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section_test.py
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section_test.py
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import argparse
from os.path import join as pjoin
import numpy as np
import torch
import torch.nn.functional as F
from tensorboardX import SummaryWriter
import torchvision.utils as vutils
from core.loader.data_loader import *
from core.metrics import runningScore
from core.utils import np_to_tb
def test(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
log_dir, model_name = os.path.split(args.model_path)
# load model:
model = torch.load(args.model_path)
model = model.to(device) # Send to GPU if available
writer = SummaryWriter(log_dir=log_dir)
class_names = ['upper_ns', 'middle_ns', 'lower_ns',
'rijnland_chalk', 'scruff', 'zechstein']
running_metrics_overall = runningScore(6)
if "both" in args.split:
splits = ["test1", "test2"]
else:
splits = args.split
for sdx, split in enumerate(splits):
# define indices of the array
labels = np.load(pjoin('data', 'test_once', split + '_labels.npy'))
irange, xrange, depth = labels.shape
if args.inline:
i_list = list(range(irange))
i_list = ['i_'+str(inline) for inline in i_list]
else:
i_list = []
if args.crossline:
x_list = list(range(xrange))
x_list = ['x_'+str(crossline) for crossline in x_list]
else:
x_list = []
list_test = i_list + x_list
file_object = open(
pjoin('data', 'splits', 'section_' + split + '.txt'), 'w')
file_object.write('\n'.join(list_test))
file_object.close()
test_set = section_loader(is_transform=True,
split=split,
augmentations=None)
n_classes = test_set.n_classes
test_loader = data.DataLoader(test_set,
batch_size=1,
num_workers=4,
shuffle=False)
# print the results of this split:
running_metrics_split = runningScore(n_classes)
# testing mode:
with torch.no_grad(): # operations inside don't track history
model.eval()
total_iteration = 0
for i, (images, labels) in enumerate(test_loader):
total_iteration = total_iteration + 1
image_original, labels_original = images, labels
images, labels = images.to(device), labels.to(device)
outputs = model(images)
pred = outputs.detach().max(1)[1].cpu().numpy()
gt = labels.detach().cpu().numpy()
running_metrics_split.update(gt, pred)
running_metrics_overall.update(gt, pred)
numbers = [0, 99, 149, 399, 499]
if i in numbers:
tb_original_image = vutils.make_grid(
image_original[0][0], normalize=True, scale_each=True)
writer.add_image('test/original_image',
tb_original_image, i)
labels_original = labels_original.numpy()[0]
correct_label_decoded = test_set.decode_segmap(np.squeeze(labels_original))
writer.add_image('test/original_label',
np_to_tb(correct_label_decoded), i)
out = F.softmax(outputs, dim=1)
# this returns the max. channel number:
prediction = out.max(1)[1].cpu().numpy()[0]
# this returns the confidence:
confidence = out.max(1)[0].cpu().detach()[0]
tb_confidence = vutils.make_grid(
confidence, normalize=True, scale_each=True)
decoded = test_set.decode_segmap(np.squeeze(prediction))
writer.add_image('test/predicted', np_to_tb(decoded), i)
writer.add_image('test/confidence', tb_confidence, i)
# uncomment if you want to visualize the different class heatmaps
unary = outputs.cpu().detach()
unary_max = torch.max(unary)
unary_min = torch.min(unary)
unary = unary.add((-1*unary_min))
unary = unary/(unary_max - unary_min)
for channel in range(0, len(class_names)):
decoded_channel = unary[0][channel]
tb_channel = vutils.make_grid(decoded_channel, normalize=True, scale_each=True)
writer.add_image(f'test_classes/_{class_names[channel]}', tb_channel, i)
# get scores and save in writer()
score, class_iou = running_metrics_split.get_scores()
# Add split results to TB:
writer.add_text(f'test__{split}/',
f'Pixel Acc: {score["Pixel Acc: "]:.3f}', 0)
for cdx, class_name in enumerate(class_names):
writer.add_text(
f'test__{split}/', f' {class_name}_accuracy {score["Class Accuracy: "][cdx]:.3f}', 0)
writer.add_text(
f'test__{split}/', f'Mean Class Acc: {score["Mean Class Acc: "]:.3f}', 0)
writer.add_text(
f'test__{split}/', f'Freq Weighted IoU: {score["Freq Weighted IoU: "]:.3f}', 0)
writer.add_text(f'test__{split}/',
f'Mean IoU: {score["Mean IoU: "]:0.3f}', 0)
running_metrics_split.reset()
# FINAL TEST RESULTS:
score, class_iou = running_metrics_overall.get_scores()
# Add split results to TB:
writer.add_text('test_final', f'Pixel Acc: {score["Pixel Acc: "]:.3f}', 0)
for cdx, class_name in enumerate(class_names):
writer.add_text(
'test_final', f' {class_name}_accuracy {score["Class Accuracy: "][cdx]:.3f}', 0)
writer.add_text(
'test_final', f'Mean Class Acc: {score["Mean Class Acc: "]:.3f}', 0)
writer.add_text(
'test_final', f'Freq Weighted IoU: {score["Freq Weighted IoU: "]:.3f}', 0)
writer.add_text('test_final', f'Mean IoU: {score["Mean IoU: "]:0.3f}', 0)
print('--------------- FINAL RESULTS -----------------')
print(f'Pixel Acc: {score["Pixel Acc: "]:.3f}')
for cdx, class_name in enumerate(class_names):
print(
f' {class_name}_accuracy {score["Class Accuracy: "][cdx]:.3f}')
print(f'Mean Class Acc: {score["Mean Class Acc: "]:.3f}')
print(f'Freq Weighted IoU: {score["Freq Weighted IoU: "]:.3f}')
print(f'Mean IoU: {score["Mean IoU: "]:0.3f}')
# Save confusion matrix:
confusion = score['confusion_matrix']
np.savetxt(pjoin(log_dir,'confusion.csv'), confusion, delimiter=" ")
writer.close()
return
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Params')
parser.add_argument('--model_path', nargs='?', type=str, default='path/to/model.pkl',
help='Path to the saved model')
parser.add_argument('--split', nargs='?', type=str, default='both',
help='Choose from: "test1", "test2", or "both" to change which region to test on')
parser.add_argument('--crossline', nargs='?', type=bool, default=True,
help='whether to test in crossline mode')
parser.add_argument('--inline', nargs='?', type=bool, default=True,
help='whether to test inline mode')
args = parser.parse_args()
test(args)