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data_workers_test.py
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data_workers_test.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
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())