def predict(model_name, model_class, weight_pth, image_size, normalize):
print(f'[+] predict {model_name}')
model = get_model(model_class)
model.load_state_dict(torch.load(weight_pth))
model.eval()
tta_preprocess = [
preprocess(normalize, image_size),
preprocess_hflip(normalize, image_size)
]
tta_preprocess += make_transforms(
[transforms.Resize((image_size + 20, image_size + 20))],
[transforms.ToTensor(), normalize], five_crops(image_size))
tta_preprocess += make_transforms(
[transforms.Resize((image_size + 20, image_size + 20))],
[HorizontalFlip(), transforms.ToTensor(), normalize],
five_crops(image_size))
print(f'[+] tta size: {len(tta_preprocess)}')
data_loaders = []
for transform in tta_preprocess:
test_dataset = FurnitureDataset('test', transform=transform)
data_loader = DataLoader(dataset=test_dataset,
num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
data_loaders.append(data_loader)
lx, px = utils.predict_tta(model, data_loaders)
data = {
'lx': lx.cpu(),
'px': px.cpu(),
}
torch.save(data, f'{model_name}_test_prediction.pth')
data_loaders = []
for transform in tta_preprocess:
test_dataset = FurnitureDataset('val', transform=transform)
data_loader = DataLoader(dataset=test_dataset,
num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
data_loaders.append(data_loader)
lx, px = utils.predict_tta(model, data_loaders)
data = {
'lx': lx.cpu(),
'px': px.cpu(),
}
torch.save(data, f'{model_name}_val_prediction.pth')
def train():
train_dataset = FurnitureDataset('train', transform=preprocess_with_augmentation(normalize_torch, IMAGE_SIZE))
val_dataset = FurnitureDataset('val', transform=preprocess(normalize_torch, IMAGE_SIZE))
training_data_loader = DataLoader(dataset=train_dataset, num_workers=8,
batch_size=BATCH_SIZE,
shuffle=True)
validation_data_loader = DataLoader(dataset=val_dataset, num_workers=1,
batch_size=BATCH_SIZE,
shuffle=False)
model = get_model()
criterion = nn.CrossEntropyLoss().cuda()
nb_learnable_params = sum(p.numel() for p in model.fresh_params())
print(f'[+] nb learnable params {nb_learnable_params}')
lx, px = utils.predict(model, validation_data_loader)
min_loss = criterion(Variable(px), Variable(lx)).data[0]
lr = 0
patience = 0
for epoch in range(20):
print(f'epoch {epoch}')
if epoch == 1:
lr = 0.00003
print(f'[+] set lr={lr}')
if patience == 2:
patience = 0
model.load_state_dict(torch.load('densenet201_15755.pth'))
lr = lr / 10
print(f'[+] set lr={lr}')
if epoch == 0:
lr = 0.001
print(f'[+] set lr={lr}')
optimizer = torch.optim.Adam(model.fresh_params(), lr=lr)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.0001)
running_loss = RunningMean()
running_score = RunningMean()
model.train()
pbar = tqdm(training_data_loader, total=len(training_data_loader))
for inputs, labels in pbar:
batch_size = inputs.size(0)
inputs = Variable(inputs)
labels = Variable(labels)
if use_gpu:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
_, preds = torch.max(outputs.data, dim=1)
loss = criterion(outputs, labels)
running_loss.update(loss.data[0], 1)
running_score.update(torch.sum(preds != labels.data), batch_size)
loss.backward()
optimizer.step()
pbar.set_description(f'{running_loss.value:.5f} {running_score.value:.3f}')
print(f'[+] epoch {epoch} {running_loss.value:.5f} {running_score.value:.3f}')
lx, px = utils.predict(model, validation_data_loader)
log_loss = criterion(Variable(px), Variable(lx))
log_loss = log_loss.data[0]
_, preds = torch.max(px, dim=1)
accuracy = torch.mean((preds != lx).float())
print(f'[+] val {log_loss:.5f} {accuracy:.3f}')
if log_loss < min_loss:
torch.save(model.state_dict(), 'densenet201_15755.pth')
print(f'[+] val score improved from {min_loss:.5f} to {log_loss:.5f}. Saved!')
min_loss = log_loss
patience = 0
else:
patience += 1
import data, pr, info_fns, G_builder, misc ########## LOCAL PATHS ############ output_path = 'C:/Users/Crbn/Documents/MPRI M2/ReSys/project/output/' hema_file = 'C:/Users/Crbn/Documents/MPRI M2/ReSys/project/data/wholecells_binary.csv' ########## MAIN PARAMETERS ########### dataset='hema' #can also try others in data.py, such as '3NAND_AND_2OR' cutoff=None G=None thresh_mult = 1 ############# MAIN ################ dataa, gene_names = data.gen_data(set=dataset, include_stages=False, cutoff=cutoff, hema_file=hema_file) dataa,gene_names = misc.preprocess(dataa, gene_names) G = G_builder.build(dataa, gene_names, G=G, thresh_mult=thresh_mult) G = misc.postprocess(G) G = misc.assign_stages(G) #print(G.nodes[edge[0]]['gene'], '->', G.nodes[edge[1]]['gene']) misc.drawG(G,output_path)