), requires_grad=True)
self.bias = nn.Parameter(torch.zeros(out_channels), requires_grad=True)
def forward(self, x):
return F.conv2d(x, self.weights.unsqueeze(0).unsqueeze(0).repeat(self.out_channels, self.in_channels, 1, 1),
bias=self.bias, stride=self.stride, padding=self.padding)
def PromptMLP(dim = 3, expansion_factor = 4, dropout = 0., dense = nn.Linear):
inner_dim = int(dim * expansion_factor)
return nn.Sequential(
dense(dim, inner_dim),
nn.GELU(),
nn.Dropout(dropout),
dense(inner_dim, 1),
nn.Dropout(dropout)
)
class PromptMixer(nn.Module):
def __init__(
self,
dim: int = 3,
depth: int = 1,
expansion_factor: int = 4,
dropout: float = 0.,
) -> None:
super().__init__()
self.depth = depth
self.dim = dim
self.expansion_factor = expansion_factor
self.dropout = dropout
self.layers = nn.Sequential(
Rearrange('k b n d -> b n d k'),
*[nn.Sequential(
PromptMLP(dim, expansion_factor, dropout),
) for _ in range(depth)],
# nn.LayerNorm(dim) # b n d
)
def forward(self, q, k, v):
qk = torch.stack([q, k, v]) # 3 b n d
res = self.layers(qk)
# print("res size is", res.size())
return res.squeeze(-1) # b n d
class PromptParser(nn.Module):
def __init__(
self,
embedding_dim: int,
token_num: int,
) -> None:
super().__init__()
self.embedding_dim = embedding_dim
self.pt_mix = PromptMixer()
self.gauss = GaussianConv2d(in_channels = token_num)
self.norm_final_attn = nn.LayerNorm(embedding_dim)
def forward(
self,
image_embedding: Tensor,
tmp_embedding: Tensor,
prompt_embedding1: Tensor,
prompt_embedding2: Tensor,
) -> Tuple[Tensor, Tensor]:
pt_pe = prompt_embedding1 + prompt_embedding2
etpp = self.pt_mix(tmp_embedding, prompt_embedding1, prompt_embedding2)
att_m = torch.einsum ('bncd, bndx -> bncx', etpp.unsqueeze(-1), image_embedding.unsqueeze(-2))
att_m = self.gauss(att_m)
etq = torch.einsum ('bncd, bndx -> bncx', image_embedding.unsqueeze(-1), (tmp_embedding + pt_pe).unsqueeze(-2))
eg = torch.max(att_m * etq, etq)
res = torch.einsum ('bncx, bnx -> bnc', eg, tmp_embedding + pt_pe)
return image_embedding, res
class OnePromptFormer(nn.Module):
def __init__(
self,
embedding_dim: int,
prompt_embed_dim: int,
token_num: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
) -> None:
super().__init__()
self.embedding_dim = embedding_dim
self.num_heads = num_heads
self.mlp_dim = mlp_dim
self.layers = nn.ModuleList()
self.nn = nn.Linear(embedding_dim, prompt_embed_dim)
self.attns1 = Attention(prompt_embed_dim, num_heads)
self.attns2 = Attention(prompt_embed_dim, num_heads)
self.mlps1 = MLPBlock(prompt_embed_dim, mlp_dim, activation)
self.norms1 = nn.LayerNorm(prompt_embed_dim)
self.norms2 = nn.LayerNorm(prompt_embed_dim)
self.parser = PromptParser(embedding_dim = prompt_embed_dim, token_num = token_num)
self.attnt1 = Attention(prompt_embed_dim, num_heads)
self.mlpt1 = MLPBlock(prompt_embed_dim, mlp_dim, activation)
self.normt1 = nn.LayerNorm(prompt_embed_dim)
self.normt2 = nn.LayerNorm(prompt_embed_dim)
self.attnm1 = Attention(prompt_embed_dim, num_heads)
self.attnm2 = Attention(prompt_embed_dim, num_heads)
self.final = nn.Sequential(
MLPBlock(prompt_embed_dim, mlp_dim, activation),
nn.LayerNorm(prompt_embed_dim)
)
def forward(
self,
emb: Tensor,
image_embedding: Tensor,
tmp_embedding: Tensor,
prompt_embedding1: Tensor,
prompt_embedding2: Tensor,
) -> Tuple[Tensor, Tensor]:
image_embedding, et = self.parser(image_embedding,tmp_embedding, prompt_embedding1, prompt_embedding2)
es = self.attns1(q=image_embedding, k= emb, v= emb)
es_bk = es
es = self.attns2(q=et, k= es, v= es)
es = self.norms1(es + et)
es = self.norms2(self.mlps1(es) + es)
et = self.attnt1(q = es_bk, k = et, v = et)
et = self.normt1(es_bk + et)
et = self.norms2(self.mlps1(et) + et)
e = self.attnm1(q = et, k = es, v = es)
e = self.attnm2(q = e, k = e, v = e)
e = self.final(e)
return e
class MixedUpScale(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
super().__init__()
self.depth = depth
self.embedding_dim = embedding_dim
self.num_heads = num_heads
self.mlp_dim = mlp_dim
self.layers = nn.ModuleList()
for i in range(depth):
self.layers.append(
CrossAttentionBlock(
embedding_dim=embedding_dim,
num_heads=num_heads,
mlp_dim=mlp_dim,
activation=activation,
attention_downsample_rate=attention_downsample_rate,
skip_first_layer_pe=(i == 0),
)
)
self.final_attn = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.norm_final_attn = nn.LayerNorm(embedding_dim)
def forward(
self,
image_embedding: Tensor,
image_pe: Tensor,
point_embedding: Tensor,
) -> Tuple[Tensor, Tensor]:
# BxCxHxW -> BxHWxC == B x N_image_tokens x C
bs, c, h, w = image_embedding.shape
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
# Prepare queries
queries = point_embedding
keys = image_embedding
# Apply transformer blocks and final layernorm
for layer in self.layers:
queries, keys = layer(
queries=queries,
keys=keys,
query_pe=point_embedding,
key_pe=image_pe,
)
# Apply the final attention layer from the points to the image
q = queries + point_embedding
k = keys + image_pe
attn_out = self.final_attn(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm_final_attn(queries)
return queries, keys
class TwoWayTransformer(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
) -> None:
super().__init__()
self.depth = depth
self.embedding_dim = embedding_dim
self.num_heads = num_heads
self.mlp_dim = mlp_dim
self.layers = nn.ModuleList()
for i in range(depth):
self.layers.append(
TwoWayAttentionBlock(
embedding_dim=embedding_dim,
num_heads=num_heads,
mlp_dim=mlp_dim,
activation=activation,
attention_downsample_rate=attention_downsample_rate,
skip_first_layer_pe=(i == 0),
)
)
self.final_attn_token_to_image = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.norm_final_attn = nn.LayerNorm(embedding_dim)
def forward(
self,
image_embedding: Tensor,
image_pe: Tensor,
point_embedding: Tensor,
) -> Tuple[Tensor, Tensor]:
bs, c, h, w = image_embedding.shape
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
image_pe = image_pe.flatten(2).permute(0, 2, 1)
# Prepare queries
queries = point_embedding
keys = image_embedding
# Apply transformer blocks and final layernorm
for layer in self.layers:
queries, keys = layer(
queries=queries,
keys=keys,
query_pe=point_embedding,
key_pe=image_pe,
)
# Apply the final attention layer from the points to the image
q = queries + point_embedding
k = keys + image_pe
attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm_final_attn(queries)
return queries, keys
class TwoWayAttentionBlock(nn.Module):
def __init__(
self,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
super().__init__()
self.self_attn = Attention(embedding_dim, num_heads)
self.norm1 = nn.LayerNorm(embedding_dim)
self.cross_attn_token_to_image = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.norm2 = nn.LayerNorm(embedding_dim)
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
self.norm3 = nn.LayerNorm(embedding_dim)
self.norm4 = nn.LayerNorm(embedding_dim)
self.cross_attn_image_to_token = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.skip_first_layer_pe = skip_first_layer_pe
def forward(
self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
) -> Tuple[Tensor, Tensor]:
# Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(queries)
# Cross attention block, tokens attending to image embedding
q = queries + query_pe
# print("key size is", keys.size())
# print("image_pe size is", key_pe.size())
k = keys + key_pe
attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm2(queries)
# MLP block
mlp_out = self.mlp(queries)
queries = queries + mlp_out
queries = self.norm3(queries)
# Cross attention block, image embedding attending to tokens
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
keys = keys + attn_out
keys = self.norm4(keys)
return queries, keys
class CrossAttentionBlock(nn.Module):
def __init__(
self,
depth: int,
embedding_dim: int,
num_heads: int,
mlp_dim: int = 2048,
activation: Type[nn.Module] = nn.ReLU,
attention_downsample_rate: int = 2,
skip_first_layer_pe: bool = False,
) -> None:
super().__init__()
self.self_attn = Attention(embedding_dim, num_heads)
self.norm1 = nn.LayerNorm(embedding_dim)
self.cross_attn_token_to_image = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.norm2 = nn.LayerNorm(embedding_dim)
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
self.norm3 = nn.LayerNorm(embedding_dim)
self.norm4 = nn.LayerNorm(embedding_dim)
self.cross_attn_image_to_token = Attention(
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
)
self.skip_first_layer_pe = skip_first_layer_pe
def forward(
self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
) -> Tuple[Tensor, Tensor]:
# Self attention block
if self.skip_first_layer_pe:
queries = self.self_attn(q=queries, k=queries, v=queries)
else:
q = queries + query_pe
attn_out = self.self_attn(q=q, k=q, v=queries)
queries = queries + attn_out
queries = self.norm1(queries)
# Cross attention block, tokens attending to image embedding
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
queries = queries + attn_out
queries = self.norm2(queries)
# MLP block
mlp_out = self.mlp(queries)
queries = queries + mlp_out
queries = self.norm3(queries)
# Cross attention block, image embedding attending to tokens
q = queries + query_pe
k = keys + key_pe
attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
keys = keys + attn_out
keys = self.norm4(keys)
return queries, keys
class Attention(nn.Module):
"""
An attention layer that allows for downscaling the size of the embedding
after projection to queries, keys, and values.
"""
def __init__(
self,
embedding_dim: int,
num_heads: int,
downsample_rate: int = 1,
) -> None:
super().__init__()
self.embedding_dim = embedding_dim
self.internal_dim = embedding_dim // downsample_rate
self.num_heads = num_heads
# print("self.embedding_dim is", self.embedding_dim)
# print("self.internal_dim is", self.internal_dim)
# print("num_heads is", num_heads)
assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
b, n, c = x.shape
x = x.reshape(b, n, num_heads, c // num_heads)
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
def _recombine_heads(self, x: Tensor) -> Tensor:
b, n_heads, n_tokens, c_per_head = x.shape
x = x.transpose(1, 2)
return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
# Input projections
q = self.q_proj(q)
k = self.k_proj(k)
v = self.v_proj(v)
# Separate into heads
q = self._separate_heads(q, self.num_heads)
k = self._separate_heads(k, self.num_heads)
v = self._separate_heads(v, self.num_heads)
# Attention
_, _, _, c_per_head = q.shape
attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
attn = attn / math.sqrt(c_per_head)
attn = torch.softmax(attn, dim=-1)
# Get output
out = attn @ v
out = self._recombine_heads(out)
out = self.out_proj(out)
return out