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How to use outer method in lettuce_webdriver

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out_Guide_posts_locking_assemble.py

Source:out_Guide_posts_locking_assemble.py

1import win32com.client as win322import global_var as gvar34def out_Guide_posts_locking_assemble(outer_Guiding_data):  # å¤å°æ±èºæ 5    (M) = out_Guide_posts_down_locking_assemble(outer_Guiding_data)  # ä¸æ¨¡åº§èºæ 6    # hide_1()7    (N) = out_Guide_posts_down_pin_assemble(outer_Guiding_data)  # ä¸æ¨¡åº§åé·8    # hide_2()9    out_Guide_posts_up_locking_assemble(M,outer_Guiding_data)  # ä¸æ¨¡åº§èºæ 10    # # hide_3()11    out_Guide_posts_up_pin_assemble(N,outer_Guiding_data)  # ä¸æ¨¡åº§åé·12    # # hide_4()13    catapp = win32.Dispatch('CATIA.Application')14    document = catapp.ActiveDocument15    product1 = document.Product16    products1 = product1.Products17    product1.Update()181920def out_Guide_posts_down_locking_assemble(outer_Guiding_data):21    catapp = win32.Dispatch('CATIA.Application')22    document = catapp.ActiveDocument23    product1 = document.Product24    products1 = product1.Products25    # if outer_Guiding_data(1, 1) == 20 and outer_Guiding_data(3, 1) == "MYJP":26    #     a = "CB_6"27    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYJP":28    #     a = "CB_8"29    # elif outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYJP":30    #     a = "CB_10"31    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYJP":32    #     a = "CB_10"33    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYJP":34    #     a = "CB_12"35    # elif outer_Guiding_data(1, 1) == 20 and outer_Guiding_data(3, 1) == "MYKP":36    #     a = "CB_8"37    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYKP":38    #     a = "CB_8"39    # elif outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYKP":40    #     a = "CB_10"41    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYKP":42    #     a = "CB_10"43    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYKP":44    #     a = "CB_12"45    M = 046    # =====================èºæ å¤æ·(æå°)===============================47    selection1 = document.Selection48    selection1.Clear()49    selection1.Search("Name=" + str(outer_Guiding_data[4][1]) + "*")50    N = selection1.Count51    selection1.Clear()52    # =====================èºæ å¤æ·(æå°)===============================53    for g in range(1, 4 + 1):54        for i in range(1, 4 + 1):55            M += 156            # ================å¯å¥æªæ¡================57            arrayOfVariantOfBSTR1 = [0]58            arrayOfVariantOfBSTR1[0] = gvar.save_path + str(outer_Guiding_data[4][1]) + ".CATPart"59            products1Variant = products160            products1Variant.AddComponentsFromFiles(arrayOfVariantOfBSTR1, "All")61            # ================å¯å¥æªæ¡================62            constraints1 = product1.Connections("CATIAConstraints")63            # ================é²è¡ææ================64            reference1 = product1.CreateReferenceFromName(65                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_down." + str(66                    g) + "/!Product1/" + str(outer_Guiding_data[3][1]) + "_down." + str(g) + "/")67            constraint1 = constraints1.AddMonoEltCst(0, reference1)68            reference2 = product1.CreateReferenceFromName(69                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_down." + str(70                    g) + "/!Locking_point_" + str(i))71            reference3 = product1.CreateReferenceFromName(72                "Product1/" + str(outer_Guiding_data[4][1]) + "." + str(M) + "/!Start_Point")73            constraint2 = constraints1.AddBiEltCst(2, reference2, reference3)74            reference4 = product1.CreateReferenceFromName(75                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_down." + str(76                    g) + "/!Locking_dir_point_" + str(i))77            reference5 = product1.CreateReferenceFromName(78                "Product1/" + str(outer_Guiding_data[4][1]) + "." + str(M) + "/!End_Point")79            constraint3 = constraints1.AddBiEltCst(2, reference4, reference5)80            # ================é²è¡ææ================81    return M828384def out_Guide_posts_down_pin_assemble(outer_Guiding_data):85    catapp = win32.Dispatch('CATIA.Application')86    document = catapp.ActiveDocument87    product1 = document.Product88    products1 = product1.Products89    # if outer_Guiding_data(1, 1) == 20 and outer_Guiding_data(3, 1) == "MYJP":90    #     a = "CB_6"91    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYJP":92    #     a = "CB_8"93    # elif outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYJP":94    #     a = "CB_10"95    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYJP":96    #     a = "CB_10"97    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYJP":98    #     a = "CB_12"99    # elif outer_Guiding_data(1, 1) == 20 and outer_Guiding_data(3, 1) == "MYKP":100    #     a = "CB_8"101    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYKP":102    #     a = "CB_8"103    # elif outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYKP":104    #     a = "CB_10"105    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYKP":106    #     a = "CB_10"107    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYKP":108    #     a = "CB_12"109    N = 0110    # =====================pinå¤æ·(æå°)===============================111    selection1 = document.Selection112    selection1.Clear()113    selection1.Search("Name=" + str(outer_Guiding_data[5][1]))114    N = selection1.Count115    selection1.Clear()116    # =====================pinå¤æ·(æå°)===============================117    for g in range(1, 4 + 1):118        for i in range(1, 2 + 1):119            N += 1120            # ================å¯å¥æªæ¡================121            arrayOfVariantOfBSTR1 = [0]122            arrayOfVariantOfBSTR1[0] = gvar.save_path + str(outer_Guiding_data[5][1]) + ".CATPart"123            products1Variant = products1124            products1Variant.AddComponentsFromFiles(arrayOfVariantOfBSTR1, "All")125            # ================å¯å¥æªæ¡================126            constraints1 = product1.Connections("CATIAConstraints")127            # ================é²è¡ææ================128            reference1 = product1.CreateReferenceFromName(129                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_down." + str(130                    g) + "/!Product1" + str(outer_Guiding_data[3][1]) + "_" + str(131                    outer_Guiding_data[1][1]) + "_down." + str(g) + "/")132            constraint1 = constraints1.AddMonoEltCst(0, reference1)133            reference2 = product1.CreateReferenceFromName(134                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_down." + str(135                    g) + "/!Pin_point_" + str(i))136            reference3 = product1.CreateReferenceFromName(137                "Product1/" + str(outer_Guiding_data[5][1]) + "." + str(N) + "/!Start_Point")138            constraint2 = constraints1.AddBiEltCst(2, reference2, reference3)139            reference4 = product1.CreateReferenceFromName(140                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_down." + str(141                    g) + "/!Pin_dir_point_" + str(i))142            # reference5 = product1.CreateReferenceFromName(143            #     "Product1/" + str(outer_Guiding_data[5][1]) + "." + str(N) + "/!End_Point")144            # constraint3 = constraints1.AddBiEltCst(1, reference4, reference5)145146            # WordCount_PinLength = len(outer_Guiding_data[5][1])147            # for j in range(0, WordCount_PinLength):148            #     word = outer_Guiding_data51[j]  # æåPin_data[2][1]ä¸çå¼149            #     if word == "1":150            #         length2 = constraint3.dimension151            #         if WordCount_PinLength < 14:152            #             k = 2153            #         else:154            #             k = 3155            #         if int(int(outer_Guiding_data51[j + 3:20]) - 30) < 0:156            #             length2.Value = int((int(outer_Guiding_data51[j + k:10]) - 30) * -1)157            #         else:158            #             length2.Value = int(int(outer_Guiding_data51[j + k:10]) - 30)159            # ================é²è¡ææ================160    return N161162163def out_Guide_posts_up_locking_assemble(M,outer_Guiding_data):164    catapp = win32.Dispatch('CATIA.Application')165    document = catapp.ActiveDocument166    product1 = document.Product167    products1 = product1.Products168    # if outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYJP":169    #     a = "CB_10"170    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYJP":171    #     a = "CB_10"172    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYJP":173    #     a = "CB_12"174    # elif outer_Guiding_data(1, 1) == 20 and outer_Guiding_data(3, 1) == "MYJP":175    #     a = "CB_8"176    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYJP":177    #     a = "CB_8"178    # elif outer_Guiding_data(1, 1) ==32 and outer_Guiding_data(3, 1) == "MYKP":179    #     a = "CB_810"180    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYKP":181    #     a = "CB_10"182    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYKP":183    #     a = "CB_12"184    # =====================èºæ å¤æ·(æå°)===============================185    # selection1 = document.Selection186    # selection1.Clear()187    # selection1.Search("Name=" + str(outer_Guiding_data[4][2]) + "*")188    # M = selection1.Count189    # selection1.Clear()190    # =====================èºæ å¤æ·(æå°)===============================191    for g in range(1, 4 + 1):192        for i in range(1, 4 + 1):193            M += 1194            # ================å¯å¥æªæ¡================195            arrayOfVariantOfBSTR1 = [0]196            arrayOfVariantOfBSTR1[0] = gvar.save_path + str(outer_Guiding_data[4][2]) + ".CATPart"197            products1Variant = products1198            products1Variant.AddComponentsFromFiles(arrayOfVariantOfBSTR1, "All")199            # ================å¯å¥æªæ¡================200            constraints1 = product1.Connections("CATIAConstraints")201            # ================é²è¡ææ================202            reference1 = product1.CreateReferenceFromName(203                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_up." + str(204                    g) + "/!Product1" + str(outer_Guiding_data[3][1]) + "_" + str(205                    outer_Guiding_data[1][1]) + "_up." + str(g) + "/")206            constraint1 = constraints1.AddMonoEltCst(0, reference1)207            reference2 = product1.CreateReferenceFromName(208                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_up." + str(209                    g) + "/!Locking_point_" + str(i))210            reference3 = product1.CreateReferenceFromName(211                "Product1/" + str(outer_Guiding_data[4][2]) + "." + str(M) + "/!Start_Point")212            constraint2 = constraints1.AddBiEltCst(2, reference2, reference3)213            reference4 = product1.CreateReferenceFromName(214                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_up." + str(215                    g) + "/!Locking_dir_point_" + str(i))216            reference5 = product1.CreateReferenceFromName(217                "Product1/" + str(outer_Guiding_data[4][2]) + "." + str(M) + "/!End_Point")218            constraint3 = constraints1.AddBiEltCst(2, reference4, reference5)219            # ================é²è¡ææ================220            # product1.Update()221222223def out_Guide_posts_up_pin_assemble(N,outer_Guiding_data):224    catapp = win32.Dispatch('CATIA.Application')225    document = catapp.ActiveDocument226    product1 = document.Product227    products1 = product1.Products228    # if outer_Guiding_data(1, 1) == 20 and outer_Guiding_data(3, 1) == "MYJP":229    #     a = "MSTM_6"230    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYJP":231    #     a = "MSTM_8"232    # elif outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYJP":233    #     a = "MSTM_8"234    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYJP":235    #     a = "MSTM_8"236    # elif outer_Guiding_data(1, 1) == 50 and outer_Guiding_data(3, 1) == "MYJP":237    #     a = "MSTM_10"238    # elif outer_Guiding_data(1, 1) ==20 and outer_Guiding_data(3, 1) == "MYKP":239    #     a = "MSTM_8"240    # elif outer_Guiding_data(1, 1) == 25 and outer_Guiding_data(3, 1) == "MYKP":241    #     a = "MSTM_8"242    # elif outer_Guiding_data(1, 1) == 32 and outer_Guiding_data(3, 1) == "MYKP":243    #     a = "MSTM_8"244    # elif outer_Guiding_data(1, 1) == 38 and outer_Guiding_data(3, 1) == "MYKP":245    #     a = "MSTM_10"246    # elif outer_Guiding_data(1, 1) ==  and outer_Guiding_data(3, 1) == "MYKP":247    #     a = "MSTM_10"248    # =====================pinå¤æ·(æå°)===============================249    # selection1 = document.Selection250    # selection1.Clear()251    # selection1.Search("Name=*" + str(outer_Guiding_data[4][2]) + ".*")252    # N = selection1.Count253    # selection1.Clear()254    # =====================pinå¤æ·(æå°)===============================255    for g in range(1, 4 + 1):256        for i in range(1, 2 + 1):257            N += 1258            # ================å¯å¥æªæ¡================259            arrayOfVariantOfBSTR1 = [0]260            arrayOfVariantOfBSTR1[0] = gvar.save_path + str(outer_Guiding_data[5][2]) + ".CATPart"261            products1Variant = products1262            products1Variant.AddComponentsFromFiles(arrayOfVariantOfBSTR1, "All")263            # ================å¯å¥æªæ¡================264            constraints1 = product1.Connections("CATIAConstraints")265            # ================é²è¡ææ================266            reference1 = product1.CreateReferenceFromName(267                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_up." + str(268                    g) + "/!Product1" + str(outer_Guiding_data[3][1]) + "_" + str(269                    outer_Guiding_data[1][1]) + "_up." + str(g) + "/")270            constraint1 = constraints1.AddMonoEltCst(0, reference1)271            reference2 = product1.CreateReferenceFromName(272                "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_up." + str(273                    g) + "/!Pin_point_" + str(i))274            reference3 = product1.CreateReferenceFromName(275                "Product1/" + str(outer_Guiding_data[5][2]) + "." + str(N) + "/!Start_Point")276            constraint2 = constraints1.AddBiEltCst(2, reference2, reference3)277            # reference4 = product1.CreateReferenceFromName(278            #     "Product1/" + str(outer_Guiding_data[3][1]) + "_" + str(outer_Guiding_data[1][1]) + "_up." + str(279            #         g) + "/!Pin_dir_point_" + str(i))280            # reference5 = product1.CreateReferenceFromName(281            #     "Product1/" + str(outer_Guiding_data[5][2]) + "." + str(N) + "/!End_Point")282            # constraint3 = constraints1.AddBiEltCst(1, reference4, reference5)283            # WordCount_PinLength = len(outer_Guiding_data[5][2])284            # for j in range(0, WordCount_PinLength):285            #     word = outer_Guiding_data51[j]  # æåPin_data[2][1]ä¸çå¼286            #     if word == "1":287            #         length2 = constraint3.dimension288            #         if WordCount_PinLength < 14:289            #             k = 2290            #         else:291            #             k = 3292            #         if int(int(outer_Guiding_data51[j + 3:20]) - 30) < 0:293            #             length2.Value = int((int(outer_Guiding_data51[j + k:10]) - 30) * -1)294            #         else:295            #             length2.Value = int(int(outer_Guiding_data51[j + k:10]) - 30)296            # ================é²è¡ææ================297            # product1.Update()298299300def hide1():301    catapp = win32.Dispatch('CATIA.Application')302303304def hide2():305    catapp = win32.Dispatch('CATIA.Application')306307308def hide3():309    catapp = win32.Dispatch('CATIA.Application')310311312def hide4():
...

ragged_merge_dims_op_test.py

Source:ragged_merge_dims_op_test.py

1# Copyright 2019 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for RaggedTensor.merge_dims."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19from absl.testing import parameterized20from tensorflow.python.eager import context21from tensorflow.python.framework import test_util22from tensorflow.python.ops import array_ops23from tensorflow.python.ops.ragged import ragged_factory_ops24from tensorflow.python.platform import googletest25from tensorflow.python.util import nest26@test_util.run_all_in_graph_and_eager_modes27class RaggedMergeDimsOpTest(test_util.TensorFlowTestCase,28                            parameterized.TestCase):29  @parameterized.named_parameters([30      {31          'testcase_name': '2DAxis0To1',32          'rt': [[1, 2], [], [3, 4, 5]],33          'outer_axis': 0,34          'inner_axis': 1,35          'expected': [1, 2, 3, 4, 5],36      },37      {38          'testcase_name': '3DAxis0To1',39          'rt': [[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]],40          'outer_axis': 0,41          'inner_axis': 1,42          'expected': [[1, 2], [], [3, 4, 5], [6], [7, 8], []],43      },44      {45          'testcase_name': '3DAxis1To2',46          'rt': [[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]],47          'outer_axis': 1,48          'inner_axis': 2,49          'expected': [[1, 2, 3, 4, 5], [6, 7, 8]],50      },51      {52          'testcase_name': '3DAxis0To2',53          'rt': [[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]],54          'outer_axis': 0,55          'inner_axis': 2,56          'expected': [1, 2, 3, 4, 5, 6, 7, 8],57      },58      {59          'testcase_name': '3DAxis0To1WithDenseValues',60          'rt': [[[1, 2], [3, 4], [5, 6]], [[7, 8]]],61          'ragged_ranks': (1, 2),62          'outer_axis': 0,63          'inner_axis': 1,64          'expected': [[1, 2], [3, 4], [5, 6], [7, 8]],65      },66      {67          'testcase_name': '3DAxis1To2WithDenseValues',68          'rt': [[[1, 2], [3, 4], [5, 6]], [[7, 8]]],69          'ragged_ranks': (1, 2),70          'outer_axis': 1,71          'inner_axis': 2,72          'expected': [[1, 2, 3, 4, 5, 6], [7, 8]],73      },74      {75          'testcase_name': '4DAxis0To1',76          'rt': [[[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]], [[[9], [0]]]],77          'outer_axis': 0,78          'inner_axis': 1,79          'expected': [[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []], [[9], [0]]],80      },81      {82          'testcase_name': '4DAxis1To2',83          'rt': [[[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]], [[[9], [0]]]],84          'outer_axis': 1,85          'inner_axis': 2,86          'expected': [[[1, 2], [], [3, 4, 5], [6], [7, 8], []], [[9], [0]]],87      },88      {89          'testcase_name': '4DAxis2To3',90          'rt': [[[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]], [[[9], [0]]]],91          'outer_axis': 2,92          'inner_axis': 3,93          'expected': [[[1, 2, 3, 4, 5], [6, 7, 8]], [[9, 0]]],94      },95      {96          'testcase_name': '4DAxis1To3',97          'rt': [[[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]], [[[9], [0]]]],98          'outer_axis': 1,99          'inner_axis': 3,100          'expected': [[1, 2, 3, 4, 5, 6, 7, 8], [9, 0]],101      },102      {103          'testcase_name': '4DAxis1ToNeg1',104          'rt': [[[[1, 2], [], [3, 4, 5]], [[6], [7, 8], []]], [[[9], [0]]]],105          'outer_axis': 1,106          'inner_axis': -1,107          'expected': [[1, 2, 3, 4, 5, 6, 7, 8], [9, 0]],108      },109      {110          'testcase_name': '4DAxis1To2WithDenseValues',111          'rt': [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]]]],112          'ragged_ranks': (1, 2, 3),113          'outer_axis': 1,114          'inner_axis': 2,115          'expected': [[[1, 2], [3, 4], [5, 6], [7, 8]], [[9, 10], [11, 12]]],116      },117      {118          'testcase_name': '4DAxis2To3WithDenseValues',119          'rt': [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]]]],120          'ragged_ranks': (1, 2, 3),121          'outer_axis': 2,122          'inner_axis': 3,123          'expected': [[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12]]],124      },125      {126          'testcase_name': '4DAxis1To3WithDenseValues',127          'rt': [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]]]],128          'ragged_ranks': (1, 2, 3),129          'outer_axis': 1,130          'inner_axis': 3,131          'expected': [[1, 2, 3, 4, 5, 6, 7, 8], [9, 10, 11, 12]],132      },133      {134          'testcase_name': '5DAxis2To3WithDenseValues',135          'rt': [[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]],136                 [[[[9, 10], [11, 12]]]]],137          'ragged_ranks': (1, 2, 3, 4),138          'outer_axis': 2,139          'inner_axis': 3,140          'expected': [[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],141                       [[[9, 10], [11, 12]]]],142      },143      {144          'testcase_name': '5DAxis3To4WithDenseValues',145          'rt': [[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]],146                 [[[[9, 10], [11, 12]]]]],147          'ragged_ranks': (1, 2, 3, 4),148          'outer_axis': 3,149          'inner_axis': 4,150          'expected': [[[[1, 2, 3, 4]], [[5, 6, 7, 8]]], [[[9, 10, 11, 12]]]],151      },152      {153          'testcase_name': '5DAxis1To3WithDenseValues',154          'rt': [[[[[1, 2], [3, 4]]], [[[5, 6], [7, 8]]]],155                 [[[[9, 10], [11, 12]]]]],156          'ragged_ranks': (1, 2, 3, 4),157          'outer_axis': 1,158          'inner_axis': 3,159          'expected': [[[1, 2], [3, 4], [5, 6], [7, 8]], [[9, 10], [11, 12]]],160      },161  ])  # pyformat: disable162  def testRaggedMergeDims(self,163                          rt,164                          outer_axis,165                          inner_axis,166                          expected,167                          ragged_ranks=(None,)):168    for ragged_rank in ragged_ranks:169      x = ragged_factory_ops.constant(rt, ragged_rank=ragged_rank)170      # Check basic behavior.171      actual = x.merge_dims(outer_axis, inner_axis)172      self.assertAllEqual(expected, actual)173      if outer_axis >= 0 and inner_axis >= 0:174        self.assertEqual(actual.shape.rank,175                         x.shape.rank - (inner_axis - outer_axis))176      # Check behavior with negative axis.177      if outer_axis >= 0 and inner_axis >= 0:178        actual_with_neg_axis = x.merge_dims(outer_axis - x.shape.rank,179                                            inner_axis - x.shape.rank)180        self.assertAllEqual(expected, actual_with_neg_axis)181      # Check behavior with placeholder input (no shape info).182      if (not context.executing_eagerly() and outer_axis >= 0 and183          inner_axis >= 0):184        x_with_placeholders = nest.map_structure(185            lambda t: array_ops.placeholder_with_default(t, None),186            x,187            expand_composites=True)188        actual_with_placeholders = x_with_placeholders.merge_dims(189            outer_axis, inner_axis)190        self.assertAllEqual(expected, actual_with_placeholders)191  @parameterized.parameters([192      {193          'rt': [[1]],194          'outer_axis': {},195          'inner_axis': 1,196          'exception': TypeError,197          'message': 'outer_axis must be an int',198      },199      {200          'rt': [[1]],201          'outer_axis': 1,202          'inner_axis': {},203          'exception': TypeError,204          'message': 'inner_axis must be an int',205      },206      {207          'rt': [[1]],208          'outer_axis': 1,209          'inner_axis': 3,210          'exception': ValueError,211          'message': 'inner_axis=3 out of bounds: expected -2<=inner_axis<2',212      },213      {214          'rt': [[1]],215          'outer_axis': 1,216          'inner_axis': -3,217          'exception': ValueError,218          'message': 'inner_axis=-3 out of bounds: expected -2<=inner_axis<2',219      },220      {221          'rt': [[1]],222          'outer_axis': 0,223          'inner_axis': 0,224          'exception': ValueError,225          'message': 'Expected outer_axis .* to be less than inner_axis .*',226      },227      {228          'rt': [[1]],229          'outer_axis': 1,230          'inner_axis': 0,231          'exception': ValueError,232          'message': 'Expected outer_axis .* to be less than inner_axis .*',233      },234      {235          'rt': [[1]],236          'outer_axis': -1,237          'inner_axis': -2,238          'exception': ValueError,239          'message': 'Expected outer_axis .* to be less than inner_axis .*',240      },241      {242          'rt': [[1]],243          'outer_axis': 1,244          'inner_axis': -1,245          'exception': ValueError,246          'message': 'Expected outer_axis .* to be less than inner_axis .*',247      },248  ])  # pyformat: disable249  def testRaggedMergeDimsError(self,250                               rt,251                               outer_axis,252                               inner_axis,253                               exception,254                               message=None,255                               ragged_rank=None):256    x = ragged_factory_ops.constant(rt, ragged_rank=ragged_rank)257    with self.assertRaisesRegexp(exception, message):258      self.evaluate(x.merge_dims(outer_axis, inner_axis))259if __name__ == '__main__':...

maml.py

Source:maml.py

1import functools2import constants3import jax4import jax.numpy as jnp5import matplotlib.pyplot as plt6import sinusoidal_task_distribution7from jax.experimental import optimizers, stax8class MAML:9    def __init__(10        self,11        key,12        optimiser_type: str,13        lr: float,14        task_distribution,15        network_specification,16    ):17        self._key = key18        self._task_distribution = task_distribution19        (20            optimiser_initialiser,21            optimiser_update,22            get_parameters,23        ) = self._setup_optimiser(optimiser_type=optimiser_type, lr=lr)24        network_forward, network_parameters = self._setup_network(25            network_specification=network_specification26        )27        self._optimiser_state = optimiser_initialiser(network_parameters)28        def loss_function(parameters, inputs, labels):29            predictions = network_forward(parameters, inputs)30            return jnp.mean((predictions - labels) ** 2)31        def inner_loop(parameters, x, y):32            # get inner loop optimiser33            # (34            #     optimiser_initialiser,35            #     optimiser_update,36            #     get_parameters,37            # ) = self._setup_optimiser(optimiser_type="adam", lr=0.001)38            # optimiser_state = optimiser_initialiser(parameters)39            gradients = jax.grad(loss_function)(parameters, x, y)40            # updated_optimiser_state = optimiser_update(0, gradients, optimiser_state)41            # updated_parameters = get_parameters(updated_optimiser_state)42            # import pdb43            # pdb.set_trace()44            # return updated_parameters45            inner_sgd_fn = lambda g, state: (state - 0.01 * g)46            return jax.tree_multimap(inner_sgd_fn, gradients, parameters)47        def compute_meta_loss(parameters, x_inner, y_inner, x_outer, y_outer):48            updated_parameters = inner_loop(49                parameters=parameters, x=x_inner, y=y_inner50            )51            loss = loss_function(updated_parameters, x_outer, y_outer)52            return loss53        def compute_batch_meta_loss(54            parameters, batch_x_inner, batch_y_inner, batch_x_outer, batch_y_outer55        ):56            task_losses = jax.vmap(functools.partial(compute_meta_loss, parameters))(57                batch_x_inner, batch_y_inner, batch_x_outer, batch_y_outer58            )59            batch_meta_loss = jnp.mean(task_losses)60            return batch_meta_loss61        def step(epoch: int, optimiser_state, inner_x, inner_y, outer_x, outer_y):62            parameters = get_parameters(optimiser_state)63            gradients = jax.grad(compute_batch_meta_loss)(64                parameters, inner_x, inner_y, outer_x, outer_y65            )66            batch_meta_loss = compute_batch_meta_loss(67                parameters, inner_x, inner_y, outer_x, outer_y68            )69            return (70                optimiser_update(epoch, gradients, optimiser_state),71                batch_meta_loss,72            )73        self._get_parameters = get_parameters74        self._network_forward = network_forward75        self._inner_loop = inner_loop76        self._step = jax.jit(step)77    def _setup_optimiser(self, optimiser_type: str, lr: float):78        if optimiser_type == constants.ADAM:79            init, update, get_params = optimizers.adam(step_size=lr)80        return init, update, get_params81    def _setup_network(self, network_specification):82        input_dimension = network_specification[constants.INPUT_DIM]83        layer_specifications = network_specification[constants.LAYER_SPECIFICATIONS]84        layers = []85        for layer_specification in layer_specifications:86            layer_type = list(layer_specification.keys())[0]87            layer_info = list(layer_specification.values())[0]88            if layer_type == constants.LINEAR:89                layer = stax.Dense(layer_info[constants.OUTPUT_DIM])90            layers.append(layer)91            activation_type = layer_info.get(constants.ACTIVATION)92            if activation_type is not None:93                if activation_type == constants.RELU:94                    activation = stax.Relu95                layers.append(activation)96        init, forward = stax.serial(*layers)97        _, params = init(self._key, (-1, input_dimension))98        return forward, params99    def _fine_tune(self, parameters, x, y, adaptation_steps):100        fine_tuned_parameters = []101        updated_parameters = parameters102        fine_tuned_parameters.append(updated_parameters)103        for i in range(adaptation_steps):104            updated_parameters = self._inner_loop(updated_parameters, x, y)105            fine_tuned_parameters.append(updated_parameters)106        return fine_tuned_parameters107    def _get_data_batch_from_tasks(self, tasks, batch_size: int):108        batch_x_inner = []109        batch_y_inner = []110        batch_x_outer = []111        batch_y_outer = []112        for task in tasks:113            x_inner, y_inner = task.sample_data(114                key=self._key, num_datapoints=batch_size115            )116            x_outer, y_outer = task.sample_data(117                key=self._key, num_datapoints=batch_size118            )119            batch_x_inner.append(x_inner)120            batch_y_inner.append(y_inner)121            batch_x_outer.append(x_outer)122            batch_y_outer.append(y_outer)123        return (124            jnp.stack(batch_x_inner),125            jnp.stack(batch_y_inner),126            jnp.stack(batch_x_outer),127            jnp.stack(batch_y_outer),128        )129    def train(self, epochs: int, num_tasks: int, batch_size: int):130        meta_losses = []131        for i in range(epochs):132            task_sample = self._task_distribution.sample(133                key=self._key, num_tasks=num_tasks134            )135            (136                batch_x_inner,137                batch_y_inner,138                batch_x_outer,139                batch_y_outer,140            ) = self._get_data_batch_from_tasks(141                tasks=task_sample, batch_size=batch_size142            )143            self._optimiser_state, meta_loss = self._step(144                epoch=i,145                optimiser_state=self._optimiser_state,146                inner_x=batch_x_inner,147                inner_y=batch_y_inner,148                outer_x=batch_x_outer,149                outer_y=batch_y_outer,150            )151            meta_losses.append(meta_loss)152            if i % 100 == 0:153                print(f"{i}: {meta_loss}")154        return meta_losses155    def test(156        self,157        num_evaluations: int,158        num_examples: int,159        num_adaptation_steps: int,160        plot: bool,161    ):162        evaluation_tasks = self._task_distribution.sample(163            key=self._key, num_tasks=num_evaluations164        )165        trained_parameters = self._get_parameters(self._optimiser_state)166        for i, task in enumerate(evaluation_tasks):167            x, y = task.sample_data(key=self._key, num_datapoints=num_examples)168            adapted_parameters = self._fine_tune(169                trained_parameters, x, y, num_adaptation_steps170            )171            if plot:172                self._plot_evaluation(x, y, task, adapted_parameters, f"{i}_test.pdf")173    def _plot_evaluation(self, x, y, task, adapted_parameters, save_name):174        fig = plt.figure()175        plt.scatter(x, y)176        x_range = jnp.linspace(-5, 5, 100).reshape(-1, 1)177        plt.plot(x_range, task(x_range), label="ground truth")178        for i, parameters in enumerate(adapted_parameters):179            regression = self._network_forward(parameters, x_range)180            plt.plot(x_range, regression, label=f"{i} tuning")181        plt.legend()182        fig.savefig(save_name)183def forward(inputs):184    mlp = hk.Sequential(185        [hk.Linear(40), jax.nn.relu, hk.Linear(40), jax.nn.relu, hk.Linear(1)]186    )187    prediction = mlp(inputs)188    return prediction189if __name__ == "__main__":190    task_distribution = sinusoidal_task_distribution.SinusoidalTaskDistribution(191        x_range=(-5, 5), amplitude_range=(0.1, 5), phase_range=(0, jnp.pi)192    )193    network_specification = {194        "input_dim": 1,195        "layer_specifications": [196            {"linear": {"output_dim": 40, "activation": "relu"}},197            {"linear": {"output_dim": 40, "activation": "relu"}},198            {"linear": {"output_dim": 1}},199        ],200    }201    rng = jax.random.PRNGKey(0)202    maml = MAML(203        key=rng,204        task_distribution=task_distribution,205        optimiser_type="adam",206        lr=0.001,207        network_specification=network_specification,208    )209    meta_losses = maml.train(10000, 5, 5)210    fig = plt.figure()211    plt.plot(range(len(meta_losses)), meta_losses)212    plt.xlabel("epochs")213    plt.ylabel("meta loss")214    fig.savefig("losses.pdf")...

summary_test.py

Source:summary_test.py

1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18from six.moves import xrange  # pylint: disable=redefined-builtin19from tensorflow.core.framework import summary_pb220from tensorflow.python.framework import constant_op21from tensorflow.python.framework import meta_graph22from tensorflow.python.framework import ops23from tensorflow.python.ops import array_ops24from tensorflow.python.ops import variables25from tensorflow.python.platform import test26from tensorflow.python.summary import summary as summary_lib27class ScalarSummaryTest(test.TestCase):28  def testScalarSummary(self):29    with self.test_session() as s:30      i = constant_op.constant(3)31      with ops.name_scope('outer'):32        im = summary_lib.scalar('inner', i)33      summary_str = s.run(im)34    summary = summary_pb2.Summary()35    summary.ParseFromString(summary_str)36    values = summary.value37    self.assertEqual(len(values), 1)38    self.assertEqual(values[0].tag, 'outer/inner')39    self.assertEqual(values[0].simple_value, 3.0)40  def testScalarSummaryWithFamily(self):41    with self.test_session() as s:42      i = constant_op.constant(7)43      with ops.name_scope('outer'):44        im1 = summary_lib.scalar('inner', i, family='family')45        self.assertEquals(im1.op.name, 'outer/family/inner')46        im2 = summary_lib.scalar('inner', i, family='family')47        self.assertEquals(im2.op.name, 'outer/family/inner_1')48      sm1, sm2 = s.run([im1, im2])49    summary = summary_pb2.Summary()50    summary.ParseFromString(sm1)51    values = summary.value52    self.assertEqual(len(values), 1)53    self.assertEqual(values[0].tag, 'family/outer/family/inner')54    self.assertEqual(values[0].simple_value, 7.0)55    summary.ParseFromString(sm2)56    values = summary.value57    self.assertEqual(len(values), 1)58    self.assertEqual(values[0].tag, 'family/outer/family/inner_1')59    self.assertEqual(values[0].simple_value, 7.0)60  def testSummarizingVariable(self):61    with self.test_session() as s:62      c = constant_op.constant(42.0)63      v = variables.Variable(c)64      ss = summary_lib.scalar('summary', v)65      init = variables.global_variables_initializer()66      s.run(init)67      summ_str = s.run(ss)68    summary = summary_pb2.Summary()69    summary.ParseFromString(summ_str)70    self.assertEqual(len(summary.value), 1)71    value = summary.value[0]72    self.assertEqual(value.tag, 'summary')73    self.assertEqual(value.simple_value, 42.0)74  def testImageSummary(self):75    with self.test_session() as s:76      i = array_ops.ones((5, 4, 4, 3))77      with ops.name_scope('outer'):78        im = summary_lib.image('inner', i, max_outputs=3)79      summary_str = s.run(im)80    summary = summary_pb2.Summary()81    summary.ParseFromString(summary_str)82    values = summary.value83    self.assertEqual(len(values), 3)84    tags = sorted(v.tag for v in values)85    expected = sorted('outer/inner/image/{}'.format(i) for i in xrange(3))86    self.assertEqual(tags, expected)87  def testImageSummaryWithFamily(self):88    with self.test_session() as s:89      i = array_ops.ones((5, 2, 3, 1))90      with ops.name_scope('outer'):91        im = summary_lib.image('inner', i, max_outputs=3, family='family')92        self.assertEquals(im.op.name, 'outer/family/inner')93      summary_str = s.run(im)94    summary = summary_pb2.Summary()95    summary.ParseFromString(summary_str)96    values = summary.value97    self.assertEqual(len(values), 3)98    tags = sorted(v.tag for v in values)99    expected = sorted('family/outer/family/inner/image/{}'.format(i)100                      for i in xrange(3))101    self.assertEqual(tags, expected)102  def testHistogramSummary(self):103    with self.test_session() as s:104      i = array_ops.ones((5, 4, 4, 3))105      with ops.name_scope('outer'):106        summ_op = summary_lib.histogram('inner', i)107      summary_str = s.run(summ_op)108    summary = summary_pb2.Summary()109    summary.ParseFromString(summary_str)110    self.assertEqual(len(summary.value), 1)111    self.assertEqual(summary.value[0].tag, 'outer/inner')112  def testHistogramSummaryWithFamily(self):113    with self.test_session() as s:114      i = array_ops.ones((5, 4, 4, 3))115      with ops.name_scope('outer'):116        summ_op = summary_lib.histogram('inner', i, family='family')117        self.assertEquals(summ_op.op.name, 'outer/family/inner')118      summary_str = s.run(summ_op)119    summary = summary_pb2.Summary()120    summary.ParseFromString(summary_str)121    self.assertEqual(len(summary.value), 1)122    self.assertEqual(summary.value[0].tag, 'family/outer/family/inner')123  def testAudioSummary(self):124    with self.test_session() as s:125      i = array_ops.ones((5, 3, 4))126      with ops.name_scope('outer'):127        aud = summary_lib.audio('inner', i, 0.2, max_outputs=3)128      summary_str = s.run(aud)129    summary = summary_pb2.Summary()130    summary.ParseFromString(summary_str)131    values = summary.value132    self.assertEqual(len(values), 3)133    tags = sorted(v.tag for v in values)134    expected = sorted('outer/inner/audio/{}'.format(i) for i in xrange(3))135    self.assertEqual(tags, expected)136  def testAudioSummaryWithFamily(self):137    with self.test_session() as s:138      i = array_ops.ones((5, 3, 4))139      with ops.name_scope('outer'):140        aud = summary_lib.audio('inner', i, 0.2, max_outputs=3, family='family')141        self.assertEquals(aud.op.name, 'outer/family/inner')142      summary_str = s.run(aud)143    summary = summary_pb2.Summary()144    summary.ParseFromString(summary_str)145    values = summary.value146    self.assertEqual(len(values), 3)147    tags = sorted(v.tag for v in values)148    expected = sorted('family/outer/family/inner/audio/{}'.format(i)149                      for i in xrange(3))150    self.assertEqual(tags, expected)151  def testSummaryNameConversion(self):152    c = constant_op.constant(3)153    s = summary_lib.scalar('name with spaces', c)154    self.assertEqual(s.op.name, 'name_with_spaces')155    s2 = summary_lib.scalar('name with many $#illegal^: characters!', c)156    self.assertEqual(s2.op.name, 'name_with_many___illegal___characters_')157    s3 = summary_lib.scalar('/name/with/leading/slash', c)158    self.assertEqual(s3.op.name, 'name/with/leading/slash')159  def testSummaryWithFamilyMetaGraphExport(self):160    with ops.name_scope('outer'):161      i = constant_op.constant(11)162      summ = summary_lib.scalar('inner', i)163      self.assertEquals(summ.op.name, 'outer/inner')164      summ_f = summary_lib.scalar('inner', i, family='family')165      self.assertEquals(summ_f.op.name, 'outer/family/inner')166    metagraph_def, _ = meta_graph.export_scoped_meta_graph(export_scope='outer')167    with ops.Graph().as_default() as g:168      meta_graph.import_scoped_meta_graph(metagraph_def, graph=g,169                                          import_scope='new_outer')170      # The summaries should exist, but with outer scope renamed.171      new_summ = g.get_tensor_by_name('new_outer/inner:0')172      new_summ_f = g.get_tensor_by_name('new_outer/family/inner:0')173      # However, the tags are unaffected.174      with self.test_session() as s:175        new_summ_str, new_summ_f_str = s.run([new_summ, new_summ_f])176        new_summ_pb = summary_pb2.Summary()177        new_summ_pb.ParseFromString(new_summ_str)178        self.assertEquals('outer/inner', new_summ_pb.value[0].tag)179        new_summ_f_pb = summary_pb2.Summary()180        new_summ_f_pb.ParseFromString(new_summ_f_str)181        self.assertEquals('family/outer/family/inner',182                          new_summ_f_pb.value[0].tag)183if __name__ == '__main__':...

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