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train_funknn.py
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train_funknn.py
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import numpy as np
import torch
import torch.nn.functional as F
from timeit import default_timer
from torch.optim import Adam
import os
import matplotlib.pyplot as plt
from funknn_model import FunkNN
from utils import *
from datasets import *
from results import evaluator
import config_funknn as config
torch.manual_seed(0)
np.random.seed(0)
epochs_funknn = config.epochs_funknn
batch_size = config.batch_size
dataset = config.dataset
gpu_num = config.gpu_num
exp_desc = config.exp_desc
image_size = config.image_size
c = config.c
train_funknn = config.train_funknn
training_mode = config.training_mode
ood_analysis = config.ood_analysis
enable_cuda = True
device = torch.device('cuda:' + str(gpu_num) if torch.cuda.is_available() and enable_cuda else 'cpu')
all_experiments = 'experiments/'
if os.path.exists(all_experiments) == False:
os.mkdir(all_experiments)
# experiment path
exp_path = all_experiments + 'funknn_' + dataset + '_' \
+ str(image_size) + '_' + training_mode + '_' + exp_desc
if os.path.exists(exp_path) == False:
os.mkdir(exp_path)
learning_rate = 1e-4
step_size = 50
gamma = 0.5
# myloss = F.mse_loss
myloss = F.l1_loss
num_batch_pixels = 3 # The number of iterations over each batch
batch_pixels = 512 # Number of pixels to optimize in each iteration
k = 2 # super resolution factor for training
# Print the experiment setup:
print('Experiment setup:')
print('---> epochs_funknn: {}'.format(epochs_funknn))
print('---> batch_size: {}'.format(batch_size))
print('---> dataset: {}'.format(dataset))
print('---> Learning rate: {}'.format(learning_rate))
print('---> experiment path: {}'.format(exp_path))
print('---> image size: {}'.format(image_size))
# Dataset:
train_dataset = Dataset_loader(dataset = 'train' ,size = (image_size,image_size), c = c)
test_dataset = Dataset_loader(dataset = 'test' ,size = (config.max_scale*image_size,config.max_scale*image_size), c = c)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=32, shuffle = True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, num_workers=32)
ntrain = len(train_loader.dataset)
n_test = len(test_loader.dataset)
n_ood = 0
if ood_analysis:
ood_dataset = Dataset_loader(dataset = 'ood',size = (2*image_size,2*image_size), c = c)
ood_loader = torch.utils.data.DataLoader(ood_dataset, batch_size=300, num_workers=32)
n_ood= len(ood_loader.dataset)
print('---> Number of training, test and ood samples: {}, {}, {}'.format(ntrain,n_test, n_ood))
# Loading model
plot_per_num_epoch = 10 if ntrain > 10000 else 10000//ntrain
model = FunkNN(c=c).to(device)
# model = torch.nn.DataParallel(model) # Using multiple GPUs
num_param_funknn = count_parameters(model)
print('---> Number of trainable parameters of funknn: {}'.format(num_param_funknn))
optimizer_funknn = Adam(model.parameters(), lr=learning_rate)
scheduler_funknn = torch.optim.lr_scheduler.StepLR(optimizer_funknn, step_size=step_size, gamma=gamma)
checkpoint_exp_path = os.path.join(exp_path, 'funknn.pt')
if os.path.exists(checkpoint_exp_path) and config.restore_funknn:
checkpoint_funknn = torch.load(checkpoint_exp_path)
model.load_state_dict(checkpoint_funknn['model_state_dict'])
optimizer_funknn.load_state_dict(checkpoint_funknn['optimizer_state_dict'])
print('funknn is restored...')
if train_funknn:
print('Training...')
if plot_per_num_epoch == -1:
plot_per_num_epoch = epochs_funknn + 1 # only plot in the last epoch
loss_funknn_plot = np.zeros([epochs_funknn])
for ep in range(epochs_funknn):
model.train()
t1 = default_timer()
loss_funknn_epoch = 0
for image in train_loader:
batch_size = image.shape[0]
image = image.to(device)
for i in range(num_batch_pixels):
image_mat = image.reshape(-1, image_size, image_size, c).permute(0,3,1,2)
image_high, image_low, image_size_high = training_strategy(image_mat, image_size, factor = k , mode = training_mode)
coords = get_mgrid(image_size_high).reshape(-1, 2)
coords = torch.unsqueeze(coords, dim = 0)
coords = coords.expand(batch_size , -1, -1).to(device)
image_high = image_high.permute(0,2,3,1).reshape(-1, image_size_high * image_size_high, c)
optimizer_funknn.zero_grad()
pixels = np.random.randint(low = 0, high = image_size_high**2, size = batch_pixels)
batch_coords = coords[:,pixels]
batch_image = image_high[:,pixels]
out = model(batch_coords, image_low)
mse_loss = myloss(out.reshape(batch_size, -1) , batch_image.reshape(batch_size, -1) )
total_loss = mse_loss
total_loss.backward()
optimizer_funknn.step()
loss_funknn_epoch += total_loss.item()
scheduler_funknn.step()
t2 = default_timer()
loss_funknn_epoch/= ntrain
loss_funknn_plot[ep] = loss_funknn_epoch
plt.plot(np.arange(epochs_funknn)[:ep] , loss_funknn_plot[:ep], 'o-', linewidth=2)
plt.title('FunkNN_loss')
plt.xlabel('epoch')
plt.ylabel('MSE loss')
plt.savefig(os.path.join(exp_path, 'funknn_loss.jpg'))
np.save(os.path.join(exp_path, 'funknn_loss.npy'), loss_funknn_plot[:ep])
plt.close()
torch.save({
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer_funknn.state_dict()
}, checkpoint_exp_path)
print('ep: {}/{} | time: {:.0f} | FunkNN_loss {:.6f} '.format(ep, epochs_funknn, t2-t1,loss_funknn_epoch))
with open(os.path.join(exp_path, 'results.txt'), 'a') as file:
file.write('ep: {}/{} | time: {:.0f} | FunkNN_loss {:.6f} '.format(ep, epochs_funknn, t2-t1,loss_funknn_epoch))
file.write('\n')
if ep % plot_per_num_epoch == 0 or (ep + 1) == epochs_funknn:
evaluator(ep = ep, subset = 'test', data_loader = test_loader, model = model, exp_path = exp_path)
if ood_analysis:
evaluator(ep = ep, subset = 'ood', data_loader = ood_loader, model = model, exp_path = exp_path)
print('Evaluating...')
evaluator(ep = -1, subset = 'test', data_loader = test_loader, model = model, exp_path = exp_path)
if ood_analysis:
evaluator(ep = -1, subset = 'ood', data_loader = ood_loader, model = model, exp_path = exp_path)