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
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cnn.py	7626	0644	edit dl rm
context.py	2841	0644	edit dl rm
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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
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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
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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
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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
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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
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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
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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
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nomnigraph_transformations_test.py	5767	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
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parallelize_bmuf_distributed_test.py	9908	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
record_queue.py	4453	0644	edit dl rm
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
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schema.py	45621	0644	edit dl rm
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scope.py	3623	0644	edit dl rm
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session.py	7642	0644	edit dl rm
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sparse_to_dense_mask_test.py	6565	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
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tt_core.py	9349	0644	edit dl rm
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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/python_op_test.py (9169B)

from caffe2.python import core, workspace
from caffe2.python.core import CreatePythonOperator
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np

class CustomError(Exception):
    pass

def SubFunctionThatThrowsCustomError():
    raise CustomError("This is an intentional exception.")

def MainOpFunctionThatThrowsCustomError(inputs, _):
    return SubFunctionThatThrowsCustomError()

def MainOpFunctionThatThrowsCustomErrorInBuilder(inputs, _):
    raise CustomError("This is an intentional exception in builder.")

def op_builder(name, index, extra):
    iterations = [0]
    assert name == 'name'
    assert index == 5
    assert extra - 4.2 < 0.0001

def my_op(inputs, outputs):
        assert inputs[0].data[0] == iterations[0]
        assert name == 'name'
        assert index == 5
        assert extra - 4.2 < 0.0001
        iterations[0] += 1

return my_op

class PythonOpTest(hu.HypothesisTestCase):
    @given(x=hu.tensor())
    def test_feed(self, x):
        def f(inputs, _):
            self.assertEqual(x.shape, inputs[0].shape)
            self.assertEqual(type(inputs[0].shape), tuple)
            self.assertEqual(type(inputs[0].data), np.ndarray)
            np.testing.assert_almost_equal(x, inputs[0].data)
        op = CreatePythonOperator(f, ["x"], [])
        workspace.FeedBlob("x", x)
        workspace.RunOperatorOnce(op)

def test_exception(self):
        op = CreatePythonOperator(MainOpFunctionThatThrowsCustomError, [], [])
        with self.assertRaisesRegex(CustomError, "This is an intentional exception."):
            workspace.RunOperatorOnce(op)

def test_exception_builder(self):
        op = CreatePythonOperator(MainOpFunctionThatThrowsCustomErrorInBuilder, [], [])
        with self.assertRaisesRegex(CustomError, "This is an intentional exception in builder."):
            workspace.RunOperatorOnce(op)

@given(x=hu.tensor())
    def test_feed_with_helper_function(self, x):
        def f(inputs, _):
            self.assertEqual(x.shape, inputs[0].shape)
            self.assertEqual(type(inputs[0].shape), tuple)
            self.assertEqual(type(inputs[0].data), np.ndarray)
            np.testing.assert_almost_equal(x, inputs[0].data)
        net = core.Net("test")
        net.Python(f)(["x"], [])
        workspace.FeedBlob("x", x)
        workspace.RunNetOnce(net)

def test_builder_tuple(self):
        net = core.Net("builder_template")
        iter_blob = 'iter'
        net.Python((op_builder, ['name', 5], {'extra': 4.2}))([iter_blob], [])
        net.Python((op_builder, ['name', 5], {'extra': 4.2}))([iter_blob], [])
        for repeat in range(2):
            # check that the builder will be called exactly once for each
            # PythonOp constructor. Cloning the net will also trigger a call
            # to the builder when the net is created.
            cloned_net = net.Clone('builder_%d' % repeat)
            workspace.FeedBlob(iter_blob, np.array([0]))
            # Builder gets called once per python op in the line below
            workspace.CreateNet(cloned_net)
            for i in range(10):
                workspace.FeedBlob(iter_blob, np.array([i]))
                workspace.RunNet(cloned_net)

@given(x=hu.tensor())
    def test_feed_with_gc(self, x):
        def f(inputs, _):
            self.assertEqual(x.shape, inputs[0].shape)
            np.testing.assert_almost_equal(x, inputs[0].data)
        op = CreatePythonOperator(f, ["x"], [])
        workspace.FeedBlob("x", x)
        workspace.RunOperatorOnce(op)
        del f
        workspace.FeedBlob("x", x)
        workspace.RunOperatorOnce(op)

@given(x=hu.tensor())
    def test_reshape(self, x):
        def f(inputs, outputs):
            outputs[0].reshape(inputs[0].shape)
            self.assertEqual(x.shape, inputs[0].shape)
            self.assertEqual(x.shape, outputs[0].shape)
            outputs[0].data[...] = inputs[0].data

op = CreatePythonOperator(f, ["x"], ["y"])
        workspace.FeedBlob("x", x)
        workspace.RunOperatorOnce(op)
        y = workspace.FetchBlob("y")
        np.testing.assert_almost_equal(x, y)

@given(x=hu.tensor())
    def test_workspace_manipulation(self, x):
        """
        Verify that python op can manipulate workspace directly
        """
        def f(inputs, outputs, ws):
            fetched = ws.blobs['internal'].fetch()
            np.testing.assert_almost_equal(fetched, x)

ws = workspace.C.Workspace()
        net = core.Net("test")
        net.GivenTensorFill([], ['internal'], values=x, shape=x.shape)
        net.Python(f, pass_workspace=True)([], [])
        ws.run(net)

@given(x=hu.tensor())
    def test_caught_exception_doesnt_terminate(self, x):
        def f(inputs, outputs):
            try:
                raise Exception("Exception in handler")
            except Exception:
                pass

op = CreatePythonOperator(f, ["x"], ["y"])
        workspace.FeedBlob("x", x)
        workspace.RunOperatorOnce(op)

@given(x=hu.tensor(),
           n=st.integers(min_value=1, max_value=20),
           w=st.integers(min_value=1, max_value=20))
    @settings(deadline=1000)
    def test_multithreaded_evaluation(self, x, n, w):
        def f(inputs, outputs):
            outputs[0].reshape(inputs[0].shape)
            outputs[0].data[...] = inputs[0].data
        ops = [CreatePythonOperator(f, ["x"], [str(i)]) for i in range(n)]
        net = core.Net("net")
        net.Proto().op.extend(ops)
        net.Proto().type = "dag"
        net.Proto().num_workers = w
        iters = 100
        plan = core.Plan("plan")
        plan.AddStep(core.ExecutionStep("test-step", net, iters))
        workspace.FeedBlob("x", x)
        workspace.RunPlan(plan.Proto().SerializeToString())
        for i in range(n):
            y = workspace.FetchBlob(str(i))
            np.testing.assert_almost_equal(x, y)

@given(x=hu.tensor(), in_place=st.booleans(), **hu.gcs)
    @settings(deadline=10000)
    def test_gradient(self, x, in_place, gc, dc):
        def f(inputs, outputs):
            outputs[0].reshape(inputs[0].shape)
            outputs[0].data[...] = inputs[0].data * 2

def grad_f(inputs, outputs):
            # Ordering is [inputs, outputs, grad_outputs]
            grad_output = inputs[2]

grad_input = outputs[0]
            grad_input.reshape(grad_output.shape)
            grad_input.data[...] = grad_output.data * 2

op = CreatePythonOperator(
            f, ["x"], ["x" if in_place else "y"], grad_f=grad_f)
        self.assertGradientChecks(gc, op, [x], 0, [0])
        self.assertDeviceChecks(dc, op, [x], [0])

@given(inputs=hu.tensors(n=2), **hu.gcs)
    @settings(deadline=10000)
    def test_gradient_multiple(self, inputs, gc, dc):
        (x1, x2) = inputs

def f(inputs, outputs):
            for idx in [0, 1]:
                self.assertEqual(type(inputs[idx].shape), tuple)
                outputs[idx].reshape(inputs[idx].shape)
                outputs[idx].data[...] = inputs[idx].data * 2

def grad_f(inputs, outputs):
            # Ordering is [inputs, outputs, grad_outputs]
            self.assertEqual(len(inputs), 6)
            self.assertEqual(len(outputs), 2)
            for (grad_output_idx, grad_input_idx) in [(4, 0), (5, 1)]:
                grad_output = inputs[grad_output_idx]
                grad_input = outputs[grad_input_idx]
                grad_input.reshape(grad_output.shape)
                grad_input.data[...] = grad_output.data * 2

op = CreatePythonOperator(f, ["x1", "x2"], ["y1", "y2"], grad_f=grad_f)

for idx in [0, 1]:
            self.assertGradientChecks(gc, op, [x1, x2], idx, [0, 1])
        self.assertDeviceChecks(dc, op, [x1, x2], [0, 1])

@given(inputs=hu.tensors(n=3), **hu.gcs)
    @settings(deadline=10000)
    def test_gradient_multiple_with_indices(self, inputs, gc, dc):
        (x1, x2, x3) = inputs

def f(inputs, outputs):
            for idx in [0, 1, 2]:
                self.assertEqual(type(inputs[idx].shape), tuple)
                outputs[idx].reshape(inputs[idx].shape)
                outputs[idx].data[...] = inputs[idx].data * 2

def grad_f(inputs, outputs):
            # Ordering is [inputs, outputs, grad_outputs]
            self.assertEqual(len(inputs), 8)
            self.assertEqual(len(outputs), 1)
            for (grad_output_idx, grad_input_idx) in [(6, 0)]:
                grad_output = inputs[grad_output_idx]
                grad_input = outputs[grad_input_idx]
                grad_input.reshape(grad_output.shape)
                grad_input.data[...] = grad_output.data * 2

op = CreatePythonOperator(
            f, ["x1", "x2", "x3"], ["y1", "y2", "y3"],
            grad_f=grad_f,
            grad_output_indices=[0, 2],  # Receive grad outputs for y1 and y3
            grad_input_indices=[0]       # Produce grad inputs for x1
        )

self.assertGradientChecks(gc, op, [x1, x2, x3], 0, [0, 2])
        self.assertDeviceChecks(dc, op, [x1, x2, x3], [0, 1, 2])

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