update three mode in separate folder

README.md

@@ -0,0 +1,28 @@
+# 4296_gp
+
+### prerequisite
+
+```
+pip install -r requirements.txt
+```
+
+### Usage
+
+Cloud computing
+
+```
+mv src/ cloud_computing/server
+mv image/ cloud_computing/client
+python3 mserver.py -ip xx
+python3 mclient.py -ip xx
+```
+
+Edge computing
+
+```
+mv src/ edge_computing/server
+mv image/ edge_computing/client
+python3 mserver.py -ip 0.0.0.0
+python3 mclient.py -ip xx
+```
+

cloud_computing/client/mclient.py

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+import socket
+import zipfile
+import os
+import argparse
+import time
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address', default="127.0.0.1")
+args = parser.parse_args()
+
+ip_port = (args.ip, 9999)
+s = socket.socket()
+s.connect(ip_port)
+
+
+def zip_and_transfer(path='images', zip_name='tmp.zip'):
+    with zipfile.ZipFile(zip_name, 'w') as file:
+        for fn in os.listdir(path):
+            file.write(os.path.join(path, fn))
+    filesize = str(os.path.getsize('tmp.zip'))
+    f = open('tmp.zip', 'rb')
+    l = f.read()
+    s.sendall(l)
+
+
+def receive_and_unzip():
+    filename = "tmp.zip"
+    f = open(filename, 'wb')
+    client_data = s.recv(1024)
+    total = 0
+    while (client_data):
+        f.write(client_data)
+        total += len(client_data)
+        client_data = s.recv(1024)
+    f.close()
+    with zipfile.ZipFile(filename, 'r') as file:
+        print('[+] Extracting files...')
+        file.extractall()
+        print('[+] Done')
+
+
+start_time = time.time()
+zip_and_transfer()
+s.close()
+
+s = socket.socket()
+s.connect(ip_port)
+server_reply = s.recv(1024).decode('utf-8')
+print(server_reply)
+
+receive_and_unzip()
+print("total time: ", time.time() - start_time)
+os.remove("tmp.zip")
+s.close()

cloud_computing/server/mserver.py

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+import socket
+import zipfile
+import torch
+import os
+import shutil
+import argparse
+import time
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address',default="127.0.0.1")
+args = parser.parse_args()
+
+
+def zip_and_transfer(path = 'images',zip_name = 'tmp.zip'):
+    with zipfile.ZipFile(zip_name, 'w') as file:
+        for fn in os.listdir(path):
+            file.write(os.path.join(path,fn))
+    filesize = str(os.path.getsize('tmp.zip'))
+    print(filesize)
+    f = open('tmp.zip', 'rb')
+    l = f.read()
+    conn.sendall(l)
+
+def receive_and_unzip():
+    print(f"receive message from {str(address)}" )
+    filename = "tmp.zip"
+    f = open(filename, 'wb')
+    client_data = conn.recv(1024)
+    total = 0    
+    while(client_data):
+        f.write(client_data)
+        total += len(client_data)
+        client_data = conn.recv(1024)
+    f.close()
+    with zipfile.ZipFile(filename, 'r') as file:
+        print('[+] Extracting files...')
+        file.extractall()
+        print('[+] Done')
+
+# 1. set up socket
+ip_port = (args.ip, 9999)
+sk = socket.socket()            
+sk.bind(ip_port)                
+sk.listen(5)                    
+print('socket service start，wait for client to connect...')
+conn, address = sk.accept()    
+
+# 2. receive transfer images
+receive_and_unzip()
+conn.close()
+
+conn, address = sk.accept()
+# 3. start inference
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+imgs = os.listdir('images')  # batch of images
+imgs = list(map(lambda img: os.path.join('images/', img), imgs))
+imgs = imgs[1:]  # remove .DS_Store file
+
+
+# Inference
+start_time = time.time()
+results = model(imgs[:40])
+print("total computing time for yolo:", time.time() - start_time, 's')
+
+results.save()
+conn.sendall('inference finished'.encode('utf-8'))
+
+# 4. reply the prediction results
+zip_and_transfer(path="runs/detect/exp")
+
+# 5. do the clear and close the connection
+os.remove("tmp.zip")
+shutil.rmtree("images")
+
+conn.close()
+sk.close()
+

client1/client01.py → distributed_computing/client1/client01.py

@@ -4,9 +4,14 @@
 import shutil
 import torch
 import time
+import argparse
 
-TCP_IP = 'localhost'
-TCP_PORT = 9001
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address', default="127.0.0.1")
+args = parser.parse_args()
+
+TCP_IP = args.ip
+TCP_PORT = 9999
 BUFFER_SIZE = 1024
 
 

client2/client02.py → distributed_computing/client2/client02.py

@@ -4,9 +4,14 @@
 import shutil
 import torch
 import time
+import argparse
 
-TCP_IP = 'localhost'
-TCP_PORT = 9001
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address', default="127.0.0.1")
+args = parser.parse_args()
+
+TCP_IP = args.ip
+TCP_PORT = 9999
 BUFFER_SIZE = 1024
 
 

server/server.py → distributed_computing/server/server.py

@@ -3,9 +3,14 @@
 import zipfile
 import os
 import time
+import argparse
 
-TCP_IP = 'localhost'
-TCP_PORT = 9001
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address', default="127.0.0.1")
+args = parser.parse_args()
+
+TCP_IP = args.ip
+TCP_PORT = 9999
 BUFFER_SIZE = 1024
 
 

edge_computing/client/mclient.py

@@ -0,0 +1,54 @@
+import socket
+import zipfile
+import os
+import argparse
+import time
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address', default="127.0.0.1")
+args = parser.parse_args()
+
+ip_port = (args.ip, 9999)
+s = socket.socket()
+s.connect(ip_port)
+
+
+def zip_and_transfer(path='images', zip_name='tmp.zip'):
+    with zipfile.ZipFile(zip_name, 'w') as file:
+        for fn in os.listdir(path):
+            file.write(os.path.join(path, fn))
+    filesize = str(os.path.getsize('tmp.zip'))
+    f = open('tmp.zip', 'rb')
+    l = f.read()
+    s.sendall(l)
+
+
+def receive_and_unzip():
+    filename = "tmp.zip"
+    f = open(filename, 'wb')
+    client_data = s.recv(1024)
+    total = 0
+    while (client_data):
+        f.write(client_data)
+        total += len(client_data)
+        client_data = s.recv(1024)
+    f.close()
+    with zipfile.ZipFile(filename, 'r') as file:
+        print('[+] Extracting files...')
+        file.extractall()
+        print('[+] Done')
+
+
+start_time = time.time()
+zip_and_transfer()
+s.close()
+
+s = socket.socket()
+s.connect(ip_port)
+server_reply = s.recv(1024).decode('utf-8')
+print(server_reply)
+
+receive_and_unzip()
+print("total time: ", time.time() - start_time)
+os.remove("tmp.zip")
+s.close()

edge_computing/server/mserver.py

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+import socket
+import zipfile
+import os
+import shutil
+import argparse
+import torch
+import time
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-ip', type=str, help='ip address', default="127.0.0.1")
+args = parser.parse_args()
+
+
+def zip_and_transfer(path='image', zip_name='tmp.zip'):
+    with zipfile.ZipFile(zip_name, 'w') as file:
+        for fn in os.listdir(path):
+            file.write(os.path.join(path, fn))
+    filesize = str(os.path.getsize('tmp.zip'))
+    print(filesize)
+    f = open('tmp.zip', 'rb')
+    l = f.read()
+    conn.sendall(l)
+
+
+def receive_and_unzip():
+    print(f"receive message from {str(address)}")
+    filename = "tmp.zip"
+    f = open(filename, 'wb')
+    client_data = conn.recv(1024)
+    total = 0
+    while (client_data):
+        f.write(client_data)
+        total += len(client_data)
+        client_data = conn.recv(1024)
+    f.close()
+    with zipfile.ZipFile(filename, 'r') as file:
+        print('[+] Extracting files...')
+        file.extractall()
+        print('[+] Done')
+
+
+# 1. set up socket
+ip_port = (args.ip, 9999)
+sk = socket.socket()
+sk.bind(ip_port)
+sk.listen(5)
+print('socket service start，wait for client to connect...')
+conn, address = sk.accept()
+
+# 2. receive transfer images
+receive_and_unzip()
+conn.close()
+
+conn, address = sk.accept()
+# 3. start inference
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
+
+imgs = os.listdir('images')  # batch of images
+imgs = list(map(lambda img: os.path.join('images/', img), imgs))
+
+
+# Inference
+start_time = time.time()
+results = model(imgs)
+print("total computing time for yolo:", time.time() - start_time, 's')
+
+results.save()
+conn.sendall('inference finished'.encode('utf-8'))
+
+# 4. reply the prediction results
+zip_and_transfer(path="runs/detect/exp")
+
+# 5. do the clear and close the connection
+os.remove("tmp.zip")
+shutil.rmtree("images")
+
+conn.close()
+sk.close()

requirements.txt

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+# pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.2
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=4.1  # CoreML export
+# onnx>=1.9.0  # ONNX export
+# onnx-simplifier>=0.3.6  # ONNX simplifier
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TFLite export
+# tensorflowjs>=3.9.0  # TF.js export
+
+# Extras --------------------------------------
+# albumentations>=1.0.3
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0  # COCO mAP
+# roboflow
+thop  # FLOPs computation