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train_script.py
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train_script.py
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import time
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils import data
from models import tiramisu
from datasets import shirts
from datasets import joint_transforms
import utils.imgs
import utils.training as train_utils
import os
CAMVID_PATH = os.path.join("gdrive","My Drive","image_extraction","data","tiramisu")
RESULTS_PATH = Path('gdrive/My Drive/tiramisu/results/')
WEIGHTS_PATH = Path('gdrive/My Drive/tiramisu/weights/')
RESULTS_PATH.mkdir(exist_ok=True)
WEIGHTS_PATH.mkdir(exist_ok=True)
batch_size = 3
mean = [0.41189489566336, 0.4251328133025, 0.4326707089857]
std = [0.27413549931506, 0.28506257482912, 0.28284674400252]
normalize = transforms.Normalize(mean=mean, std=std)
train_joint_transformer = transforms.Compose([
joint_transforms.JointCenterCrop((512,224)),
joint_transforms.JointRandomHorizontalFlip()
])
test_joint_transformer = transforms.Compose([
joint_transforms.JointCenterCrop((512,224))
])
train_dset = shirts.Shirts(CAMVID_PATH, 'train',
joint_transform=train_joint_transformer,
transform=transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dset, batch_size=batch_size, shuffle=True)
val_dset = shirts.Shirts(
CAMVID_PATH, 'val', joint_transform=test_joint_transformer,
transform=transforms.Compose([
transforms.ToTensor(),
normalize
]))
val_loader = torch.utils.data.DataLoader(
val_dset, batch_size=batch_size, shuffle=False)
test_dset = shirts.Shirts(
CAMVID_PATH, 'test', joint_transform=test_joint_transformer,
transform=transforms.Compose([
transforms.ToTensor(),
normalize
]))
test_loader = torch.utils.data.DataLoader(
test_dset, batch_size=1, shuffle=False)
print("Train: %d" %len(train_loader.dataset.imgs))
print("Val: %d" %len(val_loader.dataset.imgs))
print("Test: %d" %len(test_loader.dataset.imgs))
print("Classes: %d" % len(train_loader.dataset.classes))
inputs, targets = next(iter(train_loader))
print("Inputs: ", inputs.size())
print("Targets: ", targets.size())
utils.imgs.view_image(inputs[0])
utils.imgs.view_annotated(targets[0])
LR = 1e-4
LR_DECAY = 0.005*LR
DECAY_EVERY_N_EPOCHS = 1
N_EPOCHS = 20
torch.cuda.manual_seed(0)
#model = tiramisu.FCDenseNet67(n_classes=12).cuda()
model = tiramisu.FCDenseNet00(n_classes=2).cuda()
model.apply(train_utils.weights_init)
optimizer = torch.optim.RMSprop(model.parameters(), lr=LR, weight_decay=1e-4)
#was criterion = nn.NLLLoss2d(weight=shirts.class_weight.cuda()).cuda()
criterion = nn.NLLLoss2d().cuda()
for epoch in range(1, N_EPOCHS+1):
since = time.time()
### Train ###
trn_loss, trn_err = train_utils.train(
model, train_loader, optimizer, criterion, epoch)
print('Epoch {:d}\nTrain - Loss: {:.4f}, Acc: {:.4f}'.format(
epoch, trn_loss, 1-trn_err))
time_elapsed = time.time() - since
print('Train Time {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
### Checkpoint ###
train_utils.save_weights(model, epoch, trn_loss, trn_err)
### Test ###
val_loss, val_err = train_utils.test(model, val_loader, criterion, epoch)
print('Val - Loss: {:.4f} | Acc: {:.4f}'.format(val_loss, 1-val_err))
time_elapsed = time.time() - since
print('Total Time {:.0f}m {:.0f}s\n'.format(
time_elapsed // 60, time_elapsed % 60))
### Adjust Lr ###
train_utils.adjust_learning_rate(LR, LR_DECAY, optimizer,
epoch, DECAY_EVERY_N_EPOCHS)
train_utils.test(model, test_loader, criterion, epoch=1)
train_utils.view_sample_predictions(model, test_loader, n=1)