{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 使用Trainer和Tester快速训练和测试" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数据读入和处理" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "In total 3 datasets:\n", "\ttest has 1821 instances.\n", "\ttrain has 67349 instances.\n", "\tdev has 872 instances.\n", "In total 2 vocabs:\n", "\twords has 16292 entries.\n", "\ttarget has 2 entries.\n", "\n", "+-----------------------------------+--------+-----------------------------------+---------+\n", "| raw_words | target | words | seq_len |\n", "+-----------------------------------+--------+-----------------------------------+---------+\n", "| hide new secretions from the p... | 1 | [4110, 97, 12009, 39, 2, 6843,... | 7 |\n", "+-----------------------------------+--------+-----------------------------------+---------+\n", "Vocabulary(['hide', 'new', 'secretions', 'from', 'the']...)\n" ] } ], "source": [ "from fastNLP.io import SST2Pipe\n", "\n", "pipe = SST2Pipe()\n", "databundle = pipe.process_from_file()\n", "vocab = databundle.get_vocab('words')\n", "print(databundle)\n", "print(databundle.get_dataset('train')[0])\n", "print(databundle.get_vocab('words'))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4925 872 75\n" ] } ], "source": [ "train_data = databundle.get_dataset('train')[:5000]\n", "train_data, test_data = train_data.split(0.015)\n", "dev_data = databundle.get_dataset('dev')\n", "print(len(train_data),len(dev_data),len(test_data))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------+-----------+--------+-------+---------+\n", "| field_names | raw_words | target | words | seq_len |\n", "+-------------+-----------+--------+-------+---------+\n", "| is_input | False | False | True | True |\n", "| is_target | False | True | False | False |\n", "| ignore_type | | False | False | False |\n", "| pad_value | | 0 | 0 | 0 |\n", "+-------------+-----------+--------+-------+---------+\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_data.print_field_meta()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from fastNLP import AccuracyMetric\n", "from fastNLP import Const\n", "\n", "# metrics=AccuracyMetric() 在本例中与下面这行代码等价\n", "metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DataSetIter初探" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n", " 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n", " 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n", " 1323, 4398, 7],\n", " [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n", " 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n", " 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0]]), 'seq_len': tensor([33, 21])}\n", "batch_y: {'target': tensor([1, 0])}\n", "batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],\n", " [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}\n", "batch_y: {'target': tensor([0, 1])}\n", "batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],\n", " [15618, 3204, 5, 1675, 0]]), 'seq_len': tensor([5, 4])}\n", "batch_y: {'target': tensor([1, 1])}\n", "batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n", " 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],\n", " [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n", " 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}\n", "batch_y: {'target': tensor([0, 0])}\n", "batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n", " 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],\n", " [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n", " 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 12])}\n", "batch_y: {'target': tensor([0, 1])}\n" ] } ], "source": [ "from fastNLP import BucketSampler\n", "from fastNLP import DataSetIter\n", "\n", "tmp_data = dev_data[:10]\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", "sampler = BucketSampler(batch_size=2, seq_len_field_name='seq_len')\n", "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n", "for batch_x, batch_y in batch:\n", " print(\"batch_x: \",batch_x)\n", " print(\"batch_y: \", batch_y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n", " 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n", " 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n", " 1323, 4398, 7],\n", " [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n", " 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n", " 7, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n", " -1, -1, -1]]), 'seq_len': tensor([33, 21])}\n", "batch_y: {'target': tensor([1, 0])}\n", "batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],\n", " [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}\n", "batch_y: {'target': tensor([0, 1])}\n", "batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n", " 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],\n", " [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n", " 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}\n", "batch_y: {'target': tensor([0, 0])}\n", "batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],\n", " [15618, 3204, 5, 1675, -1]]), 'seq_len': tensor([5, 4])}\n", "batch_y: {'target': tensor([1, 1])}\n", "batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n", " 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],\n", " [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n", " 1217, 7, -1, -1, -1, -1, -1, -1, -1, -1]]), 'seq_len': tensor([20, 12])}\n", "batch_y: {'target': tensor([0, 1])}\n" ] } ], "source": [ "tmp_data.set_pad_val('words',-1)\n", "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n", "for batch_x, batch_y in batch:\n", " print(\"batch_x: \",batch_x)\n", " print(\"batch_y: \", batch_y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "batch_x: {'words': tensor([[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,\n", " 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,\n", " 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])}\n", "batch_y: {'target': tensor([1, 0])}\n", "batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,\n", " 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,\n", " 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,\n", " 1323, 4398, 7, 0, 0, 0, 0, 0, 0, 0],\n", " [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,\n", " 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,\n", " 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([33, 21])}\n", "batch_y: {'target': tensor([1, 0])}\n", "batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0],\n", " [ 14, 10, 437, 32, 78, 3, 78, 437, 7, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0]]), 'seq_len': tensor([9, 9])}\n", "batch_y: {'target': tensor([0, 1])}\n", "batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,\n", " 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,\n", " 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])}\n", "batch_y: {'target': tensor([0, 0])}\n", "batch_x: {'words': tensor([[ 4, 277, 685, 18, 7, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [15618, 3204, 5, 1675, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])}\n", "batch_y: {'target': tensor([1, 1])}\n" ] } ], "source": [ "from fastNLP.core.field import Padder\n", "import numpy as np\n", "class FixLengthPadder(Padder):\n", " def __init__(self, pad_val=0, length=None):\n", " super().__init__(pad_val=pad_val)\n", " self.length = length\n", " assert self.length is not None, \"Creating FixLengthPadder with no specific length!\"\n", "\n", " def __call__(self, contents, field_name, field_ele_dtype, dim):\n", " #计算当前contents中的最大长度\n", " max_len = max(map(len, contents))\n", " #如果当前contents中的最大长度大于指定的padder length的话就报错\n", " assert max_len <= self.length, \"Fixed padder length smaller than actual length! with length {}\".format(max_len)\n", " array = np.full((len(contents), self.length), self.pad_val, dtype=field_ele_dtype)\n", " for i, content_i in enumerate(contents):\n", " array[i, :len(content_i)] = content_i\n", " return array\n", "\n", "#设定FixLengthPadder的固定长度为40\n", "tmp_padder = FixLengthPadder(pad_val=0,length=40)\n", "#利用dataset的set_padder函数设定words field的padder\n", "tmp_data.set_padder('words',tmp_padder)\n", "batch = DataSetIter(batch_size=2, dataset=tmp_data, sampler=sampler)\n", "for batch_x, batch_y in batch:\n", " print(\"batch_x: \",batch_x)\n", " print(\"batch_y: \", batch_y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 使用DataSetIter自己编写训练过程\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----start training-----\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 2.68 seconds!\n", "Epoch 0 Avg Loss: 0.66 AccuracyMetric: acc=0.708716 29307ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.38 seconds!\n", "Epoch 1 Avg Loss: 0.41 AccuracyMetric: acc=0.770642 52200ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.51 seconds!\n", "Epoch 2 Avg Loss: 0.16 AccuracyMetric: acc=0.747706 70268ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.96 seconds!\n", "Epoch 3 Avg Loss: 0.06 AccuracyMetric: acc=0.741972 90349ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 1.04 seconds!\n", "Epoch 4 Avg Loss: 0.03 AccuracyMetric: acc=0.740826 114250ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.8 seconds!\n", "Epoch 5 Avg Loss: 0.02 AccuracyMetric: acc=0.738532 134742ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.65 seconds!\n", "Epoch 6 Avg Loss: 0.01 AccuracyMetric: acc=0.731651 154503ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.8 seconds!\n", "Epoch 7 Avg Loss: 0.01 AccuracyMetric: acc=0.738532 175397ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.36 seconds!\n", "Epoch 8 Avg Loss: 0.01 AccuracyMetric: acc=0.733945 192384ms\n" ] }, { "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…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.84 seconds!\n", "Epoch 9 Avg Loss: 0.01 AccuracyMetric: acc=0.744266 214417ms\n" ] }, { "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=5.0), HTML(value='')), layout=Layout(disp…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "Evaluate data in 0.04 seconds!\n", "[tester] \n", "AccuracyMetric: acc=0.786667\n" ] }, { "data": { "text/plain": [ "{'AccuracyMetric': {'acc': 0.786667}}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "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()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python Now", "language": "python", "name": "now" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 2 }