import argparse import sys import time import math import unittest import contextlib import numpy as np import six import paddle.fluid as fluid import paddle import net import utils def parse_args(): parser = argparse.ArgumentParser("PaddlePaddle Word2vec infer example") parser.add_argument( '--dict_path', type=str, default='./data/data_c/1-billion_dict_word_to_id_', help="The path of dic") parser.add_argument( '--infer_epoch', action='store_true', required=False, default=False, help='infer by epoch') parser.add_argument( '--infer_step', action='store_true', required=False, default=False, help='infer by step') parser.add_argument( '--test_dir', type=str, default='test_data', help='test file address') parser.add_argument( '--print_step', type=int, default='500000', help='print step') parser.add_argument( '--start_index', type=int, default='0', help='start index') parser.add_argument( '--start_batch', type=int, default='1', help='start index') parser.add_argument( '--end_batch', type=int, default='13', help='start index') parser.add_argument( '--last_index', type=int, default='100', help='last index') parser.add_argument( '--model_dir', type=str, default='model', help='model dir') parser.add_argument( '--use_cuda', type=int, default='0', help='whether use cuda') parser.add_argument( '--batch_size', type=int, default='5', help='batch_size') parser.add_argument('--emb_size', type=int, default='64', help='batch_size') args = parser.parse_args() return args def infer_epoch(args, vocab_size, test_reader, use_cuda, i2w): """ inference function """ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) emb_size = args.emb_size batch_size = args.batch_size with fluid.scope_guard(fluid.Scope()): main_program = fluid.Program() with fluid.program_guard(main_program): values, pred = net.infer_network(vocab_size, emb_size) for epoch in range(start_index, last_index + 1): copy_program = main_program.clone() model_path = model_dir + "/pass-" + str(epoch) fluid.io.load_params( executor=exe, dirname=model_path, main_program=copy_program) accum_num = 0 accum_num_sum = 0.0 t0 = time.time() step_id = 0 for data in test_reader(): step_id += 1 b_size = len([dat[0] for dat in data]) wa = np.array( [dat[0] for dat in data]).astype("int64").reshape( b_size) wb = np.array( [dat[1] for dat in data]).astype("int64").reshape( b_size) wc = np.array( [dat[2] for dat in data]).astype("int64").reshape( b_size) label = [dat[3] for dat in data] input_word = [dat[4] for dat in data] para = exe.run(copy_program, feed={ "analogy_a": wa, "analogy_b": wb, "analogy_c": wc, "all_label": np.arange(vocab_size).reshape( vocab_size).astype("int64"), }, fetch_list=[pred.name, values], return_numpy=False) pre = np.array(para[0]) val = np.array(para[1]) for ii in range(len(label)): top4 = pre[ii] accum_num_sum += 1 for idx in top4: if int(idx) in input_word[ii]: continue if int(idx) == int(label[ii][0]): accum_num += 1 break if step_id % 1 == 0: print("step:%d %d " % (step_id, accum_num)) print("epoch:%d \t acc:%.3f " % (epoch, 1.0 * accum_num / accum_num_sum)) def infer_step(args, vocab_size, test_reader, use_cuda, i2w): """ inference function """ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) emb_size = args.emb_size batch_size = args.batch_size with fluid.scope_guard(fluid.Scope()): main_program = fluid.Program() with fluid.program_guard(main_program): values, pred = net.infer_network(vocab_size, emb_size) for epoch in range(start_index, last_index + 1): for batchid in range(args.start_batch, args.end_batch): copy_program = main_program.clone() model_path = model_dir + "/pass-" + str(epoch) + ( '/batch-' + str(batchid * args.print_step)) fluid.io.load_params( executor=exe, dirname=model_path, main_program=copy_program) accum_num = 0 accum_num_sum = 0.0 t0 = time.time() step_id = 0 for data in test_reader(): step_id += 1 b_size = len([dat[0] for dat in data]) wa = np.array( [dat[0] for dat in data]).astype("int64").reshape( b_size) wb = np.array( [dat[1] for dat in data]).astype("int64").reshape( b_size) wc = np.array( [dat[2] for dat in data]).astype("int64").reshape( b_size) label = [dat[3] for dat in data] input_word = [dat[4] for dat in data] para = exe.run( copy_program, feed={ "analogy_a": wa, "analogy_b": wb, "analogy_c": wc, "all_label": np.arange(vocab_size).reshape(vocab_size), }, fetch_list=[pred.name, values], return_numpy=False) pre = np.array(para[0]) val = np.array(para[1]) for ii in range(len(label)): top4 = pre[ii] accum_num_sum += 1 for idx in top4: if int(idx) in input_word[ii]: continue if int(idx) == int(label[ii][0]): accum_num += 1 break if step_id % 1 == 0: print("step:%d %d " % (step_id, accum_num)) print("epoch:%d \t acc:%.3f " % (epoch, 1.0 * accum_num / accum_num_sum)) t1 = time.time() if __name__ == "__main__": utils.check_version() args = parse_args() start_index = args.start_index last_index = args.last_index test_dir = args.test_dir model_dir = args.model_dir batch_size = args.batch_size dict_path = args.dict_path use_cuda = True if args.use_cuda else False print("start index: ", start_index, " last_index:", last_index) vocab_size, test_reader, id2word = utils.prepare_data( test_dir, dict_path, batch_size=batch_size) print("vocab_size:", vocab_size) if args.infer_step: infer_step( args, vocab_size, test_reader=test_reader, use_cuda=use_cuda, i2w=id2word) else: infer_epoch( args, vocab_size, test_reader=test_reader, use_cuda=use_cuda, i2w=id2word)