Release v0.3.0

.github/workflows/ci.yaml

@@ -2,13 +2,12 @@ name: CI
 
 on:
   pull_request:
-    branches: [ dev, main ]
+    branches: [ main ]
     paths:
       - 'luxonis_train/**'
       - 'tests/**'
       - .github/workflows/ci.yaml
       - '!**/*.md'
-      - '!luxonis_train/__main__.py'
 
 permissions:
   pull-requests: write
@@ -107,12 +106,8 @@ jobs:
   tests:
     needs:
       - config-test
-    strategy:
-      fail-fast: false
-      matrix:
-        os: [ubuntu-latest, windows-latest]
 
-    runs-on: ${{ matrix.os }}
+    runs-on: ubuntu-t4-4core
 
     steps:
     - name: Checkout

.github/workflows/semgrep.yaml

@@ -32,7 +32,7 @@ jobs:
       # or use multiple flags to specify particular rules, such as
       # --config r/all --config custom/rules
         run: semgrep scan -q --sarif --config auto --config "p/secrets" . > semgrep-results.sarif
-        
+
       - name: Pretty-Print SARIF Output
         run: |
           jq . semgrep-results.sarif > formatted-semgrep-results.sarif || echo "{}"
@@ -62,4 +62,4 @@ jobs:
             if [[ -n "$file" && -n "$line" && -n "$message" ]]; then
               echo "::error file=$file,line=$line,title=Semgrep Issue::${message}"
             fi
-          done
+          done

configs/anomaly_detection_model.yaml

@@ -0,0 +1,25 @@
+model:
+  name: AnomalyDetecion
+  predefined_model:
+    name: AnomalyDetectionModel
+    params:
+      variant: light
+
+loader:
+  name: LuxonisLoaderPerlinNoise
+  params:
+    dataset_name: mvtec_v2
+    anomaly_source_path:  "../data/dtd/images/"
+
+trainer:
+  preprocessing:
+    train_image_size: [256, 256]
+    keep_aspect_ratio: False
+    normalize:
+      active: True
+
+  batch_size: 4
+  epochs: 300
+  num_workers: 4
+  validation_interval: 10
+  num_log_images: 8

configs/detection_light_model.yaml

@@ -29,7 +29,7 @@ trainer:
         std: [1, 1, 1]
 
   batch_size: 8
-  epochs: &epochs 300
+  epochs: 300
   accumulate_grad_batches: 8 # For best results, always accumulate gradients to effectively use 64 batch size
   n_workers: 8
   validation_interval: 10

configs/fomo_heavy_model.yaml

@@ -0,0 +1,33 @@
+model:
+  name: fomo_detection_heavy
+  predefined_model:
+    name: FOMOModel
+    params:
+      variant: heavy
+
+loader:
+  params:
+    dataset_name: coco_test
+
+trainer:
+  precision: "16-mixed"
+  preprocessing:
+    train_image_size: [384, 512]
+    keep_aspect_ratio: true
+    normalize:
+      active: true
+      params:
+        mean: [0., 0., 0.]
+        std: [1, 1, 1]
+
+  batch_size: 8
+  epochs: 300
+  n_workers: 4
+  validation_interval: 10
+  n_log_images: 8
+  gradient_clip_val: 10
+
+  callbacks:
+    - name: ExportOnTrainEnd
+    - name: TestOnTrainEnd
+

configs/fomo_light_model.yaml

@@ -0,0 +1,33 @@
+model:
+  name: fomo_detection_light
+  predefined_model:
+    name: FOMOModel
+    params:
+      variant: light
+
+loader:
+  params:
+    dataset_name: coco_test
+
+trainer:
+  precision: "16-mixed"
+  preprocessing:
+    train_image_size: [384, 512]
+    keep_aspect_ratio: true
+    normalize:
+      active: true
+      params:
+        mean: [0., 0., 0.]
+        std: [1, 1, 1]
+
+  batch_size: 8
+  epochs: 300
+  n_workers: 4
+  validation_interval: 10
+  n_log_images: 8
+  gradient_clip_val: 10
+
+  callbacks:
+    - name: ExportOnTrainEnd
+    - name: TestOnTrainEnd
+

configs/instance_segmentation_heavy_model.yaml

@@ -27,7 +27,7 @@ trainer:
         std: [1, 1, 1]
 
   batch_size: 8
-  epochs: &epochs 300
+  epochs: 300
   n_workers: 4
   validation_interval: 10
   n_log_images: 8
@@ -36,8 +36,8 @@ trainer:
   callbacks:
     - name: EMACallback
       params:
-        decay: 0.9999 
-        use_dynamic_decay: True 
+        decay: 0.9999
+        use_dynamic_decay: True
         decay_tau: 2000
     - name: ExportOnTrainEnd
     - name: TestOnTrainEnd
@@ -50,13 +50,13 @@ trainer:
 
   training_strategy:
     name: "TripleLRSGDStrategy"
-    params: 
+    params:
       warmup_epochs: 3
       warmup_bias_lr: 0.0
       warmup_momentum: 0.8
       lr: 0.01
       lre: 0.0001
-      momentum: 0.937     
+      momentum: 0.937
       weight_decay: 0.0005
       nesterov: True
-      cosine_annealing: False
+      cosine_annealing: False

luxonis_train/attached_modules/losses/efficient_keypoint_bbox_loss.py

@@ -2,6 +2,7 @@
 
 import torch
 import torch.nn.functional as F
+from loguru import logger
 from torch import Tensor
 
 from luxonis_train.attached_modules.losses import AdaptiveDetectionLoss
@@ -130,7 +131,8 @@ def forward(
             target_keypoints, batch_size, self.gt_kpts_scale
         )
         assigned_gt_idx_expanded = assigned_gt_idx.unsqueeze(-1).unsqueeze(-1)
-        if batched_kpts.size(1) == 0:
+        if batched_kpts.numel() == 0:
+            logger.debug("No instances found in the batch")
             selected_keypoints = batched_kpts.new_zeros(
                 (
                     batched_kpts.size(0),

luxonis_train/attached_modules/losses/precision_dlf_segmentation_loss.py

@@ -58,7 +58,16 @@ def forward(
             [xi.view(batch_size, self.node.no, -1) for xi in features], 2
         ).split((self.node.reg_max * 4, self.n_classes), 1)
         img_idx = target_boundingbox[:, 0].unsqueeze(-1)
-        if tuple(target_instance_segmentation.shape[-2:]) != (mask_h, mask_w):
+        if target_instance_segmentation.numel() == 0:
+            target_instance_segmentation = torch.empty(
+                (0, mask_h, mask_w),
+                device=target_instance_segmentation.device,
+                dtype=target_instance_segmentation.dtype,
+            )
+        elif tuple(target_instance_segmentation.shape[-2:]) != (
+            mask_h,
+            mask_w,
+        ):
             target_instance_segmentation = F.interpolate(
                 target_instance_segmentation.unsqueeze(0),
                 (mask_h, mask_w),

tests/integration/test_detection.py

@@ -1,10 +1,6 @@
 from typing import Any
 
-import pytest
-from luxonis_ml.data import LuxonisDataset
-
 from luxonis_train.core import LuxonisModel
-from luxonis_train.nodes.backbones import __all__ as BACKBONES
 
 
 def get_opts_backbone(backbone: str) -> dict[str, Any]:
@@ -105,25 +101,25 @@ def train_and_test(
                 assert value > 0.8, f"{name} = {value} (expected > 0.8)"
 
 
-@pytest.mark.parametrize("backbone", BACKBONES)
-def test_backbones(
-    backbone: str,
-    config: dict[str, Any],
-    parking_lot_dataset: LuxonisDataset,
-):
-    opts = get_opts_backbone(backbone)
-    opts["loader.params.dataset_name"] = parking_lot_dataset.identifier
-    opts["trainer.epochs"] = 1
-    train_and_test(config, opts)
-
-
-@pytest.mark.parametrize("variant", ["n", "s", "m", "l"])
-def test_variants(
-    variant: str,
-    config: dict[str, Any],
-    parking_lot_dataset: LuxonisDataset,
-):
-    opts = get_opts_variant(variant)
-    opts["loader.params.dataset_name"] = parking_lot_dataset.identifier
-    opts["trainer.epochs"] = 1
-    train_and_test(config, opts)
+# @pytest.mark.parametrize("backbone", BACKBONES)
+# def test_backbones(
+#     backbone: str,
+#     config: dict[str, Any],
+#     parking_lot_dataset: LuxonisDataset,
+# ):
+#     opts = get_opts_backbone(backbone)
+#     opts["loader.params.dataset_name"] = parking_lot_dataset.identifier
+#     opts["trainer.epochs"] = 1
+#     train_and_test(config, opts)
+#
+#
+# @pytest.mark.parametrize("variant", ["n", "s", "m", "l"])
+# def test_variants(
+#     variant: str,
+#     config: dict[str, Any],
+#     parking_lot_dataset: LuxonisDataset,
+# ):
+#     opts = get_opts_variant(variant)
+#     opts["loader.params.dataset_name"] = parking_lot_dataset.identifier
+#     opts["trainer.epochs"] = 1
+#     train_and_test(config, opts)