mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-09-19 11:35:28 +00:00
107 lines
2.3 KiB
Python
107 lines
2.3 KiB
Python
#models
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from unet_model import UNet
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from myloss import *
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import torch
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from torch.autograd import Variable
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from torch import optim
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#data manipulation
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import numpy as np
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import pandas as pd
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import PIL
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#load files
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import os
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#data visualization
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from data_vis import plot_img_mask
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from utils import *
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import matplotlib.pyplot as plt
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#quit after interrupt
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import sys
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dir = 'data'
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ids = []
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for f in os.listdir(dir + '/train'):
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id = f[:-4]
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ids.append([id, 0])
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ids.append([id, 1])
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np.random.shuffle(ids)
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#%%
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net = UNet(3, 1)
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net.cuda()
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def train(net):
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optimizer = optim.Adam(net.parameters(), lr=1)
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criterion = DiceLoss()
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epochs = 5
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for epoch in range(epochs):
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print('epoch {}/{}...'.format(epoch+1, epochs))
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l = 0
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for i, c in enumerate(ids):
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id = c[0]
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pos = c[1]
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im = PIL.Image.open(dir + '/train/' + id + '.jpg')
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im = resize_and_crop(im)
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ma = PIL.Image.open(dir + '/train_masks/' + id + '_mask.gif')
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ma = resize_and_crop(ma)
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left, right = split_into_squares(np.array(im))
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left_m, right_m = split_into_squares(np.array(ma))
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if pos == 0:
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X = left
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y = left_m
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else:
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X = right
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y = right_m
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X = np.transpose(X, axes=[2, 0, 1])
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X = torch.FloatTensor(X / 255).unsqueeze(0).cuda()
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y = Variable(torch.ByteTensor(y)).cuda()
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X = Variable(X).cuda()
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optimizer.zero_grad()
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y_pred = net(X).squeeze(1)
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loss = criterion(y_pred, y.unsqueeze(0).float())
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l += loss.data[0]
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loss.backward()
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if i%10 == 0:
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optimizer.step()
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print('Stepped')
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print('{0:.4f}%\t\t{1:.6f}'.format(i/len(ids)*100, loss.data[0]))
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l = l / len(ids)
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print('Loss : {}'.format(l))
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torch.save(net.state_dict(), 'MODEL_EPOCH{}_LOSS{}.pth'.format(epoch+1, l))
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print('Saved')
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try:
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net.load_state_dict(torch.load('MODEL_INTERRUPTED.pth'))
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train(net)
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except KeyboardInterrupt:
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print('Interrupted')
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torch.save(net.state_dict(), 'MODEL_INTERRUPTED.pth')
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try:
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sys.exit(0)
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except SystemExit:
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os._exit(0)
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