test_luna_size_scan_dsb.py

import sys
import lasagne as nn
import numpy as np
import theano
import os

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_size_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_size_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

#existing_preds = [f.rsplit('.') for f in os.listdir(outputs_path)]
#print existing_preds

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

prev_pid = None
candidates = []
patients_count = 0
max_malignancy = 0.
for n, (x, candidate_zyxd, id) in enumerate(data_iterator.generate()):
    pid = id[0]

    if pid != prev_pid and prev_pid is not None:
        print patients_count, prev_pid, len(candidates)
        candidates = np.asarray(candidates)
        a = np.asarray(sorted(candidates, key=lambda x: x[-1], reverse=True))
        print 'max malignancies', a[:10,-1]
        utils.save_pkl(a, outputs_path + '/%s.pkl' % prev_pid)
        print 'saved predictions'
        patients_count += 1
        candidates = []

    x_shared.set_value(x)
    predictions = get_predictions_patch()
    #print 'predictions.shape', predictions.shape
    total_malignancy = np.sum(config().malignancy_weights*predictions)

    #print 'total_malignancy', total_malignancy
    #print 'candidate_zyxd', candidate_zyxd
    candidate_zyxd_pred = np.append(candidate_zyxd, [predictions])
    candidate_zyxd_pred_mal = np.append(candidate_zyxd_pred, [[total_malignancy]])
    candidates.append(candidate_zyxd_pred_mal)

    prev_pid = pid

# save the last one
print patients_count, prev_pid, len(candidates)
candidates = np.asarray(candidates)
a = np.asarray(sorted(candidates, key=lambda x: x[-1], reverse=True))
utils.save_pkl(a, outputs_path + '/%s.pkl' % prev_pid)
print 'saved predictions'