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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,29 @@ | ||
| name: Units Tests | ||
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| on: | ||
| push: | ||
| branches: | ||
| - main | ||
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| jobs: | ||
| units-test: | ||
| runs-on: ubuntu-latest | ||
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| steps: | ||
| - name: Checkout code | ||
| uses: actions/checkout@v2 | ||
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| - name: Use Node.js 16 | ||
| uses: actions/setup-node@v3 | ||
| with: | ||
| node-version: 16 | ||
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| - name: Set up Python | ||
| uses: actions/setup-python@v2 | ||
| with: | ||
| python-version: 3.10 | ||
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| - name: Pytest | ||
| run: | | ||
| cd linguify | ||
| pytest |
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| @@ -1,4 +1,4 @@ | ||
| # NSL2AUDIOO | ||
| # NSL-2-AUDIO | ||
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| [](LICENSE) | ||
| [](https://www.python.org/) | ||
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@@ -52,13 +52,13 @@ $ make setup | |
| $ source $(poetry env info --path)/bin/activate | ||
| ``` | ||
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| ## Project Roadmap | ||
| ### Project Roadmap | ||
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| Here's a glimpse of the exciting features we plan to implement in the coming weeks: | ||
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| ## Citation | ||
| - [x] Add project's documentation | ||
| - [] Develop a Proof of Concept System | ||
| - [] Deployment of Proof of Concept System | ||
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| ## Acknowledgments | ||
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@@ -68,10 +68,6 @@ I would like to acknowledge the outstanding contributions of : | |
| **Email:** <[email protected]> | ||
| **GitHub:** [@tejuafonja](https://github.com/tejuafonja) | ||
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| **Name:** Fola Animashaun ***(```Mentor```)*** | ||
| **Email:** <[email protected]> | ||
| **GitHub:** [@Modinat-A](https://github.com/Modinat-A) | ||
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| ## Contact | ||
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| **Name:** **Ipadeola Ezekiel Ladipo** | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,203 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def resize_pad(x):\n", | ||
| " if x.shape[0] < FRAME_LEN:\n", | ||
| " x = F.pad(x, (0, 0, 0, FRAME_LEN - x.shape[0], 0, 0))\n", | ||
| " else:\n", | ||
| " x = x.unsqueeze(0) # Add batch and channel dimensions\n", | ||
| " x = torch.nn.functional.interpolate(\n", | ||
| " x, size=(FRAME_LEN, x.shape[1]), mode=\"bilinear\", align_corners=False\n", | ||
| " ).squeeze(0)\n", | ||
| "\n", | ||
| " return x\n", | ||
| "\n", | ||
| "\n", | ||
| "def frames_preprocess(x):\n", | ||
| " x = torch.tensor(x)\n", | ||
| " rhand = x[:, RHAND_IDX]\n", | ||
| " lhand = x[:, LHAND_IDX]\n", | ||
| " rpose = x[:, RPOSE_IDX]\n", | ||
| " lpose = x[:, LPOSE_IDX]\n", | ||
| "\n", | ||
| " rnan_idx = torch.any(torch.isnan(rhand), dim=1)\n", | ||
| " lnan_idx = torch.any(torch.isnan(lhand), dim=1)\n", | ||
| "\n", | ||
| " rnans = torch.sum(rnan_idx)\n", | ||
| " lnans = torch.sum(lnan_idx)\n", | ||
| "\n", | ||
| " if rnans > lnans:\n", | ||
| " hand = lhand\n", | ||
| " pose = lpose\n", | ||
| "\n", | ||
| " hand_x = hand[:, 0 * (len(LHAND_IDX) // 3) : 1 * (len(LHAND_IDX) // 3)]\n", | ||
| " hand_y = hand[:, 1 * (len(LHAND_IDX) // 3) : 2 * (len(LHAND_IDX) // 3)]\n", | ||
| " hand_z = hand[:, 2 * (len(LHAND_IDX) // 3) : 3 * (len(LHAND_IDX) // 3)]\n", | ||
| " hand = torch.cat([1 - hand_x, hand_y, hand_z], dim=1)\n", | ||
| "\n", | ||
| " pose_x = pose[:, 0 * (len(LPOSE_IDX) // 3) : 1 * (len(LPOSE_IDX) // 3)]\n", | ||
| " pose_y = pose[:, 1 * (len(LPOSE_IDX) // 3) : 2 * (len(LPOSE_IDX) // 3)]\n", | ||
| " pose_z = pose[:, 2 * (len(LPOSE_IDX) // 3) : 3 * (len(LPOSE_IDX) // 3)]\n", | ||
| " pose = torch.cat([1 - pose_x, pose_y, pose_z], dim=1)\n", | ||
| " else:\n", | ||
| " hand = rhand\n", | ||
| " pose = rpose\n", | ||
| "\n", | ||
| " hand_x = hand[:, 0 * (len(LHAND_IDX) // 3) : 1 * (len(LHAND_IDX) // 3)]\n", | ||
| " hand_y = hand[:, 1 * (len(LHAND_IDX) // 3) : 2 * (len(LHAND_IDX) // 3)]\n", | ||
| " hand_z = hand[:, 2 * (len(LHAND_IDX) // 3) : 3 * (len(LHAND_IDX) // 3)]\n", | ||
| " hand = torch.cat(\n", | ||
| " [hand_x.unsqueeze(-1), hand_y.unsqueeze(-1), hand_z.unsqueeze(-1)], dim=-1\n", | ||
| " )\n", | ||
| "\n", | ||
| " mean = torch.mean(hand, dim=1).unsqueeze(1)\n", | ||
| " std = torch.std(hand, dim=1).unsqueeze(1)\n", | ||
| " hand = (hand - mean) / std\n", | ||
| "\n", | ||
| " pose_x = pose[:, 0 * (len(LPOSE_IDX) // 3) : 1 * (len(LPOSE_IDX) // 3)]\n", | ||
| " pose_y = pose[:, 1 * (len(LPOSE_IDX) // 3) : 2 * (len(LPOSE_IDX) // 3)]\n", | ||
| " pose_z = pose[:, 2 * (len(LPOSE_IDX) // 3) : 3 * (len(LPOSE_IDX) // 3)]\n", | ||
| " pose = torch.cat(\n", | ||
| " [pose_x.unsqueeze(-1), pose_y.unsqueeze(-1), pose_z.unsqueeze(-1)], dim=-1\n", | ||
| " )\n", | ||
| "\n", | ||
| " x = torch.cat([hand, pose], dim=1)\n", | ||
| " # print(f\"befor re{x.shape}\")\n", | ||
| " x = resize_pad(x)\n", | ||
| " # print(f\"after re{x.shape}\")\n", | ||
| " x = torch.where(torch.isnan(x), torch.zeros_like(x), x)\n", | ||
| " # print(x.shape)\n", | ||
| "\n", | ||
| " #! CRITICAL Debug\n", | ||
| " # x = x.view(FRAME_LEN, len(LHAND_IDX) + len(LPOSE_IDX))\n", | ||
| " return x" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\"\"\"doc\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "FRAME_LEN = 128\n", | ||
| "\n", | ||
| "LPOSE = [13, 15, 17, 19, 21]\n", | ||
| "RPOSE = [14, 16, 18, 20, 22]\n", | ||
| "POSE = LPOSE + RPOSE\n", | ||
| "\n", | ||
| "X = (\n", | ||
| " [f\"x_right_hand_{i}\" for i in range(21)]\n", | ||
| " + [f\"x_left_hand_{i}\" for i in range(21)]\n", | ||
| " + [f\"x_pose_{i}\" for i in POSE]\n", | ||
| ")\n", | ||
| "Y = (\n", | ||
| " [f\"y_right_hand_{i}\" for i in range(21)]\n", | ||
| " + [f\"y_left_hand_{i}\" for i in range(21)]\n", | ||
| " + [f\"y_pose_{i}\" for i in POSE]\n", | ||
| ")\n", | ||
| "Z = (\n", | ||
| " [f\"z_right_hand_{i}\" for i in range(21)]\n", | ||
| " + [f\"z_left_hand_{i}\" for i in range(21)]\n", | ||
| " + [f\"z_pose_{i}\" for i in POSE]\n", | ||
| ")\n", | ||
| "\n", | ||
| "FEATURE_COLUMNS = X + Y + Z\n", | ||
| "\n", | ||
| "X_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"x_\" in col]\n", | ||
| "Y_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"y_\" in col]\n", | ||
| "Z_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"z_\" in col]\n", | ||
| "\n", | ||
| "RHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"right\" in col]\n", | ||
| "LHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS) if \"left\" in col]\n", | ||
| "RPOSE_IDX = [\n", | ||
| " i\n", | ||
| " for i, col in enumerate(FEATURE_COLUMNS)\n", | ||
| " if \"pose\" in col and int(col[-2:]) in RPOSE\n", | ||
| "]\n", | ||
| "LPOSE_IDX = [\n", | ||
| " i\n", | ||
| " for i, col in enumerate(FEATURE_COLUMNS)\n", | ||
| " if \"pose\" in col and int(col[-2:]) in LPOSE\n", | ||
| "]\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "(63, 63)" | ||
| ] | ||
| }, | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "len(RHAND_IDX), len(LHAND_IDX)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def read_file(file, file_id, landmarks_metadata_path):\n", | ||
| " phrase_list = []\n", | ||
| " frames_list = []\n", | ||
| " metadata_train_dataframe = pd.read_csv(landmarks_metadata_path)\n", | ||
| " file_id_df = metadata_train_dataframe.loc[\n", | ||
| " metadata_train_dataframe[\"file_id\"] == file_id\n", | ||
| " ]\n", | ||
| " saved_parueat_df = pq.read_table(\n", | ||
| " file, columns=[\"sequence_id\"] + FEATURE_COLUMNS\n", | ||
| " ).to_pandas()\n", | ||
| " for seq_id, phrase in zip(file_id_df.sequence_id, file_id_df.phrase):\n", | ||
| " frames = saved_parueat_df[saved_parueat_df.index == seq_id].to_numpy()\n", | ||
| " # NaN\n", | ||
| " right_num_nan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis=1) == 0)\n", | ||
| " left_num_nan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis=1) == 0)\n", | ||
| " \n", | ||
| " total_num_nan = max(right_num_nan, left_num_nan)\n", | ||
| " if 2 * len(phrase) < total_num_nan:\n", | ||
| " frames_list.append(frames)\n", | ||
| " phrase_list.append(phrase)\n", | ||
| " return (frames_list, phrase_list)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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