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17/01/2023

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Jan-Niclas Loosen 8 months ago
parent 3a520b8f97
commit 16177ffa79
3 changed files with 84 additions and 54 deletions
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6
.idea/workspace.xml
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@ -5,8 +5,7 @@
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<change beforePath="$PROJECT_DIR$/.idea/UNI_Python.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/UNI_Python.iml" afterDir="false" /> <change afterPath="$PROJECT_DIR$/hosts.txt" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/ha_09/loosen_janniclas_1540907_10.py" beforeDir="false" afterPath="$PROJECT_DIR$/ha_09/loosen_janniclas_1540907_10.py" afterDir="false" /> <change beforePath="$PROJECT_DIR$/ha_09/loosen_janniclas_1540907_10.py" beforeDir="false" afterPath="$PROJECT_DIR$/ha_09/loosen_janniclas_1540907_10.py" afterDir="false" />
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@ -160,6 +159,7 @@
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@ -179,7 +179,7 @@
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130
ha_09/loosen_janniclas_1540907_10.py
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@ -1,4 +1,5 @@
import random import random
from concurrent.futures import ThreadPoolExecutor
from io import BytesIO from io import BytesIO
import numpy as np import numpy as np
@ -19,33 +20,40 @@ def count_randomized_hits(iterations):
return counter return counter
FLAG_default = 0
FLAG_threaded = 1
FLAG_network = 2
def monte_carlo_methode(n, mode=0): def monte_carlo_methode(n, mode=0):
func_comm = MPI.COMM_WORLD
func_rank = func_comm.Get_rank()
func_size = func_comm.Get_size()
if mode == 1: # Multithreading mode if mode == 1: # Multithreading mode
num_threads = 16 if func_rank == 0:
iterations_per_thread = n // num_threads num_threads = 16
with multiprocessing.Pool(num_threads) as pool: iterations_per_thread = n // num_threads
hits = pool.map(count_randomized_hits, [iterations_per_thread] * num_threads)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
hits = list(executor.map(count_randomized_hits, [iterations_per_thread] * num_threads))
hit = sum(hits) hit = sum(hits)
elif mode == 2: # MPI parallel mode elif mode == 2: # MPI parallel mode
comm = MPI.COMM_WORLD func_comm = MPI.COMM_WORLD
rank = comm.Get_rank() func_rank = func_comm.Get_rank()
size = comm.Get_size() func_size = func_comm.Get_size()
local_hit = count_randomized_hits(n // size)
hit = comm.reduce(local_hit, op=MPI.SUM, root=0) n = func_comm.bcast(n, root=0)
local_hit = count_randomized_hits(n // func_size)
hit = func_comm.reduce(local_hit, op=MPI.SUM, root=0)
hit = int(func_comm.bcast(hit, root=0))
else: # Default mode else: # Default mode
hit = count_randomized_hits(n) if func_rank == 0:
hit = count_randomized_hits(n)
pi_approx = (hit / n) * 4 if func_rank == 0:
pi_diff = abs(np.pi - pi_approx) pi_approx = (hit / n) * 4
pi_diff = abs(np.pi - pi_approx)
return pi_approx, pi_diff
return pi_approx, pi_diff
def uniform_kernel(n): def uniform_kernel(n):
@ -65,33 +73,33 @@ def gauss_kernel(s):
return K return K
FLAG_gauss = 0
FLAG_uniform = 1
def process_image_part(data_part, kernel, padding): def process_image_part(data_part, kernel, padding):
y_part_size, x_part_size, _ = data_part.shape y_part_size, x_part_size, _ = data_part.shape
data_part_new = np.zeros((data_part.shape[0] - padding[0], data_part.shape[1] - padding[1], 3))
pad_y, pad_x = padding pad_y, pad_x = padding
data_part_new = np.zeros((y_part_size - 2 * pad_y, x_part_size - 2 * pad_x, 3))
# DO NOT CHANGE THIS LOOP
for i in range(pad_y, y_part_size - pad_y): for i in range(pad_y, y_part_size - pad_y):
for j in range(pad_x, x_part_size - pad_x): for j in range(pad_x, x_part_size - pad_x):
for k in range(3): for k in range(3):
new_value = 0.0 new_value = 0.0
for ii in range(kernel.shape[0]): for ii in range(kernel.shape[1]):
for jj in range(kernel.shape[1]): for jj in range(kernel.shape[0]):
iii = ii - pad_y iii = ii - (kernel.shape[1] - 1) // 2
jjj = jj - pad_x jjj = jj - (kernel.shape[0] - 1) // 2
new_value += kernel[ii, jj] * data_part[i + iii, j + jjj, k] new_value += kernel[ii, jj] * data_part[i + iii, j + jjj, k]
data_part_new[i - pad_y, j - pad_x, k] = new_value data_part_new[i - pad_y, j - pad_x, k] = new_value
return data_part_new return data_part_new
def split_array(arr, n, overlap): def overlapping_submatrices(arr, n, overlap):
sub_array_length = (len(arr) + (n - 1) * overlap) // n sub_array_length = len(arr) // n
sub_arrays = [arr[i * (sub_array_length - overlap): i * (sub_array_length - overlap) + sub_array_length] for i in range(n)] # Zugegeben, den Loop hab ich auch nur durch ChatGPT hinbekommen :O
return np.array(sub_arrays) sub_arrays = [
arr[i * sub_array_length - min(i * overlap, overlap): (i + 1) * sub_array_length + min((n - i - 1) * overlap,
overlap)]
for i in range(n)
]
return np.array(sub_arrays, dtype=object)
def process_image(img, func=0, mode=0): def process_image(img, func=0, mode=0):
@ -102,34 +110,44 @@ def process_image(img, func=0, mode=0):
img = img.convert(mode="RGB") img = img.convert(mode="RGB")
data = np.asarray(img, dtype=np.float64) / 255.0 data = np.asarray(img, dtype=np.float64) / 255.0
padding = 9
print(f"Before: {data.shape}")
if func == 1: if func == 1:
kernel = uniform_kernel(7) kernel = uniform_kernel(padding)
else: else:
kernel = gauss_kernel(3) kernel = gauss_kernel(padding)
padding = [(kernel.shape[0] // 2), kernel.shape[1] // 2] padding = (padding // 2, padding // 2)
if mode == 1: # Multithreading mode if mode == 1: # Multithreading mode
num_threads = 16 num_threads = 5
data_parts = overlapping_submatrices(data, num_threads, padding[0])
data_parts = split_array(data, num_threads, padding[0]) with ThreadPoolExecutor(max_workers=num_threads) as executor:
data_new_parts = list(
with multiprocessing.Pool(num_threads) as pool: executor.map(process_image_part, data_parts, [kernel] * num_threads, [padding] * num_threads))
data_new_parts = pool.starmap(process_image_part, zip(data_parts, [kernel]*num_threads, [padding]*num_threads))
data_new = np.concatenate(data_new_parts, axis=0) data_new = np.concatenate(data_new_parts, axis=0)
elif mode == 2: # MPI parallel mode elif mode == 2: # MPI parallel mode
comm = MPI.COMM_WORLD comm = MPI.COMM_WORLD
rank = comm.Get_rank() rank = comm.Get_rank()
size = comm.Get_size() size = comm.Get_size()
data_part = np.array_split(data, size, axis=0)[rank]
data_new_part = process_image_part(data_part, kernel, padding)
data_new_parts = comm.gather(data_new_part, root=0)
if rank == 0: if rank == 0:
data_parts = overlapping_submatrices(data, size, padding[0])
else:
data_parts = None
data_part = comm.scatter(data_parts, root=0)
data_part_new = process_image_part(data_part, kernel, padding)
if rank == 0:
data_new_parts = comm.gather(data_part_new, root=0)
data_new = np.concatenate(data_new_parts, axis=0) data_new = np.concatenate(data_new_parts, axis=0)
else: else:
data_new = None data_new = None
data_new = comm.bcast(data_new, root=0) data_new = comm.bcast(data_new, root=0)
else: # Default mode else: # Default mode
@ -137,20 +155,30 @@ def process_image(img, func=0, mode=0):
data_new = data_new * 255.0 data_new = data_new * 255.0
data_new = np.uint8(data_new) data_new = np.uint8(data_new)
print(f"After: {data_new.shape}")
return Image.fromarray(data_new, mode="RGB") return Image.fromarray(data_new, mode="RGB")
if __name__ == '__main__': if __name__ == '__main__':
print(monte_carlo_methode(1000, FLAG_default)) comm = MPI.COMM_WORLD
print(monte_carlo_methode(1000, FLAG_threaded)) rank = comm.Get_rank()
print(monte_carlo_methode(1000, FLAG_network)) size = comm.Get_size()
FLAG_gauss = 0
FLAG_uniform = 1
FLAG_default = 0
FLAG_threaded = 1
FLAG_network = 2
if rank == 0:
print(monte_carlo_methode(1000, mode=FLAG_default))
print(monte_carlo_methode(1000, mode=FLAG_threaded))
print(monte_carlo_methode(1000, mode=FLAG_network))
url = "https://i.wfcdn.de/teaser/660/27020.jpg" url = "https://i.wfcdn.de/teaser/660/27020.jpg"
response = requests.get(url) response = requests.get(url)
if response.status_code == 200: if response.status_code == 200:
image = Image.open(BytesIO(response.content)) image = Image.open(BytesIO(response.content))
image = process_image(image, FLAG_uniform, FLAG_threaded) image = process_image(image, FLAG_uniform, FLAG_network)
image.show() image.show()

2
hosts.txt
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@ -0,0 +1,2 @@
192.168.178.77:2
192.168.178.73:2
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