pytorch/caffe2/python/data_workers_test.py

import numpy as np
import unittest
import time

from caffe2.python import workspace, model_helper
from caffe2.python import timeout_guard
import caffe2.python.data_workers as data_workers


def dummy_fetcher(fetcher_id, batch_size):
    # Create random amount of values
    n = np.random.randint(64) + 1
    data = np.zeros((n, 3))
    labels = []
    for j in range(n):
        data[j, :] *= (j + fetcher_id)
        labels.append(data[j, 0])

    return [np.array(data), np.array(labels)]


def dummy_fetcher_rnn(fetcher_id, batch_size):
    # Hardcoding some input blobs
    T = 20
    N = batch_size
    D = 33
    data = np.random.rand(T, N, D)
    label = np.random.randint(N, size=(T, N))
    seq_lengths = np.random.randint(N, size=(N))
    return [data, label, seq_lengths]


class DataWorkersTest(unittest.TestCase):

    def testNonParallelModel(self):
        workspace.ResetWorkspace()

        model = model_helper.ModelHelper(name="test")
        old_seq_id = data_workers.global_coordinator._fetcher_id_seq
        coordinator = data_workers.init_data_input_workers(
            model,
            ["data", "label"],
            dummy_fetcher,
            32,
            2,
            input_source_name="unittest"
        )
        new_seq_id = data_workers.global_coordinator._fetcher_id_seq
        self.assertEqual(new_seq_id, old_seq_id + 2)

        coordinator.start()

        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net)

        for _i in range(500):
            with timeout_guard.CompleteInTimeOrDie(5):
                workspace.RunNet(model.net.Proto().name)

            data = workspace.FetchBlob("data")
            labels = workspace.FetchBlob("label")

            self.assertEqual(data.shape[0], labels.shape[0])
            self.assertEqual(data.shape[0], 32)

            for j in range(32):
                self.assertEqual(labels[j], data[j, 0])
                self.assertEqual(labels[j], data[j, 1])
                self.assertEqual(labels[j], data[j, 2])

        coordinator.stop_coordinator("unittest")
        self.assertEqual(coordinator._coordinators, [])

    def testRNNInput(self):
        workspace.ResetWorkspace()
        model = model_helper.ModelHelper(name="rnn_test")
        old_seq_id = data_workers.global_coordinator._fetcher_id_seq
        coordinator = data_workers.init_data_input_workers(
            model,
            ["data1", "label1", "seq_lengths1"],
            dummy_fetcher_rnn,
            32,
            2,
            dont_rebatch=False,
            batch_columns=[1, 1, 0],
        )
        new_seq_id = data_workers.global_coordinator._fetcher_id_seq
        self.assertEqual(new_seq_id, old_seq_id + 2)

        coordinator.start()

        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net)

        while coordinator._coordinators[0]._state._inputs < 100:
            time.sleep(0.01)

        # Run a couple of rounds
        workspace.RunNet(model.net.Proto().name)
        workspace.RunNet(model.net.Proto().name)

        # Wait for the enqueue thread to get blocked
        time.sleep(0.2)

        # We don't dequeue on caffe2 side (as we don't run the net)
        # so the enqueue thread should be blocked.
        # Let's now shutdown and see it succeeds.
        self.assertTrue(coordinator.stop())

    @unittest.skip("Test is flaky: https://github.com/pytorch/pytorch/issues/9064")
    def testInputOrder(self):
        #
        # Create two models (train and validation) with same input blobs
        # names and ensure that both will get the data in correct order
        #
        workspace.ResetWorkspace()
        self.counters = {0: 0, 1: 1}

        def dummy_fetcher_rnn_ordered1(fetcher_id, batch_size):
            # Hardcoding some input blobs
            T = 20
            N = batch_size
            D = 33
            data = np.zeros((T, N, D))
            data[0][0][0] = self.counters[fetcher_id]
            label = np.random.randint(N, size=(T, N))
            label[0][0] = self.counters[fetcher_id]
            seq_lengths = np.random.randint(N, size=(N))
            seq_lengths[0] = self.counters[fetcher_id]
            self.counters[fetcher_id] += 1
            return [data, label, seq_lengths]

        workspace.ResetWorkspace()
        model = model_helper.ModelHelper(name="rnn_test_order")

        coordinator = data_workers.init_data_input_workers(
            model,
            input_blob_names=["data2", "label2", "seq_lengths2"],
            fetch_fun=dummy_fetcher_rnn_ordered1,
            batch_size=32,
            max_buffered_batches=1000,
            num_worker_threads=1,
            dont_rebatch=True,
            input_source_name='train'
        )
        coordinator.start()

        val_model = model_helper.ModelHelper(name="rnn_test_order_val")
        coordinator1 = data_workers.init_data_input_workers(
            val_model,
            input_blob_names=["data2", "label2", "seq_lengths2"],
            fetch_fun=dummy_fetcher_rnn_ordered1,
            batch_size=32,
            max_buffered_batches=1000,
            num_worker_threads=1,
            dont_rebatch=True,
            input_source_name='val'
        )
        coordinator1.start()

        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net)
        workspace.CreateNet(val_model.net)

        while coordinator._coordinators[0]._state._inputs < 900:
            time.sleep(0.01)

        with timeout_guard.CompleteInTimeOrDie(5):
            for m in (model, val_model):
                print(m.net.Proto().name)
                workspace.RunNet(m.net.Proto().name)
                last_data = workspace.FetchBlob('data2')[0][0][0]
                last_lab = workspace.FetchBlob('label2')[0][0]
                last_seq = workspace.FetchBlob('seq_lengths2')[0]

                # Run few rounds
                for _i in range(10):
                    workspace.RunNet(m.net.Proto().name)
                    data = workspace.FetchBlob('data2')[0][0][0]
                    lab = workspace.FetchBlob('label2')[0][0]
                    seq = workspace.FetchBlob('seq_lengths2')[0]
                    self.assertEqual(data, last_data + 1)
                    self.assertEqual(lab, last_lab + 1)
                    self.assertEqual(seq, last_seq + 1)
                    last_data = data
                    last_lab = lab
                    last_seq = seq

            time.sleep(0.2)

            self.assertTrue(coordinator.stop())