def allcompare_process(filestore_dir, process_id, data, num_procs):
from caffe2.python import core, data_parallel_model, workspace, dyndep
from caffe2.python.model_helper import ModelHelper
from caffe2.proto import caffe2_pb2
dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:file_store_handler_ops")
workspace.RunOperatorOnce(
core.CreateOperator(
"FileStoreHandlerCreate", [], ["store_handler"], path=filestore_dir
)
)
rendezvous = dict(
kv_handler="store_handler",
shard_id=process_id,
num_shards=num_procs,
engine=op_engine,
exit_nets=None
)
model = ModelHelper()
model._rendezvous = rendezvous
workspace.FeedBlob("test_data", data)
data_parallel_model._RunComparison(
model, "test_data", core.DeviceOption(caffe2_pb2.CPU, 0)
)
def test_validate(self):
model = ModelHelper(name="test_model")
model.params.append("aaa")
model.params.append("bbb")
self.assertEqual(model._Validate(), [])
model.params.append("xxx")
model.params.append("bbb")
self.assertEqual(model._Validate(), ["bbb"])
def GenerateLossOps(
model: ModelHelper, model_id: str, output_blob: str, label_blob: str,
loss_blob: str
) -> None:
"""
Adds loss operators to net. The loss function is computed by a squared L2
distance, and then averaged over all items in the minibatch.
:param model: ModelHelper object to add loss operators to.
:param model_id: String identifier.
:param output_blob: Blob containing output of net.
:param label_blob: Blob containing labels.
:param loss_blob: Blob in which to store loss.
"""
dist = model.SquaredL2Distance([label_blob, output_blob], model_id + "dist")
model.AveragedLoss(dist, loss_blob)
def test_caffe2_to_onnx_value_info(self):
caffe2_net = tempfile.NamedTemporaryFile()
output = tempfile.NamedTemporaryFile()
model = ModelHelper(name='caffe2-to-onnx-test')
brew.relu(model, ["X"], "Y")
caffe2_net.write(model.net.Proto().SerializeToString())
caffe2_net.flush()
args = [caffe2_net.name, '--output', output.name]
self.assertRaisesRegexp(Exception,
'value info',
self._run_command, caffe2_to_onnx, args)
args.extend([
'--value-info',
json.dumps({
'X': (TensorProto.FLOAT, (2, 2)),
})])
result = self._run_command(caffe2_to_onnx, args)
onnx_model = ModelProto()
onnx_model.ParseFromString(output.read())
self.assertEqual(len(onnx_model.graph.node), 1)
self.assertEqual(onnx_model.graph.node[0].op_type, 'Relu')
self.assertEqual(len(onnx_model.graph.initializer), 0)
def setUp(self):
def myhelper(model, val=-1):
return val
if not brew.has_helper(myhelper):
brew.Register(myhelper)
self.myhelper = myhelper
def myhelper2(model, val=-1):
return val
if not brew.has_helper(myhelper2):
brew.Register(myhelper2)
self.myhelper2 = myhelper2
self.model = ModelHelper(name="test_model")
def test_crf_gradient(self, num_tags, num_words):
base_model = ModelHelper(name='base_model')
transitions = np.random.randn(num_tags + 2,
num_tags + 2).astype(np.float32)
predictions = np.random.randn(num_words, 1,
num_tags + 2).astype(np.float32)
initial = np.random.randn(1, num_tags + 2).astype(np.float32)
predictions_blob, transitions_blob, initial_blob = (
base_model.net.AddExternalInputs('predictions_blob',
'crf_transitions', 'inital_blob'))
workspace.FeedBlob(str(predictions_blob), predictions)
workspace.FeedBlob(str(transitions_blob), transitions)
workspace.FeedBlob(str(initial_blob), initial)
crf_layer = crf.CRFWithLoss(base_model, num_tags, transitions_blob)
crf_layer.build_crf_net(predictions_blob, initial_blob,
transitions_blob)
op = base_model.net._net.op[-1]
workspace.RunNetOnce(base_model.param_init_net)
gradients_to_check = (index
for (index, input_name) in enumerate(op.input)
if input_name != "crf_net/zero_segment_id")
inputs = [workspace.FetchBlob(name) for name in op.input]
for param in gradients_to_check:
self.assertGradientChecks(
device_option=hu.cpu_do,
op=op,
inputs=inputs,
outputs_to_check=param,
outputs_with_grads=[1],
threshold=0.05,
stepsize=0.001,
)
def test_layer_norm_op_c10_preallocated_outputs(self, X, gc, dc):
# This test case ensures that it works correctly when output tensors are preallocated.
axis = np.random.randint(0, len(X.shape))
epsilon = 1e-4
self.ws.create_blob('input').feed(X)
m = ModelHelper(name="test")
m.net.C10LayerNorm_DontUseThisOpYet(["input"],
["output", "mean", "stdev"],
axis=axis,
epsilon=epsilon)
self.ws.create_net(m.param_init_net).run()
net = self.ws.create_net(m.net)
net.run()
net.run(
) # run two times to be extra sure that the outputs are preallocated
expected_norm, expected_mean, expected_stdev = _layer_norm_ref(
axis, epsilon, X)
actual_norm = self.ws.fetch_blob('output')
actual_mean = self.ws.fetch_blob('mean')
actual_stdev = self.ws.fetch_blob('stdev')
torch.testing.assert_allclose(expected_norm, actual_norm)
torch.testing.assert_allclose(expected_mean, actual_mean)
torch.testing.assert_allclose(expected_stdev, actual_stdev)
def test_arg_scope_single(self):
X = np.random.rand(64, 3, 32, 32).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
with brew.arg_scope(
brew.conv,
stride=2,
pad=2,
weight_init=('XavierFill', {}),
bias_init=('ConstantFill', {})
):
brew.conv(
model=model,
blob_in="x",
blob_out="out",
dim_in=3,
dim_out=64,
kernel=3,
)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertEqual(out.shape, (64, 64, 17, 17))
def _prepare_gru_unit_op(gc,
n,
d,
outputs_with_grads,
forward_only=False,
drop_states=False,
two_d_initial_states=None):
print("Dims: (n,d) = ({},{})".format(n, d))
def generate_input_state(n, d):
if two_d_initial_states:
return np.random.randn(n, d).astype(np.float32)
else:
return np.random.randn(1, n, d).astype(np.float32)
model = ModelHelper(name='external')
with scope.NameScope("test_name_scope"):
hidden_t_prev, gates_t, seq_lengths, timestep = \
model.net.AddScopedExternalInputs(
"hidden_t_prev",
"gates_t",
'seq_lengths',
"timestep",
)
workspace.FeedBlob(hidden_t_prev,
generate_input_state(n, d).astype(np.float32),
device_option=gc)
workspace.FeedBlob(gates_t,
generate_input_state(n, 3 * d).astype(np.float32),
device_option=gc)
hidden_t = model.net.GRUUnit(
[
hidden_t_prev,
gates_t,
seq_lengths,
timestep,
],
['hidden_t'],
forget_bias=0.0,
drop_states=drop_states,
)
model.net.AddExternalOutputs(hidden_t)
workspace.RunNetOnce(model.param_init_net)
# 10 is used as a magic number to simulate some reasonable timestep
# and generate some reasonable seq. lengths
workspace.FeedBlob(seq_lengths,
np.random.randint(1, 10,
size=(n, )).astype(np.int32),
device_option=gc)
workspace.FeedBlob(
timestep,
np.random.randint(1, 10, size=(1, )).astype(np.int32),
device_option=core.DeviceOption(caffe2_pb2.CPU),
)
print("Feed {}".format(timestep))
return hidden_t, model.net
def test_relu(self):
Xpos = np.ones((5, 5)).astype(np.float32) - 0.5
Xneg = np.ones((5, 5)).astype(np.float32) - 1.5
workspace.FeedBlob("xpos", Xpos)
workspace.FeedBlob("xneg", Xneg)
model = ModelHelper(name="test_model")
brew.relu(model, "xpos", "out_xpos")
brew.relu(model, "xneg", "out_xneg")
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
pos = workspace.FetchBlob("out_xpos")
self.assertAlmostEqual(pos.mean(), 0.5)
neg = workspace.FetchBlob("out_xneg")
self.assertAlmostEqual(neg.mean(), 0)
def _create_reward_train_net(self) -> None:
self.reward_train_model = ModelHelper(name="reward_train_" +
self.model_id)
C2.set_model(self.reward_train_model)
self.update_model('states', 'actions', 'rewards')
workspace.RunNetOnce(self.reward_train_model.param_init_net)
workspace.CreateNet(self.reward_train_model.net)
C2.set_model(None)
def _create_all_q_score_net(self) -> None:
self.all_q_score_model = ModelHelper(name="all_q_score_" +
self.model_id)
C2.set_model(self.all_q_score_model)
self.all_q_score_output = self.get_q_values_all_actions("states", True)
workspace.RunNetOnce(self.all_q_score_model.param_init_net)
workspace.CreateNet(self.all_q_score_model.net)
C2.set_model(None)
def simple_mlp():
model = ModelHelper(name="r")
brew.relu(
model,
brew.fc(model,
brew.relu(model, brew.fc(model, "data", "fc1", 10, 10), "rl1"),
"fc2", 10, 10), "rl2")
return model, [(1, 10)]
def ResNet50(order, cudnn_ws, mkl):
my_arg_scope = {'order': order, 'use_cudnn': True,
'cudnn_exhaustive_search': True,
'ws_nbytes_limit': str(cudnn_ws)}
model = ModelHelper(name="alexnet", arg_scope=my_arg_scope)
resnet.create_resnet50(model, "data", 3, 1000, is_test=True,
final_avg_kernel=14)
return model, 448
def test_milstm_params(self):
model = ModelHelper(name="milstm_params_test")
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
output, _, _, _ = rnn_cell.MILSTM(
model=model,
input_blob="input",
seq_lengths="seqlengths",
initial_states=None,
dim_in=20,
dim_out=[40, 20],
scope="test",
drop_states=True,
return_last_layer_only=True,
)
for param in model.GetParams():
self.assertNotEqual(model.get_param_info(param), None)
def createTrainModel(lmdb_path, devices):
"""Create and return a training model, complete with training ops."""
model = ModelHelper(name='train', arg_scope={'order': 'NCHW'})
reader = model.CreateDB('train_reader', db=lmdb_path, db_type='lmdb')
data_parallel_model.Parallelize_GPU(
model,
input_builder_fun=functools.partial(AddInputOps,
reader=reader,
batch_size=(BATCH_SIZE //
len(devices))),
forward_pass_builder_fun=AddForwardPassOps,
optimizer_builder_fun=AddOptimizerOps,
devices=devices,
use_nccl=True)
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
return model
def test_mobile_exporter(self):
model = ModelHelper(name="mobile_exporter_test_model")
# Test LeNet
brew.conv(model, 'data', 'conv1', dim_in=1, dim_out=20, kernel=5)
brew.max_pool(model, 'conv1', 'pool1', kernel=2, stride=2)
brew.conv(model, 'pool1', 'conv2', dim_in=20, dim_out=50, kernel=5)
brew.max_pool(model, 'conv2', 'pool2', kernel=2, stride=2)
brew.fc(model, 'pool2', 'fc3', dim_in=50 * 4 * 4, dim_out=500)
brew.relu(model, 'fc3', 'fc3')
brew.fc(model, 'fc3', 'pred', 500, 10)
brew.softmax(model, 'pred', 'out')
# Create our mobile exportable networks
workspace.RunNetOnce(model.param_init_net)
init_net, predict_net = mobile_exporter.Export(workspace, model.net,
model.params)
# Populate the workspace with data
np_data = np.random.rand(1, 1, 28, 28).astype(np.float32)
workspace.FeedBlob("data", np_data)
workspace.CreateNet(model.net)
workspace.RunNet(model.net)
ref_out = workspace.FetchBlob("out")
# Clear the workspace
workspace.ResetWorkspace()
# Populate the workspace with data
workspace.RunNetOnce(init_net)
# Fake "data" is populated by init_net, we have to replace it
workspace.FeedBlob("data", np_data)
# Overwrite the old net
workspace.CreateNet(predict_net, True)
workspace.RunNet(predict_net.name)
manual_run_out = workspace.FetchBlob("out")
np.testing.assert_allclose(ref_out,
manual_run_out,
atol=1e-10,
rtol=1e-10)
# Clear the workspace
workspace.ResetWorkspace()
# Predictor interface test (simulates writing to disk)
predictor = workspace.Predictor(init_net.SerializeToString(),
predict_net.SerializeToString())
# Output is a vector of outputs but we only care about the first and only result
predictor_out = predictor.run([np_data])
assert len(predictor_out) == 1
predictor_out = predictor_out[0]
np.testing.assert_allclose(ref_out,
predictor_out,
atol=1e-10,
rtol=1e-10)
def test_extract(self, T, n, d):
model = ModelHelper(name='external')
workspace.ResetWorkspace()
input_blob, initial_input_blob = model.net.AddExternalInputs(
'input', 'initial_input')
step = ModelHelper(name='step', param_model=model)
input_t, output_t_prev = step.net.AddExternalInput(
'input_t', 'output_t_prev')
output_t = step.net.Mul([input_t, output_t_prev])
step.net.AddExternalOutput(output_t)
inputs = np.random.randn(T, n, d).astype(np.float32)
initial_input = np.random.randn(1, n, d).astype(np.float32)
recurrent.recurrent_net(
net=model.net,
cell_net=step.net,
inputs=[(input_t, input_blob)],
initial_cell_inputs=[(output_t_prev, initial_input_blob)],
links={output_t_prev: output_t},
scope="test_rnn_sum_mull",
)
workspace.blobs[input_blob] = inputs
workspace.blobs[initial_input_blob] = initial_input
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
prefix = "extractTest"
workspace.RunNet(model.net.Proto().name, T)
retrieved_blobs = recurrent.retrieve_step_blobs(model.net, prefix)
# needed for python3.6, which returns bytearrays instead of str
retrieved_blobs = [x.decode() for x in retrieved_blobs]
for i in range(T):
blob_name = prefix + "_" + "input_t" + str(i)
self.assertTrue(
blob_name in retrieved_blobs,
"blob extraction failed on timestep {}\
. \n\n Extracted Blobs: {} \n\n Looking for {}\
.".format(i, retrieved_blobs, blob_name))
def test_caffe2_simple_model(self):
model = ModelHelper(name="mnist")
# how come those inputs don't break the forward pass =.=a
workspace.FeedBlob("data",
np.random.randn(1, 3, 64, 64).astype(np.float32))
workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))
with core.NameScope("conv1"):
conv1 = brew.conv(model,
"data",
'conv1',
dim_in=1,
dim_out=20,
kernel=5)
# Image size: 24 x 24 -> 12 x 12
pool1 = brew.max_pool(model, conv1, 'pool1', kernel=2, stride=2)
# Image size: 12 x 12 -> 8 x 8
conv2 = brew.conv(model,
pool1,
'conv2',
dim_in=20,
dim_out=100,
kernel=5)
# Image size: 8 x 8 -> 4 x 4
pool2 = brew.max_pool(model, conv2, 'pool2', kernel=2, stride=2)
with core.NameScope("classifier"):
# 50 * 4 * 4 stands for dim_out from previous layer multiplied by the image size
fc3 = brew.fc(model, pool2, 'fc3', dim_in=100 * 4 * 4, dim_out=500)
relu = brew.relu(model, fc3, fc3)
pred = brew.fc(model, relu, 'pred', 500, 10)
softmax = brew.softmax(model, pred, 'softmax')
xent = model.LabelCrossEntropy([softmax, "label"], 'xent')
# compute the expected loss
loss = model.AveragedLoss(xent, "loss")
model.net.RunAllOnMKL()
model.param_init_net.RunAllOnMKL()
model.AddGradientOperators([loss], skip=1)
blob_name_tracker = {}
graph = c2_graph.model_to_graph_def(
model,
blob_name_tracker=blob_name_tracker,
shapes={},
show_simplified=False,
)
compare_proto(graph, self)
def _create_q_score_net(self) -> None:
self.q_score_model = ModelHelper(name="q_score_" + self.model_id)
C2.set_model(self.q_score_model)
self.q_score_output = self.get_q_values("states", "actions", True)
workspace.RunNetOnce(self.q_score_model.param_init_net)
self.q_score_model.net.Proto().num_workers = \
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS
workspace.CreateNet(self.q_score_model.net)
C2.set_model(None)
def createTestModel(lmdb_path, devices):
"""Create and return a test model. Does not include training ops."""
model = ModelHelper(name='test',
arg_scope={'order': 'NCHW'},
init_params=False)
reader = model.CreateDB('test_reader', db=lmdb_path, db_type='lmdb')
data_parallel_model.Parallelize_GPU(
model,
input_builder_fun=functools.partial(AddInputOps,
reader=reader,
batch_size=(BATCH_SIZE //
len(devices))),
forward_pass_builder_fun=AddForwardPassOps,
param_update_builder_fun=None,
devices=devices)
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
return model
def complex_resnet():
model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
resnet.create_resnet50(model,
"data",
num_input_channels=1,
num_labels=5,
is_test=True,
no_loss=True)
return model, [(1, 1, 224, 224)]
def simple_resnet():
model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
resnet.create_resnet_32x32(model,
"data",
num_input_channels=1,
num_groups=1,
num_labels=5,
is_test=True)
return model, [(1, 1, 32, 32)]
def _create_internal_policy_net(self) -> None:
self.internal_policy_model = ModelHelper(name="internal_policy_" +
self.model_id)
C2.set_model(self.internal_policy_model)
self.internal_policy_output = self.get_q_values_all_actions(
"states", False)
workspace.RunNetOnce(self.internal_policy_model.param_init_net)
workspace.CreateNet(self.internal_policy_model.net)
C2.set_model(None)
def _create_internal_policy_net(self) -> None:
self.internal_policy_model = ModelHelper(name="q_score_" +
self.model_id)
C2.set_model(self.internal_policy_model)
self.internal_policy_output = C2.FlattenToVec(
self.get_q_values('states', 'actions', False))
workspace.RunNetOnce(self.internal_policy_model.param_init_net)
workspace.CreateNet(self.internal_policy_model.net)
C2.set_model(None)
def test_dropout(self):
p = 0.2
X = np.ones((100, 100)).astype(np.float32) - p
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.dropout(model, "x", "out")
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertLess(abs(out.mean() - (1 - p)), 0.05)
def simple_cnn():
model = ModelHelper(name="r", arg_scope={"order": "NCHW", "is_test": True})
brew.conv(
model, "data", 'conv1', 3, 16, kernel=3, stride=1
)
brew.spatial_bn(
model, 'conv1', 'conv1_spatbn', 16, epsilon=1e-3
)
brew.relu(model, 'conv1_spatbn', 'relu1')
return model, (1, 3, 32, 32)
def test_fc(self):
m, n, k = (15, 15, 15)
X = np.random.rand(m, k).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.fc(model, "x", "out_1", k, n)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
def _create_reward_train_net(self) -> None:
self.reward_train_model = ModelHelper(name="reward_train_" +
self.model_id)
C2.set_model(self.reward_train_model)
self.update_model('states', 'actions', 'rewards')
workspace.RunNetOnce(self.reward_train_model.param_init_net)
self.reward_train_model.net.Proto().num_workers = \
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS
workspace.CreateNet(self.reward_train_model.net)
C2.set_model(None)
def test_c10_layer_norm(self, X, gc, dc):
X = X[0]
if len(X.shape) == 1:
X = np.expand_dims(X, axis=0)
axis = np.random.randint(0, len(X.shape))
scale_dim = [1] * np.ndim(X)
scale_dim[axis] = X.shape[axis]
self.ws.create_blob('input').feed(X)
model = ModelHelper(name='test_layer_norm_brew_wrapper')
model.C10LayerNorm_DontUseThisOpYet(
['input'],
['output_1', 'output_1', 'output_2'],
axis=axis,
epsilon=1e-4,
)
self.ws.create_net(model.param_init_net).run()
self.ws.create_net(model.net).run()
def _create_internal_policy_net(self) -> None:
self.internal_policy_model = ModelHelper(name="internal_policy_" +
self.model_id)
C2.set_model(self.internal_policy_model)
self.internal_policy_output = self.get_q_values_all_actions(
"states", False)
workspace.RunNetOnce(self.internal_policy_model.param_init_net)
self.internal_policy_model.net.Proto().num_workers = \
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS
workspace.CreateNet(self.internal_policy_model.net)
C2.set_model(None)
def _create_reward_train_net(self) -> None:
self.reward_train_model = ModelHelper(name="reward_train_" +
self.model_id)
C2.set_model(self.reward_train_model)
self.update_model("states", "actions", "rewards")
workspace.RunNetOnce(self.reward_train_model.param_init_net)
self.reward_train_model.net.Proto().num_workers = (
RLTrainer.DEFAULT_TRAINING_NUM_WORKERS)
self.reward_train_model.net.Proto().type = "async_scheduling"
workspace.CreateNet(self.reward_train_model.net)
C2.set_model(None)
def _create_rnn_variant(cls, init_model, pred_model, n, opset_version):
assert init_model is not None, "cannot convert RNNs without access to the full model"
assert pred_model is not None, "cannot convert RNNs without access to the full model"
attrs = dict(n.attrs) # make a copy, which is safe to mutate
hidden_size = attrs.pop('hidden_size')
direction = force_unicode(attrs.pop('direction', 'forward'))
if n.op_type == 'RNN':
activation = force_unicode(attrs.pop('activations', ('tanh',))[0])
elif n.op_type == 'GRU':
linear_before_reset = attrs.pop('linear_before_reset', 0)
assert not attrs, "unsupported RNN attributes: " + str(attrs.keys())
assert direction in ['forward', 'bidirectional'], "unsupported backwards RNN/GRU/LSTM"
if n.op_type in ['RNN', 'GRU']:
input_blob, W, R, B, sequence_lens, initial_h = n.inputs
elif n.op_type == 'LSTM':
input_blob, W, R, B, sequence_lens, initial_h, initial_c = n.inputs
if sequence_lens == "":
sequence_lens = None
for x in itertools.chain(init_model.graph.input,
init_model.graph.value_info,
pred_model.graph.input,
pred_model.graph.value_info):
if x.name == W:
input_size = x.type.tensor_type.shape.dim[1].dim_value
break
else:
raise RuntimeError("best-effort shape inference for RNN/GRU/LSTM failed")
init_net = core.Net("init-net")
pred_mh = ModelHelper()
if n.op_type == 'RNN':
def reform(*args):
pass
def make_cell(*args, **kwargs):
return rnn_cell.BasicRNN(*args, activation=activation, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h')], sequence_lens,
pred_mh, init_net, input_size, hidden_size, 1, direction_offset,
"/i2h_b", "/gates_t_b", "/i2h_w", "/gates_t_w",
reform, make_cell, lambda x: x)
elif n.op_type == 'GRU':
def reform(Bi, Br, W_, R_, name, hidden_size, init_net):
# caffe2 has a different order from onnx. We need to rearrange
# z r h -> r z h
reforms = ((W_, 'i2h_w', True, [(0,-1)]),
(R_, 'gate_t_w', False, [(0,-1)]),
(Bi, 'i2h_b', True, []),
(Br, 'gate_t_b', False, []))
cls._rnn_reform_weights(reforms, name, hidden_size, init_net,
['update', 'reset', 'output'], [1, 0, 2])
def make_cell(*args, **kwargs):
return gru_cell.GRU(*args, linear_before_reset=linear_before_reset, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h')], sequence_lens,
pred_mh, init_net, input_size, hidden_size, 3, direction_offset,
"_bias_i2h", "_bias_gates", "/i2h_w_pre", "/gates_t_w_pre",
reform, make_cell, lambda x: x)
elif n.op_type == 'LSTM':
def reform(Bi, Br, W_, R_, name, hidden_size, init_net):
# caffe2 has a different order from onnx. We need to rearrange
# i o f c -> i f o c
reforms = ((W_, 'i2h_w', True, [(0, -1)]),
(R_, 'gates_t_w', True, [(0, -1)]),
(Bi, 'i2h_b' , True, []),
(Br, 'gates_t_b', True, []))
cls._rnn_reform_weights(reforms, name, hidden_size, init_net,
['input', 'output', 'forget', 'cell'], [0, 2, 1, 3])
def make_cell(*args, **kwargs):
return rnn_cell.LSTM(*args, **kwargs)
def make_rnn(direction_offset):
return cls._make_rnn_direction(
input_blob, B, W, R, [(initial_h, '/initial_h'), (initial_c, '/initial_c')], sequence_lens,
pred_mh, init_net, input_size, hidden_size, 4, direction_offset,
"/i2h_b", "/gates_t_b", "/i2h_w", "/gates_t_w",
reform, make_cell, lambda x: [x[0], x[1], x[3]])
if direction == 'forward':
outputs = make_rnn(0)
# in the forward case, storage is shared between the
# last outputs. We need to decouple them so that the
# VariableLengthSequencePadding only mutates
# n.outputs[0]
for i in range(1, len(outputs)):
pred_mh.net.Copy(outputs[i], n.outputs[i])
pred_mh.net = pred_mh.net.Clone(
"dummy-clone-net", blob_remap={ outputs[0]: n.outputs[0] }
)
elif direction == 'bidirectional':
outputs_f = make_rnn(0)
outputs_b = make_rnn(1)
pred_mh.net.Concat([outputs_f[0], outputs_b[0]],
[n.outputs[0], cls.dummy_name()], axis=2)
for i in range(1, len(n.outputs)):
pred_mh.net.Concat([outputs_f[i], outputs_b[i]],
[n.outputs[i], cls.dummy_name()], axis=0)
if sequence_lens is not None:
pred_mh.net.VariableLengthSequencePadding(
[n.outputs[0], sequence_lens], [n.outputs[0]])
return Caffe2Ops(list(pred_mh.Proto().op),
list(init_net.Proto().op),
list(pred_mh.Proto().external_input))
