{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":46105,"databundleVersionId":5087314,"sourceType":"competition"},{"sourceId":5600436,"sourceType":"datasetVersion","datasetId":3221731}],"dockerImageVersionId":30408,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**NOTE: This submission utilizes the maximum inference time limit, so depending on the situation, a submission scoring error may occur. \n\nHowever, you can succeed by trying multiple times.**","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport json\nimport os\nfrom multiprocessing import cpu_count\n\ndef read_json_file(file_path):\n    \"\"\"Read a JSON file and parse it into a Python object.\n\n    Args:\n        file_path (str): The path to the JSON file to read.\n\n    Returns:\n        dict: A dictionary object representing the JSON data.\n        \n    Raises:\n        FileNotFoundError: If the specified file path does not exist.\n        ValueError: If the specified file path does not contain valid JSON data.\n    \"\"\"\n    try:\n        # Open the file and load the JSON data into a Python object\n        with open(file_path, 'r') as file:\n            json_data = json.load(file)\n        return json_data\n    except FileNotFoundError:\n        # Raise an error if the file path does not exist\n        raise FileNotFoundError(f\"File not found: {file_path}\")\n    except ValueError:\n        # Raise an error if the file does not contain valid JSON data\n        raise ValueError(f\"Invalid JSON data in file: {file_path}\")\n\ncpu_count()","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:00.59514Z","iopub.execute_input":"2024-03-15T02:04:00.595474Z","iopub.status.idle":"2024-03-15T02:04:08.741098Z","shell.execute_reply.started":"2024-03-15T02:04:00.595442Z","shell.execute_reply":"2024-03-15T02:04:08.740055Z"},"trusted":true},"execution_count":1,"outputs":[{"execution_count":1,"output_type":"execute_result","data":{"text/plain":"4"},"metadata":{}}]},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/asl-signs/train.csv')\nprint(\"\\n\\n... LOAD SIGN TO PREDICTION INDEX MAP FROM JSON FILE ...\\n\")\ns2p_map = {k.lower():v for k,v in read_json_file(os.path.join(\"/kaggle/input/asl-signs/sign_to_prediction_index_map.json\")).items()}\np2s_map = {v:k for k,v in read_json_file(os.path.join(\"/kaggle/input/asl-signs/sign_to_prediction_index_map.json\")).items()}\nencoder = lambda x: s2p_map.get(x.lower())\ndecoder = lambda x: p2s_map.get(x)\n# print(s2p_map)\ntrain_df['label'] = train_df.sign.map(encoder)","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:08.743395Z","iopub.execute_input":"2024-03-15T02:04:08.744035Z","iopub.status.idle":"2024-03-15T02:04:09.039412Z","shell.execute_reply.started":"2024-03-15T02:04:08.744002Z","shell.execute_reply":"2024-03-15T02:04:09.038343Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"\n\n... LOAD SIGN TO PREDICTION INDEX MAP FROM JSON FILE ...\n\n","output_type":"stream"}]},{"cell_type":"code","source":"ROWS_PER_FRAME = 543\nMAX_LEN = 384\nCROP_LEN = MAX_LEN\nNUM_CLASSES  = 250\nPAD = -100.\nNOSE=[\n    1,2,98,327\n]\nLNOSE = [98]\nRNOSE = [327]\nLIP = [ 0, \n    61, 185, 40, 39, 37, 267, 269, 270, 409,\n    291, 146, 91, 181, 84, 17, 314, 405, 321, 375,\n    78, 191, 80, 81, 82, 13, 312, 311, 310, 415,\n    95, 88, 178, 87, 14, 317, 402, 318, 324, 308,\n]\nLLIP = [84,181,91,146,61,185,40,39,37,87,178,88,95,78,191,80,81,82]\nRLIP = [314,405,321,375,291,409,270,269,267,317,402,318,324,308,415,310,311,312]\n\nPOSE = [500, 502, 504, 501, 503, 505, 512, 513]\nLPOSE = [513,505,503,501]\nRPOSE = [512,504,502,500]\n\nREYE = [\n    33, 7, 163, 144, 145, 153, 154, 155, 133,\n    246, 161, 160, 159, 158, 157, 173,\n]\nLEYE = [\n    263, 249, 390, 373, 374, 380, 381, 382, 362,\n    466, 388, 387, 386, 385, 384, 398,\n]\n\nLHAND = np.arange(468, 489).tolist()\nRHAND = np.arange(522, 543).tolist()\n\nPOINT_LANDMARKS = LIP + LHAND + RHAND + NOSE + REYE + LEYE #+POSE\n\nNUM_NODES = len(POINT_LANDMARKS)\nCHANNELS = 6*NUM_NODES\n\nprint(NUM_NODES)\nprint(CHANNELS)\n\ndef tf_nan_mean(x, axis=0, keepdims=False):\n    return tf.reduce_sum(tf.where(tf.math.is_nan(x), tf.zeros_like(x), x), axis=axis, keepdims=keepdims) / tf.reduce_sum(tf.where(tf.math.is_nan(x), tf.zeros_like(x), tf.ones_like(x)), axis=axis, keepdims=keepdims)\n\ndef tf_nan_std(x, center=None, axis=0, keepdims=False):\n    if center is None:\n        center = tf_nan_mean(x, axis=axis,  keepdims=True)\n    d = x - center\n    return tf.math.sqrt(tf_nan_mean(d * d, axis=axis, keepdims=keepdims))\n\nclass Preprocess(tf.keras.layers.Layer):\n    def __init__(self, max_len=MAX_LEN, point_landmarks=POINT_LANDMARKS, **kwargs):\n        super().__init__(**kwargs)\n        self.max_len = max_len\n        self.point_landmarks = point_landmarks\n\n    def call(self, inputs):\n        if tf.rank(inputs) == 3:\n            x = inputs[None,...]\n        else:\n            x = inputs\n        \n        mean = tf_nan_mean(tf.gather(x, [17], axis=2), axis=[1,2], keepdims=True)\n        mean = tf.where(tf.math.is_nan(mean), tf.constant(0.5,x.dtype), mean)\n        x = tf.gather(x, self.point_landmarks, axis=2) #N,T,P,C\n        std = tf_nan_std(x, center=mean, axis=[1,2], keepdims=True)\n        \n        x = (x - mean)/std\n\n        if self.max_len is not None:\n            x = x[:,:self.max_len]\n        length = tf.shape(x)[1]\n        x = x[...,:2]\n\n        dx = tf.cond(tf.shape(x)[1]>1,lambda:tf.pad(x[:,1:] - x[:,:-1], [[0,0],[0,1],[0,0],[0,0]]),lambda:tf.zeros_like(x))\n\n        dx2 = tf.cond(tf.shape(x)[1]>2,lambda:tf.pad(x[:,2:] - x[:,:-2], [[0,0],[0,2],[0,0],[0,0]]),lambda:tf.zeros_like(x))\n\n        x = tf.concat([\n            tf.reshape(x, (-1,length,2*len(self.point_landmarks))),\n            tf.reshape(dx, (-1,length,2*len(self.point_landmarks))),\n            tf.reshape(dx2, (-1,length,2*len(self.point_landmarks))),\n        ], axis = -1)\n        \n        x = tf.where(tf.math.is_nan(x),tf.constant(0.,x.dtype),x)\n        \n        return x","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:09.041013Z","iopub.execute_input":"2024-03-15T02:04:09.041441Z","iopub.status.idle":"2024-03-15T02:04:09.074625Z","shell.execute_reply.started":"2024-03-15T02:04:09.041398Z","shell.execute_reply":"2024-03-15T02:04:09.073709Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"118\n708\n","output_type":"stream"}]},{"cell_type":"code","source":"class ECA(tf.keras.layers.Layer):\n    def __init__(self, kernel_size=5, **kwargs):\n        super().__init__(**kwargs)\n        self.supports_masking = True\n        self.kernel_size = kernel_size\n        self.conv = tf.keras.layers.Conv1D(1, kernel_size=kernel_size, strides=1, padding=\"same\", use_bias=False)\n\n    def call(self, inputs, mask=None):\n        nn = tf.keras.layers.GlobalAveragePooling1D()(inputs, mask=mask)\n        nn = tf.expand_dims(nn, -1)\n        nn = self.conv(nn)\n        nn = tf.squeeze(nn, -1)\n        nn = tf.nn.sigmoid(nn)\n        nn = nn[:,None,:]\n        return inputs * nn\n\nclass LateDropout(tf.keras.layers.Layer):\n    def __init__(self, rate, noise_shape=None, start_step=0, **kwargs):\n        super().__init__(**kwargs)\n        self.supports_masking = True\n        self.rate = rate\n        self.start_step = start_step\n        self.dropout = tf.keras.layers.Dropout(rate, noise_shape=noise_shape)\n      \n    def build(self, input_shape):\n        super().build(input_shape)\n        agg = tf.VariableAggregation.ONLY_FIRST_REPLICA\n        self._train_counter = tf.Variable(0, dtype=\"int64\", aggregation=agg, trainable=False)\n\n    def call(self, inputs, training=False):\n        x = tf.cond(self._train_counter < self.start_step, lambda:inputs, lambda:self.dropout(inputs, training=training))\n        if training:\n            self._train_counter.assign_add(1)\n        return x\n\nclass CausalDWConv1D(tf.keras.layers.Layer):\n    def __init__(self, \n        kernel_size=17,\n        dilation_rate=1,\n        use_bias=False,\n        depthwise_initializer='glorot_uniform',\n        name='', **kwargs):\n        super().__init__(name=name,**kwargs)\n        self.causal_pad = tf.keras.layers.ZeroPadding1D((dilation_rate*(kernel_size-1),0),name=name + '_pad')\n        self.dw_conv = tf.keras.layers.DepthwiseConv1D(\n                            kernel_size,\n                            strides=1,\n                            dilation_rate=dilation_rate,\n                            padding='valid',\n                            use_bias=use_bias,\n                            depthwise_initializer=depthwise_initializer,\n                            name=name + '_dwconv')\n        self.supports_masking = True\n        \n    def call(self, inputs):\n        x = self.causal_pad(inputs)\n        x = self.dw_conv(x)\n        return x\n\ndef Conv1DBlock(channel_size,\n          kernel_size,\n          dilation_rate=1,\n          drop_rate=0.0,\n          expand_ratio=2,\n          se_ratio=0.25,\n          activation='swish',\n          name=None):\n    '''\n    efficient conv1d block, @hoyso48\n    '''\n    if name is None:\n        name = str(tf.keras.backend.get_uid(\"mbblock\"))\n    # Expansion phase\n    def apply(inputs):\n        channels_in = tf.keras.backend.int_shape(inputs)[-1]\n        channels_expand = channels_in * expand_ratio\n\n        skip = inputs\n\n        x = tf.keras.layers.Dense(\n            channels_expand,\n            use_bias=True,\n            activation=activation,\n            name=name + '_expand_conv')(inputs)\n\n        # Depthwise Convolution\n        x = CausalDWConv1D(kernel_size,\n            dilation_rate=dilation_rate,\n            use_bias=False,\n            name=name + '_dwconv')(x)\n\n        x = tf.keras.layers.BatchNormalization(momentum=0.95, name=name + '_bn')(x)\n\n        x  = ECA()(x)\n\n        x = tf.keras.layers.Dense(\n            channel_size,\n            use_bias=True,\n            name=name + '_project_conv')(x)\n\n        if drop_rate > 0:\n            x = tf.keras.layers.Dropout(drop_rate, noise_shape=(None,1,1), name=name + '_drop')(x)\n\n        if (channels_in == channel_size):\n            x = tf.keras.layers.add([x, skip], name=name + '_add')\n        return x\n\n    return apply","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:09.07635Z","iopub.execute_input":"2024-03-15T02:04:09.076727Z","iopub.status.idle":"2024-03-15T02:04:09.102203Z","shell.execute_reply.started":"2024-03-15T02:04:09.076695Z","shell.execute_reply":"2024-03-15T02:04:09.101273Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"class MultiHeadSelfAttention(tf.keras.layers.Layer):\n    def __init__(self, dim=256, num_heads=4, dropout=0, **kwargs):\n        super().__init__(**kwargs)\n        self.dim = dim\n        self.scale = self.dim ** -0.5\n        self.num_heads = num_heads\n        self.qkv = tf.keras.layers.Dense(3 * dim, use_bias=False)\n        self.drop1 = tf.keras.layers.Dropout(dropout)\n        self.proj = tf.keras.layers.Dense(dim, use_bias=False)\n        self.supports_masking = True\n\n    def call(self, inputs, mask=None):\n        qkv = self.qkv(inputs)\n        qkv = tf.keras.layers.Permute((2, 1, 3))(tf.keras.layers.Reshape((-1, self.num_heads, self.dim * 3 // self.num_heads))(qkv))\n        q, k, v = tf.split(qkv, [self.dim // self.num_heads] * 3, axis=-1)\n\n        attn = tf.matmul(q, k, transpose_b=True) * self.scale\n\n        if mask is not None:\n            mask = mask[:, None, None, :]\n\n        attn = tf.keras.layers.Softmax(axis=-1)(attn, mask=mask)\n        attn = self.drop1(attn)\n\n        x = attn @ v\n        x = tf.keras.layers.Reshape((-1, self.dim))(tf.keras.layers.Permute((2, 1, 3))(x))\n        x = self.proj(x)\n        return x\n\n\ndef TransformerBlock(dim=256, num_heads=4, expand=4, attn_dropout=0.2, drop_rate=0.2, activation='swish'):\n    def apply(inputs):\n        x = inputs\n        x = tf.keras.layers.BatchNormalization(momentum=0.95)(x)\n        x = MultiHeadSelfAttention(dim=dim,num_heads=num_heads,dropout=attn_dropout)(x)\n        x = tf.keras.layers.Dropout(drop_rate, noise_shape=(None,1,1))(x)\n        x = tf.keras.layers.Add()([inputs, x])\n        attn_out = x\n\n        x = tf.keras.layers.BatchNormalization(momentum=0.95)(x)\n        x = tf.keras.layers.Dense(dim*expand, use_bias=False, activation=activation)(x)\n        x = tf.keras.layers.Dense(dim, use_bias=False)(x)\n        x = tf.keras.layers.Dropout(drop_rate, noise_shape=(None,1,1))(x)\n        x = tf.keras.layers.Add()([attn_out, x])\n        return x\n    return apply","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:09.105389Z","iopub.execute_input":"2024-03-15T02:04:09.105999Z","iopub.status.idle":"2024-03-15T02:04:09.124645Z","shell.execute_reply.started":"2024-03-15T02:04:09.105968Z","shell.execute_reply":"2024-03-15T02:04:09.123805Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def get_model(max_len=MAX_LEN, dropout_step=0, dim=192):\n    inp = tf.keras.Input((max_len,CHANNELS))\n    #x = tf.keras.layers.Masking(mask_value=PAD,input_shape=(max_len,CHANNELS))(inp) #we don't need masking layer with inference\n    x = inp\n    ksize = 17\n    x = tf.keras.layers.Dense(dim, use_bias=False,name='stem_conv')(x)\n    x = tf.keras.layers.BatchNormalization(momentum=0.95,name='stem_bn')(x)\n\n    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n    x = TransformerBlock(dim,expand=2)(x)\n\n    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n    x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n    x = TransformerBlock(dim,expand=2)(x)\n\n    if dim == 384: #for the 4x sized model\n        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n        x = TransformerBlock(dim,expand=2)(x)\n\n        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n        x = Conv1DBlock(dim,ksize,drop_rate=0.2)(x)\n        x = TransformerBlock(dim,expand=2)(x)\n\n    x = tf.keras.layers.Dense(dim*2,activation=None,name='top_conv')(x)\n    x = tf.keras.layers.GlobalAveragePooling1D()(x)\n    x = LateDropout(0.8, start_step=dropout_step)(x)\n    x = tf.keras.layers.Dense(NUM_CLASSES,name='classifier')(x)\n    return tf.keras.Model(inp, x)","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:09.125681Z","iopub.execute_input":"2024-03-15T02:04:09.125941Z","iopub.status.idle":"2024-03-15T02:04:09.142431Z","shell.execute_reply.started":"2024-03-15T02:04:09.125916Z","shell.execute_reply":"2024-03-15T02:04:09.141207Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"models_path = [\n              '/kaggle/input/islr-models/islr-fp16-192-8-seed42-foldall-last.h5', #comment out other weights to check single model score\n               '/kaggle/input/islr-models/islr-fp16-192-8-seed43-foldall-last.h5',\n               '/kaggle/input/islr-models/islr-fp16-192-8-seed44-foldall-last.h5',\n               #'/kaggle/input/islr-models/islr-fp16-192-8-seed45-foldall-last.h5',\n              ]\nmodels = [get_model() for _ in models_path]\nfor model,path in zip(models,models_path):\n    model.load_weights(path)\nmodels[0].summary()","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:09.143895Z","iopub.execute_input":"2024-03-15T02:04:09.144232Z","iopub.status.idle":"2024-03-15T02:04:15.914682Z","shell.execute_reply.started":"2024-03-15T02:04:09.144201Z","shell.execute_reply":"2024-03-15T02:04:15.913613Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                   Output Shape         Param #     Connected to                     \n==================================================================================================\n input_1 (InputLayer)           [(None, 384, 708)]   0           []                               \n                                                                                                  \n stem_conv (Dense)              (None, 384, 192)     135936      ['input_1[0][0]']                \n                                                                                                  \n stem_bn (BatchNormalization)   (None, 384, 192)     768         ['stem_conv[0][0]']              \n                                                                                                  \n 1_expand_conv (Dense)          (None, 384, 384)     74112       ['stem_bn[0][0]']                \n                                                                                                  \n 1_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['1_expand_conv[0][0]']          \n                                                                                                  \n 1_bn (BatchNormalization)      (None, 384, 384)     1536        ['1_dwconv[0][0]']               \n                                                                                                  \n eca (ECA)                      (None, 384, 384)     5           ['1_bn[0][0]']                   \n                                                                                                  \n 1_project_conv (Dense)         (None, 384, 192)     73920       ['eca[0][0]']                    \n                                                                                                  \n 1_drop (Dropout)               (None, 384, 192)     0           ['1_project_conv[0][0]']         \n                                                                                                  \n 1_add (Add)                    (None, 384, 192)     0           ['1_drop[0][0]',                 \n                                                                  'stem_bn[0][0]']                \n                                                                                                  \n 2_expand_conv (Dense)          (None, 384, 384)     74112       ['1_add[0][0]']                  \n                                                                                                  \n 2_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['2_expand_conv[0][0]']          \n                                                                                                  \n 2_bn (BatchNormalization)      (None, 384, 384)     1536        ['2_dwconv[0][0]']               \n                                                                                                  \n eca_1 (ECA)                    (None, 384, 384)     5           ['2_bn[0][0]']                   \n                                                                                                  \n 2_project_conv (Dense)         (None, 384, 192)     73920       ['eca_1[0][0]']                  \n                                                                                                  \n 2_drop (Dropout)               (None, 384, 192)     0           ['2_project_conv[0][0]']         \n                                                                                                  \n 2_add (Add)                    (None, 384, 192)     0           ['2_drop[0][0]',                 \n                                                                  '1_add[0][0]']                  \n                                                                                                  \n 3_expand_conv (Dense)          (None, 384, 384)     74112       ['2_add[0][0]']                  \n                                                                                                  \n 3_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['3_expand_conv[0][0]']          \n                                                                                                  \n 3_bn (BatchNormalization)      (None, 384, 384)     1536        ['3_dwconv[0][0]']               \n                                                                                                  \n eca_2 (ECA)                    (None, 384, 384)     5           ['3_bn[0][0]']                   \n                                                                                                  \n 3_project_conv (Dense)         (None, 384, 192)     73920       ['eca_2[0][0]']                  \n                                                                                                  \n 3_drop (Dropout)               (None, 384, 192)     0           ['3_project_conv[0][0]']         \n                                                                                                  \n 3_add (Add)                    (None, 384, 192)     0           ['3_drop[0][0]',                 \n                                                                  '2_add[0][0]']                  \n                                                                                                  \n batch_normalization (BatchNorm  (None, 384, 192)    768         ['3_add[0][0]']                  \n alization)                                                                                       \n                                                                                                  \n multi_head_self_attention (Mul  (None, 384, 192)    147456      ['batch_normalization[0][0]']    \n tiHeadSelfAttention)                                                                             \n                                                                                                  \n dropout_1 (Dropout)            (None, 384, 192)     0           ['multi_head_self_attention[0][0]\n                                                                 ']                               \n                                                                                                  \n add (Add)                      (None, 384, 192)     0           ['3_add[0][0]',                  \n                                                                  'dropout_1[0][0]']              \n                                                                                                  \n batch_normalization_1 (BatchNo  (None, 384, 192)    768         ['add[0][0]']                    \n rmalization)                                                                                     \n                                                                                                  \n dense_2 (Dense)                (None, 384, 384)     73728       ['batch_normalization_1[0][0]']  \n                                                                                                  \n dense_3 (Dense)                (None, 384, 192)     73728       ['dense_2[0][0]']                \n                                                                                                  \n dropout_2 (Dropout)            (None, 384, 192)     0           ['dense_3[0][0]']                \n                                                                                                  \n add_1 (Add)                    (None, 384, 192)     0           ['add[0][0]',                    \n                                                                  'dropout_2[0][0]']              \n                                                                                                  \n 4_expand_conv (Dense)          (None, 384, 384)     74112       ['add_1[0][0]']                  \n                                                                                                  \n 4_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['4_expand_conv[0][0]']          \n                                                                                                  \n 4_bn (BatchNormalization)      (None, 384, 384)     1536        ['4_dwconv[0][0]']               \n                                                                                                  \n eca_3 (ECA)                    (None, 384, 384)     5           ['4_bn[0][0]']                   \n                                                                                                  \n 4_project_conv (Dense)         (None, 384, 192)     73920       ['eca_3[0][0]']                  \n                                                                                                  \n 4_drop (Dropout)               (None, 384, 192)     0           ['4_project_conv[0][0]']         \n                                                                                                  \n 4_add (Add)                    (None, 384, 192)     0           ['4_drop[0][0]',                 \n                                                                  'add_1[0][0]']                  \n                                                                                                  \n 5_expand_conv (Dense)          (None, 384, 384)     74112       ['4_add[0][0]']                  \n                                                                                                  \n 5_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['5_expand_conv[0][0]']          \n                                                                                                  \n 5_bn (BatchNormalization)      (None, 384, 384)     1536        ['5_dwconv[0][0]']               \n                                                                                                  \n eca_4 (ECA)                    (None, 384, 384)     5           ['5_bn[0][0]']                   \n                                                                                                  \n 5_project_conv (Dense)         (None, 384, 192)     73920       ['eca_4[0][0]']                  \n                                                                                                  \n 5_drop (Dropout)               (None, 384, 192)     0           ['5_project_conv[0][0]']         \n                                                                                                  \n 5_add (Add)                    (None, 384, 192)     0           ['5_drop[0][0]',                 \n                                                                  '4_add[0][0]']                  \n                                                                                                  \n 6_expand_conv (Dense)          (None, 384, 384)     74112       ['5_add[0][0]']                  \n                                                                                                  \n 6_dwconv (CausalDWConv1D)      (None, 384, 384)     6528        ['6_expand_conv[0][0]']          \n                                                                                                  \n 6_bn (BatchNormalization)      (None, 384, 384)     1536        ['6_dwconv[0][0]']               \n                                                                                                  \n eca_5 (ECA)                    (None, 384, 384)     5           ['6_bn[0][0]']                   \n                                                                                                  \n 6_project_conv (Dense)         (None, 384, 192)     73920       ['eca_5[0][0]']                  \n                                                                                                  \n 6_drop (Dropout)               (None, 384, 192)     0           ['6_project_conv[0][0]']         \n                                                                                                  \n 6_add (Add)                    (None, 384, 192)     0           ['6_drop[0][0]',                 \n                                                                  '5_add[0][0]']                  \n                                                                                                  \n batch_normalization_2 (BatchNo  (None, 384, 192)    768         ['6_add[0][0]']                  \n rmalization)                                                                                     \n                                                                                                  \n multi_head_self_attention_1 (M  (None, 384, 192)    147456      ['batch_normalization_2[0][0]']  \n ultiHeadSelfAttention)                                                                           \n                                                                                                  \n dropout_4 (Dropout)            (None, 384, 192)     0           ['multi_head_self_attention_1[0][\n                                                                 0]']                             \n                                                                                                  \n add_2 (Add)                    (None, 384, 192)     0           ['6_add[0][0]',                  \n                                                                  'dropout_4[0][0]']              \n                                                                                                  \n batch_normalization_3 (BatchNo  (None, 384, 192)    768         ['add_2[0][0]']                  \n rmalization)                                                                                     \n                                                                                                  \n dense_6 (Dense)                (None, 384, 384)     73728       ['batch_normalization_3[0][0]']  \n                                                                                                  \n dense_7 (Dense)                (None, 384, 192)     73728       ['dense_6[0][0]']                \n                                                                                                  \n dropout_5 (Dropout)            (None, 384, 192)     0           ['dense_7[0][0]']                \n                                                                                                  \n add_3 (Add)                    (None, 384, 192)     0           ['add_2[0][0]',                  \n                                                                  'dropout_5[0][0]']              \n                                                                                                  \n top_conv (Dense)               (None, 384, 384)     74112       ['add_3[0][0]']                  \n                                                                                                  \n global_average_pooling1d (Glob  (None, 384)         0           ['top_conv[0][0]']               \n alAveragePooling1D)                                                                              \n                                                                                                  \n late_dropout (LateDropout)     (None, 384)          1           ['global_average_pooling1d[0][0]'\n                                                                 ]                                \n                                                                                                  \n classifier (Dense)             (None, 250)          96250       ['late_dropout[0][0]']           \n                                                                                                  \n==================================================================================================\nTotal params: 1,836,569\nTrainable params: 1,830,040\nNon-trainable params: 6,529\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"class TFLiteModel(tf.Module):\n    \"\"\"\n    TensorFlow Lite model that takes input tensors and applies:\n        – a preprocessing model\n        – the ISLR model \n    \"\"\"\n\n    def __init__(self, islr_models):\n        \"\"\"\n        Initializes the TFLiteModel with the specified preprocessing model and ISLR model.\n        \"\"\"\n        super(TFLiteModel, self).__init__()\n\n        # Load the feature generation and main models\n        self.prep_inputs = Preprocess()\n        self.islr_models   = islr_models\n    \n    @tf.function(input_signature=[tf.TensorSpec(shape=[None, 543, 3], dtype=tf.float32, name='inputs')])\n    def __call__(self, inputs):\n        \"\"\"\n        Applies the feature generation model and main model to the input tensors.\n\n        Args:\n            inputs: Input tensor with shape [batch_size, 543, 3].\n\n        Returns:\n            A dictionary with a single key 'outputs' and corresponding output tensor.\n        \"\"\"\n        x = self.prep_inputs(tf.cast(inputs, dtype=tf.float32))\n        outputs = [model(x) for model in self.islr_models]\n        outputs = tf.keras.layers.Average()(outputs)[0]\n        return {'outputs': outputs}","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:15.915922Z","iopub.execute_input":"2024-03-15T02:04:15.916222Z","iopub.status.idle":"2024-03-15T02:04:15.925967Z","shell.execute_reply.started":"2024-03-15T02:04:15.916193Z","shell.execute_reply":"2024-03-15T02:04:15.924966Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"ROWS_PER_FRAME = 543  # number of landmarks per frame\ndef load_relevant_data_subset(pq_path):\n    data_columns = ['x', 'y', 'z']\n    data = pd.read_parquet('/kaggle/input/asl-signs/' + pq_path, columns=data_columns)\n    n_frames = int(len(data) / ROWS_PER_FRAME)\n    data = data.values.reshape(n_frames, ROWS_PER_FRAME, len(data_columns))\n    return data.astype(np.float32)","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:15.927449Z","iopub.execute_input":"2024-03-15T02:04:15.927749Z","iopub.status.idle":"2024-03-15T02:04:15.937827Z","shell.execute_reply.started":"2024-03-15T02:04:15.92772Z","shell.execute_reply":"2024-03-15T02:04:15.936852Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"train_df","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:28:47.979931Z","iopub.execute_input":"2024-03-15T02:28:47.980528Z","iopub.status.idle":"2024-03-15T02:28:48.001118Z","shell.execute_reply.started":"2024-03-15T02:28:47.980469Z","shell.execute_reply":"2024-03-15T02:28:48.000078Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"                                                path  participant_id  \\\n0      train_landmark_files/26734/1000035562.parquet           26734   \n1      train_landmark_files/28656/1000106739.parquet           28656   \n2       train_landmark_files/16069/100015657.parquet           16069   \n3      train_landmark_files/25571/1000210073.parquet           25571   \n4      train_landmark_files/62590/1000240708.parquet           62590   \n...                                              ...             ...   \n94472   train_landmark_files/53618/999786174.parquet           53618   \n94473   train_landmark_files/26734/999799849.parquet           26734   \n94474   train_landmark_files/25571/999833418.parquet           25571   \n94475   train_landmark_files/29302/999895257.parquet           29302   \n94476   train_landmark_files/36257/999962374.parquet           36257   \n\n       sequence_id    sign  label  \n0       1000035562    blow     25  \n1       1000106739    wait    232  \n2        100015657   cloud     48  \n3       1000210073    bird     23  \n4       1000240708    owie    164  \n...            ...     ...    ...  \n94472    999786174   white    238  \n94473    999799849    have    108  \n94474    999833418  flower     86  \n94475    999895257    room    188  \n94476    999962374   happy    105  \n\n[94477 rows x 5 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>path</th>\n      <th>participant_id</th>\n      <th>sequence_id</th>\n      <th>sign</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>train_landmark_files/26734/1000035562.parquet</td>\n      <td>26734</td>\n      <td>1000035562</td>\n      <td>blow</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>train_landmark_files/28656/1000106739.parquet</td>\n      <td>28656</td>\n      <td>1000106739</td>\n      <td>wait</td>\n      <td>232</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>train_landmark_files/16069/100015657.parquet</td>\n      <td>16069</td>\n      <td>100015657</td>\n      <td>cloud</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>train_landmark_files/25571/1000210073.parquet</td>\n      <td>25571</td>\n      <td>1000210073</td>\n      <td>bird</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>train_landmark_files/62590/1000240708.parquet</td>\n      <td>62590</td>\n      <td>1000240708</td>\n      <td>owie</td>\n      <td>164</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>94472</th>\n      <td>train_landmark_files/53618/999786174.parquet</td>\n      <td>53618</td>\n      <td>999786174</td>\n      <td>white</td>\n      <td>238</td>\n    </tr>\n    <tr>\n      <th>94473</th>\n      <td>train_landmark_files/26734/999799849.parquet</td>\n      <td>26734</td>\n      <td>999799849</td>\n      <td>have</td>\n      <td>108</td>\n    </tr>\n    <tr>\n      <th>94474</th>\n      <td>train_landmark_files/25571/999833418.parquet</td>\n      <td>25571</td>\n      <td>999833418</td>\n      <td>flower</td>\n      <td>86</td>\n    </tr>\n    <tr>\n      <th>94475</th>\n      <td>train_landmark_files/29302/999895257.parquet</td>\n      <td>29302</td>\n      <td>999895257</td>\n      <td>room</td>\n      <td>188</td>\n    </tr>\n    <tr>\n      <th>94476</th>\n      <td>train_landmark_files/36257/999962374.parquet</td>\n      <td>36257</td>\n      <td>999962374</td>\n      <td>happy</td>\n      <td>105</td>\n    </tr>\n  </tbody>\n</table>\n<p>94477 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"tflite_keras_model = TFLiteModel(islr_models=models)\ndemo_output = tflite_keras_model(load_relevant_data_subset(train_df.path[0]))[\"outputs\"]\ndecoder(np.argmax(demo_output.numpy(), axis=-1))","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:15.939172Z","iopub.execute_input":"2024-03-15T02:04:15.939563Z","iopub.status.idle":"2024-03-15T02:04:24.577477Z","shell.execute_reply.started":"2024-03-15T02:04:15.939534Z","shell.execute_reply":"2024-03-15T02:04:24.576296Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"'blow'"},"metadata":{}}]},{"cell_type":"code","source":"keras_model_converter = tf.lite.TFLiteConverter.from_keras_model(tflite_keras_model)\nkeras_model_converter.optimizations = [tf.lite.Optimize.DEFAULT]\nkeras_model_converter.target_spec.supported_types = [tf.float16]\ntflite_model = keras_model_converter.convert()\nwith open('/kaggle/working/model.tflite', 'wb') as f:\n    f.write(tflite_model)\n!zip submission.zip /kaggle/working/model.tflite","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:04:24.579104Z","iopub.execute_input":"2024-03-15T02:04:24.579542Z","iopub.status.idle":"2024-03-15T02:05:42.875958Z","shell.execute_reply.started":"2024-03-15T02:04:24.579499Z","shell.execute_reply":"2024-03-15T02:05:42.874861Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"  adding: kaggle/working/model.tflite (deflated 9%)\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install -q mediapipe","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:05:42.877686Z","iopub.execute_input":"2024-03-15T02:05:42.878036Z","iopub.status.idle":"2024-03-15T02:05:57.368412Z","shell.execute_reply.started":"2024-03-15T02:05:42.878002Z","shell.execute_reply":"2024-03-15T02:05:57.366128Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"code","source":"!wget -q -O image.jpg https://wonderwall.sg/images/default-source/content/dam/wonderwall/images/2022/09/singapore-sign-language-a-mode-of-communication-thats-uniquely-sg-leh/singapore_sign_language_rectangle.jpg.jpg?sfvrsn=c6f36_0\nimport cv2\nimport matplotlib.pyplot as plt\n\nimg = cv2.imread(\"image.jpg\")\nplt.imshow(img)","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:50:39.035982Z","iopub.execute_input":"2024-03-15T02:50:39.037087Z","iopub.status.idle":"2024-03-15T02:50:41.116518Z","shell.execute_reply.started":"2024-03-15T02:50:39.037044Z","shell.execute_reply":"2024-03-15T02:50:41.115337Z"},"trusted":true},"execution_count":73,"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x798f7c32b650>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"#@markdown We implemented some functions to visualize the hand landmark detection results. <br/> Run the following cell to activate the functions.\n\nfrom mediapipe import solutions\nfrom mediapipe.framework.formats import landmark_pb2\nimport numpy as np\n\nMARGIN = 10  # pixels\nFONT_SIZE = 1\nFONT_THICKNESS = 1\nHANDEDNESS_TEXT_COLOR = (88, 205, 54) # vibrant green\n\ndef draw_landmarks_on_image(rgb_image, detection_result):\n  hand_landmarks_list = detection_result.hand_landmarks\n  handedness_list = detection_result.handedness\n  annotated_image = np.copy(rgb_image)\n\n  # Loop through the detected hands to visualize.\n  for idx in range(len(hand_landmarks_list)):\n    hand_landmarks = hand_landmarks_list[idx]\n    handedness = handedness_list[idx]\n\n    # Draw the hand landmarks.\n    hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()\n    hand_landmarks_proto.landmark.extend([\n      landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in hand_landmarks\n    ])\n    solutions.drawing_utils.draw_landmarks(\n      annotated_image,\n      hand_landmarks_proto,\n      solutions.hands.HAND_CONNECTIONS,\n      solutions.drawing_styles.get_default_hand_landmarks_style(),\n      solutions.drawing_styles.get_default_hand_connections_style())\n\n    # Get the top left corner of the detected hand's bounding box.\n    height, width, _ = annotated_image.shape\n    x_coordinates = [landmark.x for landmark in hand_landmarks]\n    y_coordinates = [landmark.y for landmark in hand_landmarks]\n    text_x = int(min(x_coordinates) * width)\n    text_y = int(min(y_coordinates) * height) - MARGIN\n\n    # Draw handedness (left or right hand) on the image.\n    cv2.putText(annotated_image, f\"{handedness[0].category_name}\",\n                (text_x, text_y), cv2.FONT_HERSHEY_DUPLEX,\n                FONT_SIZE, HANDEDNESS_TEXT_COLOR, FONT_THICKNESS, cv2.LINE_AA)\n\n  return annotated_image","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:50:46.265691Z","iopub.execute_input":"2024-03-15T02:50:46.266097Z","iopub.status.idle":"2024-03-15T02:50:46.279388Z","shell.execute_reply.started":"2024-03-15T02:50:46.266062Z","shell.execute_reply":"2024-03-15T02:50:46.278285Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"!wget -q https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:43:55.110826Z","iopub.execute_input":"2024-03-15T02:43:55.111553Z","iopub.status.idle":"2024-03-15T02:43:57.067816Z","shell.execute_reply.started":"2024-03-15T02:43:55.111512Z","shell.execute_reply":"2024-03-15T02:43:57.066564Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"# STEP 1: Import the necessary modules.\nimport mediapipe as mp\nfrom mediapipe.tasks import python\nfrom mediapipe.tasks.python import vision\n\n# STEP 2: Create an HandLandmarker object.\nbase_options = python.BaseOptions(model_asset_path='hand_landmarker.task')\noptions = vision.HandLandmarkerOptions(base_options=base_options,\n                                       num_hands=2)\ndetector = vision.HandLandmarker.create_from_options(options)\n\n# STEP 3: Load the input image.\nimage = mp.Image.create_from_file(\"image.jpg\")\n\n# STEP 4: Detect hand landmarks from the input image.\ndetection_result = detector.detect(image)\n\n# STEP 5: Process the classification result. In this case, visualize it.\nannotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)\nplt.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:50:51.125198Z","iopub.execute_input":"2024-03-15T02:50:51.126148Z","iopub.status.idle":"2024-03-15T02:50:51.759665Z","shell.execute_reply.started":"2024-03-15T02:50:51.126108Z","shell.execute_reply":"2024-03-15T02:50:51.758652Z"},"trusted":true},"execution_count":75,"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x798f7c5adc50>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"ndata = []\nfor hand in detection_result.hand_landmarks:\n    for rec in hand:\n        ndata.append([rec.x,rec.y,rec.z])\nndata","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:51:13.206315Z","iopub.execute_input":"2024-03-15T02:51:13.207217Z","iopub.status.idle":"2024-03-15T02:51:13.216188Z","shell.execute_reply.started":"2024-03-15T02:51:13.207178Z","shell.execute_reply":"2024-03-15T02:51:13.215082Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"[[0.41600853204727173, 0.5782839059829712, -1.7087586456909776e-08],\n [0.3909696638584137, 0.5086855292320251, -0.013778266496956348],\n [0.3777078688144684, 0.4363550543785095, -0.02688114531338215],\n [0.37063130736351013, 0.3690997064113617, -0.03924405574798584],\n [0.36164671182632446, 0.32059037685394287, -0.0519232414662838],\n [0.43326687812805176, 0.3774924576282501, -0.026172533631324768],\n [0.4557206928730011, 0.2822970449924469, -0.03869980201125145],\n [0.4692821800708771, 0.2329561710357666, -0.045730240643024445],\n [0.4809442460536957, 0.19362567365169525, -0.050517596304416656],\n [0.4619608521461487, 0.3955521285533905, -0.026480942964553833],\n [0.48251932859420776, 0.2896175682544708, -0.03626544773578644],\n [0.4934448301792145, 0.23242071270942688, -0.04141230508685112],\n [0.5016483068466187, 0.19034361839294434, -0.04590306803584099],\n [0.48389098048210144, 0.42521923780441284, -0.02770877815783024],\n [0.5040891766548157, 0.3220471739768982, -0.03665708005428314],\n [0.5135798454284668, 0.26622146368026733, -0.0419255755841732],\n [0.518054187297821, 0.22392377257347107, -0.04566309228539467],\n [0.5033460259437561, 0.46230989694595337, -0.029993513599038124],\n [0.5239893198013306, 0.386481910943985, -0.03748602420091629],\n [0.532204270362854, 0.3423324227333069, -0.0406455397605896],\n [0.5364328622817993, 0.30213117599487305, -0.042395975440740585]]"},"metadata":{}}]},{"cell_type":"code","source":"while len(ndata) < 543:\n    ndata.insert(0, [None] * 3)\nndata","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:51:25.769783Z","iopub.execute_input":"2024-03-15T02:51:25.770186Z","iopub.status.idle":"2024-03-15T02:51:25.826747Z","shell.execute_reply.started":"2024-03-15T02:51:25.770141Z","shell.execute_reply":"2024-03-15T02:51:25.82559Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"[[None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, 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None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [None, None, None],\n [0.41600853204727173, 0.5782839059829712, -1.7087586456909776e-08],\n [0.3909696638584137, 0.5086855292320251, -0.013778266496956348],\n [0.3777078688144684, 0.4363550543785095, -0.02688114531338215],\n [0.37063130736351013, 0.3690997064113617, -0.03924405574798584],\n [0.36164671182632446, 0.32059037685394287, -0.0519232414662838],\n [0.43326687812805176, 0.3774924576282501, -0.026172533631324768],\n [0.4557206928730011, 0.2822970449924469, -0.03869980201125145],\n [0.4692821800708771, 0.2329561710357666, -0.045730240643024445],\n [0.4809442460536957, 0.19362567365169525, -0.050517596304416656],\n [0.4619608521461487, 0.3955521285533905, -0.026480942964553833],\n [0.48251932859420776, 0.2896175682544708, -0.03626544773578644],\n [0.4934448301792145, 0.23242071270942688, -0.04141230508685112],\n [0.5016483068466187, 0.19034361839294434, -0.04590306803584099],\n [0.48389098048210144, 0.42521923780441284, -0.02770877815783024],\n [0.5040891766548157, 0.3220471739768982, -0.03665708005428314],\n [0.5135798454284668, 0.26622146368026733, -0.0419255755841732],\n [0.518054187297821, 0.22392377257347107, -0.04566309228539467],\n [0.5033460259437561, 0.46230989694595337, -0.029993513599038124],\n [0.5239893198013306, 0.386481910943985, -0.03748602420091629],\n [0.532204270362854, 0.3423324227333069, -0.0406455397605896],\n [0.5364328622817993, 0.30213117599487305, -0.042395975440740585]]"},"metadata":{}}]},{"cell_type":"code","source":"nrec = np.array(ndata, dtype = np.float32).reshape(1,543, 3)\nnrec","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:51:33.470594Z","iopub.execute_input":"2024-03-15T02:51:33.471688Z","iopub.status.idle":"2024-03-15T02:51:33.483266Z","shell.execute_reply.started":"2024-03-15T02:51:33.471634Z","shell.execute_reply":"2024-03-15T02:51:33.48212Z"},"trusted":true},"execution_count":78,"outputs":[{"execution_count":78,"output_type":"execute_result","data":{"text/plain":"array([[[        nan,         nan,         nan],\n        [        nan,         nan,         nan],\n        [        nan,         nan,         nan],\n        ...,\n        [ 0.5239893 ,  0.3864819 , -0.03748602],\n        [ 0.5322043 ,  0.34233242, -0.04064554],\n        [ 0.53643286,  0.30213118, -0.04239598]]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"tflite_keras_model = TFLiteModel(islr_models=models)\ndemo_output = tflite_keras_model(nrec)[\"outputs\"]\ndecoder(np.argmax(demo_output.numpy(), axis=-1))","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:51:36.990985Z","iopub.execute_input":"2024-03-15T02:51:36.992111Z","iopub.status.idle":"2024-03-15T02:51:40.742171Z","shell.execute_reply.started":"2024-03-15T02:51:36.992067Z","shell.execute_reply":"2024-03-15T02:51:40.741137Z"},"trusted":true},"execution_count":79,"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"'morning'"},"metadata":{}}]},{"cell_type":"code","source":"data = df.values.reshape(1, 543, len(data_columns))","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:05:59.64151Z","iopub.status.idle":"2024-03-15T02:05:59.642019Z","shell.execute_reply.started":"2024-03-15T02:05:59.641747Z","shell.execute_reply":"2024-03-15T02:05:59.641775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#check inference time\n#code from @hengck23\nmode = 'd' #'d'ebug #'s'ubmit\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport shutil\nfrom datetime import datetime\nfrom timeit import default_timer as timer\n\n\nif mode in ['d']:  \n    try:\n        import tflite_runtime\n    except:\n        !pip install tflite-runtime\n\n    import tflite_runtime.interpreter as tflite   \n    import tflite_runtime\n    print(tflite_runtime.__version__)\n\nprint('import ok')\n'''\nYour model must also require less than 40 MB in memory and \nperform inference with less than 100 milliseconds of latency per video. \nExpect to see approximately 40,000 videos in the test set. \nWe allow an additional 10 minute buffer for loading the data and miscellaneous overhead.\n\n'''\ndef time_to_str(t, mode='min'):\n    if mode=='min':\n        t  = int(t)/60\n        hr = t//60\n        min = t%60\n        return '%2d hr %02d min'%(hr,min)\n\n    elif mode=='sec':\n        t   = int(t)\n        min = t//60\n        sec = t%60\n        return '%2d min %02d sec'%(min,sec)\n\n    else:\n        raise NotImplementedError\n\n        \nROWS_PER_FRAME = 543\ndef load_relevant_data_subset(pq_path):\n    data_columns = ['x', 'y', 'z']\n    data = pd.read_parquet(pq_path, columns=data_columns)\n    n_frames = int(len(data) / ROWS_PER_FRAME)\n    data = data.values.reshape(n_frames, ROWS_PER_FRAME, len(data_columns))\n    return data.astype(np.float32)\n\nif mode in ['d']:\n \n    interpreter = tflite.Interpreter('/kaggle/working/model.tflite')\n    prediction_fn = interpreter.get_signature_runner('serving_default')\n#     valid_df = pd.read_csv('/kaggle/input/asl-demo/train_prepared.csv') \n#     valid_df = train_df[train_df.fold==0].reset_index(drop=True)\n#     valid_df = valid_df[:1000]\n    valid_df = train_df[:1000]\n    valid_num = len(valid_df)\n    valid = {\n        'sign':[],\n    }\n\n    start_timer = timer()\n    for t, d in valid_df.iterrows():\n\n        pq_file = f'/kaggle/input/asl-signs/{d.path}'\n        #print(pq_file)\n        xyz = load_relevant_data_subset(pq_file)\n\n        output = prediction_fn(inputs=xyz)\n        p = output['outputs'].reshape(-1)\n\n        valid['sign'].append(p)\n\n        #---\n        if t%100==0:\n            time_taken = timer() - start_timer\n            print('\\r %8d / %d  %s'%(t,valid_num,time_to_str(time_taken,'sec')),end='',flush=True)\n\n    print('\\n')\n\n\n    truth = valid_df.label.values\n    sign  = np.stack(valid['sign'])\n    predict = np.argsort(-sign, -1)\n    correct = predict==truth.reshape(valid_num,1)\n    topk = correct.cumsum(-1).mean(0)[:5]\n\n\n    print(f'time_taken = {time_to_str(time_taken,\"sec\")}')\n    print(f'time_taken for LB = {time_taken*1000/valid_num:05f} msec\\n')\n    for i in range(5):\n        print(f'topk[{i}] = {topk[i]}')  \n    print('----- end -----\\n')","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:21:01.230405Z","iopub.execute_input":"2024-03-15T02:21:01.23118Z","iopub.status.idle":"2024-03-15T02:22:21.338954Z","shell.execute_reply.started":"2024-03-15T02:21:01.231137Z","shell.execute_reply":"2024-03-15T02:22:21.33764Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Collecting tflite-runtime\n  Downloading tflite_runtime-2.11.0-cp37-cp37m-manylinux2014_x86_64.whl (2.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy>=1.19.2 in /opt/conda/lib/python3.7/site-packages (from tflite-runtime) (1.21.6)\nInstalling collected packages: tflite-runtime\nSuccessfully installed tflite-runtime-2.11.0\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m2.11.0\nimport ok\n      900 / 1000   1 min 01 sec\n\ntime_taken =  1 min 01 sec\ntime_taken for LB = 61.741353 msec\n\ntopk[0] = 0.928\ntopk[1] = 0.957\ntopk[2] = 0.965\ntopk[3] = 0.967\ntopk[4] = 0.974\n----- end -----\n\n","output_type":"stream"}]},{"cell_type":"code","source":"interpreter = tflite.Interpreter('/kaggle/working/model.tflite')\nprediction_fn = interpreter.get_signature_runner('serving_default')\n#     valid_df = pd.read_csv('/kaggle/input/asl-demo/train_prepared.csv') \n#     valid_df = train_df[train_df.fold==0].reset_index(drop=True)\n#     valid_df = valid_df[:1000]\nvalid = {\n    'sign':[],\n}\n\n\noutput = prediction_fn(inputs=nrec)\np = output['outputs'].reshape(-1)\n\nvalid['sign'].append(p)\n\n\nsign  = np.stack(valid['sign'])\npredict = np.argsort(-sign, -1)\n\nprint(valid)\nprint(predict)","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:24:27.441015Z","iopub.execute_input":"2024-03-15T02:24:27.441482Z","iopub.status.idle":"2024-03-15T02:24:27.550533Z","shell.execute_reply.started":"2024-03-15T02:24:27.441444Z","shell.execute_reply":"2024-03-15T02:24:27.549372Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"{'sign': [array([-0.10954334,  0.4202963 , -0.4402346 , -0.20583458, -0.40947396,\n        0.32177237, -0.02699398,  0.23820627, -0.3868188 , -0.40804094,\n       -0.74196166, -0.77549833, -0.42998376,  0.22814219, -0.42145598,\n        0.2677334 , -0.3413164 , -0.05017353, -0.06200726, -0.40455633,\n        0.19186234,  0.5363112 , -0.07820019, -0.4798813 , -0.19742236,\n       -0.25775608, -0.49003726, -0.14375804,  0.04168924, -0.3933732 ,\n       -0.3977658 , -0.8892695 , -0.47356895, -0.4593662 , -0.38212895,\n        0.10524563, -0.45624423, -0.54967624, -0.15480444, -0.09241255,\n       -0.16525124, -0.3474116 ,  0.21466883, -0.48826957, -0.55062884,\n        0.35647452, -0.04324015, -0.6672554 , -0.27346382, -0.7017175 ,\n       -0.7486903 , -0.4971329 , -0.13602646, -0.01329957, -0.11995941,\n       -0.32108188,  0.4914538 , -0.29032642, -0.3028141 , -0.5301225 ,\n       -0.71018225,  0.14342307, -0.14821187, -0.4214047 , -0.03518893,\n       -0.45921406, -0.63300484, -0.41712853, -0.2151865 ,  0.14591418,\n        0.30370775,  0.08937735, -0.19644342, -0.07047718,  0.28865275,\n       -0.31965312, -0.0515073 ,  0.07479184, -0.30041134,  0.30856305,\n       -0.25072262, -0.37218395, -0.6237503 , -0.43567243, -0.20025612,\n       -0.27569252, -0.53606087, -0.10974529, -0.5641597 , -0.3203038 ,\n       -0.74914503, -0.31434298,  0.12277361,  0.26010475, -0.35354745,\n        0.74153477,  0.12217689,  0.05878178, -0.3541844 ,  0.01381121,\n       -0.5021014 ,  0.03961306, -0.14822726, -0.40391988, -0.567654  ,\n        0.08382415,  0.47572428,  0.39195764,  0.40420547,  0.15767592,\n        0.2714432 , -0.31843066,  0.02802361, -0.46026808, -0.25064364,\n       -0.16440651,  0.3480433 , -0.14469041, -0.62761146, -0.5193256 ,\n       -0.03694895,  0.03408654, -0.78028303,  0.06508867,  0.5158244 ,\n        0.20226686, -0.01723962, -0.3906445 ,  0.03016718, -0.15804538,\n        0.07229222, -0.5639137 ,  0.25683665, -0.2945157 , -0.46792156,\n       -0.30417204, -0.48559096, -0.18648024, -0.41917026, -0.32724175,\n       -0.1950826 , -0.13933183, -0.39358544, -0.1413083 , -0.24959338,\n       -0.11289532, -0.60857135, -0.23125502, -0.6385647 , -0.5611375 ,\n       -0.06840767,  0.17011839,  0.5624356 , -0.08108877,  0.09235302,\n       -0.21381408,  0.33931553,  0.2536148 , -0.32276908, -0.10615695,\n        0.09726796, -0.1231554 , -0.6389063 , -0.05817443, -0.09720089,\n       -0.79488885,  0.0481924 , -0.33881587, -0.26456344, -0.35373184,\n       -0.24953784, -0.19077824,  0.01387954, -0.14058836, -0.38434586,\n       -0.06124775, -0.2794551 , -0.6154995 , -0.24837707,  0.0452848 ,\n        0.13764738,  0.17619528, -0.68621534, -0.26003456,  0.03535462,\n       -0.18085505, -0.66773987,  0.08669329, -0.094877  , -0.27842388,\n        0.00914019, -0.04096794, -0.21343428, -0.19799362, -0.5217318 ,\n        0.22395161, -0.33477056, -0.5126225 , -0.13519125,  0.05986051,\n       -0.07586174,  0.00671536, -0.27063584, -0.49297294, -0.42022774,\n        0.14903916,  0.42980975, -0.11291754, -0.18525623, -0.04834896,\n       -0.29378206,  0.44108045, -0.40777227,  0.13299075,  0.34965092,\n       -0.13870215,  0.14104031, -0.03732315, -0.06157292, -0.5853266 ,\n        0.25688836, -0.42285416, -0.26445776, -0.13782637, -0.24349335,\n       -0.08694117, -0.5410592 , -0.42450115, -0.8089273 , -0.16036762,\n       -0.6763495 ,  0.53868246, -0.09150637, -0.7863289 , -0.5088374 ,\n        0.16083774, -0.10041223, -0.71379936,  0.0528497 , -0.28634244,\n       -0.23105733, -0.2400347 , -0.11538606, -0.3366039 , -0.18835796,\n       -0.7297571 ,  0.01045937,  0.18303916,  0.07985399,  0.5245837 ],\n      dtype=float32)]}\n[[ 95 152 231  21 249 124  56 106 211 206   1 108 107  45 214 116 156   5\n   79  70  74 110  15  93 220 132 157   7  13 195  42 125  20 247 181 151\n  235 109 205  69  61 216 180 213  92  96  35 160 154  71 187 105 248  77\n  130 123 199  97 238 166 179  28 101 184 121 128 112 172  99 246 190 201\n   53 126   6  64 120 217 191  46 209  17  76 163 175 218  18 150  73 200\n   22 153 225 232  39 188 164 236 159   0  87 145 207 242  54 161 198  52\n  223 215 141 173 143  27 117  62 102  38 129 229 115  40 185 208 137 244\n  171 140  72  24 193  84   3 192 155  68 240 147 241 224 178 170 144 114\n   80  25 183 222 168 202  48  85 189 176 239  57 210 133  78  58 135  91\n  111  75  89  55 158 139 196 243 167  16  41  94 169  98  81  34 174   8\n  127  29 142  30 103  19 212   9   4  67 138 204  63  14 221 227  12  83\n    2  36  65  33 113 134  32  23 136  43  26 203  51 100 234 197 119 194\n   59  86 226  37  44 149 131  88 104 219 146 177  82 118  66 148 162  47\n  186 230 182  49  60 237 245  10  50  90  11 122 233 165 228  31]]\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as ax\ndef plot_frame(frame, edges=[], idxs=[]):\n        \n    frame[np.isnan(frame)] = 0\n    x = list(frame[...,0])\n    y = list(frame[...,1])\n    if len(idxs) == 0:\n        idxs = list(range(len(x)))\n    #ax.clear()\n    ax.scatter(x, y, color='dodgerblue')\nplot_frame()","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:05:59.6459Z","iopub.status.idle":"2024-03-15T02:05:59.646432Z","shell.execute_reply.started":"2024-03-15T02:05:59.646132Z","shell.execute_reply":"2024-03-15T02:05:59.646157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install -q mediapipe","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:15:06.159505Z","iopub.execute_input":"2024-03-15T02:15:06.160331Z","iopub.status.idle":"2024-03-15T02:15:17.797296Z","shell.execute_reply.started":"2024-03-15T02:15:06.160278Z","shell.execute_reply":"2024-03-15T02:15:17.79613Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"code","source":"!wget -q https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:05:59.650657Z","iopub.status.idle":"2024-03-15T02:05:59.651264Z","shell.execute_reply.started":"2024-03-15T02:05:59.650947Z","shell.execute_reply":"2024-03-15T02:05:59.650979Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# STEP 1: Import the necessary modules.\nimport mediapipe as mp\nfrom mediapipe.tasks import python\nfrom mediapipe.tasks.python import vision\n\n# STEP 2: Create an HandLandmarker object.\nbase_options = python.BaseOptions(model_asset_path='hand_landmarker.task')\noptions = vision.HandLandmarkerOptions(base_options=base_options,\n                                       num_hands=2)\ndetector = vision.HandLandmarker.create_from_options(options)\n\n# STEP 3: Load the input image.\nimage = mp.Image.create_from_file(\"image.jpg\")\n\n# STEP 4: Detect hand landmarks from the input image.\ndetection_result = detector.detect(image)\n\n# STEP 5: Process the classification result. In this case, visualize it.\nannotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)\ncv2_imshow(cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))","metadata":{"execution":{"iopub.status.busy":"2024-03-15T02:05:59.652912Z","iopub.status.idle":"2024-03-15T02:05:59.653422Z","shell.execute_reply.started":"2024-03-15T02:05:59.653141Z","shell.execute_reply":"2024-03-15T02:05:59.653166Z"},"trusted":true},"execution_count":null,"outputs":[]}]}