""" Sentiment Classification Task """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import argparse import numpy as np import multiprocessing import sys sys.path.append("../models/classification/") sys.path.append("../") from nets import bow_net from nets import lstm_net from nets import cnn_net from nets import bilstm_net from nets import gru_net from models.model_check import check_cuda from models.model_check import check_version from config import PDConfig import paddle import paddle.fluid as fluid import reader from utils import init_checkpoint def create_model(args, pyreader_name, num_labels, is_prediction=False): """ Create Model for sentiment classification """ data = fluid.layers.data( name="src_ids", shape=[-1, args.max_seq_len], dtype='int64') label = fluid.layers.data( name="label", shape=[-1, 1], dtype="int64") seq_len = fluid.layers.data( name="seq_len", shape=[-1], dtype="int64") data_reader = fluid.io.PyReader(feed_list=[data, label, seq_len], capacity=4, iterable=False) if args.model_type == "bilstm_net": network = bilstm_net elif args.model_type == "bow_net": network = bow_net elif args.model_type == "cnn_net": network = cnn_net elif args.model_type == "lstm_net": network = lstm_net elif args.model_type == "gru_net": network = gru_net else: raise ValueError("Unknown network type!") if is_prediction: probs = network(data, seq_len, None, args.vocab_size, is_prediction=is_prediction) print("create inference model...") return data_reader, probs, [data.name, seq_len.name] ce_loss, probs = network(data, seq_len, label, args.vocab_size, is_prediction=is_prediction) loss = fluid.layers.mean(x=ce_loss) num_seqs = fluid.layers.create_tensor(dtype='int64') accuracy = fluid.layers.accuracy(input=probs, label=label, total=num_seqs) return data_reader, loss, accuracy, num_seqs def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase): """ Evaluation Function """ test_pyreader.start() total_cost, total_acc, total_num_seqs = [], [], [] time_begin = time.time() while True: #print("===============") try: np_loss, np_acc, np_num_seqs = exe.run(program=test_program, fetch_list=fetch_list, return_numpy=False) np_loss = np.array(np_loss) np_acc = np.array(np_acc) np_num_seqs = np.array(np_num_seqs) total_cost.extend(np_loss * np_num_seqs) total_acc.extend(np_acc * np_num_seqs) total_num_seqs.extend(np_num_seqs) except fluid.core.EOFException: test_pyreader.reset() break time_end = time.time() print("[%s evaluation] ave loss: %f, ave acc: %f, elapsed time: %f s" % (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs), np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin)) def inference(exe, test_program, test_pyreader, fetch_list, infer_phrase): """ Inference Function """ test_pyreader.start() time_begin = time.time() while True: try: np_props = exe.run(program=test_program, fetch_list=fetch_list, return_numpy=True) for probs in np_props[0]: print("%d\t%f\t%f" % (np.argmax(probs), probs[0], probs[1])) except fluid.core.EOFException: test_pyreader.reset() break time_end = time.time() print("[%s] elapsed time: %f s" % (infer_phrase, time_end - time_begin)) def main(args): """ Main Function """ if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) dev_count = fluid.core.get_cuda_device_count() else: place = fluid.CPUPlace() dev_count = 1 exe = fluid.Executor(place) task_name = args.task_name.lower() processor = reader.SentaProcessor(data_dir=args.data_dir, vocab_path=args.vocab_path, random_seed=args.random_seed, max_seq_len=args.max_seq_len) num_labels = len(processor.get_labels()) if not (args.do_train or args.do_val or args.do_infer): raise ValueError("For args `do_train`, `do_val` and `do_infer`, at " "least one of them must be True.") startup_prog = fluid.Program() if args.random_seed is not None: startup_prog.random_seed = args.random_seed if args.do_train: train_data_generator = processor.data_generator( batch_size=args.batch_size, phase='train', epoch=args.epoch, shuffle=True) num_train_examples = processor.get_num_examples(phase="train") max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count print("Device count: %d" % dev_count) print("Num train examples: %d" % num_train_examples) print("Max train steps: %d" % max_train_steps) train_program = fluid.Program() if args.enable_ce and args.random_seed is not None: train_program.random_seed = args.random_seed with fluid.program_guard(train_program, startup_prog): with fluid.unique_name.guard(): train_reader, loss, accuracy, num_seqs = create_model( args, pyreader_name='train_reader', num_labels=num_labels, is_prediction=False) sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr) sgd_optimizer.minimize(loss) if args.verbose: lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size) print("Theoretical memory usage in training: %.3f - %.3f %s" % (lower_mem, upper_mem, unit)) if args.do_val: test_data_generator = processor.data_generator( batch_size=args.batch_size, phase='dev', epoch=1, shuffle=False) test_prog = fluid.Program() with fluid.program_guard(test_prog, startup_prog): with fluid.unique_name.guard(): test_reader, loss, accuracy, num_seqs = create_model( args, pyreader_name='test_reader', num_labels=num_labels, is_prediction=False) test_prog = test_prog.clone(for_test=True) if args.do_infer: infer_data_generator = processor.data_generator( batch_size=args.batch_size, phase='infer', epoch=1, shuffle=False) infer_prog = fluid.Program() with fluid.program_guard(infer_prog, startup_prog): with fluid.unique_name.guard(): infer_reader, prop, _ = create_model( args, pyreader_name='infer_reader', num_labels=num_labels, is_prediction=True) infer_prog = infer_prog.clone(for_test=True) exe.run(startup_prog) if args.do_train: if args.init_checkpoint: init_checkpoint( exe, args.init_checkpoint, main_program=startup_prog) elif args.do_val or args.do_infer: if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if" "only doing validation or testing!") init_checkpoint( exe, args.init_checkpoint, main_program=startup_prog) if args.do_train: train_exe = exe train_reader.decorate_sample_list_generator(train_data_generator) else: train_exe = None if args.do_val: test_exe = exe test_reader.decorate_sample_list_generator(test_data_generator) if args.do_infer: test_exe = exe infer_reader.decorate_sample_list_generator(infer_data_generator) if args.do_train: train_reader.start() steps = 0 total_cost, total_acc, total_num_seqs = [], [], [] time_begin = time.time() while True: try: steps += 1 #print("steps...") if steps % args.skip_steps == 0: fetch_list = [loss.name, accuracy.name, num_seqs.name] else: fetch_list = [] outputs = train_exe.run(program=train_program, fetch_list=fetch_list, return_numpy=False) #print("finished one step") if steps % args.skip_steps == 0: np_loss, np_acc, np_num_seqs = outputs np_loss = np.array(np_loss) np_acc = np.array(np_acc) np_num_seqs = np.array(np_num_seqs) total_cost.extend(np_loss * np_num_seqs) total_acc.extend(np_acc * np_num_seqs) total_num_seqs.extend(np_num_seqs) if args.verbose: verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size() print(verbose) time_end = time.time() used_time = time_end - time_begin print("step: %d, ave loss: %f, " "ave acc: %f, speed: %f steps/s" % (steps, np.sum(total_cost) / np.sum(total_num_seqs), np.sum(total_acc) / np.sum(total_num_seqs), args.skip_steps / used_time)) total_cost, total_acc, total_num_seqs = [], [], [] time_begin = time.time() if steps % args.save_steps == 0: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) if steps % args.validation_steps == 0: # evaluate dev set if args.do_val: print("do evalatation") evaluate(exe, test_prog, test_reader, [loss.name, accuracy.name, num_seqs.name], "dev") except fluid.core.EOFException: save_path = os.path.join(args.checkpoints, "step_" + str(steps)) fluid.io.save_persistables(exe, save_path, train_program) train_reader.reset() break # final eval on dev set if args.do_val: print("Final validation result:") evaluate(exe, test_prog, test_reader, [loss.name, accuracy.name, num_seqs.name], "dev") # final eval on test set if args.do_infer: print("Final test result:") inference(exe, infer_prog, infer_reader, [prop.name], "infer") if __name__ == "__main__": args = PDConfig('senta_config.json') args.build() args.print_arguments() check_cuda(args.use_cuda) main(args)