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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52083 This makes minor fixes in `caffe2/python` to address all errors currently reported by Pyre. I update the code to fix errors when doing so looked simple and safe, and added `pyre-fixme` comments in other places. ghstack-source-id: 121109695 Test Plan: Confirmed that Pyre no longer reports errors under `caffe2/python` Differential Revision: D26272279 fbshipit-source-id: b1eb19d323b613f23280ce9c71e800e874ca1162
140 lines
4.8 KiB
Python
140 lines
4.8 KiB
Python
## @package layer_test_util
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# Module caffe2.python.layer_test_util
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from collections import namedtuple
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from caffe2.python import (
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core,
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layer_model_instantiator,
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layer_model_helper,
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schema,
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test_util,
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workspace,
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utils,
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)
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from caffe2.proto import caffe2_pb2
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import numpy as np
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# pyre-fixme[13]: Pyre can't detect attribute initialization through the
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# super().__new__ call
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class OpSpec(namedtuple("OpSpec", "type input output arg")):
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def __new__(cls, op_type, op_input, op_output, op_arg=None):
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return super(OpSpec, cls).__new__(cls, op_type, op_input,
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op_output, op_arg)
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class LayersTestCase(test_util.TestCase):
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def setUp(self):
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super(LayersTestCase, self).setUp()
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self.setup_example()
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def setup_example(self):
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"""
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This is undocumented feature in hypothesis,
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https://github.com/HypothesisWorks/hypothesis-python/issues/59
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"""
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workspace.ResetWorkspace()
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self.reset_model()
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def reset_model(self, input_feature_schema=None, trainer_extra_schema=None):
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input_feature_schema = input_feature_schema or schema.Struct(
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('float_features', schema.Scalar((np.float32, (32,)))),
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)
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trainer_extra_schema = trainer_extra_schema or schema.Struct()
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self.model = layer_model_helper.LayerModelHelper(
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'test_model',
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input_feature_schema=input_feature_schema,
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trainer_extra_schema=trainer_extra_schema)
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def new_record(self, schema_obj):
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return schema.NewRecord(self.model.net, schema_obj)
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def get_training_nets(self, add_constants=False):
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"""
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We don't use
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layer_model_instantiator.generate_training_nets_forward_only()
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here because it includes initialization of global constants, which make
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testing tricky
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"""
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train_net = core.Net('train_net')
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if add_constants:
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train_init_net = self.model.create_init_net('train_init_net')
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else:
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train_init_net = core.Net('train_init_net')
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for layer in self.model.layers:
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layer.add_operators(train_net, train_init_net)
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return train_init_net, train_net
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def get_eval_net(self):
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return layer_model_instantiator.generate_eval_net(self.model)
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def get_predict_net(self):
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return layer_model_instantiator.generate_predict_net(self.model)
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def run_train_net(self):
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self.model.output_schema = schema.Struct()
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train_init_net, train_net = \
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layer_model_instantiator.generate_training_nets(self.model)
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workspace.RunNetOnce(train_init_net)
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workspace.RunNetOnce(train_net)
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def run_train_net_forward_only(self, num_iter=1):
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self.model.output_schema = schema.Struct()
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train_init_net, train_net = \
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layer_model_instantiator.generate_training_nets_forward_only(
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self.model)
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workspace.RunNetOnce(train_init_net)
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assert num_iter > 0, 'num_iter must be larger than 0'
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workspace.CreateNet(train_net)
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workspace.RunNet(train_net.Proto().name, num_iter=num_iter)
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def assertBlobsEqual(self, spec_blobs, op_blobs):
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"""
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spec_blobs can either be None or a list of blob names. If it's None,
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then no assertion is performed. The elements of the list can be None,
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in that case, it means that position will not be checked.
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"""
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if spec_blobs is None:
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return
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self.assertEqual(len(spec_blobs), len(op_blobs))
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for spec_blob, op_blob in zip(spec_blobs, op_blobs):
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if spec_blob is None:
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continue
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self.assertEqual(spec_blob, op_blob)
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def assertArgsEqual(self, spec_args, op_args):
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self.assertEqual(len(spec_args), len(op_args))
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keys = [a.name for a in op_args]
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def parse_args(args):
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operator = caffe2_pb2.OperatorDef()
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# Generate the expected value in the same order
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for k in keys:
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v = args[k]
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arg = utils.MakeArgument(k, v)
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operator.arg.add().CopyFrom(arg)
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return operator.arg
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self.assertEqual(parse_args(spec_args), op_args)
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def assertNetContainOps(self, net, op_specs):
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"""
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Given a net and a list of OpSpec's, check that the net match the spec
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"""
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ops = net.Proto().op
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self.assertEqual(len(op_specs), len(ops))
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for op, op_spec in zip(ops, op_specs):
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self.assertEqual(op_spec.type, op.type)
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self.assertBlobsEqual(op_spec.input, op.input)
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self.assertBlobsEqual(op_spec.output, op.output)
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if op_spec.arg is not None:
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self.assertArgsEqual(op_spec.arg, op.arg)
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return ops
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