From 896c553f0d6a1a5775d3d9536934e7ec73532508 Mon Sep 17 00:00:00 2001
From: Pavel Kirilin <win10@list.ru>
Date: Mon, 6 Jul 2020 04:00:20 +0400
Subject: [PATCH] Fixed formatting.
Signed-off-by: Pavel Kirilin <win10@list.ru>
---
bilateral/hooks/hook-pycuda.py | 3 +-
bilateral/main.py | 56 +++++++++++++++-------------------
bilateral/src/cpu.py | 9 +++---
bilateral/src/gpu.py | 38 +++++++++++------------
4 files changed, 51 insertions(+), 55 deletions(-)
diff --git a/bilateral/hooks/hook-pycuda.py b/bilateral/hooks/hook-pycuda.py
index bf41b49..ca3780f 100644
--- a/bilateral/hooks/hook-pycuda.py
+++ b/bilateral/hooks/hook-pycuda.py
@@ -1,2 +1,3 @@
from PyInstaller.utils.hooks import copy_metadata
-datas = copy_metadata('pycuda')
+
+datas = copy_metadata("pycuda")
diff --git a/bilateral/main.py b/bilateral/main.py
index fb2c562..1ce4739 100644
--- a/bilateral/main.py
+++ b/bilateral/main.py
@@ -1,62 +1,56 @@
-import numpy as np
-from PIL import Image
-from src import gpu, cpu
-import fire
import logging
from pathlib import Path
+
+import fire
+import numpy as np
+from PIL import Image
+
+from src import cpu, gpu
+
logger = logging.getLogger(__name__)
def read_img(file_path: str) -> np.ndarray:
- logger.debug(f'Reading image {file_path}')
- src_img = Image.open(file_path).convert('L')
+ logger.debug(f"Reading image {file_path}")
+ src_img = Image.open(file_path).convert("L")
return np.asarray(src_img)
def write_img(img_arr: np.ndarray, img_name: str):
- Path('out_imgs').mkdir(exist_ok=True)
- res_img = Image.fromarray(img_arr).convert('L')
- filename = f'out_imgs/{img_name}.png'
- logger.debug(f'Writing image to {filename}')
+ Path("out_imgs").mkdir(exist_ok=True)
+ res_img = Image.fromarray(img_arr).convert("L")
+ filename = f"out_imgs/{img_name}.png"
+ logger.debug(f"Writing image to {filename}")
res_img.save(filename)
class CLI(object):
- def run(self,
- img_path: str,
- sigma_r: float = 75,
- sigma_d: float = 75):
+ def run(self, img_path: str, sigma_r: float = 75, sigma_d: float = 75):
gpu_time = self.run_gpu(img_path, sigma_r, sigma_d)
cpu_time = self.run_cpu(img_path, sigma_r, sigma_d)
- logger.info(f'CPU execution time: {cpu_time}')
- logger.info(f'GPU execution time: {gpu_time}')
- logger.info(f'Acceleration: {cpu_time / gpu_time:.5f}')
-
- def run_gpu(self,
- img_path: str,
- sigma_r: float = 75,
- sigma_d: float = 75):
+ logger.info(f"CPU execution time: {cpu_time}")
+ logger.info(f"GPU execution time: {gpu_time}")
+ logger.info(f"Acceleration: {cpu_time / gpu_time:.5f}")
+
+ def run_gpu(self, img_path: str, sigma_r: float = 75, sigma_d: float = 75):
src_img = read_img(img_path)
res_img, execution_time = gpu.filter_image(src_img, sigma_r, sigma_d)
- write_img(res_img, 'gpu')
+ write_img(res_img, "gpu")
return execution_time
- def run_cpu(self,
- img_path: str,
- sigma_r: float = 75,
- sigma_d: float = 75):
+ def run_cpu(self, img_path: str, sigma_r: float = 75, sigma_d: float = 75):
src_img = read_img(img_path)
res_img, execution_time = cpu.filter_image(src_img, sigma_r, sigma_d)
- write_img(res_img, 'cpu')
+ write_img(res_img, "cpu")
return execution_time
def setup_logging():
logging.basicConfig(
- format="[%(levelname)8s] %(msg)s {%(filename)s:%(lineno)s}",
- level=logging.DEBUG)
+ format="[%(levelname)8s] %(msg)s {%(filename)s:%(lineno)s}", level=logging.DEBUG
+ )
-if __name__ == '__main__':
+if __name__ == "__main__":
setup_logging()
fire.Fire(CLI)
diff --git a/bilateral/src/cpu.py b/bilateral/src/cpu.py
index 8b20655..92950e5 100644
--- a/bilateral/src/cpu.py
+++ b/bilateral/src/cpu.py
@@ -1,6 +1,7 @@
-import numpy as np
-import time
import logging
+import time
+
+import numpy as np
logger = logging.getLogger(__name__)
@@ -15,8 +16,8 @@ def filter_image(image: np.ndarray, sigma_r: float, sigma_d: float):
s = 0
for k in range(i - 1, i + 2):
for l in range(j - 1, j + 2):
- g = np.exp(-((k - i)**2 + (l - j)**2) / sigma_d**2)
- r = np.exp(-(image[k, l] - image[i, j])**2 / sigma_r**2)
+ g = np.exp(-((k - i) ** 2 + (l - j) ** 2) / sigma_d ** 2)
+ r = np.exp(-((image[k, l] - image[i, j]) ** 2) / sigma_r ** 2)
c += g * r
s += g * r * image[k, l]
result[i, j] = s / c
diff --git a/bilateral/src/gpu.py b/bilateral/src/gpu.py
index b183a2c..4462637 100644
--- a/bilateral/src/gpu.py
+++ b/bilateral/src/gpu.py
@@ -1,31 +1,29 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
+import logging
+import pkgutil
import time
+
import numpy as np
-import pkgutil
import pycuda.autoinit
-import pycuda.driver as drv
+from pycuda import driver as drv
from pycuda.compiler import SourceModule
-import logging
-
logger = logging.getLogger(__name__)
def filter_image(image: np.ndarray, sigma_r: float, sigma_d: float):
- kernel_code = pkgutil.get_data('src',
- 'kernel.cu').decode('UTF-8', 'ignore')
- logger.debug('Read kernel code.')
+ kernel_code = pkgutil.get_data("src", "kernel.cu").decode("UTF-8", "ignore")
+ logger.debug("Read kernel code.")
logger.debug(kernel_code)
kernel = SourceModule(kernel_code)
N, M = image.shape
logger.debug("Image shape: {N}, {M}")
result = np.zeros((N, M), dtype=np.uint32)
block_size = (8, 8, 1)
- logger.debug(f'Using block size: {block_size}')
- grid_size = (int(np.ceil(N / block_size[0])),
- int(np.ceil(M / block_size[1])))
- logger.debug(f'Using block size: {block_size}')
+ logger.debug(f"Using block size: {block_size}")
+ grid_size = (int(np.ceil(N / block_size[0])), int(np.ceil(M / block_size[1])))
+ logger.debug(f"Using block size: {block_size}")
filt_gpu = kernel.get_function("filter")
tex = kernel.get_texref("tex")
tex.set_filter_mode(drv.filter_mode.LINEAR)
@@ -33,14 +31,16 @@ def filter_image(image: np.ndarray, sigma_r: float, sigma_d: float):
tex.set_address_mode(1, drv.address_mode.MIRROR)
drv.matrix_to_texref(image.astype(np.uint32), tex, order="C")
start_time = time.time()
- filt_gpu(drv.Out(result),
- np.int32(N),
- np.int32(M),
- np.float32(sigma_d),
- np.float32(sigma_r),
- block=block_size,
- grid=grid_size,
- texrefs=[tex])
+ filt_gpu(
+ drv.Out(result),
+ np.int32(N),
+ np.int32(M),
+ np.float32(sigma_d),
+ np.float32(sigma_r),
+ block=block_size,
+ grid=grid_size,
+ texrefs=[tex],
+ )
drv.Context.synchronize()
end_time = time.time()
execution_time = end_time - start_time
--
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