How to use outputs method in localstack

Best Python code snippet using localstack_python

models.py

Source:models.py

1import tensorflow as tf2from ..utils.tf_utils import activation_fun3def UNET(nb_classes, inputs):4    """Compile a UNET model.5    Args:6        nb_classes: the number of classes to predict7        inputs: the input tensor8    Returns:9        an output tensor, with 'nb_classes' of featuremaps10    """11    padding = 'same'12    # Conv block 113    outputs = tf.compat.v1.layers.conv2d(inputs, 64, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv1-1', use_bias=True)14    outputs = tf.nn.relu(outputs)15    16    outputs = tf.compat.v1.layers.conv2d(outputs, 64, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv1-2', use_bias=True)17    outputs = tf.nn.relu(outputs)18    # Make a copy of conv1 output tensor 19    conv1_output = outputs20    21    # Down-sample 122    outputs = tf.compat.v1.layers.max_pooling2d(outputs,pool_size = 2,strides = 2,padding=padding)23    24    # Conv block 225    outputs = tf.compat.v1.layers.conv2d(outputs, 128, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv2-1', use_bias=True)26    outputs = tf.nn.relu(outputs)27    outputs = tf.compat.v1.layers.conv2d(outputs, 128, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv2-2', use_bias=True)28    outputs = tf.nn.relu(outputs)29    # Make a copy of conv2 output tensor 30    conv2_output = outputs31    32    # Down-sample 233    outputs = tf.compat.v1.layers.max_pooling2d(outputs,pool_size = 2,strides = 2,padding=padding)34    35    # Conv block 336    outputs = tf.compat.v1.layers.conv2d(outputs, 256, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv3-1', use_bias=True)37    outputs = tf.nn.relu(outputs)38    outputs = tf.compat.v1.layers.conv2d(outputs, 256, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv3-2', use_bias=True)39    outputs = tf.nn.relu(outputs)40    # Make a copy of conv3 output tensor 41    conv3_output = outputs42    43    # Down-sample 344    outputs = tf.compat.v1.layers.max_pooling2d(outputs,pool_size = 2,strides = 2,padding=padding)45    46    # Conv block 447    outputs = tf.compat.v1.layers.conv2d(outputs, 512, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv4-1', use_bias=True)48    outputs = tf.nn.relu(outputs)49    outputs = tf.compat.v1.layers.conv2d(outputs, 512, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv4-2', use_bias=True)50    outputs = tf.nn.relu(outputs)51    # Make a copy of conv4 output tensor 52    conv4_output = outputs53    54    # Down-sample 455    outputs = tf.compat.v1.layers.max_pooling2d(outputs,pool_size = 2,strides = 2,padding=padding)56    57    # Get extracted feature for RPN58    rpn_feature = outputs59    60    61    # Conv block 562    outputs = tf.compat.v1.layers.conv2d(outputs, 1024, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv5-1', use_bias=True)63    outputs = tf.nn.relu(outputs)64    outputs = tf.compat.v1.layers.conv2d(outputs, 1024, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv5-2', use_bias=True)65    outputs = tf.nn.relu(outputs)66    67    68    # Up-sample(Conv_transpose) 469    outputs = tf.compat.v1.layers.conv2d_transpose(outputs, 512, 3, strides=(2, 2),70 padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), use_bias=True)71    outputs = tf.nn.relu(outputs)72    73    # changing the former line to the line below will connect the 4th layer on both ends, 74    # and form the classic U-Net architecture, but in our experiments we found this did 75    # not gain a better performance, also thanks for Meryem Uzun-Per for pointing this out76    77    # outputs = tf.concat([conv4_output, outputs], 3)78    79    # Conv block 4'80    outputs = tf.compat.v1.layers.conv2d(outputs, 512, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv4-3', use_bias=True)81    outputs = tf.nn.relu(outputs)82    outputs = tf.compat.v1.layers.conv2d(outputs, 512, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv4-4', use_bias=True)83    outputs = tf.nn.relu(outputs)84    85    # Up-sample(Conv_transpose) 386    outputs = tf.compat.v1.layers.conv2d_transpose(outputs, 256, 3, strides=(2, 2),87 padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), use_bias=True)88    outputs = tf.concat([conv3_output, outputs], 3)89    90    # Conv block 3'91    outputs = tf.compat.v1.layers.conv2d(outputs, 256, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv3-3', use_bias=True)92    outputs = tf.nn.relu(outputs)93    outputs = tf.compat.v1.layers.conv2d(outputs, 256, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv3-4', use_bias=True)94    outputs = tf.nn.relu(outputs)95    96    # Up-sample(Conv_transpose) 297    outputs = tf.compat.v1.layers.conv2d_transpose(outputs, 128, 3, strides=(2, 2),98 padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), use_bias=True)99    outputs = tf.concat([conv2_output, outputs], 3)100    101    # Conv block 2'102    outputs = tf.compat.v1.layers.conv2d(outputs, 128, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv2-3', use_bias=True)103    outputs = tf.nn.relu(outputs)104    outputs = tf.compat.v1.layers.conv2d(outputs, 128, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv2-4', use_bias=True)105    outputs = tf.nn.relu(outputs)106    107    # Up-sample(Conv_transpose) 1108    outputs = tf.compat.v1.layers.conv2d_transpose(outputs, 64, 3, strides=(2, 2),109 padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), use_bias=True)110    outputs = tf.concat([conv1_output, outputs], 3)111    112    # Conv block 2'113    outputs = tf.compat.v1.layers.conv2d(outputs, 64, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv1-3', use_bias=True)114    outputs = tf.nn.relu(outputs)115    outputs = tf.compat.v1.layers.conv2d(outputs, 64, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='conv1-4', use_bias=True)116    outputs = tf.nn.relu(outputs)117    118    # only output 2 featuremaps at the end119    outputs = tf.compat.v1.layers.conv2d(outputs, nb_classes, 3, padding=padding, kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), name='final', use_bias=False)120    ...

DCGAN.py

Source:DCGAN.py

1import torch2import torch.nn as nn3class Generator(nn.Module):4    def __init__(self):5        super(Generator, self).__init__()6        self.encoder_conv_1 = nn.Sequential(7            nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1),8            nn.LeakyReLU(inplace=False),9        )10        self.encoder_conv_2 = nn.Sequential(11            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),12            nn.LeakyReLU(inplace=False),13        )14        self.encoder_conv_3 = nn.Sequential(15            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),16            nn.LeakyReLU(inplace=False),17        )18        self.encoder_conv_4 = nn.Sequential(19            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),20            nn.LeakyReLU(inplace=False),21        )22        self.encoder_conv_5 = nn.Sequential(23            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),24            nn.LeakyReLU(inplace=False),25        )26        self.encoder_conv_6 = nn.Sequential(27            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),28            nn.LeakyReLU(inplace=False),29        )30        self.decoder_conv_1 = nn.Sequential(31            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),32            nn.ReLU(inplace=False),33            nn.Dropout(0.5),34        )35        self.decoder_conv_2 = nn.Sequential(36            nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1),37            nn.ReLU(inplace=False),38            nn.Dropout(0.5),39        )40        self.decoder_conv_3 = nn.Sequential(41            nn.ConvTranspose2d(1024, 256, kernel_size=4, stride=2, padding=1),42            nn.ReLU(inplace=False),43        )44        self.decoder_conv_4 = nn.Sequential(45            nn.ConvTranspose2d(512, 128, kernel_size=4, stride=2, padding=1),46            nn.ReLU(inplace=False),47        )48        self.decoder_conv_5 = nn.Sequential(49            nn.ConvTranspose2d(256, 64, kernel_size=4, stride=2, padding=1),50            nn.ReLU(inplace=False),51        )52        self.output = nn.Sequential(53            nn.Conv2d(64, 3, kernel_size=1, stride=1, padding=0),54            nn.Tanh(),55        )56    def forward(self, inputs):57        encoder_conv_1_outputs = self.encoder_conv_1(inputs)58        encoder_conv_2_outputs = self.encoder_conv_2(encoder_conv_1_outputs)59        encoder_conv_3_outputs = self.encoder_conv_3(encoder_conv_2_outputs)60        encoder_conv_4_outputs = self.encoder_conv_4(encoder_conv_3_outputs)61        encoder_conv_5_outputs = self.encoder_conv_5(encoder_conv_4_outputs)62        encoder_conv_6_outputs = self.encoder_conv_6(encoder_conv_5_outputs)63        decoder_conv_1_outputs = self.decoder_conv_1(encoder_conv_6_outputs)64        decoder_conv_2_outputs = self.decoder_conv_2(torch.cat([decoder_conv_1_outputs, encoder_conv_5_outputs], dim=1))65        decoder_conv_3_outputs = self.decoder_conv_3(torch.cat([decoder_conv_2_outputs, encoder_conv_4_outputs], dim=1))66        decoder_conv_4_outputs = self.decoder_conv_4(torch.cat([decoder_conv_3_outputs, encoder_conv_3_outputs], dim=1))67        decoder_conv_5_outputs = self.decoder_conv_5(torch.cat([decoder_conv_4_outputs, encoder_conv_2_outputs], dim=1))68        outputs = self.output(decoder_conv_5_outputs)69        return outputs70class Discriminator(nn.Module):71    def __init__(self):72        super(Discriminator, self).__init__()73        self.conv1 = nn.Sequential(74            nn.Conv2d(4, 64, kernel_size=4, stride=2, padding=1),75            nn.LeakyReLU(inplace=False),76            nn.BatchNorm2d(64),77        )78        self.conv2 = nn.Sequential(79            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),80            nn.LeakyReLU(inplace=False),81            nn.BatchNorm2d(128),82        )83        self.conv3 = nn.Sequential(84            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),85            nn.LeakyReLU(inplace=False),86            nn.BatchNorm2d(256),87        )88        self.conv4 = nn.Sequential(89            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),90            nn.LeakyReLU(inplace=False),91            nn.BatchNorm2d(512),92        )93        self.conv5 = nn.Sequential(94            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),95            nn.LeakyReLU(inplace=False),96            nn.BatchNorm2d(512),97        )98        self.output = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=0)99    def forward(self, inputs):100        outputs = self.conv1(inputs)101        outputs = self.conv2(outputs)102        outputs = self.conv3(outputs)103        outputs = self.conv4(outputs)104        outputs = self.conv5(outputs)105        outputs = self.output(outputs)...

test_cleaner.py

Source:test_cleaner.py

1# coding: utf-82import os3import shutil4from abiflows.core.mastermind_abc import Cleaner5from abiflows.core.testing import AbiflowsTest6test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", "..",7                        "test_files")8class TestCleaner(AbiflowsTest):9    def test_cleaner(self):10        # Keep current working directory, create tmp directory and change to tmp directory11        cwd = os.getcwd()12        tmp_dir = '_tmp_cleaner'13        if os.path.exists(tmp_dir):14            shutil.rmtree(tmp_dir)15        os.makedirs(tmp_dir)16        os.chdir(tmp_dir)17        tmp_abs_dir = os.getcwd()18        # Create a list of files and directories19        os.makedirs('outputs')20        os.makedirs('outputs/formatted')21        os.makedirs('outputs/text')22        os.makedirs('results')23        os.makedirs('temporary')24        open('somefile.txt', "w").close()25        open('somefile.txt.backup', "w").close()26        open('outputs/text/text1.abc', "w").close()27        open('outputs/text/text2.abc', "w").close()28        open('outputs/text/text3.abc', "w").close()29        open('outputs/text/text1.def', "w").close()30        open('outputs/text/text15.def', "w").close()31        open('outputs/formatted/formatted1.txt', "w").close()32        open('outputs/formatted/formatted2.txt', "w").close()33        open('outputs/formatted/formatted3.log', "w").close()34        open('outputs/formatted/formatted4.log', "w").close()35        open('outputs/formatted/formatted5.log', "w").close()36        open('outputs/formatted/formatted6.bin', "w").close()37        open('outputs/formatted/formatted7.bog', "w").close()38        open('outputs/formatted/formatted8.beg', "w").close()39        open('temporary/item.log', "w").close()40        open('temporary/result.txt', "w").close()41        # Create a first cleaner42        cleaner1 = Cleaner(dirs_and_patterns=[{'directory': 'outputs/text',43                                               'patterns': ['text?.abc']}])44        cleaner1.clean(root_directory=tmp_abs_dir)45        # Check that the first cleaner did his job correctly46        self.assertTrue(os.path.exists('outputs/text/text1.def'))47        self.assertTrue(os.path.exists('outputs/text/text15.def'))48        self.assertFalse(os.path.exists('outputs/text/text1.abc'))49        self.assertFalse(os.path.exists('outputs/text/text2.abc'))50        self.assertFalse(os.path.exists('outputs/text/text3.abc'))51        # Create a second cleaner52        cleaner2 = Cleaner(dirs_and_patterns=[{'directory': '.',53                                               'patterns': ['temporary']},54                                              {'directory': 'outputs/formatted',55                                               'patterns': ['*[1-4].log', '*.b?g']}])56        cleaner2.clean(root_directory=tmp_abs_dir)57        # Check that the first cleaner did his job correctly58        self.assertTrue(os.path.exists('outputs/formatted/formatted1.txt'))59        self.assertTrue(os.path.exists('outputs/formatted/formatted2.txt'))60        self.assertFalse(os.path.exists('outputs/formatted/formatted3.log'))61        self.assertFalse(os.path.exists('outputs/formatted/formatted4.log'))62        self.assertTrue(os.path.exists('outputs/formatted/formatted5.log'))63        self.assertTrue(os.path.exists('outputs/formatted/formatted6.bin'))64        self.assertFalse(os.path.exists('outputs/formatted/formatted7.bog'))65        self.assertFalse(os.path.exists('outputs/formatted/formatted8.beg'))66        self.assertFalse(os.path.exists('temporary'))67        self.assertTrue(os.path.exists('outputs/formatted'))68        # Change back to the initial working directory and remove the tmp directory69        os.chdir(cwd)...

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