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682 lines
24 KiB
Plaintext
682 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 使用Trainer和Tester快速训练和测试"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 数据读入和处理"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/remote-home/ynzheng/anaconda3/envs/now/lib/python3.8/site-packages/FastNLP-0.5.0-py3.8.egg/fastNLP/io/loader/classification.py:340: UserWarning: SST2's test file has no target.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"In total 3 datasets:\n",
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"\ttest has 1821 instances.\n",
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"\ttrain has 67349 instances.\n",
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"\tdev has 872 instances.\n",
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"In total 2 vocabs:\n",
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"\twords has 16292 entries.\n",
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"\ttarget has 2 entries.\n",
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"\n",
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"+-----------------------------------+--------+-----------------------------------+---------+\n",
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"| raw_words | target | words | seq_len |\n",
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"+-----------------------------------+--------+-----------------------------------+---------+\n",
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"| hide new secretions from the p... | 1 | [4110, 97, 12009, 39, 2, 6843,... | 7 |\n",
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"+-----------------------------------+--------+-----------------------------------+---------+\n",
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"Vocabulary(['hide', 'new', 'secretions', 'from', 'the']...)\n"
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]
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}
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],
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"source": [
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"from fastNLP.io import SST2Pipe\n",
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"\n",
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"pipe = SST2Pipe()\n",
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"databundle = pipe.process_from_file()\n",
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"vocab = databundle.get_vocab('words')\n",
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"print(databundle)\n",
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"print(databundle.get_dataset('train')[0])\n",
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"print(databundle.get_vocab('words'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4925 872 75\n"
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]
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}
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],
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"source": [
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"train_data = databundle.get_dataset('train')[:5000]\n",
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"train_data, test_data = train_data.split(0.015)\n",
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"dev_data = databundle.get_dataset('dev')\n",
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"print(len(train_data),len(dev_data),len(test_data))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+-------------+-----------+--------+-------+---------+\n",
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"| field_names | raw_words | target | words | seq_len |\n",
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"+-------------+-----------+--------+-------+---------+\n",
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"| is_input | False | False | True | True |\n",
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"| is_target | False | True | False | False |\n",
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"| ignore_type | | False | False | False |\n",
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"| pad_value | | 0 | 0 | 0 |\n",
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"+-------------+-----------+--------+-------+---------+\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<prettytable.PrettyTable at 0x7f0db03d0640>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_data.print_field_meta()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import AccuracyMetric\n",
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"from fastNLP import Const\n",
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"\n",
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"# metrics=AccuracyMetric() 在本例中与下面这行代码等价\n",
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"metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## DataSetIter初探"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n",
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" 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n",
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" 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n",
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" 1323, 4398, 7],\n",
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" [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n",
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" 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n",
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" 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0]]), 'seq_len': tensor([33, 21])}\n",
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"batch_y: {'target': tensor([1, 0])}\n",
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"batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],\n",
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" [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}\n",
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"batch_y: {'target': tensor([0, 1])}\n",
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"batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],\n",
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" [15618, 3204, 5, 1675, 0]]), 'seq_len': tensor([5, 4])}\n",
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"batch_y: {'target': tensor([1, 1])}\n",
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"batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n",
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" 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],\n",
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" [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n",
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" 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}\n",
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"batch_y: {'target': tensor([0, 0])}\n",
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"batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n",
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" 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],\n",
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" [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n",
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" 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 12])}\n",
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"batch_y: {'target': tensor([0, 1])}\n"
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]
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}
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],
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"source": [
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"from fastNLP import BucketSampler\n",
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"from fastNLP import DataSetIter\n",
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"\n",
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"tmp_data = dev_data[:10]\n",
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"# 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n",
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"# 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n",
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"sampler = BucketSampler(batch_size=2, seq_len_field_name='seq_len')\n",
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"batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n",
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"for batch_x, batch_y in batch:\n",
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" print(\"batch_x: \",batch_x)\n",
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" print(\"batch_y: \", batch_y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n",
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" 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n",
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" 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n",
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" 1323, 4398, 7],\n",
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" [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n",
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" 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n",
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" 7, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
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" -1, -1, -1]]), 'seq_len': tensor([33, 21])}\n",
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"batch_y: {'target': tensor([1, 0])}\n",
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"batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],\n",
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" [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}\n",
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"batch_y: {'target': tensor([0, 1])}\n",
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"batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n",
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" 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],\n",
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" [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n",
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" 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}\n",
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"batch_y: {'target': tensor([0, 0])}\n",
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"batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],\n",
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" [15618, 3204, 5, 1675, -1]]), 'seq_len': tensor([5, 4])}\n",
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"batch_y: {'target': tensor([1, 1])}\n",
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"batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n",
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" 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],\n",
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" [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n",
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" 1217, 7, -1, -1, -1, -1, -1, -1, -1, -1]]), 'seq_len': tensor([20, 12])}\n",
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"batch_y: {'target': tensor([0, 1])}\n"
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]
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}
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],
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"source": [
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"tmp_data.set_pad_val('words',-1)\n",
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"batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n",
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"for batch_x, batch_y in batch:\n",
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" print(\"batch_x: \",batch_x)\n",
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" print(\"batch_y: \", batch_y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"batch_x: {'words': tensor([[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n",
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" 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n",
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" 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])}\n",
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"batch_y: {'target': tensor([1, 0])}\n",
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"batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n",
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" 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n",
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||
" 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n",
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||
" 1323, 4398, 7, 0, 0, 0, 0, 0, 0, 0],\n",
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||
" [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n",
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" 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n",
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" 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([33, 21])}\n",
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"batch_y: {'target': tensor([1, 0])}\n",
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"batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0],\n",
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" [ 14, 10, 437, 32, 78, 3, 78, 437, 7, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0]]), 'seq_len': tensor([9, 9])}\n",
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"batch_y: {'target': tensor([0, 1])}\n",
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"batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n",
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" 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n",
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" 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])}\n",
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"batch_y: {'target': tensor([0, 0])}\n",
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"batch_x: {'words': tensor([[ 4, 277, 685, 18, 7, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [15618, 3204, 5, 1675, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])}\n",
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"batch_y: {'target': tensor([1, 1])}\n"
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]
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}
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],
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"source": [
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"from fastNLP.core.field import Padder\n",
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"import numpy as np\n",
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"class FixLengthPadder(Padder):\n",
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" def __init__(self, pad_val=0, length=None):\n",
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" super().__init__(pad_val=pad_val)\n",
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" self.length = length\n",
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" assert self.length is not None, \"Creating FixLengthPadder with no specific length!\"\n",
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"\n",
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" def __call__(self, contents, field_name, field_ele_dtype, dim):\n",
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" #计算当前contents中的最大长度\n",
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" max_len = max(map(len, contents))\n",
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" #如果当前contents中的最大长度大于指定的padder length的话就报错\n",
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" assert max_len <= self.length, \"Fixed padder length smaller than actual length! with length {}\".format(max_len)\n",
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" array = np.full((len(contents), self.length), self.pad_val, dtype=field_ele_dtype)\n",
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" for i, content_i in enumerate(contents):\n",
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" array[i, :len(content_i)] = content_i\n",
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" return array\n",
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"\n",
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"#设定FixLengthPadder的固定长度为40\n",
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"tmp_padder = FixLengthPadder(pad_val=0,length=40)\n",
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"#利用dataset的set_padder函数设定words field的padder\n",
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"tmp_data.set_padder('words',tmp_padder)\n",
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"batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n",
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"for batch_x, batch_y in batch:\n",
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" print(\"batch_x: \",batch_x)\n",
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" print(\"batch_y: \", batch_y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 使用DataSetIter自己编写训练过程\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"-----start training-----\n"
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]
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},
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{
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||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
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||
"version_major": 2,
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||
"version_minor": 0
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||
},
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||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…"
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||
]
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||
},
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||
"metadata": {},
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||
"output_type": "display_data"
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||
},
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||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r",
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"Evaluate data in 2.68 seconds!\n",
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"Epoch 0 Avg Loss: 0.66 AccuracyMetric: acc=0.708716 29307ms\n"
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]
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},
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||
{
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||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
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||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
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||
},
|
||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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"\r",
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"Evaluate data in 0.38 seconds!\n",
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"Epoch 1 Avg Loss: 0.41 AccuracyMetric: acc=0.770642 52200ms\n"
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]
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},
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||
{
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||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(FloatProgress(value=0.0, layout=Layout(flex='2'), max=55.0), HTML(value='')), layout=Layout(dis…"
|
||
]
|
||
},
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"Epoch 2 Avg Loss: 0.16 AccuracyMetric: acc=0.747706 70268ms\n"
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"Epoch 4 Avg Loss: 0.03 AccuracyMetric: acc=0.740826 114250ms\n"
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"Epoch 5 Avg Loss: 0.02 AccuracyMetric: acc=0.738532 134742ms\n"
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"Epoch 8 Avg Loss: 0.01 AccuracyMetric: acc=0.733945 192384ms\n"
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"\r",
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"Evaluate data in 0.84 seconds!\n",
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"Epoch 9 Avg Loss: 0.01 AccuracyMetric: acc=0.744266 214417ms\n"
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
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"\r",
|
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"Evaluate data in 0.04 seconds!\n",
|
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"[tester] \n",
|
||
"AccuracyMetric: acc=0.786667\n"
|
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]
|
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},
|
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{
|
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"data": {
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
|
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"source": [
|
||
"from fastNLP import BucketSampler\n",
|
||
"from fastNLP import DataSetIter\n",
|
||
"from fastNLP.models import CNNText\n",
|
||
"from fastNLP import Tester\n",
|
||
"import torch\n",
|
||
"import time\n",
|
||
"\n",
|
||
"embed_dim = 100\n",
|
||
"model = CNNText((len(vocab),embed_dim), num_classes=2, dropout=0.1)\n",
|
||
"\n",
|
||
"def train(epoch, data, devdata):\n",
|
||
" optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
|
||
" lossfunc = torch.nn.CrossEntropyLoss()\n",
|
||
" batch_size = 32\n",
|
||
"\n",
|
||
" # 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n",
|
||
" # 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n",
|
||
" train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len')\n",
|
||
" train_batch = DataSetIter(batch_size=batch_size, dataset=data, sampler=train_sampler)\n",
|
||
"\n",
|
||
" start_time = time.time()\n",
|
||
" print(\"-\"*5+\"start training\"+\"-\"*5)\n",
|
||
" for i in range(epoch):\n",
|
||
" loss_list = []\n",
|
||
" for batch_x, batch_y in train_batch:\n",
|
||
" optimizer.zero_grad()\n",
|
||
" output = model(batch_x['words'])\n",
|
||
" loss = lossfunc(output['pred'], batch_y['target'])\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
" loss_list.append(loss.item())\n",
|
||
"\n",
|
||
" #这里verbose如果为0，在调用Tester对象的test()函数时不输出任何信息，返回评估信息; 如果为1，打印出验证结果，返回评估信息\n",
|
||
" #在调用过Tester对象的test()函数后，调用其_format_eval_results(res)函数，结构化输出验证结果\n",
|
||
" tester_tmp = Tester(devdata, model, metrics=AccuracyMetric(), verbose=0)\n",
|
||
" res=tester_tmp.test()\n",
|
||
"\n",
|
||
" print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=\" \")\n",
|
||
" print(tester_tmp._format_eval_results(res),end=\" \")\n",
|
||
" print('{:d}ms'.format(round((time.time()-start_time)*1000)))\n",
|
||
" loss_list.clear()\n",
|
||
"\n",
|
||
"train(10, train_data, dev_data)\n",
|
||
"#使用tester进行快速测试\n",
|
||
"tester = Tester(test_data, model, metrics=AccuracyMetric())\n",
|
||
"tester.test()"
|
||
]
|
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},
|
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{
|
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}
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"name": "ipython",
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