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Support SimpleViT as encoder in MAE (#272)

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support simplevit in mae
This commit is contained in:
roydenwa
2023-07-24 15:43:01 +02:00
committed by GitHub
parent 9e3fec2398
commit d4daf7bd0f
2 changed files with 21 additions and 15 deletions
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5
vit_pytorch/mae.py
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@@ -49,7 +49,10 @@ class MAE(nn.Module):
# patch to encoder tokens and add positions
tokens = self.patch_to_emb(patches)
tokens = tokens + self.encoder.pos_embedding[:, 1:(num_patches + 1)]
if self.encoder.pool == "cls":
tokens += self.encoder.pos_embedding[:, 1:(num_patches + 1)]
elif self.encoder.pool == "mean":
tokens += self.encoder.pos_embedding.to(device, dtype=tokens.dtype)
# calculate of patches needed to be masked, and get random indices, dividing it up for mask vs unmasked

31
vit_pytorch/simple_vit.py
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@@ -9,17 +9,15 @@ from einops.layers.torch import Rearrange
def pair(t):
return t if isinstance(t, tuple) else (t, t)
def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
_, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1)
omega = 1. / (temperature ** omega)
def posemb_sincos_2d(h, w, dim, temperature: int = 10000, dtype = torch.float32):
y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
assert (dim % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
omega = torch.arange(dim // 4) / (dim // 4 - 1)
omega = 1.0 / (temperature ** omega)
y = y.flatten()[:, None] * omega[None, :]
x = x.flatten()[:, None] * omega[None, :]
pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
x = x.flatten()[:, None] * omega[None, :]
pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
return pe.type(dtype)
# classes
@@ -86,16 +84,21 @@ class SimpleViT(nn.Module):
assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b h w (p1 p2 c)', p1 = patch_height, p2 = patch_width),
Rearrange("b c (h p1) (w p2) -> b (h w) (p1 p2 c)", p1 = patch_height, p2 = patch_width),
nn.LayerNorm(patch_dim),
nn.Linear(patch_dim, dim),
nn.LayerNorm(dim),
)
self.pos_embedding = posemb_sincos_2d(
h = image_height // patch_height,
w = image_width // patch_width,
dim = dim,
)
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
self.to_latent = nn.Identity()
@@ -103,13 +106,13 @@ class SimpleViT(nn.Module):
nn.LayerNorm(dim),
nn.Linear(dim, num_classes)
)
self.pool = "mean"
def forward(self, img):
*_, h, w, dtype = *img.shape, img.dtype
device = img.device
x = self.to_patch_embedding(img)
pe = posemb_sincos_2d(x)
x = rearrange(x, 'b ... d -> b (...) d') + pe
x += self.pos_embedding.to(device, dtype=x.dtype)
x = self.transformer(x)
x = x.mean(dim = 1)