research-rainfallradar/aimodel/src/rainfallwater_identity_TEST.ipynb

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 "cells": [
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   "execution_count": 6,
   "id": "1f6fdebf-69c5-46ab-a5a8-f9c91f000ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from lib.dataset.parse_heightmap import parse_heightmap\n",
    "from lib.ai.model_rainfallwater_mono import model_rainfallwater_mono\n",
    "from lib.ai.helpers.make_callbacks import make_callbacks\n",
    "from lib.ai.helpers.summarywriter import summarywriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07093079",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_heightmap=\"/mnt/research-data/main/terrain50-nimrodsized.json.gz\"\n",
    "\n",
    "dir_output = \"/tmp/x/mono_segment_TEST\"\n",
    "if not os.path.exists(os.path.join(dir_output, \"checkpoints\")):\n",
    "\tos.makedirs(os.path.join(dir_output, \"checkpoints\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4466ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RAINFALL channels 1 width 64 height 64 HEIGHTMAP_INPUT False\n",
      "convnext:shape IN x (None, 64, 64, 1)\n",
      "DEBUG:convnext shape x (None, 64, 64, 1)\n",
      "DEBUG:model ENCODER output_shape (None, 512)\n",
      "DEBUG:model BOTTLENECK:stack2image output_shape (None, 4, 4, 512)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-09 17:35:07.608 | WARNING  | lib.ai.model_rainfallwater_mono:model_rainfallwater_mono:70 - Warning: TODO implement attention from https://ieeexplore.ieee.org/document/9076883\n",
      "2022-12-09 17:35:07.656 | INFO     | lib.ai.model_rainfallwater_mono:model_rainfallwater_mono:80 - learning_rate: None\n"
     ]
    }
   ],
   "source": [
    "model = model_rainfallwater_mono(\n",
    "\tmetadata={ \"rainfallradar\": [ 1, 64, 64 ] },\n",
    "\tmodel_arch_dec=\"convnext_i_xxtiny\"\n",
    ")\n",
    "\n",
    "summarywriter(model, filepath_output=os.path.join(dir_output, \"summary.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78c633e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cells 4096 cells/2 2048.0 shape+ (64, 64, 2) tf.Tensor(2401, shape=(), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "heightmap = parse_heightmap(filepath_heightmap) / 100\n",
    "heightmap = tf.image.crop_to_bounding_box(tf.expand_dims(heightmap, axis=-1), 0, 0, 64, 64)\n",
    "heightmap_labels = tf.one_hot(tf.cast(tf.math.greater(tf.squeeze(heightmap)/10, 0.05), dtype=tf.int32), 2)\n",
    "\n",
    "dataset = tf.data.Dataset.from_tensor_slices([heightmap])\n",
    "dataset_labels = tf.data.Dataset.from_tensor_slices([heightmap_labels])\n",
    "\n",
    "for item in dataset_labels:\n",
    "\tprint(\"cells\", 64*64, \"cells/2\", (64*64)/2, \"shape+\", item.shape, tf.math.reduce_sum(tf.math.argmax(item, axis=-1)))\n",
    "\tbreak\n",
    "dataset = tf.data.Dataset.zip((dataset_labels, dataset_labels)) \\\n",
    "\t.repeat(64 * 64) \\\n",
    "\t.batch(64) \\\n",
    "\t.prefetch(tf.data.AUTOTUNE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3dbc95eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 64, 64, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 64, 64, 1), dtype=tf.float32, name='input_3'), name='input_3', description=\"created by layer 'input_3'\"), but it was called on an input with incompatible shape (None, 64, 64, 2).\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 64, 64, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 64, 64, 1), dtype=tf.float32, name='input_4'), name='input_4', description=\"created by layer 'input_4'\"), but it was called on an input with incompatible shape (None, 64, 64, 2).\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 889, in train_step\n        y_pred = self(x, training=True)\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/input_spec.py\", line 248, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer \"convnext1\" (type Functional).\n    \n    Input 0 of layer \"downsample_layers.0.0_conv\" is incompatible with the layer: expected axis -1 of input shape to have value 1, but received input with shape (None, 64, 64, 2)\n    \n    Call arguments received by layer \"convnext1\" (type Functional):\n      • inputs=tf.Tensor(shape=(None, 64, 64, 2), dtype=float32)\n      • training=True\n      • mask=None\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model\u001b[39m.\u001b[39mfit(\n\u001b[1;32m      2\u001b[0m \tdataset,\n\u001b[1;32m      3\u001b[0m \tepochs\u001b[39m=\u001b[39m\u001b[39m50\u001b[39m,\n\u001b[1;32m      4\u001b[0m \tcallbacks\u001b[39m=\u001b[39mmake_callbacks(\u001b[39m\"\u001b[39m\u001b[39m/tmp/x/mono_segment_TEST\u001b[39m\u001b[39m\"\u001b[39m, model)\n\u001b[1;32m      5\u001b[0m )\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:  \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m---> 67\u001b[0m   \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[1;32m     68\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m   \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/tmp/__autograph_generated_filey37nprp8.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[39m=\u001b[39m ag__\u001b[39m.\u001b[39mconverted_call(ag__\u001b[39m.\u001b[39mld(step_function), (ag__\u001b[39m.\u001b[39mld(\u001b[39mself\u001b[39m), ag__\u001b[39m.\u001b[39mld(iterator)), \u001b[39mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/training.py\", line 889, in train_step\n        y_pred = self(x, training=True)\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/home/bryan-smithl/.local/lib/python3.10/site-packages/keras/engine/input_spec.py\", line 248, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Exception encountered when calling layer \"convnext1\" (type Functional).\n    \n    Input 0 of layer \"downsample_layers.0.0_conv\" is incompatible with the layer: expected axis -1 of input shape to have value 1, but received input with shape (None, 64, 64, 2)\n    \n    Call arguments received by layer \"convnext1\" (type Functional):\n      • inputs=tf.Tensor(shape=(None, 64, 64, 2), dtype=float32)\n      • training=True\n      • mask=None\n"
     ]
    }
   ],
   "source": [
    "model.fit(\n",
    "\tdataset,\n",
    "\tepochs=50,\n",
    "\tcallbacks=make_callbacks(\"/tmp/x/mono_segment_TEST\", model)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7f8c33f",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(tf.expand_dims(heightmap, axis=0))\n",
    "print(tf.math.argmax(prediction, axis=-1))\n",
    "print(tf.math.reduce_sum(tf.math.argmax(prediction, axis=-1)))\n",
    "print(prediction)\n"
   ]
  }
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