defmodule Bumblebee.Vision.Deit do
alias Bumblebee.Shared
options =
[
image_size: [
default: 224,
doc: "the size of the input spatial dimensions"
],
num_channels: [
default: 3,
doc: "the number of channels in the input"
],
patch_size: [
default: 16,
doc: "the size of the patch spatial dimensions"
],
hidden_size: [
default: 768,
doc: "the dimensionality of hidden layers"
],
num_blocks: [
default: 12,
doc: "the number of Transformer blocks in the encoder"
],
num_attention_heads: [
default: 12,
doc: "the number of attention heads for each attention layer in the encoder"
],
intermediate_size: [
default: 3072,
docs:
"the dimensionality of the intermediate layer in the transformer feed-forward network (FFN) in the encoder"
],
use_qkv_bias: [
default: true,
doc: "whether to use bias in query, key, and value projections"
],
activation: [
default: :gelu,
doc: "the activation function"
],
dropout_rate: [
default: 0.0,
doc: "the dropout rate for encoder and decoder"
],
attention_dropout_rate: [
default: 0.0,
doc: "the dropout rate for attention weights"
],
layer_norm_epsilon: [
default: 1.0e-12,
doc: "the epsilon used by the layer normalization layers"
],
initializer_scale: [
default: 0.02,
doc:
"the standard deviation of the normal initializer used for initializing kernel parameters"
]
] ++
Shared.common_options([
:output_hidden_states,
:output_attentions,
:num_labels,
:id_to_label
])
@moduledoc """
DeiT model family.
## Architectures
* `:base` - plain DeiT without any head on top
* `:for_image_classification` - DeiT with a classification head.
The head consists of two dense layers on top of the final
hidden state of the CLS token
* `:for_image_classification_with teacher` - DeiT with a
classification head. The head consists of two dense layers
on top of the final hidden state of the CLS token and the
final hidden state of the distillation token
* `:for_masked_image_modeling` - DEiT with a language modeling
head on top for predicting visual tokens
## Inputs
* `"pixel_values"` - `{batch_size, image_size, image_size, num_channels}`
Featurized image pixel values.
* `"patch_mask"` - `{batch_size, num_patches}`
Mask to nullify selected embedded patches.
## Configuration
#{Shared.options_doc(options)}
## References
* [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)
"""
defstruct [architecture: :base] ++ Shared.option_defaults(options)
@behaviour Bumblebee.ModelSpec
@behaviour Bumblebee.Configurable
import Bumblebee.Utils.Model, only: [join: 2]
alias Bumblebee.Layers
@impl true
def architectures(),
do: [
:base,
:for_image_classification,
:for_image_classification_with_teacher,
:for_masked_image_modeling
]
@impl true
def config(spec, opts \\ []) do
spec
|> Shared.put_config_attrs(opts)
|> Shared.validate_label_options()
end
@impl true
def input_template(spec) do
%{
"pixel_values" =>
Nx.template({1, spec.image_size, spec.image_size, spec.num_channels}, :f32)
}
end
@impl true
def model(%__MODULE__{architecture: :base} = spec) do
inputs = inputs(spec)
inputs
|> core(spec)
|> Layers.output()
end
def model(%__MODULE__{architecture: :for_image_classification} = spec) do
inputs = inputs(spec)
outputs = core(inputs, spec)
logits =
outputs.hidden_state
|> Layers.take_token(index: 0, axis: 1)
|> Axon.dense(spec.num_labels,
kernel_initializer: kernel_initializer(spec),
name: "image_classification_head.output"
)
Layers.output(%{
logits: logits,
hidden_states: outputs.hidden_states,
attentions: outputs.attentions
})
end
def model(%__MODULE__{architecture: :for_image_classification_with_teacher} = spec) do
inputs = inputs(spec)
outputs = core(inputs, spec)
class_logits =
outputs.hidden_state
|> Layers.take_token(index: 0, axis: 1)
|> Axon.dense(spec.num_labels,
name: "image_classification_with_teacher_head.class_output"
)
distillation_logits =
outputs.hidden_state
|> Layers.take_token(index: 1, axis: 1)
|> Axon.dense(spec.num_labels,
name: "image_classification_with_teacher_head.distillation_output"
)
# TODO: Replace with mean layer in Axon
logits =
class_logits
|> Axon.add(distillation_logits)
|> Axon.nx(&Nx.divide(&1, 2))
Layers.output(%{
logits: logits,
class_logits: class_logits,
distillation_logits: distillation_logits,
hidden_states: outputs.hidden_states,
attentions: outputs.attentions
})
end
def model(%__MODULE__{architecture: :for_masked_image_modeling} = spec) do
inputs = inputs(spec)
outputs = core(inputs, spec)
logits =
outputs.hidden_state
|> Axon.nx(fn x ->
x = x[[0..-1//1, 1..-2//1]]
{batch_size, sequence_length, channels} = Nx.shape(x)
height = width = sequence_length |> :math.sqrt() |> floor()
Nx.reshape(x, {batch_size, height, width, channels})
end)
# Upsample to the original spatial resolution
|> Axon.conv(spec.patch_size ** 2 * 3,
kernel_size: 1,
kernel_initializer: kernel_initializer(spec),
name: "masked_image_modeling_head.output"
)
|> Layers.pixel_shuffle(spec.patch_size)
Layers.output(%{
logits: logits,
hidden_states: outputs.hidden_states,
attentions: outputs.attentions
})
end
defp inputs(spec) do
shape = {nil, spec.image_size, spec.image_size, spec.num_channels}
Bumblebee.Utils.Model.inputs_to_map([
Axon.input("pixel_values", shape: shape),
Axon.input("patch_mask", shape: {nil, nil}, optional: true)
])
end
defp core(inputs, spec, opts \\ []) do
name = opts[:name]
embeddings =
embedder(inputs["pixel_values"], inputs["patch_mask"], spec, name: join(name, "embedder"))
encoder_outputs = encoder(embeddings, spec, name: join(name, "encoder"))
hidden_state =
Axon.layer_norm(encoder_outputs.hidden_state,
epsilon: spec.layer_norm_epsilon,
name: join(name, "norm")
)
pooled = pooler(hidden_state, spec, name: join(name, "pooler"))
%{
hidden_state: hidden_state,
pooled_state: pooled,
hidden_states: encoder_outputs.hidden_states,
attentions: encoder_outputs.attentions
}
end
defp embedder(pixel_values, patch_mask, spec, opts) do
name = opts[:name]
patch_embeddings =
pixel_values
|> patch_embedding(spec, name: join(name, "patch_embedding"))
|> Layers.apply_vision_patch_mask(patch_mask, name: join(name, "mask_tokens"))
input_embeddings =
patch_embeddings
|> Layers.prepend_embedding(name: join(name, "distillation_embedding"))
|> Layers.prepend_embedding(name: join(name, "class_embedding"))
num_patches = div(spec.image_size, spec.patch_size) ** 2
position_embeddings =
Layers.learned_embeddings(num_patches + 2, spec.hidden_size,
initializer: :zeros,
name: join(name, "position_embedding")
)
Axon.add(input_embeddings, position_embeddings)
|> Axon.dropout(rate: spec.dropout_rate, name: join(name, "dropout"))
end
defp patch_embedding(pixel_values, spec, opts) do
name = opts[:name]
pixel_values
|> Axon.conv(spec.hidden_size,
kernel_size: spec.patch_size,
strides: spec.patch_size,
padding: :valid,
kernel_initializer: kernel_initializer(spec),
name: join(name, "projection")
)
|> Axon.reshape({:batch, :auto, spec.hidden_size}, name: join(name, "reshape"))
end
defp encoder(hidden_state, spec, opts) do
name = opts[:name]
Layers.Transformer.blocks(hidden_state,
num_blocks: spec.num_blocks,
num_attention_heads: spec.num_attention_heads,
hidden_size: spec.hidden_size,
kernel_initializer: kernel_initializer(spec),
dropout_rate: spec.dropout_rate,
attention_dropout_rate: spec.attention_dropout_rate,
query_use_bias: spec.use_qkv_bias,
key_use_bias: spec.use_qkv_bias,
value_use_bias: spec.use_qkv_bias,
layer_norm: [
epsilon: spec.layer_norm_epsilon
],
norm_placement: :first,
ffn: [
intermediate_size: spec.intermediate_size,
activation: spec.activation
],
output_hidden_states: spec.output_hidden_states,
output_attentions: spec.output_attentions,
name: join(name, "blocks")
)
end
defp pooler(hidden_state, spec, opts) do
name = opts[:name]
hidden_state
|> Layers.take_token(index: 0, axis: 1)
|> Axon.dense(spec.hidden_size,
kernel_initializer: kernel_initializer(spec),
name: join(name, "output")
)
|> Axon.tanh()
end
defp kernel_initializer(spec) do
Axon.Initializers.normal(scale: spec.initializer_scale)
end
defimpl Bumblebee.HuggingFace.Transformers.Config do
def load(spec, data) do
import Shared.Converters
opts =
convert!(data,
image_size: {"image_size", number()},
num_channels: {"num_channels", number()},
patch_size: {"patch_size", number()},
hidden_size: {"hidden_size", number()},
num_blocks: {"num_hidden_layers", number()},
num_attention_heads: {"num_attention_heads", number()},
intermediate_size: {"intermediate_size", number()},
use_qkv_bias: {"qkv_bias", boolean()},
activation: {"hidden_act", atom()},
dropout_rate: {"hidden_dropout_prob", number()},
attention_dropout_rate: {"attention_probs_dropout_prob", number()},
layer_norm_epsilon: {"layer_norm_eps", number()},
initializer_scale: {"initializer_range", number()}
) ++ Shared.common_options_from_transformers(data, spec)
@for.config(spec, opts)
end
end
defimpl Bumblebee.HuggingFace.Transformers.Model do
def params_mapping(_spec) do
%{
"embedder.patch_embedding.projection" => "deit.embeddings.patch_embeddings.projection",
"embedder.distillation_embedding" => %{
"embedding" => {
[{"deit.embeddings", "distillation_token"}],
fn [value] -> Nx.squeeze(value, axes: [0, 1]) end
}
},
"embedder.class_embedding" => %{
"embedding" => {
[{"deit.embeddings", "cls_token"}],
fn [value] -> Nx.squeeze(value, axes: [0, 1]) end
}
},
"embedder.position_embedding" => %{
"embeddings" => {
[{"deit.embeddings", "position_embeddings"}],
fn [value] -> Nx.squeeze(value, axes: [0]) end
}
},
"encoder.blocks.{n}.self_attention_norm" => "deit.encoder.layer.{n}.layernorm_before",
"encoder.blocks.{n}.self_attention.key" =>
"deit.encoder.layer.{n}.attention.attention.key",
"encoder.blocks.{n}.self_attention.query" =>
"deit.encoder.layer.{n}.attention.attention.query",
"encoder.blocks.{n}.self_attention.value" =>
"deit.encoder.layer.{n}.attention.attention.value",
"encoder.blocks.{n}.self_attention.output" =>
"deit.encoder.layer.{n}.attention.output.dense",
"encoder.blocks.{n}.ffn.intermediate" => "deit.encoder.layer.{n}.intermediate.dense",
"encoder.blocks.{n}.ffn.output" => "deit.encoder.layer.{n}.output.dense",
"encoder.blocks.{n}.output_norm" => "deit.encoder.layer.{n}.layernorm_after",
"norm" => "deit.layernorm",
"pooler.output" => "deit.pooler.dense",
"image_classification_head.output" => "classifier",
"image_classification_with_teacher_head.class_output" => "cls_classifier",
"image_classification_with_teacher_head.distillation_output" => "distillation_classifier",
"masked_image_modeling_head.output" => "decoder.0"
}
end
end
end
