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inference_analysis.py
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"""for presentations etc"""
import plotting as plg
import sys
import os
import pickle
import numpy as np
import pandas as pd
import torch
import utils.exp_utils as utils
import utils.model_utils as mutils
from predictor import Predictor
from evaluator import Evaluator
def find_pid_in_splits(pid, exp_dir=None):
if exp_dir is None:
exp_dir = cf.exp_dir
check_file = os.path.join(exp_dir, 'fold_ids.pickle')
with open(check_file, 'rb') as handle:
splits = pickle.load(handle)
finds = []
for i, split in enumerate(splits):
if pid in split:
finds.append(i)
print("Pid {} found in split {}".format(pid, i))
if not len(finds)==1:
raise Exception("pid {} found in more than one split: {}".format(pid, finds))
return finds[0]
def plot_train_forward(slices=None):
with torch.no_grad():
batch = next(val_gen)
results_dict = net.train_forward(batch, is_validation=True) #seg preds are int preds already
out_file = os.path.join(anal_dir, "straight_val_inference_fold_{}".format(str(cf.fold)))
plg.view_batch(cf, batch, res_dict=results_dict, show_info=False, legend=True,
out_file=out_file, slices=slices)
def plot_forward(pid, slices=None):
with torch.no_grad():
batch = batch_gen['test'].generate_train_batch(pid=pid)
results_dict = net.test_forward(batch) #seg preds are only seg_logits! need to take argmax.
if 'seg_preds' in results_dict.keys():
results_dict['seg_preds'] = np.argmax(results_dict['seg_preds'], axis=1)[:,np.newaxis]
out_file = os.path.join(anal_dir, "straight_inference_fold_{}_pid_{}".format(str(cf.fold), pid))
plg.view_batch(cf, batch, res_dict=results_dict, show_info=False, legend=True, show_gt_labels=True,
out_file=out_file, sample_picks=slices)
def plot_merged_boxes(results_list, pid, plot_mods=False, show_seg_ids="all", show_info=True, show_gt_boxes=True,
s_picks=None, vol_slice_picks=None, score_thres=None):
"""
:param results_list: holds (results_dict, pid)
:param pid:
:return:
"""
results_dict = [res_dict for (res_dict, pid_) in results_list if pid_==pid][0]
#seg preds are discarded in predictor pipeline.
#del results_dict['seg_preds']
batch = batch_gen['test'].generate_train_batch(pid=pid)
out_file = os.path.join(anal_dir, "merged_boxes_fold_{}_pid_{}_thres_{}.png".format(str(cf.fold), pid, str(score_thres).replace(".","_")))
utils.save_obj({'res_dict':results_dict, 'batch':batch}, os.path.join(anal_dir, "bytes_merged_boxes_fold_{}_pid_{}".format(str(cf.fold), pid)))
plg.view_batch(cf, batch, res_dict=results_dict, show_info=show_info, legend=False, sample_picks=s_picks,
show_seg_pred=True, show_seg_ids=show_seg_ids, show_gt_boxes=show_gt_boxes,
box_score_thres=score_thres, vol_slice_picks=vol_slice_picks, show_gt_labels=True,
plot_mods=plot_mods, out_file=out_file, has_colorchannels=cf.has_colorchannels, dpi=600)
return
if __name__=="__main__":
class Args():
def __init__(self):
#self.dataset_name = "datasets/prostate"
self.dataset_name = "datasets/lidc"
#self.exp_dir = "datasets/toy/experiments/mrcnnal2d_clkengal" # detunet2d_di_bs16_ps512"
#self.exp_dir = "/home/gregor/networkdrives/E132-Cluster-Projects/prostate/experiments/gs6071_retinau3d_cl_bs6"
#self.exp_dir = "/home/gregor/networkdrives/E132-Cluster-Projects/prostate/experiments/gs6071_frcnn3d_cl_bs6"
#self.exp_dir = "/home/gregor/networkdrives/E132-Cluster-Projects/prostate/experiments_t2/gs6071_mrcnn3d_cl_bs6_lessaug"
#self.exp_dir = "/home/gregor/networkdrives/E132-Cluster-Projects/prostate/experiments/gs6071_detfpn3d_cl_bs6"
#self.exp_dir = "/home/gregor/networkdrives/E132-Cluster-Projects/lidc_sa/experiments/ms12345_mrcnn3d_rgbin_bs8"
self.exp_dir = '/home/gregor/Documents/medicaldetectiontoolkit/datasets/lidc/experiments/ms12345_mrcnn3d_rg_bs8'
#self.exp_dir = '/home/gregor/Documents/medicaldetectiontoolkit/datasets/lidc/experiments/ms12345_mrcnn3d_rgbin_bs8'
self.server_env = False
args = Args()
data_loader = utils.import_module('dl', os.path.join(args.dataset_name, "data_loader.py"))
config_file = utils.import_module('cf', os.path.join(args.exp_dir, "configs.py"))
cf = config_file.Configs()
cf.exp_dir = args.exp_dir
cf.test_dir = cf.exp_dir
pid = '0811a'
cf.fold = find_pid_in_splits(pid)
#cf.fold = 0
cf.merge_2D_to_3D_preds = False
if cf.merge_2D_to_3D_preds:
cf.dim==3
cf.fold_dir = os.path.join(cf.exp_dir, 'fold_{}'.format(cf.fold))
anal_dir = os.path.join(cf.exp_dir, "inference_analysis")
logger = utils.get_logger(cf.exp_dir)
model = utils.import_module('model', os.path.join(cf.exp_dir, "model.py"))
torch.backends.cudnn.benchmark = cf.dim == 3
net = model.net(cf, logger).cuda()
test_predictor = Predictor(cf, None, logger, mode='test')
test_evaluator = Evaluator(cf, logger, mode='test')
#val_gen = data_loader.get_train_generators(cf, logger, data_statistics=False)['val_sampling']
batch_gen = data_loader.get_test_generator(cf, logger)
weight_paths = [os.path.join(cf.fold_dir, '{}_best_params.pth'.format(rank)) for rank in
test_predictor.epoch_ranking]
try:
pids = batch_gen["test"].dataset_pids
except:
pids = batch_gen["test"].generator.dataset_pids
print("pids in test set: ", pids)
#pid = pids[0]
#assert pid in pids
# load already trained model weights
rank = 0
weight_path = weight_paths[rank]
with torch.no_grad():
pass
net.load_state_dict(torch.load(weight_path))
net.eval()
# generate a batch from test set and show results
if not os.path.isdir(anal_dir):
os.mkdir(anal_dir)
#plot_train_forward()
#plot_forward(pids[0])
#net.actual_dims()
#batch_gen = data_loader.get_test_generator(cf, logger)
merged_boxes_file = os.path.join(cf.fold_dir, "merged_box_results")
try:
results_list = utils.load_obj(merged_boxes_file+".pkl")
print("loaded merged boxes from file.")
except FileNotFoundError:
results_list = test_predictor.load_saved_predictions()
utils.save_obj(results_list, merged_boxes_file)
cf.plot_class_ids = False
for pid in [pid,]:#['0317a',]:#pids[2:8]:
assert pid in [res[1] for res in results_list]
plot_merged_boxes(results_list, pid=pid, show_info=True, show_gt_boxes=True, show_seg_ids="all", score_thres=0.13,
s_picks=None, vol_slice_picks=None, plot_mods=False)