Revert "Fix node install from conda environments"

add install notes

nodes.py

@@ -31,7 +31,7 @@ class DownloadAndLoadSAM2RealtimeModel:
     def INPUT_TYPES(s):
         return {"required": {
             "model": ([ 
-                    'sam2_hiera_tiny.pt',
+                    'sam2_hiera_tiny.pt', 'sam2_hiera_small.pt',
                     ],),
             "segmentor": (
                     ['realtime'],
@@ -70,7 +70,8 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         if not os.path.exists(model_path):
             print(f"Downloading SAM2 model to: {model_path}")
-            url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
+            base_url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/"
+            url = f"{base_url}{model}"
             response = requests.get(url, stream=True)
             response.raise_for_status()
 
@@ -83,8 +84,9 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         config_dir = os.path.join(script_directory, "sam2_configs") 
 
+        model_cfg = model.replace(".pt", ".yaml")
+
         # Code ripped out of sam2.build_sam.build_sam2_camera_predictor to appease Hydra
-        model_cfg = "sam2_hiera_t.yaml" #TODO: remove hardcoded config and path
         with initialize_config_dir(config_dir=config_dir, version_base=None):
             cfg = compose(config_name=model_cfg)
 
@@ -140,12 +142,12 @@ class Sam2RealtimeSegmentation:
             "required": {
                 "images": ("IMAGE",),
                 "sam2_model": ("SAM2MODEL",),
-                "reset_tracking": ("BOOLEAN", {"default": False}),
                 # "keep_model_loaded": ("BOOLEAN", {"default": True}),
             },
             "optional": {
                 "coordinates_positive": ("STRING", ),
                 "coordinates_negative": ("STRING", ),
+                "reset_tracking": ("BOOLEAN", {"default": False}),
                 # "bboxes": ("BBOX", ),
                 # "individual_objects": ("BOOLEAN", {"default": False}),
                 # "mask": ("MASK", ),
@@ -192,9 +194,9 @@ class Sam2RealtimeSegmentation:
         images,
         sam2_model,
         # keep_model_loaded,
-        reset_tracking,
         coordinates_positive=None,
         coordinates_negative=None,
+        reset_tracking=False,
         #point_labels=None,
         # bboxes=None,
         # individual_objects=False,
@@ -250,6 +252,7 @@ class Sam2RealtimeSegmentation:
 
                 # Create colored overlay for processed frames
                 mask_colored = torch.stack([mask] * 3, dim=2)
+
                 overlayed_frame = torch.add(frame * 0.7, mask_colored * 0.3)
                 
                 processed_frames.append(overlayed_frame)

sam2_configs/sam2_hiera_small.yaml

@@ -0,0 +1,116 @@
+# @package _global_
+
+# Model
+model:
+  _target_: sam2_realtime.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: sam2_realtime.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: sam2_realtime.modeling.backbones.hieradet.Hiera
+      embed_dim: 96
+      num_heads: 1
+      stages: [1, 2, 11, 2]
+      global_att_blocks: [7, 10, 13]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+    neck:
+      _target_: sam2_realtime.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: sam2_realtime.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [768, 384, 192, 96]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: sam2_realtime.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: sam2_realtime.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: sam2_realtime.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: sam2_realtime.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: sam2_realtime.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: sam2_realtime.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: sam2_realtime.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: sam2_realtime.modeling.memory_encoder.Fuser
+        layer:
+          _target_: sam2_realtime.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 512
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: false
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  compile_image_encoder: False

sam2_configs/sam2_hiera_tiny.yaml

@@ -85,7 +85,7 @@ model:
         num_layers: 2
 
   num_maskmem: 7
-  image_size: 1024
+  image_size: 512
   # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
   # SAM decoder
   sigmoid_scale_for_mem_enc: 20.0