def testSingleImg(): NetHelper.gpu() #submission() nh = NetHelper(deploy=cfgs.deploy_pt, model=cfgs.best_model_dir) img = Data.imFromFile(os.path.join(cfgs.train_mask_path, "1_1_mask.tif")) res = nh.bin_pred_map(img) print(np.histogram(res))
def submission(): NetHelper.gpu(2) #submission() nh = NetHelper(deploy=cfgs.deploy_pt, model=cfgs.best_model_dir) if debug: l = Data.folder_opt(cfgs.train_data_path, func, nh) else: l = Data.folder_opt(cfgs.test_data_path, func, nh) l = np.array(l, dtype=[('x', int), ('y', object)]) l.sort(order='x') first_row = 'img,pixels' file_name = 'submission.csv' with open(file_name, 'w+') as f: f.write(first_row) for i in l: s = str(i[0]) + ',' + i[1] f.write(('\n' + s))
def train_model(self): for iter in range(500 * 2000): if debug: if iter % 100 == 0 and iter != 0: nethelper = NetHelper(self.solver.net) # nethelper.hist('label') # nethelper.hist('prob', filters=2,attr="blob") # nethelper.hist('data', filters=2,attr="blob") if False: for i in range( nethelper.net.blobs['data'].data.shape[0]): plt.subplot(221) plt.imshow(nethelper.net.blobs['data'].data[i, 0]) plt.subplot(222) plt.imshow(nethelper.net.blobs['prob'].data[i, 0]) plt.subplot(223) plt.imshow(nethelper.net.blobs['label'].data[i, 0]) plt.show() self.solver.step(1)