Update ViT

tests/test_super_vit.py

@@ -0,0 +1,29 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+# pytest ./tests/test_super_vit.py -s               #
+#####################################################
+import sys
+import unittest
+
+import torch
+from xautodl.xmodels import transformers
+from xautodl.utils.flop_benchmark import count_parameters
+
+class TestSuperViT(unittest.TestCase):
+    """Test the super re-arrange layer."""
+
+    def test_super_vit(self):
+        model = transformers.get_transformer("vit-base")
+        tensor = torch.rand((16, 3, 256, 256))
+        print("The tensor shape: {:}".format(tensor.shape))
+        print(model)
+        outs = model(tensor)
+        print("The output tensor shape: {:}".format(outs.shape))
+
+    def test_model_size(self):
+        name2config = transformers.name2config
+        for name, config in name2config.items():
+            model = transformers.get_transformer(config)
+            size = count_parameters(model, "mb", True)
+            print('{:10s} : size={:.2f}MB'.format(name, size))

xautodl/xlayers/super_mlp.py

@@ -0,0 +1,319 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import math
+from typing import Optional, Callable
+
+from xautodl import spaces
+from .super_module import SuperModule
+from .super_module import IntSpaceType
+from .super_module import BoolSpaceType
+
+
+class SuperLinear(SuperModule):
+    """Applies a linear transformation to the incoming data: :math:`y = xA^T + b`"""
+
+    def __init__(
+        self,
+        in_features: IntSpaceType,
+        out_features: IntSpaceType,
+        bias: BoolSpaceType = True,
+    ) -> None:
+        super(SuperLinear, self).__init__()
+
+        # the raw input args
+        self._in_features = in_features
+        self._out_features = out_features
+        self._bias = bias
+        # weights to be optimized
+        self.register_parameter(
+            "_super_weight",
+            torch.nn.Parameter(torch.Tensor(self.out_features, self.in_features)),
+        )
+        if self.bias:
+            self.register_parameter(
+                "_super_bias", torch.nn.Parameter(torch.Tensor(self.out_features))
+            )
+        else:
+            self.register_parameter("_super_bias", None)
+        self.reset_parameters()
+
+    @property
+    def in_features(self):
+        return spaces.get_max(self._in_features)
+
+    @property
+    def out_features(self):
+        return spaces.get_max(self._out_features)
+
+    @property
+    def bias(self):
+        return spaces.has_categorical(self._bias, True)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        if not spaces.is_determined(self._in_features):
+            root_node.append(
+                "_in_features", self._in_features.abstract(reuse_last=True)
+            )
+        if not spaces.is_determined(self._out_features):
+            root_node.append(
+                "_out_features", self._out_features.abstract(reuse_last=True)
+            )
+        if not spaces.is_determined(self._bias):
+            root_node.append("_bias", self._bias.abstract(reuse_last=True))
+        return root_node
+
+    def reset_parameters(self) -> None:
+        nn.init.kaiming_uniform_(self._super_weight, a=math.sqrt(5))
+        if self.bias:
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self._super_weight)
+            bound = 1 / math.sqrt(fan_in)
+            nn.init.uniform_(self._super_bias, -bound, bound)
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        if not spaces.is_determined(self._in_features):
+            expected_input_dim = self.abstract_child["_in_features"].value
+        else:
+            expected_input_dim = spaces.get_determined_value(self._in_features)
+        if input.size(-1) != expected_input_dim:
+            raise ValueError(
+                "Expect the input dim of {:} instead of {:}".format(
+                    expected_input_dim, input.size(-1)
+                )
+            )
+        # create the weight matrix
+        if not spaces.is_determined(self._out_features):
+            out_dim = self.abstract_child["_out_features"].value
+        else:
+            out_dim = spaces.get_determined_value(self._out_features)
+        candidate_weight = self._super_weight[:out_dim, :expected_input_dim]
+        # create the bias matrix
+        if not spaces.is_determined(self._bias):
+            if self.abstract_child["_bias"].value:
+                candidate_bias = self._super_bias[:out_dim]
+            else:
+                candidate_bias = None
+        else:
+            if spaces.get_determined_value(self._bias):
+                candidate_bias = self._super_bias[:out_dim]
+            else:
+                candidate_bias = None
+        return F.linear(input, candidate_weight, candidate_bias)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        return F.linear(input, self._super_weight, self._super_bias)
+
+    def extra_repr(self) -> str:
+        return "in_features={:}, out_features={:}, bias={:}".format(
+            self._in_features, self._out_features, self._bias
+        )
+
+    def forward_with_container(self, input, container, prefix=[]):
+        super_weight_name = ".".join(prefix + ["_super_weight"])
+        super_weight = container.query(super_weight_name)
+        super_bias_name = ".".join(prefix + ["_super_bias"])
+        if container.has(super_bias_name):
+            super_bias = container.query(super_bias_name)
+        else:
+            super_bias = None
+        return F.linear(input, super_weight, super_bias)
+
+
+class SuperMLPv1(SuperModule):
+    """An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
+
+    def __init__(
+        self,
+        in_features: IntSpaceType,
+        hidden_features: IntSpaceType,
+        out_features: IntSpaceType,
+        act_layer: Callable[[], nn.Module] = nn.GELU,
+        drop: Optional[float] = None,
+    ):
+        super(SuperMLPv1, self).__init__()
+        self._in_features = in_features
+        self._hidden_features = hidden_features
+        self._out_features = out_features
+        self._drop_rate = drop
+        self.fc1 = SuperLinear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = SuperLinear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop or 0.0)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        space_fc1 = self.fc1.abstract_search_space
+        space_fc2 = self.fc2.abstract_search_space
+        if not spaces.is_determined(space_fc1):
+            root_node.append("fc1", space_fc1)
+        if not spaces.is_determined(space_fc2):
+            root_node.append("fc2", space_fc2)
+        return root_node
+
+    def apply_candidate(self, abstract_child: spaces.VirtualNode):
+        super(SuperMLPv1, self).apply_candidate(abstract_child)
+        if "fc1" in abstract_child:
+            self.fc1.apply_candidate(abstract_child["fc1"])
+        if "fc2" in abstract_child:
+            self.fc2.apply_candidate(abstract_child["fc2"])
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        return self.forward_raw(input)
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(input)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return "in_features={:}, hidden_features={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
+            self._in_features,
+            self._hidden_features,
+            self._out_features,
+            self._drop_rate,
+        )
+
+
+class SuperMLPv2(SuperModule):
+    """An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
+
+    def __init__(
+        self,
+        in_features: IntSpaceType,
+        hidden_multiplier: IntSpaceType,
+        out_features: IntSpaceType,
+        act_layer: Callable[[], nn.Module] = nn.GELU,
+        drop: Optional[float] = None,
+    ):
+        super(SuperMLPv2, self).__init__()
+        self._in_features = in_features
+        self._hidden_multiplier = hidden_multiplier
+        self._out_features = out_features
+        self._drop_rate = drop
+        self._params = nn.ParameterDict({})
+
+        self._create_linear(
+            "fc1", self.in_features, int(self.in_features * self.hidden_multiplier)
+        )
+        self._create_linear(
+            "fc2", int(self.in_features * self.hidden_multiplier), self.out_features
+        )
+        self.act = act_layer()
+        self.drop = nn.Dropout(drop or 0.0)
+        self.reset_parameters()
+
+    @property
+    def in_features(self):
+        return spaces.get_max(self._in_features)
+
+    @property
+    def hidden_multiplier(self):
+        return spaces.get_max(self._hidden_multiplier)
+
+    @property
+    def out_features(self):
+        return spaces.get_max(self._out_features)
+
+    def _create_linear(self, name, inC, outC):
+        self._params["{:}_super_weight".format(name)] = torch.nn.Parameter(
+            torch.Tensor(outC, inC)
+        )
+        self._params["{:}_super_bias".format(name)] = torch.nn.Parameter(
+            torch.Tensor(outC)
+        )
+
+    def reset_parameters(self) -> None:
+        nn.init.kaiming_uniform_(self._params["fc1_super_weight"], a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self._params["fc2_super_weight"], a=math.sqrt(5))
+        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
+            self._params["fc1_super_weight"]
+        )
+        bound = 1 / math.sqrt(fan_in)
+        nn.init.uniform_(self._params["fc1_super_bias"], -bound, bound)
+        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
+            self._params["fc2_super_weight"]
+        )
+        bound = 1 / math.sqrt(fan_in)
+        nn.init.uniform_(self._params["fc2_super_bias"], -bound, bound)
+
+    @property
+    def abstract_search_space(self):
+        root_node = spaces.VirtualNode(id(self))
+        if not spaces.is_determined(self._in_features):
+            root_node.append(
+                "_in_features", self._in_features.abstract(reuse_last=True)
+            )
+        if not spaces.is_determined(self._hidden_multiplier):
+            root_node.append(
+                "_hidden_multiplier", self._hidden_multiplier.abstract(reuse_last=True)
+            )
+        if not spaces.is_determined(self._out_features):
+            root_node.append(
+                "_out_features", self._out_features.abstract(reuse_last=True)
+            )
+        return root_node
+
+    def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
+        # check inputs ->
+        if not spaces.is_determined(self._in_features):
+            expected_input_dim = self.abstract_child["_in_features"].value
+        else:
+            expected_input_dim = spaces.get_determined_value(self._in_features)
+        if input.size(-1) != expected_input_dim:
+            raise ValueError(
+                "Expect the input dim of {:} instead of {:}".format(
+                    expected_input_dim, input.size(-1)
+                )
+            )
+        # create the weight and bias matrix for fc1
+        if not spaces.is_determined(self._hidden_multiplier):
+            hmul = self.abstract_child["_hidden_multiplier"].value * expected_input_dim
+        else:
+            hmul = spaces.get_determined_value(self._hidden_multiplier)
+        hidden_dim = int(expected_input_dim * hmul)
+        _fc1_weight = self._params["fc1_super_weight"][:hidden_dim, :expected_input_dim]
+        _fc1_bias = self._params["fc1_super_bias"][:hidden_dim]
+        x = F.linear(input, _fc1_weight, _fc1_bias)
+        x = self.act(x)
+        x = self.drop(x)
+        # create the weight and bias matrix for fc2
+        if not spaces.is_determined(self._out_features):
+            out_dim = self.abstract_child["_out_features"].value
+        else:
+            out_dim = spaces.get_determined_value(self._out_features)
+        _fc2_weight = self._params["fc2_super_weight"][:out_dim, :hidden_dim]
+        _fc2_bias = self._params["fc2_super_bias"][:out_dim]
+        x = F.linear(x, _fc2_weight, _fc2_bias)
+        x = self.drop(x)
+        return x
+
+    def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
+        x = F.linear(
+            input, self._params["fc1_super_weight"], self._params["fc1_super_bias"]
+        )
+        x = self.act(x)
+        x = self.drop(x)
+        x = F.linear(
+            x, self._params["fc2_super_weight"], self._params["fc2_super_bias"]
+        )
+        x = self.drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return "in_features={:}, hidden_multiplier={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
+            self._in_features,
+            self._hidden_multiplier,
+            self._out_features,
+            self._drop_rate,
+        )

xautodl/xmodels/__init__.py

@@ -3,3 +3,5 @@
 #####################################################
 # The models in this folder is written with xlayers #
 #####################################################
+
+from .transformers import get_transformer

xautodl/xmodels/transformers.py

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