FM / python

/usr/local/lib64/python3.6/site-packages/caffe2/python/

/usr/local/lib64/python3.6/site-packages/caffe2/python

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docs/	-	0755	rm
examples/	-	0755	rm
fakelowp/	-	0755	rm
helpers/	-	0755	rm
ideep/	-	0755	rm
layers/	-	0755	rm
mint/	-	0755	rm
mkl/	-	0755	rm
modeling/	-	0755	rm
models/	-	0755	rm
onnx/	-	0755	rm
operator_test/	-	0755	rm
predictor/	-	0755	rm
rnn/	-	0755	rm
serialized_test/	-	0755	rm
test/	-	0755	rm
trt/	-	0755	rm
__pycache__/	-	0755	rm
allcompare_test.py	2255	0644	edit dl rm
attention.py	12359	0644	edit dl rm
benchmark_generator.py	4912	0644	edit dl rm
binarysize.py	5521	0644	edit dl rm
brew.py	4762	0644	edit dl rm
brew_test.py	11739	0644	edit dl rm
build.py	153	0644	edit dl rm
cached_reader.py	4394	0644	edit dl rm
caffe2_pybind11_state.cpython-36m-x86_64-linux-gnu.so	48299712	0755	edit dl rm
caffe2_pybind11_state_gpu.cpython-36m-x86_64-linux-gnu.so	49048144	0755	edit dl rm
caffe_translator.py	35227	0644	edit dl rm
caffe_translator_test.py	3553	0644	edit dl rm
checkpoint.py	32101	0644	edit dl rm
checkpoint_test.py	13405	0644	edit dl rm
cnn.py	7626	0644	edit dl rm
context.py	2841	0644	edit dl rm
context_test.py	1792	0644	edit dl rm
control.py	19309	0644	edit dl rm
control_ops_grad.py	28893	0644	edit dl rm
control_ops_grad_test.py	1752	0644	edit dl rm
control_ops_util.py	10863	0644	edit dl rm
control_test.py	12276	0644	edit dl rm
convert.py	55	0644	edit dl rm
convert_test.py	201	0644	edit dl rm
convnet_benchmarks.py	20533	0644	edit dl rm
convnet_benchmarks_test.py	839	0644	edit dl rm
core.py	119400	0644	edit dl rm
core_gradients_test.py	38022	0644	edit dl rm
core_test.py	47683	0644	edit dl rm
crf.py	13250	0644	edit dl rm
crf_predict.py	1159	0644	edit dl rm
crf_viterbi_test.py	1663	0644	edit dl rm
dataio.py	23532	0644	edit dl rm
dataio_test.py	17575	0644	edit dl rm
dataset.py	12886	0644	edit dl rm
data_parallel_model.py	83100	0644	edit dl rm
data_parallel_model_test.py	56145	0644	edit dl rm
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data_workers_test.py	6561	0644	edit dl rm
db_file_reader.py	6608	0644	edit dl rm
db_test.py	1110	0644	edit dl rm
device_checker.py	5157	0644	edit dl rm
dyndep.py	1533	0644	edit dl rm
embedding_generation_benchmark.py	5256	0644	edit dl rm
experiment_util.py	3625	0644	edit dl rm
extension_loader.py	744	0644	edit dl rm
fakefp16_transform_lib.py	322	0644	edit dl rm
filler_test.py	748	0644	edit dl rm
functional.py	4415	0644	edit dl rm
functional_test.py	4204	0644	edit dl rm
fused_8bit_rowwise_conversion_ops_test.py	3945	0644	edit dl rm
gradient_checker.py	15377	0644	edit dl rm
gradient_check_test.py	20729	0644	edit dl rm
gru_cell.py	5129	0644	edit dl rm
hip_test_util.py	405	0644	edit dl rm
hsm_util.py	2259	0644	edit dl rm
hypothesis_test.py	105762	0644	edit dl rm
hypothesis_test_util.py	26853	0644	edit dl rm
ideep_test_util.py	998	0644	edit dl rm
layers_test.py	92931	0644	edit dl rm
layer_model_helper.py	29340	0644	edit dl rm
layer_model_instantiator.py	3935	0644	edit dl rm
layer_parameter_sharing_test.py	9148	0644	edit dl rm
layer_test_util.py	4875	0644	edit dl rm
lazy.py	277	0644	edit dl rm
lazy_dyndep.py	2562	0644	edit dl rm
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lengths_reducer_fused_8bit_rowwise_ops_test.py	7575	0644	edit dl rm
lengths_reducer_rowwise_8bit_ops_test.py	5710	0644	edit dl rm
lstm_benchmark.py	10649	0644	edit dl rm
memonger.py	34041	0644	edit dl rm
memonger_test.py	36910	0644	edit dl rm
mkl_test_util.py	1142	0644	edit dl rm
model_device_test.py	4777	0644	edit dl rm
model_helper.py	23492	0644	edit dl rm
model_helper_test.py	2336	0644	edit dl rm
modifier_context.py	1772	0644	edit dl rm
muji.py	8131	0644	edit dl rm
muji_test.py	3058	0644	edit dl rm
net_builder.py	27679	0644	edit dl rm
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net_drawer.py	14264	0644	edit dl rm
net_printer.py	12704	0644	edit dl rm
net_printer_test.py	3190	0644	edit dl rm
nomnigraph.py	4216	0644	edit dl rm
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nomnigraph_transformations.py	3787	0644	edit dl rm
nomnigraph_transformations_test.py	5767	0644	edit dl rm
normalizer.py	1411	0644	edit dl rm
normalizer_context.py	1007	0644	edit dl rm
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numa_benchmark.py	2230	0644	edit dl rm
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observer_test.py	5316	0644	edit dl rm
operator_fp_exceptions_test.py	1248	0644	edit dl rm
optimizer.py	78813	0644	edit dl rm
optimizer_context.py	1462	0644	edit dl rm
optimizer_test.py	30705	0644	edit dl rm
optimizer_test_util.py	9187	0644	edit dl rm
parallelize_bmuf_distributed_test.py	9908	0644	edit dl rm
parallel_workers.py	7682	0644	edit dl rm
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pipeline.py	17283	0644	edit dl rm
pipeline_test.py	2542	0644	edit dl rm
predictor_constants.py	198	0644	edit dl rm
python_op_test.py	9169	0644	edit dl rm
queue_util.py	4459	0644	edit dl rm
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recurrent.py	13297	0644	edit dl rm
regularizer.py	21120	0644	edit dl rm
regularizer_context.py	1013	0644	edit dl rm
regularizer_test.py	10266	0644	edit dl rm
rnn_cell.py	68233	0644	edit dl rm
schema.py	45621	0644	edit dl rm
schema_test.py	15754	0644	edit dl rm
scope.py	3623	0644	edit dl rm
scope_test.py	5249	0644	edit dl rm
session.py	7642	0644	edit dl rm
session_test.py	2078	0644	edit dl rm
sparse_to_dense_mask_test.py	6565	0644	edit dl rm
sparse_to_dense_test.py	3556	0644	edit dl rm
task.py	24274	0644	edit dl rm
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text_file_reader.py	1990	0644	edit dl rm
timeout_guard.py	4054	0644	edit dl rm
toy_regression_test.py	2822	0644	edit dl rm
transformations.py	1832	0644	edit dl rm
transformations_test.py	11960	0644	edit dl rm
tt_core.py	9349	0644	edit dl rm
tt_core_test.py	2518	0644	edit dl rm
utils.py	14181	0644	edit dl rm
utils_test.py	1399	0644	edit dl rm
visualize.py	6315	0644	edit dl rm
workspace.py	25263	0644	edit dl rm
workspace_test.py	34844	0644	edit dl rm
_import_c_extension.py	2250	0644	edit dl rm
__init__.py	3925	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/optimizer_test_util.py (9187B)

## @package optimizer_test_util
# Module caffe2.python.optimizer_test_util

import unittest
import numpy as np
from caffe2.python import brew, core, workspace, cnn, optimizer
from caffe2.python.modeling.initializers import (
    Initializer, PseudoFP16Initializer)

from caffe2.python.model_helper import ModelHelper

class OptimizerTestBase(object):
    """
    This is an abstract base class.
    Don't inherit from unittest.TestCase, and don't name it 'Test*'.
    Do, however, do these things in classes which inherit from this.
    """

def _createDense(self, dtype=core.DataType.FLOAT):
        perfect_model = np.array([2, 6, 5, 0, 1]).astype(np.float32)
        np.random.seed(123)  # make test deterministic
        numpy_dtype = np.float32 if dtype == core.DataType.FLOAT else np.float16
        initializer = Initializer if dtype == core.DataType.FLOAT else \
            PseudoFP16Initializer
        data = np.random.randint(
            2,
            size=(20, perfect_model.size)).astype(numpy_dtype)
        label = np.dot(data, perfect_model)[:, np.newaxis]

model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
        out = brew.fc(
            model,
            'data', 'fc', perfect_model.size, 1, ('ConstantFill', {}),
            ('ConstantFill', {}), axis=0,
            WeightInitializer=initializer, BiasInitializer=initializer
        )
        if dtype == core.DataType.FLOAT16:
            out = model.HalfToFloat(out, out + "_fp32")
        sq = model.SquaredL2Distance([out, 'label'])
        loss = model.AveragedLoss(sq, "avg_loss")
        grad_map = model.AddGradientOperators([loss])
        self.assertIsInstance(grad_map['fc_w'], core.BlobReference)
        return (model, perfect_model, data, label)

def testDense(self):
        model, perfect_model, data, label = self._createDense()
        optimizer = self.build_optimizer(model)
        workspace.FeedBlob('data', data[0])
        workspace.FeedBlob('label', label[0])
        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net, True)
        for _ in range(2000):
            idx = np.random.randint(data.shape[0])
            workspace.FeedBlob('data', data[idx])
            workspace.FeedBlob('label', label[idx])
            workspace.RunNet(model.net.Proto().name)

np.testing.assert_allclose(
            perfect_model[np.newaxis, :],
            workspace.FetchBlob('fc_w'),
            atol=1e-2
        )
        self.check_optimizer(optimizer)

@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
    def testGPUDense(self, dtype=core.DataType.FLOAT):
        device_opt = core.DeviceOption(workspace.GpuDeviceType, 0)
        with core.DeviceScope(device_opt):
            model, _perfect_model, data, label = self._createDense(dtype)
            if dtype == core.DataType.FLOAT16:
                fc_fp32_for_host = model.HalfToFloat('fc', 'fc_fp32_for_host')
                model.CopyGPUToCPU(fc_fp32_for_host, 'fc_cpu')
            else:
                model.CopyGPUToCPU('fc', 'fc_cpu')
            workspace.FeedBlob('data', data[0])
            workspace.FeedBlob('label', label[0])

# Add some CPU ops
        brew.fc(model, 'fc_cpu', 'fc2', dim_in=1, dim_out=10, axis=0)

# Create optimizer in default device scope
        self.build_optimizer(model)

if self._skip_gpu:
            return

# Run net to see it does not crash
        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net, True)
        workspace.RunNet(model.net.Proto().name)

def testSparse(self):
        # to test duplicated indices we assign two indices to each weight and
        # thus each weight might count once or twice
        DUPLICATION = 2
        perfect_model = np.array([2, 6, 5, 0, 1]).astype(np.float32)
        np.random.seed(123)  # make test deterministic
        data = np.random.randint(
            2,
            size=(20, perfect_model.size * DUPLICATION)).astype(np.float32)
        label = np.dot(data, np.repeat(perfect_model, DUPLICATION))

model = cnn.CNNModelHelper("NCHW", name="test")
        # imitate what model wrapper does
        w = model.param_init_net.ConstantFill(
            [], 'w', shape=[perfect_model.size], value=0.0)
        model.params.append(w)
        picked = model.net.Gather([w, 'indices'], 'gather')
        out = model.ReduceFrontSum(picked, 'sum')

sq = model.SquaredL2Distance([out, 'label'])
        loss = model.AveragedLoss(sq, "avg_loss")
        grad_map = model.AddGradientOperators([loss])
        self.assertIsInstance(grad_map['w'], core.GradientSlice)
        optimizer = self.build_optimizer(model)

workspace.CreateBlob('indices')
        workspace.CreateBlob('label')

for indices_type in [np.int32, np.int64]:
            workspace.RunNetOnce(model.param_init_net)
            workspace.CreateNet(model.net, True)
            for _ in range(2000):
                idx = np.random.randint(data.shape[0])
                # transform into indices of binary features
                indices = np.repeat(np.arange(perfect_model.size),
                                    DUPLICATION)[data[idx] == 1]
                if indices.size == 0:
                    continue
                workspace.FeedBlob(
                    'indices',
                    indices.reshape((indices.size,)).astype(indices_type)
                )
                workspace.FeedBlob('label',
                                   np.array(label[idx]).astype(np.float32))
                workspace.RunNet(model.net.Proto().name)

np.testing.assert_allclose(
                perfect_model,
                workspace.FetchBlob('w'),
                atol=1e-2
            )
        self.check_optimizer(optimizer)

class LRModificationTestBase(object):
    """
    This is an abstract base class.
    Don't inherit from unittest.TestCase, and don't name it 'Test*'.
    Do, however, do these things in classes which inherit from this.
    """

def _gradient_ratio_reference(self, model, params, max_gradient_norm):
        from caffe2.python import core
        sum_squared_norms = 0.0
        for param in params:
            grad = (
                model.param_to_grad[param]
                if not isinstance(
                    model.param_to_grad[param],
                    core.GradientSlice,
                ) else model.param_to_grad[param].values
            )
            val = workspace.FetchBlob(grad)
            sum_squared_norms += np.power(np.linalg.norm(val), 2.0)
        global_norm = np.sqrt(sum_squared_norms)
        clip_norm = max_gradient_norm
        norm_ratio = clip_norm / np.maximum(clip_norm, global_norm)
        return norm_ratio

def test_global_norm_based_gradient_clipping(self):
        max_gradient_norm = 1.0
        model, perfect_model, data, label = self._createDense()
        opt = self.build_optimizer(model, max_gradient_norm=max_gradient_norm)

params = []
        for param in model.GetParams(top_scope=True):
            if param in model.param_to_grad:
                if not isinstance(
                    model.param_to_grad[param],
                    core.GradientSlice,
                ):
                    params.append(param)

workspace.FeedBlob('data', data[0])
        workspace.FeedBlob('label', label[0])
        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net, True)
        self.assertIsNotNone(opt._lr_multiplier)

# Run net once
        idx = np.random.randint(data.shape[0])
        workspace.FeedBlob('data', data[idx])
        workspace.FeedBlob('label', label[idx])
        workspace.RunNet(model.net.Proto().name)

reference = self._gradient_ratio_reference(
            model,
            params,
            max_gradient_norm,
        )
        norm_ratio = workspace.FetchBlob(
            'norm_clipped_grad_update/norm_ratio')
        np.testing.assert_almost_equal(norm_ratio, reference)
        self.assertTrue(
            reference < 1.0, "Bad test, gradient not being scaled."
        )

def test_lr_injection(self):
        model, perfect_model, data, label = self._createDense()
        opt = self.build_optimizer(
            model, max_gradient_norm=1, allow_lr_injection=True
        )

workspace.FeedBlob('data', data[0])
        workspace.FeedBlob('label', label[0])
        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net, True)

# Test LR injection initialized properly
        self.assertIsNotNone(opt._lr_multiplier)
        self.assertEqual(optimizer.get_lr_injection(), 1)

# Test that we're able to modify the value of the lr_injection
        optimizer.set_lr_injection(0)
        self.assertEqual(optimizer.get_lr_injection(), 0)

# Test that setting the lr_injector properly propagates to the
        # lr_multiplier. Here, we have both lr_injector and norm_ratio that
        # affect the lr_multiplier
        workspace.RunNet(model.net.Proto().name)
        self.assertEqual(workspace.FetchBlob('lr_multiplier'), 0)

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