improve gpu computation in HHD computation
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@@ -19,11 +19,11 @@ def main():
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# 步骤1：创建传感器对象 (必要步骤)，config_name为配置文件名（内置配置文件名）或路径（自定义配置文件路径）
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# 方式1: 从视频文件读取
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# sensor = orisys.Sensor("s1.mp4", config_name="prototype", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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sensor = orisys.Sensor("s1.mp4", config_name="prototype", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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# 方式2: 从摄像头读取（取消注释使用）
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sensor = orisys.Sensor(0, config_name="./config/lens_test.json", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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# sensor = orisys.Sensor(0, config_name="./config/lens_test.json", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
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@@ -33,12 +33,12 @@ def main():
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# 步骤 2.1: 获取并拼接图像 (必要步骤)
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sensor.get_img()
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print("img_size_raw:", sensor.img_size_raw)
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if sensor.detect_motion(threshold=2):
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print("motion detected")
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# 步骤 2.2: 计算形变场 (必要步骤)
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sensor.compute_deformation()
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# 步骤 2.3: 计算触碰点
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sensor.compute_contact()
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# if sensor.detect_motion(threshold=2):
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# print("motion detected")
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# 步骤 2.2: 计算形变场 (必要步骤)
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sensor.compute_deformation()
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# 步骤 2.3: 计算触碰点
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sensor.compute_contact()
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#步骤 2.4: 读取信息
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+204
-145
@@ -104,6 +104,10 @@ class nHHD(object):
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self.psolver.prepare(False)
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# Example pattern for stream usage:
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self.stream1 = cp.cuda.Stream()
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self.stream2 = cp.cuda.Stream()
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# create the 3-component decomposition
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def decompose(self, vfield, verbose=0):
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@@ -228,20 +232,21 @@ class nHHD(object):
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dudy, dudx = cp.gradient(vfield_gpu[:,:,0], dx0, dx1)
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dvdy, dvdx = cp.gradient(vfield_gpu[:,:,1], dx0, dx1)
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div_gpu = dudx + dvdy
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curlw_gpu = dvdx - dudy
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div_gpu = cp.add(dudx, dvdy)
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curlw_gpu = cp.subtract(dvdx, dudy)
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if verbose > 0:
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print('div =', div_gpu.shape, float(div_gpu.min()), float(div_gpu.max()))
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print('curlw =', curlw_gpu.shape, float(curlw_gpu.min()), float(curlw_gpu.max()))
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# Compute potentials on GPU (solve_gpu returns numpy, so we convert back)
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nD_cpu = self.psolver.solve_gpu(cp.asnumpy(div_gpu), verbose > 0)
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nRu_cpu = self.psolver.solve_gpu(cp.asnumpy(curlw_gpu), verbose > 0)
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# Keep potentials on GPU
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nD_gpu = cp.asarray(nD_cpu, dtype=cp.float32)
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nRu_gpu = cp.asarray(nRu_cpu, dtype=cp.float32)
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# Compute potentials on GPU (solve_gpu returns numpy, so we convert back)
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with self.stream1:
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nD_gpu = self.psolver._solve_gpu_internal(div_gpu)
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with self.stream2:
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nRu_gpu = self.psolver._solve_gpu_internal(curlw_gpu)
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self.stream1.synchronize()
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self.stream2.synchronize()
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if verbose > 0:
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print('nD =', nD_gpu.shape, float(nD_gpu.min()), float(nD_gpu.max()))
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@@ -256,15 +261,15 @@ class nHHD(object):
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# Compute rotated gradient on GPU
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rdy, rdx = cp.gradient(nRu_gpu, dx0, dx1)
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rdy *= -1.0
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rdy = cp.multiply(rdy, -1.0)
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r_gpu = cp.stack((rdy, rdx), axis=-1)
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if verbose > 0:
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print('r =', r_gpu.shape, float(r_gpu.min()), float(r_gpu.max()))
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# Compute h on GPU
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h_gpu = d_gpu + r_gpu
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h_gpu = vfield_gpu - h_gpu
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h_gpu = cp.add(d_gpu, r_gpu)
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h_gpu = cp.subtract(vfield_gpu, h_gpu)
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if verbose > 0:
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print('h =', h_gpu.shape, float(h_gpu.min()), float(h_gpu.max()))
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@@ -276,11 +281,11 @@ class nHHD(object):
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self._nRu_gpu = nRu_gpu
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# Also store CPU copies for compatibility
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self.d = cp.asnumpy(d_gpu)
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self.h = cp.asnumpy(h_gpu)
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self.r = cp.asnumpy(r_gpu)
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self.nRu = cp.asnumpy(nRu_gpu)
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self.nD = nD_cpu
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# self.d = cp.asnumpy(d_gpu)
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# self.h = cp.asnumpy(h_gpu)
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# self.r = cp.asnumpy(r_gpu)
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# self.nRu = cp.asnumpy(nRu_gpu)
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# self.nD = cp.asnumpy(nD_gpu)
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return (d_gpu, h_gpu, r_gpu, nRu_gpu)
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@@ -375,13 +380,18 @@ class nHHD(object):
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return self.get_force(vfield, verbose)
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# Decompose the vector field
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self.decompose(vfield, verbose)
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self.decompose_gpu(vfield, verbose)
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# Keep data on GPU - avoid transfers by computing directly
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d_gpu = cp.asarray(self.d, dtype=cp.float32)
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h_gpu = cp.asarray(self.h, dtype=cp.float32)
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r_gpu = cp.asarray(self.r, dtype=cp.float32)
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nRu_gpu = cp.asarray(self.nRu, dtype=cp.float32)
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# d_gpu = cp.asarray(self.d, dtype=cp.float32)
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# h_gpu = cp.asarray(self.h, dtype=cp.float32)
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# r_gpu = cp.asarray(self.r, dtype=cp.float32)
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# nRu_gpu = cp.asarray(self.nRu, dtype=cp.float32)
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d_gpu = self._d_gpu
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h_gpu = self._h_gpu
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r_gpu = self._r_gpu
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nRu_gpu = self._nRu_gpu
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# Use reduction kernels for better performance
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# Compute Sn using L1 norm (sum of absolute values)
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@@ -410,10 +420,17 @@ class nHHD(object):
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r_y = r_gpu[:, :, 1]
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# Compute torque with fused sum
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Stor = float(cp.sum(
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(mgrid_0 - max_0) * r_y - (mgrid_1 - max_1) * r_x +
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(mgrid_0 - min_0) * r_y - (mgrid_1 - min_1) * r_x
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))
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# Stor = float(cp.sum(
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# (mgrid_0 - max_0) * r_y - (mgrid_1 - max_1) * r_x +
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# (mgrid_0 - min_0) * r_y - (mgrid_1 - min_1) * r_x
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# ))
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# Replace lines 413-416:
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if KERNEL_AVAILABLE and _torque_kernel is not None:
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torque = _torque_kernel(
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r_x, r_y, mgrid_0, mgrid_1,
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float(max_0), float(max_1), float(min_0), float(min_1)
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)
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Stor = float(cp.sum(torque))
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self.Sn, self.Stx, self.Sty, self.Stor = Sn, Stx, Sty, Stor
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output = numpy.array([Sn, Stx, Sty, Stor], dtype=numpy.float32)
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@@ -430,12 +447,14 @@ class nHHD(object):
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cp.get_default_memory_pool().free_all_blocks()
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def get_shear(self):
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return self.h
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if not CUPY_AVAILABLE:
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return self.h
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return cp.asnumpy(self._h_gpu)
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def get_normal(self):
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return self.d
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if not CUPY_AVAILABLE:
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return self.d
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return cp.asnumpy(self._d_gpu)
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# ---------------------------------------------------------------------------
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@@ -574,72 +593,72 @@ class StructuredGrid(object):
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return (dudx, dwdy, dudz, dvdx)
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def divcurl_gpu(self, vfield, verbose=False):
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"""
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GPU-accelerated computation of divergence and curl of vector field.
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Uses CuPy for faster gradient computation on GPU.
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"""
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if not CUPY_AVAILABLE:
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if verbose:
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print('CuPy not available, falling back to CPU')
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return self.divcurl(vfield, verbose)
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# def divcurl_gpu(self, vfield, verbose=False):
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# """
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# GPU-accelerated computation of divergence and curl of vector field.
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# Uses CuPy for faster gradient computation on GPU.
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# """
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# if not CUPY_AVAILABLE:
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# if verbose:
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# print('CuPy not available, falling back to CPU')
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# return self.divcurl(vfield, verbose)
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if (vfield.shape[-1] != self.dim) or (vfield.shape[0:self.dim] != self.dims):
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raise ValueError("Dimensions of vector field should match that of the grid")
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# if (vfield.shape[-1] != self.dim) or (vfield.shape[0:self.dim] != self.dims):
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# raise ValueError("Dimensions of vector field should match that of the grid")
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if verbose:
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print(' Computing div and curl on GPU...', end ='')
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sys.stdout.flush()
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mtimer = Timer()
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# if verbose:
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# print(' Computing div and curl on GPU...', end ='')
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# sys.stdout.flush()
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# mtimer = Timer()
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if self.dim == 2:
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# Transfer to GPU
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vfield_gpu = cp.asarray(vfield, dtype=cp.float32)
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# if self.dim == 2:
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# # Transfer to GPU
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# vfield_gpu = cp.asarray(vfield, dtype=cp.float32)
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# Compute gradients on GPU
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dudy, dudx = cp.gradient(vfield_gpu[:,:,0], self.dx[0], self.dx[1])
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dvdy, dvdx = cp.gradient(vfield_gpu[:,:,1], self.dx[0], self.dx[1])
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# # Compute gradients on GPU
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# dudy, dudx = cp.gradient(vfield_gpu[:,:,0], self.dx[0], self.dx[1])
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# dvdy, dvdx = cp.gradient(vfield_gpu[:,:,1], self.dx[0], self.dx[1])
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# Compute div and curl on GPU
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div_gpu = dudx + dvdy
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curl_gpu = dvdx - dudy
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# # Compute div and curl on GPU
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# div_gpu = dudx + dvdy
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# curl_gpu = dvdx - dudy
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# Transfer back to CPU
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div = cp.asnumpy(div_gpu)
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curl = cp.asnumpy(curl_gpu)
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# # Transfer back to CPU
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# div = cp.asnumpy(div_gpu)
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# curl = cp.asnumpy(curl_gpu)
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if verbose:
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print(' Done!', end ='')
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mtimer.end()
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# if verbose:
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# print(' Done!', end ='')
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# mtimer.end()
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return (div, curl)
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# return (div, curl)
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elif self.dim == 3:
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# Transfer to GPU
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vfield_gpu = cp.asarray(vfield, dtype=cp.float32)
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# elif self.dim == 3:
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# # Transfer to GPU
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# vfield_gpu = cp.asarray(vfield, dtype=cp.float32)
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# Compute gradients on GPU
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dudz, dudy, dudx = cp.gradient(vfield_gpu[:,:,:,0], self.dx[0], self.dx[1], self.dx[2])
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dvdz, dvdy, dvdx = cp.gradient(vfield_gpu[:,:,:,1], self.dx[0], self.dx[1], self.dx[2])
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dwdz, dwdy, dwdx = cp.gradient(vfield_gpu[:,:,:,2], self.dx[0], self.dx[1], self.dx[2])
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# # Compute gradients on GPU
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# dudz, dudy, dudx = cp.gradient(vfield_gpu[:,:,:,0], self.dx[0], self.dx[1], self.dx[2])
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# dvdz, dvdy, dvdx = cp.gradient(vfield_gpu[:,:,:,1], self.dx[0], self.dx[1], self.dx[2])
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# dwdz, dwdy, dwdx = cp.gradient(vfield_gpu[:,:,:,2], self.dx[0], self.dx[1], self.dx[2])
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# Compute div and curl components on GPU
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div_gpu = dudx + dvdy + dwdz
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curlu_gpu = dwdy - dvdz
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curlv_gpu = dudz - dwdx
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curlw_gpu = dvdx - dudy
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# # Compute div and curl components on GPU
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# div_gpu = dudx + dvdy + dwdz
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# curlu_gpu = dwdy - dvdz
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# curlv_gpu = dudz - dwdx
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# curlw_gpu = dvdx - dudy
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# Transfer back to CPU
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div = cp.asnumpy(div_gpu)
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curlu = cp.asnumpy(curlu_gpu)
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curlv = cp.asnumpy(curlv_gpu)
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curlw = cp.asnumpy(curlw_gpu)
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# # Transfer back to CPU
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# div = cp.asnumpy(div_gpu)
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# curlu = cp.asnumpy(curlu_gpu)
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# curlv = cp.asnumpy(curlv_gpu)
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# curlw = cp.asnumpy(curlw_gpu)
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if verbose:
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print(' Done!', end ='')
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mtimer.end()
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# if verbose:
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# print(' Done!', end ='')
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# mtimer.end()
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return (div, curlu, curlv, curlw)
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# return (div, curlu, curlv, curlw)
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def curl3D(self, vfield, verbose=False):
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@@ -1187,7 +1206,7 @@ class PoissonSolver(object):
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# --------------------------------------------------------------------------
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# GPU-accelerated solve using CuPy
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# --------------------------------------------------------------------------
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def solve_gpu(self, f, verbose=False):
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def solve_gpu(self, f, verbose=False, return_gpu=False):
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"""
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GPU-accelerated Poisson solver using CuPy.
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Falls back to CPU if CuPy is not available.
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@@ -1241,110 +1260,150 @@ class PoissonSolver(object):
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# Scale by grid spacing (on GPU)
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p_gpu *= self._gpu_cache['gdx_prod']
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if verbose:
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print('Poisson solver (GPU) finished')
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gtimer.end()
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# Transfer result back to CPU
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p = cp.asnumpy(p_gpu)
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if return_gpu:
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return p_gpu # Return CuPy array
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return cp.asnumpy(p_gpu)
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# p = cp.asnumpy(p_gpu)
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elif self.stype == 'S':
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raise SyntaxError('Spatial solver not suggested for structured grids')
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# ----------------------------------------------------------------------
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# unstructured points - not optimized for GPU
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elif self.ptype == 'P':
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if verbose:
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print(' - GPU solver not optimized for unstructured points, using CPU')
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return self.solve(f, verbose)
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# elif self.ptype == 'P':
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# if verbose:
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# print(' - GPU solver not optimized for unstructured points, using CPU')
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# return self.solve(f, verbose)
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# ----------------------------------------------------------------------
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if verbose:
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print('Poisson solver (GPU) finished')
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gtimer.end()
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return p
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# return p
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def solve_gpu_batch(self, f_list, verbose=False):
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def _solve_gpu_internal(self, f_gpu):
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"""Internal GPU solve that accepts and returns GPU arrays."""
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"""
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Batch GPU-accelerated Poisson solver for multiple inputs.
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Solves multiple Poisson equations in a single GPU call.
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GPU-accelerated Poisson solver using CuPy.
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Falls back to CPU if CuPy is not available.
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Args:
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f_list: List of input fields (numpy arrays)
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verbose: Print timing information
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f_gpu: Input field (numpy array)
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Returns:
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List of solutions (numpy arrays on CPU)
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p_gpu: Solution (CuPy array)
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"""
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if not CUPY_AVAILABLE:
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if verbose:
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print("CuPy not available, falling back to CPU solver")
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return [self.solve(f, verbose) for f in f_list]
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if not self.ready:
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self.prepare(verbose)
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gtimer = Timer()
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if verbose:
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print(f'\nSolving {len(f_list)} Poisson Eq. on GPU (batched)')
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# Verify all inputs have same shape
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fshape = f_list[0].shape
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for f in f_list:
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if f.shape != fshape:
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raise ValueError("All input fields must have the same shape for batch processing")
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# ----------------------------------------------------------------------
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# regular grid
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if self.ptype == 'G':
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if self.gdims != fshape:
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raise ValueError("Shape of function should match shape of grid")
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# convolution in frequency domain using GPU
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if self.stype == 'F':
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# Initialize GPU cache if needed
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if self._gpu_cache is None:
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if verbose:
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print(' - Transferring Green\'s function to GPU...')
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self._gpu_cache = {
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'G_gpu': cp.asarray(self.G, dtype=cp.float32),
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'gdx_prod': numpy.prod(self.gdx)
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}
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results = []
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# Perform FFT convolution on GPU
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# CuPy's fftconvolve is GPU-accelerated
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p_gpu = cp_signal.fftconvolve(f_gpu, self._gpu_cache['G_gpu'], mode='same')
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# Process all inputs in batch
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for i, f in enumerate(f_list):
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# Transfer input to GPU
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f_gpu = cp.asarray(f, dtype=cp.float32)
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# Scale by grid spacing (on GPU)
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p_gpu *= self._gpu_cache['gdx_prod']
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# Perform FFT convolution on GPU
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p_gpu = cp_signal.fftconvolve(f_gpu, self._gpu_cache['G_gpu'], mode='same')
|
||||
return p_gpu # Return CuPy array
|
||||
|
||||
# Scale by grid spacing (on GPU)
|
||||
p_gpu *= self._gpu_cache['gdx_prod']
|
||||
|
||||
# Transfer result back to CPU
|
||||
results.append(cp.asnumpy(p_gpu))
|
||||
# def solve_gpu_batch(self, f_list, verbose=False):
|
||||
# """
|
||||
# Batch GPU-accelerated Poisson solver for multiple inputs.
|
||||
# Solves multiple Poisson equations in a single GPU call.
|
||||
|
||||
if verbose:
|
||||
print(f' - Solved {i+1}/{len(f_list)}')
|
||||
# Args:
|
||||
# f_list: List of input fields (numpy arrays)
|
||||
# verbose: Print timing information
|
||||
|
||||
if verbose:
|
||||
print('Poisson solver (GPU batch) finished')
|
||||
gtimer.end()
|
||||
# Returns:
|
||||
# List of solutions (numpy arrays on CPU)
|
||||
# """
|
||||
# if not CUPY_AVAILABLE:
|
||||
# if verbose:
|
||||
# print("CuPy not available, falling back to CPU solver")
|
||||
# return [self.solve(f, verbose) for f in f_list]
|
||||
|
||||
return results
|
||||
# if not self.ready:
|
||||
# self.prepare(verbose)
|
||||
|
||||
elif self.stype == 'S':
|
||||
raise SyntaxError('Spatial solver not suggested for structured grids')
|
||||
# gtimer = Timer()
|
||||
# if verbose:
|
||||
# print(f'\nSolving {len(f_list)} Poisson Eq. on GPU (batched)')
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# unstructured points - not optimized for GPU
|
||||
elif self.ptype == 'P':
|
||||
if verbose:
|
||||
print(' - GPU solver not optimized for unstructured points, using CPU')
|
||||
return [self.solve(f, verbose) for f in f_list]
|
||||
# # Verify all inputs have same shape
|
||||
# fshape = f_list[0].shape
|
||||
# for f in f_list:
|
||||
# if f.shape != fshape:
|
||||
# raise ValueError("All input fields must have the same shape for batch processing")
|
||||
|
||||
# If we reach here, it's an unsupported case
|
||||
raise ValueError(f"Unsupported point type '{self.ptype}' for GPU batch solver")
|
||||
# # ----------------------------------------------------------------------
|
||||
# # regular grid
|
||||
# if self.ptype == 'G':
|
||||
# if self.gdims != fshape:
|
||||
# raise ValueError("Shape of function should match shape of grid")
|
||||
|
||||
# # convolution in frequency domain using GPU
|
||||
# if self.stype == 'F':
|
||||
# # Initialize GPU cache if needed
|
||||
# if self._gpu_cache is None:
|
||||
# if verbose:
|
||||
# print(' - Transferring Green\'s function to GPU...')
|
||||
# self._gpu_cache = {
|
||||
# 'G_gpu': cp.asarray(self.G, dtype=cp.float32),
|
||||
# 'gdx_prod': numpy.prod(self.gdx)
|
||||
# }
|
||||
|
||||
# results = []
|
||||
|
||||
# # Process all inputs in batch
|
||||
# for i, f in enumerate(f_list):
|
||||
# # Transfer input to GPU
|
||||
# f_gpu = cp.asarray(f, dtype=cp.float32)
|
||||
|
||||
# # Perform FFT convolution on GPU
|
||||
# p_gpu = cp_signal.fftconvolve(f_gpu, self._gpu_cache['G_gpu'], mode='same')
|
||||
|
||||
# # Scale by grid spacing (on GPU)
|
||||
# p_gpu *= self._gpu_cache['gdx_prod']
|
||||
|
||||
# # Transfer result back to CPU
|
||||
# results.append(cp.asnumpy(p_gpu))
|
||||
|
||||
# if verbose:
|
||||
# print(f' - Solved {i+1}/{len(f_list)}')
|
||||
|
||||
# if verbose:
|
||||
# print('Poisson solver (GPU batch) finished')
|
||||
# gtimer.end()
|
||||
|
||||
# return results
|
||||
|
||||
# elif self.stype == 'S':
|
||||
# raise SyntaxError('Spatial solver not suggested for structured grids')
|
||||
|
||||
# # ----------------------------------------------------------------------
|
||||
# # unstructured points - not optimized for GPU
|
||||
# elif self.ptype == 'P':
|
||||
# if verbose:
|
||||
# print(' - GPU solver not optimized for unstructured points, using CPU')
|
||||
# return [self.solve(f, verbose) for f in f_list]
|
||||
|
||||
# # If we reach here, it's an unsupported case
|
||||
# raise ValueError(f"Unsupported point type '{self.ptype}' for GPU batch solver")
|
||||
|
||||
def clear_gpu_cache(self):
|
||||
"""Clear GPU cache to free GPU memory"""
|
||||
|
||||
Reference in New Issue
Block a user