How to use test_input_value method in Playwright Python

Best Python code snippet using playwright-python

maml_regression.py

Source:maml_regression.py Github

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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)...

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predict.py

Source:predict.py Github

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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) # 移动模型到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')...

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test_twitter_connector.py

Source:test_twitter_connector.py Github

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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="")...

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test.py

Source:test.py Github

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...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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Playwright tutorial

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