Update ViT
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D-X-Y
@@ -3,3 +3,5 @@
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#####################################################
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# The models in this folder is written with xlayers #
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#####################################################
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from .transformers import get_transformer
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@@ -1,6 +1,8 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
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#####################################################
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# Vision Transformer: arxiv.org/pdf/2010.11929.pdf #
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#####################################################
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import math
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from functools import partial
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from typing import Optional, Text, List
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@@ -10,186 +12,163 @@ import torch.nn as nn
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import torch.nn.functional as F
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from xautodl import spaces
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from xautodl.xlayers import trunc_normal_
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from xautodl.xlayers import super_core
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from xautodl import xlayers
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from xautodl.xlayers import weight_init
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__all__ = ["DefaultSearchSpace", "DEFAULT_NET_CONFIG", "get_transformer"]
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def pair(t):
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return t if isinstance(t, tuple) else (t, t)
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def _get_mul_specs(candidates, num):
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results = []
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for i in range(num):
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results.append(spaces.Categorical(*candidates))
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return results
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def _init_weights(m):
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if isinstance(m, nn.Linear):
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weight_init.trunc_normal_(m.weight, std=0.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, xlayers.SuperLinear):
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weight_init.trunc_normal_(m._super_weight, std=0.02)
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if m._super_bias is not None:
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nn.init.constant_(m._super_bias, 0)
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elif isinstance(m, xlayers.SuperLayerNorm1D):
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nn.init.constant_(m.weight, 1.0)
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nn.init.constant_(m.bias, 0)
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def _get_list_mul(num, multipler):
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results = []
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for i in range(1, num + 1):
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results.append(i * multipler)
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return results
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name2config = {
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"vit-base": dict(
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type="vit",
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image_size=256,
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patch_size=16,
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num_classes=1000,
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dim=768,
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depth=12,
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heads=12,
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dropout=0.1,
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emb_dropout=0.1,
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),
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"vit-large": dict(
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type="vit",
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image_size=256,
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patch_size=16,
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num_classes=1000,
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dim=1024,
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depth=24,
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heads=16,
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dropout=0.1,
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emb_dropout=0.1,
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),
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"vit-huge": dict(
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type="vit",
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image_size=256,
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patch_size=16,
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num_classes=1000,
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dim=1280,
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depth=32,
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heads=16,
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dropout=0.1,
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emb_dropout=0.1,
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),
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}
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def _assert_types(x, expected_types):
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if not isinstance(x, expected_types):
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raise TypeError(
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"The type [{:}] is expected to be {:}.".format(type(x), expected_types)
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)
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DEFAULT_NET_CONFIG = None
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_default_max_depth = 5
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DefaultSearchSpace = dict(
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d_feat=6,
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embed_dim=spaces.Categorical(*_get_list_mul(8, 16)),
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num_heads=_get_mul_specs((1, 2, 4, 8), _default_max_depth),
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mlp_hidden_multipliers=_get_mul_specs((0.5, 1, 2, 4, 8), _default_max_depth),
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qkv_bias=True,
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pos_drop=0.0,
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other_drop=0.0,
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)
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class SuperTransformer(super_core.SuperModule):
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class SuperViT(xlayers.SuperModule):
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"""The super model for transformer."""
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def __init__(
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self,
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d_feat: int = 6,
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embed_dim: List[super_core.IntSpaceType] = DefaultSearchSpace["embed_dim"],
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num_heads: List[super_core.IntSpaceType] = DefaultSearchSpace["num_heads"],
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mlp_hidden_multipliers: List[super_core.IntSpaceType] = DefaultSearchSpace[
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"mlp_hidden_multipliers"
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],
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qkv_bias: bool = DefaultSearchSpace["qkv_bias"],
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pos_drop: float = DefaultSearchSpace["pos_drop"],
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other_drop: float = DefaultSearchSpace["other_drop"],
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max_seq_len: int = 65,
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image_size,
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patch_size,
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num_classes,
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dim,
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depth,
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heads,
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mlp_multiplier=4,
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channels=3,
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dropout=0.0,
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emb_dropout=0.0,
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):
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super(SuperTransformer, self).__init__()
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self._embed_dim = embed_dim
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self._num_heads = num_heads
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self._mlp_hidden_multipliers = mlp_hidden_multipliers
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super(SuperViT, self).__init__()
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image_height, image_width = pair(image_size)
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patch_height, patch_width = pair(patch_size)
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# the stem part
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self.input_embed = super_core.SuperAlphaEBDv1(d_feat, embed_dim)
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self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
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self.pos_embed = super_core.SuperPositionalEncoder(
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d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop
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if image_height % patch_height != 0 or image_width % patch_width != 0:
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raise ValueError("Image dimensions must be divisible by the patch size.")
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num_patches = (image_height // patch_height) * (image_width // patch_width)
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patch_dim = channels * patch_height * patch_width
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self.to_patch_embedding = xlayers.SuperSequential(
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xlayers.SuperReArrange(
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"b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
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p1=patch_height,
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p2=patch_width,
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),
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xlayers.SuperLinear(patch_dim, dim),
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)
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# build the transformer encode layers -->> check params
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_assert_types(num_heads, (tuple, list))
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_assert_types(mlp_hidden_multipliers, (tuple, list))
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assert len(num_heads) == len(mlp_hidden_multipliers), "{:} vs {:}".format(
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len(num_heads), len(mlp_hidden_multipliers)
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)
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# build the transformer encode layers -->> backbone
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
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self.dropout = nn.Dropout(emb_dropout)
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# build the transformer encode layers
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layers = []
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for num_head, mlp_hidden_multiplier in zip(num_heads, mlp_hidden_multipliers):
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layer = super_core.SuperTransformerEncoderLayer(
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embed_dim,
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num_head,
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qkv_bias,
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mlp_hidden_multiplier,
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other_drop,
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for ilayer in range(depth):
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layers.append(
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xlayers.SuperTransformerEncoderLayer(
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dim, heads, False, mlp_multiplier, dropout
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)
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)
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layers.append(layer)
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self.backbone = super_core.SuperSequential(*layers)
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# the regression head
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self.head = super_core.SuperSequential(
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super_core.SuperLayerNorm1D(embed_dim), super_core.SuperLinear(embed_dim, 1)
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self.backbone = xlayers.SuperSequential(*layers)
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self.cls_head = xlayers.SuperSequential(
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xlayers.SuperLayerNorm1D(dim), xlayers.SuperLinear(dim, num_classes)
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)
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trunc_normal_(self.cls_token, std=0.02)
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self.apply(self._init_weights)
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@property
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def embed_dim(self):
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return spaces.get_max(self._embed_dim)
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weight_init.trunc_normal_(self.cls_token, std=0.02)
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self.apply(_init_weights)
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@property
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def abstract_search_space(self):
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root_node = spaces.VirtualNode(id(self))
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if not spaces.is_determined(self._embed_dim):
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root_node.append("_embed_dim", self._embed_dim.abstract(reuse_last=True))
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xdict = dict(
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input_embed=self.input_embed.abstract_search_space,
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pos_embed=self.pos_embed.abstract_search_space,
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backbone=self.backbone.abstract_search_space,
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head=self.head.abstract_search_space,
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)
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for key, space in xdict.items():
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if not spaces.is_determined(space):
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root_node.append(key, space)
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return root_node
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raise NotImplementedError
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def apply_candidate(self, abstract_child: spaces.VirtualNode):
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super(SuperTransformer, self).apply_candidate(abstract_child)
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xkeys = ("input_embed", "pos_embed", "backbone", "head")
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for key in xkeys:
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if key in abstract_child:
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getattr(self, key).apply_candidate(abstract_child[key])
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=0.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, super_core.SuperLinear):
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trunc_normal_(m._super_weight, std=0.02)
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if m._super_bias is not None:
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nn.init.constant_(m._super_bias, 0)
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elif isinstance(m, super_core.SuperLayerNorm1D):
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nn.init.constant_(m.weight, 1.0)
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nn.init.constant_(m.bias, 0)
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super(SuperViT, self).apply_candidate(abstract_child)
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raise NotImplementedError
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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batch, flatten_size = input.shape
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feats = self.input_embed(input) # batch * 60 * 64
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if not spaces.is_determined(self._embed_dim):
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embed_dim = self.abstract_child["_embed_dim"].value
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else:
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embed_dim = spaces.get_determined_value(self._embed_dim)
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cls_tokens = self.cls_token.expand(batch, -1, -1)
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cls_tokens = F.interpolate(
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cls_tokens, size=(embed_dim), mode="linear", align_corners=True
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)
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feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
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feats_w_tp = self.pos_embed(feats_w_ct)
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xfeats = self.backbone(feats_w_tp)
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xfeats = xfeats[:, 0, :] # use the feature for the first token
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predicts = self.head(xfeats).squeeze(-1)
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return predicts
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raise NotImplementedError
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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batch, flatten_size = input.shape
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feats = self.input_embed(input) # batch * 60 * 64
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tensors = self.to_patch_embedding(input)
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batch, seq, _ = tensors.shape
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cls_tokens = self.cls_token.expand(batch, -1, -1)
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feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
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feats_w_tp = self.pos_embed(feats_w_ct)
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xfeats = self.backbone(feats_w_tp)
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xfeats = xfeats[:, 0, :] # use the feature for the first token
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predicts = self.head(xfeats).squeeze(-1)
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return predicts
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feats = torch.cat((cls_tokens, tensors), dim=1)
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feats = feats + self.pos_embedding[:, : seq + 1, :]
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feats = self.dropout(feats)
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feats = self.backbone(feats)
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x = feats[:, 0] # the features for cls-token
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return self.cls_head(x)
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def get_transformer(config):
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if config is None:
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return SuperTransformer(6)
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if isinstance(config, str) and config.lower() in name2config:
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config = name2config[config.lower()]
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if not isinstance(config, dict):
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raise ValueError("Invalid Configuration: {:}".format(config))
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name = config.get("name", "basic")
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if name == "basic":
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model = SuperTransformer(
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d_feat=config.get("d_feat"),
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embed_dim=config.get("embed_dim"),
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num_heads=config.get("num_heads"),
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mlp_hidden_multipliers=config.get("mlp_hidden_multipliers"),
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qkv_bias=config.get("qkv_bias"),
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pos_drop=config.get("pos_drop"),
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other_drop=config.get("other_drop"),
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model_type = config.get("type", "vit").lower()
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if model_type == "vit":
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model = SuperViT(
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image_size=config.get("image_size"),
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patch_size=config.get("patch_size"),
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num_classes=config.get("num_classes"),
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dim=config.get("dim"),
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depth=config.get("depth"),
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heads=config.get("heads"),
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dropout=config.get("dropout"),
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emb_dropout=config.get("emb_dropout"),
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)
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else:
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raise ValueError("Unknown model name: {:}".format(name))
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raise ValueError("Unknown model type: {:}".format(model_type))
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return model
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