Update ViT
This commit is contained in:
D-X-Y
29
tests/test_super_vit.py
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29
tests/test_super_vit.py
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@@ -0,0 +1,29 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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# pytest ./tests/test_super_vit.py -s #
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#####################################################
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import sys
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import unittest
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import torch
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from xautodl.xmodels import transformers
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from xautodl.utils.flop_benchmark import count_parameters
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class TestSuperViT(unittest.TestCase):
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"""Test the super re-arrange layer."""
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def test_super_vit(self):
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model = transformers.get_transformer("vit-base")
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tensor = torch.rand((16, 3, 256, 256))
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print("The tensor shape: {:}".format(tensor.shape))
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print(model)
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outs = model(tensor)
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print("The output tensor shape: {:}".format(outs.shape))
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def test_model_size(self):
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name2config = transformers.name2config
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for name, config in name2config.items():
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model = transformers.get_transformer(config)
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size = count_parameters(model, "mb", True)
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print('{:10s} : size={:.2f}MB'.format(name, size))
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319
xautodl/xlayers/super_mlp.py
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319
xautodl/xlayers/super_mlp.py
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@@ -0,0 +1,319 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, Callable
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from xautodl import spaces
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from .super_module import SuperModule
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from .super_module import IntSpaceType
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from .super_module import BoolSpaceType
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class SuperLinear(SuperModule):
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"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`"""
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def __init__(
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self,
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in_features: IntSpaceType,
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out_features: IntSpaceType,
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bias: BoolSpaceType = True,
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) -> None:
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super(SuperLinear, self).__init__()
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# the raw input args
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self._in_features = in_features
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self._out_features = out_features
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self._bias = bias
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# weights to be optimized
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self.register_parameter(
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"_super_weight",
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torch.nn.Parameter(torch.Tensor(self.out_features, self.in_features)),
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)
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if self.bias:
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self.register_parameter(
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"_super_bias", torch.nn.Parameter(torch.Tensor(self.out_features))
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)
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else:
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self.register_parameter("_super_bias", None)
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self.reset_parameters()
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@property
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def in_features(self):
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return spaces.get_max(self._in_features)
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@property
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def out_features(self):
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return spaces.get_max(self._out_features)
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@property
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def bias(self):
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return spaces.has_categorical(self._bias, True)
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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._in_features):
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root_node.append(
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"_in_features", self._in_features.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._out_features):
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root_node.append(
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"_out_features", self._out_features.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._bias):
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root_node.append("_bias", self._bias.abstract(reuse_last=True))
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return root_node
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def reset_parameters(self) -> None:
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nn.init.kaiming_uniform_(self._super_weight, a=math.sqrt(5))
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if self.bias:
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self._super_weight)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(self._super_bias, -bound, bound)
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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# check inputs ->
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if not spaces.is_determined(self._in_features):
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expected_input_dim = self.abstract_child["_in_features"].value
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else:
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expected_input_dim = spaces.get_determined_value(self._in_features)
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if input.size(-1) != expected_input_dim:
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raise ValueError(
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"Expect the input dim of {:} instead of {:}".format(
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expected_input_dim, input.size(-1)
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)
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)
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# create the weight matrix
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if not spaces.is_determined(self._out_features):
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out_dim = self.abstract_child["_out_features"].value
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else:
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out_dim = spaces.get_determined_value(self._out_features)
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candidate_weight = self._super_weight[:out_dim, :expected_input_dim]
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# create the bias matrix
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if not spaces.is_determined(self._bias):
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if self.abstract_child["_bias"].value:
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candidate_bias = self._super_bias[:out_dim]
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else:
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candidate_bias = None
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else:
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if spaces.get_determined_value(self._bias):
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candidate_bias = self._super_bias[:out_dim]
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else:
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candidate_bias = None
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return F.linear(input, candidate_weight, candidate_bias)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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return F.linear(input, self._super_weight, self._super_bias)
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def extra_repr(self) -> str:
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return "in_features={:}, out_features={:}, bias={:}".format(
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self._in_features, self._out_features, self._bias
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)
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def forward_with_container(self, input, container, prefix=[]):
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super_weight_name = ".".join(prefix + ["_super_weight"])
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super_weight = container.query(super_weight_name)
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super_bias_name = ".".join(prefix + ["_super_bias"])
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if container.has(super_bias_name):
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super_bias = container.query(super_bias_name)
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else:
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super_bias = None
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return F.linear(input, super_weight, super_bias)
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class SuperMLPv1(SuperModule):
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"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
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def __init__(
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self,
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in_features: IntSpaceType,
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hidden_features: IntSpaceType,
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out_features: IntSpaceType,
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act_layer: Callable[[], nn.Module] = nn.GELU,
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drop: Optional[float] = None,
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):
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super(SuperMLPv1, self).__init__()
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self._in_features = in_features
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self._hidden_features = hidden_features
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self._out_features = out_features
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self._drop_rate = drop
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self.fc1 = SuperLinear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = SuperLinear(hidden_features, out_features)
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self.drop = nn.Dropout(drop or 0.0)
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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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space_fc1 = self.fc1.abstract_search_space
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space_fc2 = self.fc2.abstract_search_space
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if not spaces.is_determined(space_fc1):
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root_node.append("fc1", space_fc1)
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if not spaces.is_determined(space_fc2):
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root_node.append("fc2", space_fc2)
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return root_node
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def apply_candidate(self, abstract_child: spaces.VirtualNode):
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super(SuperMLPv1, self).apply_candidate(abstract_child)
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if "fc1" in abstract_child:
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self.fc1.apply_candidate(abstract_child["fc1"])
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if "fc2" in abstract_child:
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self.fc2.apply_candidate(abstract_child["fc2"])
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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return self.forward_raw(input)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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x = self.fc1(input)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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def extra_repr(self) -> str:
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return "in_features={:}, hidden_features={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
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self._in_features,
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self._hidden_features,
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self._out_features,
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self._drop_rate,
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)
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class SuperMLPv2(SuperModule):
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"""An MLP layer: FC -> Activation -> Drop -> FC -> Drop."""
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def __init__(
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self,
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in_features: IntSpaceType,
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hidden_multiplier: IntSpaceType,
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out_features: IntSpaceType,
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act_layer: Callable[[], nn.Module] = nn.GELU,
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drop: Optional[float] = None,
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):
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super(SuperMLPv2, self).__init__()
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self._in_features = in_features
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self._hidden_multiplier = hidden_multiplier
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self._out_features = out_features
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self._drop_rate = drop
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self._params = nn.ParameterDict({})
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self._create_linear(
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"fc1", self.in_features, int(self.in_features * self.hidden_multiplier)
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)
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self._create_linear(
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"fc2", int(self.in_features * self.hidden_multiplier), self.out_features
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)
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self.act = act_layer()
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self.drop = nn.Dropout(drop or 0.0)
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self.reset_parameters()
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@property
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def in_features(self):
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return spaces.get_max(self._in_features)
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@property
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def hidden_multiplier(self):
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return spaces.get_max(self._hidden_multiplier)
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@property
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def out_features(self):
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return spaces.get_max(self._out_features)
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def _create_linear(self, name, inC, outC):
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self._params["{:}_super_weight".format(name)] = torch.nn.Parameter(
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torch.Tensor(outC, inC)
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)
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self._params["{:}_super_bias".format(name)] = torch.nn.Parameter(
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torch.Tensor(outC)
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)
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def reset_parameters(self) -> None:
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nn.init.kaiming_uniform_(self._params["fc1_super_weight"], a=math.sqrt(5))
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nn.init.kaiming_uniform_(self._params["fc2_super_weight"], a=math.sqrt(5))
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
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self._params["fc1_super_weight"]
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)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(self._params["fc1_super_bias"], -bound, bound)
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
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self._params["fc2_super_weight"]
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)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(self._params["fc2_super_bias"], -bound, bound)
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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._in_features):
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root_node.append(
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"_in_features", self._in_features.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._hidden_multiplier):
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root_node.append(
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"_hidden_multiplier", self._hidden_multiplier.abstract(reuse_last=True)
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)
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if not spaces.is_determined(self._out_features):
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root_node.append(
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"_out_features", self._out_features.abstract(reuse_last=True)
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)
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return root_node
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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# check inputs ->
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if not spaces.is_determined(self._in_features):
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expected_input_dim = self.abstract_child["_in_features"].value
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else:
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expected_input_dim = spaces.get_determined_value(self._in_features)
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if input.size(-1) != expected_input_dim:
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raise ValueError(
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"Expect the input dim of {:} instead of {:}".format(
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expected_input_dim, input.size(-1)
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)
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)
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# create the weight and bias matrix for fc1
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if not spaces.is_determined(self._hidden_multiplier):
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hmul = self.abstract_child["_hidden_multiplier"].value * expected_input_dim
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else:
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hmul = spaces.get_determined_value(self._hidden_multiplier)
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hidden_dim = int(expected_input_dim * hmul)
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_fc1_weight = self._params["fc1_super_weight"][:hidden_dim, :expected_input_dim]
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_fc1_bias = self._params["fc1_super_bias"][:hidden_dim]
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x = F.linear(input, _fc1_weight, _fc1_bias)
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x = self.act(x)
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x = self.drop(x)
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# create the weight and bias matrix for fc2
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if not spaces.is_determined(self._out_features):
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out_dim = self.abstract_child["_out_features"].value
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else:
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out_dim = spaces.get_determined_value(self._out_features)
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_fc2_weight = self._params["fc2_super_weight"][:out_dim, :hidden_dim]
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_fc2_bias = self._params["fc2_super_bias"][:out_dim]
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x = F.linear(x, _fc2_weight, _fc2_bias)
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x = self.drop(x)
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return x
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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x = F.linear(
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input, self._params["fc1_super_weight"], self._params["fc1_super_bias"]
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)
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x = self.act(x)
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x = self.drop(x)
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x = F.linear(
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x, self._params["fc2_super_weight"], self._params["fc2_super_bias"]
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)
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x = self.drop(x)
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return x
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def extra_repr(self) -> str:
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return "in_features={:}, hidden_multiplier={:}, out_features={:}, drop={:}, fc1 -> act -> drop -> fc2 -> drop,".format(
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self._in_features,
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self._hidden_multiplier,
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self._out_features,
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self._drop_rate,
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)
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@@ -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),
|
||||
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):
|
||||
class SuperViT(xlayers.SuperModule):
|
||||
"""The super model for transformer."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
d_feat: int = 6,
|
||||
embed_dim: List[super_core.IntSpaceType] = DefaultSearchSpace["embed_dim"],
|
||||
num_heads: List[super_core.IntSpaceType] = DefaultSearchSpace["num_heads"],
|
||||
mlp_hidden_multipliers: List[super_core.IntSpaceType] = DefaultSearchSpace[
|
||||
"mlp_hidden_multipliers"
|
||||
],
|
||||
qkv_bias: bool = DefaultSearchSpace["qkv_bias"],
|
||||
pos_drop: float = DefaultSearchSpace["pos_drop"],
|
||||
other_drop: float = DefaultSearchSpace["other_drop"],
|
||||
max_seq_len: int = 65,
|
||||
image_size,
|
||||
patch_size,
|
||||
num_classes,
|
||||
dim,
|
||||
depth,
|
||||
heads,
|
||||
mlp_multiplier=4,
|
||||
channels=3,
|
||||
dropout=0.0,
|
||||
emb_dropout=0.0,
|
||||
):
|
||||
super(SuperTransformer, self).__init__()
|
||||
self._embed_dim = embed_dim
|
||||
self._num_heads = num_heads
|
||||
self._mlp_hidden_multipliers = mlp_hidden_multipliers
|
||||
super(SuperViT, self).__init__()
|
||||
image_height, image_width = pair(image_size)
|
||||
patch_height, patch_width = pair(patch_size)
|
||||
|
||||
# the stem part
|
||||
self.input_embed = super_core.SuperAlphaEBDv1(d_feat, embed_dim)
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
|
||||
self.pos_embed = super_core.SuperPositionalEncoder(
|
||||
d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop
|
||||
if image_height % patch_height != 0 or image_width % patch_width != 0:
|
||||
raise ValueError("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 = 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))
|
||||
_assert_types(mlp_hidden_multipliers, (tuple, list))
|
||||
assert len(num_heads) == len(mlp_hidden_multipliers), "{:} vs {:}".format(
|
||||
len(num_heads), len(mlp_hidden_multipliers)
|
||||
)
|
||||
# build the transformer encode layers -->> backbone
|
||||
|
||||
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
|
||||
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
|
||||
self.dropout = nn.Dropout(emb_dropout)
|
||||
|
||||
# build the transformer encode layers
|
||||
layers = []
|
||||
for num_head, mlp_hidden_multiplier in zip(num_heads, mlp_hidden_multipliers):
|
||||
layer = super_core.SuperTransformerEncoderLayer(
|
||||
embed_dim,
|
||||
num_head,
|
||||
qkv_bias,
|
||||
mlp_hidden_multiplier,
|
||||
other_drop,
|
||||
for ilayer in range(depth):
|
||||
layers.append(
|
||||
xlayers.SuperTransformerEncoderLayer(
|
||||
dim, heads, False, mlp_multiplier, dropout
|
||||
)
|
||||
)
|
||||
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)
|
||||
self.backbone = xlayers.SuperSequential(*layers)
|
||||
self.cls_head = xlayers.SuperSequential(
|
||||
xlayers.SuperLayerNorm1D(dim), xlayers.SuperLinear(dim, num_classes)
|
||||
)
|
||||
trunc_normal_(self.cls_token, std=0.02)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
@property
|
||||
def embed_dim(self):
|
||||
return spaces.get_max(self._embed_dim)
|
||||
weight_init.trunc_normal_(self.cls_token, std=0.02)
|
||||
self.apply(_init_weights)
|
||||
|
||||
@property
|
||||
def abstract_search_space(self):
|
||||
root_node = spaces.VirtualNode(id(self))
|
||||
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
|
||||
raise NotImplementedError
|
||||
|
||||
def apply_candidate(self, abstract_child: spaces.VirtualNode):
|
||||
super(SuperTransformer, self).apply_candidate(abstract_child)
|
||||
xkeys = ("input_embed", "pos_embed", "backbone", "head")
|
||||
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)
|
||||
super(SuperViT, self).apply_candidate(abstract_child)
|
||||
raise NotImplementedError
|
||||
|
||||
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
||||
batch, flatten_size = input.shape
|
||||
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
|
||||
raise NotImplementedError
|
||||
|
||||
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
||||
batch, flatten_size = input.shape
|
||||
feats = self.input_embed(input) # batch * 60 * 64
|
||||
tensors = self.to_patch_embedding(input)
|
||||
batch, seq, _ = tensors.shape
|
||||
|
||||
cls_tokens = self.cls_token.expand(batch, -1, -1)
|
||||
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
|
||||
feats = torch.cat((cls_tokens, tensors), dim=1)
|
||||
feats = feats + self.pos_embedding[:, : seq + 1, :]
|
||||
feats = self.dropout(feats)
|
||||
|
||||
feats = self.backbone(feats)
|
||||
|
||||
x = feats[:, 0] # the features for cls-token
|
||||
|
||||
return self.cls_head(x)
|
||||
|
||||
|
||||
def get_transformer(config):
|
||||
if config is None:
|
||||
return SuperTransformer(6)
|
||||
if isinstance(config, str) and config.lower() in name2config:
|
||||
config = name2config[config.lower()]
|
||||
if not isinstance(config, dict):
|
||||
raise ValueError("Invalid Configuration: {:}".format(config))
|
||||
name = config.get("name", "basic")
|
||||
if name == "basic":
|
||||
model = SuperTransformer(
|
||||
d_feat=config.get("d_feat"),
|
||||
embed_dim=config.get("embed_dim"),
|
||||
num_heads=config.get("num_heads"),
|
||||
mlp_hidden_multipliers=config.get("mlp_hidden_multipliers"),
|
||||
qkv_bias=config.get("qkv_bias"),
|
||||
pos_drop=config.get("pos_drop"),
|
||||
other_drop=config.get("other_drop"),
|
||||
model_type = config.get("type", "vit").lower()
|
||||
if model_type == "vit":
|
||||
model = SuperViT(
|
||||
image_size=config.get("image_size"),
|
||||
patch_size=config.get("patch_size"),
|
||||
num_classes=config.get("num_classes"),
|
||||
dim=config.get("dim"),
|
||||
depth=config.get("depth"),
|
||||
heads=config.get("heads"),
|
||||
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
|
||||
|
||||
Reference in New Issue
Block a user