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"""
Copyright (c) 2024 The D-FINE Authors. All Rights Reserved.
"""
import argparse
import glob
import os
import time
from collections import OrderedDict, namedtuple
import numpy as np
import pycuda.driver as cuda
import tensorrt as trt
import torch
from dataset import Dataset
from tqdm import tqdm
from utils import TimeProfiler
def parse_args():
parser = argparse.ArgumentParser(description="Argument Parser Example")
parser.add_argument(
"--infer_dir",
type=str,
default="/data/COCO2017/val2017",
help="Directory for images to perform inference on.",
)
parser.add_argument("--engine_dir", type=str, help="Directory containing model engine files.")
parser.add_argument(
"--busy", action="store_true", help="Flag to indicate that other processes may be running."
)
args = parser.parse_args()
return args
class TRTInference(object):
def __init__(
self, engine_path, device="cuda", backend="torch", max_batch_size=32, verbose=False
):
self.engine_path = engine_path
self.device = device
self.backend = backend
self.max_batch_size = max_batch_size
self.logger = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger(trt.Logger.INFO)
self.engine = self.load_engine(engine_path)
self.context = self.engine.create_execution_context()
self.bindings = self.get_bindings(
self.engine, self.context, self.max_batch_size, self.device
)
self.bindings_addr = OrderedDict((n, v.ptr) for n, v in self.bindings.items())
self.input_names = self.get_input_names()
self.output_names = self.get_output_names()
if self.backend == "cuda":
self.stream = cuda.Stream()
self.time_profile = TimeProfiler()
self.time_profile_dataset = TimeProfiler()
def init(self):
self.dynamic = False
def load_engine(self, path):
trt.init_libnvinfer_plugins(self.logger, "")
with open(path, "rb") as f, trt.Runtime(self.logger) as runtime:
return runtime.deserialize_cuda_engine(f.read())
def get_input_names(self):
names = []
for _, name in enumerate(self.engine):
if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
names.append(name)
return names
def get_output_names(self):
names = []
for _, name in enumerate(self.engine):
if self.engine.get_tensor_mode(name) == trt.TensorIOMode.OUTPUT:
names.append(name)
return names
def get_bindings(self, engine, context, max_batch_size=32, device=None):
Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
bindings = OrderedDict()
for i, name in enumerate(engine):
shape = engine.get_tensor_shape(name)
dtype = trt.nptype(engine.get_tensor_dtype(name))
if shape[0] == -1:
dynamic = True
shape[0] = max_batch_size
if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
context.set_input_shape(name, shape)
if self.backend == "cuda":
if engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
data = np.random.randn(*shape).astype(dtype)
ptr = cuda.mem_alloc(data.nbytes)
bindings[name] = Binding(name, dtype, shape, data, ptr)
else:
data = cuda.pagelocked_empty(trt.volume(shape), dtype)
ptr = cuda.mem_alloc(data.nbytes)
bindings[name] = Binding(name, dtype, shape, data, ptr)
else:
data = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
bindings[name] = Binding(name, dtype, shape, data, data.data_ptr())
return bindings
def run_torch(self, blob):
for n in self.input_names:
if self.bindings[n].shape != blob[n].shape:
self.context.set_input_shape(n, blob[n].shape)
self.bindings[n] = self.bindings[n]._replace(shape=blob[n].shape)
self.bindings_addr.update({n: blob[n].data_ptr() for n in self.input_names})
self.context.execute_v2(list(self.bindings_addr.values()))
outputs = {n: self.bindings[n].data for n in self.output_names}
return outputs
def async_run_cuda(self, blob):
for n in self.input_names:
cuda.memcpy_htod_async(self.bindings_addr[n], blob[n], self.stream)
bindings_addr = [int(v) for _, v in self.bindings_addr.items()]
self.context.execute_async_v2(bindings=bindings_addr, stream_handle=self.stream.handle)
outputs = {}
for n in self.output_names:
cuda.memcpy_dtoh_async(self.bindings[n].data, self.bindings[n].ptr, self.stream)
outputs[n] = self.bindings[n].data
self.stream.synchronize()
return outputs
def __call__(self, blob):
if self.backend == "torch":
return self.run_torch(blob)
elif self.backend == "cuda":
return self.async_run_cuda(blob)
def synchronize(self):
if self.backend == "torch" and torch.cuda.is_available():
torch.cuda.synchronize()
elif self.backend == "cuda":
self.stream.synchronize()
def warmup(self, blob, n):
for _ in range(n):
_ = self(blob)
def speed(self, blob, n, nonempty_process=False):
times = []
self.time_profile_dataset.reset()
for i in tqdm(range(n), desc="Running Inference", unit="iteration"):
self.time_profile.reset()
with self.time_profile_dataset:
img = blob[i]
if img["images"] is not None:
img["image"] = img["input"] = img["images"].unsqueeze(0)
else:
img["images"] = img["input"] = img["image"].unsqueeze(0)
with self.time_profile:
_ = self(img)
times.append(self.time_profile.total)
# end-to-end model only
times = sorted(times)
if len(times) > 100 and nonempty_process:
times = times[:100]
avg_time = sum(times) / len(times) # Calculate the average of the remaining times
return avg_time
def main():
FLAGS = parse_args()
dataset = Dataset(FLAGS.infer_dir)
im = torch.ones(1, 3, 640, 640).cuda()
blob = {
"image": im,
"images": im,
"input": im,
"im_shape": torch.tensor([640, 640]).to(im.device),
"scale_factor": torch.tensor([1, 1]).to(im.device),
"orig_target_sizes": torch.tensor([640, 640]).to(im.device),
}
engine_files = glob.glob(os.path.join(FLAGS.engine_dir, "*.engine"))
results = []
for engine_file in engine_files:
print(f"Testing engine: {engine_file}")
model = TRTInference(engine_file, max_batch_size=1, verbose=False)
model.init()
model.warmup(blob, 1000)
t = []
for _ in range(1):
t.append(model.speed(dataset, 1000, FLAGS.busy))
avg_latency = 1000 * torch.tensor(t).mean()
results.append((engine_file, avg_latency))
print(f"Engine: {engine_file}, Latency: {avg_latency:.2f} ms")
del model
torch.cuda.empty_cache()
time.sleep(1)
sorted_results = sorted(results, key=lambda x: x[1])
for engine_file, latency in sorted_results:
print(f"Engine: {engine_file}, Latency: {latency:.2f} ms")
if __name__ == "__main__":
main()
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