mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
4bc40d0658
Summary: Hypothesis tests only call `setUp()` once per test. It's annoying to reset manually. Reviewed By: xianjiec Differential Revision: D4911862 fbshipit-source-id: 6b1c11daf002d51c8a0d532261506bcb20429438
107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
## @package layer_test_util
|
|
# Module caffe2.python.layer_test_util
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from collections import namedtuple
|
|
|
|
from caffe2.python import (
|
|
core,
|
|
layer_model_instantiator,
|
|
layer_model_helper,
|
|
schema,
|
|
test_util,
|
|
workspace,
|
|
)
|
|
import numpy as np
|
|
|
|
|
|
OpSpec = namedtuple("OpSpec", "type input output")
|
|
|
|
|
|
class LayersTestCase(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
super(LayersTestCase, self).setUp()
|
|
self.setup_example()
|
|
|
|
def setup_example(self):
|
|
"""
|
|
This is undocumented feature in hypothesis,
|
|
https://github.com/HypothesisWorks/hypothesis-python/issues/59
|
|
"""
|
|
workspace.ResetWorkspace()
|
|
self.reset_model()
|
|
|
|
def reset_model(self, input_feature_schema=None, trainer_extra_schema=None):
|
|
input_feature_schema = input_feature_schema or schema.Struct(
|
|
('float_features', schema.Scalar((np.float32, (32,)))),
|
|
)
|
|
trainer_extra_schema = trainer_extra_schema or schema.Struct()
|
|
self.model = layer_model_helper.LayerModelHelper(
|
|
'test_model',
|
|
input_feature_schema=input_feature_schema,
|
|
trainer_extra_schema=trainer_extra_schema)
|
|
|
|
def new_record(self, schema_obj):
|
|
return schema.NewRecord(self.model.net, schema_obj)
|
|
|
|
def get_training_nets(self):
|
|
"""
|
|
We don't use
|
|
layer_model_instantiator.generate_training_nets_forward_only()
|
|
here because it includes initialization of global constants, which make
|
|
testing tricky
|
|
"""
|
|
train_net = core.Net('train_net')
|
|
train_init_net = core.Net('train_init_net')
|
|
for layer in self.model.layers:
|
|
layer.add_operators(train_net, train_init_net)
|
|
return train_init_net, train_net
|
|
|
|
def get_predict_net(self):
|
|
return layer_model_instantiator.generate_predict_net(self.model)
|
|
|
|
def run_train_net(self):
|
|
self.model.output_schema = schema.Struct()
|
|
train_init_net, train_net = \
|
|
layer_model_instantiator.generate_training_nets(self.model)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
|
|
def run_train_net_forward_only(self):
|
|
self.model.output_schema = schema.Struct()
|
|
train_init_net, train_net = \
|
|
layer_model_instantiator.generate_training_nets_forward_only(
|
|
self.model)
|
|
workspace.RunNetOnce(train_init_net)
|
|
workspace.RunNetOnce(train_net)
|
|
|
|
def assertBlobsEqual(self, spec_blobs, op_blobs):
|
|
"""
|
|
spec_blobs can either be None or a list of blob names. If it's None,
|
|
then no assertion is performed. The elements of the list can be None,
|
|
in that case, it means that position will not be checked.
|
|
"""
|
|
if spec_blobs is None:
|
|
return
|
|
self.assertEqual(len(spec_blobs), len(op_blobs))
|
|
for spec_blob, op_blob in zip(spec_blobs, op_blobs):
|
|
if spec_blob is None:
|
|
continue
|
|
self.assertEqual(spec_blob, op_blob)
|
|
|
|
def assertNetContainOps(self, net, op_specs):
|
|
"""
|
|
Given a net and a list of OpSpec's, check that the net match the spec
|
|
"""
|
|
ops = net.Proto().op
|
|
self.assertEqual(len(op_specs), len(ops))
|
|
for op, op_spec in zip(ops, op_specs):
|
|
self.assertEqual(op_spec.type, op.type)
|
|
self.assertBlobsEqual(op_spec.input, op.input)
|
|
self.assertBlobsEqual(op_spec.output, op.output)
|
|
return ops
|