fgerlits commented on code in PR #2258:
URL: https://github.com/apache/nifi-minifi-cpp/pull/2258#discussion_r4060487812
##########
minifi_rust/extensions/minifi_tensor/features/resources/.gitignore:
##########
Review Comment:
could these resources go in the build directory?
##########
behave_framework/pyproject.toml:
##########
@@ -9,7 +9,8 @@ dependencies = [
"PyYAML==6.0.3",
"humanfriendly==10.0",
"cryptography==50.0.0",
- "pyjks==20.0.0"
+ "pyjks==20.0.0",
+ "certifi==2026.7.22"
Review Comment:
`certifi` is just a list of root certificates, right? if so, then newer is
better:
```suggestion
"certifi>=2026.7.22"
```
##########
minifi_rust/extensions/minifi_tensor/src/low_level_processors/classify_output/classify_output_def.rs:
##########
@@ -0,0 +1,164 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use super::{ClassifyOutput, ScoreActivation};
+use minifi_native::{
+ OutputAttribute, ProcessorDefinition, ProcessorInputRequirement, Property,
PropertyDefinition,
+ Relationship, property_definitions,
+};
+use std::path::PathBuf;
+
+pub(crate) const TOP_K: Property<usize> = Property::new(
+ "Top K",
+ "Number of highest-scoring classes to include in the output JSON, in
descending \
+ order of confidence. Values above the total class count are
clamped. Set to 1 \
+ for pure top-1 classification.",
+)
+.with_default("5");
+
+pub(crate) const SCORE_OUTPUT_INDEX: Property<usize> = Property::new(
+ "Score output index",
+ "Zero-based index of the model output tensor that holds classification
scores. \
+ The processor slices the concatenated payload from
InvokeTractModel according \
+ to the 'tensor.N.bytes' attributes. Almost always 0 for
single-head \
+ classifiers.",
+)
+.with_default("0");
+
+pub(crate) const SCORE_ACTIVATION: Property<ScoreActivation> = Property::new(
+ "Score activation",
+ "Activation applied to the raw score vector before ranking. \
+ Softmax = mutually-exclusive classes (ImageNet-trained
ResNet/MobileNet/\
+ EfficientNet raw logits). \
+ Sigmoid = independent classes (multi-label classifiers). \
+ None = the model already emits probabilities/scores; rank
the raw values.",
+)
+.with_default(ScoreActivation::Softmax.into_str());
+
+pub(crate) const CONFIDENCE_THRESHOLD: Property<f32> = Property::new(
+ "Confidence Threshold",
+ "Minimum confidence a class must reach to be included in the output JSON. \
+ Applied AFTER activation, so the units match the chosen
activation \
+ (0.0..=1.0 for Softmax/Sigmoid, model-native for None). Set
to 0.0 to always \
+ emit exactly Top K predictions.",
+)
+.with_default("0.0")
+.supports_expression_language();
+
+pub(crate) const LABELS_FILE_PATH: Property<Option<PathBuf>> = Property::new(
+ "Labels file path",
+ "Optional path to a newline-separated labels file (line N = name of class
N). \
+ Loaded once at service enable time. When set, each
prediction in the output \
+ JSON gains a 'class_name' field and the 'class.top1.name'
flow file attribute \
+ is populated. Leave empty to emit numeric class IDs only.",
+);
+
+pub(crate) const LABEL_INDEX_OFFSET: Property<usize> = Property::new(
+ "Label index offset",
+ "Offset added to the model's class ID when looking up a name in the labels
file. \
+ Defaults to 0 (labels file line N = class N). Set to 1 for
label files that \
+ start with a dummy/background entry — e.g. the ONNX
MobileNetV2 model emits \
+ 1000 class scores while 'imagenet_slim_labels.txt' has 1001
lines (line 0 = \
+ 'dummy'), so class ID 653 maps to line 654 = 'military
uniform'.",
+)
+.with_default("0");
+
+pub(super) const SUCCESS: Relationship = Relationship {
+ name: "success",
+ description: "Classification completed. The flow file content is a JSON
array of the Top K \
+ predictions (possibly fewer if the confidence threshold
filtered some out).",
+};
+
+pub(super) const FAILURE: Relationship = Relationship {
+ name: "failure",
+ description: "The upstream output attributes were missing/invalid or the
score tensor could \
+ not be interpreted as f32 values.",
+};
+
+const MIME_TYPE_ATTR: OutputAttribute = OutputAttribute {
+ name: "mime.type",
+ relationships: &["success"],
+ description: "Always 'application/json' — the output payload is a JSON
array of prediction \
+ objects with fields class_id, confidence, and optional
class_name.",
+};
+
+const CLASS_COUNT_ATTR: OutputAttribute = OutputAttribute {
+ name: "class.count",
+ relationships: &["success"],
+ description: "Number of predictions retained after Top K selection and
confidence filtering.",
+};
+
+const CLASS_TOP1_ID_ATTR: OutputAttribute = OutputAttribute {
+ name: "class.top1.id",
+ relationships: &["success"],
+ description: "Numeric class ID of the highest-confidence prediction, when
at least one \
+ prediction cleared the confidence threshold.",
+};
+
+const CLASS_TOP1_CONFIDENCE_ATTR: OutputAttribute = OutputAttribute {
+ name: "class.top1.confidence",
+ relationships: &["success"],
+ description: "Confidence (post-activation) of the highest-confidence
prediction, when at \
+ least one prediction cleared the confidence threshold.",
+};
+
+const CLASS_TOP1_NAME_ATTR: OutputAttribute = OutputAttribute {
+ name: "class.top1.name",
+ relationships: &["success"],
+ description: "Label of the highest-confidence prediction. Only present
when 'Labels file \
+ path' was configured and at least one prediction cleared the
threshold.",
+};
+
+pub(crate) const OUTPUT_ATTRIBUTE_NAME: Property<Option<String>> =
Property::new(
+ "Output attribute name",
+ "Specify the attribute to use as output, if not provided, the content is
overridden instead.",
+)
+.supports_expression_language();
Review Comment:
please move this up in this file, so all the properties are together
##########
minifi_rust/extensions/minifi_tensor/src/low_level_processors/classify_output.rs:
##########
@@ -0,0 +1,476 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use crate::utils::score_activation::{ScoreActivation, SoftmaxTerms};
+use crate::utils::tensor_helpers::{deserialize_tensors, tensor_as_f32,
tensor_shape};
+use classify_output_def::SUCCESS;
+pub(crate) use classify_output_def::{
+ CLASSIFY_OUTPUT_ATTRIBUTES, CONFIDENCE_THRESHOLD, LABEL_INDEX_OFFSET,
LABELS_FILE_PATH,
+ OUTPUT_ATTRIBUTE_NAME, SCORE_ACTIVATION, SCORE_OUTPUT_INDEX, TOP_K,
+};
+use minifi_native::macros::ComponentIdentifier;
+use minifi_native::{
+ Content, FlowFileTransform, GetAttribute, GetId, GetProperty, InputStream,
Logger, MinifiError,
+ ProcessError, RouteErrorExt, Schedule, TransformedFlowFile, warn,
+};
+use serde::Serialize;
+use std::path::Path;
+use tract::Tensor;
+
+mod classify_output_def;
+
+#[derive(Serialize, Clone, Debug, PartialEq)]
+struct Prediction {
+ class_id: usize,
+ confidence: f32,
+ #[serde(skip_serializing_if = "Option::is_none")]
+ class_name: Option<String>,
+}
+
+fn load_labels(path: &Path) -> Result<Vec<String>, MinifiError> {
+ let content = std::fs::read_to_string(path).map_err(|e| {
+ MinifiError::custom(format!("Failed to read labels file '{:?}': {}",
path, e))
+ })?;
+ Ok(content
+ .lines()
+ .map(|line| line.trim().to_string())
+ .collect())
+}
+
+fn top_k(mut scored: Vec<(usize, f32)>, k: usize) -> Vec<(usize, f32)> {
+ scored.sort_by(|&(ai, a), &(bi, b)| b.total_cmp(&a).then(ai.cmp(&bi)));
+ scored.truncate(k);
+ scored
+}
+
+#[derive(ComponentIdentifier)]
+pub(crate) struct ClassifyOutput {
+ top_k: usize,
+ score_output_index: usize,
+ score_activation: ScoreActivation,
+ confidence_threshold: f32,
+ labels: Vec<String>,
+ label_index_offset: usize,
+}
+
+impl Schedule for ClassifyOutput {
+ fn schedule<Ctx: GetProperty, L: Logger>(
+ context: &Ctx,
+ _logger: &L,
+ ) -> Result<Self, MinifiError>
+ where
+ Self: Sized,
+ {
+ let top_k = context.get_property(&TOP_K)?;
+ if top_k == 0 {
+ return Err(MinifiError::validation("Top K must be >= 1"));
+ }
+ let score_output_index = context.get_property(&SCORE_OUTPUT_INDEX)?;
+ let score_activation = context.get_property(&SCORE_ACTIVATION)?;
+ let confidence_threshold =
context.get_property(&CONFIDENCE_THRESHOLD)?;
+
+ let labels = match context.get_property(&LABELS_FILE_PATH)? {
+ Some(path) => load_labels(&path)?,
+ _ => Vec::new(),
Review Comment:
Nitpicking, but why isn't `labels` an `Option<Vec<String>>`, set to `None`
if the property is unset? I think the code would read better that way, and
`label_for` could throw an error if the index is not found.
##########
minifi_rust/extensions/minifi_tensor/src/low_level_processors/mod.rs:
##########
Review Comment:
How does `#[cfg(feature = "low-level-processors")]` work? Specifically, why
do we have it only on `invoke_tract_model` here, but on all 4 low-level
processors in `lib.rs`?
##########
minifi_rust/extensions/minifi_tensor/src/low_level_processors/classify_output.rs:
##########
Review Comment:
There are no unit tests for `ScoreActivation::Sigmoid`, only for `None` and
`Softmax`.
##########
minifi_rust/extensions/minifi_tensor/features/detection.feature:
##########
@@ -0,0 +1,131 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+@SUPPORTS_WINDOWS
+Feature: Face detection with UltraFace (SSD)
Review Comment:
Can we write this abbreviation out, please? I had to google it, and it isn't
even easy to google, because most of the pages which come up are about
"solid-state drive".
```suggestion
Feature: Face detection with UltraFace Single Shot MultiBox Detector (SSD)
```
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