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fit.py
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import torch
import torch.nn as nn
import numpy as np
import time
import os, glob, sys
from torch.utils.data import Dataset
import models
import utils
def fit(input_args, device):
epochs = input_args.epochs
lr = input_args.lr
batch_size = input_args.batch_size
print_freq = input_args.print_freq
model = models.UNet().to(device)
loss_fn = nn.MSELoss()
opt_func=torch.optim.Adam
train_loader, val_loader = utils.load_data(input_args)
if not input_args.restart:
print('\nfrom scratch')
train_epoch_loss = []
val_epoch_loss = []
running_train_loss = []
running_val_loss = []
epochs_till_now = 0
else:
ckpt_path = os.path.join(input_args.log_dir, input_args.log_file)
ckpt = torch.load(ckpt_path)
print(f'\nckpt loaded: {ckpt_path}')
model_state_dict = ckpt['model_state_dict']
model.load_state_dict(model_state_dict)
model.to(device)
losses = ckpt['losses']
running_train_loss = losses['running_train_loss']
running_val_loss = losses['running_val_loss']
train_epoch_loss = losses['train_epoch_loss']
val_epoch_loss = losses['val_epoch_loss']
epochs_till_now = ckpt['epochs_till_now']
print('\nmodel has {} M parameters'.format(count_parameters(model)))
print(f'\nloss_fn : {loss_fn}')
print(f'lr : {lr}')
print(f'epochs_till_now: {epochs_till_now}')
print(f'epochs from now: {epochs}')
optimizer = opt_func(filter(lambda p: p.requires_grad, model.parameters()), lr = lr)
for epoch in range(epochs_till_now, epochs_till_now + epochs):
print('\nTRAINING...')
epoch_train_start_time = time.time()
# Training Phase
model.train()
epoch_loss_train = []
for idx, (density, density_target) in enumerate(train_loader):
batch_start_time = time.time()
density_target = density_target.to(device)
density = density.to(device)
density_predict = model(density)
loss = loss_fn(density_predict, density_target)
running_train_loss.append(loss.item())
epoch_loss_train.append(loss.item())
loss.backward()
optimizer.step()
optimizer.zero_grad()
batch_time = time.time() - batch_start_time
print('train loss for epoch: {} batch id: {}, loss: {}, time: {}'.format(epoch, idx, loss.item(), batch_time), flush=True)
print('\nAverage TRAIN LOSS for epoch: {}, loss: {}'.format(epoch, np.array(epoch_loss_train).mean()), flush=True)
train_epoch_loss.append(np.array(running_train_loss).mean())
epoch_train_time = time.time() - epoch_train_start_time
m,s = divmod(epoch_train_time, 60)
h,m = divmod(m, 60)
print('\nepoch train time: {} hrs {} mins {} secs'.format(int(h), int(m), int(s)))
print('\nVALIDATION...')
epoch_val_start_time = time.time()
model.eval()
epoch_loss_val = []
with torch.no_grad():
for idx, (density, density_target) in enumerate(val_loader):
density_target = density_target.to(device)
density = density.to(device)
density_predict = model(density)
loss = loss_fn(density_predict, density_target)
running_val_loss.append(loss.item())
epoch_loss_val.append(loss.item())
print('val loss for epoch: {} batch id: {}, loss: {}'.format(epoch, idx, loss.item()), flush=True)
print('\nAverage VAL LOSS for epoch: {}, loss: {}'.format(epoch, np.array(epoch_loss_val).mean()), flush=True)
val_epoch_loss.append(np.array(running_val_loss).mean())
epoch_val_time = time.time() - epoch_val_start_time
m,s = divmod(epoch_val_time, 60)
h,m = divmod(m, 60)
print('\nepoch val time: {} hrs {} mins {} secs'.format(int(h), int(m), int(s)))
if (epoch % print_freq ) == 0:
torch.save({'model_state_dict': model.state_dict(),
'losses': {'running_train_loss': running_train_loss,
'running_val_loss': running_val_loss,
'train_epoch_loss': train_epoch_loss,
'val_epoch_loss': val_epoch_loss},
'epochs_till_now': epoch+1},
os.path.join(input_args.log_dir, 'model{}.pth'.format(str(epoch + 1).zfill(2))))
return
def predict(input_args, device):
model = models.UNet().to(device)
loss_fn = nn.MSELoss()
test_loader = utils.load_test_data(input_args)
predict_dir = input_args.pred_dir
ckpt_path = os.path.join(input_args.log_dir, input_args.log_file)
ckpt = torch.load(ckpt_path)
print(f'\nckpt loaded: {ckpt_path}')
model_state_dict = ckpt['model_state_dict']
model.load_state_dict(model_state_dict)
model.to(device)
print('\nTESTING...')
model.eval()
with torch.no_grad():
for idx, (density, density_target) in enumerate(test_loader):
density_target = density_target.to(device)
density = density.to(device)
density_predict = model(density)
loss = loss_fn(density_predict, density_target)
if input_args.print_density:
#save density_predict to file with index number in filename
name = 'density-target-{}'.format(str(idx).zfill(2))
path = os.path.join(predict_dir, name)
#print(path)
density_target = density_target.cpu().detach().numpy()
np.savetxt(path, np.reshape(density_target, (-1,1)))
name = 'density-predict-{}'.format(str(idx).zfill(2))
path = os.path.join(predict_dir, name)
#print(path)
density_predict = density_predict.cpu().detach().numpy()
np.savetxt(path, np.reshape(density_predict, (-1,1)))
name = 'density-actual-{}'.format(str(idx).zfill(2))
path = os.path.join(predict_dir, name)
#print(path)
density = density.cpu().detach().numpy()
np.savetxt(path, np.reshape(density, (-1,1)))
print('test loss for batch id: {}, loss: {}'.format(idx, loss.item()), flush=True)
return
def count_parameters(model):
num_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
return num_parameters/1e6 # in terms of millions