How to use test_input_value method in Playwright Python

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maml_regression.py

Source:maml_regression.py

1import torch2import torchvision3import torch.nn as nn4import numpy as np5import matplotlib.pyplot as plt6import pandas as pd7from sklearn.model_selection import train_test_split8import torchmeta9from torchmeta.modules import (MetaModule, MetaSequential, MetaLinear)10import os11import torch12import torch.nn.functional as F13from tqdm import tqdm14import logging15from torchmeta.utils.data import BatchMetaDataLoader16from torchmeta.utils.gradient_based import gradient_update_parameters17class RegressionNeuralNetwork(MetaModule):18    def __init__(self, in_channels, hidden1_size=40, hidden2_size=80):19        super(RegressionNeuralNetwork, self).__init__()20        self.in_channels = in_channels21        self.hidden1_size = hidden1_size22        self.hidden2_size = hidden2_size23        self.regressor = MetaSequential(24            MetaLinear(in_channels, hidden1_size),25            nn.ReLU(),26            MetaLinear(hidden1_size, hidden2_size),27            nn.ReLU(),28            MetaLinear(hidden2_size, hidden1_size),29            nn.ReLU(),30            MetaLinear(hidden1_size, 1)31        )32    def forward(self, inputs, params=None):33        values = self.regressor(inputs, params=self.get_subdict(params, 'regressor'))34        # values = values.view(values.size(0),-1)35        return values36class RegressionNeuralNetwork_v2(MetaModule):37    def __init__(self, in_channels, hidden1_size=40, hidden2_size=80):38        super(RegressionNeuralNetwork_v2, self).__init__()39        self.in_channels = in_channels40        self.hidden1_size = hidden1_size41        self.hidden2_size = hidden2_size42        self.regressor = MetaSequential(43            MetaLinear(in_channels, hidden1_size),44            nn.LeakyReLU(),45            MetaLinear(hidden1_size, hidden2_size),46            nn.LeakyReLU(),47            MetaLinear(hidden2_size, hidden1_size),48            nn.LeakyReLU(),49            MetaLinear(hidden1_size, 1)50        )51    def forward(self, inputs, params=None):52        values = self.regressor(inputs, params=self.get_subdict(params, 'regressor'))53        # values = values.view(values.size(0),-1)54        return values55def meta_train(args, metaDataloader):56    model = RegressionNeuralNetwork(args['in_channels'], hidden1_size=args['hidden1_size'],57                                    hidden2_size=args['hidden2_size'])58    model.train()59    meta_optimizer = torch.optim.Adam(model.parameters(), lr=args['beta'])60    loss_record = []61    # training loop62    for it_outer in range(args['num_it_outer']):63        model.zero_grad()64        train_dataloader = metaDataloader['train']65        test_dataloader = metaDataloader['test']66        outer_loss = torch.tensor(0., dtype=torch.float)67        for task in train_dataloader:68            iterator = iter(train_dataloader[task])69            train_sample = iterator.next()70            # get true h value71            # h_value = torch.tensor(train_sample[:,-1], dtype=torch.float)72            h_value = train_sample[:, -1].clone().detach().to(dtype=torch.float)73            # get input74            # input_value = torch.tensor(train_sample[:,:-1], dtype=torch.float)75            input_value = train_sample[:, :-1].clone().detach().to(dtype=torch.float)76            #77            train_h_value = model(input_value)78            inner_loss = F.mse_loss(train_h_value.view(-1, 1), h_value.view(-1, 1))79            model.zero_grad()80            # print('It {}, task {}, Start updating parameters'.format(it_outer, task))81            params = gradient_update_parameters(model, inner_loss, step_size=args['alpha'],82                                                first_order=args['first_order'])83            # adaptation84            # get test sample85            test_iterator = iter(test_dataloader[task])86            test_sample = test_iterator.next()87            # h_value2 = torch.tensor(test_sample[:,-1], dtype=torch.float)88            h_value2 = test_sample[:, -1].clone().detach().to(dtype=torch.float)89            # test_input_value = torch.tensor(test_sample[:,:-1], dtype=torch.float)90            test_input_value = test_sample[:, :-1].clone().detach().to(dtype=torch.float)91            test_h_value = model(test_input_value, params=params)92            outer_loss += F.mse_loss(test_h_value.view(-1, 1), h_value2.view(-1, 1))93        outer_loss.div_(args['num_tasks'])94        outer_loss.backward()95        meta_optimizer.step()96        loss_record.append(outer_loss.detach())97        if it_outer % 50 == 0:98            print('It {}, outer traning loss: {}'.format(it_outer, outer_loss))99            # print the loss plot100    plt.plot(loss_record)101    plt.title('Outer Training Loss (MSE Loss) in MAML')102    plt.xlabel('Iteration number')103    plt.show()104    # save model105    if args['output_model'] is not None:106        with open(args['output_model'], 'wb') as f:107            state_dict = model.state_dict()108            torch.save(state_dict, f)109def meta_train_v2(args, metaDataloader):110    model = RegressionNeuralNetwork_v2(args['in_channels'], hidden1_size=args['hidden1_size'],111                                    hidden2_size=args['hidden2_size'])112    model.train()113    meta_optimizer = torch.optim.Adam(model.parameters(), lr=args['beta'])114    loss_record = []115    # training loop116    for it_outer in range(args['num_it_outer']):117        model.zero_grad()118        train_dataloader = metaDataloader['train']119        test_dataloader = metaDataloader['test']120        outer_loss = torch.tensor(0., dtype=torch.float)121        for task in train_dataloader:122            iterator = iter(train_dataloader[task])123            train_sample = iterator.next()124            # get true h value125            # h_value = torch.tensor(train_sample[:,-1], dtype=torch.float)126            h_value = train_sample[:, -1].clone().detach().to(dtype=torch.float)127            # get input128            # input_value = torch.tensor(train_sample[:,:-1], dtype=torch.float)129            input_value = train_sample[:, :-1].clone().detach().to(dtype=torch.float)130            #131            train_h_value = model(input_value)132            inner_loss = F.mse_loss(train_h_value.view(-1, 1), h_value.view(-1, 1))133            model.zero_grad()134            # print('It {}, task {}, Start updating parameters'.format(it_outer, task))135            params = gradient_update_parameters(model, inner_loss, step_size=args['alpha'],136                                                first_order=args['first_order'])137            # adaptation138            # get test sample139            test_iterator = iter(test_dataloader[task])140            test_sample = test_iterator.next()141            # h_value2 = torch.tensor(test_sample[:,-1], dtype=torch.float)142            h_value2 = test_sample[:, -1].clone().detach().to(dtype=torch.float)143            # test_input_value = torch.tensor(test_sample[:,:-1], dtype=torch.float)144            test_input_value = test_sample[:, :-1].clone().detach().to(dtype=torch.float)145            test_h_value = model(test_input_value, params=params)146            outer_loss += F.mse_loss(test_h_value.view(-1, 1), h_value2.view(-1, 1))147        outer_loss.div_(args['num_tasks'])148        outer_loss.backward()149        meta_optimizer.step()150        loss_record.append(outer_loss.detach())151        if it_outer % 50 == 0:152            print('It {}, outer traning loss: {}'.format(it_outer, outer_loss))153            # print the loss plot154    plt.plot(loss_record)155    plt.title('Outer Training Loss (MSE Loss) in MAML')156    plt.xlabel('Iteration number')157    plt.show()158    # save model159    if args['output_model'] is not None:160        with open(args['output_model'], 'wb') as f:161            state_dict = model.state_dict()162            torch.save(state_dict, f)163def fine_tune(args, model, dataloader, validation_set):164    # set the model to be train mode165    model.train()166    # set the optimizer167    opt = torch.optim.Adam(model.parameters(), lr=args['learning_rate'])168    #169    train_it = 0170    loss_record = []171    validation_loss_record = []172    flag_stop = False173    for ep in range(args['epoch']):174        print("Run Epoch {}".format(ep))175        for data in dataloader:176            # input_value = torch.tensor(data[:,:-1], dtype=torch.float)177            input_value = data[:, :-1].clone().detach().to(dtype=torch.float)178            # h_value = torch.tensor(data[:,-1], dtype=torch.float)179            h_value = data[:, -1].clone().detach().to(dtype=torch.float)180            # zero out gradients181            opt.zero_grad()182            # forward recursion183            est_h_value = model(input_value)184            # loss185            loss = F.mse_loss(est_h_value.view(-1, 1), h_value.view(-1, 1))186            # backward recursion187            loss.backward()188            # update the weights189            opt.step()190            # calculate validation loss191            loss_record.append(loss.detach())192            with torch.no_grad():193                input_value_val = torch.tensor(validation_set[:, :-1], dtype=torch.float)194                h_value_val = torch.tensor(validation_set[:, -1], dtype=torch.float)195                est_h_value_val = model(input_value_val)196                val_loss = F.mse_loss(est_h_value_val.view(-1, 1), h_value_val.view(-1, 1))197                validation_loss_record.append(val_loss)198            if train_it % 5 == 0:199                print("It {}, L2 training loss: {} ".format(train_it, loss.item()))200                print("It {}, L2 validation loss: {}".format(train_it, val_loss.item()))201            if train_it > args['Max_it']:202                print('Stop fine-tuning')203                flag_stop = True204                break205            train_it += 1206        if flag_stop == True:207            break208    # print the loss plot209    fig = plt.figure(figsize=(10, 5))210    ax1 = plt.subplot(121)211    ax1.plot(loss_record)212    ax1.title.set_text('Training Loss (MSE Loss)')213    ax1.set_xlabel('Iteration Number')214    ax2 = plt.subplot(122)215    ax2.plot(validation_loss_record)216    ax2.title.set_text('Validation Loss (MSE Loss)')217    ax2.set_xlabel('Iteration Number')218    plt.show()219    # save the model220    if args['output_model'] is not None:221        with open(args['output_model'], 'wb') as f:222            state_dict = model.state_dict()223            torch.save(state_dict, f)...

predict.py

Source:predict.py

1import os2import torch3import torch.utils.data as Data4import numpy as np5import matplotlib.pyplot as plt6from network import DeepSeparator7model_name = 'DeepSeparator'8# choose one sample for visualization9index = 3510test_input = np.load('../data/test_input.npy')11test_input = test_input[index]12test_output = np.load('../data/test_output.npy')13test_output = test_output[index]14test_input = torch.from_numpy(test_input)15test_output = torch.from_numpy(test_output)16test_input = torch.unsqueeze(test_input, 0)17test_output = torch.unsqueeze(test_output, 0)18test_torch_dataset = Data.TensorDataset(test_input)19print("torch.cuda.is_available() = ", torch.cuda.is_available())20device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")21model = DeepSeparator()22model.to(device)  # ç§»å¨æ¨¡åå°cuda23if os.path.exists('checkpoint/' + model_name + '.pkl'):24    print('load model')25    model.load_state_dict(torch.load('checkpoint/' + model_name + '.pkl'))26test_input = test_input.float().to(device)27extracted_signal = model(test_input, 0)  # 0 for denoising, 1 for extracting artifact28extracted_artifact = model(test_input, 1)  # 0 for denoising, 1 for extracting artifact29test_input_value = test_input.cpu()30test_input_value = test_input_value.detach().numpy()31test_input_value = test_input_value[0]32test_output_value = test_output.cpu()33test_output_value = test_output_value.detach().numpy()34test_output_value = test_output_value[0]35extracted_signal_value = extracted_signal.cpu()36extracted_signal_value = extracted_signal_value.detach().numpy()37extracted_signal_value = extracted_signal_value[0]38extracted_artifact_value = extracted_artifact.cpu()39extracted_artifact_value = extracted_artifact_value.detach().numpy()40extracted_artifact_value = extracted_artifact_value[0]41l0, = plt.plot(test_input_value)42l1, = plt.plot(extracted_signal_value)43# l2, = plt.plot(extracted_artifact_value)44l3, = plt.plot(test_output_value)45# plt.legend([l0, l1, l2, l3], ['Raw EEG', 'Denoised EEG', 'Extracted Artifact', 'Clean EEG'], loc='upper right')46plt.legend([l0, l1, l3], ['Raw EEG', 'Denoised EEG', 'Clean EEG'], loc='upper right')...

test_twitter_connector.py

Source:test_twitter_connector.py

1#!/usr/bin/env python2import sys3import os.path4sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))5sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.join(os.path.pardir, 'tweetbot'))))6import tweet_config, twitter_connector7from argparse import ArgumentParser8from unittest import mock9def test_twitter_connector_init():10    # set test config file to use11    #test_config_file = 'tests/test_files/test_config.ini'12    #tc = tweet_config.tweet_config(config_file=test_config_file)13    tc = tweet_config.tweet_config()14    # create tweet_config object15    tconn = twitter_connector.twitter_connector(tweet_config=tc)16    assert isinstance(tconn, twitter_connector.twitter_connector)17@mock.patch('twitter_connector.quote')18def test_percent_encode(mocked_twitter_connector_quote):19    #tc = tweet_config.tweet_config(config_file=test_config_file)20    tc = tweet_config.tweet_config()21    # create tweet_config object22    tconn = twitter_connector.twitter_connector(tweet_config=tc)23    # set test string24    test_input_value = 'this is a test string'25    # call method we want to test26    output = tconn._percent_encode(test_input_value)27    mocked_twitter_connector_quote.assert_called_with(test_input_value, safe="")...

test.py

Source:test.py

...9        """10        data = [1, 2, 3]11        result = find_average(data)12        self.assertEqual(result, 2.0)13    def test_input_value(self):14        """15        Provide an assertion level for arg input16        """17        18        self.assertRaises(TypeError, find_average, True)19class TestCountOccurence(unittest.TestCase):20    def test_count_occurence(self):21        """22        Test that it returns the count of each unique values in the given list23        """24        data = [0,0,9,0,8,9,0,7]25        result = count_occurence(data)26        output = {0: 4, 9: 2, 8: 1, 7: 1}27        self.assertAlmostEqual(result, output)28    def test_input_value(self):29        """30        Provide an assertion level for arg input31        """32        self.assertRaises(TypeError, count_occurence, True)33if __name__ == '__main__':...

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