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import torch
import numpy as np
from pathlib import Path
from collections import OrderedDict, namedtuple
from boxmot.utils import logger as LOGGER
from boxmot.appearance.backends.base_backend import BaseModelBackend
class TensorRTBackend(BaseModelBackend):
def __init__(self, weights, device, half):
self.is_trt10 = False
super().__init__(weights, device, half)
self.nhwc = False
self.half = half
self.device = device
self.weights = weights
self.fp16 = False # Will be updated in load_model
self.load_model(self.weights)
def load_model(self, w):
LOGGER.info(f"Loading {w} for TensorRT inference...")
self.checker.check_packages(("nvidia-tensorrt",))
try:
import tensorrt as trt # TensorRT library
except ImportError:
raise ImportError("Please install tensorrt to use this backend.")
if self.device.type == "cpu":
if torch.cuda.is_available():
self.device = torch.device("cuda:0")
else:
raise ValueError("CUDA device not available for TensorRT inference.")
Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
logger = trt.Logger(trt.Logger.INFO)
# Deserialize the engine
with open(w, "rb") as f, trt.Runtime(logger) as runtime:
self.model_ = runtime.deserialize_cuda_engine(f.read())
# Execution context
self.context = self.model_.create_execution_context()
self.bindings = OrderedDict()
self.is_trt10 = not hasattr(self.model_, "num_bindings")
num = range(self.model_.num_io_tensors) if self.is_trt10 else range(self.model_.num_bindings)
# Parse bindings
for index in num:
if self.is_trt10:
name = self.model_.get_tensor_name(index)
dtype = trt.nptype(self.model_.get_tensor_dtype(name))
is_input = self.model_.get_tensor_mode(name) == trt.TensorIOMode.INPUT
if is_input and -1 in tuple(self.model_.get_tensor_shape(name)):
self.context.set_input_shape(name, tuple(self.model_.get_tensor_profile_shape(name, 0)[1]))
if is_input and dtype == np.float16:
self.fp16 = True
shape = tuple(self.context.get_tensor_shape(name))
else:
name = self.model_.get_binding_name(index)
dtype = trt.nptype(self.model_.get_binding_dtype(index))
is_input = self.model_.binding_is_input(index)
# Handle dynamic shapes
if is_input and -1 in self.model_.get_binding_shape(index):
profile_index = 0
min_shape, opt_shape, max_shape = self.model_.get_profile_shape(profile_index, index)
self.context.set_binding_shape(index, opt_shape)
if is_input and dtype == np.float16:
self.fp16 = True
shape = tuple(self.context.get_binding_shape(index))
data = torch.from_numpy(np.empty(shape, dtype=dtype)).to(self.device)
self.bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
self.binding_addrs = OrderedDict((n, d.ptr) for n, d in self.bindings.items())
def forward(self, im_batch):
temp_im_batch = im_batch.clone()
batch_array = []
inp_batch = im_batch.shape[0]
out_batch = self.bindings["output"].shape[0]
resultant_features = []
# Divide batch to sub batches
while inp_batch > out_batch:
batch_array.append(temp_im_batch[:out_batch])
temp_im_batch = temp_im_batch[out_batch:]
inp_batch = temp_im_batch.shape[0]
if temp_im_batch.shape[0] > 0:
batch_array.append(temp_im_batch)
for temp_batch in batch_array:
# Adjust for dynamic shapes
if temp_batch.shape != self.bindings["images"].shape:
if self.is_trt10:
self.context.set_input_shape("images", temp_batch.shape)
self.bindings["images"] = self.bindings["images"]._replace(shape=temp_batch.shape)
self.bindings["output"].data.resize_(tuple(self.context.get_tensor_shape("output")))
else:
i_in = self.model_.get_binding_index("images")
i_out = self.model_.get_binding_index("output")
self.context.set_binding_shape(i_in, temp_batch.shape)
self.bindings["images"] = self.bindings["images"]._replace(shape=temp_batch.shape)
output_shape = tuple(self.context.get_binding_shape(i_out))
self.bindings["output"].data.resize_(output_shape)
s = self.bindings["images"].shape
assert temp_batch.shape == s, f"Input size {temp_batch.shape} does not match model size {s}"
self.binding_addrs["images"] = int(temp_batch.data_ptr())
# Execute inference
self.context.execute_v2(list(self.binding_addrs.values()))
features = self.bindings["output"].data
resultant_features.append(features.clone())
if len(resultant_features)== 1:
return resultant_features[0]
else:
rslt_features = torch.cat(resultant_features,dim=0)
rslt_features= rslt_features[:im_batch.shape[0]]
return rslt_features
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