mirror of
https://github.com/zebrajr/pytorch.git
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edb88b5f3a
* add opencl + fpga context adds an opencl context inside caffe2/fb which can be used for fpga access * [Caffe2] Force tensor inference checks to be triggered during testing We've started to rely on TensorInference functions more for different analysis. This diff ensures that the TensorInference function's result matches what is expected from the definition of the operator. * Enable building //caffe2:torch with @mode/opt In @mode/opt, python runs out of a PAR, which breaks a lot of assumptions in the code about where templates/ folders live relative to __file__. Rather than introduce hacks with parutil, I simply turn template_path into a parameter for all the relevant functions and thread it through from the top level. * [Caffe2] Fix cost models for DotProduct and Div. Update Tensor Inference for dot product As title. DotProduct states that output is a 1-D tensor (https://caffe2.ai/docs/operators-catalogue.html#dotproduct) though code suggests it is either 0- or 1-D depending on inputs. TensorInference defined to support implementation. * [SG-MoE] Add an option to make the experts NOT as components * [nomnigraph] Rename and fixup convertToNeuralNetOperator API This will make things a bit cleaner * no longer symlink THNN.h and THCUNN.h * forced decoder network (onnx export) Closes https://github.com/pytorch/translate/pull/95 Add networks in ensemble_export.py to create a forced decoding network from PyTorch NMT checkpoints. This network takes an arbitrary numberized (source, target) pair and returns the model score for the translation, including penalties. Vocabulary reduction networks are also supported, but note that target indices which are not in the possible_translation_tokens generated for the source input will be trea * Revert schema change to fix production models Revert schema change to fix production models * MockLogDeviceReader - rebase on FIX # Goal 1), Build a make_mock_log_device_reader using make_mock_reader 2), Replace the real log_device_reader here: https://fburl.com/raihwf1p # Log by D8151734 Real log_device_reader: ``` I0529 20:29:05.373108 954994 tensor.h:839] Tensor print_net/log of type std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >. Dims: (): read_net/ParseOpenTrainingRow:0 I0529 20:29:05.373244 954994 tensor.h:839] Tensor read_net/ParseOpenTrainin * [C2/D2][1/n]: Nonnegative-Constrained Optimization -- log barrier implement log barrier as a regularization method * Add teacher weight screening. Add teacher weight sceening according to teacher labels. If teacher label is zero, we do not use the distill loss in the objective function. * Add NormalizerContext See task for more detail. This implementation is a copy of what exists for RegularizerContext except for how the parameters are defined in the model_definition thrift file. I'll try an alternative implementation which overrides the default arguments of functions instead like for argscopes in tensorflow. https://github.com/pytorch/pytorch/compare/master...MaximeBoucher:update-from-facebook-0939578c068c?expand=1 * Adding cosine similarity option in dot processor Add pairwise cosine similarity option in dot product. Add an option to concate dot product and cosine similarity. Add test cases. * [nomnigraph][redo] Concat elim for sparseNN Same as D7962948, which was reverted because Operator Schema was not defined * [pytorch] Revert pytorch/pytorch#7918 'Release GIL when copying to shared memory', breaks ASAN Revert this pytorch diff that breaks ASAN when running Filament in dev mode; in opt mode it gives "bad file descriptor" errors. Looks like a race when copying tensors to shared memory in multiple mp.Queue's (which spawn separate threads). https://github.com/pytorch/pytorch/pull/7918/files * [nomnigraph][mobile] Enable nomnigraph by default, use -Oz on nomnigraph related code to reduce code size enables nomnigraph and reduces codesize * [Warmup] Allow both offline incremental training and online training Change plan name on saving side and reading side to support both training type This diff depends on D8128530 and D8168651. * Revert D7802642: [Warmup] Allow both offline incremental training and online training This reverts commit afc213cf9b36cecf75333a788391c4d09f4afccc @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * Add legacy grad logic to fix div op on old graphs. Add legacy grad logic to fix div op on old graphs. * Correctly propagate operator failures Propagate errors from operators that throw exceptions and return false * Revert D8374829: [caffe2][nomnigraph][redo] Concat elim for sparseNN This reverts commit 6dda028c463e54bb5c32188bbbe9202107e188a5 @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * [Caffe2] Added extra_info to core.DeviceOption(), enforced extra_info to be inherited in scope.DeviceScope extra_info is a newly defined field in DeviceOption proto. This diff added extra_info to the core.DeviceOption(). And, In scope.DeviceScope(), this diff enforce the new scope to inherit the extra_info from old scope. * [opt] hgdirsync wasn't enabled, merge diverged code Here's the damage, P59732616 basically xplat was left behind but had the change from assert to CAFFE_ENFORCE * OMP parallelism over RoIs for RoIAlign op Simpler to parallelize over RoIs. Shouldn't affect other uses as it relies on the number of OMP threads set during startup. PR: https://github.com/pytorch/pytorch/pull/8562 * Use int64_t for shape in FillOps to avoid overflow of int32 * Implement Rotated RoIAlign op Based on Rotated RPNs as explained in https://arxiv.org/abs/1703.01086. The idea is simple - orientation/angle is added as an RPN anchor parameter and then the angle is further regressed similar to bbox coords. There are some additional changes related to NMS and IoU, but besides that it's a direct extension to Faster-RCNN. Further details in https://fb.quip.com/sZHlA1iMfWPZ. RoIs are represented in [center_x, center_y, width, height, angle] format. `angle` repre * Rotated RoIAlign op CUDA forward implementation CUDA forward impl for D8415490 * RoIAlignRotated op CUDA backward pass implementation TSIA * All remaining fixes to eliminate process_github.sh Most of this diff has already been reviewed separately, except for the parts relating to _thnn/utils.py and _utils._internal.py remove skipIf(True, 'Fbcode') line from process_github.sh replace sed of cpp file with #ifdef to control cudnnDestroy use undo sync-time deletion of .gitattributes, remove process_github.sh switch to using _utils._internal rather than try-import-except This diff also fixes the open-source bug where rebuilds have * Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training" Original commit changeset: 7707d2efe60e The original diff is backout becuase the online trainer package is backed out. This code would only work with new online trainer package * [easy] improve error log in adagrad op as title * re-allow use of thnn_h_path This fixes cffi usage in OSS * [4/4] [tum] paralyzing layerNorm for GPU full sync as title * add compile=False to pytorch tests, remove hack with pyc * Add shape and type inference for RowWiseArgMax operator See title * Revert D8515341: Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training" This reverts commit 78167eeef0af16b60f72c82f9dcdda9b41b4dcbd @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * [fix-flaky-test] mock_hive_reader_test flaky, because GlobalCounter collects local counts intervally # Problem `MockHiveReader` uses `GlobalCounter` to limit `max_examples`. GlobalCounter on server node collect local counts from worker nodes every 1 sec. This 1 sec delay makes it impossible to limit exactly to the `max_examples`, it will definitely exceed `max_examples`. # Plan Given, ``` Expected num_examples = max_examples + num_examples/sec (Read Speed) x 1 sec (GlobalCounter Sync Int * [Caffe2] Fix FCGradient cost inference. Prevent overflow in cost inference FCGradient missed a factor 2 in the `num_outputs == 3` case. Overflow was occurring with flop calculation for FC. Changed types to `uint64_t` to prevent future problems. * Fix binary ops with empty inputs Fix binary ops with empty inputs * Support the filling of input blob with provided data as title for Biz Integrity case * Back out "Revert D8515341: Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training"" Original commit changeset: 30c55dd38816 Original diff is reverted due to introducing bad integration test. Fixed the integration test. * [c2][easy] improve pack ops error loggings as desc. * Add ShapeTypeInference for LpNorm operator As desc * Shard test_nn to reduce runtime for each test target Closes https://github.com/pytorch/pytorch/pull/8793 The current test_nn would time out and be disabled in GreenWarden, and we need to have an option to split it up in order to pass the stress test. Right now GreenWarden roughly allows running 100 test cases in test_nn before timing out, and here we have an option to divide test_nn into 30 shards (with ~40 tests in each shard) to allow for some test suite growth in the future. * Change default caffe2_streams_per_gpu to 1 * Remove IN_SANDCASTLE from common.py and test_nn.py We prefer to disable the failing tests through Sandcastle UI instead. * Add a new class for an updated prof_dag.proto This diff contains: - An updated prof_dag.proto that contains blob profiles. - A class to deserialize this information (serialization is in a follow up diff) - Update to separate profiling information from NeuralNet (and use it as part of the class above). - Unit tests * Lambdarank for SparseNN This diff adds a lambda_rank_layer for SparseNN. changes include 1) Adds support for multi sessions in c2 op 2) Adds support for two different loss functions in c2 op 3) Unit tests for op * Revert D8586950: Back out "Revert D8515341: Back out "Revert D7802642: [Warmup] Allow both offline incremental training and online training"" This reverts commit 012220ed63eccc35659a57b31d16a3625da6317b @bypass-lint An infra SEV is better than not reverting this diff. If you copy this password, see you in SEV Review! @cause_a_sev_many_files * [easy] A few fixups to multithread predictor benchmark (1) support perf on T6 server (2) remove dead code * fix a bug about the map size as title * Fix reduce sum on in-place case. Fix reduce sum on in-place case. * [Warmup] Reland reverted diff Allow both offline incremental training and online training Closes https://github.com/pytorch/pytorch/pull/8827 fix net transform integration test. Allow offline and online trainer to coexist D7802642. * Add StoreHandlerNotAvailableException Add an exception for a store that is not available or has been deleted. * Use exception handling for fault tolerance, missing KV store Remove status blobs to communication ops so that exceptions propagate on failure. * [C2/D2][2/n]: Nonnegative-Constrained Optimization -- bounded grad proj for simple bounded constrained optimization, incl non-negative box constraints. * [GanH]: Adaptive Weighting with More Estimations With implemented postivity optimization, we now learn adaptive weights with different parameterizations. This improves parameter estimation and training stability. * Revert some changes for landing * Remove AutoNoGIL in StorageSharing * Temporarily disable net_tests * Revert "[Caffe2] Force tensor inference checks to be triggered during testing" This reverts commit 67ef05c22b2f71b4a489695384932f968384a2a4. * Revert "Fix reduce sum on in-place case." This reverts commit 6cb8a8e1b3db7b6d20941b0053e3f3836068eb64. * Revert "Revert "Fix reduce sum on in-place case."" This reverts commit 130a257c0893dc09f4bd6e6a45d112261807fd2c.
1989 lines
72 KiB
Python
1989 lines
72 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import hypothesis.strategies as st
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import numpy as np
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import numpy.testing as npt
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import unittest
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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from caffe2.python import (
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layer_model_instantiator,
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core,
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schema,
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workspace,
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)
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from caffe2.python.layers.layers import (
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InstantiationContext,
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)
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from caffe2.python.layers.tags import Tags
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from caffe2.python.layer_test_util import (
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LayersTestCase,
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OpSpec,
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)
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from caffe2.python.layers.layers import (
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IdList,
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set_request_only,
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is_request_only_scalar,
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get_key,
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)
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import logging
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logger = logging.getLogger(__name__)
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class TestLayers(LayersTestCase):
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def testAddLoss(self):
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input_record_LR = self.new_record(
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schema.Struct(
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('label', schema.Scalar((np.float64, (1, )))),
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('logit', schema.Scalar((np.float32, (2, )))),
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('weight', schema.Scalar((np.float64, (1, ))))
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)
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)
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loss_LR = self.model.BatchLRLoss(input_record_LR)
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self.model.add_loss(loss_LR)
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assert 'unnamed' in self.model.loss
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self.assertEqual(
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schema.Scalar((np.float32, tuple())), self.model.loss.unnamed
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)
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self.assertEqual(loss_LR, self.model.loss.unnamed)
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self.model.add_loss(loss_LR, 'addLoss')
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assert 'addLoss' in self.model.loss
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self.assertEqual(
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schema.Scalar((np.float32, tuple())), self.model.loss.addLoss
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)
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self.assertEqual(loss_LR, self.model.loss.addLoss)
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self.model.add_loss(
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schema.Scalar(
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dtype=np.float32, blob=core.BlobReference('loss_blob_1')
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), 'addLoss'
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)
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assert 'addLoss_auto_0' in self.model.loss
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self.assertEqual(
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schema.Scalar((np.float32, tuple())), self.model.loss.addLoss_auto_0
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)
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assert core.BlobReference('loss_blob_1') in self.model.loss.field_blobs()
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self.model.add_loss(
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schema.Struct(
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(
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'structName', schema.Scalar(
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dtype=np.float32,
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blob=core.BlobReference('loss_blob_2')
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)
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)
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), 'addLoss'
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)
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assert 'addLoss_auto_1' in self.model.loss
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self.assertEqual(
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schema.Struct(('structName', schema.Scalar((np.float32, tuple())))),
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self.model.loss.addLoss_auto_1
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)
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assert core.BlobReference('loss_blob_2') in self.model.loss.field_blobs()
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loss_in_tuple_0 = schema.Scalar(
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dtype=np.float32, blob=core.BlobReference('loss_blob_in_tuple_0')
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)
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loss_in_tuple_1 = schema.Scalar(
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dtype=np.float32, blob=core.BlobReference('loss_blob_in_tuple_1')
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)
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loss_tuple = schema.NamedTuple(
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'loss_in_tuple', * [loss_in_tuple_0, loss_in_tuple_1]
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)
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self.model.add_loss(loss_tuple, 'addLoss')
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assert 'addLoss_auto_2' in self.model.loss
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self.assertEqual(
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schema.Struct(
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('loss_in_tuple_0', schema.Scalar((np.float32, tuple()))),
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('loss_in_tuple_1', schema.Scalar((np.float32, tuple())))
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), self.model.loss.addLoss_auto_2
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)
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assert core.BlobReference('loss_blob_in_tuple_0')\
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in self.model.loss.field_blobs()
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assert core.BlobReference('loss_blob_in_tuple_1')\
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in self.model.loss.field_blobs()
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def testAddOutputSchema(self):
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# add the first field
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self.model.add_output_schema('struct', schema.Struct())
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expected_output_schema = schema.Struct(('struct', schema.Struct()))
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self.assertEqual(
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self.model.output_schema,
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expected_output_schema,
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)
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# add the second field
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self.model.add_output_schema('scalar', schema.Scalar(np.float64))
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expected_output_schema = schema.Struct(
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('struct', schema.Struct()),
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('scalar', schema.Scalar(np.float64)),
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)
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self.assertEqual(
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self.model.output_schema,
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expected_output_schema,
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)
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# overwrite a field should raise
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with self.assertRaises(AssertionError):
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self.model.add_output_schema('scalar', schema.Struct())
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def _test_net(self, net, ops_list):
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"""
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Helper function to assert the net contains some set of operations and
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then to run the net.
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Inputs:
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net -- the network to test and run
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ops_list -- the list of operation specifications to check for
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in the net
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"""
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ops_output = self.assertNetContainOps(net, ops_list)
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workspace.RunNetOnce(net)
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return ops_output
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def testFCWithoutBias(self):
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output_dims = 2
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fc_without_bias = self.model.FCWithoutBias(
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self.model.input_feature_schema.float_features, output_dims)
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self.model.output_schema = fc_without_bias
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self.assertEqual(
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schema.Scalar((np.float32, (output_dims, ))),
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fc_without_bias
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)
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train_init_net, train_net = self.get_training_nets()
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init_ops = self.assertNetContainOps(
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train_init_net,
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[
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OpSpec("UniformFill", None, None),
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]
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)
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mat_mul_spec = OpSpec(
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"MatMul",
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[
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self.model.input_feature_schema.float_features(),
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init_ops[0].output[0],
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],
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fc_without_bias.field_blobs()
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)
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self.assertNetContainOps(train_net, [mat_mul_spec])
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predict_net = self.get_predict_net()
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self.assertNetContainOps(predict_net, [mat_mul_spec])
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def testSparseLookupSumPooling(self):
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record = schema.NewRecord(self.model.net, schema.Struct(
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('sparse', schema.Struct(
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('sparse_feature_0', schema.List(
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schema.Scalar(np.int64,
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metadata=schema.Metadata(categorical_limit=1000)))),
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)),
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))
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embedding_dim = 64
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embedding_after_pooling = self.model.SparseLookup(
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record.sparse.sparse_feature_0, [embedding_dim], 'Sum')
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self.model.output_schema = schema.Struct()
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self.assertEqual(
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schema.Scalar((np.float32, (embedding_dim, ))),
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embedding_after_pooling
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)
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train_init_net, train_net = self.get_training_nets()
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init_ops = self.assertNetContainOps(
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train_init_net,
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[
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OpSpec("UniformFill", None, None),
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OpSpec("ConstantFill", None, None),
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]
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)
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sparse_lookup_op_spec = OpSpec(
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'SparseLengthsSum',
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[
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init_ops[0].output[0],
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record.sparse.sparse_feature_0.items(),
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record.sparse.sparse_feature_0.lengths(),
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],
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[embedding_after_pooling()]
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)
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self.assertNetContainOps(train_net, [sparse_lookup_op_spec])
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predict_net = self.get_predict_net()
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self.assertNetContainOps(predict_net, [sparse_lookup_op_spec])
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@given(
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use_hashing=st.booleans(),
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modulo=st.integers(min_value=100, max_value=200),
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)
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def testSparseFeatureHashIdList(self, use_hashing, modulo):
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record = schema.NewRecord(
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self.model.net,
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schema.List(schema.Scalar(
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np.int64,
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metadata=schema.Metadata(categorical_limit=60000)
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))
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)
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output_schema = self.model.SparseFeatureHash(
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record,
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modulo=modulo,
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use_hashing=use_hashing)
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self.model.output_schema = output_schema
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self.assertEqual(len(self.model.layers), 1)
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self.assertEqual(output_schema._items.metadata.categorical_limit,
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modulo)
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train_init_net, train_net = self.get_training_nets()
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@given(
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use_hashing=st.booleans(),
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modulo=st.integers(min_value=100, max_value=200),
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)
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def testSparseFeatureHashIdScoreList(self, use_hashing, modulo):
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record = schema.NewRecord(self.model.net,
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schema.Map(schema.Scalar(np.int64,
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metadata=schema.Metadata(
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categorical_limit=60000)),
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np.float32))
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output_schema = self.model.SparseFeatureHash(
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record,
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modulo=modulo,
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use_hashing=use_hashing)
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self.model.output_schema = output_schema
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self.assertEqual(len(self.model.layers), 1)
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self.assertEqual(output_schema._items.keys.metadata.categorical_limit,
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modulo)
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train_init_net, train_net = self.get_training_nets()
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def testSparseLookupIncorrectPositionWeightedOnIdList(self):
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'''
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Currently the implementation of SparseLookup assumed input is id_score_list
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when use PositionWeighted.
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'''
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record = schema.NewRecord(self.model.net, schema.Struct(
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('sparse', schema.Struct(
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('sparse_feature_0', schema.List(
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schema.Scalar(np.int64,
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metadata=schema.Metadata(categorical_limit=1000)))),
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)),
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))
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embedding_dim = 64
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with self.assertRaises(AssertionError):
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self.model.SparseLookup(
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record.sparse.sparse_feature_0, [embedding_dim], 'PositionWeighted')
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def testSparseLookupPositionWeightedOnIdList(self):
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record = schema.NewRecord(self.model.net, schema.Struct(
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('sparse', schema.Struct(
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('sparse_feature_0', schema.List(
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schema.Scalar(np.int64,
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metadata=schema.Metadata(categorical_limit=1000)))),
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)),
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))
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# convert id_list to id_score_list with PositionWeighted layer
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sparse_segment = record.sparse.sparse_feature_0
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pos_w_layer = self.model.PositionWeighted(sparse_segment)
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sparse_segment = schema.Map(
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keys=get_key(sparse_segment),
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values=pos_w_layer.position_weights,
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lengths_blob=sparse_segment.lengths
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)
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embedding_dim = 64
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embedding_after_pooling = self.model.SparseLookup(
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sparse_segment, [embedding_dim], 'PositionWeighted')
|
|
self.model.output_schema = schema.Struct()
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (embedding_dim, ))),
|
|
embedding_after_pooling
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
self.assertNetContainOps(
|
|
train_init_net,
|
|
[
|
|
OpSpec("ConstantFill", None, None), # position_weights/pos_w
|
|
OpSpec("UniformFill", None, None),
|
|
OpSpec("ConstantFill", None, None),
|
|
]
|
|
)
|
|
self.assertNetContainOps(train_net, [
|
|
OpSpec("LengthsRangeFill", None, None),
|
|
OpSpec("Gather", None, None),
|
|
OpSpec("SparseLengthsWeightedSum", None, None),
|
|
])
|
|
|
|
predict_net = self.get_predict_net()
|
|
self.assertNetContainOps(predict_net, [
|
|
OpSpec("LengthsRangeFill", None, None),
|
|
OpSpec("Gather", None, None),
|
|
OpSpec("SparseLengthsWeightedSum", None, None),
|
|
])
|
|
|
|
def testSparseLookupPositionWeightedOnIdScoreList(self):
|
|
record = schema.NewRecord(self.model.net, schema.Struct(
|
|
('sparse', schema.Struct(
|
|
('id_score_list_0', schema.Map(
|
|
schema.Scalar(
|
|
np.int64,
|
|
metadata=schema.Metadata(
|
|
categorical_limit=1000
|
|
),
|
|
),
|
|
np.float32
|
|
)),
|
|
)),
|
|
))
|
|
|
|
embedding_dim = 64
|
|
embedding_after_pooling = self.model.SparseLookup(
|
|
record.sparse.id_score_list_0, [embedding_dim], 'PositionWeighted')
|
|
self.model.output_schema = schema.Struct()
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (embedding_dim, ))),
|
|
embedding_after_pooling
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
init_ops = self.assertNetContainOps(
|
|
train_init_net,
|
|
[
|
|
OpSpec("UniformFill", None, None),
|
|
OpSpec("ConstantFill", None, None),
|
|
]
|
|
)
|
|
sparse_lookup_op_spec = OpSpec(
|
|
'SparseLengthsWeightedSum',
|
|
[
|
|
init_ops[0].output[0],
|
|
record.sparse.id_score_list_0.values(),
|
|
record.sparse.id_score_list_0.keys(),
|
|
record.sparse.id_score_list_0.lengths(),
|
|
],
|
|
[embedding_after_pooling()]
|
|
)
|
|
self.assertNetContainOps(train_net, [sparse_lookup_op_spec])
|
|
|
|
predict_net = self.get_predict_net()
|
|
self.assertNetContainOps(predict_net, [sparse_lookup_op_spec])
|
|
|
|
def testPairwiseSimilarityWithAllEmbeddings(self):
|
|
embedding_dim = 64
|
|
N = 5
|
|
record = schema.NewRecord(self.model.net, schema.Struct(
|
|
('all_embeddings', schema.Scalar(
|
|
((np.float32, (N, embedding_dim)))
|
|
)),
|
|
))
|
|
current = self.model.PairwiseSimilarity(
|
|
record, N * N)
|
|
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (N * N, ))),
|
|
current
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
self.assertNetContainOps(train_init_net, [])
|
|
self.assertNetContainOps(train_net, [
|
|
OpSpec("BatchMatMul", None, None),
|
|
OpSpec("Flatten", None, None),
|
|
])
|
|
|
|
def testPairwiseSimilarityWithXandYEmbeddings(self):
|
|
embedding_dim = 64
|
|
record = schema.NewRecord(self.model.net, schema.Struct(
|
|
('x_embeddings', schema.Scalar(
|
|
((np.float32, (5, embedding_dim)))
|
|
)),
|
|
('y_embeddings', schema.Scalar(
|
|
((np.float32, (6, embedding_dim)))
|
|
)),
|
|
))
|
|
current = self.model.PairwiseSimilarity(
|
|
record, 5 * 6)
|
|
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (5 * 6, ))),
|
|
current
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
self.assertNetContainOps(train_init_net, [])
|
|
self.assertNetContainOps(train_net, [
|
|
OpSpec("BatchMatMul", None, None),
|
|
OpSpec("Flatten", None, None),
|
|
])
|
|
|
|
def testPairwiseSimilarityWithXandYEmbeddingsAndGather(self):
|
|
embedding_dim = 64
|
|
|
|
output_idx = [1, 3, 5]
|
|
output_idx_blob = self.model.add_global_constant(
|
|
str(self.model.net.NextScopedBlob('pairwise_dot_product_gather')),
|
|
output_idx,
|
|
dtype=np.int32,
|
|
)
|
|
indices_to_gather = schema.Scalar(
|
|
(np.int32, len(output_idx)),
|
|
output_idx_blob,
|
|
)
|
|
|
|
record = schema.NewRecord(self.model.net, schema.Struct(
|
|
('x_embeddings', schema.Scalar(
|
|
((np.float32, (5, embedding_dim)))
|
|
)),
|
|
('y_embeddings', schema.Scalar(
|
|
((np.float32, (6, embedding_dim)))
|
|
)),
|
|
('indices_to_gather', indices_to_gather),
|
|
))
|
|
current = self.model.PairwiseSimilarity(
|
|
record, len(output_idx))
|
|
|
|
# This assert is not necessary,
|
|
# output size is passed into PairwiseSimilarity
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (len(output_idx), ))),
|
|
current
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
self.assertNetContainOps(train_init_net, [])
|
|
self.assertNetContainOps(train_net, [
|
|
OpSpec("BatchMatMul", None, None),
|
|
OpSpec("Flatten", None, None),
|
|
OpSpec("BatchGather", None, None),
|
|
])
|
|
|
|
def testPairwiseSimilarityIncorrectInput(self):
|
|
embedding_dim = 64
|
|
record = schema.NewRecord(self.model.net, schema.Struct(
|
|
('x_embeddings', schema.Scalar(
|
|
((np.float32, (5, embedding_dim)))
|
|
)),
|
|
))
|
|
with self.assertRaises(AssertionError):
|
|
self.model.PairwiseSimilarity(
|
|
record, 25)
|
|
|
|
record = schema.NewRecord(self.model.net, schema.Struct(
|
|
('all_embeddings', schema.List(np.float32))
|
|
))
|
|
with self.assertRaises(AssertionError):
|
|
self.model.PairwiseSimilarity(
|
|
record, 25)
|
|
|
|
def testConcat(self):
|
|
embedding_dim = 64
|
|
input_record = self.new_record(schema.Struct(
|
|
('input1', schema.Scalar((np.float32, (embedding_dim, )))),
|
|
('input2', schema.Scalar((np.float32, (embedding_dim, )))),
|
|
('input3', schema.Scalar((np.float32, (embedding_dim, )))),
|
|
))
|
|
|
|
output = self.model.Concat(input_record)
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, ((len(input_record.fields) * embedding_dim, )))),
|
|
output
|
|
)
|
|
|
|
# Note that in Concat layer we assume first dimension is batch.
|
|
# so input is B * embedding_dim
|
|
# add_axis=1 make it B * 1 * embedding_dim
|
|
# concat on axis=1 make it B * N * embedding_dim
|
|
output = self.model.Concat(input_record, axis=1, add_axis=1)
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, ((len(input_record.fields), embedding_dim)))),
|
|
output
|
|
)
|
|
|
|
def testSamplingTrain(self):
|
|
output_dims = 1000
|
|
|
|
indices = self.new_record(schema.Scalar((np.int32, (10,))))
|
|
sampling_prob = self.new_record(schema.Scalar((np.float32, (10, ))))
|
|
|
|
sampled_fc = self.model.SamplingTrain(
|
|
schema.Struct(
|
|
('input', self.model.input_feature_schema.float_features),
|
|
('indices', indices),
|
|
('sampling_prob', sampling_prob),
|
|
),
|
|
"FC",
|
|
output_dims,
|
|
)
|
|
self.model.output_schema = sampled_fc
|
|
|
|
# Check that we don't add prediction layer into the model
|
|
self.assertEqual(1, len(self.model.layers))
|
|
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (output_dims, ))),
|
|
sampled_fc
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
init_ops = self.assertNetContainOps(
|
|
train_init_net,
|
|
[
|
|
OpSpec("UniformFill", None, None),
|
|
OpSpec("UniformFill", None, None),
|
|
]
|
|
)
|
|
|
|
sampled_fc_layer = self.model.layers[0]
|
|
|
|
gather_w_spec = OpSpec(
|
|
"Gather",
|
|
[
|
|
init_ops[0].output[0],
|
|
indices(),
|
|
],
|
|
[
|
|
sampled_fc_layer._prediction_layer.train_param_blobs[0]
|
|
]
|
|
)
|
|
gather_b_spec = OpSpec(
|
|
"Gather",
|
|
[
|
|
init_ops[1].output[0],
|
|
indices(),
|
|
],
|
|
[
|
|
sampled_fc_layer._prediction_layer.train_param_blobs[1]
|
|
]
|
|
)
|
|
train_fc_spec = OpSpec(
|
|
"FC",
|
|
[
|
|
self.model.input_feature_schema.float_features(),
|
|
] + sampled_fc_layer._prediction_layer.train_param_blobs,
|
|
sampled_fc.field_blobs()
|
|
)
|
|
log_spec = OpSpec("Log", [sampling_prob()], [None])
|
|
sub_spec = OpSpec(
|
|
"Sub",
|
|
[sampled_fc.field_blobs()[0], None],
|
|
sampled_fc.field_blobs()
|
|
)
|
|
|
|
train_ops = self.assertNetContainOps(
|
|
train_net,
|
|
[gather_w_spec, gather_b_spec, train_fc_spec, log_spec, sub_spec])
|
|
|
|
self.assertEqual(train_ops[3].output[0], train_ops[4].input[1])
|
|
|
|
predict_net = self.get_predict_net()
|
|
self.assertNetContainOps(
|
|
predict_net,
|
|
[
|
|
OpSpec(
|
|
"FC",
|
|
[
|
|
self.model.input_feature_schema.float_features(),
|
|
init_ops[0].output[0],
|
|
init_ops[1].output[0],
|
|
],
|
|
sampled_fc.field_blobs()
|
|
)
|
|
]
|
|
)
|
|
|
|
def testDistillBatchLRLoss(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float64, (1,)))),
|
|
('logit', schema.Scalar((np.float32, (2,)))),
|
|
('teacher_label', schema.Scalar((np.float32(1,)))),
|
|
('weight', schema.Scalar((np.float64, (1,))))
|
|
))
|
|
loss = self.model.BatchDistillLRLoss(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, tuple())), loss)
|
|
|
|
def testDistillBatchLRLossWithTeacherWeightScreen(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float32, (2,)))),
|
|
('logit', schema.Scalar((np.float32, (2, 1)))),
|
|
('teacher_label', schema.Scalar((np.float32(2,)))),
|
|
('weight', schema.Scalar((np.float64, (2,))))
|
|
))
|
|
label_items = np.array([1.0, 1.0], dtype=np.float32)
|
|
logit_items = np.array([[1.0], [1.0]], dtype=np.float32)
|
|
teacher_label_items = np.array([0.8, -1.0], dtype=np.float32)
|
|
weight_items = np.array([1.0, 1.0], dtype=np.float32)
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[label_items, logit_items, teacher_label_items, weight_items]
|
|
)
|
|
loss = self.model.BatchDistillLRLoss(
|
|
input_record,
|
|
teacher_weight=0.5,
|
|
filter_invalid_teacher_label=True
|
|
)
|
|
self.run_train_net_forward_only()
|
|
tensor_loss = workspace.FetchBlob(loss.field_blobs()[0])
|
|
|
|
def cross_entropy(label, logit):
|
|
return logit - logit * label + np.log(1 + np.exp(-1.0 * logit))
|
|
|
|
def cal_cross_entropy(
|
|
label_items, logit_items, teacher_label_items, weight_items
|
|
):
|
|
total_ce = 0
|
|
for i in range(label_items.shape[0]):
|
|
true_xent = cross_entropy(label_items[i], logit_items[i, 0])
|
|
if teacher_label_items[i] > 0:
|
|
teacher_xent = cross_entropy(
|
|
teacher_label_items[i], logit_items[i, 0]
|
|
)
|
|
else:
|
|
teacher_xent = 0
|
|
teacher_weight = 0.5 if teacher_label_items[i] > 0 else 0
|
|
total_ce += (true_xent * (1 - teacher_weight) +
|
|
teacher_xent * teacher_weight) * weight_items[i]
|
|
return total_ce / label_items.shape[0]
|
|
|
|
correct_ace = cal_cross_entropy(
|
|
label_items,
|
|
logit_items,
|
|
teacher_label_items,
|
|
weight_items
|
|
)
|
|
self.assertAlmostEqual(
|
|
tensor_loss,
|
|
np.array(correct_ace),
|
|
delta=0.0000001,
|
|
msg="Wrong cross entropy {}".format(tensor_loss)
|
|
)
|
|
|
|
def testBatchLRLoss(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float64, (1,)))),
|
|
('logit', schema.Scalar((np.float32, (2,)))),
|
|
('weight', schema.Scalar((np.float64, (1,))))
|
|
))
|
|
loss = self.model.BatchLRLoss(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, tuple())), loss)
|
|
|
|
def testMarginRankLoss(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('pos_prediction', schema.Scalar((np.float32, (1,)))),
|
|
('neg_prediction', schema.List(np.float32)),
|
|
))
|
|
pos_items = np.array([0.1, 0.2, 0.3], dtype=np.float32)
|
|
neg_lengths = np.array([1, 2, 3], dtype=np.int32)
|
|
neg_items = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype=np.float32)
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[pos_items, neg_lengths, neg_items]
|
|
)
|
|
loss = self.model.MarginRankLoss(input_record)
|
|
self.run_train_net_forward_only()
|
|
self.assertEqual(schema.Scalar((np.float32, tuple())), loss)
|
|
|
|
def testBatchMSELoss(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float64, (1,)))),
|
|
('prediction', schema.Scalar((np.float32, (2,)))),
|
|
))
|
|
loss = self.model.BatchMSELoss(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, tuple())), loss)
|
|
|
|
def testBatchSigmoidCrossEntropyLoss(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float32, (32,)))),
|
|
('prediction', schema.Scalar((np.float32, (32,))))
|
|
))
|
|
loss = self.model.BatchSigmoidCrossEntropyLoss(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, tuple())), loss)
|
|
|
|
def testBatchSoftmaxLoss(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float32, tuple()))),
|
|
('prediction', schema.Scalar((np.float32, (32,))))
|
|
))
|
|
loss = self.model.BatchSoftmaxLoss(input_record)
|
|
self.assertEqual(schema.Struct(
|
|
('softmax', schema.Scalar((np.float32, (32,)))),
|
|
('loss', schema.Scalar(np.float32)),
|
|
), loss)
|
|
|
|
def testBatchSoftmaxLossWeight(self):
|
|
input_record = self.new_record(schema.Struct(
|
|
('label', schema.Scalar((np.float32, tuple()))),
|
|
('prediction', schema.Scalar((np.float32, (32,)))),
|
|
('weight', schema.Scalar((np.float64, (1,))))
|
|
))
|
|
loss = self.model.BatchSoftmaxLoss(input_record)
|
|
self.assertEqual(schema.Struct(
|
|
('softmax', schema.Scalar((np.float32, (32,)))),
|
|
('loss', schema.Scalar(np.float32)),
|
|
), loss)
|
|
|
|
@given(
|
|
X=hu.arrays(dims=[2, 5]),
|
|
)
|
|
def testBatchNormalization(self, X):
|
|
input_record = self.new_record(schema.Scalar((np.float32, (5,))))
|
|
schema.FeedRecord(input_record, [X])
|
|
bn_output = self.model.BatchNormalization(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, (5,))), bn_output)
|
|
self.model.output_schema = schema.Struct()
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
init_ops = self.assertNetContainOps(
|
|
train_init_net,
|
|
[
|
|
OpSpec("ConstantFill", None, None),
|
|
OpSpec("ConstantFill", None, None),
|
|
OpSpec("ConstantFill", None, None),
|
|
OpSpec("ConstantFill", None, None),
|
|
]
|
|
)
|
|
|
|
input_blob = input_record.field_blobs()[0]
|
|
output_blob = bn_output.field_blobs()[0]
|
|
|
|
expand_dims_spec = OpSpec(
|
|
"ExpandDims",
|
|
[input_blob],
|
|
None,
|
|
)
|
|
|
|
train_bn_spec = OpSpec(
|
|
"SpatialBN",
|
|
[None, init_ops[0].output[0], init_ops[1].output[0],
|
|
init_ops[2].output[0], init_ops[3].output[0]],
|
|
[output_blob, init_ops[2].output[0], init_ops[3].output[0], None, None],
|
|
{'is_test': 0, 'order': 'NCHW', 'momentum': 0.9},
|
|
)
|
|
|
|
test_bn_spec = OpSpec(
|
|
"SpatialBN",
|
|
[None, init_ops[0].output[0], init_ops[1].output[0],
|
|
init_ops[2].output[0], init_ops[3].output[0]],
|
|
[output_blob],
|
|
{'is_test': 1, 'order': 'NCHW', 'momentum': 0.9},
|
|
)
|
|
|
|
squeeze_spec = OpSpec(
|
|
"Squeeze",
|
|
[output_blob],
|
|
[output_blob],
|
|
)
|
|
|
|
self.assertNetContainOps(
|
|
train_net,
|
|
[expand_dims_spec, train_bn_spec, squeeze_spec]
|
|
)
|
|
|
|
eval_net = self.get_eval_net()
|
|
|
|
self.assertNetContainOps(
|
|
eval_net,
|
|
[expand_dims_spec, test_bn_spec, squeeze_spec]
|
|
)
|
|
|
|
predict_net = self.get_predict_net()
|
|
|
|
self.assertNetContainOps(
|
|
predict_net,
|
|
[expand_dims_spec, test_bn_spec, squeeze_spec]
|
|
)
|
|
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
|
|
schema.FeedRecord(input_record, [X])
|
|
workspace.RunNetOnce(eval_net)
|
|
|
|
schema.FeedRecord(input_record, [X])
|
|
workspace.RunNetOnce(predict_net)
|
|
|
|
@given(
|
|
X=hu.arrays(dims=[2, 5, 6]),
|
|
)
|
|
def testLayerNormalization(self, X):
|
|
input_record = self.new_record(schema.Scalar((np.float32, (5, 6,))))
|
|
schema.FeedRecord(input_record, [X])
|
|
ln_output = self.model.LayerNormalization(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, (5, 6,))), ln_output)
|
|
self.model.output_schema = schema.Struct()
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
|
|
@given(
|
|
X=hu.arrays(dims=[5, 2]),
|
|
num_to_collect=st.integers(min_value=1, max_value=10),
|
|
)
|
|
def testLastNWindowCollector(self, X, num_to_collect):
|
|
input_record = self.new_record(schema.Scalar(np.float32))
|
|
schema.FeedRecord(input_record, [X])
|
|
last_n = self.model.LastNWindowCollector(input_record, num_to_collect)
|
|
self.run_train_net_forward_only()
|
|
output_record = schema.FetchRecord(last_n.last_n)
|
|
start = max(0, 5 - num_to_collect)
|
|
npt.assert_array_equal(X[start:], output_record())
|
|
num_visited = schema.FetchRecord(last_n.num_visited)
|
|
npt.assert_array_equal([5], num_visited())
|
|
|
|
@given(
|
|
X=hu.arrays(dims=[5, 2]),
|
|
num_to_collect=st.integers(min_value=3, max_value=3),
|
|
)
|
|
def testReservoirSamplingWithID(self, X, num_to_collect):
|
|
ID = np.array([1, 2, 3, 1, 2], dtype=np.int64)
|
|
input_record = self.new_record(
|
|
schema.Struct(
|
|
('record', schema.Struct(
|
|
('dense', schema.Scalar()),
|
|
)),
|
|
('object_id', schema.Scalar(np.int64)),
|
|
)
|
|
)
|
|
schema.FeedRecord(input_record, [X, ID])
|
|
packed_record = self.model.PackRecords(
|
|
input_record.record, 1, fields=input_record.record.field_names())
|
|
reservoir_input = schema.Struct(
|
|
('data', packed_record),
|
|
('object_id', input_record.object_id),
|
|
)
|
|
reservoir = self.model.ReservoirSampling(
|
|
reservoir_input, num_to_collect)
|
|
self.model.output_schema = schema.Struct()
|
|
train_init_net, train_net = \
|
|
layer_model_instantiator.generate_training_nets_forward_only(
|
|
self.model)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.CreateNet(train_net)
|
|
workspace.RunNet(train_net.Proto().name, num_iter=2)
|
|
num_visited = schema.FetchRecord(reservoir.num_visited)
|
|
npt.assert_array_equal([3], num_visited())
|
|
for param in self.model.params:
|
|
serialized = workspace.SerializeBlob(str(param))
|
|
workspace.DeserializeBlob(str(param), serialized)
|
|
ID = np.array([3, 5, 3, 3, 5], dtype=np.int64)
|
|
schema.FeedRecord(input_record.object_id, [ID])
|
|
workspace.RunNet(train_net.Proto().name, num_iter=2)
|
|
num_visited = schema.FetchRecord(reservoir.num_visited)
|
|
npt.assert_array_equal([2], num_visited())
|
|
|
|
def testUniformSampling(self):
|
|
input_record = self.new_record(schema.Scalar(np.int32))
|
|
input_array = np.array([3, 10, 11, 15, 20, 99], dtype=np.int32)
|
|
schema.FeedRecord(input_record, [input_array])
|
|
num_samples = 20
|
|
num_elements = 100
|
|
uniform_sampling_output = self.model.UniformSampling(
|
|
input_record, num_samples, num_elements)
|
|
self.model.loss = uniform_sampling_output
|
|
self.run_train_net()
|
|
samples = workspace.FetchBlob(uniform_sampling_output.samples())
|
|
sampling_prob = workspace.FetchBlob(
|
|
uniform_sampling_output.sampling_prob())
|
|
self.assertEqual(num_samples, len(samples))
|
|
np.testing.assert_array_equal(input_array, samples[:len(input_array)])
|
|
np.testing.assert_almost_equal(
|
|
np.array([float(num_samples) / num_elements] * num_samples,
|
|
dtype=np.float32),
|
|
sampling_prob
|
|
)
|
|
|
|
def testUniformSamplingWithIncorrectSampleSize(self):
|
|
input_record = self.new_record(schema.Scalar(np.int32))
|
|
num_samples = 200
|
|
num_elements = 100
|
|
with self.assertRaises(AssertionError):
|
|
self.model.UniformSampling(input_record, num_samples, num_elements)
|
|
|
|
def testGatherRecord(self):
|
|
indices = np.array([1, 3, 4], dtype=np.int32)
|
|
dense = np.array(list(range(20)), dtype=np.float32).reshape(10, 2)
|
|
lengths = np.array(list(range(10)), dtype=np.int32)
|
|
items = np.array(list(range(lengths.sum())), dtype=np.int64)
|
|
items_lengths = np.array(list(range(lengths.sum())), dtype=np.int32)
|
|
items_items = np.array(list(range(items_lengths.sum())), dtype=np.int64)
|
|
record = self.new_record(schema.Struct(
|
|
('dense', schema.Scalar(np.float32)),
|
|
('sparse', schema.Struct(
|
|
('list', schema.List(np.int64)),
|
|
('list_of_list', schema.List(schema.List(np.int64))),
|
|
)),
|
|
('empty_struct', schema.Struct())
|
|
))
|
|
indices_record = self.new_record(schema.Scalar(np.int32))
|
|
input_record = schema.Struct(
|
|
('indices', indices_record),
|
|
('record', record),
|
|
)
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[indices, dense, lengths, items, lengths, items_lengths,
|
|
items_items])
|
|
gathered_record = self.model.GatherRecord(input_record)
|
|
self.assertTrue(schema.equal_schemas(gathered_record, record))
|
|
|
|
self.run_train_net_forward_only()
|
|
gathered_dense = workspace.FetchBlob(gathered_record.dense())
|
|
np.testing.assert_array_equal(
|
|
np.concatenate([dense[i:i + 1] for i in indices]), gathered_dense)
|
|
gathered_lengths = workspace.FetchBlob(
|
|
gathered_record.sparse.list.lengths())
|
|
np.testing.assert_array_equal(
|
|
np.concatenate([lengths[i:i + 1] for i in indices]),
|
|
gathered_lengths)
|
|
gathered_items = workspace.FetchBlob(
|
|
gathered_record.sparse.list.items())
|
|
offsets = lengths.cumsum() - lengths
|
|
np.testing.assert_array_equal(
|
|
np.concatenate([
|
|
items[offsets[i]: offsets[i] + lengths[i]]
|
|
for i in indices
|
|
]), gathered_items)
|
|
|
|
gathered_items_lengths = workspace.FetchBlob(
|
|
gathered_record.sparse.list_of_list.items.lengths())
|
|
np.testing.assert_array_equal(
|
|
np.concatenate([
|
|
items_lengths[offsets[i]: offsets[i] + lengths[i]]
|
|
for i in indices
|
|
]),
|
|
gathered_items_lengths
|
|
)
|
|
|
|
nested_offsets = []
|
|
nested_lengths = []
|
|
nested_offset = 0
|
|
j = 0
|
|
for l in lengths:
|
|
nested_offsets.append(nested_offset)
|
|
nested_length = 0
|
|
for _i in range(l):
|
|
nested_offset += items_lengths[j]
|
|
nested_length += items_lengths[j]
|
|
j += 1
|
|
nested_lengths.append(nested_length)
|
|
|
|
gathered_items_items = workspace.FetchBlob(
|
|
gathered_record.sparse.list_of_list.items.items())
|
|
np.testing.assert_array_equal(
|
|
np.concatenate([
|
|
items_items[nested_offsets[i]:
|
|
nested_offsets[i] + nested_lengths[i]]
|
|
for i in indices
|
|
]),
|
|
gathered_items_items
|
|
)
|
|
|
|
def testMapToRange(self):
|
|
input_record = self.new_record(schema.Scalar(np.int32))
|
|
indices_blob = self.model.MapToRange(input_record,
|
|
max_index=100).indices
|
|
self.model.output_schema = schema.Struct()
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[np.array([10, 3, 20, 99, 15, 11, 3, 11], dtype=np.int32)]
|
|
)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
indices = workspace.FetchBlob(indices_blob())
|
|
np.testing.assert_array_equal(
|
|
np.array([1, 2, 3, 4, 5, 6, 2, 6], dtype=np.int32),
|
|
indices
|
|
)
|
|
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[np.array([10, 3, 23, 35, 60, 15, 10, 15], dtype=np.int32)]
|
|
)
|
|
workspace.RunNetOnce(train_net)
|
|
indices = workspace.FetchBlob(indices_blob())
|
|
np.testing.assert_array_equal(
|
|
np.array([1, 2, 7, 8, 9, 5, 1, 5], dtype=np.int32),
|
|
indices
|
|
)
|
|
|
|
eval_net = self.get_eval_net()
|
|
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[np.array([10, 3, 23, 35, 60, 15, 200], dtype=np.int32)]
|
|
)
|
|
workspace.RunNetOnce(eval_net)
|
|
indices = workspace.FetchBlob(indices_blob())
|
|
np.testing.assert_array_equal(
|
|
np.array([1, 2, 7, 8, 9, 5, 0], dtype=np.int32),
|
|
indices
|
|
)
|
|
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[np.array([10, 3, 23, 15, 101, 115], dtype=np.int32)]
|
|
)
|
|
workspace.RunNetOnce(eval_net)
|
|
indices = workspace.FetchBlob(indices_blob())
|
|
np.testing.assert_array_equal(
|
|
np.array([1, 2, 7, 5, 0, 0], dtype=np.int32),
|
|
indices
|
|
)
|
|
|
|
predict_net = self.get_predict_net()
|
|
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[np.array([3, 3, 20, 23, 151, 35, 60, 15, 200], dtype=np.int32)]
|
|
)
|
|
workspace.RunNetOnce(predict_net)
|
|
indices = workspace.FetchBlob(indices_blob())
|
|
np.testing.assert_array_equal(
|
|
np.array([2, 2, 3, 7, 0, 8, 9, 5, 0], dtype=np.int32),
|
|
indices
|
|
)
|
|
|
|
def testSelectRecordByContext(self):
|
|
float_features = self.model.input_feature_schema.float_features
|
|
|
|
float_array = np.array([1.0, 2.0], dtype=np.float32)
|
|
|
|
schema.FeedRecord(float_features, [float_array])
|
|
|
|
with Tags(Tags.EXCLUDE_FROM_PREDICTION):
|
|
log_float_features = self.model.Log(float_features, 1)
|
|
joined = self.model.SelectRecordByContext(
|
|
schema.Struct(
|
|
(InstantiationContext.PREDICTION, float_features),
|
|
(InstantiationContext.TRAINING, log_float_features),
|
|
# TODO: TRAIN_ONLY layers are also generated in eval
|
|
(InstantiationContext.EVAL, log_float_features),
|
|
)
|
|
)
|
|
|
|
# model.output_schema has to a struct
|
|
self.model.output_schema = schema.Struct((
|
|
'joined', joined
|
|
))
|
|
predict_net = layer_model_instantiator.generate_predict_net(self.model)
|
|
workspace.RunNetOnce(predict_net)
|
|
predict_output = schema.FetchRecord(predict_net.output_record())
|
|
npt.assert_array_equal(float_array,
|
|
predict_output['joined']())
|
|
eval_net = layer_model_instantiator.generate_eval_net(self.model)
|
|
workspace.RunNetOnce(eval_net)
|
|
eval_output = schema.FetchRecord(eval_net.output_record())
|
|
npt.assert_array_equal(np.log(float_array),
|
|
eval_output['joined']())
|
|
_, train_net = (
|
|
layer_model_instantiator.generate_training_nets_forward_only(
|
|
self.model
|
|
)
|
|
)
|
|
workspace.RunNetOnce(train_net)
|
|
train_output = schema.FetchRecord(train_net.output_record())
|
|
npt.assert_array_equal(np.log(float_array),
|
|
train_output['joined']())
|
|
|
|
def testFunctionalLayer(self):
|
|
def normalize(net, in_record, out_record):
|
|
mean = net.ReduceFrontMean(in_record(), 1)
|
|
net.Sub(
|
|
[in_record(), mean],
|
|
out_record(),
|
|
broadcast=1)
|
|
normalized = self.model.Functional(
|
|
self.model.input_feature_schema.float_features, 1,
|
|
normalize, name="normalizer")
|
|
|
|
# Attach metadata to one of the outputs and use it in FC
|
|
normalized.set_type((np.float32, 32))
|
|
self.model.output_schema = self.model.FC(normalized, 2)
|
|
|
|
predict_net = layer_model_instantiator.generate_predict_net(
|
|
self.model)
|
|
ops = predict_net.Proto().op
|
|
assert len(ops) == 3
|
|
assert ops[0].type == "ReduceFrontMean"
|
|
assert ops[1].type == "Sub"
|
|
assert ops[2].type == "FC"
|
|
assert len(ops[0].input) == 1
|
|
assert ops[0].input[0] ==\
|
|
self.model.input_feature_schema.float_features()
|
|
assert len(ops[1].output) == 1
|
|
assert ops[1].output[0] in ops[2].input
|
|
|
|
def testFunctionalLayerHelper(self):
|
|
mean = self.model.ReduceFrontMean(
|
|
self.model.input_feature_schema.float_features, 1)
|
|
normalized = self.model.Sub(
|
|
schema.Tuple(
|
|
self.model.input_feature_schema.float_features, mean),
|
|
1, broadcast=1)
|
|
# Attach metadata to one of the outputs and use it in FC
|
|
normalized.set_type((np.float32, (32,)))
|
|
self.model.output_schema = self.model.FC(normalized, 2)
|
|
|
|
predict_net = layer_model_instantiator.generate_predict_net(
|
|
self.model)
|
|
ops = predict_net.Proto().op
|
|
assert len(ops) == 3
|
|
assert ops[0].type == "ReduceFrontMean"
|
|
assert ops[1].type == "Sub"
|
|
assert ops[2].type == "FC"
|
|
assert len(ops[0].input) == 1
|
|
assert ops[0].input[0] ==\
|
|
self.model.input_feature_schema.float_features()
|
|
assert len(ops[1].output) == 1
|
|
assert ops[1].output[0] in ops[2].input
|
|
|
|
def testFunctionalLayerHelperAutoInference(self):
|
|
softsign = self.model.Softsign(
|
|
schema.Tuple(self.model.input_feature_schema.float_features),
|
|
1)
|
|
assert softsign.field_type().base == np.float32
|
|
assert softsign.field_type().shape == (32,)
|
|
self.model.output_schema = self.model.FC(softsign, 2)
|
|
|
|
predict_net = layer_model_instantiator.generate_predict_net(
|
|
self.model)
|
|
ops = predict_net.Proto().op
|
|
assert len(ops) == 2
|
|
assert ops[0].type == "Softsign"
|
|
assert ops[1].type == "FC"
|
|
assert len(ops[0].input) == 1
|
|
assert ops[0].input[0] ==\
|
|
self.model.input_feature_schema.float_features()
|
|
assert len(ops[0].output) == 1
|
|
assert ops[0].output[0] in ops[1].input
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
|
|
def testHalfToFloatTypeInference(self):
|
|
input = self.new_record(schema.Scalar((np.float32, (32,))))
|
|
|
|
output = self.model.FloatToHalf(input, 1)
|
|
assert output.field_type().base == np.float16
|
|
assert output.field_type().shape == (32, )
|
|
|
|
output = self.model.HalfToFloat(output, 1)
|
|
assert output.field_type().base == np.float32
|
|
assert output.field_type().shape == (32, )
|
|
|
|
def testFunctionalLayerHelperAutoInferenceScalar(self):
|
|
loss = self.model.AveragedLoss(self.model.input_feature_schema, 1)
|
|
self.assertEqual(1, len(loss.field_types()))
|
|
self.assertEqual(np.float32, loss.field_types()[0].base)
|
|
self.assertEqual(tuple(), loss.field_types()[0].shape)
|
|
|
|
def testFunctionalLayerInputCoercion(self):
|
|
one = self.model.global_constants['ONE']
|
|
two = self.model.Add([one, one], 1)
|
|
self.model.loss = two
|
|
self.run_train_net()
|
|
data = workspace.FetchBlob(two.field_blobs()[0])
|
|
np.testing.assert_array_equal([2.0], data)
|
|
|
|
def testFunctionalLayerWithOutputNames(self):
|
|
k = 3
|
|
topk = self.model.TopK(
|
|
self.model.input_feature_schema,
|
|
output_names_or_num=['values', 'indices'],
|
|
k=k,
|
|
)
|
|
self.assertEqual(2, len(topk.field_types()))
|
|
self.assertEqual(np.float32, topk.field_types()[0].base)
|
|
self.assertEqual((k,), topk.field_types()[0].shape)
|
|
self.assertEqual(np.int32, topk.field_types()[1].base)
|
|
self.assertEqual((k,), topk.field_types()[1].shape)
|
|
self.assertEqual(['TopK/values', 'TopK/indices'], topk.field_blobs())
|
|
|
|
def testFunctionalLayerSameOperatorOutputNames(self):
|
|
Con1 = self.model.ConstantFill([], 1, value=1)
|
|
Con2 = self.model.ConstantFill([], 1, value=2)
|
|
self.assertNotEqual(str(Con1), str(Con2))
|
|
|
|
def testFunctionalLayerWithOutputDtypes(self):
|
|
loss = self.model.AveragedLoss(
|
|
self.model.input_feature_schema,
|
|
1,
|
|
output_dtypes=(np.float32, (1,)),
|
|
)
|
|
self.assertEqual(1, len(loss.field_types()))
|
|
self.assertEqual(np.float32, loss.field_types()[0].base)
|
|
self.assertEqual((1,), loss.field_types()[0].shape)
|
|
|
|
def testPropagateRequestOnly(self):
|
|
# test case when output is request only
|
|
input_record = self.new_record(schema.Struct(
|
|
('input1', schema.Scalar((np.float32, (32, )))),
|
|
('input2', schema.Scalar((np.float32, (64, )))),
|
|
('input3', schema.Scalar((np.float32, (16, )))),
|
|
))
|
|
|
|
set_request_only(input_record)
|
|
concat_output = self.model.Concat(input_record)
|
|
self.assertEqual(is_request_only_scalar(concat_output), True)
|
|
|
|
# test case when output is not request only
|
|
input_record2 = self.new_record(schema.Struct(
|
|
('input4', schema.Scalar((np.float32, (100, ))))
|
|
)) + input_record
|
|
|
|
concat_output2 = self.model.Concat(input_record2)
|
|
self.assertEqual(is_request_only_scalar(concat_output2), False)
|
|
|
|
def testSetRequestOnly(self):
|
|
input_record = schema.Scalar(np.int64)
|
|
schema.attach_metadata_to_scalars(
|
|
input_record,
|
|
schema.Metadata(
|
|
categorical_limit=100000000,
|
|
expected_value=99,
|
|
feature_specs=schema.FeatureSpec(
|
|
feature_ids=[1, 100, 1001]
|
|
)
|
|
)
|
|
)
|
|
|
|
set_request_only(input_record)
|
|
self.assertEqual(input_record.metadata.categorical_limit, 100000000)
|
|
self.assertEqual(input_record.metadata.expected_value, 99)
|
|
self.assertEqual(
|
|
input_record.metadata.feature_specs.feature_ids,
|
|
[1, 100, 1001]
|
|
)
|
|
|
|
@given(
|
|
X=hu.arrays(dims=[5, 5]), # Shape of X is irrelevant
|
|
)
|
|
def testDropout(self, X):
|
|
input_record = self.new_record(schema.Scalar((np.float32, (1,))))
|
|
schema.FeedRecord(input_record, [X])
|
|
d_output = self.model.Dropout(input_record)
|
|
self.assertEqual(schema.Scalar((np.float32, (1,))), d_output)
|
|
self.model.output_schema = schema.Struct()
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
input_blob = input_record.field_blobs()[0]
|
|
output_blob = d_output.field_blobs()[0]
|
|
|
|
train_d_spec = OpSpec(
|
|
"Dropout",
|
|
[input_blob],
|
|
[output_blob, None],
|
|
{'is_test': 0, 'ratio': 0.5}
|
|
)
|
|
|
|
test_d_spec = OpSpec(
|
|
"Dropout",
|
|
[input_blob],
|
|
[output_blob, None],
|
|
{'is_test': 1, 'ratio': 0.5}
|
|
)
|
|
|
|
self.assertNetContainOps(
|
|
train_net,
|
|
[train_d_spec]
|
|
)
|
|
|
|
eval_net = self.get_eval_net()
|
|
|
|
self.assertNetContainOps(
|
|
eval_net,
|
|
[test_d_spec]
|
|
)
|
|
|
|
predict_net = self.get_predict_net()
|
|
|
|
self.assertNetContainOps(
|
|
predict_net,
|
|
[test_d_spec]
|
|
)
|
|
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
|
|
schema.FeedRecord(input_record, [X])
|
|
workspace.RunNetOnce(eval_net)
|
|
|
|
schema.FeedRecord(input_record, [X])
|
|
workspace.RunNetOnce(predict_net)
|
|
|
|
@given(
|
|
num_inputs=st.integers(1, 3),
|
|
batch_size=st.integers(5, 10)
|
|
)
|
|
def testMergeIdListsLayer(self, num_inputs, batch_size):
|
|
inputs = []
|
|
for _ in range(num_inputs):
|
|
lengths = np.random.randint(5, size=batch_size).astype(np.int32)
|
|
size = lengths.sum()
|
|
values = np.random.randint(1, 10, size=size).astype(np.int64)
|
|
inputs.append(lengths)
|
|
inputs.append(values)
|
|
input_schema = schema.Tuple(
|
|
*[schema.List(
|
|
schema.Scalar(dtype=np.int64, metadata=schema.Metadata(
|
|
categorical_limit=20
|
|
))) for _ in range(num_inputs)]
|
|
)
|
|
|
|
input_record = schema.NewRecord(self.model.net, input_schema)
|
|
schema.FeedRecord(input_record, inputs)
|
|
output_schema = self.model.MergeIdLists(input_record)
|
|
assert schema.equal_schemas(
|
|
output_schema, IdList,
|
|
check_field_names=False)
|
|
|
|
@given(
|
|
batch_size=st.integers(min_value=2, max_value=10),
|
|
input_dims=st.integers(min_value=5, max_value=10),
|
|
output_dims=st.integers(min_value=5, max_value=10),
|
|
bandwidth=st.floats(min_value=0.1, max_value=5),
|
|
)
|
|
def testRandomFourierFeatures(self, batch_size, input_dims, output_dims, bandwidth):
|
|
|
|
def _rff_hypothesis_test(rff_output, X, W, b, scale):
|
|
"""
|
|
Runs hypothesis test for Semi Random Features layer.
|
|
|
|
Inputs:
|
|
rff_output -- output of net after running random fourier features layer
|
|
X -- input data
|
|
W -- weight parameter from train_init_net
|
|
b -- bias parameter from train_init_net
|
|
scale -- value by which to scale the output vector
|
|
"""
|
|
output = workspace.FetchBlob(rff_output)
|
|
output_ref = scale * np.cos(np.dot(X, np.transpose(W)) + b)
|
|
npt.assert_allclose(output, output_ref, rtol=1e-3, atol=1e-3)
|
|
|
|
X = np.random.random((batch_size, input_dims)).astype(np.float32)
|
|
scale = np.sqrt(2.0 / output_dims)
|
|
input_record = self.new_record(schema.Scalar((np.float32, (input_dims,))))
|
|
schema.FeedRecord(input_record, [X])
|
|
input_blob = input_record.field_blobs()[0]
|
|
rff_output = self.model.RandomFourierFeatures(input_record,
|
|
output_dims,
|
|
bandwidth)
|
|
self.model.output_schema = schema.Struct()
|
|
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (output_dims, ))),
|
|
rff_output
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
# Init net assertions
|
|
init_ops_list = [
|
|
OpSpec("GaussianFill", None, None),
|
|
OpSpec("UniformFill", None, None),
|
|
]
|
|
init_ops = self._test_net(train_init_net, init_ops_list)
|
|
W = workspace.FetchBlob(self.model.layers[0].w)
|
|
b = workspace.FetchBlob(self.model.layers[0].b)
|
|
|
|
# Operation specifications
|
|
fc_spec = OpSpec("FC", [input_blob, init_ops[0].output[0],
|
|
init_ops[1].output[0]], None)
|
|
cosine_spec = OpSpec("Cos", None, None)
|
|
scale_spec = OpSpec("Scale", None, rff_output.field_blobs(),
|
|
{'scale': scale})
|
|
ops_list = [
|
|
fc_spec,
|
|
cosine_spec,
|
|
scale_spec
|
|
]
|
|
|
|
# Train net assertions
|
|
self._test_net(train_net, ops_list)
|
|
_rff_hypothesis_test(rff_output(), X, W, b, scale)
|
|
|
|
# Eval net assertions
|
|
eval_net = self.get_eval_net()
|
|
self._test_net(eval_net, ops_list)
|
|
_rff_hypothesis_test(rff_output(), X, W, b, scale)
|
|
|
|
# Predict net assertions
|
|
predict_net = self.get_predict_net()
|
|
self._test_net(predict_net, ops_list)
|
|
_rff_hypothesis_test(rff_output(), X, W, b, scale)
|
|
|
|
@given(
|
|
batch_size=st.integers(min_value=2, max_value=10),
|
|
input_dims=st.integers(min_value=5, max_value=10),
|
|
output_dims=st.integers(min_value=5, max_value=10),
|
|
s=st.integers(min_value=0, max_value=3),
|
|
scale=st.floats(min_value=0.1, max_value=5),
|
|
set_weight_as_global_constant=st.booleans()
|
|
)
|
|
def testArcCosineFeatureMap(self, batch_size, input_dims, output_dims, s, scale,
|
|
set_weight_as_global_constant):
|
|
|
|
def _arc_cosine_hypothesis_test(ac_output, X, W, b, s):
|
|
"""
|
|
Runs hypothesis test for Arc Cosine layer.
|
|
|
|
Inputs:
|
|
ac_output -- output of net after running arc cosine layer
|
|
X -- input data
|
|
W -- weight parameter from train_init_net
|
|
b -- bias parameter from train_init_net
|
|
s -- degree parameter
|
|
"""
|
|
# Get output from net
|
|
net_output = workspace.FetchBlob(ac_output)
|
|
|
|
# Computing output directly
|
|
x_rand = np.matmul(X, np.transpose(W)) + b
|
|
x_pow = np.power(x_rand, s)
|
|
if s > 0:
|
|
h_rand_features = np.piecewise(x_rand,
|
|
[x_rand <= 0, x_rand > 0],
|
|
[0, 1])
|
|
else:
|
|
h_rand_features = np.piecewise(x_rand,
|
|
[x_rand <= 0, x_rand > 0],
|
|
[0, lambda x: x / (1 + x)])
|
|
output_ref = np.multiply(x_pow, h_rand_features)
|
|
|
|
# Comparing net output and computed output
|
|
npt.assert_allclose(net_output, output_ref, rtol=1e-3, atol=1e-3)
|
|
|
|
X = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
|
|
input_record = self.new_record(schema.Scalar((np.float32, (input_dims,))))
|
|
schema.FeedRecord(input_record, [X])
|
|
input_blob = input_record.field_blobs()[0]
|
|
|
|
ac_output = self.model.ArcCosineFeatureMap(
|
|
input_record,
|
|
output_dims,
|
|
s=s,
|
|
scale=scale,
|
|
set_weight_as_global_constant=set_weight_as_global_constant
|
|
)
|
|
self.model.output_schema = schema.Struct()
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (output_dims, ))),
|
|
ac_output
|
|
)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
# Run create_init_net to initialize the global constants, and W and b
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(self.model.create_init_net(name='init_net'))
|
|
|
|
if set_weight_as_global_constant:
|
|
W = workspace.FetchBlob(
|
|
self.model.global_constants['arc_cosine_feature_map_fixed_rand_W']
|
|
)
|
|
b = workspace.FetchBlob(
|
|
self.model.global_constants['arc_cosine_feature_map_fixed_rand_b']
|
|
)
|
|
else:
|
|
W = workspace.FetchBlob(self.model.layers[0].random_w)
|
|
b = workspace.FetchBlob(self.model.layers[0].random_b)
|
|
|
|
# Operation specifications
|
|
fc_spec = OpSpec("FC", [input_blob, None, None], None)
|
|
softsign_spec = OpSpec("Softsign", None, None)
|
|
relu_spec = OpSpec("Relu", None, None)
|
|
relu_spec_output = OpSpec("Relu", None, ac_output.field_blobs())
|
|
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
|
|
mul_spec = OpSpec("Mul", None, ac_output.field_blobs())
|
|
|
|
if s == 0:
|
|
ops_list = [
|
|
fc_spec,
|
|
softsign_spec,
|
|
relu_spec_output,
|
|
]
|
|
elif s == 1:
|
|
ops_list = [
|
|
fc_spec,
|
|
relu_spec_output,
|
|
]
|
|
else:
|
|
ops_list = [
|
|
fc_spec,
|
|
relu_spec,
|
|
pow_spec,
|
|
mul_spec,
|
|
]
|
|
|
|
# Train net assertions
|
|
self._test_net(train_net, ops_list)
|
|
_arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
|
|
|
|
# Eval net assertions
|
|
eval_net = self.get_eval_net()
|
|
self._test_net(eval_net, ops_list)
|
|
_arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
|
|
|
|
# Predict net assertions
|
|
predict_net = self.get_predict_net()
|
|
self._test_net(predict_net, ops_list)
|
|
_arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
|
|
|
|
@given(
|
|
batch_size=st.integers(min_value=2, max_value=10),
|
|
input_dims=st.integers(min_value=5, max_value=10),
|
|
output_dims=st.integers(min_value=5, max_value=10),
|
|
s=st.integers(min_value=0, max_value=3),
|
|
scale=st.floats(min_value=0.1, max_value=5),
|
|
set_weight_as_global_constant=st.booleans(),
|
|
use_struct_input=st.booleans(),
|
|
)
|
|
def testSemiRandomFeatures(self, batch_size, input_dims, output_dims, s, scale,
|
|
set_weight_as_global_constant, use_struct_input):
|
|
|
|
def _semi_random_hypothesis_test(srf_output, X_full, X_random, rand_w,
|
|
rand_b, s):
|
|
"""
|
|
Runs hypothesis test for Semi Random Features layer.
|
|
|
|
Inputs:
|
|
srf_output -- output of net after running semi random features layer
|
|
X_full -- full input data
|
|
X_random -- random-output input data
|
|
rand_w -- random-initialized weight parameter from train_init_net
|
|
rand_b -- random-initialized bias parameter from train_init_net
|
|
s -- degree parameter
|
|
|
|
"""
|
|
# Get output from net
|
|
net_output = workspace.FetchBlob(srf_output)
|
|
|
|
# Fetch learned parameter blobs
|
|
learned_w = workspace.FetchBlob(self.model.layers[0].learned_w)
|
|
learned_b = workspace.FetchBlob(self.model.layers[0].learned_b)
|
|
|
|
# Computing output directly
|
|
x_rand = np.matmul(X_random, np.transpose(rand_w)) + rand_b
|
|
x_learn = np.matmul(X_full, np.transpose(learned_w)) + learned_b
|
|
x_pow = np.power(x_rand, s)
|
|
if s > 0:
|
|
h_rand_features = np.piecewise(x_rand,
|
|
[x_rand <= 0, x_rand > 0],
|
|
[0, 1])
|
|
else:
|
|
h_rand_features = np.piecewise(x_rand,
|
|
[x_rand <= 0, x_rand > 0],
|
|
[0, lambda x: x / (1 + x)])
|
|
output_ref = np.multiply(np.multiply(x_pow, h_rand_features), x_learn)
|
|
|
|
# Comparing net output and computed output
|
|
npt.assert_allclose(net_output, output_ref, rtol=1e-3, atol=1e-3)
|
|
|
|
X_full = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
|
|
if use_struct_input:
|
|
X_random = np.random.normal(size=(batch_size, input_dims)).\
|
|
astype(np.float32)
|
|
input_data = [X_full, X_random]
|
|
input_record = self.new_record(schema.Struct(
|
|
('full', schema.Scalar(
|
|
(np.float32, (input_dims,))
|
|
)),
|
|
('random', schema.Scalar(
|
|
(np.float32, (input_dims,))
|
|
))
|
|
))
|
|
else:
|
|
X_random = X_full
|
|
input_data = [X_full]
|
|
input_record = self.new_record(schema.Scalar(
|
|
(np.float32, (input_dims,))
|
|
))
|
|
|
|
schema.FeedRecord(input_record, input_data)
|
|
srf_output = self.model.SemiRandomFeatures(
|
|
input_record,
|
|
output_dims,
|
|
s=s,
|
|
scale_random=scale,
|
|
scale_learned=scale,
|
|
set_weight_as_global_constant=set_weight_as_global_constant
|
|
)
|
|
|
|
self.model.output_schema = schema.Struct()
|
|
|
|
self.assertEqual(
|
|
schema.Struct(
|
|
('full', schema.Scalar(
|
|
(np.float32, (output_dims,))
|
|
)),
|
|
('random', schema.Scalar(
|
|
(np.float32, (output_dims,))
|
|
))
|
|
),
|
|
srf_output
|
|
)
|
|
|
|
init_ops_list = [
|
|
OpSpec("GaussianFill", None, None),
|
|
OpSpec("UniformFill", None, None),
|
|
OpSpec("GaussianFill", None, None),
|
|
OpSpec("UniformFill", None, None),
|
|
]
|
|
train_init_net, train_net = self.get_training_nets()
|
|
|
|
# Need to run to initialize the global constants for layer
|
|
workspace.RunNetOnce(self.model.create_init_net(name='init_net'))
|
|
|
|
if set_weight_as_global_constant:
|
|
# If weight params are global constants, they won't be in train_init_net
|
|
init_ops = self._test_net(train_init_net, init_ops_list[:2])
|
|
rand_w = workspace.FetchBlob(
|
|
self.model.global_constants['semi_random_features_fixed_rand_W']
|
|
)
|
|
rand_b = workspace.FetchBlob(
|
|
self.model.global_constants['semi_random_features_fixed_rand_b']
|
|
)
|
|
|
|
# Operation specifications
|
|
fc_random_spec = OpSpec("FC", [None, None, None], None)
|
|
fc_learned_spec = OpSpec("FC", [None, init_ops[0].output[0],
|
|
init_ops[1].output[0]], None)
|
|
else:
|
|
init_ops = self._test_net(train_init_net, init_ops_list)
|
|
rand_w = workspace.FetchBlob(self.model.layers[0].random_w)
|
|
rand_b = workspace.FetchBlob(self.model.layers[0].random_b)
|
|
|
|
# Operation specifications
|
|
fc_random_spec = OpSpec("FC", [None, init_ops[0].output[0],
|
|
init_ops[1].output[0]], None)
|
|
fc_learned_spec = OpSpec("FC", [None, init_ops[2].output[0],
|
|
init_ops[3].output[0]], None)
|
|
|
|
softsign_spec = OpSpec("Softsign", None, None)
|
|
relu_spec = OpSpec("Relu", None, None)
|
|
relu_output_spec = OpSpec("Relu", None, srf_output.random.field_blobs())
|
|
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
|
|
mul_interim_spec = OpSpec("Mul", None, srf_output.random.field_blobs())
|
|
mul_spec = OpSpec("Mul", None, srf_output.full.field_blobs())
|
|
|
|
if s == 0:
|
|
ops_list = [
|
|
fc_learned_spec,
|
|
fc_random_spec,
|
|
softsign_spec,
|
|
relu_output_spec,
|
|
mul_spec,
|
|
]
|
|
elif s == 1:
|
|
ops_list = [
|
|
fc_learned_spec,
|
|
fc_random_spec,
|
|
relu_output_spec,
|
|
mul_spec,
|
|
]
|
|
else:
|
|
ops_list = [
|
|
fc_learned_spec,
|
|
fc_random_spec,
|
|
relu_spec,
|
|
pow_spec,
|
|
mul_interim_spec,
|
|
mul_spec,
|
|
]
|
|
|
|
# Train net assertions
|
|
self._test_net(train_net, ops_list)
|
|
_semi_random_hypothesis_test(srf_output.full(), X_full, X_random,
|
|
rand_w, rand_b, s)
|
|
|
|
# Eval net assertions
|
|
eval_net = self.get_eval_net()
|
|
self._test_net(eval_net, ops_list)
|
|
_semi_random_hypothesis_test(srf_output.full(), X_full, X_random,
|
|
rand_w, rand_b, s)
|
|
|
|
# Predict net assertions
|
|
predict_net = self.get_predict_net()
|
|
self._test_net(predict_net, ops_list)
|
|
_semi_random_hypothesis_test(srf_output.full(), X_full, X_random,
|
|
rand_w, rand_b, s)
|
|
|
|
def testConv(self):
|
|
batch_size = 50
|
|
H = 1
|
|
W = 10
|
|
C = 50
|
|
output_dims = 32
|
|
kernel_h = 1
|
|
kernel_w = 3
|
|
stride_h = 1
|
|
stride_w = 1
|
|
pad_t = 0
|
|
pad_b = 0
|
|
pad_r = None
|
|
pad_l = None
|
|
|
|
input_record = self.new_record(schema.Scalar((np.float32, (H, W, C))))
|
|
X = np.random.random((batch_size, H, W, C)).astype(np.float32)
|
|
schema.FeedRecord(input_record, [X])
|
|
conv = self.model.Conv(
|
|
input_record,
|
|
output_dims,
|
|
kernel_h=kernel_h,
|
|
kernel_w=kernel_w,
|
|
stride_h=stride_h,
|
|
stride_w=stride_w,
|
|
pad_t=pad_t,
|
|
pad_b=pad_b,
|
|
pad_r=pad_r,
|
|
pad_l=pad_l,
|
|
order='NHWC'
|
|
)
|
|
|
|
self.assertEqual(
|
|
schema.Scalar((np.float32, (output_dims,))),
|
|
conv
|
|
)
|
|
|
|
self.run_train_net_forward_only()
|
|
output_record = schema.FetchRecord(conv)
|
|
# check the number of output channels is the same as input in this example
|
|
assert output_record.field_types()[0].shape == (H, W, output_dims)
|
|
assert output_record().shape == (batch_size, H, W, output_dims)
|
|
|
|
train_init_net, train_net = self.get_training_nets()
|
|
# Init net assertions
|
|
init_ops = self.assertNetContainOps(
|
|
train_init_net,
|
|
[
|
|
OpSpec("XavierFill", None, None),
|
|
OpSpec("ConstantFill", None, None),
|
|
]
|
|
)
|
|
conv_spec = OpSpec(
|
|
"Conv",
|
|
[
|
|
input_record.field_blobs()[0],
|
|
init_ops[0].output[0],
|
|
init_ops[1].output[0],
|
|
],
|
|
conv.field_blobs()
|
|
)
|
|
|
|
# Train net assertions
|
|
self.assertNetContainOps(train_net, [conv_spec])
|
|
|
|
# Predict net assertions
|
|
predict_net = self.get_predict_net()
|
|
self.assertNetContainOps(predict_net, [conv_spec])
|
|
|
|
# Eval net assertions
|
|
eval_net = self.get_eval_net()
|
|
self.assertNetContainOps(eval_net, [conv_spec])
|
|
|
|
@given(
|
|
num=st.integers(min_value=10, max_value=100),
|
|
feed_weight=st.booleans(),
|
|
**hu.gcs
|
|
)
|
|
def testAdaptiveWeight(self, num, feed_weight, gc, dc):
|
|
input_record = self.new_record(schema.RawTuple(num))
|
|
data = np.random.random(num)
|
|
schema.FeedRecord(
|
|
input_record,
|
|
[np.array(x).astype(np.float32) for x in data]
|
|
)
|
|
weights = np.random.random(num) if feed_weight else None
|
|
result = self.model.AdaptiveWeight(input_record, weights=weights)
|
|
train_init_net, train_net = self.get_training_nets(True)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
result = workspace.FetchBlob(result())
|
|
if not feed_weight:
|
|
weights = 1. / num
|
|
expected = np.sum(weights * data + 0.5 * np.log(1. / 2. / weights))
|
|
npt.assert_allclose(expected, result, atol=1e-4, rtol=1e-4)
|
|
|
|
@given(num=st.integers(min_value=10, max_value=100), **hu.gcs)
|
|
def testConstantWeight(self, num, gc, dc):
|
|
input_record = self.new_record(schema.RawTuple(num))
|
|
data = np.random.random(num)
|
|
schema.FeedRecord(
|
|
input_record, [np.array(x).astype(np.float32) for x in data]
|
|
)
|
|
weights = np.random.random(num)
|
|
result = self.model.ConstantWeight(input_record, weights=weights)
|
|
train_init_net, train_net = self.get_training_nets(True)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
result = workspace.FetchBlob(result())
|
|
expected = np.sum(weights * data)
|
|
npt.assert_allclose(expected, result, atol=1e-4, rtol=1e-4)
|
|
|
|
@given(**hu.gcs)
|
|
def testHomotopyWeight(self, gc, dc):
|
|
input_record = self.new_record(schema.RawTuple(2))
|
|
data = np.random.random(2)
|
|
schema.FeedRecord(
|
|
input_record, [np.array(x).astype(np.float32) for x in data]
|
|
)
|
|
# ensure: quad_life > 2 * half_life
|
|
half_life = int(np.random.random() * 1e2 + 1)
|
|
quad_life = int(np.random.random() * 1e3 + 2 * half_life + 1)
|
|
min_weight = np.random.random()
|
|
max_weight = np.random.random() + min_weight + 1e-5
|
|
result = self.model.HomotopyWeight(
|
|
input_record,
|
|
min_weight=min_weight,
|
|
max_weight=max_weight,
|
|
half_life=half_life,
|
|
quad_life=quad_life,
|
|
)
|
|
train_init_net, train_net = self.get_training_nets(True)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.CreateNet(train_net)
|
|
workspace.RunNet(train_net.Name(), num_iter=half_life)
|
|
half_life_result = workspace.FetchBlob(result())
|
|
workspace.RunNet(train_net.Name(), num_iter=quad_life - half_life)
|
|
quad_life_result = workspace.FetchBlob(result())
|
|
|
|
alpha = (min_weight + max_weight) / 2.
|
|
beta = (min_weight + max_weight) / 2.
|
|
expected_half_life_result = alpha * data[0] + beta * data[1]
|
|
alpha = (3 * min_weight + max_weight) / 4.
|
|
beta = (min_weight + 3 * max_weight) / 4.
|
|
expected_quad_life_result = alpha * data[0] + beta * data[1]
|
|
npt.assert_allclose(
|
|
expected_half_life_result, half_life_result, atol=1e-2, rtol=1e-2
|
|
)
|
|
npt.assert_allclose(
|
|
expected_quad_life_result, quad_life_result, atol=1e-2, rtol=1e-2
|
|
)
|
|
|
|
def _testLabelSmooth(self, categories, binary_prob_label, bsz):
|
|
label = self.new_record(schema.Scalar((np.float32, (1, ))))
|
|
label_np = np.random.randint(categories, size=bsz).astype(np.float32)
|
|
schema.FeedRecord(label, [label_np])
|
|
smooth_matrix_shape = (
|
|
2 if binary_prob_label else (categories, categories)
|
|
)
|
|
smooth_matrix = np.random.random(smooth_matrix_shape)
|
|
smoothed_label = self.model.LabelSmooth(label, smooth_matrix)
|
|
train_init_net, train_net = self.get_training_nets(True)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
smoothed_label_np = workspace.FetchBlob(smoothed_label())
|
|
if binary_prob_label:
|
|
expected = np.array(
|
|
[
|
|
smooth_matrix[0] if x == 0.0 else smooth_matrix[1]
|
|
for x in label_np
|
|
]
|
|
)
|
|
else:
|
|
expected = np.array([smooth_matrix[int(x)] for x in label_np])
|
|
npt.assert_allclose(expected, smoothed_label_np, atol=1e-4, rtol=1e-4)
|
|
|
|
@given(
|
|
categories=st.integers(min_value=2, max_value=10),
|
|
bsz=st.integers(min_value=10, max_value=100),
|
|
**hu.gcs
|
|
)
|
|
def testLabelSmoothForCategoricalLabel(self, categories, bsz, gc, dc):
|
|
self._testLabelSmooth(categories, False, bsz)
|
|
|
|
@given(
|
|
bsz=st.integers(min_value=10, max_value=100),
|
|
**hu.gcs
|
|
)
|
|
def testLabelSmoothForBinaryProbLabel(self, bsz, gc, dc):
|
|
self._testLabelSmooth(2, True, bsz)
|
|
|
|
@given(
|
|
num_inputs=st.integers(min_value=2, max_value=10),
|
|
batch_size=st.integers(min_value=2, max_value=10),
|
|
input_dim=st.integers(min_value=5, max_value=10),
|
|
seed=st.integers(1, 10),
|
|
)
|
|
def testBlobWeightedSum(self, num_inputs, batch_size, input_dim, seed):
|
|
|
|
def get_blob_weighted_sum():
|
|
weights = []
|
|
for i in range(num_inputs):
|
|
w_blob_name = 'blob_weighted_sum/w_{0}'.format(i)
|
|
assert workspace.HasBlob(w_blob_name), (
|
|
"cannot fine blob {}".format(w_blob_name)
|
|
)
|
|
w = workspace.FetchBlob(w_blob_name)
|
|
weights.append(w)
|
|
|
|
result = np.sum([
|
|
input_data[idx] * weights[idx] for idx in range(num_inputs)
|
|
], axis=0)
|
|
return result
|
|
|
|
np.random.seed(seed)
|
|
expected_output_schema = schema.Scalar((np.float32, (input_dim,)))
|
|
input_schema = schema.Tuple(
|
|
*[expected_output_schema for _ in range(num_inputs)]
|
|
)
|
|
input_data = [
|
|
np.random.random((batch_size, input_dim)).astype(np.float32)
|
|
for _ in range(num_inputs)
|
|
]
|
|
input_record = self.new_record(input_schema)
|
|
schema.FeedRecord(input_record, input_data)
|
|
|
|
# test output schema
|
|
ws_output = self.model.BlobWeightedSum(input_record)
|
|
self.assertEqual(len(self.model.layers), 1)
|
|
assert schema.equal_schemas(ws_output, expected_output_schema)
|
|
|
|
# test train net
|
|
train_init_net, train_net = self.get_training_nets()
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
output = workspace.FetchBlob(ws_output())
|
|
npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5)
|
|
|
|
self.run_train_net_forward_only()
|
|
output = workspace.FetchBlob(ws_output())
|
|
npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5)
|
|
|
|
# test eval net
|
|
eval_net = self.get_eval_net()
|
|
workspace.RunNetOnce(eval_net)
|
|
output = workspace.FetchBlob(ws_output())
|
|
npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5)
|
|
|
|
# test pred net
|
|
pred_net = self.get_predict_net()
|
|
workspace.RunNetOnce(pred_net)
|
|
output = workspace.FetchBlob(ws_output())
|
|
npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5)
|