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3 Commits
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e3ca2ae502
| Author | SHA1 | Date | |
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e3ca2ae502
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6dd99e3b0a
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c450fa7f89
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594
Cargo.lock
generated
594
Cargo.lock
generated
@@ -430,6 +430,12 @@ version = "1.0.4"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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||||
@@ -529,6 +535,19 @@ dependencies = [
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||||
"crossbeam-utils",
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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@@ -541,6 +560,16 @@ version = "0.4.2"
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||||
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@@ -777,6 +806,27 @@ dependencies = [
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||||
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@@ -809,6 +859,21 @@ dependencies = [
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||||
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||||
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||||
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@@ -967,6 +1032,21 @@ version = "1.3.0"
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||||
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||||
[[package]]
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||||
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||||
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||||
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||||
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||||
[[package]]
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||||
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||||
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||||
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||||
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||||
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||||
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@@ -1055,8 +1148,10 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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[[package]]
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@@ -1066,9 +1161,11 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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@@ -1156,12 +1253,42 @@ version = "0.5.0"
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|
||||
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@@ -1266,6 +1393,40 @@ dependencies = [
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||||
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||||
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"want",
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[[package]]
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@@ -1462,6 +1633,35 @@ dependencies = [
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||||
"serde_core",
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|
||||
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||||
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|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b9d782e804c2f632e395708e99a94275910eb9100b2114651e04744e9b125006"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnu"
|
||||
version = "0.48.5"
|
||||
@@ -3948,12 +4498,24 @@ version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8e9b5ad5ab802e97eb8e295ac6720e509ee4c243f69d781394014ebfe8bbfa0b"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnu"
|
||||
version = "0.53.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "960e6da069d81e09becb0ca57a65220ddff016ff2d6af6a223cf372a506593a3"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnullvm"
|
||||
version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0eee52d38c090b3caa76c563b86c3a4bd71ef1a819287c19d586d7334ae8ed66"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_gnullvm"
|
||||
version = "0.53.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "fa7359d10048f68ab8b09fa71c3daccfb0e9b559aed648a8f95469c27057180c"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_msvc"
|
||||
version = "0.48.5"
|
||||
@@ -3966,6 +4528,12 @@ version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "240948bc05c5e7c6dabba28bf89d89ffce3e303022809e73deaefe4f6ec56c66"
|
||||
|
||||
[[package]]
|
||||
name = "windows_i686_msvc"
|
||||
version = "0.53.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1e7ac75179f18232fe9c285163565a57ef8d3c89254a30685b57d83a38d326c2"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnu"
|
||||
version = "0.48.5"
|
||||
@@ -3978,6 +4546,12 @@ version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "147a5c80aabfbf0c7d901cb5895d1de30ef2907eb21fbbab29ca94c5b08b1a78"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnu"
|
||||
version = "0.53.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9c3842cdd74a865a8066ab39c8a7a473c0778a3f29370b5fd6b4b9aa7df4a499"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnullvm"
|
||||
version = "0.48.5"
|
||||
@@ -3990,6 +4564,12 @@ version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "24d5b23dc417412679681396f2b49f3de8c1473deb516bd34410872eff51ed0d"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_gnullvm"
|
||||
version = "0.53.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0ffa179e2d07eee8ad8f57493436566c7cc30ac536a3379fdf008f47f6bb7ae1"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_msvc"
|
||||
version = "0.48.5"
|
||||
@@ -4002,6 +4582,12 @@ version = "0.52.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
|
||||
|
||||
[[package]]
|
||||
name = "windows_x86_64_msvc"
|
||||
version = "0.53.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d6bbff5f0aada427a1e5a6da5f1f98158182f26556f345ac9e04d36d0ebed650"
|
||||
|
||||
[[package]]
|
||||
name = "wit-bindgen"
|
||||
version = "0.51.0"
|
||||
|
||||
@@ -12,6 +12,7 @@ members = [
|
||||
"crates/rbv-search",
|
||||
"crates/rbv-cli",
|
||||
"crates/rbv-api",
|
||||
"crates/rbv-caption",
|
||||
]
|
||||
|
||||
[workspace.package]
|
||||
@@ -80,3 +81,9 @@ rbv-cluster = { path = "crates/rbv-cluster" }
|
||||
rbv-ingest = { path = "crates/rbv-ingest" }
|
||||
rbv-auth = { path = "crates/rbv-auth" }
|
||||
rbv-search = { path = "crates/rbv-search" }
|
||||
|
||||
# HTTP client
|
||||
reqwest = { version = "0.12", default-features = false, features = ["rustls-tls"] }
|
||||
|
||||
# HuggingFace model hub
|
||||
hf-hub = { version = "0.4", features = ["tokio"] }
|
||||
|
||||
17
asset/systemd/rbv-caption@.service
Normal file
17
asset/systemd/rbv-caption@.service
Normal file
@@ -0,0 +1,17 @@
|
||||
[Unit]
|
||||
Description=rbv caption (%i)
|
||||
After=network.target
|
||||
ConditionFileIsExecutable=/usr/local/bin/rbv-caption
|
||||
|
||||
[Service]
|
||||
Environment=RUST_LOG=debug,ort=off,sqlx::query=off,hyper_util=off
|
||||
ExecStart=/usr/local/bin/rbv-caption \
|
||||
--model %i \
|
||||
--model-dir /var/lib/rbv/models \
|
||||
--database postgres://rbv:password@gramathea.kosherinata.internal:4432/rbv \
|
||||
--cdn-map /tank/data/rbv/vault=https://rbv.internal/vault \
|
||||
--batch-size 100
|
||||
Restart=always
|
||||
|
||||
[Install]
|
||||
WantedBy=multi-user.target
|
||||
@@ -18,6 +18,7 @@ pub fn router() -> Router<AppState> {
|
||||
.route("/{id}/persons", get(get_gallery_persons))
|
||||
.route("/{id}/tags", axum::routing::post(add_tag))
|
||||
.route("/{id}/subjects", axum::routing::post(add_subject))
|
||||
.route("/{id}/captions", get(get_gallery_captions))
|
||||
}
|
||||
|
||||
#[derive(Deserialize)]
|
||||
@@ -277,3 +278,24 @@ async fn add_subject(
|
||||
.ok_or_else(|| ApiError::not_found("gallery not found"))?;
|
||||
Ok((StatusCode::OK, Json(gallery.into())))
|
||||
}
|
||||
|
||||
// ── Captions ────────────────────────────────────────────────────────────────
|
||||
|
||||
#[derive(Deserialize)]
|
||||
pub struct CaptionQuery {
|
||||
#[serde(default = "default_caption_model")]
|
||||
pub model: String,
|
||||
}
|
||||
fn default_caption_model() -> String { "florence-2-base".into() }
|
||||
|
||||
async fn get_gallery_captions(
|
||||
State(state): State<AppState>,
|
||||
Path(id): Path<String>,
|
||||
Query(q): Query<CaptionQuery>,
|
||||
) -> ApiResult<Json<std::collections::HashMap<String, String>>> {
|
||||
let bytes = rbv_hash::from_hex(&id)
|
||||
.map_err(|_| ApiError::bad_request("invalid gallery id"))?;
|
||||
let gid = rbv_entity::GalleryId(bytes);
|
||||
let captions = rbv_data::caption::gallery_captions(&state.pool, &gid, &q.model).await?;
|
||||
Ok(Json(captions.into_iter().collect()))
|
||||
}
|
||||
|
||||
@@ -15,6 +15,8 @@ pub struct SearchRequest {
|
||||
pub tag: Option<String>,
|
||||
pub subject: Option<String>,
|
||||
pub person_name: Option<String>,
|
||||
pub caption: Option<String>,
|
||||
pub label: Option<String>,
|
||||
#[serde(default = "default_limit")]
|
||||
pub limit: i64,
|
||||
}
|
||||
@@ -41,6 +43,8 @@ async fn search(
|
||||
tag: req.tag.as_deref(),
|
||||
subject: req.subject.as_deref(),
|
||||
person_name: req.person_name.as_deref(),
|
||||
caption: req.caption.as_deref(),
|
||||
label: req.label.as_deref(),
|
||||
limit: req.limit,
|
||||
};
|
||||
|
||||
|
||||
25
crates/rbv-caption/Cargo.toml
Normal file
25
crates/rbv-caption/Cargo.toml
Normal file
@@ -0,0 +1,25 @@
|
||||
[package]
|
||||
name = "rbv-caption"
|
||||
version.workspace = true
|
||||
edition.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
[[bin]]
|
||||
name = "rbv-caption"
|
||||
path = "src/main.rs"
|
||||
|
||||
[dependencies]
|
||||
rbv-entity = { workspace = true }
|
||||
rbv-data = { workspace = true }
|
||||
rbv-hash = { workspace = true }
|
||||
clap = { workspace = true }
|
||||
sqlx = { workspace = true }
|
||||
tokio = { workspace = true }
|
||||
anyhow = { workspace = true }
|
||||
tracing = { workspace = true }
|
||||
tracing-subscriber = { workspace = true }
|
||||
ort = { workspace = true }
|
||||
tokenizers = { workspace = true }
|
||||
image = { workspace = true }
|
||||
reqwest = { workspace = true }
|
||||
hf-hub = { workspace = true }
|
||||
114
crates/rbv-caption/src/download.rs
Normal file
114
crates/rbv-caption/src/download.rs
Normal file
@@ -0,0 +1,114 @@
|
||||
use anyhow::{Context, Result};
|
||||
use std::path::{Path, PathBuf};
|
||||
use tracing::info;
|
||||
|
||||
/// HuggingFace repo and file mapping for a captioning model.
|
||||
struct ModelSource {
|
||||
repo_id: &'static str,
|
||||
files: &'static [(&'static str, &'static str)], // (remote_path, local_filename)
|
||||
}
|
||||
|
||||
fn model_source(model_name: &str) -> Option<ModelSource> {
|
||||
match model_name {
|
||||
"florence-2-base" => Some(ModelSource {
|
||||
repo_id: "onnx-community/Florence-2-base-ft",
|
||||
files: &[
|
||||
("onnx/vision_encoder.onnx", "vision_encoder.onnx"),
|
||||
("onnx/encoder_model.onnx", "encoder.onnx"),
|
||||
("onnx/embed_tokens.onnx", "embed_tokens.onnx"),
|
||||
("onnx/decoder_model.onnx", "decoder.onnx"),
|
||||
("tokenizer.json", "tokenizer.json"),
|
||||
],
|
||||
}),
|
||||
"vit-gpt2" => Some(ModelSource {
|
||||
repo_id: "Xenova/vit-gpt2-image-captioning",
|
||||
files: &[
|
||||
("onnx/encoder_model.onnx", "encoder.onnx"),
|
||||
("onnx/decoder_model.onnx", "decoder.onnx"),
|
||||
("tokenizer.json", "tokenizer.json"),
|
||||
],
|
||||
}),
|
||||
// OCR variant reuses the same florence-2-base files.
|
||||
"florence-2-base-ocr" => Some(ModelSource {
|
||||
repo_id: "onnx-community/Florence-2-base-ft",
|
||||
files: &[
|
||||
("onnx/vision_encoder.onnx", "vision_encoder.onnx"),
|
||||
("onnx/encoder_model.onnx", "encoder.onnx"),
|
||||
("onnx/embed_tokens.onnx", "embed_tokens.onnx"),
|
||||
("onnx/decoder_model.onnx", "decoder.onnx"),
|
||||
("tokenizer.json", "tokenizer.json"),
|
||||
],
|
||||
}),
|
||||
_ => None,
|
||||
}
|
||||
}
|
||||
|
||||
/// Ensure all required model files are present in the local model directory.
|
||||
/// Downloads from HuggingFace if any are missing.
|
||||
/// Returns the model directory path.
|
||||
pub async fn ensure_model(model_name: &str, model_dir: &Path) -> Result<PathBuf> {
|
||||
// OCR variant shares the same model files as the base caption model.
|
||||
let dir_name = match model_name {
|
||||
"florence-2-base-ocr" => "florence-2-base",
|
||||
other => other,
|
||||
};
|
||||
let local_dir = model_dir.join("captioning").join(dir_name);
|
||||
|
||||
let source = match model_source(model_name) {
|
||||
Some(s) => s,
|
||||
None => {
|
||||
// No auto-download — check if files were placed manually.
|
||||
if local_dir.exists() {
|
||||
return Ok(local_dir);
|
||||
}
|
||||
anyhow::bail!(
|
||||
"No auto-download source for model '{model_name}'. \
|
||||
Manually place model files in {}. \
|
||||
Models with auto-download: florence-2-base, vit-gpt2",
|
||||
local_dir.display()
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
// Check if all required files already exist.
|
||||
let all_present = source.files.iter().all(|(_, local_name)| local_dir.join(local_name).exists());
|
||||
if all_present {
|
||||
return Ok(local_dir);
|
||||
}
|
||||
|
||||
info!(
|
||||
"Downloading model '{}' from HuggingFace repo '{}'...",
|
||||
model_name, source.repo_id
|
||||
);
|
||||
|
||||
std::fs::create_dir_all(&local_dir)
|
||||
.with_context(|| format!("Failed to create directory: {}", local_dir.display()))?;
|
||||
|
||||
let api = hf_hub::api::tokio::Api::new()
|
||||
.context("Failed to initialize HuggingFace API")?;
|
||||
let repo = api.model(source.repo_id.to_string());
|
||||
|
||||
for (remote_path, local_name) in source.files {
|
||||
let local_path = local_dir.join(local_name);
|
||||
if local_path.exists() {
|
||||
info!(" {} already exists, skipping", local_name);
|
||||
continue;
|
||||
}
|
||||
|
||||
info!(" Downloading {} ...", remote_path);
|
||||
let cached_path = repo.get(remote_path).await
|
||||
.with_context(|| format!("Failed to download {remote_path} from {}", source.repo_id))?;
|
||||
|
||||
// hf-hub caches files in its own directory. Copy to our model dir.
|
||||
std::fs::copy(&cached_path, &local_path)
|
||||
.with_context(|| format!(
|
||||
"Failed to copy {} to {}",
|
||||
cached_path.display(),
|
||||
local_path.display()
|
||||
))?;
|
||||
info!(" Saved {}", local_path.display());
|
||||
}
|
||||
|
||||
info!("Model '{}' ready.", model_name);
|
||||
Ok(local_dir)
|
||||
}
|
||||
191
crates/rbv-caption/src/main.rs
Normal file
191
crates/rbv-caption/src/main.rs
Normal file
@@ -0,0 +1,191 @@
|
||||
mod download;
|
||||
mod models;
|
||||
|
||||
use std::path::PathBuf;
|
||||
use anyhow::Result;
|
||||
use clap::Parser;
|
||||
use tracing::info;
|
||||
use rbv_entity::{Caption, ImageId, Label};
|
||||
|
||||
#[derive(Parser)]
|
||||
#[command(name = "rbv-caption", about = "Generate image captions using ONNX models")]
|
||||
struct Args {
|
||||
/// PostgreSQL connection string
|
||||
#[arg(long)]
|
||||
database: String,
|
||||
|
||||
/// Model to use (blip-base, git-base, florence-2-base, florence-2-base-ocr, vit-gpt2)
|
||||
#[arg(long)]
|
||||
model: String,
|
||||
|
||||
/// Path to model directory (expects captioning/{model}/ subdirectory)
|
||||
#[arg(long)]
|
||||
model_dir: PathBuf,
|
||||
|
||||
/// CDN mapping in the form fs_prefix=url_prefix
|
||||
#[arg(long)]
|
||||
cdn_map: String,
|
||||
|
||||
/// Number of images per batch
|
||||
#[arg(long, default_value = "100")]
|
||||
batch_size: i64,
|
||||
|
||||
/// Number of concurrent download+caption tasks
|
||||
#[arg(long, default_value = "1")]
|
||||
concurrency: usize,
|
||||
}
|
||||
|
||||
struct CdnMap {
|
||||
fs_prefix: String,
|
||||
url_prefix: String,
|
||||
}
|
||||
|
||||
impl CdnMap {
|
||||
fn resolve(&self, gallery_path: &str, filename: &str) -> Option<String> {
|
||||
if gallery_path.starts_with(&self.fs_prefix) {
|
||||
let rel = &gallery_path[self.fs_prefix.len()..];
|
||||
Some(format!("{}{rel}/{filename}", self.url_prefix))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn is_ocr_model(model_name: &str) -> bool {
|
||||
model_name.ends_with("-ocr")
|
||||
}
|
||||
|
||||
#[tokio::main]
|
||||
async fn main() -> Result<()> {
|
||||
tracing_subscriber::fmt()
|
||||
.with_env_filter(
|
||||
tracing_subscriber::EnvFilter::try_from_default_env()
|
||||
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
|
||||
)
|
||||
.init();
|
||||
|
||||
let args = Args::parse();
|
||||
|
||||
let (fs_prefix, url_prefix) = args.cdn_map.split_once('=')
|
||||
.ok_or_else(|| anyhow::anyhow!("--cdn-map must be in the form fs_prefix=url_prefix"))?;
|
||||
let cdn = CdnMap {
|
||||
fs_prefix: fs_prefix.trim_end_matches('/').to_string(),
|
||||
url_prefix: url_prefix.trim_end_matches('/').to_string(),
|
||||
};
|
||||
|
||||
// Download model from HuggingFace if not present locally.
|
||||
download::ensure_model(&args.model, &args.model_dir).await?;
|
||||
|
||||
info!("Loading model: {}", args.model);
|
||||
let model: std::sync::Arc<dyn models::CaptionModel> = std::sync::Arc::from(
|
||||
models::load_model(&args.model, &args.model_dir)?
|
||||
);
|
||||
info!("Model loaded.");
|
||||
|
||||
let pool = rbv_data::connect(&args.database, 2).await?;
|
||||
let ocr = is_ocr_model(&args.model);
|
||||
|
||||
let total = if ocr {
|
||||
rbv_data::label::count_unlabelled(&pool, &args.model).await?
|
||||
} else {
|
||||
rbv_data::caption::count_uncaptioned(&pool, &args.model).await?
|
||||
};
|
||||
let task = if ocr { "unlabelled" } else { "uncaptioned" };
|
||||
info!("{total} {task} images for model '{}'", args.model);
|
||||
|
||||
if total == 0 {
|
||||
info!("Nothing to do.");
|
||||
return Ok(());
|
||||
}
|
||||
|
||||
let http = reqwest::Client::builder()
|
||||
.timeout(std::time::Duration::from_secs(30))
|
||||
.build()?;
|
||||
|
||||
let mut processed = 0u64;
|
||||
let mut errors = 0u64;
|
||||
|
||||
loop {
|
||||
let batch = if ocr {
|
||||
rbv_data::label::unlabelled_images(&pool, &args.model, args.batch_size).await?
|
||||
} else {
|
||||
rbv_data::caption::uncaptioned_images(&pool, &args.model, args.batch_size).await?
|
||||
};
|
||||
if batch.is_empty() {
|
||||
break;
|
||||
}
|
||||
|
||||
let batch_len = batch.len();
|
||||
|
||||
for (image_id_hex, gallery_path, filename) in &batch {
|
||||
let result = process_image(
|
||||
&http, &model, &pool, &cdn, &args.model, ocr,
|
||||
image_id_hex, gallery_path, filename,
|
||||
).await;
|
||||
|
||||
match result {
|
||||
Ok(()) => processed += 1,
|
||||
Err(e) => {
|
||||
tracing::warn!("Error for {image_id_hex}: {e}");
|
||||
errors += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
info!(
|
||||
"Progress: {processed}/{total} {task}, {errors} errors ({batch_len} in last batch)"
|
||||
);
|
||||
}
|
||||
|
||||
info!("Done. {processed} {task}, {errors} errors.");
|
||||
Ok(())
|
||||
}
|
||||
|
||||
async fn process_image(
|
||||
http: &reqwest::Client,
|
||||
model: &std::sync::Arc<dyn models::CaptionModel>,
|
||||
pool: &sqlx::PgPool,
|
||||
cdn: &CdnMap,
|
||||
model_name: &str,
|
||||
ocr: bool,
|
||||
image_id_hex: &str,
|
||||
gallery_path: &str,
|
||||
filename: &str,
|
||||
) -> Result<()> {
|
||||
let url = cdn.resolve(gallery_path, filename)
|
||||
.ok_or_else(|| anyhow::anyhow!(
|
||||
"CDN mapping does not match gallery path: {gallery_path}"
|
||||
))?;
|
||||
|
||||
let response = http.get(&url).send().await?;
|
||||
if !response.status().is_success() {
|
||||
anyhow::bail!("HTTP {} fetching {url}", response.status());
|
||||
}
|
||||
let image_bytes = response.bytes().await?;
|
||||
|
||||
// Run inference (blocking — ONNX inference is CPU-bound)
|
||||
let bytes = image_bytes.to_vec();
|
||||
let model_clone = model.clone();
|
||||
let text = tokio::task::spawn_blocking(move || {
|
||||
model_clone.caption(&bytes)
|
||||
}).await??;
|
||||
|
||||
let id_bytes = rbv_hash::from_hex(image_id_hex)?;
|
||||
let image_id = ImageId(id_bytes);
|
||||
|
||||
if ocr {
|
||||
rbv_data::label::upsert_label(pool, &Label {
|
||||
image_id,
|
||||
model: model_name.to_string(),
|
||||
label: text,
|
||||
}).await?;
|
||||
} else {
|
||||
rbv_data::caption::upsert_caption(pool, &Caption {
|
||||
image_id,
|
||||
model: model_name.to_string(),
|
||||
caption: text,
|
||||
}).await?;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
145
crates/rbv-caption/src/models/blip.rs
Normal file
145
crates/rbv-caption/src/models/blip.rs
Normal file
@@ -0,0 +1,145 @@
|
||||
use anyhow::{Context, Result};
|
||||
use image::GenericImageView;
|
||||
use ort::session::Session;
|
||||
use ort::value::Tensor;
|
||||
use std::path::Path;
|
||||
use std::sync::Mutex;
|
||||
use tokenizers::Tokenizer;
|
||||
use super::CaptionModel;
|
||||
|
||||
const IMAGE_SIZE: u32 = 384;
|
||||
const MAX_TOKENS: usize = 50;
|
||||
const MEAN: [f32; 3] = [0.48145466, 0.4578275, 0.40821073];
|
||||
const STD: [f32; 3] = [0.26862954, 0.26130258, 0.27577711];
|
||||
|
||||
pub struct BlipBase {
|
||||
encoder: Mutex<Session>,
|
||||
decoder: Mutex<Session>,
|
||||
tokenizer: Tokenizer,
|
||||
}
|
||||
|
||||
impl BlipBase {
|
||||
pub fn load(model_dir: &Path) -> Result<Self> {
|
||||
let encoder_path = model_dir.join("encoder.onnx");
|
||||
let decoder_path = model_dir.join("decoder.onnx");
|
||||
let tokenizer_path = model_dir.join("tokenizer.json");
|
||||
|
||||
for p in [&encoder_path, &decoder_path, &tokenizer_path] {
|
||||
anyhow::ensure!(p.exists(), "Missing model file: {}", p.display());
|
||||
}
|
||||
|
||||
let encoder = Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?
|
||||
.commit_from_file(&encoder_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load BLIP encoder: {e}"))?;
|
||||
|
||||
let decoder = Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?
|
||||
.commit_from_file(&decoder_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load BLIP decoder: {e}"))?;
|
||||
|
||||
let tokenizer = Tokenizer::from_file(&tokenizer_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {e}"))?;
|
||||
|
||||
Ok(Self {
|
||||
encoder: Mutex::new(encoder),
|
||||
decoder: Mutex::new(decoder),
|
||||
tokenizer,
|
||||
})
|
||||
}
|
||||
|
||||
fn preprocess_image(&self, image_bytes: &[u8]) -> Result<Vec<f32>> {
|
||||
let img = image::load_from_memory(image_bytes)
|
||||
.context("Failed to decode image")?;
|
||||
let img = img.resize_exact(IMAGE_SIZE, IMAGE_SIZE, image::imageops::FilterType::Lanczos3);
|
||||
|
||||
let mut pixels = vec![0f32; 3 * IMAGE_SIZE as usize * IMAGE_SIZE as usize];
|
||||
for y in 0..IMAGE_SIZE {
|
||||
for x in 0..IMAGE_SIZE {
|
||||
let pixel = img.get_pixel(x, y);
|
||||
for c in 0..3usize {
|
||||
let idx = c * (IMAGE_SIZE as usize * IMAGE_SIZE as usize)
|
||||
+ y as usize * IMAGE_SIZE as usize
|
||||
+ x as usize;
|
||||
pixels[idx] = (pixel[c] as f32 / 255.0 - MEAN[c]) / STD[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(pixels)
|
||||
}
|
||||
}
|
||||
|
||||
impl CaptionModel for BlipBase {
|
||||
fn caption(&self, image_bytes: &[u8]) -> Result<String> {
|
||||
let pixels = self.preprocess_image(image_bytes)?;
|
||||
let pixel_input = Tensor::<f32>::from_array(
|
||||
([1usize, 3, IMAGE_SIZE as usize, IMAGE_SIZE as usize], pixels),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
// Encode image
|
||||
let (enc_data, enc_shape) = {
|
||||
let mut enc_guard = self.encoder.lock().unwrap();
|
||||
let encoder_outputs = enc_guard.run(ort::inputs![pixel_input])
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let (shape, data) = encoder_outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
// Autoregressive decoding
|
||||
let bos_id = self.tokenizer.token_to_id("[CLS]").unwrap_or(101) as i64;
|
||||
let sep_id = self.tokenizer.token_to_id("[SEP]").unwrap_or(102) as i64;
|
||||
|
||||
let mut token_ids: Vec<i64> = vec![bos_id];
|
||||
|
||||
for _ in 0..MAX_TOKENS {
|
||||
let seq_len = token_ids.len();
|
||||
let input_ids = Tensor::<i64>::from_array(
|
||||
([1usize, seq_len], token_ids.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let attention_mask = Tensor::<i64>::from_array(
|
||||
([1usize, seq_len], vec![1i64; seq_len]),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let enc_hidden = Tensor::<f32>::from_array(
|
||||
(enc_shape.clone(), enc_data.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (logits_data, vocab_size) = {
|
||||
let mut dec_guard = self.decoder.lock().unwrap();
|
||||
let decoder_outputs = dec_guard.run(ort::inputs![input_ids, attention_mask, enc_hidden])
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let (shape, data) = decoder_outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let vs = *shape.last().unwrap_or(&0) as usize;
|
||||
(data.to_vec(), vs)
|
||||
};
|
||||
|
||||
let last_pos = seq_len - 1;
|
||||
let offset = last_pos * vocab_size;
|
||||
|
||||
let mut best_id = 0usize;
|
||||
let mut best_score = f32::NEG_INFINITY;
|
||||
for v in 0..vocab_size {
|
||||
let score = logits_data[offset + v];
|
||||
if score > best_score {
|
||||
best_score = score;
|
||||
best_id = v;
|
||||
}
|
||||
}
|
||||
|
||||
let next_id = best_id as i64;
|
||||
if next_id == sep_id || next_id == 0 {
|
||||
break;
|
||||
}
|
||||
token_ids.push(next_id);
|
||||
}
|
||||
|
||||
let output_ids: Vec<u32> = token_ids[1..].iter().map(|&id| id as u32).collect();
|
||||
let caption = self.tokenizer.decode(&output_ids, true)
|
||||
.map_err(|e| anyhow::anyhow!("Tokenizer decode failed: {e}"))?;
|
||||
|
||||
Ok(caption.trim().to_string())
|
||||
}
|
||||
}
|
||||
232
crates/rbv-caption/src/models/florence.rs
Normal file
232
crates/rbv-caption/src/models/florence.rs
Normal file
@@ -0,0 +1,232 @@
|
||||
use anyhow::{Context, Result};
|
||||
use image::GenericImageView;
|
||||
use ort::session::Session;
|
||||
use ort::value::Tensor;
|
||||
use std::path::Path;
|
||||
use std::sync::Mutex;
|
||||
use tokenizers::Tokenizer;
|
||||
use super::CaptionModel;
|
||||
|
||||
const IMAGE_SIZE: u32 = 768;
|
||||
const MAX_TOKENS: usize = 120;
|
||||
const MEAN: [f32; 3] = [0.485, 0.456, 0.406];
|
||||
const STD: [f32; 3] = [0.229, 0.224, 0.225];
|
||||
|
||||
pub struct Florence2Base {
|
||||
vision_encoder: Mutex<Session>,
|
||||
encoder: Mutex<Session>,
|
||||
embed_tokens: Mutex<Session>,
|
||||
decoder: Mutex<Session>,
|
||||
tokenizer: Tokenizer,
|
||||
prompt: String,
|
||||
}
|
||||
|
||||
impl Florence2Base {
|
||||
pub fn load(model_dir: &Path) -> Result<Self> {
|
||||
Self::load_with_prompt(model_dir, "Describe with a paragraph what is shown in the image.")
|
||||
}
|
||||
|
||||
pub fn load_ocr(model_dir: &Path) -> Result<Self> {
|
||||
Self::load_with_prompt(model_dir, "What is the text in the image?")
|
||||
}
|
||||
|
||||
pub fn load_with_prompt(model_dir: &Path, prompt: &str) -> Result<Self> {
|
||||
let vision_enc_path = model_dir.join("vision_encoder.onnx");
|
||||
let encoder_path = model_dir.join("encoder.onnx");
|
||||
let embed_path = model_dir.join("embed_tokens.onnx");
|
||||
let decoder_path = model_dir.join("decoder.onnx");
|
||||
let tokenizer_path = model_dir.join("tokenizer.json");
|
||||
|
||||
for p in [&vision_enc_path, &encoder_path, &embed_path, &decoder_path, &tokenizer_path] {
|
||||
anyhow::ensure!(p.exists(), "Missing model file: {}", p.display());
|
||||
}
|
||||
|
||||
let load = |path: &Path, name: &str| -> Result<Session> {
|
||||
Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))
|
||||
.and_then(|mut b| b.commit_from_file(path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load Florence-2 {name}: {e}")))
|
||||
};
|
||||
|
||||
let vision_encoder = load(&vision_enc_path, "vision encoder")?;
|
||||
let encoder = load(&encoder_path, "text encoder")?;
|
||||
let embed_tokens = load(&embed_path, "embed_tokens")?;
|
||||
let decoder = load(&decoder_path, "decoder")?;
|
||||
|
||||
let tokenizer = Tokenizer::from_file(&tokenizer_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {e}"))?;
|
||||
|
||||
Ok(Self {
|
||||
vision_encoder: Mutex::new(vision_encoder),
|
||||
encoder: Mutex::new(encoder),
|
||||
embed_tokens: Mutex::new(embed_tokens),
|
||||
decoder: Mutex::new(decoder),
|
||||
tokenizer,
|
||||
prompt: prompt.to_string(),
|
||||
})
|
||||
}
|
||||
|
||||
fn preprocess_image(&self, image_bytes: &[u8]) -> Result<Vec<f32>> {
|
||||
let img = image::load_from_memory(image_bytes)
|
||||
.context("Failed to decode image")?;
|
||||
let img = img.resize_exact(IMAGE_SIZE, IMAGE_SIZE, image::imageops::FilterType::Lanczos3);
|
||||
|
||||
let mut pixels = vec![0f32; 3 * IMAGE_SIZE as usize * IMAGE_SIZE as usize];
|
||||
for y in 0..IMAGE_SIZE {
|
||||
for x in 0..IMAGE_SIZE {
|
||||
let pixel = img.get_pixel(x, y);
|
||||
for c in 0..3usize {
|
||||
let idx = c * (IMAGE_SIZE as usize * IMAGE_SIZE as usize)
|
||||
+ y as usize * IMAGE_SIZE as usize
|
||||
+ x as usize;
|
||||
pixels[idx] = (pixel[c] as f32 / 255.0 - MEAN[c]) / STD[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(pixels)
|
||||
}
|
||||
}
|
||||
|
||||
impl CaptionModel for Florence2Base {
|
||||
fn caption(&self, image_bytes: &[u8]) -> Result<String> {
|
||||
let pixels = self.preprocess_image(image_bytes)?;
|
||||
let pixel_input = Tensor::<f32>::from_array(
|
||||
([1usize, 3, IMAGE_SIZE as usize, IMAGE_SIZE as usize], pixels),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
// Step 1: Run vision encoder on image pixels.
|
||||
let (image_features, image_shape) = {
|
||||
let mut guard = self.vision_encoder.lock().unwrap();
|
||||
let outputs = guard.run(ort::inputs![pixel_input])
|
||||
.map_err(|e| anyhow::anyhow!("vision_encoder: {e}"))?;
|
||||
let (shape, data) = outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("vision_encoder output: {e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
// Step 2: Tokenize the task prompt and get text embeddings.
|
||||
// Florence-2 maps task tokens to natural language prompts before tokenization.
|
||||
let encoding = self.tokenizer.encode(self.prompt.as_str(), false)
|
||||
.map_err(|e| anyhow::anyhow!("Tokenizer encode failed: {e}"))?;
|
||||
let prompt_ids: Vec<i64> = encoding.get_ids().iter().map(|&id| id as i64).collect();
|
||||
let prompt_len = prompt_ids.len();
|
||||
|
||||
let (text_embeds, text_shape) = {
|
||||
let input_ids = Tensor::<i64>::from_array(
|
||||
([1usize, prompt_len], prompt_ids.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let mut guard = self.embed_tokens.lock().unwrap();
|
||||
let outputs = guard.run(ort::inputs![input_ids])
|
||||
.map_err(|e| anyhow::anyhow!("embed_tokens: {e}"))?;
|
||||
let (shape, data) = outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("embed_tokens output: {e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
// Step 3: Concatenate image features and text embeddings, run encoder.
|
||||
// inputs_embeds shape: [1, image_seq_len + text_seq_len, hidden_dim]
|
||||
let hidden_dim = image_shape.last().copied().unwrap_or(0);
|
||||
let image_seq_len = image_shape.get(1).copied().unwrap_or(0);
|
||||
let text_seq_len = text_shape.get(1).copied().unwrap_or(0);
|
||||
let total_seq_len = image_seq_len + text_seq_len;
|
||||
|
||||
|
||||
|
||||
let mut combined = Vec::with_capacity(total_seq_len * hidden_dim);
|
||||
combined.extend_from_slice(&image_features);
|
||||
combined.extend_from_slice(&text_embeds);
|
||||
|
||||
let attention_mask = Tensor::<i64>::from_array(
|
||||
([1usize, total_seq_len], vec![1i64; total_seq_len]),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let inputs_embeds = Tensor::<f32>::from_array(
|
||||
([1usize, total_seq_len, hidden_dim], combined),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (enc_hidden, enc_shape) = {
|
||||
let mut guard = self.encoder.lock().unwrap();
|
||||
let outputs = guard.run(ort::inputs![attention_mask, inputs_embeds])
|
||||
.map_err(|e| anyhow::anyhow!("encoder: {e}"))?;
|
||||
let (shape, data) = outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("encoder output: {e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
// Step 4: Autoregressive decoding.
|
||||
let eos_id = self.tokenizer.token_to_id("</s>").unwrap_or(2) as i64;
|
||||
// decoder_start_token_id = 2 (</s>) per generation_config.json
|
||||
let decoder_start_id = eos_id;
|
||||
let mut token_ids: Vec<i64> = vec![decoder_start_id];
|
||||
|
||||
for _ in 0..MAX_TOKENS {
|
||||
let seq_len = token_ids.len();
|
||||
|
||||
// Decoder expects: encoder_attention_mask, encoder_hidden_states, inputs_embeds
|
||||
// inputs_embeds = embed_tokens(decoder token IDs)
|
||||
let dec_input_ids = Tensor::<i64>::from_array(
|
||||
([1usize, seq_len], token_ids.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (dec_embeds, dec_embed_shape) = {
|
||||
let mut guard = self.embed_tokens.lock().unwrap();
|
||||
let outputs = guard.run(ort::inputs![dec_input_ids])
|
||||
.map_err(|e| anyhow::anyhow!("embed_tokens (decoder): {e}"))?;
|
||||
let (shape, data) = outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("embed_tokens output: {e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
let enc_attn_mask = Tensor::<i64>::from_array(
|
||||
([1usize, total_seq_len], vec![1i64; total_seq_len]),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let enc_hidden_tensor = Tensor::<f32>::from_array(
|
||||
(enc_shape.clone(), enc_hidden.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let inputs_embeds = Tensor::<f32>::from_array(
|
||||
(dec_embed_shape, dec_embeds),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (logits_data, vocab_size) = {
|
||||
let mut guard = self.decoder.lock().unwrap();
|
||||
let outputs = guard.run(
|
||||
ort::inputs![enc_attn_mask, enc_hidden_tensor, inputs_embeds]
|
||||
).map_err(|e| anyhow::anyhow!("decoder: {e}"))?;
|
||||
let (shape, data) = outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("decoder output: {e}"))?;
|
||||
let vs = *shape.last().unwrap_or(&0) as usize;
|
||||
(data.to_vec(), vs)
|
||||
};
|
||||
|
||||
let last_pos = seq_len - 1;
|
||||
let offset = last_pos * vocab_size;
|
||||
|
||||
let mut best_id = 0usize;
|
||||
let mut best_score = f32::NEG_INFINITY;
|
||||
for v in 0..vocab_size {
|
||||
let score = logits_data[offset + v];
|
||||
if score > best_score {
|
||||
best_score = score;
|
||||
best_id = v;
|
||||
}
|
||||
}
|
||||
|
||||
let next_id = best_id as i64;
|
||||
if next_id == eos_id {
|
||||
break;
|
||||
}
|
||||
token_ids.push(next_id);
|
||||
}
|
||||
|
||||
// Skip BOS token for decoding.
|
||||
let output_ids: Vec<u32> = token_ids[1..].iter().map(|&id| id as u32).collect();
|
||||
let caption = self.tokenizer.decode(&output_ids, true)
|
||||
.map_err(|e| anyhow::anyhow!("Tokenizer decode failed: {e}"))?;
|
||||
|
||||
let caption = caption.trim();
|
||||
|
||||
Ok(caption.to_string())
|
||||
}
|
||||
}
|
||||
139
crates/rbv-caption/src/models/git.rs
Normal file
139
crates/rbv-caption/src/models/git.rs
Normal file
@@ -0,0 +1,139 @@
|
||||
use anyhow::{Context, Result};
|
||||
use image::GenericImageView;
|
||||
use ort::session::Session;
|
||||
use ort::value::Tensor;
|
||||
use std::path::Path;
|
||||
use std::sync::Mutex;
|
||||
use tokenizers::Tokenizer;
|
||||
use super::CaptionModel;
|
||||
|
||||
const IMAGE_SIZE: u32 = 224;
|
||||
const MAX_TOKENS: usize = 50;
|
||||
const MEAN: [f32; 3] = [0.48145466, 0.4578275, 0.40821073];
|
||||
const STD: [f32; 3] = [0.26862954, 0.26130258, 0.27577711];
|
||||
|
||||
pub struct GitBase {
|
||||
encoder: Mutex<Session>,
|
||||
decoder: Mutex<Session>,
|
||||
tokenizer: Tokenizer,
|
||||
}
|
||||
|
||||
impl GitBase {
|
||||
pub fn load(model_dir: &Path) -> Result<Self> {
|
||||
let encoder_path = model_dir.join("encoder.onnx");
|
||||
let decoder_path = model_dir.join("decoder.onnx");
|
||||
let tokenizer_path = model_dir.join("tokenizer.json");
|
||||
|
||||
for p in [&encoder_path, &decoder_path, &tokenizer_path] {
|
||||
anyhow::ensure!(p.exists(), "Missing model file: {}", p.display());
|
||||
}
|
||||
|
||||
let encoder = Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?
|
||||
.commit_from_file(&encoder_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load GIT encoder: {e}"))?;
|
||||
|
||||
let decoder = Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?
|
||||
.commit_from_file(&decoder_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load GIT decoder: {e}"))?;
|
||||
|
||||
let tokenizer = Tokenizer::from_file(&tokenizer_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {e}"))?;
|
||||
|
||||
Ok(Self {
|
||||
encoder: Mutex::new(encoder),
|
||||
decoder: Mutex::new(decoder),
|
||||
tokenizer,
|
||||
})
|
||||
}
|
||||
|
||||
fn preprocess_image(&self, image_bytes: &[u8]) -> Result<Vec<f32>> {
|
||||
let img = image::load_from_memory(image_bytes)
|
||||
.context("Failed to decode image")?;
|
||||
let img = img.resize_exact(IMAGE_SIZE, IMAGE_SIZE, image::imageops::FilterType::Lanczos3);
|
||||
|
||||
let mut pixels = vec![0f32; 3 * IMAGE_SIZE as usize * IMAGE_SIZE as usize];
|
||||
for y in 0..IMAGE_SIZE {
|
||||
for x in 0..IMAGE_SIZE {
|
||||
let pixel = img.get_pixel(x, y);
|
||||
for c in 0..3usize {
|
||||
let idx = c * (IMAGE_SIZE as usize * IMAGE_SIZE as usize)
|
||||
+ y as usize * IMAGE_SIZE as usize
|
||||
+ x as usize;
|
||||
pixels[idx] = (pixel[c] as f32 / 255.0 - MEAN[c]) / STD[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(pixels)
|
||||
}
|
||||
}
|
||||
|
||||
impl CaptionModel for GitBase {
|
||||
fn caption(&self, image_bytes: &[u8]) -> Result<String> {
|
||||
let pixels = self.preprocess_image(image_bytes)?;
|
||||
let pixel_input = Tensor::<f32>::from_array(
|
||||
([1usize, 3, IMAGE_SIZE as usize, IMAGE_SIZE as usize], pixels),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (enc_data, enc_shape) = {
|
||||
let mut enc_guard = self.encoder.lock().unwrap();
|
||||
let encoder_outputs = enc_guard.run(ort::inputs![pixel_input])
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let (shape, data) = encoder_outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
let bos_id = self.tokenizer.token_to_id("[CLS]").unwrap_or(101) as i64;
|
||||
let eos_id = self.tokenizer.token_to_id("[SEP]").unwrap_or(102) as i64;
|
||||
|
||||
let mut token_ids: Vec<i64> = vec![bos_id];
|
||||
|
||||
for _ in 0..MAX_TOKENS {
|
||||
let seq_len = token_ids.len();
|
||||
let input_ids = Tensor::<i64>::from_array(
|
||||
([1usize, seq_len], token_ids.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let enc_hidden = Tensor::<f32>::from_array(
|
||||
(enc_shape.clone(), enc_data.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (logits_data, vocab_size) = {
|
||||
let mut dec_guard = self.decoder.lock().unwrap();
|
||||
let decoder_outputs = dec_guard.run(ort::inputs![input_ids, enc_hidden])
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let (shape, data) = decoder_outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let vs = *shape.last().unwrap_or(&0) as usize;
|
||||
(data.to_vec(), vs)
|
||||
};
|
||||
|
||||
let last_pos = seq_len - 1;
|
||||
let offset = last_pos * vocab_size;
|
||||
|
||||
let mut best_id = 0usize;
|
||||
let mut best_score = f32::NEG_INFINITY;
|
||||
for v in 0..vocab_size {
|
||||
let score = logits_data[offset + v];
|
||||
if score > best_score {
|
||||
best_score = score;
|
||||
best_id = v;
|
||||
}
|
||||
}
|
||||
|
||||
let next_id = best_id as i64;
|
||||
if next_id == eos_id || next_id == 0 {
|
||||
break;
|
||||
}
|
||||
token_ids.push(next_id);
|
||||
}
|
||||
|
||||
let output_ids: Vec<u32> = token_ids[1..].iter().map(|&id| id as u32).collect();
|
||||
let caption = self.tokenizer.decode(&output_ids, true)
|
||||
.map_err(|e| anyhow::anyhow!("Tokenizer decode failed: {e}"))?;
|
||||
|
||||
Ok(caption.trim().to_string())
|
||||
}
|
||||
}
|
||||
40
crates/rbv-caption/src/models/mod.rs
Normal file
40
crates/rbv-caption/src/models/mod.rs
Normal file
@@ -0,0 +1,40 @@
|
||||
pub mod blip;
|
||||
pub mod git;
|
||||
pub mod florence;
|
||||
pub mod vit_gpt2;
|
||||
|
||||
use anyhow::Result;
|
||||
use std::path::Path;
|
||||
|
||||
pub trait CaptionModel: Send + Sync {
|
||||
fn caption(&self, image_bytes: &[u8]) -> Result<String>;
|
||||
}
|
||||
|
||||
pub fn load_model(model_name: &str, model_dir: &Path) -> Result<Box<dyn CaptionModel>> {
|
||||
match model_name {
|
||||
"blip-base" | "git-base" | "florence-2-base" | "vit-gpt2" => {
|
||||
let base = model_dir.join("captioning").join(model_name);
|
||||
if !base.exists() {
|
||||
anyhow::bail!("Model directory not found: {}", base.display());
|
||||
}
|
||||
match model_name {
|
||||
"blip-base" => Ok(Box::new(blip::BlipBase::load(&base)?)),
|
||||
"git-base" => Ok(Box::new(git::GitBase::load(&base)?)),
|
||||
"florence-2-base" => Ok(Box::new(florence::Florence2Base::load(&base)?)),
|
||||
"vit-gpt2" => Ok(Box::new(vit_gpt2::VitGpt2::load(&base)?)),
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
"florence-2-base-ocr" => {
|
||||
// OCR variant reuses the same ONNX files as florence-2-base.
|
||||
let base = model_dir.join("captioning").join("florence-2-base");
|
||||
if !base.exists() {
|
||||
anyhow::bail!("Model directory not found: {}", base.display());
|
||||
}
|
||||
Ok(Box::new(florence::Florence2Base::load_ocr(&base)?))
|
||||
}
|
||||
_ => anyhow::bail!(
|
||||
"Unknown model: {model_name}. Supported: blip-base, git-base, florence-2-base, florence-2-base-ocr, vit-gpt2"
|
||||
),
|
||||
}
|
||||
}
|
||||
145
crates/rbv-caption/src/models/vit_gpt2.rs
Normal file
145
crates/rbv-caption/src/models/vit_gpt2.rs
Normal file
@@ -0,0 +1,145 @@
|
||||
use anyhow::{Context, Result};
|
||||
use image::GenericImageView;
|
||||
use ort::session::Session;
|
||||
use ort::value::Tensor;
|
||||
use std::path::Path;
|
||||
use std::sync::Mutex;
|
||||
use tokenizers::Tokenizer;
|
||||
use super::CaptionModel;
|
||||
|
||||
const IMAGE_SIZE: u32 = 224;
|
||||
const MAX_TOKENS: usize = 50;
|
||||
// ViT uses ImageNet normalization
|
||||
const MEAN: [f32; 3] = [0.5, 0.5, 0.5];
|
||||
const STD: [f32; 3] = [0.5, 0.5, 0.5];
|
||||
|
||||
pub struct VitGpt2 {
|
||||
encoder: Mutex<Session>,
|
||||
decoder: Mutex<Session>,
|
||||
tokenizer: Tokenizer,
|
||||
}
|
||||
|
||||
impl VitGpt2 {
|
||||
pub fn load(model_dir: &Path) -> Result<Self> {
|
||||
let encoder_path = model_dir.join("encoder.onnx");
|
||||
let decoder_path = model_dir.join("decoder.onnx");
|
||||
let tokenizer_path = model_dir.join("tokenizer.json");
|
||||
|
||||
for p in [&encoder_path, &decoder_path, &tokenizer_path] {
|
||||
anyhow::ensure!(p.exists(), "Missing model file: {}", p.display());
|
||||
}
|
||||
|
||||
let encoder = Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?
|
||||
.commit_from_file(&encoder_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load ViT-GPT2 encoder: {e}"))?;
|
||||
|
||||
let decoder = Session::builder()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?
|
||||
.commit_from_file(&decoder_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load ViT-GPT2 decoder: {e}"))?;
|
||||
|
||||
let tokenizer = Tokenizer::from_file(&tokenizer_path)
|
||||
.map_err(|e| anyhow::anyhow!("Failed to load tokenizer: {e}"))?;
|
||||
|
||||
Ok(Self {
|
||||
encoder: Mutex::new(encoder),
|
||||
decoder: Mutex::new(decoder),
|
||||
tokenizer,
|
||||
})
|
||||
}
|
||||
|
||||
fn preprocess_image(&self, image_bytes: &[u8]) -> Result<Vec<f32>> {
|
||||
let img = image::load_from_memory(image_bytes)
|
||||
.context("Failed to decode image")?;
|
||||
let img = img.resize_exact(IMAGE_SIZE, IMAGE_SIZE, image::imageops::FilterType::Lanczos3);
|
||||
|
||||
let mut pixels = vec![0f32; 3 * IMAGE_SIZE as usize * IMAGE_SIZE as usize];
|
||||
for y in 0..IMAGE_SIZE {
|
||||
for x in 0..IMAGE_SIZE {
|
||||
let pixel = img.get_pixel(x, y);
|
||||
for c in 0..3usize {
|
||||
let idx = c * (IMAGE_SIZE as usize * IMAGE_SIZE as usize)
|
||||
+ y as usize * IMAGE_SIZE as usize
|
||||
+ x as usize;
|
||||
pixels[idx] = (pixel[c] as f32 / 255.0 - MEAN[c]) / STD[c];
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(pixels)
|
||||
}
|
||||
}
|
||||
|
||||
impl CaptionModel for VitGpt2 {
|
||||
fn caption(&self, image_bytes: &[u8]) -> Result<String> {
|
||||
let pixels = self.preprocess_image(image_bytes)?;
|
||||
let pixel_input = Tensor::<f32>::from_array(
|
||||
([1usize, 3, IMAGE_SIZE as usize, IMAGE_SIZE as usize], pixels),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (enc_data, enc_shape) = {
|
||||
let mut enc_guard = self.encoder.lock().unwrap();
|
||||
let encoder_outputs = enc_guard.run(ort::inputs![pixel_input])
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let (shape, data) = encoder_outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
(data.to_vec(), shape.iter().map(|&d| d as usize).collect::<Vec<_>>())
|
||||
};
|
||||
|
||||
// GPT-2 uses BOS token to start generation
|
||||
let bos_id = self.tokenizer.token_to_id("<|endoftext|>").unwrap_or(50256) as i64;
|
||||
let eos_id = bos_id; // GPT-2 uses same token for BOS and EOS
|
||||
|
||||
let mut token_ids: Vec<i64> = vec![bos_id];
|
||||
|
||||
for _ in 0..MAX_TOKENS {
|
||||
let seq_len = token_ids.len();
|
||||
let input_ids = Tensor::<i64>::from_array(
|
||||
([1usize, seq_len], token_ids.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let enc_hidden = Tensor::<f32>::from_array(
|
||||
(enc_shape.clone(), enc_data.clone()),
|
||||
).map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
|
||||
let (logits_data, vocab_size) = {
|
||||
let mut dec_guard = self.decoder.lock().unwrap();
|
||||
let decoder_outputs = dec_guard.run(ort::inputs![input_ids, enc_hidden])
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let (shape, data) = decoder_outputs[0].try_extract_tensor::<f32>()
|
||||
.map_err(|e| anyhow::anyhow!("{e}"))?;
|
||||
let vs = *shape.last().unwrap_or(&0) as usize;
|
||||
(data.to_vec(), vs)
|
||||
};
|
||||
|
||||
let last_pos = seq_len - 1;
|
||||
let offset = last_pos * vocab_size;
|
||||
|
||||
let mut best_id = 0usize;
|
||||
let mut best_score = f32::NEG_INFINITY;
|
||||
for v in 0..vocab_size {
|
||||
let score = logits_data[offset + v];
|
||||
if score > best_score {
|
||||
best_score = score;
|
||||
best_id = v;
|
||||
}
|
||||
}
|
||||
|
||||
let next_id = best_id as i64;
|
||||
// Stop on EOS or after generating enough tokens
|
||||
if next_id == eos_id && token_ids.len() > 1 {
|
||||
break;
|
||||
}
|
||||
if next_id == 0 {
|
||||
break;
|
||||
}
|
||||
token_ids.push(next_id);
|
||||
}
|
||||
|
||||
let output_ids: Vec<u32> = token_ids[1..].iter().map(|&id| id as u32).collect();
|
||||
let caption = self.tokenizer.decode(&output_ids, true)
|
||||
.map_err(|e| anyhow::anyhow!("Tokenizer decode failed: {e}"))?;
|
||||
|
||||
Ok(caption.trim().to_string())
|
||||
}
|
||||
}
|
||||
148
crates/rbv-data/src/caption.rs
Normal file
148
crates/rbv-data/src/caption.rs
Normal file
@@ -0,0 +1,148 @@
|
||||
use anyhow::Result;
|
||||
use sqlx::{PgPool, Row};
|
||||
use rbv_entity::{Caption, Gallery, GalleryId, ImageId};
|
||||
|
||||
pub async fn upsert_caption(pool: &PgPool, caption: &Caption) -> Result<()> {
|
||||
sqlx::query(
|
||||
r#"
|
||||
INSERT INTO captions (image_id, model, caption)
|
||||
VALUES ($1, $2, $3)
|
||||
ON CONFLICT (image_id, model) DO UPDATE SET
|
||||
caption = EXCLUDED.caption,
|
||||
created_at = now()
|
||||
"#,
|
||||
)
|
||||
.bind(caption.image_id.as_bytes())
|
||||
.bind(&caption.model)
|
||||
.bind(&caption.caption)
|
||||
.execute(pool)
|
||||
.await?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn get_captions(pool: &PgPool, image_id: &ImageId) -> Result<Vec<Caption>> {
|
||||
let rows = sqlx::query(
|
||||
"SELECT image_id, model, caption FROM captions WHERE image_id = $1 ORDER BY model",
|
||||
)
|
||||
.bind(image_id.as_bytes())
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
let id_bytes: Vec<u8> = r.get("image_id");
|
||||
Caption {
|
||||
image_id: ImageId(id_bytes.try_into().expect("32-byte id")),
|
||||
model: r.get("model"),
|
||||
caption: r.get("caption"),
|
||||
}
|
||||
}).collect())
|
||||
}
|
||||
|
||||
/// Fetch a batch of images that have no caption for the given model,
|
||||
/// returning (image_id_hex, gallery_path, filename) tuples.
|
||||
pub async fn uncaptioned_images(
|
||||
pool: &PgPool,
|
||||
model: &str,
|
||||
batch_size: i64,
|
||||
) -> Result<Vec<(String, String, String)>> {
|
||||
let rows = sqlx::query(
|
||||
r#"
|
||||
SELECT encode(gi.image_id, 'hex') AS image_id_hex, g.path, gi.filename
|
||||
FROM gallery_images gi
|
||||
JOIN galleries g ON g.id = gi.gallery_id
|
||||
WHERE NOT EXISTS (
|
||||
SELECT 1 FROM captions c
|
||||
WHERE c.image_id = gi.image_id AND c.model = $1
|
||||
)
|
||||
LIMIT $2
|
||||
"#,
|
||||
)
|
||||
.bind(model)
|
||||
.bind(batch_size)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
(
|
||||
r.get::<String, _>("image_id_hex"),
|
||||
r.get::<String, _>("path"),
|
||||
r.get::<String, _>("filename"),
|
||||
)
|
||||
}).collect())
|
||||
}
|
||||
|
||||
pub async fn count_uncaptioned(pool: &PgPool, model: &str) -> Result<i64> {
|
||||
let row = sqlx::query(
|
||||
r#"
|
||||
SELECT COUNT(*) AS count
|
||||
FROM gallery_images gi
|
||||
WHERE NOT EXISTS (
|
||||
SELECT 1 FROM captions c
|
||||
WHERE c.image_id = gi.image_id AND c.model = $1
|
||||
)
|
||||
"#,
|
||||
)
|
||||
.bind(model)
|
||||
.fetch_one(pool)
|
||||
.await?;
|
||||
Ok(row.get("count"))
|
||||
}
|
||||
|
||||
/// Get captions for all images in a gallery, for a specific model.
|
||||
/// Returns (image_id_hex, caption) pairs.
|
||||
pub async fn gallery_captions(
|
||||
pool: &PgPool,
|
||||
gallery_id: &GalleryId,
|
||||
model: &str,
|
||||
) -> Result<Vec<(String, String)>> {
|
||||
let rows = sqlx::query(
|
||||
r#"
|
||||
SELECT encode(c.image_id, 'hex') AS image_id_hex, c.caption
|
||||
FROM captions c
|
||||
JOIN gallery_images gi ON gi.image_id = c.image_id
|
||||
WHERE gi.gallery_id = $1 AND c.model = $2
|
||||
"#,
|
||||
)
|
||||
.bind(gallery_id.as_bytes())
|
||||
.bind(model)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
(r.get::<String, _>("image_id_hex"), r.get::<String, _>("caption"))
|
||||
}).collect())
|
||||
}
|
||||
|
||||
/// Search for galleries containing images with captions matching the query.
|
||||
pub async fn search_captions(pool: &PgPool, query: &str, limit: i64) -> Result<Vec<Gallery>> {
|
||||
let rows = sqlx::query(
|
||||
r#"
|
||||
SELECT DISTINCT g.id, g.source_id, g.collection, g.source_name,
|
||||
g.source_url, g.subjects, g.tags, g.path
|
||||
FROM galleries g
|
||||
JOIN gallery_images gi ON gi.gallery_id = g.id
|
||||
JOIN captions c ON c.image_id = gi.image_id
|
||||
WHERE c.caption ILIKE '%' || $1 || '%'
|
||||
ORDER BY g.collection, g.source_name
|
||||
LIMIT $2
|
||||
"#,
|
||||
)
|
||||
.bind(query)
|
||||
.bind(limit)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
let id_bytes: Vec<u8> = r.get("id");
|
||||
Gallery {
|
||||
id: GalleryId(id_bytes.try_into().expect("32-byte id")),
|
||||
source_id: r.get::<i64, _>("source_id") as u64,
|
||||
collection: r.get("collection"),
|
||||
source_name: r.get("source_name"),
|
||||
source_url: r.get("source_url"),
|
||||
subjects: r.get("subjects"),
|
||||
tags: r.get("tags"),
|
||||
path: r.get::<String, _>("path").into(),
|
||||
}
|
||||
}).collect())
|
||||
}
|
||||
127
crates/rbv-data/src/label.rs
Normal file
127
crates/rbv-data/src/label.rs
Normal file
@@ -0,0 +1,127 @@
|
||||
use anyhow::Result;
|
||||
use sqlx::{PgPool, Row};
|
||||
use rbv_entity::{Gallery, GalleryId, Label};
|
||||
|
||||
pub async fn upsert_label(pool: &PgPool, label: &Label) -> Result<()> {
|
||||
sqlx::query(
|
||||
r#"
|
||||
INSERT INTO labels (image_id, model, label)
|
||||
VALUES ($1, $2, $3)
|
||||
ON CONFLICT (image_id, model) DO UPDATE SET
|
||||
label = EXCLUDED.label,
|
||||
created_at = now()
|
||||
"#,
|
||||
)
|
||||
.bind(label.image_id.as_bytes())
|
||||
.bind(&label.model)
|
||||
.bind(&label.label)
|
||||
.execute(pool)
|
||||
.await?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub async fn unlabelled_images(
|
||||
pool: &PgPool,
|
||||
model: &str,
|
||||
batch_size: i64,
|
||||
) -> Result<Vec<(String, String, String)>> {
|
||||
let rows = sqlx::query(
|
||||
r#"
|
||||
SELECT encode(gi.image_id, 'hex') AS image_id_hex, g.path, gi.filename
|
||||
FROM gallery_images gi
|
||||
JOIN galleries g ON g.id = gi.gallery_id
|
||||
WHERE NOT EXISTS (
|
||||
SELECT 1 FROM labels l
|
||||
WHERE l.image_id = gi.image_id AND l.model = $1
|
||||
)
|
||||
LIMIT $2
|
||||
"#,
|
||||
)
|
||||
.bind(model)
|
||||
.bind(batch_size)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
(
|
||||
r.get::<String, _>("image_id_hex"),
|
||||
r.get::<String, _>("path"),
|
||||
r.get::<String, _>("filename"),
|
||||
)
|
||||
}).collect())
|
||||
}
|
||||
|
||||
pub async fn count_unlabelled(pool: &PgPool, model: &str) -> Result<i64> {
|
||||
let row = sqlx::query(
|
||||
r#"
|
||||
SELECT COUNT(*) AS count
|
||||
FROM gallery_images gi
|
||||
WHERE NOT EXISTS (
|
||||
SELECT 1 FROM labels l
|
||||
WHERE l.image_id = gi.image_id AND l.model = $1
|
||||
)
|
||||
"#,
|
||||
)
|
||||
.bind(model)
|
||||
.fetch_one(pool)
|
||||
.await?;
|
||||
Ok(row.get("count"))
|
||||
}
|
||||
|
||||
/// Search for galleries containing images with labels matching the query.
|
||||
/// Excludes empty labels (scanned but no text found).
|
||||
pub async fn search_labels(pool: &PgPool, query: &str, limit: i64) -> Result<Vec<Gallery>> {
|
||||
let rows = sqlx::query(
|
||||
r#"
|
||||
SELECT DISTINCT g.id, g.source_id, g.collection, g.source_name,
|
||||
g.source_url, g.subjects, g.tags, g.path
|
||||
FROM galleries g
|
||||
JOIN gallery_images gi ON gi.gallery_id = g.id
|
||||
JOIN labels l ON l.image_id = gi.image_id
|
||||
WHERE l.label <> '' AND l.label ILIKE '%' || $1 || '%'
|
||||
ORDER BY g.collection, g.source_name
|
||||
LIMIT $2
|
||||
"#,
|
||||
)
|
||||
.bind(query)
|
||||
.bind(limit)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
let id_bytes: Vec<u8> = r.get("id");
|
||||
Gallery {
|
||||
id: GalleryId(id_bytes.try_into().expect("32-byte id")),
|
||||
source_id: r.get::<i64, _>("source_id") as u64,
|
||||
collection: r.get("collection"),
|
||||
source_name: r.get("source_name"),
|
||||
source_url: r.get("source_url"),
|
||||
subjects: r.get("subjects"),
|
||||
tags: r.get("tags"),
|
||||
path: r.get::<String, _>("path").into(),
|
||||
}
|
||||
}).collect())
|
||||
}
|
||||
|
||||
pub async fn gallery_labels(
|
||||
pool: &PgPool,
|
||||
gallery_id: &GalleryId,
|
||||
model: &str,
|
||||
) -> Result<Vec<(String, String)>> {
|
||||
let rows = sqlx::query(
|
||||
r#"
|
||||
SELECT encode(l.image_id, 'hex') AS image_id_hex, l.label
|
||||
FROM labels l
|
||||
JOIN gallery_images gi ON gi.image_id = l.image_id
|
||||
WHERE gi.gallery_id = $1 AND l.model = $2 AND l.label <> ''
|
||||
"#,
|
||||
)
|
||||
.bind(gallery_id.as_bytes())
|
||||
.bind(model)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
|
||||
Ok(rows.iter().map(|r| {
|
||||
(r.get::<String, _>("image_id_hex"), r.get::<String, _>("label"))
|
||||
}).collect())
|
||||
}
|
||||
@@ -6,6 +6,8 @@ pub mod person;
|
||||
pub mod clip;
|
||||
pub mod user;
|
||||
pub mod favourite;
|
||||
pub mod caption;
|
||||
pub mod label;
|
||||
|
||||
pub use pool::{connect, run_migrations, rebuild_vector_indexes};
|
||||
pub use sqlx::PgPool;
|
||||
|
||||
9
crates/rbv-entity/src/caption.rs
Normal file
9
crates/rbv-entity/src/caption.rs
Normal file
@@ -0,0 +1,9 @@
|
||||
use serde::{Deserialize, Serialize};
|
||||
use crate::ImageId;
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct Caption {
|
||||
pub image_id: ImageId,
|
||||
pub model: String,
|
||||
pub caption: String,
|
||||
}
|
||||
9
crates/rbv-entity/src/label.rs
Normal file
9
crates/rbv-entity/src/label.rs
Normal file
@@ -0,0 +1,9 @@
|
||||
use serde::{Deserialize, Serialize};
|
||||
use crate::ImageId;
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct Label {
|
||||
pub image_id: ImageId,
|
||||
pub model: String,
|
||||
pub label: String,
|
||||
}
|
||||
@@ -4,6 +4,7 @@ pub mod face;
|
||||
pub mod person;
|
||||
pub mod clip;
|
||||
pub mod user;
|
||||
pub mod caption;
|
||||
|
||||
pub use gallery::{Gallery, GalleryId};
|
||||
pub use image::{Image, GalleryImage, ImageId};
|
||||
@@ -11,3 +12,7 @@ pub use face::{FaceDetection, FaceId, BoundingBox};
|
||||
pub use person::{Person, PersonId, PersonName};
|
||||
pub use clip::ClipEmbedding;
|
||||
pub use user::{User, Session};
|
||||
pub use caption::Caption;
|
||||
|
||||
pub mod label;
|
||||
pub use label::Label;
|
||||
|
||||
@@ -18,6 +18,8 @@ pub struct SearchParams<'a> {
|
||||
pub tag: Option<&'a str>,
|
||||
pub subject: Option<&'a str>,
|
||||
pub person_name: Option<&'a str>,
|
||||
pub caption: Option<&'a str>,
|
||||
pub label: Option<&'a str>,
|
||||
pub limit: i64,
|
||||
}
|
||||
|
||||
@@ -47,11 +49,13 @@ async fn search_quick(
|
||||
let fetch_limit = params.limit * 2;
|
||||
|
||||
// Run all searches concurrently.
|
||||
let (tag_galleries, subject_galleries, matched_person_ids, clip_hits) = tokio::try_join!(
|
||||
let (tag_galleries, subject_galleries, matched_person_ids, clip_hits, caption_galleries, label_galleries) = tokio::try_join!(
|
||||
rbv_data::gallery::search_galleries_by_tag(pool, query, fetch_limit),
|
||||
rbv_data::gallery::search_galleries_by_subject(pool, query, fetch_limit),
|
||||
rbv_data::person::search_persons_by_name(pool, query),
|
||||
search_by_text(pool, ml, query, fetch_limit),
|
||||
rbv_data::caption::search_captions(pool, query, fetch_limit),
|
||||
rbv_data::label::search_labels(pool, query, fetch_limit),
|
||||
)?;
|
||||
|
||||
// Resolve person matches to galleries.
|
||||
@@ -79,6 +83,16 @@ async fn search_quick(
|
||||
let entry = scored.entry(id).or_insert_with(|| (g, 0.0));
|
||||
entry.1 += 1.0;
|
||||
}
|
||||
for g in caption_galleries {
|
||||
let id = g.id.clone();
|
||||
let entry = scored.entry(id).or_insert_with(|| (g, 0.0));
|
||||
entry.1 += 1.0;
|
||||
}
|
||||
for g in label_galleries {
|
||||
let id = g.id.clone();
|
||||
let entry = scored.entry(id).or_insert_with(|| (g, 0.0));
|
||||
entry.1 += 1.0;
|
||||
}
|
||||
|
||||
// Resolve CLIP image hits to galleries.
|
||||
for (image_id, score) in clip_hits {
|
||||
@@ -151,6 +165,30 @@ async fn search_advanced(
|
||||
filter_sets.push(ids);
|
||||
}
|
||||
|
||||
// Caption filter.
|
||||
if let Some(caption_query) = params.caption.filter(|c| !c.is_empty()) {
|
||||
let galleries = rbv_data::caption::search_captions(pool, caption_query, fetch_limit).await?;
|
||||
let mut ids = HashSet::new();
|
||||
for g in galleries {
|
||||
let id = g.id.clone();
|
||||
ids.insert(id.clone());
|
||||
gallery_map.entry(id).or_insert_with(|| (g, 1.0));
|
||||
}
|
||||
filter_sets.push(ids);
|
||||
}
|
||||
|
||||
// Label (OCR text) filter.
|
||||
if let Some(label_query) = params.label.filter(|l| !l.is_empty()) {
|
||||
let galleries = rbv_data::label::search_labels(pool, label_query, fetch_limit).await?;
|
||||
let mut ids = HashSet::new();
|
||||
for g in galleries {
|
||||
let id = g.id.clone();
|
||||
ids.insert(id.clone());
|
||||
gallery_map.entry(id).or_insert_with(|| (g, 1.0));
|
||||
}
|
||||
filter_sets.push(ids);
|
||||
}
|
||||
|
||||
// Person name filter.
|
||||
if let Some(name) = params.person_name.filter(|n| !n.is_empty()) {
|
||||
let person_ids = rbv_data::person::search_persons_by_name(pool, name).await?;
|
||||
|
||||
9
migrations/0013_captions.sql
Normal file
9
migrations/0013_captions.sql
Normal file
@@ -0,0 +1,9 @@
|
||||
CREATE TABLE IF NOT EXISTS captions (
|
||||
image_id BYTEA NOT NULL REFERENCES images(id) ON DELETE CASCADE,
|
||||
model TEXT NOT NULL,
|
||||
caption TEXT NOT NULL,
|
||||
created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
|
||||
PRIMARY KEY (image_id, model)
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_captions_image ON captions (image_id);
|
||||
9
migrations/0014_labels.sql
Normal file
9
migrations/0014_labels.sql
Normal file
@@ -0,0 +1,9 @@
|
||||
CREATE TABLE IF NOT EXISTS labels (
|
||||
image_id BYTEA NOT NULL REFERENCES images(id) ON DELETE CASCADE,
|
||||
model TEXT NOT NULL,
|
||||
label TEXT NOT NULL,
|
||||
created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
|
||||
PRIMARY KEY (image_id, model)
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_labels_image ON labels (image_id);
|
||||
@@ -4,6 +4,7 @@ postgres_host=gramathea.kosherinata.internal
|
||||
api_host=gramathea.kosherinata.internal
|
||||
index_host=gramathea.kosherinata.internal
|
||||
ui_host=gramathea.kosherinata.internal
|
||||
caption_host=quadbrat.hanzalova.internal
|
||||
|
||||
deploy_db() {
|
||||
if rsync \
|
||||
@@ -109,6 +110,48 @@ deploy_api() {
|
||||
fi
|
||||
}
|
||||
|
||||
deploy_caption() {
|
||||
cargo build --release -p rbv-caption
|
||||
postgres_password=$(grep POSTGRES_PASSWORD asset/quadlet/.env | cut -d '=' -f 2)
|
||||
|
||||
for unit in rbv-caption@{florence-2-base,florence-2-base-ocr,vit-gpt2}.service; do
|
||||
state=$(ssh ${caption_host} "systemctl is-active ${unit} 2>/dev/null")
|
||||
if [[ ${state} == "active" || ${state} == "activating" ]]; then
|
||||
if ssh ${caption_host} sudo systemctl stop ${unit}; then
|
||||
echo "${unit} stopped successfully"
|
||||
else
|
||||
echo "failed to stop ${unit}"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
done
|
||||
if rsync \
|
||||
--archive \
|
||||
--compress \
|
||||
--rsync-path 'sudo rsync' \
|
||||
--chown root:root \
|
||||
--chmod '+x' \
|
||||
target/release/rbv-caption \
|
||||
${caption_host}:/usr/local/bin/rbv-caption \
|
||||
&& rsync \
|
||||
--archive \
|
||||
--compress \
|
||||
--rsync-path 'sudo rsync' \
|
||||
--chown root:root \
|
||||
asset/systemd/rbv-caption@.service \
|
||||
${caption_host}:/etc/systemd/system/rbv-caption@.service \
|
||||
&& ssh ${caption_host} sudo sed -i -e "s/password/${postgres_password}/" /etc/systemd/system/rbv-caption@.service \
|
||||
&& ssh ${caption_host} sudo systemctl daemon-reload \
|
||||
&& ssh ${caption_host} sudo systemctl start --no-block rbv-caption@florence-2-base.service \
|
||||
&& ssh ${caption_host} sudo systemctl start --no-block rbv-caption@florence-2-base-ocr.service \
|
||||
&& ssh ${caption_host} sudo systemctl start --no-block rbv-caption@vit-gpt2.service; then
|
||||
echo "rbv caption deployed successfully"
|
||||
else
|
||||
echo "failed to deploy rbv caption"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
deploy_ui() {
|
||||
if ssh ${ui_host} sudo step certificate verify \
|
||||
/etc/nginx/tls/rbv/rbv.pem \
|
||||
@@ -202,15 +245,16 @@ components=("${@}")
|
||||
if [ ${#components[@]} -eq 0 ]; then
|
||||
components=(api ui)
|
||||
elif [ "${components[0]}" = "all" ]; then
|
||||
components=(db index api ui)
|
||||
components=(db index api ui caption)
|
||||
fi
|
||||
|
||||
for component in "${components[@]}"; do
|
||||
case ${component} in
|
||||
db) deploy_db ;;
|
||||
index) deploy_index ;;
|
||||
api) deploy_api ;;
|
||||
ui) deploy_ui ;;
|
||||
*) echo "unknown component: ${component}"; exit 1 ;;
|
||||
db) deploy_db ;;
|
||||
index) deploy_index ;;
|
||||
api) deploy_api ;;
|
||||
ui) deploy_ui ;;
|
||||
caption) deploy_caption ;;
|
||||
*) echo "unknown component: ${component}"; exit 1 ;;
|
||||
esac
|
||||
done
|
||||
|
||||
@@ -66,6 +66,9 @@ export const getGalleryImages = (id: string) => request<GalleryImage[]>(`/galler
|
||||
|
||||
export const getGalleryPersons = (id: string) => request<Person[]>(`/galleries/${id}/persons`)
|
||||
|
||||
export const getGalleryCaptions = (id: string, model = 'florence-2-base') =>
|
||||
request<Record<string, string>>(`/galleries/${id}/captions?model=${encodeURIComponent(model)}`)
|
||||
|
||||
export const galleriesByTag = (tag: string, page = 1, perPage = 24) =>
|
||||
request<Gallery[]>(`/galleries/by-tag?tag=${encodeURIComponent(tag)}&page=${page}&per_page=${perPage}`)
|
||||
|
||||
@@ -220,6 +223,8 @@ export const search = (params: {
|
||||
tag?: string
|
||||
subject?: string
|
||||
person_name?: string
|
||||
caption?: string
|
||||
label?: string
|
||||
limit?: number
|
||||
}) =>
|
||||
request<SearchResult[]>('/search', { method: 'POST', body: JSON.stringify(params) })
|
||||
|
||||
@@ -82,6 +82,10 @@
|
||||
"subjectPlaceholder": "beach, concert...",
|
||||
"personNameLabel": "Filter by person",
|
||||
"personNamePlaceholder": "jane, john-doe...",
|
||||
"captionLabel": "Filter by image caption",
|
||||
"captionPlaceholder": "birthday cake, group photo...",
|
||||
"labelLabel": "Filter by image text",
|
||||
"labelPlaceholder": "watermark, sign text...",
|
||||
"quickPlaceholder": "Search galleries...",
|
||||
"quickResults": "Results for \"{{query}}\"",
|
||||
"submit": "Search",
|
||||
|
||||
@@ -2,7 +2,7 @@ import { useEffect, useState, useCallback, useRef } from 'react'
|
||||
import { useParams, Link } from 'react-router-dom'
|
||||
import { useTranslation } from 'react-i18next'
|
||||
import {
|
||||
getGallery, getGalleryImages, getGalleryPersons,
|
||||
getGallery, getGalleryImages, getGalleryPersons, getGalleryCaptions,
|
||||
imageFileUrl, thumbnailUrl,
|
||||
listFavouriteIds, addFavourite, removeFavourite,
|
||||
addGalleryTag, listTags,
|
||||
@@ -28,6 +28,7 @@ export function Gallery() {
|
||||
const [subjectInput, setSubjectInput] = useState('')
|
||||
const [allSubjects, setAllSubjects] = useState<string[]>([])
|
||||
const [showSubjectInput, setShowSubjectInput] = useState(false)
|
||||
const [captions, setCaptions] = useState<Record<string, string>>({})
|
||||
const [zoom, setZoom] = useState(1)
|
||||
const [offset, setOffset] = useState({ x: 0, y: 0 })
|
||||
const [dragging, setDragging] = useState(false)
|
||||
@@ -45,6 +46,7 @@ export function Gallery() {
|
||||
listTags().then(setAllTags).catch(() => setAllTags([]))
|
||||
listSubjects().then(setAllSubjects).catch(() => setAllSubjects([]))
|
||||
getConfig().then(setConfig).catch(() => setConfig(null))
|
||||
getGalleryCaptions(id).then(setCaptions).catch(() => setCaptions({}))
|
||||
}, [id])
|
||||
|
||||
// Reset zoom/pan and scroll active thumbnail into view
|
||||
@@ -233,7 +235,7 @@ export function Gallery() {
|
||||
key={currentImage.image_id}
|
||||
className="gallery-main-img"
|
||||
src={imgUrl(currentImage)}
|
||||
alt={currentImage.filename}
|
||||
alt={captions[currentImage.image_id] || currentImage.filename}
|
||||
style={{ transform: `translate(${offset.x}px, ${offset.y}px) scale(${zoom})` }}
|
||||
draggable={false}
|
||||
/>
|
||||
@@ -247,7 +249,7 @@ export function Gallery() {
|
||||
<div className="thumb-strip" ref={thumbStripRef}>
|
||||
{images.map((img, i) => (
|
||||
<button key={img.image_id} className={`thumb-btn${i === current ? ' active' : ''}`} onClick={() => setCurrent(i)}>
|
||||
<img src={thumbUrl(img)} alt={img.filename} loading="lazy" />
|
||||
<img src={thumbUrl(img)} alt={captions[img.image_id] || img.filename} loading="lazy" />
|
||||
</button>
|
||||
))}
|
||||
</div>
|
||||
|
||||
@@ -12,6 +12,8 @@ export function Search() {
|
||||
const [tag, setTag] = useState('')
|
||||
const [subject, setSubject] = useState('')
|
||||
const [personName, setPersonName] = useState('')
|
||||
const [caption, setCaption] = useState('')
|
||||
const [label, setLabel] = useState('')
|
||||
const [results, setResults] = useState<SearchResult[]>([])
|
||||
const [thumbs, setThumbs] = useState<Record<string, GalleryImage | null>>({})
|
||||
const [loading, setLoading] = useState(false)
|
||||
@@ -61,11 +63,13 @@ export function Search() {
|
||||
tag: tag.trim() || undefined,
|
||||
subject: subject.trim() || undefined,
|
||||
person_name: personName.trim() || undefined,
|
||||
caption: caption.trim() || undefined,
|
||||
label: label.trim() || undefined,
|
||||
limit: 24,
|
||||
})
|
||||
}
|
||||
|
||||
const hasInput = text.trim() || tag.trim() || subject.trim() || personName.trim()
|
||||
const hasInput = text.trim() || tag.trim() || subject.trim() || personName.trim() || caption.trim() || label.trim()
|
||||
|
||||
return (
|
||||
<div className="page">
|
||||
@@ -108,6 +112,22 @@ export function Search() {
|
||||
placeholder={t('search.personNamePlaceholder')}
|
||||
/>
|
||||
|
||||
<label className="field-label">{t('search.captionLabel')}</label>
|
||||
<input
|
||||
type="text"
|
||||
value={caption}
|
||||
onChange={e => setCaption(e.target.value)}
|
||||
placeholder={t('search.captionPlaceholder')}
|
||||
/>
|
||||
|
||||
<label className="field-label">{t('search.labelLabel')}</label>
|
||||
<input
|
||||
type="text"
|
||||
value={label}
|
||||
onChange={e => setLabel(e.target.value)}
|
||||
placeholder={t('search.labelPlaceholder')}
|
||||
/>
|
||||
|
||||
<button className="btn" type="submit" disabled={loading || !hasInput}>
|
||||
{t('search.submit')}
|
||||
</button>
|
||||
|
||||
Reference in New Issue
Block a user