Feat/train on apple silicon

focoos/hub/focoos_hub.py

@@ -37,7 +37,12 @@
 logger = get_logger("HUB")
 
 
-SUPPORTED_MODEL_FAMILIES = [ModelFamily.BISENETFORMER, ModelFamily.DETR, ModelFamily.MASKFORMER]
+SUPPORTED_MODEL_FAMILIES = [
+    ModelFamily.BISENETFORMER,
+    ModelFamily.DETR,
+    ModelFamily.MASKFORMER,
+    ModelFamily.IMAGE_CLASSIFIER,
+]
 
 
 class FocoosHUB:

focoos/models/focoos_model.py

@@ -114,10 +114,17 @@ def __init__(self, model: BaseModelNN, model_info: ModelInfo):
         self.processor.eval()
         self.model = model.eval()
 
-        try:
-            self.model = self.model.cuda()
-        except Exception:
-            logger.warning("Unable to use CUDA")
+        if torch.cuda.is_available():
+            try:
+                self.model = self.model.cuda()
+            except Exception:
+                logger.warning("Unable to use CUDA")
+
+        if torch.backends.mps.is_available():
+            try:
+                self.model = self.model.to(device="mps")
+            except Exception:
+                logger.warning("Unable to use MPS")
 
         if self.model_info.weights_uri:
             self._load_weights()
@@ -153,8 +160,17 @@ def _setup_model_for_training(self, train_args: TrainerArgs, data_train: MapData
         """
         device = get_cpu_name()
         system_info = get_system_info()
-        if system_info.gpu_info and system_info.gpu_info.devices and len(system_info.gpu_info.devices) > 0:
+        if (
+            train_args.device == "cuda"
+            and system_info.gpu_info
+            and system_info.gpu_info.devices
+            and len(system_info.gpu_info.devices) > 0
+        ):
             device = system_info.gpu_info.devices[0].gpu_name
+        elif train_args.device == "mps" and torch.backends.mps.is_available():
+            device = "mps"
+        else:
+            device = "cpu"
         self.model_info.ref = None
 
         self.model_info.train_args = train_args  # type: ignore
@@ -392,7 +408,7 @@ def export(
         runtime_type: RuntimeType = RuntimeType.TORCHSCRIPT_32,
         onnx_opset: int = 18,
         out_dir: Optional[str] = None,
-        device: Literal["cuda", "cpu", "auto"] = "auto",
+        device: Literal["cuda", "cpu", "mps", "auto"] = "auto",
         simplify_onnx: bool = True,
         overwrite: bool = True,
         image_size: Optional[Union[int, Tuple[int, int]]] = None,

focoos/ports.py

@@ -676,6 +676,7 @@ class GPUInfo(PydanticBase):
     gpu_cuda_version: Optional[str] = None
     total_gpu_memory_gb: Optional[float] = None
     devices: Optional[list[GPUDevice]] = None
+    mps_available: Optional[bool] = None
 
 
 class SystemInfo(PydanticBase):
@@ -743,6 +744,7 @@ def pprint(self, level: Literal["INFO", "DEBUG"] = "DEBUG"):
                 output_lines.append(f"  - total_memory_gb: {value.get('total_gpu_memory_gb')} GB")
                 output_lines.append(f"  - gpu_driver: {value.get('gpu_driver')}")
                 output_lines.append(f"  - gpu_cuda_version: {value.get('gpu_cuda_version')}")
+                output_lines.append(f"  - mps_available: {value.get('mps_available')}")
                 if value.get("devices"):
                     output_lines.append("  - devices:")
                     for device in value.get("devices", []):
@@ -950,16 +952,21 @@ class DatasetSplitType(str, Enum):
 
 def get_gpus_count():
     try:
-        import torch.cuda
+        import torch
 
-        return torch.cuda.device_count()
+        if torch.backends.mps.is_available():
+            return 1
+        elif torch.cuda.is_available():
+            return torch.cuda.device_count()
+        else:
+            return 0
     except ImportError:
         return 0
 
 
 SchedulerType = Literal["POLY", "FIXED", "COSINE", "MULTISTEP"]
 OptimizerType = Literal["ADAMW", "SGD", "RMSPROP"]
-DeviceType = Literal["cuda", "cpu"]
+DeviceType = Literal["cuda", "cpu", "mps"]
 
 
 @dataclass

focoos/trainer/trainer.py

@@ -77,6 +77,8 @@ def train(
         self.resume = args.resume
         self.finished = False
 
+        self.model.to(self.args.device)
+
         self.args.run_name = self.args.run_name.strip()
         # Setup logging and environment
         self.output_dir = os.path.join(self.args.output_dir, self.args.run_name)
@@ -631,8 +633,12 @@ def __init__(
         self.gather_metric_period = gather_metric_period
         self.zero_grad_before_forward = zero_grad_before_forward
 
+        if not torch.cuda.is_available():
+            logger.warning("[UnifiedTrainerLoop] CUDA is not available, training without AMP!")
+            amp = False
+
         # AMP setup
-        if amp:
+        if amp and torch.cuda.is_available():
             if grad_scaler is None:
                 # the init_scale avoids the first step to be too large
                 # and the scheduler.step() warning
@@ -725,8 +731,7 @@ def run_step(self):
         if self.zero_grad_before_forward:
             self.optimizer.zero_grad()
 
-        if self.amp:
-            assert torch.cuda.is_available(), "[UnifiedTrainerLoop] CUDA is required for AMP training!"
+        if self.amp and torch.cuda.is_available():
             with autocast(enabled=self.amp, dtype=self.precision, device_type="cuda"):
                 # we need to have preprocess data here
                 images, targets = self.processor.preprocess(data, dtype=self.precision, device=self.model.device)

focoos/utils/system.py

@@ -66,7 +66,7 @@ def get_gpu_info() -> GPUInfo:
         GPUInfo: An object containing comprehensive GPU information including devices list,
                 driver version, CUDA version and GPU count.
     """
-    gpu_info = GPUInfo()
+    gpu_info = GPUInfo(mps_available=torch.backends.mps.is_available())
     gpus_device = []
     try:
         # Get all GPU information in a single query