Merge pull request #32 from AmbiqAI/canary

Version 1.5.1
AmbiqAI · Oct 2, 2024 · f9a4e64 · f9a4e64
2 parents aa40aed + 131d307
commit f9a4e64
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diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
@@ -1,11 +1,11 @@
 {
     "name": "Python 3",
-    "image": "mcr.microsoft.com/devcontainers/python:1-3.12",
+    "image": "mcr.microsoft.com/devcontainers/python:1-3.11",
 
     "features": {
         "ghcr.io/devcontainers/features/git:1": {
             "ppa": true,
-            "version": "latest"
+            "version": "system"
         },
         "ghcr.io/devcontainers-contrib/features/pipenv:2": {
             "version": "latest"

diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
@@ -13,7 +13,7 @@ jobs:
         run: pipx install poetry
       - uses: actions/setup-python@v4
         with:
-          python-version: '3.12'
+          python-version: '3.11'
           cache: 'poetry'
       - run: poetry install
       - run: poetry run task lint

diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
@@ -35,7 +35,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.12'
+          python-version: '3.11'
           cache: 'poetry'
       - name: Install and Build 🔧
         env:

diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
@@ -21,7 +21,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.12'
+          python-version: '3.11'
           cache: 'poetry'
       - name: Install and Build 🔧
         run: |

diff --git a/configs/arr-4-eff-lg.json b/configs/arr-4-eff-lg.json
@@ -61,7 +61,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/arr-4-eff-sm.json b/configs/arr-4-eff-sm.json
@@ -61,7 +61,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/beat-2-eff-sm.json b/configs/beat-2-eff-sm.json
@@ -58,7 +58,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/beat-3-eff-sm.json b/configs/beat-3-eff-sm.json
@@ -58,7 +58,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/den-ppg-tcn-lg.json b/configs/den-ppg-tcn-lg.json
@@ -50,7 +50,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/den-ppg-tcn-sm.json b/configs/den-ppg-tcn-sm.json
@@ -50,7 +50,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/den-tcn-lg.json b/configs/den-tcn-lg.json
@@ -59,7 +59,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/den-tcn-sm.json b/configs/den-tcn-sm.json
@@ -59,7 +59,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/fnd-eff-lg.json b/configs/fnd-eff-lg.json
@@ -52,7 +52,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/fnd-eff-sm.json b/configs/fnd-eff-sm.json
@@ -52,7 +52,7 @@
         "qat": false,
         "mode": "FP32",
         "io_type": "float32",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/seg-2-tcn-sm.json b/configs/seg-2-tcn-sm.json
@@ -76,7 +76,7 @@
         "qat": false,
         "mode": "INT8",
         "io_type": "int8",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/seg-4-tcn-lg.json b/configs/seg-4-tcn-lg.json
@@ -77,7 +77,7 @@
         "qat": false,
         "mode": "INT8",
         "io_type": "int8",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/seg-4-tcn-sm.json b/configs/seg-4-tcn-sm.json
@@ -78,7 +78,7 @@
         "qat": false,
         "mode": "INT8",
         "io_type": "int8",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/configs/seg-ppg-2-tcn-sm.json b/configs/seg-ppg-2-tcn-sm.json
@@ -62,7 +62,7 @@
         "qat": false,
         "mode": "INT8",
         "io_type": "int8",
-        "concrete": true,
+       "conversion": "CONCRETE",
         "debug": false
     },
 

diff --git a/docs/assets/guides/hk-dashboard.jpeg b/docs/assets/guides/hk-dashboard.jpeg
diff --git a/docs/assets/guides/tileio-dashboard.png b/docs/assets/guides/tileio-dashboard.png
diff --git a/docs/assets/heartkit-banner.png b/docs/assets/heartkit-banner.png
diff --git a/docs/guides/heartkit-demo.md b/docs/guides/heartkit-demo.md
diff --git a/docs/guides/index.md b/docs/guides/index.md
@@ -17,4 +17,4 @@ This section contains guides to help with various aspects of HeartKit. The guide
 ## <span class="sk-h2-span">Hardware Guides</span>
 
 - **[Run simple demo on EVB]()**: Running a demo using Ambiq SoC as backend inference engine.
-- **[Full HeartKit EVB App](heartkit-demo.md)**: A guide to running a multi-headed model demo on Ambiq EVB.
+- **[HeartKit Tileio Demo](https://ambiqai.github.io/tileio-docs/demos/heartkit/)**: A guide to running a multi-headed model demo on Ambiq EVB.
diff --git a/docs/tasks/denoise.md b/docs/tasks/denoise.md
@@ -44,7 +44,7 @@ Dataloaders are available for the following datasets:
 
 ## <span class="sk-h2-span">Pre-trained Models</span>
 
-The following table provides the latest performance and accuracy results of denoising models. Additional result details can be found in [Model Zoo → Denoise](../zoo/denoise.md).
+The following table provides the latest performance and accuracy results of denoising models. Additional result details can be found in [Model Zoo → Denoise](../zoo/index.md).
 
 
 --8<-- "assets/zoo/denoise/denoise-model-zoo-table.md"

diff --git a/docs/tasks/rhythm.md b/docs/tasks/rhythm.md
@@ -77,7 +77,7 @@ Dataloaders are available for the following datasets:
 
 ## <span class="sk-h2-span">Pre-trained Models</span>
 
-The following table provides the latest performance and accuracy results for rhythm models. Additional result details can be found in [Model Zoo → Rhythm](../zoo/rhythm.md).
+The following table provides the latest performance and accuracy results for rhythm models. Additional result details can be found in [Model Zoo → Rhythm](../zoo/index.md).
 
 
 --8<-- "assets/zoo/rhythm/rhythm-model-zoo-table.md"

diff --git a/docs/tasks/segmentation.md b/docs/tasks/segmentation.md
@@ -56,7 +56,7 @@ Dataloaders are available for the following datasets:
 
 ## <span class="sk-h2-span">Pre-Trained Models</span>
 
-The following table provides the latest performance and accuracy results for segmentation models. Additional result details can be found in [Model Zoo → Segmentation](../zoo/segmentation.md).
+The following table provides the latest performance and accuracy results for segmentation models. Additional result details can be found in [Model Zoo → Segmentation](../zoo/index.md).
 
 --8<-- "assets/zoo/segmentation/segmentation-model-zoo-table.md"
 

diff --git a/docs/zoo/index.md b/docs/zoo/index.md
@@ -13,7 +13,7 @@ The following table provides the latest performance and accuracy results for den
 | __DEN-PPG-TCN-SM__  | Synthetic         | 100Hz | 2.5s     | TCN            | 3.5K   | 1.1M    | 92.1% COS   |
 
 
-## <span class="sk-h2-span">[Signal Segmentation Task](./tasks/segmentation.md)</span>
+## <span class="sk-h2-span">[Signal Segmentation Task](../tasks/segmentation.md)</span>
 
 The following table provides the latest performance and accuracy results for ECG segmentation models.
 
@@ -25,7 +25,7 @@ The following table provides the latest performance and accuracy results for ECG
 | __SEG-PPG-2-TCN-SM__ | Synthetic                | 100Hz | 2.5s     | 2         | TCN           | 4K     | 1.43M   | 98.6% F1  |
 
 
-## <span class="sk-h2-span">[Rhythm Classification Task](./tasks/rhythm.md)</span>
+## <span class="sk-h2-span">[Rhythm Classification Task](../tasks/rhythm.md)</span>
 
 The following table provides the latest performance and accuracy results for rhythm classification models.
 
@@ -35,7 +35,7 @@ The following table provides the latest performance and accuracy results for rhy
 | __ARR-4-EFF-SM__ | LSAD                     | 100Hz | 5s       | 4         | EfficientNetV2 | 27K    |  1.6M   | 95.9% F1 |
 
 
-## <span class="sk-h2-span">[Beat Classification Task](./tasks/beat.md)</span>
+## <span class="sk-h2-span">[Beat Classification Task](../tasks/beat.md)</span>
 
 The following table provides the latest performance and accuracy results for beat classification models.
 

diff --git a/heartkit/backends/evb.py b/heartkit/backends/evb.py
@@ -60,7 +60,8 @@ def open(self):
         3. Send model to EVB via eRPC
 
         """
-        self._transport = get_serial_transport(vid_pid="51966:16385", baudrate=115200)
+
+        self._transport = get_serial_transport(vid_pid="51966:16401", baudrate=115200)
         client_manager = erpc.client.ClientManager(self._transport, erpc.basic_codec.BasicCodec)
         self._client = pc2evb.client.pc_to_evbClient(client_manager)
         self.send_model()

diff --git a/heartkit/datasets/lsad.py b/heartkit/datasets/lsad.py
@@ -390,7 +390,10 @@ def signal_label_generator(
                 y[pt_lbls] = 1
                 num_samples = sum((samples_per_tgt[tgt_labels.index(i)] for i in pt_lbls))
             elif label_format == "one_hot":
-                raise NotImplementedError()
+                y = np.zeros(num_classes, dtype=np.int32)
+                pt_lbl = random.choices(pt_lbls, pt_lbl_weights, k=1)[0]
+                y[pt_lbl] = 1
+                num_samples = samples_per_tgt[tgt_labels.index(pt_lbl)]
             elif label_format is None:
                 # Its possible to have multiple labels, we assign based on weights
                 y = random.choices(pt_lbls, pt_lbl_weights, k=1)[0]
@@ -478,7 +481,7 @@ def filter_patients_for_labels(
         patients_labels = self.get_patients_labels(patient_ids, label_map, label_type)
         # Find any patient with empty list
         label_mask = np.array([len(x) > 0 for x in patients_labels])
-        neg_mask = label_mask == -1
+        neg_mask = ~label_mask
         num_neg = neg_mask.sum()
         if num_neg > 0:
             logger.debug(f"Removed {num_neg} of {patient_ids.size} patients w/ no target class")

diff --git a/heartkit/datasets/ptbxl.py b/heartkit/datasets/ptbxl.py
@@ -422,7 +422,7 @@ def signal_label_generator(
         # END IF
 
         # Target labels and mapping
-        tgt_labels = sorted(list(set((lbl for lbl in label_map.values() if lbl != -1))))
+        tgt_labels = sorted(set((lbl for lbl in label_map.values() if lbl != -1)))
         label_key = self.label_key(label_type)
         num_classes = len(tgt_labels)
 
@@ -471,7 +471,10 @@ def signal_label_generator(
                 # y = np.expand_dims(y, axis=0)
                 num_samples = sum((samples_per_tgt[tgt_labels.index(i)] for i in pt_lbls))
             elif label_format == "one_hot":
-                raise NotImplementedError()
+                y = np.zeros(num_classes, dtype=np.int32)
+                pt_lbl = random.choices(pt_lbls, pt_lbl_weights, k=1)[0]
+                y[pt_lbl] = 1
+                num_samples = samples_per_tgt[tgt_labels.index(pt_lbl)]
             elif label_format is None:
                 # Its possible to have multiple labels, we assign based on weights
                 y = random.choices(pt_lbls, pt_lbl_weights, k=1)[0]
@@ -562,7 +565,7 @@ def filter_patients_for_labels(
         patients_labels = self.get_patients_labels(patient_ids, label_map, label_type)
         # Find any patient with empty list
         label_mask = np.array([len(x) > 0 for x in patients_labels])
-        neg_mask = label_mask == -1
+        neg_mask = ~label_mask
         num_neg = neg_mask.sum()
         if num_neg > 0:
             logger.debug(f"Removed {num_neg} of {patient_ids.size} patients w/ no target class")