test_fpred_scan.py

import sys
import lasagne as nn
import numpy as np
import theano
import pathfinder
import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))
givens_valid = {}
givens_valid[model.l_in.input_var] = x_shared

get_predictions_patch = theano.function([],
                                        nn.layers.get_output(model.l_out, deterministic=True),
                                        givens=givens_valid,
                                        on_unused_input='ignore')

data_iterator = config().data_iterator

print
print 'Data'
print 'n samples: %d' % data_iterator.nsamples

nblob2prob, nblob2label = {}, {}
pid2candidates = defaultdict(list)
for n, (x, candidate_zyxd, id) in enumerate(data_iterator.generate()):
    pid = id[0]
    x_shared.set_value(x)
    predictions = get_predictions_patch()
    label = candidate_zyxd[-1]
    p1 = predictions[0][1]
    nblob2prob[n] = p1
    nblob2label[n] = label
    candidate_zyxdp = np.append(candidate_zyxd, [[p1]])
    pid2candidates[pid].append(candidate_zyxdp)


for k in pid2candidates.iterkeys():
    candidates = np.asarray(pid2candidates[k])
    candidates_wo_dupes = utils_lung.filter_close_neighbors(candidates)
    a = np.asarray(sorted(candidates_wo_dupes, key=lambda x: x[-1], reverse=True))
    utils.save_pkl(a, outputs_path + '/%s.pkl' % k)