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How to use get_stage method in localstack

Best Python code snippet using localstack_python

hrnet.py

Source:hrnet.py

...134        # self.bn2 = tf.keras.layers.BatchNormalization(momentum=0.1, epsilon=1e-5)135        # self.layer1 = make_bottleneck_layer(filter_num=64, blocks=4)136        # self.transition1 = self.__make_transition_layer(previous_branches_num=1,137        #                                                 previous_channels=[256],138        #                                                 current_branches_num=self.config_params.get_stage("s2")[1],139        #                                                 current_channels=self.config_params.get_stage("s2")[0])140        # self.stage2 = self.__make_stages("s2", self.config_params.get_stage("s2")[0])141        # self.transition2 = self.__make_transition_layer(previous_branches_num=self.config_params.get_stage("s2")[1],142        #                                                 previous_channels=self.config_params.get_stage("s2")[0],143        #                                                 current_branches_num=self.config_params.get_stage("s3")[1],144        #                                                 current_channels=self.config_params.get_stage("s3")[0])145        # self.stage3 = self.__make_stages("s3", self.config_params.get_stage("s3")[0])146        # self.transition3 = self.__make_transition_layer(previous_branches_num=self.config_params.get_stage("s3")[1],147        #                                                 previous_channels=self.config_params.get_stage("s3")[0],148        #                                                 current_branches_num=self.config_params.get_stage("s4")[1],149        #                                                 current_channels=self.config_params.get_stage("s4")[0])150        # self.stage4 = self.__make_stages("s4", self.config_params.get_stage("s4")[0], False)151        # self.conv3 = tf.keras.layers.Conv2D(filters=self.config_params.num_of_joints,152        #                                     kernel_size=self.config_params.conv3_kernel,153        #                                     strides=1,154        #                                     padding="same")155    # def __choose_config(self, config_name):156    #     return get_config_params(config_name)157    def __make_stages(self, inputs, stage_name, in_channels, multi_scale_output=True):158        stage_info = self.config_params.get_stage(stage_name)159        channels, num_branches, num_modules, block, num_blocks, fusion_method = stage_info160        fusion = []161        for i in range(num_modules):162            if not multi_scale_output and i == num_modules - 1:163                reset_multi_scale_output = False164            else:165                reset_multi_scale_output = True166            module_list = HighResolutionModule(num_branches=num_branches,167                                               num_in_channels=in_channels,168                                               num_channels=channels,169                                               block=block,170                                               num_blocks=num_blocks,171                                               fusion_method=fusion_method,172                                               multi_scale_output=reset_multi_scale_output)173            fusion = module_list.call(inputs)174        return fusion175    @staticmethod176    def __make_transition_layer(x, previous_branches_num, previous_channels, current_branches_num, current_channels):177        transition_layers = []178        for i in range(current_branches_num):179            if i < previous_branches_num:180                if current_channels[i] != previous_channels[i]:181                    temp = _conv2d_layer(name="trans1"+str(i), input=x, filters=current_channels[i], kernel_size=3,182                                         strides=1, padding="SAME", use_bias=False)183                    temp = _batch_norm(inputs=temp, momentum=0.1, epsilon=1e-5)184                    transition_layers.append(temp)185                    # transition_layers.append(186                    #     tf.keras.Sequential([187                    #         tf.keras.layers.Conv2D(filters=current_channels[i], kernel_size=(3, 3), strides=1, padding="same", use_bias=False),188                    #         tf.keras.layers.BatchNormalization(momentum=0.1, epsilon=1e-5),189                    #         tf.keras.layers.ReLU()190                    #     ])191                    # )192                else:193                    transition_layers.append(x)194            else:195                down_sampling_layers = []196                for j in range(i + 1 - previous_branches_num):197                    in_channels = previous_channels[-1],198                    out_channels = current_channels[i] if j == i - previous_branches_num else in_channels199                    with flow.scope.namespace('transition_layers_'+str(j)):200                        temp = _conv2d_layer(name="fuse11", input=x, filters=out_channels,201                                             kernel_size=3, strides=2, padding="SAME", use_bias=False)202                        temp = _batch_norm(inputs=temp, momentum=0.1, epsilon=1e-5)203                        temp = flow.nn.relu(temp)204                        down_sampling_layers.append(temp)205                        # down_sampling_layers.append(206                        #     tf.keras.Sequential([207                        #         tf.keras.layers.Conv2D(filters=out_channels, kernel_size=(3, 3), strides=2,208                        #                                padding="same", use_bias=False),209                        #         tf.keras.layers.BatchNormalization(momentum=0.1, epsilon=1e-5),210                        #         tf.keras.layers.ReLU()211                        #     ])212                        # )213                transition_layers.append(down_sampling_layers)214        return transition_layers215    def call(self, inputs, training=None):#mask=None216        x = _conv2d_layer(name="conv1", input=inputs, filters=64,217                          kernel_size=3, strides=2, padding="SAME", use_bias=False)218        # x = self.conv1(inputs)219        x = _batch_norm(inputs=x, momentum=0.1, epsilon=1e-5)220        # x = self.bn1(x, training=training)221        x = flow.nn.relu(x)222        # x = tf.nn.relu(x)223        x = _conv2d_layer(name="conv2", input=x, filters=64,224                          kernel_size=3, strides=2, padding="SAME", use_bias=False)225        # x = self.conv2(x)226        x = _batch_norm(inputs=x, momentum=0.1, epsilon=1e-5)227        # x = self.bn2(x, training=training)228        x = flow.nn.relu(x)229        # x = tf.nn.relu(x)230        x = make_bottleneck_layer(x, training=training, filter_num=64, blocks=4)231        # x = self.layer1(x, training=training)232        feature_list = []233        for i in range(self.config_params.get_stage("s2")[1]):234            result = self.__make_transition_layer(x=x,235                                                  previous_branches_num=1,236                                                  previous_channels=[256],237                                                  current_branches_num=self.config_params.get_stage("s2")[1],238                                                  current_channels=self.config_params.get_stage("s2")[0])239            if result[i] is not None:240                feature_list.append(result[i])241            # if self.transition1[i] is not None:242            #     feature_list.append(self.transition1[i](x, training=training))243            else:244                feature_list.append(x)245        y_list = self.__make_stages(feature_list, "s2", self.config_params.get_stage("s2")[0])246        # y_list = self.stage2(feature_list, training=training)247        feature_list = []248        for i in range(self.config_params.get_stage("s3")[1]):249            result = self.__make_transition_layer(x=y_list[-1],250                                                  previous_branches_num=self.config_params.get_stage("s2")[1],251                                                  previous_channels=self.config_params.get_stage("s2")[0],252                                                  current_branches_num=self.config_params.get_stage("s3")[1],253                                                  current_channels=self.config_params.get_stage("s3")[0])254            if result[i] is not None:255                feature_list.append(result[i])256            # if self.transition2[i] is not None:257            #     feature_list.append(self.transition2[i](y_list[-1], training=training))258            else:259                feature_list.append(y_list[i])260        y_list = self.__make_stages(feature_list, "s3", self.config_params.get_stage("s3")[0])261        # y_list = self.stage3(feature_list, training=training)262        feature_list = []263        for i in range(self.config_params.get_stage("s4")[1]):264            result = self.__make_transition_layer(x=y_list[-1],265                                                  previous_branches_num=self.config_params.get_stage("s3")[1],266                                                  previous_channels=self.config_params.get_stage("s3")[0],267                                                  current_branches_num=self.config_params.get_stage("s4")[1],268                                                  current_channels=self.config_params.get_stage("s4")[0])269            if result[i] is not None:270                feature_list.append(result[i])271        # for i in range(self.config_params.get_stage("s4")[1]):272        #     if self.transition3[i] is not None:273        #         feature_list.append(self.transition3[i](y_list[-1], training=training))274            else:275                feature_list.append(y_list[i])276        y_list = self.__make_stages(feature_list, "s4", self.config_params.get_stage("s4")[0], False)277        # y_list = self.stage4(feature_list, training=training)278        outputs = _conv2d_layer(name="conv3",279                                input=y_list[0],280                                filters=self.config_params.num_of_joints,281                                kernel_size=self.config_params.conv3_kernel,282                                strides=1,283                                padding="SAME")284        # outputs = self.conv3(y_list[0])...

test_dungeon.py

Source:test_dungeon.py

...21    d = dungeon.Dungeon(hero)22    d.mk_next_stage.assert_called_once()23def test_dungeon_init__hero_exists_on_stage(hero):24    d = dungeon.Dungeon(hero)25    m = d.get_stage()26    assert any([True for e in m.entities if e.has_comp('human')])27def test_dungeon_init__move_hero_called(mocker, hero):28    mocker.patch.object(dungeon.Dungeon, 'move_hero')29    d = dungeon.Dungeon(hero)30    d.move_hero.assert_called_once()31def test_dungeon_init__populate_called(mocker, hero):32    mocker.patch.object(stages.Stage, 'populate')33    d = dungeon.Dungeon(hero)34    m = d.get_stage()35    m.populate.assert_called_once()36def test_get_stage__1_level(hero):37    d = dungeon.Dungeon(hero)38    m = d.get_stage()39    assert m.dungeon_lvl == d.current_stage + 140def test_get_stage__2_stages(hero):41    d = dungeon.Dungeon(hero)42    d.mk_next_stage()43    m = d.get_stage()44    assert m.dungeon_lvl == d.current_stage + 145    d.current_stage = 146    m = d.get_stage()47    assert m.dungeon_lvl == d.current_stage + 148# def test_place_hero(, level):49    # Should this take the hero as a parameter?50    # Test that the hero is put somewhere51def test_mk_next_stage__stages_increases(hero):52    d = dungeon.Dungeon(hero)53    assert len(d.stages) == 154    d.mk_next_stage()55    assert len(d.stages) == 256def test_mk_next_stage__stages_are_numbered_correctly(hero):57    d = dungeon.Dungeon(hero)58    assert d.stages[0].dungeon_lvl == 159    d.mk_next_stage()60    assert d.stages[1].dungeon_lvl == 261def test_hero_at_stairs__valid_returns_True(hero):62    d = dungeon.Dungeon(hero)63    down_stair = d.get_stage().find_stair('>')64    d.hero.x, d.hero.y = down_stair.x, down_stair.y65    assert d.hero_at_stairs('>')66def test_hero_at_stairs__invalid_returns_False(hero):67    d = dungeon.Dungeon(hero)68    assert d.hero_at_stairs('>') is False69def test_hero_at_stairs__starting_upstair_returns_True(hero):70    d = dungeon.Dungeon(hero)71    # Hero starts on an upstair, so this should be True.72    assert d.hero_at_stairs('<')73def test_move_downstairs__not_on_down_stair_returns_False(hero):74    d = dungeon.Dungeon(hero)75    d.mk_next_stage()76    result = d.move_downstairs()77    assert result is False78def test_move_downstairs__hero_moved_to_next_upstair(hero):79    d = dungeon.Dungeon(hero)80    down_stair = d.get_stage().find_stair('>')81    d.hero.x, d.hero.y = down_stair.x, down_stair.y82    d.mk_next_stage()83    d.move_downstairs()84    up_stair = d.get_stage().find_stair('<')85    assert d.hero.x == up_stair.x86    assert d.hero.y == up_stair.y87def test_move_downstairs__dungeon_lvl_incremented(hero):88    d = dungeon.Dungeon(hero)89    prev_lvl = d.current_stage90    down_stair = d.get_stage().find_stair('>')91    d.hero.x, d.hero.y = down_stair.x, down_stair.y92    d.mk_next_stage()93    d.move_downstairs()94    assert d.current_stage == prev_lvl + 195def test_move_downstairs__success_returns_True(hero):96    d = dungeon.Dungeon(hero)97    down_stair = d.get_stage().find_stair('>')98    d.hero.x, d.hero.y = down_stair.x, down_stair.y99    d.mk_next_stage()100    assert d.move_downstairs()101def test_move_upstairs__not_on_up_stair_returns_False(hero):102    d = dungeon.Dungeon(hero)103    down_stair = d.get_stage().find_stair('>')104    # Move hero to downstair (won't be on upstair)105    d.hero.x, d.hero.y = down_stair.x, down_stair.y106    assert d.move_upstairs() is False107def test_move_upstairs__hero_moved_to_prev_downstair(hero):108    d = dungeon.Dungeon(hero)109    down_stair = d.get_stage().find_stair('>')110    d.hero.x, d.hero.y = down_stair.x, down_stair.y111    d.mk_next_stage()112    # Move the hero downstairs first113    d.move_downstairs()114    # Hero moves back to previous down-stair115    d.move_upstairs()116    assert d.hero.x == down_stair.x117    assert d.hero.y == down_stair.y118def test_move_upstairs__success_returns_True(hero):119    d = dungeon.Dungeon(hero)120    down_stair = d.get_stage().find_stair('>')121    d.hero.x, d.hero.y = down_stair.x, down_stair.y122    d.mk_next_stage()123    d.move_downstairs()124    assert d.move_upstairs()125# def test_move_upstairs__when_at_the_top_lvl():126    # Return False?127# Wait on this test - might want to remove some calls from Dungeon init128# def test_move_hero__hero_not_placed_yet(hero):129    # d = dungeon.Dungeon(hero)130def test_move_hero__to_wall_returns_False(hero):131    d = dungeon.Dungeon(hero)132    m = stages.Stage(10, 10, 2)133    d.stages.append(m)134    assert d.move_hero(dest_stage_index=1, dest_x=0, dest_y=0) is False135def test_move_hero__to_occupied_spot_returns_False(hero):136    d = dungeon.Dungeon(hero)137    rnd_monster = [e for e in d.get_stage().entities if e.has_comp('ai')].pop()138    dest_x = rnd_monster.x139    dest_y = rnd_monster.y140    assert d.move_hero(dest_stage_index=0, dest_x=dest_x, dest_y=dest_y) is False141def test_move_hero__same_floor_returns_True(hero):142    d = dungeon.Dungeon(hero)143    dest_x, dest_y = d.get_stage().get_random_open_spot()144    assert d.move_hero(dest_stage_index=0, dest_x=dest_x, dest_y=dest_y)145def test_move_hero__same_floor_hero_xy_updated(hero):146    d = dungeon.Dungeon(hero)147    dest_x, dest_y = d.get_stage().get_random_open_spot()148    d.move_hero(dest_stage_index=0, dest_x=dest_x, dest_y=dest_y)149    assert d.hero.x == dest_x150    assert d.hero.y == dest_y151def test_move_hero__same_floor_lvl_remains_same(hero):152    d = dungeon.Dungeon(hero)153    d_lvl = d.current_stage154    dest_x, dest_y = d.get_stage().get_random_open_spot()155    d.move_hero(dest_stage_index=0, dest_x=dest_x, dest_y=dest_y)156    assert d.current_stage == d_lvl157def test_move_hero__diff_floor_returns_True(hero):158    dest_stage_index = 1159    d = dungeon.Dungeon(hero)160    d.mk_next_stage()161    dest_x, dest_y = d.stages[dest_stage_index].get_random_open_spot()162    assert d.move_hero(dest_stage_index=dest_stage_index, dest_x=dest_x, dest_y=dest_y)163def test_move_hero__diff_floor_hero_xy_updated(hero):164    dest_stage_index = 1165    d = dungeon.Dungeon(hero)166    d.mk_next_stage()167    dest_x, dest_y = d.stages[dest_stage_index].get_random_open_spot()168    d.move_hero(dest_stage_index=dest_stage_index, dest_x=dest_x, dest_y=dest_y)...

models.py

Source:models.py

...8POSTTEST    = "posttest"9class PolicyworldWork(object):10    def __init__(self, stages):11        self.stages = stages12    def get_stage(self, name):13        for s in self.stages:14            if s.name == name:15                return s16        return None17    18    def get_time(self):19        t = 020        for s in self.stages:21            t = t + s.get_minutes()22        return t23    24    def to_grade(self):25        #return "%s %s %s %s %s %s time:%s" % (self._passed_to_string(self.get_stage(PRETEST).passed),26        #                                      self._passed_to_string(self.get_stage(PROBLEM_1).passed),27        #                                      self._passed_to_string(self.get_stage(PROBLEM_2).passed),28        #                                      self._passed_to_string(self.get_stage(PROBLEM_3).passed),29        #                                      self._passed_to_string(self.get_stage(DEBATETEST).passed),30        #                                      self._passed_to_string(self.get_stage(POSTTEST).passed),31        #                                      self.get_time())32        return "Pre:%s Post1:%s Post2:%s Time:%s" % (self._grade_stage(PRETEST),33                                                    self._grade_stage(DEBATETEST),34                                                    self._grade_stage(POSTTEST),35                                                    self.get_time())36    def _grade_stage(self, stage_name):37        s = self.get_stage(stage_name)38        if s == None:39            return "?"40        41        if s.passed:42            return '+'43        else:44            return '-'45    46class StageGrade(object):47    def __init__(self, name, attempt, completed, passed, msec):48        self.name       = name49        self.attempt    = attempt50        self.completed  = completed51        self.passed     = passed...

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