from models.GoogLeNet import *
model = GoogLeNet()
elif modelName == "ResNet18":
from models.ResNet18 import *
model = ResNet18()
elif modelName == "DenseNet121":
from models.DenseNet import *
model = DenseNet121()
elif modelName == "MobileNet":
from models.MobileNet import *
model = MobileNet()
else:
model = None
if model is not None:
model = model.to(Device)
else:
raise Exception("model not found")
# optimizer and loss function
optimizer = optim.SGD(model.parameters(), lr=learning_rate,
momentum=momentum, weight_decay=weightDecay)
loss_func = torch.nn.CrossEntropyLoss()
# use testset to evaluate
def evaluateModel():
total_acc = 0.0
total_loss = 0.0
step = 0
cat_samples = train_dataset.images[:
10] # The first images on the list are cats
dog_samples = train_dataset.images[
-10:] # The last images on the list are dogs
for i in range(cat_samples.shape[0]):
tensorboard.add_images("Cat/{}".format(i), None, cat_samples[i:i + 1])
tensorboard.add_images("Dog/{}".format(i), None, dog_samples[i:i + 1])
model = AlexNet(input_size, output_size)
cur_epoch = 0
if RESUME_TRAINING:
cur_epoch, optimizer_states_dict, loss = load_checkpoint(model)
cur_epoch += 1
model = model.to(device)
print(model)
loss_func = torch.nn.BCELoss(reduction="mean")
optimizer = opt(model.parameters(), lr=lr, momentum=momentum)
if RESUME_TRAINING:
optimizer.load_state_dict(optimizer_states_dict)
# Train
for epoch in range(cur_epoch, epochs):
accumulated_train_loss = []
# Set model in trainng mode
model.train()
iteration = 0
for batch_x, batch_y in tqdm(train_generator):
