112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
import math
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import torch
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import torch.nn as nn
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from einops import repeat
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from timm.models.layers import to_2tuple
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class PatchEmbed(nn.Module):
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""" 2D Image to Patch Embedding
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Image to Patch Embedding using Conv2d
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A convolution based approach to patchifying a 2D image w/ embedding projection.
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Based on the impl in https://github.com/google-research/vision_transformer
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Hacked together by / Copyright 2020 Ross Wightman
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Remove the _assert function in forward function to be compatible with multi-resolution images.
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"""
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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norm_layer=None,
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flatten=True,
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bias=True,
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):
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super().__init__()
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if isinstance(img_size, int):
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img_size = to_2tuple(img_size)
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elif isinstance(img_size, (tuple, list)) and len(img_size) == 2:
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img_size = tuple(img_size)
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else:
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raise ValueError(f"img_size must be int or tuple/list of length 2. Got {img_size}")
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patch_size = to_2tuple(patch_size)
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self.img_size = img_size
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self.patch_size = patch_size
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self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.flatten = flatten
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def update_image_size(self, img_size):
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self.img_size = img_size
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self.grid_size = (img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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def forward(self, x):
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# B, C, H, W = x.shape
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# _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
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# _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
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x = self.proj(x)
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if self.flatten:
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x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
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x = self.norm(x)
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return x
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def timestep_embedding(t, dim, max_period=10000, repeat_only=False):
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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
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if not repeat_only:
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period)
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* torch.arange(start=0, end=half, dtype=torch.float32)
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/ half
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).to(device=t.device) # size: [dim/2], 一个指数衰减的曲线
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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else:
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embedding = repeat(t, "b -> b d", d=dim)
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return embedding
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class TimestepEmbedder(nn.Module):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(self, hidden_size, frequency_embedding_size=256, out_size=None):
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super().__init__()
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if out_size is None:
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out_size = hidden_size
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, out_size, bias=True),
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)
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self.frequency_embedding_size = frequency_embedding_size
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def forward(self, t):
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t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype)
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t_emb = self.mlp(t_freq)
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return t_emb
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