def run(self, session): self.model.setup_testing(session) if self.args.use_dataset: test_data= data_parser(self.args) n_data = len(test_data[1]) else: test_data=get_single_image(self.args) n_data = len(test_data) print_info('Writing PNGs at {}'.format(self.args.base_dir_results)) if self.args.batch_size_test==1 and self.args.use_dataset: for i in range(n_data): im, em, file_name = get_testing_batch(self.args, [test_data[0][test_data[1][i]], test_data[1][i]], use_batch=False) self.img_info = file_name edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]}) self.save_egdemaps(edgemap, single_image=True) print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1])) # for individual images elif self.args.batch_size_test==1 and not self.args.use_dataset: for i in range(n_data): im, file_name = get_single_image(self.args,file_path=test_data[i]) self.img_info = file_name edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]}) self.save_egdemaps(edgemap, single_image=True) print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1]))
def run(self, session): self.model.setup_testing(session) if self.args.use_dataset: test_data= data_parser(self.args) n_data = len(test_data[1]) else: test_data=get_single_image(self.args) n_data = len(test_data) print_info('Writing PNGs at {}'.format(self.args.base_dir_results)) if self.args.batch_size_test==1 and self.args.use_dataset: for i in range(n_data): im, em, file_name = get_testing_batch(self.args, [test_data[0][test_data[1][i]], test_data[1][i]], use_batch=False) self.img_info = file_name #Dexi Start Time startDexi = time.time() #Edge map creation from the pretrained model edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]}) #Dexi End Time endDexi = time.time() secondsDexi = endDexi - startDexi print_info('Time taken for DexiNED: {} seconds'.format(secondsDexi)) self.save_egdemaps(edgemap, single_image=True) print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1])) # for individual images elif self.args.batch_size_test==1 and not self.args.use_dataset: for i in range(n_data): im, file_name = get_single_image(self.args,file_path=test_data[i]) self.img_info = file_name #Dexi Start Time startDexi = time.time() edgemap = session.run(self.model.predictions, feed_dict={self.model.images: [im]}) #Dexi End Time endDexi = time.time() secondsDexi = endDexi - startDexi print_info('Time taken for DexiNED: {} seconds'.format(secondsDexi)) self.save_egdemaps(edgemap, single_image=True) print_info('Done testing {}, {}'.format(self.img_info[0], self.img_info[1]))