workspace_test.py

import numpy as np
import os
import shutil
import tempfile
import unittest

import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core, test_util, workspace, model_helper, brew

import caffe2.python.hypothesis_test_util as htu
import hypothesis.strategies as st
from hypothesis import given, settings


class TestWorkspace(unittest.TestCase):
    def setUp(self):
        self.net = core.Net("test-net")
        self.testblob_ref = self.net.ConstantFill(
            [], "testblob", shape=[1, 2, 3, 4], value=1.0)
        workspace.ResetWorkspace()

    def testRootFolder(self):
        self.assertEqual(workspace.ResetWorkspace(), True)
        self.assertEqual(workspace.RootFolder(), ".")
        self.assertEqual(
            workspace.ResetWorkspace("/tmp/caffe-workspace-test"), True)
        self.assertEqual(workspace.RootFolder(), "/tmp/caffe-workspace-test")

    def testWorkspaceHasBlobWithNonexistingName(self):
        self.assertEqual(workspace.HasBlob("non-existing"), False)

    def testRunOperatorOnce(self):
        self.assertEqual(
            workspace.RunOperatorOnce(
                self.net.Proto().op[0].SerializeToString()
            ), True
        )
        self.assertEqual(workspace.HasBlob("testblob"), True)
        blobs = workspace.Blobs()
        self.assertEqual(len(blobs), 1)
        self.assertEqual(blobs[0], "testblob")

    def testGetOperatorCost(self):
        op = core.CreateOperator(
            "Conv2D",
            ["X", "W"], ["Y"],
            stride_h=1,
            stride_w=1,
            pad_t=1,
            pad_l=1,
            pad_b=1,
            pad_r=1,
            kernel=3,
        )
        X = np.zeros((1, 8, 8, 8))
        W = np.zeros((1, 1, 3, 3))
        workspace.FeedBlob("X", X)
        workspace.FeedBlob("W", W)
        flops, _ = workspace.GetOperatorCost(op.SerializeToString(), ["X", "W"])
        self.assertEqual(flops, 1152)

    def testRunNetOnce(self):
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        self.assertEqual(workspace.HasBlob("testblob"), True)

    def testCurrentWorkspaceWrapper(self):
        self.assertNotIn("testblob", workspace.C.Workspace.current.blobs)
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        self.assertEqual(workspace.HasBlob("testblob"), True)
        self.assertIn("testblob", workspace.C.Workspace.current.blobs)
        workspace.ResetWorkspace()
        self.assertNotIn("testblob", workspace.C.Workspace.current.blobs)

    def testRunPlan(self):
        plan = core.Plan("test-plan")
        plan.AddStep(core.ExecutionStep("test-step", self.net))
        self.assertEqual(
            workspace.RunPlan(plan.Proto().SerializeToString()), True)
        self.assertEqual(workspace.HasBlob("testblob"), True)

    def testRunPlanInBackground(self):
        plan = core.Plan("test-plan")
        plan.AddStep(core.ExecutionStep("test-step", self.net))
        background_plan = workspace.RunPlanInBackground(plan)
        while not background_plan.is_done():
            pass
        self.assertEqual(background_plan.is_succeeded(), True)
        self.assertEqual(workspace.HasBlob("testblob"), True)

    def testConstructPlanFromSteps(self):
        step = core.ExecutionStep("test-step-as-plan", self.net)
        self.assertEqual(workspace.RunPlan(step), True)
        self.assertEqual(workspace.HasBlob("testblob"), True)

    def testResetWorkspace(self):
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        self.assertEqual(workspace.HasBlob("testblob"), True)
        self.assertEqual(workspace.ResetWorkspace(), True)
        self.assertEqual(workspace.HasBlob("testblob"), False)

    def testTensorAccess(self):
        ws = workspace.C.Workspace()

        """ test in-place modification """
        ws.create_blob("tensor").feed(np.array([1.1, 1.2, 1.3]))
        tensor = ws.blobs["tensor"].tensor()
        tensor.data[0] = 3.3
        val = np.array([3.3, 1.2, 1.3])
        np.testing.assert_array_equal(tensor.data, val)
        np.testing.assert_array_equal(ws.blobs["tensor"].fetch(), val)

        """ test in-place initialization """
        tensor.init([2, 3], core.DataType.INT32)
        for x in range(2):
            for y in range(3):
                tensor.data[x, y] = 0
        tensor.data[1, 1] = 100
        val = np.zeros([2, 3], dtype=np.int32)
        val[1, 1] = 100
        np.testing.assert_array_equal(tensor.data, val)
        np.testing.assert_array_equal(ws.blobs["tensor"].fetch(), val)

        """ strings cannot be initialized from python """
        with self.assertRaises(RuntimeError):
            tensor.init([3, 4], core.DataType.STRING)

        """ feed (copy) data into tensor """
        val = np.array([[b'abc', b'def'], [b'ghi', b'jkl']], dtype=np.object)
        tensor.feed(val)
        self.assertEquals(tensor.data[0, 0], b'abc')
        np.testing.assert_array_equal(ws.blobs["tensor"].fetch(), val)

        val = np.array([1.1, 10.2])
        tensor.feed(val)
        val[0] = 5.2
        self.assertEquals(tensor.data[0], 1.1)

        """ fetch (copy) data from tensor """
        val = np.array([1.1, 1.2])
        tensor.feed(val)
        val2 = tensor.fetch()
        tensor.data[0] = 5.2
        val3 = tensor.fetch()
        np.testing.assert_array_equal(val, val2)
        self.assertEquals(val3[0], 5.2)

    def testFetchFeedBlob(self):
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        fetched = workspace.FetchBlob("testblob")
        # check if fetched is correct.
        self.assertEqual(fetched.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched, 1.0)
        fetched[:] = 2.0
        self.assertEqual(workspace.FeedBlob("testblob", fetched), True)
        fetched_again = workspace.FetchBlob("testblob")
        self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched_again, 2.0)

    def testFetchFeedBlobViaBlobReference(self):
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        fetched = workspace.FetchBlob(self.testblob_ref)
        # check if fetched is correct.
        self.assertEqual(fetched.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched, 1.0)
        fetched[:] = 2.0
        self.assertEqual(workspace.FeedBlob(self.testblob_ref, fetched), True)
        fetched_again = workspace.FetchBlob("testblob")  # fetch by name now
        self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched_again, 2.0)

    def testFetchFeedBlobTypes(self):
        for dtype in [np.float16, np.float32, np.float64, np.bool,
                      np.int8, np.int16, np.int32, np.int64,
                      np.uint8, np.uint16]:
            try:
                rng = np.iinfo(dtype).max * 2
            except ValueError:
                rng = 1000
            data = ((np.random.rand(2, 3, 4) - 0.5) * rng).astype(dtype)
            self.assertEqual(workspace.FeedBlob("testblob_types", data), True)
            fetched_back = workspace.FetchBlob("testblob_types")
            self.assertEqual(fetched_back.shape, (2, 3, 4))
            self.assertEqual(fetched_back.dtype, dtype)
            np.testing.assert_array_equal(fetched_back, data)

    def testFetchFeedBlobBool(self):
        """Special case for bool to ensure coverage of both true and false."""
        data = np.zeros((2, 3, 4)).astype(np.bool)
        data.flat[::2] = True
        self.assertEqual(workspace.FeedBlob("testblob_types", data), True)
        fetched_back = workspace.FetchBlob("testblob_types")
        self.assertEqual(fetched_back.shape, (2, 3, 4))
        self.assertEqual(fetched_back.dtype, np.bool)
        np.testing.assert_array_equal(fetched_back, data)

    def testGetBlobSizeBytes(self):
        for dtype in [np.float16, np.float32, np.float64, np.bool,
                      np.int8, np.int16, np.int32, np.int64,
                      np.uint8, np.uint16]:
            data = np.random.randn(2, 3).astype(dtype)
            self.assertTrue(workspace.FeedBlob("testblob_sizeBytes", data), True)
            self.assertEqual(
                workspace.GetBlobSizeBytes("testblob_sizeBytes"),
                6 * np.dtype(dtype).itemsize)
        strs1 = np.array([b'Hello World!', b'abcd'])
        strs2 = np.array([b'element1', b'element2'])
        strs1_len, strs2_len = 0, 0
        for str in strs1:
            strs1_len += len(str)
        for str in strs2:
            strs2_len += len(str)
        self.assertTrue(workspace.FeedBlob("testblob_str1", strs1), True)
        self.assertTrue(workspace.FeedBlob("testblob_str2", strs2), True)
        # size of blob "testblob_str1" = size_str1 * meta_.itemsize() + strs1_len
        # size of blob "testblob_str2" = size_str2 * meta_.itemsize() + strs2_len
        self.assertEqual(
            workspace.GetBlobSizeBytes("testblob_str1") -
            workspace.GetBlobSizeBytes("testblob_str2"), strs1_len - strs2_len)

    def testFetchFeedBlobZeroDim(self):
        data = np.empty(shape=(2, 0, 3), dtype=np.float32)
        self.assertEqual(workspace.FeedBlob("testblob_empty", data), True)
        fetched_back = workspace.FetchBlob("testblob_empty")
        self.assertEqual(fetched_back.shape, (2, 0, 3))
        self.assertEqual(fetched_back.dtype, np.float32)

    def testFetchFeedLongStringTensor(self):
        # long strings trigger array of object creation
        strs = np.array([
            b' '.join(10 * [b'long string']),
            b' '.join(128 * [b'very long string']),
            b'small \0\1\2 string',
            b"Hello, world! I have special \0 symbols \1!"])
        workspace.FeedBlob('my_str_tensor', strs)
        strs2 = workspace.FetchBlob('my_str_tensor')
        self.assertEqual(strs.shape, strs2.shape)
        for i in range(0, strs.shape[0]):
            self.assertEqual(strs[i], strs2[i])

    def testFetchFeedShortStringTensor(self):
        # small strings trigger NPY_STRING array
        strs = np.array([b'elem1', b'elem 2', b'element 3'])
        workspace.FeedBlob('my_str_tensor_2', strs)
        strs2 = workspace.FetchBlob('my_str_tensor_2')
        self.assertEqual(strs.shape, strs2.shape)
        for i in range(0, strs.shape[0]):
            self.assertEqual(strs[i], strs2[i])

    def testFetchFeedPlainString(self):
        # this is actual string, not a tensor of strings
        s = b"Hello, world! I have special \0 symbols \1!"
        workspace.FeedBlob('my_plain_string', s)
        s2 = workspace.FetchBlob('my_plain_string')
        self.assertEqual(s, s2)

    def testFetchBlobs(self):
        s1 = b"test1"
        s2 = b"test2"
        workspace.FeedBlob('s1', s1)
        workspace.FeedBlob('s2', s2)
        fetch1, fetch2 = workspace.FetchBlobs(['s1', 's2'])
        self.assertEquals(s1, fetch1)
        self.assertEquals(s2, fetch2)

    def testFetchFeedViaBlobDict(self):
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        fetched = workspace.blobs["testblob"]
        # check if fetched is correct.
        self.assertEqual(fetched.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched, 1.0)
        fetched[:] = 2.0
        workspace.blobs["testblob"] = fetched
        fetched_again = workspace.blobs["testblob"]
        self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched_again, 2.0)

        self.assertTrue("testblob" in workspace.blobs)
        self.assertFalse("non_existant" in workspace.blobs)
        self.assertEqual(len(workspace.blobs), 1)
        for key in workspace.blobs:
            self.assertEqual(key, "testblob")

    def testTorchInterop(self):
        workspace.RunOperatorOnce(core.CreateOperator(
            "ConstantFill", [], "foo", shape=(4,), value=2, dtype=10))
        t = workspace.FetchTorch("foo")
        t.resize_(5)
        t[4] = t[2] = 777
        np.testing.assert_array_equal(t.numpy(), np.array([2,2,777,2,777]))
        np.testing.assert_array_equal(
            workspace.FetchBlob("foo"), np.array([2,2,777,2,777]))

        z = torch.ones((4,), dtype=torch.int64)
        workspace.FeedBlob('bar', z)
        workspace.RunOperatorOnce(
            core.CreateOperator("Reshape", ['bar'], ['bar', '_'], shape=(2,2)))
        z[0,1] = 123
        np.testing.assert_array_equal(
            workspace.FetchBlob("bar"), np.array([[1,123],[1,1]]))
        np.testing.assert_array_equal(z, np.array([[1,123],[1,1]]))


class TestMultiWorkspaces(unittest.TestCase):
    def setUp(self):
        workspace.SwitchWorkspace("default")
        workspace.ResetWorkspace()

    def testCreateWorkspace(self):
        self.net = core.Net("test-net")
        self.net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True
        )
        self.assertEqual(workspace.HasBlob("testblob"), True)
        self.assertEqual(workspace.SwitchWorkspace("test", True), None)
        self.assertEqual(workspace.HasBlob("testblob"), False)
        self.assertEqual(workspace.SwitchWorkspace("default"), None)
        self.assertEqual(workspace.HasBlob("testblob"), True)

        try:
            # The following should raise an error.
            workspace.SwitchWorkspace("non-existing")
            # so this should never happen.
            self.assertEqual(True, False)
        except RuntimeError:
            pass

        workspaces = workspace.Workspaces()
        self.assertTrue("default" in workspaces)
        self.assertTrue("test" in workspaces)


@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
class TestWorkspaceGPU(test_util.TestCase):

    def setUp(self):
        workspace.ResetWorkspace()
        self.net = core.Net("test-net")
        self.net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
        self.net.RunAllOnGPU()

    def testFetchBlobGPU(self):
        self.assertEqual(
            workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
        fetched = workspace.FetchBlob("testblob")
        # check if fetched is correct.
        self.assertEqual(fetched.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched, 1.0)
        fetched[:] = 2.0
        self.assertEqual(workspace.FeedBlob("testblob", fetched), True)
        fetched_again = workspace.FetchBlob("testblob")
        self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
        np.testing.assert_array_equal(fetched_again, 2.0)

    def testGetGpuPeerAccessPattern(self):
        pattern = workspace.GetGpuPeerAccessPattern()
        self.assertEqual(type(pattern), np.ndarray)
        self.assertEqual(pattern.ndim, 2)
        self.assertEqual(pattern.shape[0], pattern.shape[1])
        self.assertEqual(pattern.shape[0], workspace.NumGpuDevices())

    @unittest.skipIf(not workspace.has_cuda_support,
                     "Tensor interop doesn't yet work on ROCm")
    def testTorchInterop(self):
        # CUDA has convenient mem stats, let's use them to make sure we didn't
        # leak memory
        initial_mem = torch.cuda.memory_allocated()
        workspace.RunOperatorOnce(core.CreateOperator(
            "ConstantFill", [], "foo", shape=(4,), value=2, dtype=10,
            device_option=core.DeviceOption(workspace.GpuDeviceType)))
        t = workspace.FetchTorch("foo")
        t.resize_(5)
        self.assertTrue(t.is_cuda)
        t[4] = t[2] = 777
        np.testing.assert_array_equal(
            t.cpu().numpy(), np.array([2,2,777,2,777]))
        np.testing.assert_array_equal(
            workspace.FetchBlob("foo"), np.array([2,2,777,2,777]))

        z = torch.ones((4,), dtype=torch.int64, device="cuda")
        workspace.FeedBlob('bar', z)
        workspace.RunOperatorOnce(
            core.CreateOperator("Reshape", ['bar'], ['bar', '_'], shape=(2,2),
            device_option=core.DeviceOption(workspace.GpuDeviceType)))
        z[0,1] = 123
        np.testing.assert_array_equal(
            workspace.FetchBlob("bar"), np.array([[1,123],[1,1]]))
        np.testing.assert_array_equal(z.cpu(), np.array([[1,123],[1,1]]))

        self.assertGreater(torch.cuda.memory_allocated(), initial_mem)
        # clean up everything
        del t
        del z
        workspace.ResetWorkspace()
        self.assertEqual(torch.cuda.memory_allocated(), initial_mem)


@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestWorkspaceIDEEP(test_util.TestCase):

    def testFeedFetchBlobIDEEP(self):
        arr = np.random.randn(2, 3).astype(np.float32)
        workspace.FeedBlob(
            "testblob_ideep", arr, core.DeviceOption(caffe2_pb2.IDEEP))
        fetched = workspace.FetchBlob("testblob_ideep")
        np.testing.assert_array_equal(arr, fetched)

class TestImmedibate(test_util.TestCase):
    def testImmediateEnterExit(self):
        workspace.StartImmediate(i_know=True)
        self.assertTrue(workspace.IsImmediate())
        workspace.StopImmediate()
        self.assertFalse(workspace.IsImmediate())

    def testImmediateRunsCorrectly(self):
        workspace.StartImmediate(i_know=True)
        net = core.Net("test-net")
        net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
        self.assertEqual(
            workspace.ImmediateBlobs(), ["testblob"])
        content = workspace.FetchImmediate("testblob")
        # Also, the immediate mode should not invade the original namespace,
        # so we check if this is so.
        with self.assertRaises(RuntimeError):
            workspace.FetchBlob("testblob")
        np.testing.assert_array_equal(content, 1.0)
        content[:] = 2.0
        self.assertTrue(workspace.FeedImmediate("testblob", content))
        np.testing.assert_array_equal(
            workspace.FetchImmediate("testblob"), 2.0)
        workspace.StopImmediate()
        with self.assertRaises(RuntimeError):
            content = workspace.FetchImmediate("testblob")

    def testImmediateRootFolder(self):
        workspace.StartImmediate(i_know=True)
        # for testing we will look into the _immediate_root_folder variable
        # but in normal usage you should not access that.
        self.assertTrue(len(workspace._immediate_root_folder) > 0)
        root_folder = workspace._immediate_root_folder
        self.assertTrue(os.path.isdir(root_folder))
        workspace.StopImmediate()
        self.assertTrue(len(workspace._immediate_root_folder) == 0)
        # After termination, immediate mode should have the root folder
        # deleted.
        self.assertFalse(os.path.exists(root_folder))


class TestCppEnforceAsException(test_util.TestCase):
    def testEnforce(self):
        op = core.CreateOperator("Relu", ["X"], ["Y"])
        with self.assertRaises(RuntimeError):
            workspace.RunOperatorOnce(op)


class TestCWorkspace(htu.HypothesisTestCase):
    def test_net_execution(self):
        ws = workspace.C.Workspace()
        self.assertEqual(ws.nets, {})
        self.assertEqual(ws.blobs, {})
        net = core.Net("test-net")
        net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
        ws.create_net(net)
        # If we do not specify overwrite, this should raise an error.
        with self.assertRaises(RuntimeError):
            ws.create_net(net)
        # But, if we specify overwrite, this should pass.
        ws.create_net(net, True)
        # Overwrite can also be a kwarg.
        ws.create_net(net, overwrite=True)
        self.assertIn("testblob", ws.blobs)
        self.assertEqual(len(ws.nets), 1)
        net_name = net.Proto().name
        self.assertIn("test-net", net_name)
        net = ws.nets[net_name].run()
        blob = ws.blobs["testblob"]
        np.testing.assert_array_equal(
            np.ones((1, 2, 3, 4), dtype=np.float32),
            blob.fetch())

    @given(name=st.text(), value=st.floats(min_value=-1, max_value=1.0))
    def test_operator_run(self, name, value):
        ws = workspace.C.Workspace()
        op = core.CreateOperator(
            "ConstantFill", [], [name], shape=[1], value=value)
        ws.run(op)
        self.assertIn(name, ws.blobs)
        np.testing.assert_allclose(
            [value], ws.blobs[name].fetch(), atol=1e-4, rtol=1e-4)

    @given(blob_name=st.text(),
           net_name=st.text(),
           value=st.floats(min_value=-1, max_value=1.0))
    def test_net_run(self, blob_name, net_name, value):
        ws = workspace.C.Workspace()
        net = core.Net(net_name)
        net.ConstantFill([], [blob_name], shape=[1], value=value)
        ws.run(net)
        self.assertIn(blob_name, ws.blobs)
        self.assertNotIn(net_name, ws.nets)
        np.testing.assert_allclose(
            [value], ws.blobs[blob_name].fetch(), atol=1e-4, rtol=1e-4)

    @given(blob_name=st.text(),
           net_name=st.text(),
           plan_name=st.text(),
           value=st.floats(min_value=-1, max_value=1.0))
    def test_plan_run(self, blob_name, plan_name, net_name, value):
        ws = workspace.C.Workspace()
        plan = core.Plan(plan_name)
        net = core.Net(net_name)
        net.ConstantFill([], [blob_name], shape=[1], value=value)

        plan.AddStep(core.ExecutionStep("step", nets=[net], num_iter=1))

        ws.run(plan)
        self.assertIn(blob_name, ws.blobs)
        self.assertIn(net.Name(), ws.nets)
        np.testing.assert_allclose(
            [value], ws.blobs[blob_name].fetch(), atol=1e-4, rtol=1e-4)

    @given(blob_name=st.text(),
           net_name=st.text(),
           value=st.floats(min_value=-1, max_value=1.0))
    def test_net_create(self, blob_name, net_name, value):
        ws = workspace.C.Workspace()
        net = core.Net(net_name)
        net.ConstantFill([], [blob_name], shape=[1], value=value)
        ws.create_net(net).run()
        self.assertIn(blob_name, ws.blobs)
        self.assertIn(net.Name(), ws.nets)
        np.testing.assert_allclose(
            [value], ws.blobs[blob_name].fetch(), atol=1e-4, rtol=1e-4)

    @given(name=st.text(),
           value=htu.tensor(),
           device_option=st.sampled_from(htu.device_options))
    def test_array_serde(self, name, value, device_option):
        ws = workspace.C.Workspace()
        ws.create_blob(name).feed(value, device_option=device_option)
        self.assertIn(name, ws.blobs)
        blob = ws.blobs[name]
        np.testing.assert_equal(value, ws.blobs[name].fetch())
        serde_blob = ws.create_blob("{}_serde".format(name))
        serde_blob.deserialize(blob.serialize(name))
        np.testing.assert_equal(value, serde_blob.fetch())

    @given(name=st.text(), value=st.text())
    def test_string_serde(self, name, value):
        value = value.encode('ascii', 'ignore')
        ws = workspace.C.Workspace()
        ws.create_blob(name).feed(value)
        self.assertIn(name, ws.blobs)
        blob = ws.blobs[name]
        self.assertEqual(value, ws.blobs[name].fetch())
        serde_blob = ws.create_blob("{}_serde".format(name))
        serde_blob.deserialize(blob.serialize(name))
        self.assertEqual(value, serde_blob.fetch())

    def test_exception(self):
        ws = workspace.C.Workspace()

        with self.assertRaises(TypeError):
            ws.create_net("...")


class TestPredictor(unittest.TestCase):
    def _create_model(self):
        m = model_helper.ModelHelper()
        y = brew.fc(m, "data", "y",
                    dim_in=4, dim_out=2,
                    weight_init=('ConstantFill', dict(value=1.0)),
                    bias_init=('ConstantFill', dict(value=0.0)),
                    axis=0)
        m.net.AddExternalOutput(y)
        return m

    # Use this test with a bigger model to see how using Predictor allows to
    # avoid issues with low protobuf size limit in Python
    #
    # def test_predictor_predefined(self):
    #     workspace.ResetWorkspace()
    #     path = 'caffe2/caffe2/test/assets/'
    #     with open(path + 'squeeze_predict_net.pb') as f:
    #         self.predict_net = f.read()
    #     with open(path + 'squeeze_init_net.pb') as f:
    #         self.init_net = f.read()
    #     self.predictor = workspace.Predictor(self.init_net, self.predict_net)

    #     inputs = [np.zeros((1, 3, 256, 256), dtype='f')]
    #     outputs = self.predictor.run(inputs)
    #     self.assertEqual(len(outputs), 1)
    #     self.assertEqual(outputs[0].shape, (1, 1000, 1, 1))
    #     self.assertAlmostEqual(outputs[0][0][0][0][0], 5.19026289e-05)

    def test_predictor_memory_model(self):
        workspace.ResetWorkspace()
        m = self._create_model()
        workspace.FeedBlob("data", np.zeros([4], dtype='float32'))
        self.predictor = workspace.Predictor(
            workspace.StringifyProto(m.param_init_net.Proto()),
            workspace.StringifyProto(m.net.Proto()))

        inputs = np.array([1, 3, 256, 256], dtype='float32')
        outputs = self.predictor.run([inputs])
        np.testing.assert_array_almost_equal(
            np.array([[516, 516]], dtype='float32'), outputs)


class TestTransform(htu.HypothesisTestCase):
    @given(input_dim=st.integers(min_value=1, max_value=10),
           output_dim=st.integers(min_value=1, max_value=10),
           batch_size=st.integers(min_value=1, max_value=10))
    def test_simple_transform(self, input_dim, output_dim, batch_size):
        m = model_helper.ModelHelper()
        fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
        fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
        conv = brew.conv(m, fc2, "conv",
                            dim_in=output_dim,
                            dim_out=output_dim,
                            use_cudnn=True,
                            engine="CUDNN",
                            kernel=3)

        conv.Relu([], conv)\
           .Softmax([], "pred") \
           .LabelCrossEntropy(["label"], ["xent"]) \
           .AveragedLoss([], "loss")

        transformed_net_proto = workspace.ApplyTransform(
            "ConvToNNPack",
            m.net.Proto())

        self.assertEqual(transformed_net_proto.op[2].engine, "NNPACK")

    @given(input_dim=st.integers(min_value=1, max_value=10),
           output_dim=st.integers(min_value=1, max_value=10),
           batch_size=st.integers(min_value=1, max_value=10))
    @settings(deadline=10000)
    def test_registry_invalid(self, input_dim, output_dim, batch_size):
        m = model_helper.ModelHelper()
        brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
        with self.assertRaises(RuntimeError):
            workspace.ApplyTransform(
                "definitely_not_a_real_transform",
                m.net.Proto())

    @given(value=st.floats(min_value=-1, max_value=1))
    @settings(deadline=10000)
    def test_apply_transform_if_faster(self, value):

        init_net = core.Net("init_net")
        init_net.ConstantFill([], ["data"], shape=[5, 5, 5, 5], value=value)
        init_net.ConstantFill([], ["conv_w"], shape=[5, 5, 3, 3], value=value)
        init_net.ConstantFill([], ["conv_b"], shape=[5], value=value)

        self.assertEqual(
            workspace.RunNetOnce(init_net.Proto().SerializeToString()), True)

        m = model_helper.ModelHelper()
        conv = brew.conv(m, "data", "conv",
                            dim_in=5,
                            dim_out=5,
                            kernel=3,
                            use_cudnn=True,
                            engine="CUDNN")

        conv.Relu([], conv)\
           .Softmax([], "pred") \
           .AveragedLoss([], "loss")

        self.assertEqual(
            workspace.RunNetOnce(m.net.Proto().SerializeToString()), True)

        proto = workspace.ApplyTransformIfFaster(
            "ConvToNNPack",
            m.net.Proto(),
            init_net.Proto())
        self.assertEqual(
            workspace.RunNetOnce(proto.SerializeToString()), True)
        proto = workspace.ApplyTransformIfFaster(
            "ConvToNNPack",
            m.net.Proto(),
            init_net.Proto(),
            warmup_runs=10,
            main_runs=100,
            improvement_threshold=2.0)
        self.assertEqual(
            workspace.RunNetOnce(proto.SerializeToString()), True)


class MyModule(torch.jit.ScriptModule):
    def __init__(self):
        super(MyModule, self).__init__()
        self.mult = torch.nn.Parameter(torch.tensor([[1, 2, 3, 4, 5.0]]))

    @torch.jit.script_method
    def forward(self, x):
        return self.mult.mm(x)

    @torch.jit.script_method
    def multi_input(self, x, y, z=2):
        # type: (Tensor, Tensor, int) -> Tensor
        return x + y + z

    @torch.jit.script_method
    def multi_output(self, x):
        return (x, x + 1)


@unittest.skipIf(
    "ScriptModule" not in core._REGISTERED_OPERATORS,
    "Script module integration in Caffe2 is not enabled")
class TestScriptModule(test_util.TestCase):
    def _createFeedModule(self):
        workspace.FeedBlob('m', MyModule())

    def testCreation(self):
        m = MyModule()
        workspace.FeedBlob('module', m)
        m2 = workspace.FetchBlob('module')
        self.assertTrue(m2 is not None)

    def testForward(self):
        self._createFeedModule()
        val = np.random.rand(5, 5).astype(np.float32)
        param = np.array([[1, 2, 3, 4, 5]]).astype(np.float32)
        workspace.FeedBlob('w', val)
        workspace.RunOperatorOnce(core.CreateOperator("ScriptModule", ["m", "w"], ["y"]))
        np.testing.assert_almost_equal(workspace.FetchBlob("y"), np.matmul(param, val), decimal=5)

    def testMultiInputOutput(self):
        self._createFeedModule()
        val = np.random.rand(5, 5).astype(np.float32)
        workspace.FeedBlob('w', val)
        val2 = np.random.rand(5, 5).astype(np.float32)
        workspace.FeedBlob('w2', val2)
        workspace.RunOperatorOnce(core.CreateOperator("ScriptModule", ["m", "w", "w2"], ["y"], method="multi_input"))
        workspace.RunOperatorOnce(core.CreateOperator("ScriptModule", ["m", "w"], ["y1", "y2"], method="multi_output"))
        np.testing.assert_almost_equal(workspace.FetchBlob("y"), val + val2 + 2, decimal=5)
        np.testing.assert_almost_equal(workspace.FetchBlob("y1"), val, decimal=5)
        np.testing.assert_almost_equal(workspace.FetchBlob("y2"), val + 1, decimal=5)

    def testSerialization(self):
        tmpdir = tempfile.mkdtemp()
        try:
            self._createFeedModule()
            workspace.RunOperatorOnce(core.CreateOperator(
                "Save",
                ["m"], [],
                absolute_path=1,
                db=os.path.join(tmpdir, "db"), db_type="minidb"))
            workspace.ResetWorkspace()

            self.assertFalse(workspace.HasBlob('m'))
            workspace.RunOperatorOnce(core.CreateOperator(
                "Load",
                [], [],
                absolute_path=1,
                db=os.path.join(tmpdir, "db"), db_type="minidb",
                load_all=1))
            self.assertTrue(workspace.HasBlob('m'))
            # TODO: make caffe2 side load return python-sided module
            # right now it returns the base class (torch._C.ScriptModule)
            # self.assertTrue(isinstance(workspace.FetchBlob('m'), torch.jit.ScriptModule))

            # do something with the module
            val = np.random.rand(5, 5).astype(np.float32)
            param = np.array([[1, 2, 3, 4, 5]]).astype(np.float32)
            workspace.FeedBlob('w', val)
            workspace.RunOperatorOnce(core.CreateOperator("ScriptModule", ["m", "w"], ["y"]))
            np.testing.assert_almost_equal(workspace.FetchBlob("y"), np.matmul(param, val), decimal=5)
        finally:
            # clean up temp folder.
            try:
                shutil.rmtree(tmpdir)
            except OSError as e:
                if e.errno != errno.ENOENT:
                    raise


class TestScriptModuleFromString(TestScriptModule):
    def _createFeedModule(self):
        workspace.RunOperatorOnce(
            core.CreateOperator(
                "ScriptModuleLoad", [], ["m"],
                serialized_binary=self._get_modules_bytes(MyModule())))

    def _get_modules_bytes(self, the_module):
        import io
        buffer = io.BytesIO()
        torch.jit.save(the_module, buffer)
        return buffer.getvalue()


if __name__ == '__main__':
    unittest.main()