Merge branch 'DeepLabCut:main' into main

Author: xiu-cs

deeplabcut/core/metrics/bbox.py

@@ -17,6 +17,8 @@
 
 from unittest.mock import Mock, patch
 
+from datetime import datetime
+
 import numpy as np
 
 try:
@@ -66,6 +68,7 @@ def compute_bbox_metrics(
     coco.dataset["annotations"] = []
     coco.dataset["categories"] = [{"id": 1, "name": "animals", "supercategory": "obj"}]
     coco.dataset["images"] = []
+    coco.dataset['info'] = {"description": "Generated by DeepLabCut","year": datetime.now().year,"date_created": datetime.now().strftime("%Y-%m-%d")}
     predictions = []
     for idx, (img, gt) in enumerate(ground_truth.items()):
         img_id = idx + 1

deeplabcut/modelzoo/model_configs/rtmpose_s.yaml

@@ -0,0 +1,98 @@
+data:
+  colormode: RGB
+  inference:
+    normalize_images: true
+  train:
+    affine:
+      p: 0.5
+      scaling: [1.0, 1.0]
+      rotation: 30
+      translation: 0
+    gaussian_noise: 12.75
+    normalize_images: true
+device: auto
+method: td
+model:
+  backbone:
+    type: CSPNeXt
+    model_name: cspnext_s
+    freeze_bn_stats: false
+    freeze_bn_weights: false
+    deepen_factor: 0.33
+    widen_factor: 0.5
+  backbone_output_channels: 512
+  heads:
+    bodypart:
+      type: RTMCCHead
+      weight_init: RTMPose
+      target_generator:
+        type: SimCCGenerator
+        input_size:
+        - 256
+        - 256
+        smoothing_type: gaussian
+        sigma:
+        - 5.66
+        - 5.66
+        simcc_split_ratio: 2.0
+        label_smooth_weight: 0.0
+        normalize: false
+      criterion:
+        x:
+          type: KLDiscreteLoss
+          use_target_weight: true
+          beta: 10.0
+          label_softmax: true
+        y:
+          type: KLDiscreteLoss
+          use_target_weight: true
+          beta: 10.0
+          label_softmax: true
+      predictor:
+        type: SimCCPredictor
+        simcc_split_ratio: 2.0
+      input_size:
+      - 256
+      - 256
+      in_channels: 512
+      out_channels: 39
+      in_featuremap_size:
+      - 8
+      - 8
+      simcc_split_ratio: 2.0
+      final_layer_kernel_size: 7
+      gau_cfg:
+        hidden_dims: 256
+        s: 128
+        expansion_factor: 2
+        dropout_rate: 0
+        drop_path: 0.0
+        act_fn: SiLU
+        use_rel_bias: false
+        pos_enc: false
+net_type: rtmpose_s
+runner:
+  type: PoseTrainingRunner
+  key_metric: "test.mAP"
+  key_metric_asc: true
+  eval_interval: 10
+  optimizer:
+    type: AdamW
+    params:
+      lr: 1e-5
+  scheduler:
+    type: LRListScheduler
+    params:
+      lr_list: [ [ 1e-6 ], [ 1e-7 ] ]
+      milestones: [ 160, 190 ]
+  snapshots:
+    max_snapshots: 5
+    save_epochs: 25
+    save_optimizer_state: false
+train_settings:
+  batch_size: 1
+  dataloader_workers: 0
+  dataloader_pin_memory: false
+  display_iters: 500
+  epochs: 100
+  seed: 42

deeplabcut/modelzoo/models_to_framework.json

@@ -1,5 +1,6 @@
 {
     "dlcrnet": "tensorflow",
     "hrnet_w32": "pytorch",
-    "resnet_50": "pytorch"
+    "resnet_50": "pytorch",
+    "rtmpose_s": "pytorch"
 }

tests/core/inferenceutils/test_map_computation.py

@@ -419,6 +419,7 @@ def eval_coco(
         coco.dataset["annotations"] = ground_truth["annotations"]
         coco.dataset["categories"] = ground_truth["categories"]
         coco.dataset["images"] = ground_truth["images"]
+        coco.dataset["info"] = {"description": "Generated by DeepLabCut"}
         coco.createIndex()
 
         coco_det = coco.loadRes(predictions)

tests/core/metrics/test_metrics_map_computation.py

@@ -394,6 +394,7 @@ def eval_coco(
         coco.dataset["annotations"] = ground_truth["annotations"]
         coco.dataset["categories"] = ground_truth["categories"]
         coco.dataset["images"] = ground_truth["images"]
+        coco.dataset["info"] = {"description": "Generated by DeepLabCut"}
         coco.createIndex()
 
         coco_det = coco.loadRes(predictions)