1156 downsampling experiment

adam/curriculum/curriculum_from_files.py

@@ -30,6 +30,14 @@
     "m5_objects_v0_with_mugs",
     "m5_actions",
     "m5_actions_person_only",
+    "m6_objects_downsampled_2pertype_post_gnn",
+    "m6_objects_downsampled_10pertype_post_gnn",
+    "m5_objects_v0_with_mugs_post_gnn",
+    "m6_unknown_objects",
+    "m6_objects_v0_with_mugs_post_gnn_top2",
+    "m6_objects_v0_with_mugs_post_gnn_top3",
+    "m6_objects_v0_with_mugs_post_gnn_top4",
+    "m6_objects_v0_with_mugs_post_gnn_top5",
 ]
 
 PHASE_3_TESTING_CURRICULUM_OPTIONS = [
@@ -40,6 +48,14 @@
     "m5_objects_v0_with_mugs_eval",
     "m5_actions_eval",
     "m5_actions_person_only_eval",
+    "m5_objects_v0_with_mugs_eval_2pertype_post_gnn",
+    "m5_objects_v0_with_mugs_eval_10pertype_post_gnn",
+    "m5_objects_v0_with_mugs_eval_post_gnn",
+    "m6_unknown_objects_eval",
+    "m6_objects_v0_with_mugs_eval_post_gnn_top2",
+    "m6_objects_v0_with_mugs_eval_post_gnn_top3",
+    "m6_objects_v0_with_mugs_eval_post_gnn_top4",
+    "m6_objects_v0_with_mugs_eval_post_gnn_top5",
 ]
 
 TRAINING_CUR = "training"

adam/perception/visual_perception.py

@@ -156,6 +156,15 @@ def from_mapping(
                         weight=strokes_map["confidence_score"],
                     )
                 )
+            if "concept_names" in strokes_map:
+                for concept_name in strokes_map["concept_names"]:
+                    properties.append(
+                        StrokeGNNRecognitionNode(
+                            object_recognized=concept_name,
+                            confidence=strokes_map["confidence_score"],
+                            weight=strokes_map["confidence_score"],
+                        )
+                    )
 
             # Cluster ID is cleaned so that only the digit ID is displayed and not 'object'
             clusters.append(

adam_preprocessing/shape_stroke_graph_inference.py

@@ -4,6 +4,7 @@
 from copy import deepcopy
 import logging
 from pathlib import Path
+from typing import Sequence
 
 import torch
 import torch.nn as nn
@@ -22,12 +23,19 @@
 from utils import accuracy, get_stroke_data, LinearModel, load_data, STRING_OBJECT_LABELS
 
 
-def update_features_yaml(features_yaml, *, predicted_object: str):
+def update_features_yaml(features_yaml, *, predicted_objects: Sequence[str]):
     # jac: not terribly efficient to do a full deepcopy, but who cares, this should be a small dict
     # anyway... also this is probably much less expensive than the GNN inference itself.
     result = deepcopy(features_yaml)
     for object_ in result["objects"]:
-        object_["stroke_graph"]["concept_name"] = predicted_object
+        if len(predicted_objects) == 1:
+            object_["stroke_graph"]["concept_name"] = predicted_objects[0]
+            if "concept_names" in object_["stroke_graph"]:
+                del object_["stroke_graph"]["concept_names"]
+        else:
+            object_["stroke_graph"]["concept_names"] = predicted_objects
+            if "concept_name" in object_["stroke_graph"]:
+                del object_["stroke_graph"]["concept_name"]
     return result
 
 
@@ -50,6 +58,12 @@ def main():
         help="Directory where we should write the feature outputs to. Outputs are structured as if "
         "the output path is a curriculum directory.",
     )
+    parser.add_argument(
+        "--top_k",
+        type=int,
+        default=1,
+        help="Top k decodes to retrieve from the GNN",
+    )
     args = parser.parse_args()
     logging.basicConfig(level=logging.INFO)
 
@@ -129,22 +143,22 @@ def main():
 
         assert outputs.size(0) == curriculum_params["num_dirs"]
 
-        predicted_label_ints = outputs.argmax(dim=1)
+        _, predicted_label_ints = outputs.topk(args.top_k)
         n_saved = 0
         for situation_num in range(curriculum_params["num_dirs"]):
             input_situation_dir = args.curriculum_path / f"situation_{situation_num}"
             feature_yamls = sorted(input_situation_dir.glob("feature*"))
             if len(feature_yamls) == 1:
                 # Load features, update them, then save
                 with open(
-                    input_situation_dir / feature_yamls[0], encoding="utf-8"
+                    feature_yamls[0], encoding="utf-8"
                 ) as feature_yaml_in:
                     features = yaml.safe_load(feature_yaml_in)
 
                 updated_features = update_features_yaml(
                     features,
-                    predicted_object=STRING_OBJECT_LABELS[
-                        predicted_label_ints[situation_num]
+                    predicted_object=[STRING_OBJECT_LABELS[
+                        predicted_label_ints[situation_num][i]] for i in range(args.top_k)
                     ],
                 )
 

adam_preprocessing/shape_stroke_graph_learner.py

@@ -161,8 +161,7 @@ def main():
         logging.info(
             "{}, loss: {:.4e}, acc: {}, test acc :{}".format(
                 step, train_loss.item(), acc, test_acc
-            ),
-            flush=True,
+            )
         )
     logging.info("Best test acc is {}".format(best_acc))
     logging.info(f"Saving model state dict to {args.save_model_to}")

adam_preprocessing/utils.py

@@ -66,7 +66,7 @@ def get_stroke_data(
         feature_yamls = sorted(situation_dir.glob("feature*"))
         if len(feature_yamls) == 1:
             with open(
-                situation_dir / feature_yamls[0], encoding="utf-8"
+                feature_yamls[0], encoding="utf-8"
             ) as feature_yaml_in:
                 features = yaml.safe_load(feature_yaml_in)
 

parameters/experiments/p3/m5_objects_v0_with_mugs_full_subset.params

@@ -0,0 +1,60 @@
+_includes:
+   - "../../root.params"
+
+# Learner Configuration
+learner: simulated-integrated-learner-params
+object_learner:
+    learner_type: "subset"
+    ontology: "phase3"
+    min_continuous_feature_match_score: 0.05
+attribute_learner:
+    learner_type: "none"
+relation_learner:
+    learner_type: "none"
+action_learner:
+    learner_type: "none"
+plural_learner:
+    learner_type: "none"
+affordance_learner:
+ learner_type: "none"
+include_functional_learner: false
+include_generics_learner: false
+suppress_error: false
+
+# Curriculum Configuration
+curriculum: "phase3"
+train_curriculum:
+    curriculum_type: "training"
+    curriculum: "m5_objects_v0_with_mugs_post_gnn"
+    color_is_rgb: True
+test_curriculum:
+    curriculum_type: "testing"
+    curriculum: "m5_objects_v0_with_mugs_eval_post_gnn"
+    color_is_rgb: True
+
+# Experiment Configuration
+experiment: "m5_objects_v0_with_mugs_full"
+experiment_group_dir: '%adam_experiment_root%/learners/%learner%/experiments/%train_curriculum.curriculum%/'
+log_learner_state: true
+experiment_type: simulated
+
+# Hypothesis Logging
+hypothesis_log_dir: "%experiment_group_dir%/hypotheses"
+log_hypothesis_every_n_steps: 250
+
+# Debug Configuration
+debug_log_directory: "%experiment_group_dir%/graphs"
+debug_perception_log_dir: "%experiment_group_dir%/perception_graphs"
+
+# Observer Params
+post_observer:
+    experiment_output_path: "%experiment_group_dir%"
+    copy_curriculum: true
+    file_name: "post_decode"
+
+test_observer:
+    experiment_output_path: "%post_observer.experiment_output_path%/test_curriculums/%test_curriculum.curriculum%/"
+    copy_curriculum: true
+    file_name: "post_decode"
+    calculate_accuracy_by_language: true
+    calculate_overall_accuracy: true

parameters/experiments/p3/m6_objects_v0_with_mugs_10pertype_subset.params

@@ -0,0 +1,60 @@
+_includes:
+   - "../../root.params"
+
+# Learner Configuration
+learner: simulated-integrated-learner-params
+object_learner:
+    learner_type: "subset"
+    ontology: "phase3"
+    min_continuous_feature_match_score: 0.05
+attribute_learner:
+    learner_type: "none"
+relation_learner:
+    learner_type: "none"
+action_learner:
+    learner_type: "none"
+plural_learner:
+    learner_type: "none"
+affordance_learner:
+ learner_type: "none"
+include_functional_learner: false
+include_generics_learner: false
+suppress_error: false
+
+# Curriculum Configuration
+curriculum: "phase3"
+train_curriculum:
+    curriculum_type: "training"
+    curriculum: "m6_objects_downsampled_10pertype_post_gnn"
+    color_is_rgb: True
+test_curriculum:
+    curriculum_type: "testing"
+    curriculum: "m5_objects_v0_with_mugs_eval_10pertype_post_gnn"
+    color_is_rgb: True
+
+# Experiment Configuration
+experiment: "m6_objects_downsampled_10pertype"
+experiment_group_dir: '%adam_experiment_root%/learners/%learner%/experiments/%train_curriculum.curriculum%/'
+log_learner_state: true
+experiment_type: simulated
+
+# Hypothesis Logging
+hypothesis_log_dir: "%experiment_group_dir%/hypotheses"
+log_hypothesis_every_n_steps: 250
+
+# Debug Configuration
+debug_log_directory: "%experiment_group_dir%/graphs"
+debug_perception_log_dir: "%experiment_group_dir%/perception_graphs"
+
+# Observer Params
+post_observer:
+    experiment_output_path: "%experiment_group_dir%"
+    copy_curriculum: true
+    file_name: "post_decode"
+
+test_observer:
+    experiment_output_path: "%post_observer.experiment_output_path%/test_curriculums/%test_curriculum.curriculum%/"
+    copy_curriculum: true
+    file_name: "post_decode"
+    calculate_accuracy_by_language: true
+    calculate_overall_accuracy: true

parameters/experiments/p3/m6_objects_v0_with_mugs_2pertype_subset.params

@@ -0,0 +1,60 @@
+_includes:
+   - "../../root.params"
+
+# Learner Configuration
+learner: simulated-integrated-learner-params
+object_learner:
+    learner_type: "subset"
+    ontology: "phase3"
+    min_continuous_feature_match_score: 0.05
+attribute_learner:
+    learner_type: "none"
+relation_learner:
+    learner_type: "none"
+action_learner:
+    learner_type: "none"
+plural_learner:
+    learner_type: "none"
+affordance_learner:
+ learner_type: "none"
+include_functional_learner: false
+include_generics_learner: false
+suppress_error: false
+
+# Curriculum Configuration
+curriculum: "phase3"
+train_curriculum:
+    curriculum_type: "training"
+    curriculum: "m6_objects_downsampled_2pertype_post_gnn"
+    color_is_rgb: True
+test_curriculum:
+    curriculum_type: "testing"
+    curriculum: "m5_objects_v0_with_mugs_eval_2pertype_post_gnn"
+    color_is_rgb: True
+
+# Experiment Configuration
+experiment: "m6_objects_downsampled_2pertype"
+experiment_group_dir: '%adam_experiment_root%/learners/%learner%/experiments/%train_curriculum.curriculum%/'
+log_learner_state: true
+experiment_type: simulated
+
+# Hypothesis Logging
+hypothesis_log_dir: "%experiment_group_dir%/hypotheses"
+log_hypothesis_every_n_steps: 250
+
+# Debug Configuration
+debug_log_directory: "%experiment_group_dir%/graphs"
+debug_perception_log_dir: "%experiment_group_dir%/perception_graphs"
+
+# Observer Params
+post_observer:
+    experiment_output_path: "%experiment_group_dir%"
+    copy_curriculum: true
+    file_name: "post_decode"
+
+test_observer:
+    experiment_output_path: "%post_observer.experiment_output_path%/test_curriculums/%test_curriculum.curriculum%/"
+    copy_curriculum: true
+    file_name: "post_decode"
+    calculate_accuracy_by_language: true
+    calculate_overall_accuracy: true

parameters/experiments/p3/m6_objects_v0_with_mugs_top2.params

@@ -0,0 +1,60 @@
+_includes:
+   - "../../root.params"
+
+# Learner Configuration
+learner: simulated-integrated-learner-params
+object_learner:
+    learner_type: "subset"
+    ontology: "phase3"
+    min_continuous_feature_match_score: 0.05
+attribute_learner:
+    learner_type: "none"
+relation_learner:
+    learner_type: "none"
+action_learner:
+    learner_type: "none"
+plural_learner:
+    learner_type: "none"
+affordance_learner:
+ learner_type: "none"
+include_functional_learner: false
+include_generics_learner: false
+suppress_error: false
+
+# Curriculum Configuration
+curriculum: "phase3"
+train_curriculum:
+    curriculum_type: "training"
+    curriculum: "m6_objects_v0_with_mugs_post_gnn_top2"
+    color_is_rgb: True
+test_curriculum:
+    curriculum_type: "testing"
+    curriculum: "m6_objects_v0_with_mugs_eval_post_gnn_top2"
+    color_is_rgb: True
+
+# Experiment Configuration
+experiment: "m6_unknown_objects"
+experiment_group_dir: '%adam_experiment_root%/learners/%learner%/experiments/%train_curriculum.curriculum%/'
+log_learner_state: true
+experiment_type: simulated
+
+# Hypothesis Logging
+hypothesis_log_dir: "%experiment_group_dir%/hypotheses"
+log_hypothesis_every_n_steps: 250
+
+# Debug Configuration
+debug_log_directory: "%experiment_group_dir%/graphs"
+debug_perception_log_dir: "%experiment_group_dir%/perception_graphs"
+
+# Observer Params
+post_observer:
+    experiment_output_path: "%experiment_group_dir%"
+    copy_curriculum: true
+    file_name: "post_decode"
+
+test_observer:
+    experiment_output_path: "%post_observer.experiment_output_path%/test_curriculums/%test_curriculum.curriculum%/"
+    copy_curriculum: true
+    file_name: "post_decode"
+    calculate_accuracy_by_language: true
+    calculate_overall_accuracy: true