{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":52950,"databundleVersionId":5973250,"sourceType":"competition"}],"dockerImageVersionId":30581,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-03-01T13:15:45.492072Z","iopub.execute_input":"2024-03-01T13:15:45.49238Z","iopub.status.idle":"2024-03-01T13:15:45.915809Z","shell.execute_reply.started":"2024-03-01T13:15:45.492353Z","shell.execute_reply":"2024-03-01T13:15:45.914862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install mediapipe ","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:36.881018Z","iopub.execute_input":"2024-03-01T13:18:36.881776Z","iopub.status.idle":"2024-03-01T13:18:51.345061Z","shell.execute_reply.started":"2024-03-01T13:18:36.881735Z","shell.execute_reply":"2024-03-01T13:18:51.344038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport shutil\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport tensorflow as tf\nimport json\nimport mediapipe\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport random\n\nfrom skimage.transform import resize\nfrom mediapipe.framework.formats import landmark_pb2\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tqdm.notebook import tqdm\nfrom matplotlib import animation, rc\nfrom ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:51.347008Z","iopub.execute_input":"2024-03-01T13:18:51.347334Z","iopub.status.idle":"2024-03-01T13:18:51.918879Z","shell.execute_reply.started":"2024-03-01T13:18:51.347304Z","shell.execute_reply":"2024-03-01T13:18:51.918109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_train = pd.read_csv('/kaggle/input/asl-fingerspelling/train.csv')\nprint(f\" the size : { data_train.shape }\")\ndata_train.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:51.920021Z","iopub.execute_input":"2024-03-01T13:18:51.920345Z","iopub.status.idle":"2024-03-01T13:18:52.113894Z","shell.execute_reply.started":"2024-03-01T13:18:51.92032Z","shell.execute_reply":"2024-03-01T13:18:52.113016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The train.csv is a normal csv file which contains a parquet file path and the **phrase** value which corresponds to the sign . \n\nThe real trainable data is contained in the **train_landmark** parquet file which has the x,y,z coordinates of the landmarks . ","metadata":{}},{"cell_type":"code","source":"#reading and handling a parquet file \n# inspecting what a parquet file contains \npar_train = pq.read_table(\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\")\npar_train_df = par_train.to_pandas().reset_index()\npar_train_df['sequence_id'] = par_train_df['sequence_id'].astype(str)\npar_train_df","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:52.116508Z","iopub.execute_input":"2024-03-01T13:18:52.117132Z","iopub.status.idle":"2024-03-01T13:18:57.605161Z","shell.execute_reply.started":"2024-03-01T13:18:52.117097Z","shell.execute_reply":"2024-03-01T13:18:57.60424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"l = [col for col in par_train_df.columns if 'face' not in col]\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.606449Z","iopub.execute_input":"2024-03-01T13:18:57.606766Z","iopub.status.idle":"2024-03-01T13:18:57.611674Z","shell.execute_reply.started":"2024-03-01T13:18:57.606739Z","shell.execute_reply":"2024-03-01T13:18:57.610724Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sequence_id = data_train.iloc[0][['sequence_id']]\nprint(f\"each sequence of lasts for : {len(par_train_df[par_train_df['sequence_id'] == '1816796431'])}\")","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.612778Z","iopub.execute_input":"2024-03-01T13:18:57.613126Z","iopub.status.idle":"2024-03-01T13:18:57.653652Z","shell.execute_reply.started":"2024-03-01T13:18:57.613094Z","shell.execute_reply":"2024-03-01T13:18:57.652711Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Each sequence lasts for almost 123 frames ,\n","metadata":{}},{"cell_type":"markdown","source":"Now lets visualize our sequences using the mediapipe library .\nSince there are no actual images or frames of hands , we cannot directly see them , but with the help of mediapipe library , we can see the outline of the hands which is based on the following image .","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:f4024d70-bc23-4638-b4f9-cdf574cfd8f4.png)","metadata":{},"attachments":{"f4024d70-bc23-4638-b4f9-cdf574cfd8f4.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Fetch the pose landmark coordinates related to hand movement.\n![image.png](attachment:f4660c83-eb03-4c1f-8040-bc3409879990.png)!","metadata":{},"attachments":{"f4660c83-eb03-4c1f-8040-bc3409879990.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Pose coordinates for hand movement using above Image","metadata":{}},{"cell_type":"markdown","source":"Definition of Hand and Pose Coordinates:\n\nLPOSE and RPOSE are lists representing the indices of key points for the left and right hand poses.\n\nCreating x, y, z Label Names:\nX, Y, and Z are lists of label names for the x, y, and z coordinates of right hand, left hand, and pose.\nThe format is \"coordinate_type_hand/pose_index\".\n\nCreating Feature Columns:\nFEATURE_COLUMNS is a concatenation of X, Y, and Z, representing all feature column names.\n\nStoring Indices for X, Y, and Z:\nX_IDX, Y_IDX, and Z_IDX are lists containing the indices of feature columns corresponding to x, y, and z coordinates, respectively.\n\nStoring Indices for Right Hand, Left Hand, and Pose:\nRHAND_IDX contains indices of feature columns related to the right hand.\nLHAND_IDX contains indices of feature columns related to the left hand.\nRPOSE_IDX contains indices of feature columns related to the right hand pose.\nLPOSE_IDX contains indices of feature columns related to the left hand pose.","metadata":{}},{"cell_type":"code","source":"LPOSE = [13, 15, 17, 19, 21]\nRPOSE = [14, 16, 18, 20, 22]\nPOSE = LPOSE + RPOSE\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.654846Z","iopub.execute_input":"2024-03-01T13:18:57.655223Z","iopub.status.idle":"2024-03-01T13:18:57.661791Z","shell.execute_reply.started":"2024-03-01T13:18:57.655174Z","shell.execute_reply":"2024-03-01T13:18:57.660947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LPOSE = [13, 15, 17, 19, 21]\nRPOSE = [14, 16, 18, 20, 22]\nPOSE = LPOSE + RPOSE\nX = [f'x_right_hand_{i}' for i in range(21)] + [f'x_left_hand_{i}' for i in range(21)] + [f'x_pose_{i}' for i in POSE]\nY = [f'y_right_hand_{i}' for i in range(21)] + [f'y_left_hand_{i}' for i in range(21)] + [f'y_pose_{i}' for i in POSE]\nZ = [f'z_right_hand_{i}' for i in range(21)] + [f'z_left_hand_{i}' for i in range(21)] + [f'z_pose_{i}' for i in POSE]\nFEATURE_COLUMNS = X + Y + Z","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.662937Z","iopub.execute_input":"2024-03-01T13:18:57.663234Z","iopub.status.idle":"2024-03-01T13:18:57.672457Z","shell.execute_reply.started":"2024-03-01T13:18:57.663209Z","shell.execute_reply":"2024-03-01T13:18:57.671649Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(len(X))\n\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.673344Z","iopub.execute_input":"2024-03-01T13:18:57.673601Z","iopub.status.idle":"2024-03-01T13:18:57.682105Z","shell.execute_reply.started":"2024-03-01T13:18:57.673577Z","shell.execute_reply":"2024-03-01T13:18:57.681153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"FEATURE_COLUMNS = X + Y + Z","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.686678Z","iopub.execute_input":"2024-03-01T13:18:57.687267Z","iopub.status.idle":"2024-03-01T13:18:57.692241Z","shell.execute_reply.started":"2024-03-01T13:18:57.687232Z","shell.execute_reply":"2024-03-01T13:18:57.691365Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(FEATURE_COLUMNS)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.693118Z","iopub.execute_input":"2024-03-01T13:18:57.693388Z","iopub.status.idle":"2024-03-01T13:18:57.702985Z","shell.execute_reply.started":"2024-03-01T13:18:57.693366Z","shell.execute_reply":"2024-03-01T13:18:57.702218Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"x_\" in col]\nY_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"y_\" in col]\nZ_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"z_\" in col]\n\nRHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"right\" in col]\nLHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"left\" in col]\nRPOSE_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"pose\" in col and int(col[-2:]) in RPOSE]\nLPOSE_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"pose\" in col and int(col[-2:]) in LPOSE]","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.704095Z","iopub.execute_input":"2024-03-01T13:18:57.704427Z","iopub.status.idle":"2024-03-01T13:18:57.713437Z","shell.execute_reply.started":"2024-03-01T13:18:57.704397Z","shell.execute_reply":"2024-03-01T13:18:57.71225Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"r = [col for col in FEATURE_COLUMNS if \"right\" in col]\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.714913Z","iopub.execute_input":"2024-03-01T13:18:57.715261Z","iopub.status.idle":"2024-03-01T13:18:57.727358Z","shell.execute_reply.started":"2024-03-01T13:18:57.715228Z","shell.execute_reply":"2024-03-01T13:18:57.726255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(X_IDX) , len(RHAND_IDX) , len(Y_IDX) , len(Z_IDX),len(LHAND_IDX) , len(RPOSE_IDX)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.728584Z","iopub.execute_input":"2024-03-01T13:18:57.728879Z","iopub.status.idle":"2024-03-01T13:18:57.738868Z","shell.execute_reply.started":"2024-03-01T13:18:57.728854Z","shell.execute_reply":"2024-03-01T13:18:57.737975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pq_file = f\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\"\n    # Filter train.csv and fetch entries only for the relevant file_id\nfile_df = data_train.loc[data_train[\"file_id\"] == '5414471']\n# Fetch the parquet file\nparquet_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\",\n                          columns=['sequence_id'] + FEATURE_COLUMNS).to_pandas()\n# File name for the updated data\nparquet_numpy = parquet_df.to_numpy()\nphrase = '3 creekhouse 3 creekhouse 3 creekhouse 3 creekhouse 3 creekhouse 3 creekhouse'\nprint(2*len(phrase))\nseq_id = 1816796431\nfile_df\nframes = parquet_numpy[parquet_df.index == seq_id]\nframes.shape\nparquet_df \n\nr_nonan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis = 1) == 0)\nl_nonan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis = 1) == 0)\nno_nan = max(r_nonan, l_nonan)\nprint(no_nan)\nfeatures={}\n#if 2*len(phrase)<no_nan:\nfeatures = {FEATURE_COLUMNS[i]: tf.train.Feature(\nfloat_list=tf.train.FloatList(value=frames[:, i])) for i in range(len(FEATURE_COLUMNS))}\nfeatures[\"phrase\"] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(phrase, 'utf-8')]))\nrecord_bytes = tf.train.Example(features=tf.train.Features(feature=features)).SerializeToString()\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.740033Z","iopub.execute_input":"2024-03-01T13:18:57.740403Z","iopub.status.idle":"2024-03-01T13:18:57.993518Z","shell.execute_reply.started":"2024-03-01T13:18:57.740367Z","shell.execute_reply":"2024-03-01T13:18:57.99251Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Preprocessing**","metadata":{}},{"cell_type":"code","source":"FRAME_LEN = 128\n\n# Create directory to store the new data\nif not os.path.isdir(\"preprocessed\"):\n    os.mkdir(\"preprocessed\")\nelse:\n    shutil.rmtree(\"preprocessed\")\n    os.mkdir(\"preprocessed\")\n\n# Loop through each file_id\nfor file_id in tqdm(data_train.file_id.unique()):\n    # Parquet file name\n    pq_file = f\"/kaggle/input/asl-fingerspelling/train_landmarks/{file_id}.parquet\"\n    # Filter train.csv and fetch entries only for the relevant file_id\n    file_df = data_train.loc[data_train[\"file_id\"] == file_id]\n    # Fetch the parquet file\n    parquet_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/{str(file_id)}.parquet\",\n                              columns=['sequence_id'] + FEATURE_COLUMNS).to_pandas()\n    # File name for the updated data\n    tf_file = f\"preprocessed/{file_id}.tfrecord\"\n    parquet_numpy = parquet_df.to_numpy()\n    # Initialize the pointer to write the output of \n    # each `for loop` below as a sequence into the file.\n    with tf.io.TFRecordWriter(tf_file) as file_writer:\n        # Loop through each sequence in file.\n        for seq_id, phrase in zip(file_df.sequence_id, file_df.phrase):\n            # Fetch sequence data\n            frames = parquet_numpy[parquet_df.index == seq_id]\n            \n            # Calculate the number of NaN values in each hand landmark\n            r_nonan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis = 1) == 0)\n            l_nonan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis = 1) == 0)\n            no_nan = max(r_nonan, l_nonan)\n            \n            if 2*len(phrase)<no_nan:\n                features = {FEATURE_COLUMNS[i]: tf.train.Feature(\n                    float_list=tf.train.FloatList(value=frames[:, i])) for i in range(len(FEATURE_COLUMNS))}\n                features[\"phrase\"] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(phrase, 'utf-8')]))\n                record_bytes = tf.train.Example(features=tf.train.Features(feature=features)).SerializeToString()\n                file_writer.write(record_bytes)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:18:57.996884Z","iopub.execute_input":"2024-03-01T13:18:57.997213Z","iopub.status.idle":"2024-03-01T13:28:48.63394Z","shell.execute_reply.started":"2024-03-01T13:18:57.997176Z","shell.execute_reply":"2024-03-01T13:28:48.633002Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"file_id = 5414471\npq_file = f\"/kaggle/input/asl-fingerspelling/train_landmarks/{file_id}.parquet\"\n# Filter train.csv and fetch entries only for the relevant file_id\nfile_df = data_train.loc[data_train[\"file_id\"] == file_id]\n# Fetch the parquet file\nparquet_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/{str(file_id)}.parquet\",\n                          columns=['sequence_id'] + FEATURE_COLUMNS).to_pandas()\nparquet_numpy = parquet_df.to_numpy()\nframes = parquet_numpy[parquet_df.index == 1816796431]\n            \nframes.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.635131Z","iopub.execute_input":"2024-03-01T13:28:48.635454Z","iopub.status.idle":"2024-03-01T13:28:48.838232Z","shell.execute_reply.started":"2024-03-01T13:28:48.635428Z","shell.execute_reply":"2024-03-01T13:28:48.837321Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"phrase = '3 creekhouse'\n2*len(phrase)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.839468Z","iopub.execute_input":"2024-03-01T13:28:48.839831Z","iopub.status.idle":"2024-03-01T13:28:48.845676Z","shell.execute_reply.started":"2024-03-01T13:28:48.8398Z","shell.execute_reply":"2024-03-01T13:28:48.8448Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.sum(np.isnan(frames[:, RHAND_IDX]) , axis = 1 )\n#np.isnan(frames[:, LHAND_IDX])","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.84689Z","iopub.execute_input":"2024-03-01T13:28:48.847167Z","iopub.status.idle":"2024-03-01T13:28:48.856673Z","shell.execute_reply.started":"2024-03-01T13:28:48.847136Z","shell.execute_reply":"2024-03-01T13:28:48.85582Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tf_records = data_train.file_id.map(lambda x: f'/kaggle/working/preprocessed/{x}.tfrecord').unique()\nprint(f\"List of {len(tf_records)} TFRecord files.\")\n\ntf_record_test = data_train.file_id.map(lambda x: f'/kaggle/working/preprocessed/5414471.tfrecord').unique()\ntf_record_test","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.857674Z","iopub.execute_input":"2024-03-01T13:28:48.857923Z","iopub.status.idle":"2024-03-01T13:28:48.941402Z","shell.execute_reply.started":"2024-03-01T13:28:48.857901Z","shell.execute_reply":"2024-03-01T13:28:48.940579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load character_to_prediction json file","metadata":{}},{"cell_type":"markdown","source":"character-to-index mapping for use in a machine learning model, especially one involving sequence generation or prediction. contributes to the effective handling of sequences in the training provess","metadata":{}},{"cell_type":"code","source":"with open (\"/kaggle/input/asl-fingerspelling/character_to_prediction_index.json\", \"r\") as f:\n    char_to_num = json.load(f)\n\n# Add pad_token, start pointer and end pointer to the dict\npad_token = 'P'\nstart_token = '<'\nend_token = '>'\npad_token_idx = 59\nstart_token_idx = 60\nend_token_idx = 61\n\nchar_to_num[pad_token] = pad_token_idx\nchar_to_num[start_token] = start_token_idx\nchar_to_num[end_token] = end_token_idx\nnum_to_char = {j:i for i,j in char_to_num.items()}","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.942734Z","iopub.execute_input":"2024-03-01T13:28:48.943093Z","iopub.status.idle":"2024-03-01T13:28:48.957257Z","shell.execute_reply.started":"2024-03-01T13:28:48.94306Z","shell.execute_reply":"2024-03-01T13:28:48.956524Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Function to resize and add padding.\ndef resize_pad(x):\n    if tf.shape(x)[0] < FRAME_LEN:\n        x = tf.pad(x, ([[0, FRAME_LEN-tf.shape(x)[0]], [0, 0], [0, 0]]))\n    else:\n        x = tf.image.resize(x, (FRAME_LEN, tf.shape(x)[1]))\n    return x\n\n# Detect the dominant hand from the number of NaN values.\n# Dominant hand will have less NaN values since it is in frame moving.\n#This function preprocesses a tensor x representing hand and pose coordinates. \n#It includes steps such as handling NaN values, normalizing the data, and resizing/padding the sequence.\ndef pre_process(x):\n    rhand = tf.gather(x, RHAND_IDX, axis=1)\n    lhand = tf.gather(x, LHAND_IDX, axis=1)\n    rpose = tf.gather(x, RPOSE_IDX, axis=1)\n    lpose = tf.gather(x, LPOSE_IDX, axis=1)\n    \n    rnan_idx = tf.reduce_any(tf.math.is_nan(rhand), axis=1)\n    lnan_idx = tf.reduce_any(tf.math.is_nan(lhand), axis=1)\n    \n    rnans = tf.math.count_nonzero(rnan_idx)\n    lnans = tf.math.count_nonzero(lnan_idx)\n    \n    # For dominant hand\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 = tf.concat([1-hand_x, hand_y, hand_z], axis=1) # REVIEW NEEDED \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 = tf.concat([1-pose_x, pose_y, pose_z], axis=1) # REVIEW NEEDED \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 = tf.concat([hand_x[..., tf.newaxis], hand_y[..., tf.newaxis], hand_z[..., tf.newaxis]], axis=-1)\n    \n    mean = tf.math.reduce_mean(hand, axis=1)[:, tf.newaxis, :]\n    std = tf.math.reduce_std(hand, axis=1)[:, tf.newaxis, :]\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 = tf.concat([pose_x[..., tf.newaxis], pose_y[..., tf.newaxis], pose_z[..., tf.newaxis]], axis=-1)\n    \n    x = tf.concat([hand, pose], axis=1)\n    x = resize_pad(x)\n    \n    x = tf.where(tf.math.is_nan(x), tf.zeros_like(x), x)\n    x = tf.reshape(x, (FRAME_LEN, len(LHAND_IDX) + len(LPOSE_IDX)))\n    return x","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.958423Z","iopub.execute_input":"2024-03-01T13:28:48.958678Z","iopub.status.idle":"2024-03-01T13:28:48.976953Z","shell.execute_reply.started":"2024-03-01T13:28:48.958655Z","shell.execute_reply":"2024-03-01T13:28:48.976212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create function to parse data from TFRecord forma","metadata":{}},{"cell_type":"markdown","source":"for reading and decoding TFRecord examples. It defines the expected schema, parses the serialized examples, extracts features, and prepares the data for use in training a model.","metadata":{}},{"cell_type":"code","source":"def decode_fn(record_bytes):\n    schema = {COL: tf.io.VarLenFeature(dtype=tf.float32) for COL in FEATURE_COLUMNS if COL != 'phrase'}\n    schema[\"phrase\"] = tf.io.FixedLenFeature([], dtype=tf.string)\n    features = tf.io.parse_single_example(record_bytes, schema)\n    phrase = features[\"phrase\"]\n    landmarks = ([tf.sparse.to_dense(features[COL]) for COL in FEATURE_COLUMNS])\n    # Transpose to maintain the original shape of landmarks data.\n    landmarks = tf.transpose(landmarks)\n    #print(landmarks)\n    return landmarks , phrase\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.978018Z","iopub.execute_input":"2024-03-01T13:28:48.978305Z","iopub.status.idle":"2024-03-01T13:28:48.990996Z","shell.execute_reply.started":"2024-03-01T13:28:48.978282Z","shell.execute_reply":"2024-03-01T13:28:48.990018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create function to convert the data","metadata":{}},{"cell_type":"code","source":"table = tf.lookup.StaticHashTable(\n    initializer=tf.lookup.KeyValueTensorInitializer(\n        keys=list(char_to_num.keys()),\n        values=list(char_to_num.values()),\n    ),\n    default_value=tf.constant(-1),\n    name=\"class_weight\"\n)\n\ndef convert_fn(landmarks,phrase):\n    #Add start and end pointers to phrase.\n    phrase = start_token + phrase + end_token\n    phrase = tf.strings.bytes_split(phrase)\n    phrase = table.lookup(phrase)\n    #Vectorize and add padding.\n    phrase = tf.pad(phrase, paddings=[[0, 64 - tf.shape(phrase)[0]]], mode = 'CONSTANT',\n                    constant_values = pad_token_idx)\n    #Apply pre_process function to the landmarks.\n    #Output: the preprocessed landmarks and the preprocessed padded phrase\n    return pre_process(landmarks) , phrase","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:48.991969Z","iopub.execute_input":"2024-03-01T13:28:48.992265Z","iopub.status.idle":"2024-03-01T13:28:49.846423Z","shell.execute_reply.started":"2024-03-01T13:28:48.992237Z","shell.execute_reply":"2024-03-01T13:28:49.845395Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pq_file = f\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\"\n    # Filter train.csv and fetch entries only for the relevant file_id\nfile_df = data_train.loc[data_train[\"file_id\"] == '5414471']\n# Fetch the parquet file\nparquet_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\",\n                          columns=['sequence_id'] + FEATURE_COLUMNS).to_pandas()\n# File name for the updated data\nparquet_numpy = parquet_df.to_numpy()\nphrase = ''\nprint(2*len(phrase))\nseq_id = 1816796431\nfile_df\nframes = parquet_numpy[parquet_df.index == seq_id]\nframes.shape\nparquet_df \n\nr_nonan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis = 1) == 0)\nl_nonan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis = 1) == 0)\nno_nan = max(r_nonan, l_nonan)\nprint(no_nan)\nfeatures={}\n#if 2*len(phrase)<no_nan:\nfeatures = {FEATURE_COLUMNS[i]: tf.train.Feature(\nfloat_list=tf.train.FloatList(value=frames[:, i])) for i in range(len(FEATURE_COLUMNS))}\nfeatures[\"phrase\"] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(phrase, 'utf-8')]))\nrecord_bytes = tf.train.Example(features=tf.train.Features(feature=features)).SerializeToString()\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:49.847875Z","iopub.execute_input":"2024-03-01T13:28:49.848501Z","iopub.status.idle":"2024-03-01T13:28:50.059404Z","shell.execute_reply.started":"2024-03-01T13:28:49.848466Z","shell.execute_reply":"2024-03-01T13:28:50.058426Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"convert_fn(decode_fn(record_bytes)[0] , decode_fn(record_bytes)[1])","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:50.060593Z","iopub.execute_input":"2024-03-01T13:28:50.060873Z","iopub.status.idle":"2024-03-01T13:28:50.640779Z","shell.execute_reply.started":"2024-03-01T13:28:50.060849Z","shell.execute_reply":"2024-03-01T13:28:50.639902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_ds = tf.data.TFRecordDataset(tf_record_test).map(decode_fn).map(convert_fn).batch(1).prefetch(buffer_size=tf.data.AUTOTUNE).cache()\nsample_batch = next(iter(sample_ds))\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:50.641861Z","iopub.execute_input":"2024-03-01T13:28:50.642153Z","iopub.status.idle":"2024-03-01T13:28:52.126498Z","shell.execute_reply.started":"2024-03-01T13:28:50.642114Z","shell.execute_reply":"2024-03-01T13:28:52.125639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_batch[0]","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:52.132813Z","iopub.execute_input":"2024-03-01T13:28:52.133176Z","iopub.status.idle":"2024-03-01T13:28:52.140158Z","shell.execute_reply.started":"2024-03-01T13:28:52.13315Z","shell.execute_reply":"2024-03-01T13:28:52.139065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"batch_size = 64\ntrain_len = int(0.8 * len(tf_records))\n\ntrain_ds = tf.data.TFRecordDataset(tf_records[:train_len]).map(decode_fn).map(convert_fn).batch(batch_size).prefetch(buffer_size=tf.data.AUTOTUNE).cache()\nvalid_ds = tf.data.TFRecordDataset(tf_records[train_len:]).map(decode_fn).map(convert_fn).batch(batch_size).prefetch(buffer_size=tf.data.AUTOTUNE).cache()","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:28:52.141338Z","iopub.execute_input":"2024-03-01T13:28:52.14164Z","iopub.status.idle":"2024-03-01T13:28:52.979141Z","shell.execute_reply.started":"2024-03-01T13:28:52.141608Z","shell.execute_reply":"2024-03-01T13:28:52.978142Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class TokenEmbedding(layers.Layer):\n    def __init__(self, num_vocab=1000, maxlen=100, num_hid=64):\n        super().__init__()\n        self.emb = tf.keras.layers.Embedding(num_vocab, num_hid)\n        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=num_hid)\n\n    def call(self, x):\n        maxlen = tf.shape(x)[-1]\n        x = self.emb(x)\n        positions = tf.range(start=0, limit=maxlen, delta=1)\n        positions = self.pos_emb(positions)\n        return x + positions\n\n\nclass LandmarkEmbedding(layers.Layer):\n    def __init__(self, num_hid=64, maxlen=100):\n        super().__init__()\n        self.conv1 = tf.keras.layers.Conv1D(\n            num_hid, 11, strides=2, padding=\"same\", activation=\"relu\"\n        )\n        self.conv2 = tf.keras.layers.Conv1D(\n            num_hid, 11, strides=2, padding=\"same\", activation=\"relu\"\n        )\n        self.conv3 = tf.keras.layers.Conv1D(\n            num_hid, 11, strides=2, padding=\"same\", activation=\"relu\"\n        )\n        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=num_hid)\n\n    def call(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        return self.conv3(x)\n\n    \n    \n    \n    \nclass TransformerEncoder(layers.Layer):\n    def __init__(self, embed_dim, num_heads, feed_forward_dim, rate=0.1):\n        super().__init__()\n        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n        self.ffn = keras.Sequential(\n            [\n                layers.Dense(feed_forward_dim, activation=\"relu\"),\n                layers.Dense(embed_dim),\n            ]\n        )\n        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n        self.dropout1 = layers.Dropout(rate)\n        self.dropout2 = layers.Dropout(rate)\n\n    def call(self, inputs, training):\n        attn_output = self.att(inputs, inputs)\n        attn_output = self.dropout1(attn_output, training=training)\n        out1 = self.layernorm1(inputs + attn_output)\n        ffn_output = self.ffn(out1)\n        ffn_output = self.dropout2(ffn_output, training=training)\n        return self.layernorm2(out1 + ffn_output)\n    \n #Customized to add `training` variable\n# Reference: https://www.kaggle.com/code/shlomoron/aslfr-a-simple-transformer/notebook\n\n\n\n\n\nclass TransformerDecoder(layers.Layer):\n    def __init__(self, embed_dim, num_heads, feed_forward_dim, dropout_rate=0.1):\n        super().__init__()\n        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm3 = layers.LayerNormalization(epsilon=1e-6)\n        self.self_att = layers.MultiHeadAttention(\n            num_heads=num_heads, key_dim=embed_dim\n        )\n        self.enc_att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n        self.self_dropout = layers.Dropout(0.5)\n        self.enc_dropout = layers.Dropout(0.1)\n        self.ffn_dropout = layers.Dropout(0.1)\n        self.ffn = keras.Sequential(\n            [\n                layers.Dense(feed_forward_dim, activation=\"relu\"),\n                layers.Dense(embed_dim),\n            ]\n        )\n\n    def causal_attention_mask(self, batch_size, n_dest, n_src, dtype):\n        \"\"\"Masks the upper half of the dot product matrix in self attention.\n\n        This prevents flow of information from future tokens to current token.\n        1's in the lower triangle, counting from the lower right corner.\n        \"\"\"\n        i = tf.range(n_dest)[:, None]\n        j = tf.range(n_src)\n        m = i >= j - n_src + n_dest\n        mask = tf.cast(m, dtype)\n        mask = tf.reshape(mask, [1, n_dest, n_src])\n        mult = tf.concat(\n            [batch_size[..., tf.newaxis], tf.constant([1, 1], dtype=tf.int32)], 0\n        )\n        return tf.tile(mask, mult)\n\n    def call(self, enc_out, target, training):\n        input_shape = tf.shape(target)\n        batch_size = input_shape[0]\n        seq_len = input_shape[1]\n        causal_mask = self.causal_attention_mask(batch_size, seq_len, seq_len, tf.bool)\n        target_att = self.self_att(target, target, attention_mask=causal_mask)\n        target_norm = self.layernorm1(target + self.self_dropout(target_att, training = training))\n        enc_out = self.enc_att(target_norm, enc_out)\n        enc_out_norm = self.layernorm2(self.enc_dropout(enc_out, training = training) + target_norm)\n        ffn_out = self.ffn(enc_out_norm)\n        ffn_out_norm = self.layernorm3(enc_out_norm + self.ffn_dropout(ffn_out, training = training))\n        return ffn_out_norm\n\n    \n    \n    \n    \n    \n    \n# Customized to add edit_dist metric and training variable.\n# Reference:\n# https://www.kaggle.com/code/irohith/aslfr-transformer/notebook\n# https://www.kaggle.com/code/shlomoron/aslfr-a-simple-transformer/notebook\n\nclass Transformer(keras.Model):\n    def __init__(\n        self,\n        num_hid=64,\n        num_head=2,\n        num_feed_forward=128,\n        source_maxlen=100,\n        target_maxlen=100,\n        num_layers_enc=4,\n        num_layers_dec=1,\n        num_classes=60,\n    ):\n        super().__init__()\n        self.loss_metric = keras.metrics.Mean(name=\"loss\")\n        self.acc_metric = keras.metrics.Mean(name=\"edit_dist\")\n        self.num_layers_enc = num_layers_enc\n        self.num_layers_dec = num_layers_dec\n        self.target_maxlen = target_maxlen\n        self.num_classes = num_classes\n\n        self.enc_input = LandmarkEmbedding(num_hid=num_hid, maxlen=source_maxlen)\n        self.dec_input = TokenEmbedding(\n            num_vocab=num_classes, maxlen=target_maxlen, num_hid=num_hid\n        )\n\n        self.encoder = keras.Sequential(\n            [self.enc_input]\n            + [\n                TransformerEncoder(num_hid, num_head, num_feed_forward)\n                for _ in range(num_layers_enc)\n            ]\n        )\n\n        for i in range(num_layers_dec):\n            setattr(\n                self,\n                f\"dec_layer_{i}\",\n                TransformerDecoder(num_hid, num_head, num_feed_forward),\n            )\n\n        self.classifier = layers.Dense(num_classes)\n\n    def decode(self, enc_out, target, training):\n        y = self.dec_input(target)\n        for i in range(self.num_layers_dec):\n            y = getattr(self, f\"dec_layer_{i}\")(enc_out, y, training)\n        return y\n\n    def call(self, inputs, training):\n        source = inputs[0]\n        target = inputs[1]\n        x = self.encoder(source, training)\n        y = self.decode(x, target, training)\n        return self.classifier(y)\n\n    @property\n    def metrics(self):\n        return [self.loss_metric]\n\n    def train_step(self, batch):\n        \"\"\"Processes one batch inside model.fit().\"\"\"\n        source = batch[0]\n        target = batch[1]\n\n        input_shape = tf.shape(target)\n        batch_size = input_shape[0]\n        \n        dec_input = target[:, :-1]\n        dec_target = target[:, 1:]\n        with tf.GradientTape() as tape:\n            preds = self([source, dec_input])\n            one_hot = tf.one_hot(dec_target, depth=self.num_classes)\n            mask = tf.math.logical_not(tf.math.equal(dec_target, pad_token_idx))\n            loss = self.compiled_loss(one_hot, preds, sample_weight=mask)\n        trainable_vars = self.trainable_variables\n        gradients = tape.gradient(loss, trainable_vars)\n        self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n        # Computes the Levenshtein distance between sequences since the evaluation\n        # metric for this contest is the normalized total levenshtein distance.\n        edit_dist = tf.edit_distance(tf.sparse.from_dense(target), \n                                     tf.sparse.from_dense(tf.cast(tf.argmax(preds, axis=1), tf.int32)))\n        edit_dist = tf.reduce_mean(edit_dist)\n        self.acc_metric.update_state(edit_dist)\n        self.loss_metric.update_state(loss)\n        return {\"loss\": self.loss_metric.result(), \"edit_dist\": self.acc_metric.result()}\n\n    def test_step(self, batch):        \n        source = batch[0]\n        target = batch[1]\n\n        input_shape = tf.shape(target)\n        batch_size = input_shape[0]\n        \n        dec_input = target[:, :-1]\n        dec_target = target[:, 1:]\n        preds = self([source, dec_input])\n        one_hot = tf.one_hot(dec_target, depth=self.num_classes)\n        mask = tf.math.logical_not(tf.math.equal(dec_target, pad_token_idx))\n        loss = self.compiled_loss(one_hot, preds, sample_weight=mask)\n        # Computes the Levenshtein distance between sequences since the evaluation\n        # metric for this contest is the normalized total levenshtein distance.\n        edit_dist = tf.edit_distance(tf.sparse.from_dense(target), \n                                     tf.sparse.from_dense(tf.cast(tf.argmax(preds, axis=1), tf.int32)))\n        edit_dist = tf.reduce_mean(edit_dist)\n        self.acc_metric.update_state(edit_dist)\n        self.loss_metric.update_state(loss)\n        return {\"loss\": self.loss_metric.result(), \"edit_dist\": self.acc_metric.result()}\n\n    def generate(self, source, target_start_token_idx):\n        \"\"\"Performs inference over one batch of inputs using greedy decoding.\"\"\"\n        bs = tf.shape(source)[0]\n        enc = self.encoder(source, training = False)\n        dec_input = tf.ones((bs, 1), dtype=tf.int32) * target_start_token_idx\n        dec_logits = []\n        for i in range(self.target_maxlen - 1):\n            dec_out = self.decode(enc, dec_input, training = False)\n            logits = self.classifier(dec_out)\n            logits = tf.argmax(logits, axis=-1, output_type=tf.int32)\n            last_logit = logits[:, -1][..., tf.newaxis]\n            dec_logits.append(last_logit)\n            dec_input = tf.concat([dec_input, last_logit], axis=-1)\n        return dec_input\n    \n    \n    \n    \n    \n    \n    \n    \nclass DisplayOutputs(keras.callbacks.Callback):\n    def __init__(\n        self, batch, idx_to_token, target_start_token_idx=60, target_end_token_idx=61\n    ):\n        \"\"\"Displays a batch of outputs after every 4 epoch\n\n        Args:\n            batch: A test batch\n            idx_to_token: A List containing the vocabulary tokens corresponding to their indices\n            target_start_token_idx: A start token index in the target vocabulary\n            target_end_token_idx: An end token index in the target vocabulary\n        \"\"\"\n        self.batch = batch\n        self.target_start_token_idx = target_start_token_idx\n        self.target_end_token_idx = target_end_token_idx\n        self.idx_to_char = idx_to_token\n\n    def on_epoch_end(self, epoch, logs=None):\n        if epoch % 4 != 0:\n            return\n        source = self.batch[0]\n        target = self.batch[1].numpy()\n        bs = tf.shape(source)[0]\n        preds = self.model.generate(source, self.target_start_token_idx)\n        preds = preds.numpy()\n        for i in range(bs):\n            target_text = \"\".join([self.idx_to_char[_] for _ in target[i, :]])\n            prediction = \"\"\n            for idx in preds[i, :]:\n                prediction += self.idx_to_char[idx]\n                if idx == self.target_end_token_idx:\n                    break\n            print(f\"target:     {target_text.replace('-','')}\")\n            print(f\"prediction: {prediction}\\n\")\n            \n            \n            \n            \n            \n            \nbatch = next(iter(valid_ds))\n\n# The vocabulary to convert predicted indices into characters\nidx_to_char = list(char_to_num.keys())\ndisplay_cb = DisplayOutputs(\n    batch, idx_to_char, target_start_token_idx=char_to_num['<'], target_end_token_idx=char_to_num['>']\n)  # set the arguments as per vocabulary index for '<' and '>'\n\nmodel = Transformer(\n    num_hid=200,\n    num_head=4,\n    num_feed_forward=400,\n    source_maxlen = FRAME_LEN,\n    target_maxlen=64,\n    num_layers_enc=2,\n    num_layers_dec=1,\n    num_classes=62\n)\nloss_fn = tf.keras.losses.CategoricalCrossentropy(\n    from_logits=True, label_smoothing=0.1,\n)\n\n\noptimizer = keras.optimizers.Adam(0.00085)\nmodel.compile(optimizer=optimizer, loss=loss_fn)\n\nhistory = model.fit(train_ds, validation_data=valid_ds, callbacks=[display_cb], epochs=40)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-03-01T13:28:52.980678Z","iopub.execute_input":"2024-03-01T13:28:52.980963Z","iopub.status.idle":"2024-03-01T13:44:23.769666Z","shell.execute_reply.started":"2024-03-01T13:28:52.980939Z","shell.execute_reply":"2024-03-01T13:44:23.7688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.legend(['training loss', 'val_loss'])","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:44:23.770886Z","iopub.execute_input":"2024-03-01T13:44:23.771244Z","iopub.status.idle":"2024-03-01T13:44:24.102625Z","shell.execute_reply.started":"2024-03-01T13:44:23.771182Z","shell.execute_reply":"2024-03-01T13:44:24.101704Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pickle \nimport os \nif not os.path.exists('/kaggle/working/models/'):\n    os.makedirs('/kaggle/working/models/')\npickle.dump(model, open('/kaggle/working/models/ASL30_model.pkl', 'wb'))","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:44:24.103778Z","iopub.execute_input":"2024-03-01T13:44:24.104038Z","iopub.status.idle":"2024-03-01T13:44:24.554636Z","shell.execute_reply.started":"2024-03-01T13:44:24.104015Z","shell.execute_reply":"2024-03-01T13:44:24.553749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" class TFLiteModel(tf.Module):\n    def __init__(self, model):\n        super(TFLiteModel, self).__init__()\n        self.target_start_token_idx = start_token_idx\n        self.target_end_token_idx = end_token_idx\n        # Load the feature generation and main models\n        self.model = model\n    \n    @tf.function(input_signature=[tf.TensorSpec(shape=[None, len(FEATURE_COLUMNS)], dtype=tf.float32, name='inputs')])\n    def __call__(self, inputs, training=False):\n        # Preprocess Data\n        x = tf.cast(inputs, tf.float32)\n        x = x[None]\n        x = tf.cond(tf.shape(x)[1] == 0, lambda: tf.zeros((1, 1, len(FEATURE_COLUMNS))), lambda: tf.identity(x))\n        x = x[0]\n        x = pre_process(x)\n        x = x[None]\n        x = self.model.generate(x, self.target_start_token_idx)\n        x = x[0]\n        idx = tf.argmax(tf.cast(tf.equal(x, self.target_end_token_idx), tf.int32))\n        idx = tf.where(tf.math.less(idx, 1), tf.constant(2, dtype=tf.int64), idx)\n        x = x[1:idx]\n        x = tf.one_hot(x, 59)\n        return {'outputs': x}\n    \ntflitemodel_base = TFLiteModel(model)\n\n\nkeras_model_converter = tf.lite.TFLiteConverter.from_keras_model(tflitemodel_base)\nkeras_model_converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]#, tf.lite.OpsSet.SELECT_TF_OPS]\ntflite_model = keras_model_converter.convert()\nwith open('/kaggle/working/models/model.tflite', 'wb') as f:\n    f.write(tflite_model)\n    \ninfargs = {\"selected_columns\" : FEATURE_COLUMNS}\n\nwith open('inference_args.json', \"w\") as json_file:\n    json.dump(infargs, json_file)\n\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:44:24.555948Z","iopub.execute_input":"2024-03-01T13:44:24.556328Z","iopub.status.idle":"2024-03-01T13:45:11.681335Z","shell.execute_reply.started":"2024-03-01T13:44:24.556293Z","shell.execute_reply":"2024-03-01T13:45:11.680274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"interpreter = tf.lite.Interpreter(\"/kaggle/working/models/model.tflite\")\n\nREQUIRED_SIGNATURE = \"serving_default\"\nREQUIRED_OUTPUT = \"outputs\"\n\nwith open (\"/kaggle/input/asl-fingerspelling/character_to_prediction_index.json\", \"r\") as f:\n    character_map = json.load(f)\nrev_character_map = {j:i for i,j in character_map.items()}\nfound_signatures = list(interpreter.get_signature_list().keys())\nif REQUIRED_SIGNATURE not in found_signatures:\n    raise KernelEvalException('Required input signature not found.')\n\nprediction_fn = interpreter.get_signature_runner(\"serving_default\")\noutput = prediction_fn(inputs=batch[0][39])\nprediction_str = \"\".join([rev_character_map.get(s, \"\") for s in np.argmax(output[REQUIRED_OUTPUT], axis=1)])\nprint(prediction_str)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T13:45:11.682863Z","iopub.execute_input":"2024-03-01T13:45:11.683271Z","iopub.status.idle":"2024-03-01T13:45:12.042332Z","shell.execute_reply.started":"2024-03-01T13:45:11.683228Z","shell.execute_reply":"2024-03-01T13:45:12.041219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class prediction_pipeline():\n    FRAME_LEN = 128\n    LPOSE = [13, 15, 17, 19, 21]\n    RPOSE = [14, 16, 18, 20, 22]\n    POSE = LPOSE + RPOSE\n    X = [f'x_right_hand_{i}' for i in range(21)] + [f'x_left_hand_{i}' for i in range(21)] + [f'x_pose_{i}' for i in POSE]\n    Y = [f'y_right_hand_{i}' for i in range(21)] + [f'y_left_hand_{i}' for i in range(21)] + [f'y_pose_{i}' for i in POSE]\n    Z = [f'z_right_hand_{i}' for i in range(21)] + [f'z_left_hand_{i}' for i in range(21)] + [f'z_pose_{i}' for i in POSE]\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 = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"pose\" in col and int(col[-2:]) in RPOSE]\n    LPOSE_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"pose\" in col and int(col[-2:]) in LPOSE]\n    pad_token = 'P'\n    start_token = '<'\n    end_token = '>'\n    pad_token_idx = 59\n    start_token_idx = 60\n    end_token_idx = 61\n    with open (\"/kaggle/input/asl-fingerspelling/character_to_prediction_index.json\", \"r\") as f:\n        char_to_num = json.load(f)\n\n    # Add pad_token, start pointer and end pointer to the dict\n    pad_token = 'P'\n    start_token = '<'\n    end_token = '>'\n    pad_token_idx = 59\n    start_token_idx = 60\n    end_token_idx = 61\n\n    char_to_num[pad_token] = pad_token_idx\n    char_to_num[start_token] = start_token_idx\n    char_to_num[end_token] = end_token_idx\n    num_to_char = {j:i for i,j in char_to_num.items()}\n    \n    def __init__(self , filename= None):\n        self.file = filename\n        \n    def get_record_bytes(self , dataframe):\n        parquet_numpy = dataframe.to_numpy()\n        features={}\n        #if 2*len(phrase)<no_nan:\n        features = {FEATURE_COLUMNS[i]: tf.train.Feature(\n        float_list=tf.train.FloatList(value=parquet_numpy[:, i])) for i in range(len(FEATURE_COLUMNS))}\n        features[\"phrase\"] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes('phrase', 'utf-8')]))\n        record_bytes = tf.train.Example(features=tf.train.Features(feature=features)).SerializeToString()\n        return record_bytes\n    \n    def decode_fn(self , record_bytes):\n        schema = {COL: tf.io.VarLenFeature(dtype=tf.float32) for COL in FEATURE_COLUMNS}\n        schema[\"phrase\"] = tf.io.FixedLenFeature([], dtype=tf.string)\n        features = tf.io.parse_single_example(record_bytes, schema)\n        phrase = features[\"phrase\"]\n        landmarks = ([tf.sparse.to_dense(features[COL]) for COL in FEATURE_COLUMNS])\n        # Transpose to maintain the original shape of landmarks data.\n        landmarks = tf.transpose(landmarks)\n        #print(landmarks)\n        return landmarks , phrase\n    \n\n        # Detect the dominant hand from the number of NaN values.\n        # Dominant hand will have less NaN values since it is in frame moving.\n        #This function preprocesses a tensor x representing hand and pose coordinates. \n        #It includes steps such as handling NaN values, normalizing the data, and resizing/padding the sequence.\n    def pre_process(self,x):\n        \n        \n        def resize_pad(x):\n            if tf.shape(x)[0] < FRAME_LEN:\n                x = tf.pad(x, ([[0, FRAME_LEN-tf.shape(x)[0]], [0, 0], [0, 0]]))\n            else:\n                x = tf.image.resize(x, (FRAME_LEN, tf.shape(x)[1]))\n            return x\n            \n        rhand = tf.gather(x, RHAND_IDX, axis=1)\n        lhand = tf.gather(x, LHAND_IDX, axis=1)\n        rpose = tf.gather(x, RPOSE_IDX, axis=1)\n        lpose = tf.gather(x, LPOSE_IDX, axis=1)\n\n        rnan_idx = tf.reduce_any(tf.math.is_nan(rhand), axis=1)\n        lnan_idx = tf.reduce_any(tf.math.is_nan(lhand), axis=1)\n\n        rnans = tf.math.count_nonzero(rnan_idx)\n        lnans = tf.math.count_nonzero(lnan_idx)\n\n        # For dominant hand\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 = tf.concat([1-hand_x, hand_y, hand_z], axis=1) # REVIEW NEEDED \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 = tf.concat([1-pose_x, pose_y, pose_z], axis=1) # REVIEW NEEDED \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 = tf.concat([hand_x[..., tf.newaxis], hand_y[..., tf.newaxis], hand_z[..., tf.newaxis]], axis=-1)\n\n        mean = tf.math.reduce_mean(hand, axis=1)[:, tf.newaxis, :]\n        std = tf.math.reduce_std(hand, axis=1)[:, tf.newaxis, :]\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 = tf.concat([pose_x[..., tf.newaxis], pose_y[..., tf.newaxis], pose_z[..., tf.newaxis]], axis=-1)\n\n        x = tf.concat([hand, pose], axis=1)\n        x = resize_pad(x)\n\n        x = tf.where(tf.math.is_nan(x), tf.zeros_like(x), x)\n        x = tf.reshape(x, (FRAME_LEN, len(LHAND_IDX) + len(LPOSE_IDX)))\n        \n        return x\n\n\n    def convert_fn(self,landmarks,phrase):\n        \n        table = tf.lookup.StaticHashTable(\n            initializer=tf.lookup.KeyValueTensorInitializer(\n                keys=list(char_to_num.keys()),\n                values=list(char_to_num.values()),\n            ),\n            default_value=tf.constant(-1),\n            name=\"class_weight\"\n        )\n\n        #Add start and end pointers to phrase.\n        phrase = start_token + phrase + end_token\n        phrase = tf.strings.bytes_split(phrase)\n        phrase = table.lookup(phrase)\n        #Vectorize and add padding.\n        phrase = tf.pad(phrase, paddings=[[0, 64 - tf.shape(phrase)[0]]], mode = 'CONSTANT',\n                        constant_values = pad_token_idx)\n        #Apply pre_process function to the landmarks.\n        #Output: the preprocessed landmarks and the preprocessed padded phrase\n        return self.pre_process(landmarks) , phrase\n    \n    def prediction(self , batch):\n        \n        interpreter = tf.lite.Interpreter(\"/kaggle/working/models/model.tflite\")\n\n        REQUIRED_SIGNATURE = \"serving_default\"\n        REQUIRED_OUTPUT = \"outputs\"\n\n        with open (\"/kaggle/input/asl-fingerspelling/character_to_prediction_index.json\", \"r\") as f:\n            character_map = json.load(f)\n        rev_character_map = {j:i for i,j in character_map.items()}\n        found_signatures = list(interpreter.get_signature_list().keys())\n        if REQUIRED_SIGNATURE not in found_signatures:\n            raise KernelEvalException('Required input signature not found.')\n\n        prediction_fn = interpreter.get_signature_runner(\"serving_default\")\n        output = prediction_fn(inputs=batch[0])\n        prediction_str = \"\".join([rev_character_map.get(s, \"\") for s in np.argmax(output[REQUIRED_OUTPUT], axis=1)])\n        \n        return prediction_str\n        \n\n    def call(self , dataframe):\n        record_bytes = self.get_record_bytes(dataframe)\n        landmarks , phrases = self.decode_fn(record_bytes)\n        preprocessed_data = self.convert_fn(landmarks,phrases)\n        prediction = self.prediction(preprocessed_data)\n        \n        return prediction\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-03-01T14:02:41.73003Z","iopub.execute_input":"2024-03-01T14:02:41.730408Z","iopub.status.idle":"2024-03-01T14:02:41.773449Z","shell.execute_reply.started":"2024-03-01T14:02:41.730377Z","shell.execute_reply":"2024-03-01T14:02:41.77256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pq_file = f\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\"\n    # Filter train.csv and fetch entries only for the relevant file_id\nfile_df = data_train.loc[data_train[\"file_id\"] == '5414471']\n# Fetch the parquet file\nparquet_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/5414471.parquet\",\n                          columns=['sequence_id'] + FEATURE_COLUMNS).to_pandas()\n# File name for the updated data\nseq_id = 1816796431\nparquest_test_df = parquet_df[parquet_df.index ==1816796431]\nparquest_test_df\n# r_nonan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis = 1) == 0)\n# l_nonan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis = 1) == 0)\n# no_nan = max(r_nonan, l_nonan)\n# print(no_nan)\n# features={}\n# #if 2*len(phrase)<no_nan:\n# features = {FEATURE_COLUMNS[i]: tf.train.Feature(\n# float_list=tf.train.FloatList(value=frames[:, i])) for i in range(len(FEATURE_COLUMNS))}\n# features[\"phrase\"] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(phrase, 'utf-8')]))\n# record_bytes = tf.train.Example(features=tf.train.Features(feature=features)).SerializeToString()\n\n# sample_input = convert_fn(decode_fn(record_bytes)[0],decode_fn(record_bytes)[1])\n# sample_input[0]\n\npred_pipe = prediction_pipeline()\npred_pipe.call(parquest_test_df)","metadata":{"execution":{"iopub.status.busy":"2024-03-01T14:02:42.062596Z","iopub.execute_input":"2024-03-01T14:02:42.062932Z","iopub.status.idle":"2024-03-01T14:02:42.740682Z","shell.execute_reply.started":"2024-03-01T14:02:42.062902Z","shell.execute_reply":"2024-03-01T14:02:42.739733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}