Commit 396cd548 authored by Mark Hoffmann's avatar Mark Hoffmann

updated annotations

parent d45dbbd3
......@@ -18,7 +18,7 @@
"installation": [
{
"type": "PIP",
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@4058da7d3ee87fa1d968514d8bf121b6101fa69c#egg=jpl_primitives"
"package_uri": "git+https://gitlab.com/datadrivendiscovery/jpl-primitives.git@8422018ded8c75052c0a30dcb5f898b6eea4d4b2#egg=jpl_primitives"
}
],
"python_path": "d3m.primitives.object_detection.retina_net.JPLPrimitives",
......@@ -342,5 +342,5 @@
},
"structural_type": "jpl_primitives.retina_net.RetinaNet",
"description": "Implementation of RetinaNet for object detection https://arxiv.org/abs/1708.02002\n\nAttributes\n----------\nmetadata : PrimitiveMetadata\n Primitive's metadata. Available as a class attribute.\nlogger : Logger\n Primitive's logger. Available as a class attribute.\nhyperparams : Hyperparams\n Hyperparams passed to the constructor.\nrandom_seed : int\n Random seed passed to the constructor.\ndocker_containers : Dict[str, DockerContainer]\n A dict mapping Docker image keys from primitive's metadata to (named) tuples containing\n container's address under which the container is accessible by the primitive, and a\n dict mapping exposed ports to ports on that address.\nvolumes : Dict[str, str]\n A dict mapping volume keys from primitive's metadata to file and directory paths\n where downloaded and extracted files are available to the primitive.\ntemporary_directory : str\n An absolute path to a temporary directory a primitive can use to store any files\n for the duration of the current pipeline run phase. Directory is automatically\n cleaned up after the current pipeline run phase finishes.",
"digest": "6986424941f930410893e20669ba3993589714b7278bc0a5389206cc63227f66"
"digest": "906d82abb62596107b9761049f6c35bd2824961635cc65c03c50ff4ec7a9e60b"
}
{
"problem": "LL1_penn_fudan_pedestrian_problem",
"full_inputs": ["LL1_penn_fudan_pedestrian_dataset"],
"train_inputs": ["LL1_penn_fudan_pedestrian_dataset_TRAIN"],
"test_inputs": ["LL1_penn_fudan_pedestrian_dataset_TEST"],
"score_inputs": ["LL1_penn_fudan_pedestrian_dataset_SCORE"]
}
---
context: TESTING
created: '2019-04-26T01:09:44.343543Z'
id: 2aa021cf-4dfc-4d46-82bc-738017555bd2
inputs:
- name: inputs
outputs:
- data: steps.4.produce
name: output predictions
schema: https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json
steps:
# STEP 0
- arguments:
inputs:
data: inputs.0
type: CONTAINER
outputs:
- id: produce
primitive:
id: f31f8c1f-d1c5-43e5-a4b2-2ae4a761ef2e
name: Denormalize datasets
python_path: d3m.primitives.data_transformation.denormalize.Common
version: 0.2.0
type: PRIMITIVE
# STEP 1
- arguments:
inputs:
data: steps.0.produce
type: CONTAINER
outputs:
- id: produce
primitive:
id: 4b42ce1e-9b98-4a25-b68e-fad13311eb65
name: Extract a DataFrame from a Dataset
python_path: d3m.primitives.data_transformation.dataset_to_dataframe.Common
version: 0.3.0
type: PRIMITIVE
# STEP 2
- arguments:
inputs: {data: steps.1.produce, type: CONTAINER}
hyperparams:
parse_semantic_types: {data: ["http://schema.org/Boolean",
"http://schema.org/Integer",
"http://schema.org/Float",
"https://metadata.datadrivendiscovery.org/types/FloatVector",
"http://schema.org/DateTime"
], type: VALUE}
outputs:
- {id: produce}
primitive:
id: d510cb7a-1782-4f51-b44c-58f0236e47c7
name: 'Parses strings into their types'
python_path: d3m.primitives.data_transformation.column_parser.DataFrameCommon
version: 0.5.0
type: PRIMITIVE
# STEP 3
- arguments:
inputs:
data: steps.2.produce
type: CONTAINER
hyperparams:
semantic_types:
data:
- 'https://metadata.datadrivendiscovery.org/types/TrueTarget'
type: VALUE
outputs:
-
id: produce
primitive:
id: 4503a4c6-42f7-45a1-a1d4-ed69699cf5e1
name: 'Extracts columns by semantic type'
python_path: d3m.primitives.data_transformation.extract_columns_by_semantic_types.DataFrameCommon
version: 0.2.0
type: PRIMITIVE
# STEP 4
- arguments:
inputs:
data: steps.1.produce
type: CONTAINER
outputs:
data: steps.2.produce
type: CONTAINER
hyperparams:
use_gpu:
data: false
type: VALUE
batch_size:
data: 3
type: VALUE
outputs:
- id: produce
primitive:
id: 176e7277-fe37-402b-86e6-bdb7f7a9cf89
name: retina_net
python_path: d3m.primitives.object_detection.retina_net.JPLPrimitives
version: 0.2.1
type: PRIMITIVE
# STEP 5
#- arguments:
# inputs:
# data: steps.2.produce
# type: CONTAINER
# reference:
# data: steps.2.produce
# type: CONTAINER
# outputs:
# -
# id: produce
# primitive:
# id: 8d38b340-f83f-4877-baaa-162f8e551736
# name: 'Construct pipeline predictions output'
# python_path: d3m.primitives.data_transformation.construct_predictions.DataFrameCommon
# version: 0.3.0
# type: PRIMITIVE
\ No newline at end of file
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