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05_02-eval_multi.py
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05_02-eval_multi.py
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from ops import *
import time
import tensorflow as tf
import os
import json
import importlib
from multiprocessing import Process
import matplotlib.pyplot as plt
import sys
import logging
import gc
def eval_model(exp, img_type, train_cond, test_cond, method):
img_path = 'imgs'
path_exp = os.path.join(img_path, 'experiments', f'exp_{exp}')
path_models = os.path.join(path_exp, 'models')
path_maps = os.path.join(path_exp, 'pred_maps')
if not os.path.exists(path_exp):
os.makedirs(path_exp)
if not os.path.exists(path_models):
os.makedirs(path_models)
if not os.path.exists(path_maps):
os.makedirs(path_maps)
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s:%(levelname)s:%(name)s:%(message)s',
filename=os.path.join(path_exp, 'eval.log'),
filemode='a'
)
log = logging.getLogger('eval')
sys.stdout = StreamToLogger(log,logging.INFO)
sys.stderr = StreamToLogger(log,logging.ERROR)
with open(f'experiments_multi.json') as param_file:
params = json.load(param_file)
n_opt_layer = 26 #number of OPT layers, used to split de input data between OPT and SAR
number_class = 3
weights = params['weights']
overlap = params['overlap']
patch_size = params['patch_size']
batch_size = params['batch_size']
nb_filters = params['nb_filters']
module = importlib.import_module('model.models')
exp_model = getattr(module, method)
grid_size = params['grid_size']
tiles_tr = params['tiles_tr']
tiles_val = params['tiles_val']
print(f'Evaluating Experiment {exp}')
print(f'Conditions: {method}_{img_type}_{train_cond}_{test_cond}')
final_mask1 = np.lib.format.open_memmap(os.path.join(img_path, 'final_mask1.npy'))
print('Labels stack:', final_mask1.shape)
path_exp = os.path.join(img_path, 'experiments', f'exp_{exp}')
path_models = os.path.join(path_exp, 'models')
path_maps = os.path.join(path_exp, 'pred_maps')
if not os.path.exists(path_exp):
os.makedirs(path_exp)
if not os.path.exists(path_models):
os.makedirs(path_models)
if not os.path.exists(path_maps):
os.makedirs(path_maps)
datasets = np.load(os.path.join(path_exp,'datasets.npy'))
test_ds = np.zeros_like(datasets, dtype=np.uint8)
test_ds[datasets==0] = 1
if img_type == 'FUS':
mean_prob_opt = np.lib.format.open_memmap(os.path.join(path_maps, 'prob_mean_opt.npy'))
mean_prob_sar = np.lib.format.open_memmap(os.path.join(path_maps, 'prob_mean_sar.npy'))
mean_prob_fus = np.lib.format.open_memmap(os.path.join(path_maps, 'prob_mean_fus.npy'))
fig = plt.figure(figsize=(20,10))
ax1 = fig.add_subplot(141)
plt.title('Prediction OPT')
ax1.imshow(mean_prob_opt, cmap ='jet')
ax1.axis('off')
ax2 = fig.add_subplot(142)
plt.title('Prediction SAR')
ax2.imshow(mean_prob_sar, cmap ='jet')
ax2.axis('off')
ax3 = fig.add_subplot(143)
plt.title('Prediction FUS')
ax3.imshow(mean_prob_fus, cmap ='jet')
ax3.axis('off')
ax4 = fig.add_subplot(144)
plt.title('Reference')
ax4.imshow(tf.keras.utils.to_categorical(final_mask1, 3)[:,:,1], cmap ='jet')
ax4.axis('off')
plt.savefig(os.path.join(path_exp, 'prediction.png'))
t0 = time.time()
mean_prob_opt = mean_prob_opt[:final_mask1.shape[0], :final_mask1.shape[1]]
mean_prob_sar = mean_prob_sar[:final_mask1.shape[0], :final_mask1.shape[1]]
mean_prob_fus = mean_prob_fus[:final_mask1.shape[0], :final_mask1.shape[1]]
ref1 = np.ones_like(final_mask1).astype(np.float32)
ref1 [final_mask1 == 2] = 0
TileMask = test_ds * ref1
GTTruePositives = final_mask1==1
Npoints = 50
Pmax_opt = np.max(mean_prob_opt[GTTruePositives * TileMask ==1])
ProbList_opt = np.linspace(Pmax_opt,0,Npoints)
Pmax_sar = np.max(mean_prob_sar[GTTruePositives * TileMask ==1])
ProbList_sar = np.linspace(Pmax_sar,0,Npoints)
Pmax_fus = np.max(mean_prob_fus[GTTruePositives * TileMask ==1])
ProbList_fus = np.linspace(Pmax_fus,0,Npoints)
del ref1, TileMask, GTTruePositives
gc.collect()
print('Evaluating metrics...')
metrics_opt = metrics_AP(ProbList_opt, mean_prob_opt, final_mask1, test_ds, 625, 4)
metrics_sar = metrics_AP(ProbList_sar, mean_prob_sar, final_mask1, test_ds, 625, 4)
metrics_fus = metrics_AP(ProbList_fus, mean_prob_fus, final_mask1, test_ds, 625, 4)
np.save(os.path.join(path_exp, 'acc_metrics_opt.npy'), metrics_opt)
np.save(os.path.join(path_exp, 'acc_metrics_sar.npy'), metrics_sar)
np.save(os.path.join(path_exp, 'acc_metrics_fus.npy'), metrics_fus)
print(f'elapsed time: {(time.time()-t0)/60} mins')
metrics_copy_opt = np.array(metrics_opt)
del metrics_opt
metrics_copy_opt = complete_nan_values(metrics_copy_opt)
metrics_copy_sar = np.array(metrics_sar)
del metrics_sar
metrics_copy_sar = complete_nan_values(metrics_copy_sar)
metrics_copy_fus = np.array(metrics_fus)
del metrics_fus
metrics_copy_fus = complete_nan_values(metrics_copy_fus)
Recall_opt = metrics_copy_opt[:,0]
Precision_opt = metrics_copy_opt[:,1]
AA_opt = metrics_copy_opt[:,2]
Recall_sar = metrics_copy_sar[:,0]
Precision_sar = metrics_copy_sar[:,1]
AA_sar = metrics_copy_sar[:,2]
Recall_fus = metrics_copy_fus[:,0]
Precision_fus = metrics_copy_fus[:,1]
AA_fus = metrics_copy_fus[:,2]
Recall_opt = np.insert(Recall_opt, 0, 0)
Precision_opt = np.insert(Precision_opt, 0, Precision_opt[0])
DeltaR_opt = Recall_opt[1:]-Recall_opt[:-1]
AP_opt = np.sum(Precision_opt[1:]*DeltaR_opt)
print('OPT AP', AP_opt)
Recall_sar = np.insert(Recall_sar, 0, 0)
Precision_sar = np.insert(Precision_sar, 0, Precision_sar[0])
DeltaR_sar = Recall_sar[1:]-Recall_sar[:-1]
AP_sar = np.sum(Precision_sar[1:]*DeltaR_sar)
print('SAR AP', AP_sar)
Recall_fus = np.insert(Recall_fus, 0, 0)
Precision_fus = np.insert(Precision_fus, 0, Precision_fus[0])
DeltaR_fus = Recall_fus[1:]-Recall_fus[:-1]
AP_fus = np.sum(Precision_fus[1:]*DeltaR_fus)
print('FUSION AP', AP_fus)
# Plot Recall vs. Precision curve
plt.figure(figsize=(10,10))
plt.plot(metrics_copy_opt[:,0],metrics_copy_opt[:,1], 'r-', label = f'OPT (AP: {AP_opt:.4f})')
plt.plot(metrics_copy_sar[:,0],metrics_copy_sar[:,1], 'g-', label = f'SAR (AP: {AP_sar:.4f})')
plt.plot(metrics_copy_fus[:,0],metrics_copy_fus[:,1], 'b-', label = f'FUSION (AP: {AP_fus:.4f})')
plt.legend(loc="lower left")
ax = plt.gca()
ax.set_ylim([0,1.01])
ax.set_xlim([0,1.01])
plt.grid()
plt.savefig(os.path.join(path_exp, 'result.png'))
else:
mean_prob = np.lib.format.open_memmap(os.path.join(path_maps, 'prob_mean.npy'))
fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(121)
plt.title('Prediction')
ax1.imshow(mean_prob, cmap ='jet')
ax1.axis('off')
ax2 = fig.add_subplot(122)
plt.title('Reference')
ax2.imshow(tf.keras.utils.to_categorical(final_mask1, 3)[:,:,1], cmap ='jet')
ax2.axis('off')
plt.savefig(os.path.join(path_exp, 'prediction.png'))
t0 = time.time()
mean_prob = mean_prob[:final_mask1.shape[0], :final_mask1.shape[1]]
ref1 = np.ones_like(final_mask1).astype(np.float32)
ref1 [final_mask1 == 2] = 0
TileMask = test_ds * ref1
GTTruePositives = final_mask1==1
Npoints = 50
Pmax = np.max(mean_prob[GTTruePositives * TileMask ==1])
ProbList = np.linspace(Pmax,0,Npoints)
del ref1, TileMask, GTTruePositives
gc.collect()
print('Evaluating metrics...')
metrics = metrics_AP(ProbList, mean_prob, final_mask1, test_ds, 625, 5)
np.save(os.path.join(path_exp, 'acc_metrics.npy'), metrics)
print(f'elapsed time: {(time.time()-t0)/60} mins')
metrics_copy = np.array(metrics)
metrics_copy = complete_nan_values(metrics_copy)
Recall = metrics_copy[:,0]
Precision = metrics_copy[:,1]
AA = metrics_copy[:,2]
Recall_ = np.insert(Recall, 0, 0)
Precision_ = np.insert(Precision, 0, Precision[0])
DeltaR = Recall_[1:]-Recall_[:-1]
AP = np.sum(Precision_[1:]*DeltaR)
print('FUSION mAP', AP)
# Plot Recall vs. Precision curve
plt.figure(figsize=(10,10))
plt.plot(metrics_copy[:,0],metrics_copy[:,1], 'b-', label = f'FUSION (AP: {AP:.4f})')
plt.legend(loc="lower left")
ax = plt.gca()
ax.set_ylim([0,1.01])
ax.set_xlim([0,1.01])
plt.grid()
plt.savefig(os.path.join(path_exp, 'result.png'))
if __name__ == '__main__':
with open(f'experiments_multi.json') as param_file:
params = json.load(param_file)
times=params['times']
exps = []
img_types = []
train_cond = []
test_cond = []
methods = []
for exp in params['experiments']:
exps.append(exp['num'])
img_types.append(exp['img_type'])
train_cond.append(exp['train_cond'])
test_cond.append(exp['test_cond'])
methods.append(exp['method'])
for i, exp in enumerate(exps):
p = Process(target=eval_model, args=(exp,img_types[i], train_cond[i], test_cond[i], methods[i]))
p.start()
p.join()