feat(timm_regnet): add timm RegNet image-classification family

benchmarks/performance/baselines/task_reference.py

@@ -573,7 +573,7 @@ def _load_asr(
     max_new_tokens = int(request.get("max_new_tokens", 100))
     device = torch.device("cuda")
 
-    if arguments.family in {"canary", "nemotron_speech_streaming", "timm_mobilenetv3", "timm_efficientnet", "timm_densenet", "timm_mnasnet", "timm_inception", "timm_repvgg"}:
+    if arguments.family in {"canary", "nemotron_speech_streaming", "timm_mobilenetv3", "timm_efficientnet", "timm_densenet", "timm_mnasnet", "timm_inception", "timm_repvgg", "timm_regnet"}:
         from tools.validation.engine import _transcription_text
 
         model = _load_nemo_asr_reference_model(arguments, device=device).eval().to(device)

benchmarks/performance/baselines/timing_contracts.py

@@ -31,6 +31,7 @@
     "timm_mnasnet",
     "timm_inception",
     "timm_repvgg",
+    "timm_regnet",
     "timm_resnet",
     "timm_vgg",
     "timm_vit",

benchmarks/performance/release.yaml

@@ -978,6 +978,19 @@ entries:
       reference_backend: hf_transformers
       timing_scope: task-model-call-wall
       input_preparation_included: false
+  - id: timm_regnet.classify
+    family: timm_regnet
+    operation: classify
+    model: regnety-040-ra3-in1k
+    workload:
+      testcase: regnety-040-ra3-in1k
+    baseline:
+      runner: task-reference
+      adapter: hf-transformers-vision
+      mode: hf-eager
+      reference_backend: hf_transformers
+      timing_scope: task-model-call-wall
+      input_preparation_included: false
   - id: timm_repvgg.classify
     family: timm_repvgg
     operation: classify

python/tensorrt_model_connect/families/timm_regnet/MODEL.toml

@@ -0,0 +1,21 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+id = "timm_regnet"
+plugin = "timm_regnet"
+module = "plugin"
+python_profile_specs = [
+  "timm_regnet_reference|families/timm_regnet/python_profile_requirements/timm_regnet_reference.lock.txt|families/timm_regnet/python_profile_verify.py|true",
+]
+default_execution_profiles = [
+  "reference|timm_regnet_reference",
+]
+aliases = [
+  "timm_regnet",
+  "regnet",
+  "regnety_040",
+]
+prefixes = [
+  "timm_regnet",
+  "regnet",
+]

python/tensorrt_model_connect/families/timm_regnet/__init__.py

@@ -0,0 +1,6 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from .plugin import plugin
+
+__all__ = ["plugin"]

python/tensorrt_model_connect/families/timm_regnet/config.py

@@ -0,0 +1,239 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""ModelConfig — parse HF config.json into a typed dataclass."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+@dataclass
+class ModelConfig:
+    """Parsed model architecture from HF config.json."""
+
+    model_type: str = ""
+    architectures: list[str] = field(default_factory=list)
+    vocab_size: int = 0
+    hidden_size: int = 0
+    intermediate_size: int = 0
+    num_hidden_layers: int = 0
+    num_attention_heads: int = 1
+    num_key_value_heads: int = 1
+    rms_norm_eps: float = 1e-5
+    rope_theta: float = 10000.0
+    bos_token_id: int = -1
+    eos_token_id: int = -1
+    pad_token_id: int = -1
+    tie_word_embeddings: bool = False
+    max_position_embeddings: int = 8192
+    hidden_act: str = ""
+
+    # Explicit head_dim from config.json (0 = not set, fall back to computed).
+    _head_dim: int = 0
+
+    # Raw JSON dict for family-specific fields
+    raw: dict = field(default_factory=dict, repr=False)
+
+    @property
+    def head_dim(self) -> int:
+        if self._head_dim > 0:
+            return self._head_dim
+        if self.num_attention_heads <= 0:
+            return 0
+        return self.hidden_size // self.num_attention_heads
+
+    @property
+    def attention_size(self) -> int:
+        return self.num_attention_heads * self.head_dim
+
+    @staticmethod
+    def from_json(text: str) -> ModelConfig:
+        d = json.loads(text)
+
+        # Some multimodal configs nest decoder fields under "text_config".
+        # Merge text_config into top level so standard key lookup works.
+        # Preserve top-level model_type and architectures (these identify the
+        # top-level model, not the nested decoder).
+        original_raw = d
+        text_config = d.get("text_config")
+        if text_config and isinstance(text_config, dict):
+            top_model_type = d.get("model_type")
+            top_architectures = d.get("architectures")
+            merged = {**d, **text_config}
+            if top_model_type:
+                merged["model_type"] = top_model_type
+            if top_architectures:
+                merged["architectures"] = top_architectures
+            d = merged
+
+        # Some multimodal configs nest the language decoder config under
+        # "language_config".  Merge into top level like text_config.
+        if not d.get("hidden_size"):
+            lang_config = d.get("language_config")
+            if isinstance(lang_config, dict):
+                top_model_type = d.get("model_type")
+                top_architectures = d.get("architectures")
+                top_vision_config = d.get("vision_config")
+                merged = {**d, **lang_config}
+                if top_model_type:
+                    merged["model_type"] = top_model_type
+                if top_architectures:
+                    merged["architectures"] = top_architectures
+                if top_vision_config:
+                    merged["vision_config"] = top_vision_config
+                d = merged
+
+        # Some multimodal configs nest LLM config under "llm_config".
+        # Merge into top level like text_config, preserving top-level
+        # model_type, architectures, and vision_config.
+        if not d.get("hidden_size"):
+            llm_config = d.get("llm_config")
+            if isinstance(llm_config, dict):
+                top_model_type = d.get("model_type")
+                top_architectures = d.get("architectures")
+                top_vision_config = d.get("vision_config")
+                merged = {**d, **llm_config}
+                if top_model_type:
+                    merged["model_type"] = top_model_type
+                if top_architectures:
+                    merged["architectures"] = top_architectures
+                if top_vision_config:
+                    merged["vision_config"] = top_vision_config
+                d = merged
+
+        # Some multimodal audio/text configs nest the primary decoder config
+        # under thinker_config.text_config. If top-level hidden_size is
+        # still missing after the text_config merge above, look there.
+        if not d.get("hidden_size"):
+            thinker_cfg = d.get("thinker_config")
+            if isinstance(thinker_cfg, dict):
+                thinker_text = thinker_cfg.get("text_config")
+                if isinstance(thinker_text, dict):
+                    top_model_type = d.get("model_type")
+                    top_architectures = d.get("architectures")
+                    merged = {**d, **thinker_text}
+                    if top_model_type:
+                        merged["model_type"] = top_model_type
+                    if top_architectures:
+                        merged["architectures"] = top_architectures
+                    # Also propagate vision_config from thinker_config
+                    # so VL pipelines can find it.
+                    if "vision_config" not in merged and "vision_config" in thinker_cfg:
+                        merged["vision_config"] = thinker_cfg["vision_config"]
+                    d = merged
+
+        # Handle non-standard config key names:
+        #   GPT-2: n_embd, n_head, n_layer, n_inner
+        #   XGLM/Bloom: d_model, attention_heads, num_layers, ffn_dim
+        #   DistilBERT: dim, n_heads, n_layers, hidden_dim
+        hidden_size = (
+            d.get("hidden_size", 0)
+            or d.get("n_embd", 0)
+            or d.get("d_model", 0)
+            or d.get("n_embed", 0)
+            or d.get("dim", 0)
+        )
+        num_heads = (
+            d.get("num_attention_heads", 0)
+            or d.get("n_head", 0)
+            or d.get("attention_heads", 0)
+            or d.get("num_heads", 0)
+            or d.get("n_heads", 0)
+            or d.get("decoder_attention_heads", 0)
+            or 1
+        )
+        num_layers = (
+            d.get("num_hidden_layers", 0)
+            or d.get("n_layer", 0)
+            or d.get("num_layers", 0)
+            or d.get("n_layers", 0)
+        )
+        intermediate = (
+            d.get("intermediate_size", 0)
+            or d.get("n_inner", 0)
+            or d.get("ffn_dim", 0)
+            or d.get("hidden_dim", 0)
+            or hidden_size * 4
+        )
+
+        # Norm epsilon: try rms_norm_eps, then layer_norm_epsilon, then
+        # layer_norm_eps, then norm_epsilon, then norm_eps.
+        eps = (
+            d.get("rms_norm_eps")
+            or d.get("layer_norm_epsilon")
+            or d.get("layer_norm_eps")
+            or d.get("norm_epsilon")
+            or d.get("norm_eps")
+            or 1e-5
+        )
+
+        # rope_theta: check top-level first, then rope_parameters dict
+        # (some model configs store it there),
+        # then rope_scaling dict.
+        rope_theta = d.get("rope_theta", None)
+        if rope_theta is None:
+            rope_params = d.get("rope_parameters")
+            if isinstance(rope_params, dict):
+                rope_theta = rope_params.get("rope_theta", 10000.0)
+            else:
+                rope_scaling = d.get("rope_scaling")
+                if isinstance(rope_scaling, dict):
+                    rope_theta = rope_scaling.get("rope_theta", 10000.0)
+                else:
+                    rope_theta = 10000.0
+        rope_theta = float(rope_theta)
+
+        architecture = d.get("architecture", "")
+        architectures = d.get("architectures", [])
+        if not architectures and architecture:
+            architectures = [architecture]
+
+        return ModelConfig(
+            model_type=d.get("model_type", "") or architecture,
+            architectures=architectures,
+            vocab_size=d.get("vocab_size", 0),
+            hidden_size=hidden_size or d.get("num_features", 0),
+            intermediate_size=intermediate,
+            num_hidden_layers=num_layers,
+            num_attention_heads=num_heads,
+            num_key_value_heads=d.get("num_key_value_heads", num_heads),
+            rms_norm_eps=eps,
+            rope_theta=rope_theta,
+            bos_token_id=d.get("bos_token_id", -1) or -1,
+            eos_token_id=d.get("eos_token_id", -1) or -1,
+            pad_token_id=d.get("pad_token_id", -1) or -1,
+            tie_word_embeddings=d.get("tie_word_embeddings", False),
+            max_position_embeddings=d.get("max_position_embeddings", d.get("n_positions", 8192)),
+            hidden_act=d.get("hidden_act", "") or d.get("activation_function", ""),
+            _head_dim=d.get("head_dim", 0),
+            raw=original_raw,
+        )
+
+    @classmethod
+    def create_tiny(cls, model_type: str, **overrides) -> "ModelConfig":
+        """Create a minimal ModelConfig for testing (2 layers, hidden=16, vocab=32)."""
+        defaults = {
+            "model_type": model_type,
+            "vocab_size": 32,
+            "hidden_size": 16,
+            "intermediate_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 4,
+            "rms_norm_eps": 1e-6,
+            "rope_theta": 10000.0,
+            "max_position_embeddings": 128,
+        }
+        defaults.update(overrides)
+        return cls.from_json(json.dumps(defaults))
+
+    @staticmethod
+    def from_dir(model_dir: str | Path) -> ModelConfig:
+        model_path = Path(model_dir)
+        config_path = model_path / "config.json"
+        if config_path.exists():
+            return ModelConfig.from_json(config_path.read_text())
+        return ModelConfig.from_json(config_path.read_text())

python/tensorrt_model_connect/families/timm_regnet/model/__init__.py

@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Family-owned TensorRT model construction components."""