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Fused model in deployment #51

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Fused model in deployment #51

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dd03656
fused
Linyou May 14, 2023
4a154d9
add deployment fused model
Linyou May 14, 2023
3c99c64
upd cpp
Linyou May 14, 2023
eb3effa
rename kernel to utils
Linyou May 15, 2023
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18 changes: 0 additions & 18 deletions deployment/InstantNGP/taichi_ngp/new_kernels.py
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139 changes: 96 additions & 43 deletions deployment/InstantNGP/taichi_ngp/taichi_ngp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,28 +5,28 @@

import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../"))
from modules.intersection import ray_aabb_intersect
from modules.intersection import ray_aabb_intersect, get_rays_test_kernel

import numpy as np
import taichi as ti
from matplotlib import pyplot as plt
from kernels import args, np_type, data_type,\
from .utils import args, np_type, data_type,\
rotate_scale, reset,\
ray_intersect, raymarching_test_kernel,\
rearange_index, hash_encode,\
sigma_rgb_layer, composite_test,\
raymarching_test_kernel,\
re_arrange_index, radiance_field, composite_test,\
re_order, fill_ndarray,\
init_current_index, rotate_scale,\
initialize, load_deployment_model,\
cascades, grid_size, scale, \
NGP_res, NGP_N_rays, NGP_min_samples

from new_kernels import get_rays

ti.init(arch=ti.vulkan,
enable_fallback=False,
debug=False,
kernel_profiler=False)
ti.init(
arch=ti.vulkan,
enable_fallback=False,
debug=False,
kernel_profiler=False
)

#########################
# Compile for AOT files #
Expand Down Expand Up @@ -89,11 +89,11 @@ def prepare_aot_files(model):
m = ti.aot.Module(
caps=['spirv_has_int8', 'spirv_has_int16', 'spirv_has_float16'])
m.add_kernel(reset)
m.add_kernel(ray_intersect)
m.add_field(get_rays_test_kernel)
m.add_kernel(ray_aabb_intersect)
m.add_kernel(raymarching_test_kernel)
m.add_kernel(rearange_index)
m.add_kernel(hash_encode)
m.add_kernel(sigma_rgb_layer)
m.add_kernel(re_arrange_index)
m.add_kernel(radiance_field)
m.add_kernel(composite_test)
m.add_kernel(re_order)
m.add_kernel(fill_ndarray)
Expand Down Expand Up @@ -123,16 +123,30 @@ def update_model_weights(model):



def run_inference(max_samples,
T_threshold,
dist_to_focus=0.8,
len_dis=0.0) -> Tuple[float, int, int]:
def run_inference(
max_samples,
T_threshold,
) -> Tuple[float, int, int]:
samples = 0
#rotate_scale(NGP_pose, 0.5, 0.5, 0.0, 2.5)
reset(NGP_counter, NGP_alive_indices, NGP_opacity, NGP_rgb)

get_rays(NGP_pose, NGP_directions, NGP_rays_o, NGP_rays_d)
ray_aabb_intersect(NGP_hits_t, NGP_rays_o, NGP_rays_d, scale)
reset(
NGP_counter,
NGP_alive_indices,
NGP_opacity,
NGP_rgb
)
get_rays_test_kernel(
NGP_pose,
NGP_directions,
NGP_rays_o,
NGP_rays_d
)
ray_aabb_intersect(
NGP_hits_t,
NGP_rays_o,
NGP_rays_d,
scale
)

while samples < max_samples:
N_alive = NGP_counter[0]
Expand All @@ -144,40 +158,79 @@ def run_inference(max_samples,
samples += N_samples
launch_model_total = N_alive * N_samples

raymarching_test_kernel(NGP_counter, NGP_density_bitfield, NGP_hits_t,
NGP_alive_indices, NGP_rays_o, NGP_rays_d,
NGP_current_index, NGP_xyzs, NGP_dirs,
NGP_deltas, NGP_ts, NGP_run_model_ind,
NGP_N_eff_samples, N_samples)
rearange_index(NGP_model_launch, NGP_padd_block_network, NGP_temp_hit,
NGP_run_model_ind, launch_model_total)
hash_encode(NGP_hash_embedding, NGP_model_launch, NGP_xyzs, NGP_dirs,
NGP_deltas, NGP_xyzs_embedding, NGP_temp_hit)
sigma_rgb_layer(NGP_sigma_weights, NGP_rgb_weights, NGP_model_launch,
NGP_padd_block_network, NGP_xyzs_embedding, NGP_dirs,
NGP_out_1, NGP_out_3, NGP_temp_hit)

composite_test(NGP_counter, NGP_alive_indices, NGP_rgb, NGP_opacity,
NGP_current_index, NGP_deltas, NGP_ts, NGP_out_3,
NGP_out_1, NGP_N_eff_samples, N_samples, T_threshold)
re_order(NGP_counter, NGP_alive_indices, NGP_current_index, N_alive)
raymarching_test_kernel(
NGP_counter,
NGP_density_bitfield,
NGP_hits_t,
NGP_alive_indices,
NGP_rays_o,
NGP_rays_d,
NGP_current_index,
NGP_xyzs,
NGP_dirs,
NGP_deltas,
NGP_ts,
NGP_run_model_ind,
NGP_N_eff_samples,
N_samples,
)
re_arrange_index(
NGP_model_launch,
NGP_padd_block_network,
NGP_temp_hit,
NGP_run_model_ind,
launch_model_total,
)
radiance_field(
NGP_hash_embedding,
NGP_model_launch,
NGP_xyzs,
NGP_sigma_weights,
NGP_rgb_weights,
NGP_padd_block_network,
NGP_dirs,
NGP_out_1,
NGP_out_3,
NGP_temp_hit,
)
composite_test(
NGP_counter,
NGP_alive_indices,
NGP_rgb,
NGP_opacity,
NGP_current_index,
NGP_deltas,
NGP_ts,
NGP_out_3,
NGP_out_1,
NGP_N_eff_samples,
N_samples,
T_threshold,
)
re_order(
NGP_counter,
NGP_alive_indices,
NGP_current_index,
N_alive
)

return samples, N_alive, N_samples


def inference_local(n=1):

for _ in range(n):
samples, N_alive, N_samples = run_inference(max_samples=100, T_threshold=1e-2)

_, _, _ = run_inference(
max_samples=100,
T_threshold=1e-2
)
ti.sync()

# Show inferenced image
rgb_np = NGP_rgb.to_numpy().reshape(NGP_res[1], NGP_res[0], 3)
plt.imshow((rgb_np * 255).astype(np.uint8))
plt.show()


if __name__ == '__main__':
model = load_deployment_model(args.model_path)
initialize()
Expand Down Expand Up @@ -230,7 +283,7 @@ def inference_local(n=1):
# model parameters
sigma_layer1_base = 16 * 16
layer1_base = 32 * 16
NGP_hash_embedding = ti.ndarray(dtype=data_type, shape=(17956864, ))
NGP_hash_embedding = ti.ndarray(dtype=data_type, shape=(11176096, ))
NGP_sigma_weights = ti.ndarray(dtype=data_type,
shape=(sigma_layer1_base + 16 * 16, ))
NGP_rgb_weights = ti.ndarray(dtype=data_type,
Expand Down
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