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validate.py
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validate.py
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import torch as th
import torch.nn as nn
import numpy as np
import model
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from loader import LandslideDataset, DistLandslideDataset
from time import ctime
from sacred import Experiment
from unet import UNet
ex = Experiment('validate_CNNpatch')
def validate(params, data_loader, _log, flag):
with th.no_grad():
sig = nn.Sigmoid()
criterion = nn.BCEWithLogitsLoss()
running_loss = 0
shape = params['shape']
res = th.zeros(shape)
prune = params['prune']
if params['model'] == 'FCNwBottleneck':
trained_model = model.FCNwBottleneck(params['feature_num'], params['pix_res'])
elif params['model'] == 'UNet':
trained_model = UNet(params['feature_num'], 1)
elif params['model'] == 'SimplerFCNwBottleneck':
trained_model = model.SimplerFCNwBottleneck(params['feature_num'])
elif params['model'] == 'Logistic':
trained_model = model.Logistic(params['feature_num'])
elif params['model'] == 'PolyLogistic':
trained_model = model.PolyLogistic(params['feature_num'])
trained_model = trained_model.cuda()
if th.cuda.device_count() > 1:
trained_model = nn.DataParallel(trained_model)
trained_model.load_state_dict(th.load(params['load_model']))
_log.info('[{}] model is successfully loaded.'.format(ctime()))
data_iter = iter(data_loader)
for iter_ in range(len(data_iter)):
sample = data_iter.next()
data, gt = sample['data'].cuda(), sample['gt'].cuda()
ignore = gt < 0
prds = trained_model.forward(data)[:, :, prune:-prune, prune:-prune]
loss = criterion(prds[1-ignore], gt[1-ignore])
running_loss += loss.item()
prds = sig(prds)
prds[ignore] = 0
for idx in range(prds.shape[0]):
row, col = sample['index'][0][idx], sample['index'][1][idx]
res[
row*params['ws']:(row+1)*params['ws'],
col*params['ws']:(col+1)*params['ws']
] = prds[idx, 0, :, :]
_log.info('[{}]: writing [{}/{}]'.format(ctime(), iter_, len(data_iter)))
_log.info('all image patches are written!')
save_image(res, '{}{}_{}_predictions.tif'.format(params['save_to'], params['region'], flag))
np.save('{}{}_{}_predictions.npy'.format(params['save_to'],params['region'], flag), res.data.numpy())
return running_loss/len(data_iter)
@ex.config
def ex_cfg():
params = {
'data_path': '/tmp/Veneto_data.h5',
'index_path': '/home/ainaz/Projects/Landslides/image_data/new_partitioning/',
'load_model': '',
'save_to': '',
'region': 'Veneto',
'ws': 500,
'pad': 64,
'prune': 64,
'shape': (21005, 19500), # final shape of the image
'bs': 4,
'n_workers': 2,
'model': 'FCNwBottleneck',
'feature_num': 94,
'pix_res': 10,
'write_image': True,
'dist_feature': False,
'dist_num': 3,
}
@ex.automain
def main(params, _log):
data = []
for flag in ['test', 'train']:
if params['dist_feature']:
data.append(
DistLandslideDataset(
params['data_path'],
np.load(params['index_path']+'{}_{}_indices.npy'.format(params['region'], flag)),
params['region'],
params['ws'],
params['pad'],
params['prune'],
params['dist_num']
)
)
else:
data.append(
LandslideDataset(
params['data_path'],
np.load(params['index_path']+'{}_{}_indices.npy'.format(params['region'], flag)),
params['region'],
params['ws'],
params['pad'],
params['prune']
)
)
test_loader = DataLoader(data[0], batch_size=params['bs'], shuffle=False, num_workers=params['n_workers'])
_log.info('[{}] prepared the dataset and the data loader for test.'.format(ctime()))
test_loss = validate(params, test_loader, _log, 'test')
_log.info('[{}] average loss on test set is {}'.format(ctime(), test_loss))
train_loader = DataLoader(data[1], batch_size=params['bs'], shuffle=False, num_workers=params['n_workers'])
_log.info('[{}] prepared the dataset and the data loader for train.'.format(ctime()))
train_loss = validate(params, train_loader, _log, 'train')
_log.info('[{}] average loss on train set is {}'.format(ctime(), train_loss))
if params['write_image']:
if params['dist_feature']:
dataset = DistLandslideDataset(
params['data_path'],
np.load(params['index_path']+'{}_data_indices.npy'.format(params['region'])),
params['region'],
params['ws'],
params['pad'],
params['prune'],
params['dist_num']
)
else:
dataset = LandslideDataset(
params['data_path'],
np.load(params['index_path']+'{}_data_indices.npy'.format(params['region'])),
params['region'],
params['ws'],
params['pad'],
params['prune']
)
data_loader = DataLoader(dataset, batch_size=params['bs'], num_workers=params['n_workers'])
validate(params, data_loader, _log, 'data')