change node input to json

nodes.py

@@ -1,25 +1,20 @@
-#nodes.py
-
 import torch
 import os
 import requests
+import numpy as np
 import logging
+import json
 import ast
+
 import sys
 
-import numpy as np #ugh
-import cv2 #double ugh
-
-import pycuda.driver as cuda
-import pycuda.autoinit  # Automatically initializes a CUDA context
-
 # Add the directory containing 'sam2_realtime' to sys.path
 current_directory = os.path.dirname(os.path.abspath(__file__))
 sam2_realtime_path = os.path.join(current_directory)  # Adjust the relative path
 sys.path.append(sam2_realtime_path)
 
 from sam2_realtime.sam2_tensor_predictor import SAM2TensorPredictor
-from sam2_realtime.sam2_tensorrt_predictor import Sam2TensorrtPredictor
+from comfy.utils import load_torch_file
 
 from omegaconf import OmegaConf
 from hydra.utils import instantiate
@@ -36,7 +31,7 @@ class DownloadAndLoadSAM2RealtimeModel:
     def INPUT_TYPES(s):
         return {"required": {
             "model": ([ 
-                    'sam2_hiera_tiny.pt', 'sam2_hiera_small.pt',
+                    'sam2_hiera_tiny.pt',
                     ],),
             "segmentor": (
                     ['realtime'],
@@ -75,8 +70,7 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         if not os.path.exists(model_path):
             print(f"Downloading SAM2 model to: {model_path}")
-            base_url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/"
-            url = f"{base_url}{model}"
+            url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
             response = requests.get(url, stream=True)
             response.raise_for_status()
 
@@ -89,9 +83,8 @@ 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)
 
@@ -146,13 +139,13 @@ class Sam2RealtimeSegmentation:
         return {
             "required": {
                 "images": ("IMAGE",),
-                # "sam2_model": ("SAM2MODEL",),
+                "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}),
+                "coordinates_positive": ("STRING", ),
+                "coordinates_negative": ("STRING", ),
                 # "bboxes": ("BBOX", ),
                 # "individual_objects": ("BOOLEAN", {"default": False}),
                 # "mask": ("MASK", ),
@@ -165,26 +158,7 @@ class Sam2RealtimeSegmentation:
     CATEGORY = "SAM2-Realtime"
 
     def __init__(self):
-        ##############################################################################
-        # SETUP: Load Engines, Create Contexts, Allocate Buffers
-        ##############################################################################
-        self.device = torch.device("cuda")
-        
-        cuda.init()
-        device = cuda.Device(0)
-        context = device.make_context()
-        print(f"Using device: {device.name()}")
-
-        # Allocate memory
-        mem = cuda.mem_alloc(1024)
-        print(f"Memory allocated at: {mem}")
-        # context.pop()
-
-        tensor_models_path = os.path.join(folder_paths.models_dir, "tensorrt")
-        self.predictor = Sam2TensorrtPredictor(
-            encoder_engine_path=os.path.join(tensor_models_path, "sam2_hiera_tiny.encoder.engine"),
-            decoder_engine_path=os.path.join(tensor_models_path, "sam2_hiera_tiny.decoder.engine")
-        )
+        self.predictor = None
         self.if_init = False
 
     def _process_coordinate_input(self, coordinates, label):
@@ -216,60 +190,70 @@ class Sam2RealtimeSegmentation:
     def segment_images(
         self,
         images,
-        # sam2_model,
-        # coordinates_positive=None,
-        # coordinates_negative=None,
-        # reset_tracking=False,
+        sam2_model,
+        # keep_model_loaded,
+        reset_tracking,
+        coordinates_positive=None,
+        coordinates_negative=None,
+        #point_labels=None,
+        # bboxes=None,
+        # individual_objects=False,
+        # mask=None,
     ):
-        
+        model = sam2_model["model"]
+        device = torch.device("cuda")
+        model.to(device)
 
         processed_frames = []
         mask_list = []
+        # The `model` variable is now ready and equivalent to `predictor` returned by sam2.build_sam.build_sam2_camera_predictor
         
-        # if reset_tracking:
-        #     self.if_init = False
+        if reset_tracking:
+            self.if_init = False
+            self.predictor = None
+
+        if self.predictor is None:
+            self.predictor = model
+
+        # Process coordinates once, outside the frame loop
+        pos_points, pos_labels = self._process_coordinate_input(coordinates_positive, 1)
+        neg_points, neg_labels = self._process_coordinate_input(coordinates_negative, 0)
+        all_points = pos_points + neg_points
+        all_labels = pos_labels + neg_labels
+
+        if all_points:
+            points_tensor = torch.tensor([all_points], device=device)
+            labels_tensor = torch.tensor([all_labels], device=device)
 
         with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16):
             for frame_idx, frame in enumerate(images):
-                
-                # Tensor -> numpy array
-                frame_numpy = frame.numpy()  # shape: already (H,W,C)!!
-
-                # Convert from RGB -> BGR
-                frame_bgr = cv2.cvtColor(frame_numpy, cv2.COLOR_RGB2BGR)  # shape: (H,W,3), float32 in [0..1]
-
-                # Scale to [0..255] and cast to uint8
-                frame_bgr = (frame_bgr * 255).clip(0,255).astype(np.uint8)
+                frame = frame.to(device).float()
 
                 if not self.if_init:
+                    self.predictor.load_first_frame(frame)
                     self.if_init = True
 
-                    out_mask_logits = self.predictor.load_first_frame_and_prompt(
-                        frame_bgr,
-                        point_coord=(512, 512)
-                    )
+                    if all_points:
+                        _, _, out_mask_logits = self.predictor.add_new_prompt(
+                            frame_idx=0,
+                            obj_id=1,
+                            points=points_tensor,
+                            labels=labels_tensor,
+                        )
+                    else:
+                        out_mask_logits = torch.zeros((0,), device=device)
                 else:
-                    out_mask_logits = self.predictor.track(frame_bgr)
+                    out_obj_ids, out_mask_logits = self.predictor.track(frame)
 
                 # Process mask logits
-                # mask = self._process_mask_logits(out_mask_logits, frame.shape, self.device)
-                mask = out_mask_logits
+                mask = self._process_mask_logits(out_mask_logits, frame.shape, device)
 
-                # # 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)
-                
                 # Create colored overlay for processed frames
-                mask_colored = np.stack([mask] * 3, axis=2)  # Stack along the last dimension (HxWxC format)
-
-                mask_colored_resized = cv2.resize(mask_colored, (frame.shape[1], frame.shape[0]))
-
-                # Overlay the frame and the colored mask
-                overlaid_frame = frame * 0.7 + mask_colored_resized * 0.3
-
-                processed_frames.append(torch.tensor(overlaid_frame))
-                mask_list.append(torch.tensor(mask))
+                mask_colored = torch.stack([mask] * 3, dim=2)
+                overlayed_frame = torch.add(frame * 0.7, mask_colored * 0.3)
+                
+                processed_frames.append(overlayed_frame)
+                mask_list.append(mask)
 
         # Stack masks and frames
         stacked_masks = torch.stack(mask_list, dim=0)
@@ -277,63 +261,44 @@ class Sam2RealtimeSegmentation:
         
         return (stacked_frames, stacked_masks)
 
-
-
-
-class Sam2RealtimeSegmentationTest:
+class BoundingBoxToCenter:
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "images": ("IMAGE",),
-                # "sam2_model": ("SAM2MODEL",),
-                # "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", ),
-            },
+                "bbox_data": ("JSON",),
+            }
         }
 
-    RETURN_NAMES = ("PROCESSED_IMAGES", "MASK",)
-    RETURN_TYPES = ("IMAGE", "MASK",)
-    FUNCTION = "segment_images"
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("center_coordinates",)
+    FUNCTION = "convert_bbox_to_center"
     CATEGORY = "SAM2-Realtime"
 
-    def segment_images(
-        self,
-        images,
-        # sam2_model,
-        # coordinates_positive=None,
-        # coordinates_negative=None,
-        # reset_tracking=False,
-    ):
-        processed_frames = []
-        mask_list = []
-        # processed_frames.append(images)
-        # images_temp = images
-        # coordinates_positive_temp = coordinates_positive.__str__
-        # reset_tracking_temp = reset_tracking
-
-        # Stack masks and frames
-        stacked_masks = torch.stack(mask_list, dim=0)
-        stacked_frames = torch.stack(processed_frames, dim=0)
+    def convert_bbox_to_center(self, bbox_data):
+        try:
+            bbox_list = ast.literal_eval(bbox_data)
+            tlx, tly, brx, bry = bbox_list[0][0]
         
-        return (stacked_frames, stacked_masks)
-
+            center_x = int((tlx + brx) / 2)
+            center_y = int((tly + bry) / 2)
+        
+            center_coords = f"[[{center_x}, {center_y}]]"
+            
+            return (center_coords,)
+            
+        except (ValueError, SyntaxError, IndexError) as e:
+            print(f"Error processing bounding box data: {e}")
+            return ("[[0, 0]]",)
 
 NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadSAM2RealtimeModel": DownloadAndLoadSAM2RealtimeModel,
     "Sam2RealtimeSegmentation": Sam2RealtimeSegmentation,
-    "Sam2RealtimeSegmentationTest": Sam2RealtimeSegmentationTest
+    "BoundingBoxToCenter": BoundingBoxToCenter
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "DownloadAndLoadSAM2RealtimeModel": "(Down)Load sam2_realtime Model",
     "Sam2RealtimeSegmentation": "Sam2RealtimeSegmentation",
-    "Sam2RealtimeSegmentationTest": "Sam2RealtimeSegmentationTest"
+    "BoundingBoxToCenter": "BoundingBox To Center"
 }

requirements.txt

@@ -1,16 +1,7 @@
 pyyaml
-ninja
 numpy>=1.24.4
 tqdm>=4.66.1
 hydra-core>=1.3.2
 iopath>=0.1.10
 pillow>=9.4.0
-opencv-python>=4.5.0
-pycuda>=2022.1
-tensorrt-cu12==10.7.0
-tensorrt-cu12-bindings==10.7.0
-tensorrt-cu12-libs==10.7.0
-tensorrt==10.7.0
-# git+https://github.com/pschroedl/ComfyUI-SAM2-Realtime.git@main#egg=sam2_realtime
-pip install -e .
-
+-e .

sam2_configs/sam2_hiera_small.yaml

@@ -1,116 +0,0 @@
-# @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_t.yaml

@@ -85,7 +85,7 @@ model:
         num_layers: 2
 
   num_maskmem: 7
-  image_size: 512
+  image_size: 1024
   # 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

sam2_realtime/sam2_tensor_predictor.py

@@ -14,6 +14,7 @@ from tqdm import tqdm
 from sam2_realtime.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
 from sam2_realtime.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames
 
+
 class SAM2TensorPredictor(SAM2Base):
     """The predictor class to handle user interactions and manage inference states."""
 
@@ -54,9 +55,7 @@ class SAM2TensorPredictor(SAM2Base):
             img = img.float()
         else:
             raise ValueError("Input must be a numpy array or a PyTorch tensor")
-        #save original height/width
-        orig_h, orig_w = img.shape[1:]
-        
+
         # Resize to the target size (supports tensor resizing)
         img = torch.nn.functional.interpolate(
             img.unsqueeze(0), size=(image_size, image_size), mode="bilinear", align_corners=False
@@ -69,29 +68,28 @@ class SAM2TensorPredictor(SAM2Base):
         img /= img_std
 
         height, width = img.shape[1:]  # CHW format
-        return img, width, height, orig_w, orig_h
+        return img, width, height
 
     @torch.inference_mode()
     def load_first_frame(self, img):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
-            
+
         self.condition_state = self._init_state(
             offload_video_to_cpu=False, offload_state_to_cpu=False
         )
-        img, width, height, orig_w, orig_h = self.prepare_data(img, image_size=self.image_size)
-        self._orig_hw = (orig_w, orig_h)
+        img, width, height = self.prepare_data(img, image_size=self.image_size)
         self.condition_state["images"] = [img]
         self.condition_state["num_frames"] = len(self.condition_state["images"])
         self.condition_state["video_height"] = height
         self.condition_state["video_width"] = width
         self._get_image_feature(frame_idx=0, batch_size=1)
-    
+
     def add_conditioning_frame(self, img):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
 
-        img, width, height, _, _ = self.prepare_data(img, image_size=self.image_size)
+        img, width, height = self.prepare_data(img, image_size=self.image_size)
         self.condition_state["images"].append(img)
         self.condition_state["num_frames"] = len(self.condition_state["images"])
         self._get_image_feature(
@@ -237,15 +235,14 @@ class SAM2TensorPredictor(SAM2Base):
                     points = torch.cat([box_coords, points], dim=1)
                     labels = torch.cat([box_labels, labels], dim=1)
         if normalize_coords:
-            #video_H = self.condition_state["video_height"]
-            #video_W = self.condition_state["video_width"]
-            orig_w, orig_h = self._orig_hw
-            
-            points = points / torch.tensor([orig_w, orig_h]).to(points.device)
+            video_H = self.condition_state["video_height"]
+            video_W = self.condition_state["video_width"]
+            points = points / torch.tensor([video_W, video_H]).to(points.device)
         # scale the (normalized) coordinates by the model's internal image size
         points = points * self.image_size
         points = points.to(self.condition_state["device"])
         labels = labels.to(self.condition_state["device"])
+
         if not clear_old_points:
             point_inputs = point_inputs_per_frame.get(frame_idx, None)
         else:
@@ -345,16 +342,14 @@ class SAM2TensorPredictor(SAM2Base):
         if labels.dim() == 1:
             labels = labels.unsqueeze(0)  # add batch dimension
         if normalize_coords:
-            #video_H = self.condition_state["video_height"]
-            #video_W = self.condition_state["video_width"]
-            orig_w, orig_h = self._orig_hw
-            
-            points = points / torch.tensor([orig_w, orig_h]).to(points.device)
-            
+            video_H = self.condition_state["video_height"]
+            video_W = self.condition_state["video_width"]
+            points = points / torch.tensor([video_W, video_H]).to(points.device)
         # scale the (normalized) coordinates by the model's internal image size
         points = points * self.image_size
         points = points.to(self.condition_state["device"])
         labels = labels.to(self.condition_state["device"])
+
         if not clear_old_points:
             point_inputs = point_inputs_per_frame.get(frame_idx, None)
         else:
@@ -774,7 +769,7 @@ class SAM2TensorPredictor(SAM2Base):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
 
-        img, _, _ , _, _ = self.prepare_data(img, image_size=self.image_size)
+        img, _, _ = self.prepare_data(img, image_size=self.image_size)
 
         output_dict = self.condition_state["output_dict"]
         obj_ids = self.condition_state["obj_ids"]

sam2_realtime/sam2_tensorrt.py

@@ -1,241 +0,0 @@
-import sys
-import numpy as np
-import cv2
-
-import pycuda.driver as cuda
-import pycuda.autoinit  # Automatically initializes a CUDA context
-import tensorrt as trt
-
-TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
-
-ENCODER_ENGINE_PATH = "sam2_hiera_tiny.encoder.engine"
-DECODER_ENGINE_PATH = "sam2_hiera_tiny.decoder.engine"
-
-def load_engine(engine_path: str) -> trt.ICudaEngine:
-    """
-    Load a TensorRT engine from a file.
-    """
-    with open(engine_path, "rb") as f:
-        engine_data = f.read()
-    runtime = trt.Runtime(TRT_LOGGER)
-    engine = runtime.deserialize_cuda_engine(engine_data)
-    return engine
-
-def allocate_io_tensors(engine: trt.ICudaEngine):
-    """
-    Allocates device+host buffers for each I/O tensor in name-based I/O.
-    Returns (device_buffers, host_buffers, input_names, output_names).
-    """
-    device_buffers = {}
-    host_buffers = {}
-    input_names = []
-    output_names = []
-
-    n_tensors = engine.num_io_tensors
-    for i in range(n_tensors):
-        tname = engine.get_tensor_name(i)
-        mode = engine.get_tensor_mode(tname)  # trt.TensorIOMode.INPUT or OUTPUT
-        shape = engine.get_tensor_shape(tname)
-        dtype = engine.get_tensor_dtype(tname)
-
-        # Convert TRT dtype -> numpy dtype
-        if dtype == trt.float32:
-            np_dtype = np.float32
-        elif dtype == trt.float16:
-            np_dtype = np.float16
-        else:
-            raise ValueError(f"Unsupported TRT dtype: {dtype}")
-
-        volume = np.prod(shape)
-        host_mem = np.zeros(volume, dtype=np_dtype)
-        device_mem = cuda.mem_alloc(host_mem.nbytes)
-
-        device_buffers[tname] = device_mem
-        host_buffers[tname] = host_mem
-
-        if mode == trt.TensorIOMode.INPUT:
-            input_names.append(tname)
-        else:
-            output_names.append(tname)
-
-    return device_buffers, host_buffers, input_names, output_names
-
-def do_inference_async(
-    context: trt.IExecutionContext,
-    device_buffers: dict,
-    host_buffers: dict,
-    input_names: list,
-    output_names: list,
-    cuda_stream: cuda.Stream
-):
-    """
-    1) Copy inputs (host->device)
-    2) context.set_tensor_address(...)
-    3) context.execute_async_v3(cuda_stream.handle)
-    4) stream.synchronize()
-    5) Copy outputs (device->host)
-    6) return output in the same order as output_names
-    """
-    # 1) Copy inputs to device
-    for tname in input_names:
-        cuda.memcpy_htod_async(device_buffers[tname], host_buffers[tname], stream=cuda_stream)
-
-    # 2) Set addresses in context
-    for tname in input_names + output_names:
-        context.set_tensor_address(tname, int(device_buffers[tname]))
-
-    # 3) Execute
-    success = context.execute_async_v3(cuda_stream.handle)
-    if not success:
-        raise RuntimeError("TensorRT execute_async_v3() returned False.")
-
-    # 4) Synchronize to ensure inference is done before copying back
-    cuda_stream.synchronize()
-
-    # 5) Copy outputs back to host
-    for tname in output_names:
-        cuda.memcpy_dtoh_async(host_buffers[tname], device_buffers[tname], stream=cuda_stream)
-
-    # Wait until all D->H copies are done
-    cuda_stream.synchronize()
-
-    # 6) Gather outputs
-    outputs = [host_buffers[t].copy() for t in output_names]
-    return outputs
-
-def run_encoder_async(
-    image_bgr: np.ndarray,
-    context: trt.IExecutionContext,
-    device_buffers: dict,
-    host_buffers: dict,
-    input_names: list,
-    output_names: list,
-    image_size: tuple = (1024, 1024),
-    cuda_stream: cuda.Stream = None
-):
-    """
-    Run the SAM encoder in async mode.
-    """
-    if cuda_stream is None:
-        cuda_stream = cuda.Stream()
-
-    # Preprocess image
-    H, W = image_size
-    resized = cv2.resize(image_bgr, (W, H))
-    rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
-    nchw = np.transpose(rgb, (2, 0, 1))[None, ...]  # (1,3,H,W)
-
-    if len(input_names) != 1:
-        raise RuntimeError(f"Expected exactly 1 input for encoder, got {input_names}")
-
-    in_name = input_names[0]
-    host_buffers[in_name][:] = nchw.flatten()
-
-    # Async inference
-    outputs = do_inference_async(
-        context,
-        device_buffers,
-        host_buffers,
-        input_names,
-        output_names,
-        cuda_stream
-    )
-
-    # Expect 3 outputs
-    if len(outputs) != 3:
-        raise RuntimeError(f"Encoder expected 3 outputs, got {len(outputs)}")
-
-    hr0_flat, hr1_flat, emb_flat = outputs
-    # Reshape according to model's output shapes
-    # Shapes for sam2_hiera_tiny:
-    #   hr0: (1, 32, 256, 256)
-    #   hr1: (1, 64, 128, 128)
-    #   emb: (1, 256, 64, 64)
-    hr0 = hr0_flat.reshape((1, 32, 256, 256))
-    hr1 = hr1_flat.reshape((1, 64, 128, 128))
-    emb = emb_flat.reshape((1, 256, 64, 64))
-
-    print("[DEBUG] Encoder outputs shapes:", hr0.shape, hr1.shape, emb.shape)
-    return hr0, hr1, emb
-
-def run_decoder_async(
-    high_res_feats_0: np.ndarray,
-    high_res_feats_1: np.ndarray,
-    image_embed: np.ndarray,
-    point_coords: np.ndarray,
-    point_labels: np.ndarray,
-    mask_input: np.ndarray,
-    has_mask_input: np.ndarray,
-    context: trt.IExecutionContext,
-    device_buffers: dict,
-    host_buffers: dict,
-    input_names: list,
-    output_names: list,
-    cuda_stream: cuda.Stream = None
-):
-    """
-    Run the SAM decoder in async mode with 7 inputs:
-      0) image_embed
-      1) high_res_feats_0
-      2) high_res_feats_1
-      3) point_coords
-      4) point_labels
-      5) mask_input
-      6) has_mask_input
-    """
-    if cuda_stream is None:
-        cuda_stream = cuda.Stream()
-
-    if len(input_names) != 7:
-        raise RuntimeError(f"Decoder expects 7 inputs, got {len(input_names)}: {input_names}")
-
-    # Flatten & place data
-    host_buffers[input_names[0]][:] = image_embed.flatten()
-    host_buffers[input_names[1]][:] = high_res_feats_0.flatten()
-    host_buffers[input_names[2]][:] = high_res_feats_1.flatten()
-    host_buffers[input_names[3]][:] = point_coords.flatten()
-    host_buffers[input_names[4]][:] = point_labels.flatten()
-    host_buffers[input_names[5]][:] = mask_input.flatten()
-    host_buffers[input_names[6]][:] = has_mask_input.flatten()
-
-    outputs = do_inference_async(
-        context,
-        device_buffers,
-        host_buffers,
-        input_names,
-        output_names,
-        cuda_stream
-    )
-
-    # Typically 2 outputs: masks, iou_predictions
-    if len(outputs) != 2:
-        raise RuntimeError(f"Decoder expected 2 outputs, got {len(outputs)}")
-
-    masks_flat, iou_flat = outputs
-    # Reshape as needed for your model
-    # Example: (1,3,256,256) for masks and (1,3) for iou_preds
-    masks = masks_flat.reshape((1, 3, 256, 256))
-    iou_preds = iou_flat.reshape((1, 3))
-    print("Decoder outputs shapes:", masks.shape, iou_preds.shape)
-    return masks, iou_preds
-
-def prepare_prompts(
-    batch_size: int = 1,
-    image_size: tuple = (1024, 1024),
-):
-    """
-    Create minimal prompt inputs for the decoder:
-      - point_coords (B, 1, 2)
-      - point_labels (B, 1)
-      - mask_input   (B, 1, 256, 256) (for sam2_hiera_tiny)
-      - has_mask_input (B,)
-    """
-    H, W = image_size
-    point_coords = np.array([[[W // 2, H // 2]]], dtype=np.float32)  # shape (B,1,2)
-    point_labels = np.array([[1]], dtype=np.float32)  # shape (B,1)
-
-    # For sam2_hiera_tiny => final embedding is 64×64, so mask_input is 4× that => 256×256
-    mask_input = np.zeros((batch_size, 1, 256, 256), dtype=np.float32)
-    has_mask_input = np.zeros((batch_size,), dtype=np.float32)
-
-    return point_coords, point_labels, mask_input, has_mask_input

sam2_realtime/sam2_tensorrt_predictor.py

@@ -1,203 +0,0 @@
-import numpy as np
-import cv2
-
-from sam2_realtime.sam2_tensorrt import (
-    load_engine,
-    allocate_io_tensors,
-    run_encoder_async,
-    run_decoder_async,
-    prepare_prompts
-)
-
-class Sam2TensorrtPredictor:
-    """
-    A simplified single-object predictor that:
-      - Uses the TensorRT-based SAM2 encoder/decoder.
-      - Tracks across frames by feeding the previously predicted mask as a prompt.
-      - Allows resetting the tracking with a new point prompt (i.e., ignoring prior mask).
-    """
-
-    def __init__(
-        self,
-        encoder_engine_path: str,
-        decoder_engine_path: str,
-        image_size=(1024, 1024)
-    ):
-        """
-        1) Load TensorRT engines
-        2) Create execution contexts
-        3) Allocate device+host buffers
-        """
-        self.image_size = image_size  # (height, width)
-
-        print("[Sam2TensorrtPredictor] Loading TensorRT engines...")
-        self.encoder_engine = load_engine(encoder_engine_path)
-        self.decoder_engine = load_engine(decoder_engine_path)
-
-        # Create execution contexts
-        self.encoder_context = self.encoder_engine.create_execution_context()
-        self.decoder_context = self.decoder_engine.create_execution_context()
-
-        # Allocate buffers for encoder
-        (
-            self.enc_device_bufs,
-            self.enc_host_bufs,
-            self.enc_in_names,
-            self.enc_out_names
-        ) = allocate_io_tensors(self.encoder_engine)
-
-        # Allocate buffers for decoder
-        (
-            self.dec_device_bufs,
-            self.dec_host_bufs,
-            self.dec_in_names,
-            self.dec_out_names
-        ) = allocate_io_tensors(self.decoder_engine)
-
-        print("[Sam2TensorrtPredictor] Engines loaded and buffers allocated.")
-
-        # Internal states
-        self._initialized = False
-        self.prev_mask  = None
-        self.prev_feats0 = None
-        self.prev_feats1 = None
-        self.prev_embed  = None
-
-    def load_first_frame_and_prompt(self, frame_bgr: np.ndarray, point_coord, point_label=1):
-        """
-        1) Encode the first frame to get feats0, feats1, embed.
-        2) Use the user-provided point prompt to decode an initial mask.
-        3) Store that mask in self.prev_mask (for subsequent frames).
-        """
-        self._initialized = False
-        self.prev_mask  = None
-        self.prev_feats0 = None
-        self.prev_feats1 = None
-        self.prev_embed  = None
-
-        # -- (1) run the encoder
-        feats0, feats1, embed = run_encoder_async(
-            image_bgr=frame_bgr,
-            context=self.encoder_context,
-            device_buffers=self.enc_device_bufs,
-            host_buffers=self.enc_host_bufs,
-            input_names=self.enc_in_names,
-            output_names=self.enc_out_names,
-            image_size=self.image_size,
-        )
-
-        # -- (2) build the prompt arrays
-        batch_size = 1
-        pt_coords, pt_labels, zero_mask, zero_has_mask = prepare_prompts(
-            batch_size=batch_size,
-            image_size=self.image_size
-        )
-
-        # Overwrite the default center prompt with the user-provided point
-        #   Note: point_coord = (x, y)
-        pt_coords[0, 0, 0] = float(point_coord[0])  # x
-        pt_coords[0, 0, 1] = float(point_coord[1])  # y
-        pt_labels[0, 0]    = float(point_label)
-
-        # decode (prompt-based)
-        masks, iou_preds = run_decoder_async(
-            high_res_feats_0=feats0,
-            high_res_feats_1=feats1,
-            image_embed=embed,
-            point_coords=pt_coords,
-            point_labels=pt_labels,
-            mask_input=zero_mask,      # no prior mask for the first frame
-            has_mask_input=zero_has_mask,
-            context=self.decoder_context,
-            device_buffers=self.dec_device_bufs,
-            host_buffers=self.dec_host_bufs,
-            input_names=self.dec_in_names,
-            output_names=self.dec_out_names,
-        )
-
-        # pick best mask
-        best_idx = np.argmax(iou_preds[0])  # choose the highest IOU channel out of 3
-        mask_logit = masks[0, best_idx]     # shape: (256, 256)
-        mask_prob = 1 / (1 + np.exp(-mask_logit))
-        mask_bin = (mask_prob > 0.5).astype(np.uint8)
-
-        # -- (3) store for next step
-        self.prev_mask  = mask_bin
-        self.prev_feats0 = feats0
-        self.prev_feats1 = feats1
-        self.prev_embed  = embed
-        self._initialized = True
-
-        print("[load_first_frame_and_prompt] First mask computed from point prompt.")
-        return mask_bin
-
-    def track(self, frame_bgr: np.ndarray):
-        """
-        For each subsequent frame:
-          1) Encode the new frame
-          2) Provide the previously predicted mask as 'mask_input' prompt
-          3) Pick best mask => new self.prev_mask
-        """
-        if not self._initialized or self.prev_mask is None:
-            raise RuntimeError(
-                "Sam2TensorrtPredictor not initialized with a first frame/prompt. "
-                "Call load_first_frame_and_prompt(...) first."
-            )
-
-        # 1) Encode the new frame
-        feats0, feats1, embed = run_encoder_async(
-            image_bgr=frame_bgr,
-            context=self.encoder_context,
-            device_buffers=self.enc_device_bufs,
-            host_buffers=self.enc_host_bufs,
-            input_names=self.enc_in_names,
-            output_names=self.enc_out_names,
-            image_size=self.image_size,
-        )
-
-        # 2) Prepare mask_input from the previously predicted binary mask
-        if self.prev_mask.shape != (256, 256):
-            # if stored differently, resize to 256x256
-            pm = cv2.resize(
-                self.prev_mask.astype(np.float32),
-                (256, 256),
-                interpolation=cv2.INTER_LINEAR
-            )[None, None]  # shape: (1,1,256,256)
-        else:
-            pm = self.prev_mask[None, None].astype(np.float32)
-
-        has_mask_input = np.array([1.0], dtype=np.float32)  # shape: (1,)
-
-        # 3) We want no new user points, we set them to dummy zero
-        point_coords = np.zeros((1, 1, 2), dtype=np.float32)
-        point_labels = np.zeros((1, 1), dtype=np.float32)
-
-        masks, iou_preds = run_decoder_async(
-            high_res_feats_0=feats0,
-            high_res_feats_1=feats1,
-            image_embed=embed,
-            point_coords=point_coords,
-            point_labels=point_labels,
-            mask_input=pm,
-            has_mask_input=has_mask_input,
-            context=self.decoder_context,
-            device_buffers=self.dec_device_bufs,
-            host_buffers=self.dec_host_bufs,
-            input_names=self.dec_in_names,
-            output_names=self.dec_out_names,
-        )
-
-        # 3.5) pick best mask
-        best_idx = np.argmax(iou_preds[0])  # shape: (3,)
-        mask_logit = masks[0, best_idx]     # shape: (256, 256)
-        mask_prob = 1 / (1 + np.exp(-mask_logit))
-        mask_bin = (mask_prob > 0.5).astype(np.uint8)
-
-        # 4) update internal states
-        self.prev_mask  = mask_bin
-        self.prev_feats0 = feats0
-        self.prev_feats1 = feats1
-        self.prev_embed  = embed
-
-        print("[track_next_frame] Tracking updated with previous mask as prompt.")
-        return mask_bin

setup.py

@@ -4,10 +4,6 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-import os
-# Set the CUDA architecture list
-os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0 8.6+PTX 8.7 9.0 9.0a"
-
 from setuptools import find_packages, setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
 
@@ -36,7 +32,6 @@ def get_extensions():
     compile_args = {
         "cxx": [],
         "nvcc": [
-            "--compiler-bindir=/usr/bin/gcc-12",
             "-DCUDA_HAS_FP16=1",
             "-D__CUDA_NO_HALF_OPERATORS__",
             "-D__CUDA_NO_HALF_CONVERSIONS__",
@@ -58,7 +53,7 @@ setup(
     license=LICENSE,
     packages=find_packages(),
     install_requires=REQUIRED_PACKAGES,
-    python_requires=">=3.10.15",
+    python_requires=">=3.11.10",
     ext_modules=get_extensions(),
     cmdclass={"build_ext": BuildExtension.with_options(no_python_abi_suffix=True)},
 )