got rid of "postbin" from trainer configuration file (temporarily pla…

…ced under classifier config yaml)

app.py

@@ -76,6 +76,9 @@ def _annotate(self, mmif: Union[str, dict, Mmif], **parameters) -> Mmif:
             configs['model_file'] = default_model_storage / f'{parameters["modelName"]}.pt'
             # model files from k-fold training have the fold number as three-digit suffix, trim it
             configs['model_config_file'] = default_model_storage / f'{parameters["modelName"][:-4]}_config.yml'
+            # TODO (krim @ 2024-03-14): make this into a runtime parameter once 
+            #  https://github.com/clamsproject/clams-python/issues/197 is resolved
+            configs['postbin'] = configs['postbins'].get(parameters['modelName'], None)
         t = time.perf_counter()
         self.logger.info(f"Initiating classifier with {configs['model_file']}")
         if self.logger.isEnabledFor(logging.DEBUG):
@@ -117,8 +120,8 @@ def _annotate(self, mmif: Union[str, dict, Mmif], **parameters) -> Mmif:
         if self.logger.isEnabledFor(logging.DEBUG):
             self.logger.debug(f"Processing took {time.perf_counter() - t} seconds")
 
-        new_view.new_contain(AnnotationTypes.TimePoint, 
-                             document=vd.id, timeUnit='milliseconds', labelset=self.classifier.postbin_labels)
+        new_view.new_contain(AnnotationTypes.TimePoint,
+                             document=vd.id, timeUnit='milliseconds', labelset=self.stitcher.stitch_label)
 
         for prediction in predictions:
             timepoint_annotation = new_view.new_annotation(AnnotationTypes.TimePoint)
@@ -143,18 +146,6 @@ def _annotate(self, mmif: Union[str, dict, Mmif], **parameters) -> Mmif:
                                               [p.annotation.id for p in tf.representative_predictions()])
         return mmif
 
-    @staticmethod
-    def _transform(classification: dict, bins: dict):
-        """Take the raw classification and turn it into a classification of user
-        labels. Also includes modeling.negative_label."""
-        # TODO: this may not work when there is pre-binning
-        transformed = {}
-        for postlabel in bins['post'].keys():
-            score = sum([classification[lbl] for lbl in bins['post'][postlabel]])
-            transformed[postlabel] = score
-        transformed[negative_label] = 1 - sum(transformed.values())
-        return transformed
-
 
 if __name__ == "__main__":
 

modeling/classify.py

@@ -44,16 +44,15 @@ def __init__(self, **config):
         self.config = config
         self.model_config = yaml.safe_load(open(config["model_config_file"]))
         self.prebin_labels = train.pretraining_binned_label(self.model_config)
-        self.postbin_labels = train.post_bin_label_names(self.model_config)
         self.featurizer = data_loader.FeatureExtractor(
             img_enc_name=self.model_config["img_enc_name"],
             pos_enc_name=self.model_config.get("pos_enc_name", None),
             pos_enc_dim=self.model_config.get("pos_enc_dim", 0),
             max_input_length=self.model_config.get("max_input_length", 0),
             pos_unit=self.model_config.get("pos_unit", 0))
         label_count = len(FRAME_TYPES) + 1
-        if 'pre' in self.model_config['bins']:
-            label_count = len(self.model_config['bins']['pre'].keys()) + 1
+        if 'bins' in self.model_config:
+            label_count = len(self.model_config['pre'].keys()) + 1
         self.classifier = train.get_net(
             in_dim=self.featurizer.feature_vector_dim(),
             n_labels=label_count,

modeling/config/classifier.yml

@@ -18,4 +18,77 @@ minFrameCount: 2
 staticFrames: [bars, slate, chyron]
 
 # Set to False to turn off the stitcher
-useStitcher: True
+useStitcher: True
+
+postbins:
+  20240126-180026.convnext_lg.kfold_000:
+    bars:
+      - B
+    slate:
+      - S
+      - S:H
+      - S:C
+      - S:D
+      - S:G
+    chyron:
+      - I
+      - N
+      - Y
+    credits:
+      - C
+  20240212-131937.convnext_tiny.kfold_000: 
+    bars:
+      - "B"
+    slate:
+      - "S"
+      - "S:H"
+      - "S:C"
+      - "S:D"
+      - "S:G"
+    other_opening:
+      - "W"
+      - "L"
+      - "O"
+      - "M"
+    chyron:
+      - "I"
+      - "N"
+      - "Y"
+    credit:
+      - "C"
+      - "R"
+    other_text:
+      - "E"
+      - "K"
+      - "G"
+      - 'T'
+      - 'F'
+  20240212-132306.convnext_lg.kfold_000:
+    bars:
+      - "B"
+    slate:
+      - "S"
+      - "S:H"
+      - "S:C"
+      - "S:D"
+      - "S:G"
+    other_opening:
+      - "W"
+      - "L"
+      - "O"
+      - "M"
+    chyron:
+      - "I"
+      - "N"
+      - "Y"
+    credit:
+      - "C"
+      - "R"
+    other_text:
+      - "E"
+      - "K"
+      - "G"
+      - 'T'
+      - 'F'
+
+

modeling/models/20240126-180026.convnext_lg.kfold_config.yml

@@ -31,19 +31,3 @@ block_guids_valid:
 - cpb-aacip-512-4b2x34nt7g
 - cpb-aacip-512-3n20c4tr34
 - cpb-aacip-512-3f4kk9534t
-bins:
-  post:
-    bars:
-    - B
-    slate:
-    - S
-    - S:H
-    - S:C
-    - S:D
-    - S:G
-    chyron:
-    - I
-    - N
-    - Y
-    credits:
-    - C

modeling/models/20240212-131937.convnext_tiny.kfold_config.yml

@@ -39,32 +39,3 @@ pos_enc_dim: 512
 # cpb-aacip-259-4j09zf95    Duration: 01:33:59.57, start: 0.000000, bitrate: 852 kb/s
 # 94 mins = 5640 secs = 5640000 ms
 max_input_length: 5640000
-
-bins:
-  post:
-    bars:
-      - "B"
-    slate:
-      - "S"
-      - "S:H"
-      - "S:C"
-      - "S:D"
-      - "S:G"
-    other_opening:
-      - "W"
-      - "L"
-      - "O"
-      - "M"
-    chyron:
-      - "I"
-      - "N"
-      - "Y"
-    credit:
-      - "C"
-      - "R"
-    other_text:
-      - "E"
-      - "K"
-      - "G"
-      - 'T'
-      - 'F'

modeling/models/20240212-132306.convnext_lg.kfold_config.yml

@@ -39,32 +39,3 @@ pos_enc_dim: 512
 # cpb-aacip-259-4j09zf95    Duration: 01:33:59.57, start: 0.000000, bitrate: 852 kb/s
 # 94 mins = 5640 secs = 5640000 ms
 max_input_length: 5640000
-
-bins:
-  post:
-    bars:
-      - "B"
-    slate:
-      - "S"
-      - "S:H"
-      - "S:C"
-      - "S:D"
-      - "S:G"
-    other_opening:
-      - "W"
-      - "L"
-      - "O"
-      - "M"
-    chyron:
-      - "I"
-      - "N"
-      - "Y"
-    credit:
-      - "C"
-      - "R"
-    other_text:
-      - "E"
-      - "K"
-      - "G"
-      - 'T'
-      - 'F'

modeling/stitch.py

@@ -26,9 +26,8 @@ def __init__(self, **config):
         self.min_timeframe_score = config.get("minTimeframeScore")
         self.min_frame_count = config.get("minFrameCount")
         self.static_frames = self.config.get("staticFrames")
-        self.prebin_labels = train.pretraining_binned_label(self.model_config)
-        self.postbin_labels = train.post_bin_label_names(self.model_config)
-        self.use_postbinning = "post" in self.model_config["bins"]
+        self.model_label = train.pretraining_binned_label(self.model_config)
+        self.stitch_label = config.get("postbin")
         self.debug = False
 
     def __str__(self):
@@ -38,8 +37,8 @@ def __str__(self):
 
     def create_timeframes(self, predictions: list) -> list:
         if self.debug:
-            print('pre-bin labels', self.prebin_labels)
-            print('post-bin labels', self.postbin_labels)
+            print('TimePoint labels', self.model_label)
+            print('TimeFrame labels', list(self.stitch_label.keys()))
         timeframes = self.collect_timeframes(predictions)
         if self.debug:
             print_timeframes('Collected frames', timeframes)
@@ -57,7 +56,7 @@ def create_timeframes(self, predictions: list) -> list:
     def collect_timeframes(self, predictions: list) -> list:
         """Find sequences of frames for all labels where the score of each frame
         is at least the mininum value as defined in self.min_frame_score."""
-        labels = self.postbin_labels if self.use_postbinning else self.prebin_labels
+        labels = self.stitch_label if self.stitch_label is not None else self.model_label
         if self.debug:
             print('>>> labels', labels)
         timeframes = []
@@ -112,11 +111,10 @@ def is_included(self, frame, outlawed_timepoints: set) -> bool:
     def _score_for_label(self, label: str, prediction):
         """Return the score for the label, this is somewhat more complicated when
         postbinning is used."""
-        if not self.use_postbinning:
+        if self.stitch_label is None:
             return prediction.score_for_label(label)
         else:
-            postbins = self.model_config['bins']['post']
-            return prediction.score_for_labels(postbins[label])
+            return prediction.score_for_labels(self.stitch_label[label])
 
 
 class TimeFrame:

modeling/train.py

@@ -234,14 +234,6 @@ def pretraining_binned_label(config):
     return modeling.FRAME_TYPES + [modeling.negative_label]
 
 
-def post_bin_label_names(config):
-    post_labels = list(config["bins"].get("post", {}).keys())
-    if post_labels:
-        return post_labels + [modeling.negative_label]
-    else:
-        return pretraining_binned_label(config)
-
-
 def train_model(model, loss_fn, device, train_loader, configs):
     since = time.perf_counter()
     optimizer = torch.optim.Adam(model.parameters(), lr=0.001)