ashhadulislam

PATH = './alex_net.pth'

# setup
model_ft = models.alexnet(pretrained=True,)
model_ft.classifier[6] = nn.Linear(4096,len(classes))
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

ashhadulislam

PATH = './densenet169.pth'

# setup
model_ft = models.densenet169(pretrained=True,)
print(model_ft.classifier)
model_ft.classifier=nn.Linear(1664,len(classes))
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

ashhadulislam

PATH = './densenet161.pth'

# setup model
model_ft = models.densenet161(pretrained=True,)
model_ft.classifier=nn.Linear(2208,len(classes))
model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

ashhadulislam

PATH = './resnet18_net.pth'

#setup model
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, len(classes))
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

ashhadulislam

from collections import Counter

train_classes = [dataset.targets[i] for i in train_data.indices]
print("train:",Counter(train_classes)) # if doesn' work: Counter(i.item() for i in train_classes)
test_classes = [dataset.targets[i] for i in test_data.indices]
print("Test:",Counter(test_classes)) # if doesn' work: Counter(i.item() for i in train_classes)
print("Total:",dict(Counter(test_data.dataset.targets)))

ashhadulislam

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
num_epochs=30

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):

ashhadulislam

# Looping through it, get a batch on each loop 
for images, labels in train_loader:
    pass

# Get one batch
images, labels = next(iter(train_loader))

indx=10
plt.imshow(images[indx].reshape(64,64))
plt.title(label_map[int(labels[indx].numpy())])

ashhadulislam

root = 'data/'

data_transform = transforms.Compose([transforms.Grayscale(num_output_channels=1),
                                     transforms.Resize((64,64)),
                                     transforms.ToTensor()])
dataset = ImageFolder(root, transform=data_transform)

print(dataset.classes)
print(dataset.class_to_idx)

ashhadulislam

import torch
import torchvision
from torchvision import datasets, models, transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, random_split

import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler

ashhadulislam

import os

! python yolov5/detect.py --source data/test/ --weights yolov5/runs/train/RoadTrainModel4/weights/best.pt --conf 0.25 --name RoadTestModel