Merge branch 'f/rewrite_subsystem' into 'main'

Target Hardware Decription Proposal

See merge request es/ai/hannah/hannah!365

.gitlab-ci.yml

@@ -27,6 +27,7 @@ variables:
   PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip"
   POETRY_HOME: "$CI_PROJECT_DIR/.poetry"
   POETRY_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pypoetry"
+  POETRY_VIRTUALENVS_CREATE: false
   GIT_SUBMODULE_STRATEGY: recursive
   DEBIAN_FRONTEND: "noninteractive"
 

experiments/rhode_island/model/localization_net.yaml

@@ -26,10 +26,10 @@ qconfig:
   _target_: hannah.models.factory.qconfig.get_trax_qat_qconfig
   config:
     bw_b: 8
-    bw_w: 6
+    bw_w: 4
     bw_f: 8
     power_of_2: false  # Use power of two quantization for weights
-    noise_prob: 0.7   # Probability of quantizing a value during training
+    noise_prob: 0.9   # Probability of quantizing a value during training
 conv:
   - target: forward
     stride: 1

hannah/__init__.py

@@ -1,8 +1,8 @@
 #
-# Copyright (c) 2022 University of Tübingen.
+# Copyright (c) 2024 Hannah contributors.
 #
 # This file is part of hannah.
-# See https://atreus.informatik.uni-tuebingen.de/ties/ai/hannah/hannah for further info.
+# See https://github.com/ekut-es/hannah for further info.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,3 +16,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+
+
+try:
+    from beartype.claw import beartype_this_package
+
+    beartype_this_package()
+except ImportError:
+    pass  # beartype is not installed in production environment

hannah/callbacks/optimization.py

@@ -1,8 +1,8 @@
 #
-# Copyright (c) 2022 University of Tübingen.
+# Copyright (c) 2024 Hannah contributors.
 #
 # This file is part of hannah.
-# See https://atreus.informatik.uni-tuebingen.de/ties/ai/hannah/hannah for further info.
+# See https://github.com/ekut-es/hannah for further info.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -20,6 +20,7 @@
 from collections import defaultdict
 from typing import Any, Iterable, List, Mapping, Union
 
+import pytorch_lightning as pl
 from pytorch_lightning.callbacks import Callback
 from torch import Tensor
 
@@ -94,8 +95,15 @@ def _add_monitor_mapping(self, monitor):
         else:
             self.directions.append(-1.0)
 
-    def on_train_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule",  outputs: 'STEP_OUTPUT', batch: Any, batch_idx: int) -> None:  # noqa: F821
-        callback_metrics =  trainer.callback_metrics
+    def on_train_batch_end(
+        self,
+        trainer: "pl.Trainer",
+        pl_module: "pl.LightningModule",
+        outputs: Any,
+        batch: Any,
+        batch_idx: int,
+    ) -> None:
+        callback_metrics = trainer.callback_metrics
 
         for k, v in callback_metrics.items():
             if k.startswith("train"):
@@ -112,7 +120,7 @@ def on_train_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModu
                 monitor_val = callback_metrics[monitor] * direction
                 if monitor.startswith("train"):
                     self._curves[monitor][trainer.global_step] = monitor_val
-                    
+
                     self.values[monitor] = monitor_val
 
     def on_test_end(self, trainer, pl_module):
@@ -156,7 +164,7 @@ def on_validation_end(self, trainer, pl_module):
         # Skip evaluation of validation metrics during sanity check
         if trainer.sanity_checking:
             return
-        
+
         callback_metrics = trainer.callback_metrics
 
         for k, v in callback_metrics.items():
@@ -168,7 +176,7 @@ def on_validation_end(self, trainer, pl_module):
                 try:
                     monitor_val = float(callback_metrics[monitor])
                     directed_monitor_val = monitor_val * direction
-                   
+
                     self.values[monitor] = directed_monitor_val
                     self._curves[monitor][trainer.global_step] = directed_monitor_val
                 except Exception:
@@ -230,4 +238,3 @@ def curves(self, dict=False):
             return list(return_values.values())[0]
 
         return return_values
-

hannah/conf/dataset/sensor/naneye-raw.yaml

@@ -0,0 +1,24 @@
+##
+## Copyright (c) 2022 University of Tübingen.
+##
+## This file is part of hannah.
+## See https://atreus.informatik.uni-tuebingen.de/ties/ai/hannah/hannah for further info.
+##
+## Licensed under the Apache License, Version 2.0 (the "License");
+## you may not use this file except in compliance with the License.
+## You may obtain a copy of the License at
+##
+##     http://www.apache.org/licenses/LICENSE-2.0
+##
+## Unless required by applicable law or agreed to in writing, software
+## distributed under the License is distributed on an "AS IS" BASIS,
+## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+## See the License for the specific language governing permissions and
+## limitations under the License.
+##
+
+# Very approximative simulation of a raw sensor image naneya-m with some noise model
+
+name: naneye-m
+resolution: [320,320]
+pattern: GRBG

hannah/conf/dataset/sensor/naneye.yaml

@@ -18,3 +18,4 @@
 ##
 name: naneye-m
 resolution: [320,320]
+pattern: [1,1]

hannah/conf/model/conv-net-2d.yaml

@@ -26,17 +26,17 @@ qconfig:
   _target_: hannah.models.factory.qconfig.get_trax_qat_qconfig
   config:
     bw_b: 8
-    bw_w: 6
+    bw_w: 8
     bw_f: 8
     power_of_2: false  # Use power of two quantization for weights
-    noise_prob: 0.7   # Probability of quantizing a value during training
+    noise_prob: 0.5   # Probability of quantizing a value during training
 conv:
   - target: forward
-    stride: 1
+    stride: 2
     blocks:
       - target: conv2d
         kernel_size: 3
-        act: false
+        act: true
         norm: true
         out_channels: 16
   - target: residual
@@ -46,43 +46,43 @@ conv:
         kernel_size: 3
         act: true
         norm: true
-        out_channels: 24
+        out_channels: 32
       - target: conv2d
         kernel_size: 1
         parallel: true
-        out_channels: 24
+        out_channels: 32
       - target: conv2d
         kernel_size: 3
         act: true
         norm: true
-        out_channels: 24
+        out_channels: 32
   - target: residual
     stride: 2
     blocks:
       - target: conv2d
         kernel_size: 3
         act: true
         norm: true
-        out_channels: 32
+        out_channels: 64
       - target: conv2d
         kernel_size: 3
         act: true
         norm: true
-        out_channels: 32
+        out_channels: 64
   - target: residual
     stride: 2
     blocks:
       - target: conv2d
         kernel_size: 3
         act: true
         norm: true
-        out_channels: 48
+        out_channels: 128
       - target: conv2d
         kernel_size: 1
         parallel: true
-        out_channels: 48
+        out_channels: 128
       - target: conv2d
         kernel_size: 3
         act: true
         norm: true
-        out_channels: 48
+        out_channels: 128

hannah/conf/nas_new.yaml

@@ -26,8 +26,8 @@ defaults:
     - override normalizer: fixedpoint
     - override module: stream_classifier
     - override checkpoint: default
-    - override backend: trax_ut
-    - override nas: aging_evolution_nas
+    #- override backend: trax_ut
+    - override nas: aging_evolution_nas_legacy
     - _self_
 
 experiment_id: test
@@ -39,9 +39,9 @@ trainer:
 
 nas:
   parametrization:
-    backend:
-      cols: [2,4,6,8,16]
-      rows: null
+    #backend:
+    #  cols: [2,4,6,8,16]
+    #  rows: null
     model:
       qconfig:
         config:

hannah/conf/trainer/default.yaml

@@ -34,3 +34,4 @@ plugins: null
 strategy: auto
 reload_dataloaders_every_n_epochs: 0
 precision: 32
+enable_model_summary: False

hannah/datasets/speech.py

@@ -51,7 +51,6 @@ def snr_factor(snr, psig, pnoise):
 
 def _load_audio(file_name, sr=16000, backend="torchaudio"):
     if backend == "torchaudio":
-        torchaudio.set_audio_backend("sox_io")
         try:
             data, samplingrate = torchaudio.load(file_name)
         except Exception as e:
@@ -60,7 +59,6 @@ def _load_audio(file_name, sr=16000, backend="torchaudio"):
             msglogger.warning(
                 "Could not load %s with default backend trying sndfile", str(file_name)
             )
-            torchaudio.set_audio_backend("soundfile")
             data, samplingrate = torchaudio.load(file_name)
         if samplingrate != sr:
             data = torchaudio.transforms.Resample(samplingrate, sr).forward(data)

hannah/models/ai8x/models.py

@@ -115,8 +115,8 @@ def block(
     weight1_quantized = quantize_weight(weight1)
     bias1 = Tensor(
         "b1",
-        (Int(channels)),
-        axis=["O", "I", "kH", "kW"],
+        (Int(channels),),
+        axis=["C"],
         grad=True,
     )
     bias1_quantized = quantize_weight(bias1)

hannah/models/ai8x/models_simplified.py

@@ -101,7 +101,7 @@ def block(input, channels: int, kernel_size: int):
     weight_quantized = quantize_weight(weight)
     bias = Tensor(
         "b1",
-        (Int(channels)),
+        (Int(channels),),
         axis=["O", "I", "kH", "kW"],
         grad=True,
     )