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import math
import torch
import torch.nn as nn
from ..utils.helpers import to_2tuple
class PatchEmbed2D(nn.Module):
def __init__(
self,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
bias=True,
dtype=None,
device=None,
):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.flatten = flatten
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, device=device, dtype=dtype
)
nn.init.xavier_uniform_(self.proj.weight.view(self.proj.weight.size(0), -1))
if bias:
nn.init.zeros_(self.proj.bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding
Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on the impl in https://github.com/google-research/vision_transformer
Hacked together by / Copyright 2020 Ross Wightman
Remove the _assert function in forward function to be compatible with multi-resolution images.
"""
def __init__(
self,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
bias=True,
dtype=None,
device=None,
):
factory_kwargs = {"dtype": dtype, "device": device}
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.flatten = flatten
self.proj = nn.Conv3d(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=patch_size,
bias=bias,
**factory_kwargs
)
nn.init.xavier_uniform_(self.proj.weight.view(self.proj.weight.size(0), -1))
if bias:
nn.init.zeros_(self.proj.bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
class TextProjection(nn.Module):
"""
Projects text embeddings. Also handles dropout for classifier-free guidance.
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
"""
def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
factory_kwargs = {"dtype": dtype, "device": device}
super().__init__()
self.linear_1 = nn.Linear(
in_features=in_channels,
out_features=hidden_size,
bias=True,
**factory_kwargs
)
self.act_1 = act_layer()
self.linear_2 = nn.Linear(
in_features=hidden_size,
out_features=hidden_size,
bias=True,
**factory_kwargs
)
def forward(self, caption):
hidden_states = self.linear_1(caption)
hidden_states = self.act_1(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
Args:
t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional.
dim (int): the dimension of the output.
max_period (int): controls the minimum frequency of the embeddings.
Returns:
embedding (torch.Tensor): An (N, D) Tensor of positional embeddings.
.. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(
self,
hidden_size,
act_layer,
frequency_embedding_size=256,
max_period=10000,
out_size=None,
dtype=None,
device=None,
):
factory_kwargs = {"dtype": dtype, "device": device}
super().__init__()
self.frequency_embedding_size = frequency_embedding_size
self.max_period = max_period
if out_size is None:
out_size = hidden_size
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True, **factory_kwargs),
act_layer(),
nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
)
nn.init.normal_(self.mlp[0].weight, std=0.02)
nn.init.normal_(self.mlp[2].weight, std=0.02)
def forward(self, t):
t_freq = timestep_embedding(t, self.frequency_embedding_size, self.max_period).type(self.mlp[0].weight.dtype)
t_emb = self.mlp(t_freq)
return t_emb |