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benchmark.py
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benchmark.py
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import torch
import cv2
import numpy as np
import pycuda.driver as cuda
import tensorrt as trt
from torchvision.transforms.functional import normalize
import argparse
import timeit
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float32 (bool): Whether to change to float32.
Returns:
list[tensor] | tensor: Tensor images. If returned results only have
one element, just return tensor.
"""
def _totensor(img, bgr2rgb, float32):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb, float32) for img in imgs]
else:
return _totensor(imgs, bgr2rgb, float32)
def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
"""Convert torch Tensors into image numpy arrays.
After clamping to [min, max], values will be normalized to [0, 1].
Args:
tensor (Tensor or list[Tensor]): Accept shapes:
1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
2) 3D Tensor of shape (3/1 x H x W);
3) 2D Tensor of shape (H x W).
Tensor channel should be in RGB order.
rgb2bgr (bool): Whether to change rgb to bgr.
out_type (numpy type): output types. If ``np.uint8``, transform outputs
to uint8 type with range [0, 255]; otherwise, float type with
range [0, 1]. Default: ``np.uint8``.
min_max (tuple[int]): min and max values for clamp.
Returns:
(Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
shape (H x W). The channel order is BGR.
"""
if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
if torch.is_tensor(tensor):
tensor = [tensor]
result = []
for _tensor in tensor:
_tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
n_dim = _tensor.dim()
if n_dim == 4:
img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
img_np = img_np.transpose(1, 2, 0)
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 3:
img_np = _tensor.numpy()
img_np = img_np.transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image
img_np = np.squeeze(img_np, axis=2)
else:
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 2:
img_np = _tensor.numpy()
else:
raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
if out_type == np.uint8:
# Unlike MATLAB, numpy.unit8() WILL NOT round by default.
img_np = (img_np * 255.0).round()
img_np = img_np.astype(out_type)
result.append(img_np)
if len(result) == 1:
result = result[0]
return result
def build_engine_context(trt_model_path):
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(TRT_LOGGER)
trt.init_libnvinfer_plugins(None, '')
engine = runtime.deserialize_cuda_engine(open(trt_model_path, 'rb').read())
assert engine
context = engine.create_execution_context()
assert context
return engine, context
def alloc_buffers(engine, context):
inputs = []
outputs = []
allocations = []
# Allocate buffers
for i in range(engine.num_bindings):
name = engine.get_tensor_name(i)
is_input = engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT
dtype = np.dtype(trt.nptype(engine.get_tensor_dtype(name)))
shape = context.get_tensor_shape(name)
if is_input and shape[0] < 0:
assert engine.num_optimization_profiles > 0
profile_shape = engine.get_profile_shape(0, name)
assert len(profile_shape) == 3 # min, opt, max
context.set_input_shape(i, profile_shape[2])
shape = context.get_tensor_shape(name)
if is_input:
batch_size = shape[0]
size = dtype.itemsize
for s in shape:
size *= s
allocation = cuda.mem_alloc(size)
host_allocation = None if is_input else np.zeros(shape, dtype)
binding = {
'index': i,
'name': name,
'dtype': dtype,
'shape': list(shape),
'allocation': allocation,
'host_allocation': host_allocation
}
allocations.append(allocation)
if is_input:
inputs.append(binding)
else:
outputs.append(binding)
return inputs, outputs, allocations
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input',type=str, default='./input.png', help='Input image path', required=True)
parser.add_argument('--output', type=str, default="./output.png", help='Output image path')
parser.add_argument('--engine', type=str, default=None, help='Tensorrt engine path', required=True)
args = parser.parse_args()
img = cv2.imread(args.input, cv2.IMREAD_COLOR)
import pycuda.autoprimaryctx as ctx
engine, context = build_engine_context(args.engine)
inputs, outputs, allocations = alloc_buffers(engine, context)
img_t = img2tensor(img / 255., bgr2rgb=True, float32=True)
normalize(img_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
input_data = np.ascontiguousarray(img_t.cpu().numpy(), dtype=np.float32)
for i in range(50):
start_time = timeit.default_timer()
cuda.memcpy_htod(inputs[0]['allocation'], input_data)
context.execute_v2(allocations)
cuda.memcpy_dtoh(outputs[2]['host_allocation'], outputs[2]['allocation'])
output = outputs[2]['host_allocation']
output=torch.from_numpy(output)
end_time = timeit.default_timer()
print(f"Execution time: {end_time-start_time} seconds")
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
cv2.imwrite(args.output,restored_face)