def create_model(base_model, ckpt_file=None, from_measurement=None):
# prepare model
if base_model == 'modelnet_x3_l4':
net = PointCNN.modelnet_x3_l4()
elif base_model == 'shapenet_x8_2048_fps':
net = PointCNN.shapenet_x8_2048_fps()
else:
raise NotImplementedError
if ckpt_file is not None:
ModelRecorder.resume_model(net,
ckpt_file,
from_measurement=from_measurement)
return net
def visualization_process():
# prepare data
image_num = 2
image_file = "assets/1/000{}.jpg".format(image_num)
pred_img = preprocess_image(Image.open(image_file))
# prepare model
print("evaluation on : {}".format(config.base_model))
net = create_model(config.base_model)
print("load pretained model from {}".format(config.test.pretrained_model))
ModelRecorder.resume_model(net,
config.test.pretrained_model,
from_measurement="acc")
show_first_conv_feature(net, pred_img, "{}_C".format(image_num))
visualize(net, pred_img, 1, "{}_C".format(image_num))
def evaluation_process(detail=False):
valid_set = ds.ClipArt(config.validation.data_set, data_aug=False)
valid_loader = DataLoader(valid_set,
batch_size=config.validation.batch_size,
shuffle=False,
num_workers=config.num_workers,
drop_last=False)
print('valid set size: {}'.format(len(valid_set)))
# prepare model
print("evaluation on : {}".format(config.base_model))
net = create_model(config.base_model)
print("load pretained model from {}".format(config.test.pretrained_model))
ModelRecorder.resume_model(net,
config.test.pretrained_model,
from_measurement="acc")
net = nn.DataParallel(net)
if not detail:
with torch.no_grad():
acc, conf_matrix = evaluate(valid_loader, net, True)
plot_conf_matrix(valid_set.get_categories(),
conf_matrix,
save_file='{}/{}.pdf'.format(config.result_sub_folder,
config.comment))
else:
with torch.no_grad():
acc, conf_matrix, features, labels = evaluate(
valid_loader, net, True, True)
plot_conf_matrix(valid_set.get_categories(),
conf_matrix,
save_file='{}/{}.pdf'.format(config.result_sub_folder,
config.comment))
array2tsv(features, '{}/{}.tsv'.format(config.result_sub_folder,
config.comment))
labels_file = open(
'{}/{}_label.tsv'.format(config.result_sub_folder, config.comment),
'w')
labels_txt = ""
for label in labels:
labels_txt += '{}\n'.format(int(label))
labels_file.write(labels_txt[:-1])
labels_file.close()
print("Finished!")
def extract_test_rst_process():
test_set = ds.ClipArtTest(config.test.data_set)
test_loader = DataLoader(test_set,
batch_size=config.test.batch_size,
shuffle=False,
num_workers=config.num_workers,
drop_last=False)
print('test set size: {}'.format(len(test_set)))
# prepare model
print("evaluation on : {}".format(config.base_model))
net = create_model(config.base_model)
print("load pretained model from {}".format(config.test.pretrained_model))
ModelRecorder.resume_model(net,
config.test.pretrained_model,
from_measurement="acc")
net = nn.DataParallel(net)
net.eval()
rst = "id,label\n"
with torch.no_grad():
for i, (batch_data, img_names) in enumerate(test_loader):
batch_data = batch_data.to(config.device)
batch_label = batch_label.to(config.device)
raw_out = net(batch_data)
pred = torch.argmax(raw_out.detach(), dim=1)
pred = list(pred.cpu().numpy())
assert len(pred) == len(img_names)
for j, p in enumerate(pred):
rst += '{}, {}\n'.format(img_names[j], ds.CONVERT_TABLE[p])
save_file_name = 's_test_rst.txt'
save_file = open(save_file_name, 'w')
save_file.write(rst)
save_file.close()
print("Finished!")
def evaluation_process(detail=False):
if config.dataset == "ModelNet40":
valid_set = ModelNet40(partition='test')
elif config.dataset == "ModelNet10":
valid_set = ModelNet10(partition='test')
elif config.dataset == "ShapeNetParts":
valid_set = ShapeNetPart(partition='test')
valid_loader = DataLoader(valid_set,
batch_size=config.validation.batch_size,
shuffle=False,
num_workers=config.num_workers,
drop_last=False)
print('valid set size: {}'.format(len(valid_set)))
# prepare model
print("evaluation on : {}".format(config.base_model))
net = create_model(config.base_model).to(config.device)
print("load pretained model from {}".format(config.test.pretrained_model))
ModelRecorder.resume_model(net,
config.test.pretrained_model,
from_measurement="acc")
net = nn.DataParallel(net)
if not detail:
with torch.no_grad():
if config.task == "seg":
validation_loss, validation_acc, avg_per_class_acc, val_ious = evaluate(
valid_loader, net, html_path="validation_output")
else:
validation_loss, acc, conf_matrix = evaluate(
valid_loader, net, True)
if not config.task == "seg":
plot_conf_matrix(valid_set.get_categories(),
conf_matrix,
save_file='{}conf_matrix.pdf'.format(
config.result_sub_folder))
else:
if not config.task == "seg":
with torch.no_grad():
validation_loss, acc, conf_matrix, features, labels = evaluate(
valid_loader, net, True, True)
plot_conf_matrix(valid_set.get_categories(),
conf_matrix,
save_file='{}conf_matrix.pdf'.format(
config.result_sub_folder))
array2tsv(features,
'{}features.tsv'.format(config.result_sub_folder))
np.save('{}features.npy'.format(config.result_sub_folder),
features)
np.save('{}labels.npy'.format(config.result_sub_folder), labels)
labels_file = open('{}labels.tsv'.format(config.result_sub_folder),
'w')
labels_txt = ""
for label in labels:
labels_txt += '{}\n'.format(int(label))
labels_file.write(labels_txt[:-1])
labels_file.close()
else:
raise NotImplementedError
print("Finished!")