Update difpoint/src/models/predictor.py

difpoint/src/models/predictor.py

@@ -1,261 +1,261 @@
-import pdb
-import threading
-import os
-import time
-
-import numpy as np
-import onnxruntime
-
-import torch
-from torch.cuda import nvtx
-from collections import OrderedDict
-import platform
-
-try:
-    import tensorrt as trt
-    import ctypes
-except ModuleNotFoundError:
-    print("No TensorRT Found")
-
-numpy_to_torch_dtype_dict = {
-    np.uint8: torch.uint8,
-    np.int8: torch.int8,
-    np.int16: torch.int16,
-    np.int32: torch.int32,
-    np.int64: torch.int64,
-    np.float16: torch.float16,
-    np.float32: torch.float32,
-    np.float64: torch.float64,
-    np.complex64: torch.complex64,
-    np.complex128: torch.complex128,
-}
-if np.version.full_version >= "1.24.0":
-    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
-else:
-    numpy_to_torch_dtype_dict[np.bool] = torch.bool
-
-
-class TensorRTPredictor:
-    """
-    Implements inference for the EfficientDet TensorRT engine.
-    """
-
-    def __init__(self, **kwargs):
-        """
-        :param engine_path: The path to the serialized engine to load from disk.
-        """
-        if platform.system().lower() == 'linux':
-            ctypes.CDLL("./difpoint/checkpoints/liveportrait_onnx/libgrid_sample_3d_plugin.so", mode=ctypes.RTLD_GLOBAL)
-        else:
-            ctypes.CDLL("./difpoint/checkpoints/liveportrait_onnx/grid_sample_3d_plugin.dll", mode=ctypes.RTLD_GLOBAL)
-        # Load TRT engine
-        self.logger = trt.Logger(trt.Logger.VERBOSE)
-        trt.init_libnvinfer_plugins(self.logger, "")
-        engine_path = os.path.abspath(kwargs.get("model_path", None))
-        print('engine_path', engine_path)
-        self.debug = kwargs.get("debug", False)
-        assert engine_path, f"model:{engine_path} must exist!"
-        print(f"loading trt model:{engine_path}")
-        with open(engine_path, "rb") as f, trt.Runtime(self.logger) as runtime:
-            assert runtime
-            self.engine = runtime.deserialize_cuda_engine(f.read())
-            print('self.engine', self.engine)
-        assert self.engine
-        self.context = self.engine.create_execution_context()
-        assert self.context
-
-        # Setup I/O bindings
-        self.inputs = []
-        self.outputs = []
-        self.tensors = OrderedDict()
-
-        # TODO: 支持动态shape输入
-        for idx in range(self.engine.num_io_tensors):
-            name = self.engine[idx]
-            is_input = self.engine.get_tensor_mode(name).name == "INPUT"
-            shape = self.engine.get_tensor_shape(name)
-            dtype = trt.nptype(self.engine.get_tensor_dtype(name))
-
-            binding = {
-                "index": idx,
-                "name": name,
-                "dtype": dtype,
-                "shape": list(shape)
-            }
-            if is_input:
-                self.inputs.append(binding)
-            else:
-                self.outputs.append(binding)
-
-        assert len(self.inputs) > 0
-        assert len(self.outputs) > 0
-        self.allocate_max_buffers()
-
-    def allocate_max_buffers(self, device="cuda"):
-        nvtx.range_push("allocate_max_buffers")
-        # 目前仅支持 batch 维度的动态处理
-        batch_size = 1
-        for idx in range(self.engine.num_io_tensors):
-            binding = self.engine[idx]
-            shape = self.engine.get_tensor_shape(binding)
-            is_input = self.engine.get_tensor_mode(binding).name == "INPUT"
-            if -1 in shape:
-                if is_input:
-                    shape = self.engine.get_tensor_profile_shape(binding, 0)[-1]
-                    batch_size = shape[0]
-                else:
-                    shape[0] = batch_size
-            dtype = trt.nptype(self.engine.get_tensor_dtype(binding))
-            tensor = torch.empty(
-                tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]
-            ).to(device=device)
-            self.tensors[binding] = tensor
-        nvtx.range_pop()
-
-    def input_spec(self):
-        """
-        Get the specs for the input tensor of the network. Useful to prepare memory allocations.
-        :return: Two items, the shape of the input tensor and its (numpy) datatype.
-        """
-        specs = []
-        for i, o in enumerate(self.inputs):
-            specs.append((o["name"], o['shape'], o['dtype']))
-            if self.debug:
-                print(f"trt input {i} -> {o['name']} -> {o['shape']}")
-        return specs
-
-    def output_spec(self):
-        """
-        Get the specs for the output tensors of the network. Useful to prepare memory allocations.
-        :return: A list with two items per element, the shape and (numpy) datatype of each output tensor.
-        """
-        specs = []
-        for i, o in enumerate(self.outputs):
-            specs.append((o["name"], o['shape'], o['dtype']))
-            if self.debug:
-                print(f"trt output {i} -> {o['name']} -> {o['shape']}")
-        return specs
-
-    def adjust_buffer(self, feed_dict):
-        nvtx.range_push("adjust_buffer")
-        for name, buf in feed_dict.items():
-            input_tensor = self.tensors[name]
-            current_shape = list(buf.shape)
-            slices = tuple(slice(0, dim) for dim in current_shape)
-            input_tensor[slices].copy_(buf)
-            self.context.set_input_shape(name, current_shape)
-        nvtx.range_pop()
-
-    def predict(self, feed_dict, stream):
-        """
-        Execute inference on a batch of images.
-        :param data: A list of inputs as numpy arrays.
-        :return A list of outputs as numpy arrays.
-        """
-        nvtx.range_push("set_tensors")
-        self.adjust_buffer(feed_dict)
-        for name, tensor in self.tensors.items():
-            self.context.set_tensor_address(name, tensor.data_ptr())
-        nvtx.range_pop()
-        nvtx.range_push("execute")
-        noerror = self.context.execute_async_v3(stream)
-        if not noerror:
-            raise ValueError("ERROR: inference failed.")
-        nvtx.range_pop()
-        return self.tensors
-
-    def __del__(self):
-        del self.engine
-        del self.context
-        del self.inputs
-        del self.outputs
-        del self.tensors
-
-
-class OnnxRuntimePredictor:
-    """
-    OnnxRuntime Prediction
-    """
-
-    def __init__(self, **kwargs):
-        model_path = kwargs.get("model_path", "")  # 用模型路径区分是否是一样的实例
-        assert os.path.exists(model_path), "model path must exist!"
-        # print("loading ort model:{}".format(model_path))
-        self.debug = kwargs.get("debug", False)
-        providers = ['CUDAExecutionProvider', 'CoreMLExecutionProvider', 'CPUExecutionProvider']
-
-        print(f"OnnxRuntime use {providers}")
-        opts = onnxruntime.SessionOptions()
-        # opts.inter_op_num_threads = kwargs.get("num_threads", 4)
-        # opts.intra_op_num_threads = kwargs.get("num_threads", 4)
-        # opts.log_severity_level = 3
-        self.onnx_model = onnxruntime.InferenceSession(model_path, providers=providers, sess_options=opts)
-        self.inputs = self.onnx_model.get_inputs()
-        self.outputs = self.onnx_model.get_outputs()
-
-    def input_spec(self):
-        """
-        Get the specs for the input tensor of the network. Useful to prepare memory allocations.
-        :return: Two items, the shape of the input tensor and its (numpy) datatype.
-        """
-        specs = []
-        for i, o in enumerate(self.inputs):
-            specs.append((o.name, o.shape, o.type))
-            if self.debug:
-                print(f"ort {i} -> {o.name} -> {o.shape}")
-        return specs
-
-    def output_spec(self):
-        """
-        Get the specs for the output tensors of the network. Useful to prepare memory allocations.
-        :return: A list with two items per element, the shape and (numpy) datatype of each output tensor.
-        """
-        specs = []
-        for i, o in enumerate(self.outputs):
-            specs.append((o.name, o.shape, o.type))
-            if self.debug:
-                print(f"ort output {i} -> {o.name} -> {o.shape}")
-        return specs
-
-    def predict(self, *data):
-        input_feeds = {}
-        for i in range(len(data)):
-            if self.inputs[i].type == 'tensor(float16)':
-                input_feeds[self.inputs[i].name] = data[i].astype(np.float16)
-            else:
-                input_feeds[self.inputs[i].name] = data[i].astype(np.float32)
-        results = self.onnx_model.run(None, input_feeds)
-        return results
-
-    def __del__(self):
-        del self.onnx_model
-        self.onnx_model = None
-
-
-class OnnxRuntimePredictorSingleton(OnnxRuntimePredictor):
-    """
-    单例模式，防止模型被加载多次
-    """
-    _instance_lock = threading.Lock()
-    _instance = {}
-
-    def __new__(cls, *args, **kwargs):
-        model_path = kwargs.get("model_path", "")  # 用模型路径区分是否是一样的实例
-        assert os.path.exists(model_path), "model path must exist!"
-        # 单例模式，避免重复加载模型
-        with OnnxRuntimePredictorSingleton._instance_lock:
-            if model_path not in OnnxRuntimePredictorSingleton._instance or \
-                    OnnxRuntimePredictorSingleton._instance[model_path].onnx_model is None:
-                OnnxRuntimePredictorSingleton._instance[model_path] = OnnxRuntimePredictor(**kwargs)
-
-        return OnnxRuntimePredictorSingleton._instance[model_path]
-
-
-def get_predictor(**kwargs):
-    predict_type = kwargs.get("predict_type", "trt")
-    if predict_type == "ort":
-        return OnnxRuntimePredictorSingleton(**kwargs)
-    elif predict_type == "trt":
-        return TensorRTPredictor(**kwargs)
-    else:
-        raise NotImplementedError
+import pdb
+import threading
+import os
+import time
+
+import numpy as np
+import onnxruntime
+
+import torch
+from torch.cuda import nvtx
+from collections import OrderedDict
+import platform
+
+try:
+    import tensorrt as trt
+    import ctypes
+except ModuleNotFoundError:
+    print("No TensorRT Found")
+
+numpy_to_torch_dtype_dict = {
+    np.uint8: torch.uint8,
+    np.int8: torch.int8,
+    np.int16: torch.int16,
+    np.int32: torch.int32,
+    np.int64: torch.int64,
+    np.float16: torch.float16,
+    np.float32: torch.float32,
+    np.float64: torch.float64,
+    np.complex64: torch.complex64,
+    np.complex128: torch.complex128,
+}
+if np.version.full_version >= "1.24.0":
+    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
+else:
+    numpy_to_torch_dtype_dict[np.bool] = torch.bool
+
+
+class TensorRTPredictor:
+    """
+    Implements inference for the EfficientDet TensorRT engine.
+    """
+
+    def __init__(self, **kwargs):
+        """
+        :param engine_path: The path to the serialized engine to load from disk.
+        """
+        if platform.system().lower() == 'linux':
+            ctypes.CDLL("./downloaded_repo/pretrained_weights/liveportrait_onnx/libgrid_sample_3d_plugin.so", mode=ctypes.RTLD_GLOBAL)
+        else:
+            ctypes.CDLL("./downloaded_repo/pretrained_weights/liveportrait_onnx/grid_sample_3d_plugin.dll", mode=ctypes.RTLD_GLOBAL)
+        # Load TRT engine
+        self.logger = trt.Logger(trt.Logger.VERBOSE)
+        trt.init_libnvinfer_plugins(self.logger, "")
+        engine_path = os.path.abspath(kwargs.get("model_path", None))
+        print('engine_path', engine_path)
+        self.debug = kwargs.get("debug", False)
+        assert engine_path, f"model:{engine_path} must exist!"
+        print(f"loading trt model:{engine_path}")
+        with open(engine_path, "rb") as f, trt.Runtime(self.logger) as runtime:
+            assert runtime
+            self.engine = runtime.deserialize_cuda_engine(f.read())
+            print('self.engine', self.engine)
+        assert self.engine
+        self.context = self.engine.create_execution_context()
+        assert self.context
+
+        # Setup I/O bindings
+        self.inputs = []
+        self.outputs = []
+        self.tensors = OrderedDict()
+
+        # TODO: 支持动态shape输入
+        for idx in range(self.engine.num_io_tensors):
+            name = self.engine[idx]
+            is_input = self.engine.get_tensor_mode(name).name == "INPUT"
+            shape = self.engine.get_tensor_shape(name)
+            dtype = trt.nptype(self.engine.get_tensor_dtype(name))
+
+            binding = {
+                "index": idx,
+                "name": name,
+                "dtype": dtype,
+                "shape": list(shape)
+            }
+            if is_input:
+                self.inputs.append(binding)
+            else:
+                self.outputs.append(binding)
+
+        assert len(self.inputs) > 0
+        assert len(self.outputs) > 0
+        self.allocate_max_buffers()
+
+    def allocate_max_buffers(self, device="cuda"):
+        nvtx.range_push("allocate_max_buffers")
+        # 目前仅支持 batch 维度的动态处理
+        batch_size = 1
+        for idx in range(self.engine.num_io_tensors):
+            binding = self.engine[idx]
+            shape = self.engine.get_tensor_shape(binding)
+            is_input = self.engine.get_tensor_mode(binding).name == "INPUT"
+            if -1 in shape:
+                if is_input:
+                    shape = self.engine.get_tensor_profile_shape(binding, 0)[-1]
+                    batch_size = shape[0]
+                else:
+                    shape[0] = batch_size
+            dtype = trt.nptype(self.engine.get_tensor_dtype(binding))
+            tensor = torch.empty(
+                tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]
+            ).to(device=device)
+            self.tensors[binding] = tensor
+        nvtx.range_pop()
+
+    def input_spec(self):
+        """
+        Get the specs for the input tensor of the network. Useful to prepare memory allocations.
+        :return: Two items, the shape of the input tensor and its (numpy) datatype.
+        """
+        specs = []
+        for i, o in enumerate(self.inputs):
+            specs.append((o["name"], o['shape'], o['dtype']))
+            if self.debug:
+                print(f"trt input {i} -> {o['name']} -> {o['shape']}")
+        return specs
+
+    def output_spec(self):
+        """
+        Get the specs for the output tensors of the network. Useful to prepare memory allocations.
+        :return: A list with two items per element, the shape and (numpy) datatype of each output tensor.
+        """
+        specs = []
+        for i, o in enumerate(self.outputs):
+            specs.append((o["name"], o['shape'], o['dtype']))
+            if self.debug:
+                print(f"trt output {i} -> {o['name']} -> {o['shape']}")
+        return specs
+
+    def adjust_buffer(self, feed_dict):
+        nvtx.range_push("adjust_buffer")
+        for name, buf in feed_dict.items():
+            input_tensor = self.tensors[name]
+            current_shape = list(buf.shape)
+            slices = tuple(slice(0, dim) for dim in current_shape)
+            input_tensor[slices].copy_(buf)
+            self.context.set_input_shape(name, current_shape)
+        nvtx.range_pop()
+
+    def predict(self, feed_dict, stream):
+        """
+        Execute inference on a batch of images.
+        :param data: A list of inputs as numpy arrays.
+        :return A list of outputs as numpy arrays.
+        """
+        nvtx.range_push("set_tensors")
+        self.adjust_buffer(feed_dict)
+        for name, tensor in self.tensors.items():
+            self.context.set_tensor_address(name, tensor.data_ptr())
+        nvtx.range_pop()
+        nvtx.range_push("execute")
+        noerror = self.context.execute_async_v3(stream)
+        if not noerror:
+            raise ValueError("ERROR: inference failed.")
+        nvtx.range_pop()
+        return self.tensors
+
+    def __del__(self):
+        del self.engine
+        del self.context
+        del self.inputs
+        del self.outputs
+        del self.tensors
+
+
+class OnnxRuntimePredictor:
+    """
+    OnnxRuntime Prediction
+    """
+
+    def __init__(self, **kwargs):
+        model_path = kwargs.get("model_path", "")  # 用模型路径区分是否是一样的实例
+        assert os.path.exists(model_path), "model path must exist!"
+        # print("loading ort model:{}".format(model_path))
+        self.debug = kwargs.get("debug", False)
+        providers = ['CUDAExecutionProvider', 'CoreMLExecutionProvider', 'CPUExecutionProvider']
+
+        print(f"OnnxRuntime use {providers}")
+        opts = onnxruntime.SessionOptions()
+        # opts.inter_op_num_threads = kwargs.get("num_threads", 4)
+        # opts.intra_op_num_threads = kwargs.get("num_threads", 4)
+        # opts.log_severity_level = 3
+        self.onnx_model = onnxruntime.InferenceSession(model_path, providers=providers, sess_options=opts)
+        self.inputs = self.onnx_model.get_inputs()
+        self.outputs = self.onnx_model.get_outputs()
+
+    def input_spec(self):
+        """
+        Get the specs for the input tensor of the network. Useful to prepare memory allocations.
+        :return: Two items, the shape of the input tensor and its (numpy) datatype.
+        """
+        specs = []
+        for i, o in enumerate(self.inputs):
+            specs.append((o.name, o.shape, o.type))
+            if self.debug:
+                print(f"ort {i} -> {o.name} -> {o.shape}")
+        return specs
+
+    def output_spec(self):
+        """
+        Get the specs for the output tensors of the network. Useful to prepare memory allocations.
+        :return: A list with two items per element, the shape and (numpy) datatype of each output tensor.
+        """
+        specs = []
+        for i, o in enumerate(self.outputs):
+            specs.append((o.name, o.shape, o.type))
+            if self.debug:
+                print(f"ort output {i} -> {o.name} -> {o.shape}")
+        return specs
+
+    def predict(self, *data):
+        input_feeds = {}
+        for i in range(len(data)):
+            if self.inputs[i].type == 'tensor(float16)':
+                input_feeds[self.inputs[i].name] = data[i].astype(np.float16)
+            else:
+                input_feeds[self.inputs[i].name] = data[i].astype(np.float32)
+        results = self.onnx_model.run(None, input_feeds)
+        return results
+
+    def __del__(self):
+        del self.onnx_model
+        self.onnx_model = None
+
+
+class OnnxRuntimePredictorSingleton(OnnxRuntimePredictor):
+    """
+    单例模式，防止模型被加载多次
+    """
+    _instance_lock = threading.Lock()
+    _instance = {}
+
+    def __new__(cls, *args, **kwargs):
+        model_path = kwargs.get("model_path", "")  # 用模型路径区分是否是一样的实例
+        assert os.path.exists(model_path), "model path must exist!"
+        # 单例模式，避免重复加载模型
+        with OnnxRuntimePredictorSingleton._instance_lock:
+            if model_path not in OnnxRuntimePredictorSingleton._instance or \
+                    OnnxRuntimePredictorSingleton._instance[model_path].onnx_model is None:
+                OnnxRuntimePredictorSingleton._instance[model_path] = OnnxRuntimePredictor(**kwargs)
+
+        return OnnxRuntimePredictorSingleton._instance[model_path]
+
+
+def get_predictor(**kwargs):
+    predict_type = kwargs.get("predict_type", "trt")
+    if predict_type == "ort":
+        return OnnxRuntimePredictorSingleton(**kwargs)
+    elif predict_type == "trt":
+        return TensorRTPredictor(**kwargs)
+    else:
+        raise NotImplementedError