Training updates

pirequirements.txt

@@ -9,7 +9,7 @@ scipy==1.9.3
 python-dateutil
 scikit-learn==1.1.3
 tables==3.8.0
-h5py==3.8.0
+h5py==3.10.0
 pyyaml==6.0
 pillow==10.0.1
 attrs==24.2.0
@@ -26,4 +26,4 @@ dbus-python==1.3.2
 importlib_resources==5.10.2
 opencv-python==4.8.0.76
 inotify_simple==1.3.5
-python-cptv==0.0.3
+python-cptv==0.0.5

pyproject.toml

@@ -48,7 +48,7 @@ dependencies = [
   "importlib_resources==5.10.2",
   "opencv-python==4.8.0.76",
   "inotify_simple==1.3.5",
-  "python-cptv==0.0.3"
+  "python-cptv==0.0.5"
 ]
 
 [project.scripts]

requirements.txt

@@ -1,4 +1,4 @@
-tensorflow~=2.14.0
+tensorflow~=2.17.0
 matplotlib~=3.0
 pytz
 cptv~=1.5.4
@@ -7,7 +7,7 @@ scipy
 python-dateutil
 scikit-learn
 tables~=3.8.0
-h5py~=3.9.0
+h5py~=3.10.0
 pyyaml>=4.2b1
 pillow~=10.0.1
 attrs~=24.2.0
@@ -26,4 +26,4 @@ joblib
 #requires sudo apt-get install libopencv-dev used for ir track extraction on server
 # pybgs==3.2.0.post1 this was used for ir
 inotify_simple==1.3.5
-python-cptv==0.0.3
+python-cptv==0.0.5

src/autobuild-cron

@@ -0,0 +1,5 @@
+#run the first of every month
+SHELL=/bin/bash
+BASH_ENV=~/.bashrc_conda
+
+* * 1 * * cp ( cd /home/cp/cacophony/classifier-pipeline/src && ./autobuild.sh /data2/cptv-files) 2>&1 | logger --tag classifier-auto-build

src/autobuild.sh

@@ -1,11 +1,15 @@
-#!/bin/sh
-
+#!/bin/bash
 set -e
 set -x
+conda init bash
+conda activate tf
 config="classifier-thermal.yaml"
-month_ago=$(python3 rebuildDate.py -c $config)
+echo "Saving into $1"
+month_ago=$(python3 rebuildDate.py $1)
 echo $month_ago
-python3 ../../cptv-download/cptv-download.py -l 0 -i 'poor tracking' -i 'untagged' -i 'part' -i 'untagged-by-humans' -i 'unknown' -i 'unidentified' -m 'human-tagged' --start-date "$month_ago" "../clips$month_ago" [email protected] userpassword
-echo "Downloading into ../clips$month_ago"
-python3 load.py -target "../clips$month_ago"  -c $config
-python3 build.py -c $config
+python3 ../../cptv-download/cptv-download.py -l 0 -i 'poor tracking' -i 'untagged' -i 'part' -i 'untagged-by-humans' -i 'unknown' -i 'unidentified' -m 'human-tagged' --start-date "$month_ago" "$1" [email protected] userpassword
+echo "Downloading into $1"
+python3 build.py -c $config --ext ".cptv" $1
+dt=$(date '+%d%m%Y-%H%M%S');
+export XLA_FLAGS=--xla_gpu_cuda_data_dir=/home/cp/miniconda3/envs/tf/lib/
+python3 train.py -c $config $dt 

src/build.py

@@ -19,8 +19,8 @@
 from ml_tools.tfwriter import create_tf_records
 from ml_tools.irwriter import save_data as save_ir_data
 from ml_tools.thermalwriter import save_data as save_thermal_data
-
-
+from ml_tools.tools import CustomJSONEncoder
+import attrs
 import numpy as np
 
 from pathlib import Path
@@ -57,7 +57,7 @@ def parse_args():
     )
     parser.add_argument("--split-file", help="Json file defining a split")
     parser.add_argument(
-        "--ext", default=".hdf5", help="Extension of files to load .mp4,.cptv,.hdf5"
+        "--ext", default=".cptv", help="Extension of files to load .mp4,.cptv,.hdf5"
     )
 
     parser.add_argument("-c", "--config-file", help="Path to config file to use")
@@ -571,7 +571,7 @@ def add_samples(
     dataset.add_samples(samples)
 
 
-def validate_datasets(datasets, test_bins, date):
+def validate_datasets(datasets, test_bins, after_date):
     # check that clips are only in one dataset
     # that only test set has clips after date
     # that test set is the only dataset with test_clips
@@ -580,7 +580,7 @@ def validate_datasets(datasets, test_bins, date):
     #     for track in dataset.tracks:
     #         assert track.start_time < date
 
-    for i, dataset in enumerate(datasets):
+    for i, dataset in enumerate(datasets[:2]):
         dont_check = set(
             [
                 sample.bin_id
@@ -608,6 +608,15 @@ def validate_datasets(datasets, test_bins, date):
                     if sample.label in split_by_clip
                 ]
             )
+            if other.name == "test" and after_date is not None:
+                dont_check_other = set(
+                    [
+                        sample.bin_id
+                        for sample in other.samples_by_id.values()
+                        if sample.rec_time > after_date
+                    ]
+                )
+                dont_check = dont_check | dont_check_other
             other_bins = set([sample.bin_id for sample in other.samples_by_id.values()])
             other_bins = other_bins - dont_check
             other_clips = set(
@@ -717,6 +726,42 @@ def dump_split_ids(datasets, out_file="datasplit.json"):
     return
 
 
+def rough_balance(datasets):
+    dev_threshold = 2000
+    logging.info("Roughly Balancing")
+    print_counts(*datasets)
+
+    for dataset in datasets:
+        lbl_counts = {}
+        counts = []
+        for label in dataset.labels:
+            label_count = len(dataset.samples_by_label.get(label, []))
+            lbl_counts[label] = label_count
+            counts.append(label_count)
+        counts.sort()
+        std_dev = np.std(counts)
+        logging.info("Counts are %s std dev %s", counts, std_dev)
+        if std_dev < dev_threshold or len(counts) <= 1:
+            logging.info("Not balancing")
+            continue
+        if len(counts) <= 2:
+            cap_at = counts[-2]
+        elif len(counts) < 7:
+            cap_at = counts[-2]
+        else:
+            cap_at = counts[-2]
+        logging.info("Capping dataset %s at %s", dataset.name, cap_at)
+        for lbl, count in lbl_counts.items():
+            if count <= cap_at:
+                continue
+            samples_to_remove = count - cap_at
+            by_labels = dataset.samples_by_label[lbl]
+            np.random.shuffle(by_labels)
+            for i in range(samples_to_remove):
+                dataset.remove_sample(by_labels[i])
+    print_counts(*datasets)
+
+
 def main():
     init_logging()
     args = parse_args()
@@ -782,6 +827,8 @@ def main():
         print("Splitting data set into train / validation")
 
         datasets = split_randomly(master_dataset, config, args.date, test_clips)
+
+        rough_balance(datasets)
         validate_datasets(datasets, test_clips, args.date)
         dump_split_ids(datasets, record_dir / "datasplit.json")
 
@@ -849,15 +896,20 @@ def main():
                     {
                         "segment_frame_spacing": master_dataset.segment_spacing * 9,
                         "segment_width": master_dataset.segment_length,
-                        "segment_type": master_dataset.segment_type,
+                        "segment_types": master_dataset.segment_types,
                         "segment_min_avg_mass": master_dataset.segment_min_avg_mass,
                         "max_segments": master_dataset.max_segments,
                         "dont_filter_segment": True,
                         "skip_ffc": True,
-                        "tag_precedence": config.load.tag_precedence,
+                        "tag_precedence": config.build.tag_precedence,
                         "min_mass": master_dataset.min_frame_mass,
+                        "thermal_diff_norm": config.build.thermal_diff_norm,
+                        "filter_by_lq": master_dataset.filter_by_lq,
+                        "max_frames": master_dataset.max_frames,
                     }
                 )
+            # dont filter the test set,
+            extra_args["filter_by_fp"] = dataset.name != "test"
             create_tf_records(
                 dataset,
                 dir,
@@ -879,10 +931,12 @@ def main():
         "type": config.train.type,
         "counts": dataset_counts,
         "by_label": False,
+        "config": attrs.asdict(config),
+        "segment_types": master_dataset.segment_types,
     }
 
     with open(meta_filename, "w") as f:
-        json.dump(meta_data, f, indent=4)
+        json.dump(meta_data, f, indent=4, cls=CustomJSONEncoder)
 
 
 if __name__ == "__main__":

src/classify/clipclassifier.py

@@ -11,13 +11,8 @@
 from track.clip import Clip
 from track.cliptrackextractor import ClipTrackExtractor, is_affected_by_ffc
 from ml_tools import tools
-from ml_tools.kerasmodel import KerasModel
 from track.irtrackextractor import IRTrackExtractor
 from ml_tools.previewer import Previewer
-from track.track import Track
-
-from cptv import CPTVReader
-from datetime import datetime
 from ml_tools.interpreter import get_interpreter
 
 
@@ -134,7 +129,7 @@ def process_file(self, filename, cache=None, reuse_frames=None):
         clip = Clip(track_extractor.config, filename)
         clip.load_metadata(
             meta_data,
-            self.config.load.tag_precedence,
+            self.config.build.tag_precedence,
         )
         track_extractor.parse_clip(clip)
 
@@ -250,7 +245,6 @@ def save_metadata(
                 prediction = predictions.prediction_for(track.get_id())
                 if prediction is None:
                     continue
-
                 prediction_meta = prediction.get_metadata()
                 prediction_meta["model_id"] = model_id
                 prediction_info.append(prediction_meta)

src/classify/trackprediction.py

@@ -79,6 +79,9 @@ def clarity(self):
         best = np.argsort(self.prediction)
         return self.prediction[best[-1]] - self.prediction[best[-2]]
 
+    def __str__(self):
+        return f"{self.frames} conf: {np.round(100*self.prediction)}"
+
 
 class TrackPrediction:
     """
@@ -107,18 +110,23 @@ def __init__(self, track_id, labels, keep_all=True, start_frame=None):
         self.masses = []
 
     def classified_clip(
-        self, predictions, smoothed_predictions, prediction_frames, top_score=None
+        self,
+        predictions,
+        smoothed_predictions,
+        prediction_frames,
+        masses,
+        top_score=None,
     ):
         self.num_frames_classified = len(predictions)
-        for prediction, smoothed_prediction, frames in zip(
-            predictions, smoothed_predictions, prediction_frames
+        for prediction, smoothed_prediction, frames, mass in zip(
+            predictions, smoothed_predictions, prediction_frames, masses
         ):
             prediction = Prediction(
                 prediction,
                 smoothed_prediction,
                 frames,
                 np.amax(frames),
-                None,
+                mass,
             )
             self.predictions.append(prediction)
 
@@ -162,11 +170,10 @@ def classified_frames(self, frame_numbers, predictions, mass):
             self.class_best_score += smoothed_prediction
 
     def classified_frame(self, frame_number, predictions, mass):
-        self.prediction_frames.append([frame_number])
         self.last_frame_classified = frame_number
         self.num_frames_classified += 1
         self.masses.append(mass)
-        smoothed_prediction = prediction * prediction * mass
+        smoothed_prediction = predictions**2 * mass
 
         prediction = Prediction(
             predictions,

src/config/buildconfig.py

@@ -22,6 +22,7 @@
 import logging
 from os import path
 from .defaultconfig import DefaultConfig
+from ml_tools.rectangle import Rectangle
 
 
 @attr.s
@@ -34,6 +35,45 @@ class BuildConfig(DefaultConfig):
     min_frame_mass = attr.ib()
     filter_by_lq = attr.ib()
     max_segments = attr.ib()
+    thermal_diff_norm = attr.ib()
+    tag_precedence = attr.ib()
+    excluded_tags = attr.ib()
+    country = attr.ib()
+    use_segments = attr.ib()
+    max_frames = attr.ib()
+
+    EXCLUDED_TAGS = ["poor tracking", "part", "untagged", "unidentified"]
+    NO_MIN_FRAMES = ["stoat", "mustelid", "weasel", "ferret"]
+    # country bounding boxs
+    COUNTRY_LOCATIONS = {
+        "AU": Rectangle.from_ltrb(
+            113.338953078, -10.6681857235, 153.569469029, -43.6345972634
+        ),
+        "NZ": Rectangle.from_ltrb(
+            166.509144322, -34.4506617165, 178.517093541, -46.641235447
+        ),
+    }
+
+    DEFAULT_GROUPS = {
+        0: [
+            "bird",
+            "false-positive",
+            "hedgehog",
+            "possum",
+            "rodent",
+            "mustelid",
+            "cat",
+            "kiwi",
+            "dog",
+            "leporidae",
+            "human",
+            "insect",
+            "pest",
+        ],
+        1: ["unidentified", "other"],
+        2: ["part", "bad track"],
+        3: ["default"],
+    }
 
     @classmethod
     def load(cls, build):
@@ -46,6 +86,12 @@ def load(cls, build):
             min_frame_mass=build["min_frame_mass"],
             filter_by_lq=build["filter_by_lq"],
             max_segments=build["max_segments"],
+            thermal_diff_norm=build["thermal_diff_norm"],
+            tag_precedence=build["tag_precedence"],
+            excluded_tags=build["excluded_tags"],
+            country=build["country"],
+            use_segments=build["use_segments"],
+            max_frames=build["max_frames"],
         )
 
     @classmethod
@@ -58,7 +104,13 @@ def get_defaults(cls):
             segment_min_avg_mass=10,
             min_frame_mass=10,
             filter_by_lq=False,
-            max_segments=5,
+            max_segments=3,
+            thermal_diff_norm=False,
+            tag_precedence=BuildConfig.DEFAULT_GROUPS,
+            excluded_tags=BuildConfig.EXCLUDED_TAGS,
+            country=None,
+            use_segments=True,
+            max_frames=75,
         )
 
     def validate(self):