diff --git a/README.md b/README.md index 100ff53..3fd4500 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ from aces.model_trainer import ModelTrainer if __name__ == "__main__": config_file = "config.env" - config = Config() + config = Config(config_file) trainer = ModelTrainer(config) trainer.train_model() ``` diff --git a/aces/config.py b/aces/config.py index 2b73f49..82ab8a7 100644 --- a/aces/config.py +++ b/aces/config.py @@ -96,7 +96,13 @@ class Config: """ def __init__(self, config_file, override=False) -> None: + """ + ACES Configuration Class Constructor + Args: + config_file (str): The path to the configuration file. + override (bool): Flag to override the configuration settings. + """ load_dotenv(config_file, override=override) self.BASEDIR = Path(os.getenv("BASEDIR")) diff --git a/aces/ee_utils.py b/aces/ee_utils.py index 3c0bef5..ec3347b 100644 --- a/aces/ee_utils.py +++ b/aces/ee_utils.py @@ -39,10 +39,14 @@ def get_credentials_by_service_account_key(key): def initialize_session(use_highvolume : bool = False, key : Union[str, None] = None, project: str = None): """ Initialize the Earth Engine session. + If use_highvolume is True, the high-volume Earth Engine API will be used. + If a project is provided, the session will be initialized with the project ID. Recommended to use project. + If a key is provided, the service account key will be used. Parameters: use_highvolume (bool): Whether to use the high-volume Earth Engine API. key (str or None): The path to the service account key JSON file. If None, the default credentials will be used. + project (str): The Google Cloud project ID to use for the session. """ if key is None: if use_highvolume and project: diff --git a/aces/model_trainer.py b/aces/model_trainer.py index caaf662..68babea 100644 --- a/aces/model_trainer.py +++ b/aces/model_trainer.py @@ -365,13 +365,15 @@ def save_plots(self) -> None: Saves the model architecture plot, training history plot, and model object. """ print(f"Saving plots and model visualization at {self.config.MODEL_SAVE_DIR}...") + + Utils.plot_metrics([key.replace("val_", "") for key in self.history.history.keys() if key.startswith("val_")], + self.history.history, len(self.history.epoch), self.config.MODEL_SAVE_DIR) + if self.config.USE_AI_PLATFORM: keras.utils.plot_model(self._model, f"{self.config.MODEL_SAVE_DIR}/model.png", show_shapes=True, rankdir="TB") keras.utils.plot_model(self.model, f"{self.config.MODEL_SAVE_DIR}/wrapped_model.png", show_shapes=True, rankdir="LR") # rankdir='TB' else: keras.utils.plot_model(self.model, f"{self.config.MODEL_SAVE_DIR}/model.png", show_shapes=True, rankdir="TB") # rankdir='TB' - Utils.plot_metrics([key.replace("val_", "") for key in self.history.history.keys() if key.startswith("val_")], - self.history.history, len(self.history.epoch), self.config.MODEL_SAVE_DIR) def save_history_object(self) -> None: """ diff --git a/notebook/aces_rice_classification_paro_2021.ipynb b/notebook/aces_rice_classification_paro_2021.ipynb index c8b717c..310f247 100644 --- a/notebook/aces_rice_classification_paro_2021.ipynb +++ b/notebook/aces_rice_classification_paro_2021.ipynb @@ -42,7 +42,8 @@ " \"GitHub\n", " View on GitHub\n", " \n", - "" + "\n", + "\n" ] }, { @@ -74,7 +75,7 @@ "base_uri": "https://localhost:8080/" }, "id": "bbjHWUAcpzse", - "outputId": "55b4fd01-c266-42f2-f851-2ef02eb8d724" + "outputId": "6dd87e72-c6ba-4c64-e3dc-9ccec0ec093b" }, "execution_count": 2, "outputs": [ @@ -97,7 +98,7 @@ "base_uri": "https://localhost:8080/" }, "id": "FuDztFucmhpB", - "outputId": "70157f58-379e-4ec7-9c4a-46f2d3c8ba2d" + "outputId": "fdbc82b8-b02a-48d8-aee8-9bd0a57ac2d2" }, "execution_count": 3, "outputs": [ @@ -111,37 +112,37 @@ "Requirement already satisfied: tensorflow>=2.9.3 in /usr/local/lib/python3.10/dist-packages (from servir-aces) (2.15.0)\n", "Collecting apache-beam>=2.38.0 (from servir-aces)\n", " Downloading 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sha256=20dddae8b8b37703777ba3433716280d2fe6cf164012536142ed0b4471e2eed4\n", + " Created wheel for crcmod: filename=crcmod-1.7-cp310-cp310-linux_x86_64.whl size=31405 sha256=7dc20302597ff13a5e202ce50d49dba8702645552e57a4856389a21bcc16fcf7\n", " Stored in directory: /root/.cache/pip/wheels/85/4c/07/72215c529bd59d67e3dac29711d7aba1b692f543c808ba9e86\n", " Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78541 sha256=e414f543f9dc7f9461eb5ba90fac7699a367826cab175eb597900909f2bca32d\n", + " Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78541 sha256=a25ab436989830665c62a2386f19b6e38266a71ea331381dbae319bff6d4375f\n", " Stored in directory: /root/.cache/pip/wheels/ea/e2/86/64980d90e297e7bf2ce588c2b96e818f5399c515c4bb8a7e4f\n", " Building wheel for hdfs (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for hdfs: filename=hdfs-2.7.3-py3-none-any.whl size=34324 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filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=20fabab8db778cb3cbc3e74e8513063680c175158affcddb604bc10eb776b8e3\n", + " Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=f342c7b684d45a58ba1ed6e57a208e4b1c9d739dff4a90ed9c8e0edd08fcae88\n", " Stored in directory: /root/.cache/pip/wheels/fc/ab/d4/5da2067ac95b36618c629a5f93f809425700506f72c9732fac\n", "Successfully built crcmod dill hdfs pyjsparser docopt\n", "Installing collected packages: pyjsparser, docopt, crcmod, zstandard, python-dotenv, orjson, objsize, js2py, fasteners, fastavro, dnspython, dill, pymongo, hdfs, apache-beam, servir-aces\n", @@ -259,7 +260,7 @@ "base_uri": "https://localhost:8080/" }, "id": "6PvoP9Sgqz4e", - "outputId": "76ff8ff4-0311-40b2-ab1c-aff69f83d2e1" + "outputId": "63c9913f-9557-4b32-f2d6-fbf47315a42f" }, "execution_count": 4, "outputs": [ @@ -268,12 +269,12 @@ "name": "stdout", "text": [ "Cloning into 'servir-aces'...\n", - "remote: Enumerating objects: 680, done.\u001b[K\n", - "remote: Counting objects: 100% (107/107), done.\u001b[K\n", - "remote: Compressing objects: 100% (58/58), done.\u001b[K\n", - "remote: Total 680 (delta 64), reused 62 (delta 49), pack-reused 573\u001b[K\n", - "Receiving objects: 100% (680/680), 898.59 KiB | 7.13 MiB/s, done.\n", - "Resolving deltas: 100% (384/384), done.\n" + "remote: Enumerating objects: 695, done.\u001b[K\n", + "remote: Counting objects: 100% (122/122), done.\u001b[K\n", + "remote: Compressing objects: 100% (66/66), done.\u001b[K\n", + "remote: Total 695 (delta 73), reused 76 (delta 56), pack-reused 573\u001b[K\n", + "Receiving objects: 100% (695/695), 2.20 MiB | 8.09 MiB/s, done.\n", + "Resolving deltas: 100% (393/393), done.\n" ] } ] @@ -317,7 +318,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "47224a19-ecec-4050-b658-a3964e918f4e" + "outputId": "5ff38614-f7b6-4cba-8c01-c55e3913d9d1" }, "execution_count": 6, "outputs": [ @@ -325,16 +326,16 @@ "output_type": "stream", "name": "stdout", "text": [ - "Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/training/training.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/352.3 KiB] \rCopying gs://dl-book/chapter-1/images/image_202100000.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/ 50.7 MiB] \rCopying gs://dl-book/chapter-1/dnn_planet_wo_indices/testing/testing.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/ 50.8 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100002.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/ 97.8 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100003.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/143.8 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100004.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/186.2 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100001.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/232.0 MiB] \rCopying gs://dl-book/chapter-1/dnn_planet_wo_indices/validation/validation.tfrecord.gz...\n", - "/ [0 files][ 0.0 B/232.1 MiB] \rCopying gs://dl-book/chapter-1/images/image_2021mixer.json...\n", - "Copying gs://dl-book/chapter-1/images/image_202100005.tfrecord.gz...\n", + "Copying gs://dl-book/chapter-1/dnn_planet_wo_indices/testing/testing.tfrecord.gz...\n", + "/ [0 files][ 0.0 B/ 52.1 KiB] \rCopying gs://dl-book/chapter-1/dnn_planet_wo_indices/training/training.tfrecord.gz...\n", + "/ [0 files][ 0.0 B/404.4 KiB] \rCopying gs://dl-book/chapter-1/images/image_202100002.tfrecord.gz...\n", + "/ [0 files][ 0.0 B/ 47.4 MiB] \rCopying gs://dl-book/chapter-1/dnn_planet_wo_indices/validation/validation.tfrecord.gz...\n", + "/ [0 files][ 0.0 B/ 47.5 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100005.tfrecord.gz...\n", + "/ [0 files][ 0.0 B/ 49.1 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100003.tfrecord.gz...\n", + "Copying gs://dl-book/chapter-1/images/image_202100001.tfrecord.gz...\n", + "/ [0 files][ 0.0 B/ 95.1 MiB] \r/ [0 files][ 0.0 B/140.8 MiB] \rCopying gs://dl-book/chapter-1/images/image_202100004.tfrecord.gz...\n", + "Copying gs://dl-book/chapter-1/images/image_202100000.tfrecord.gz...\n", + "Copying gs://dl-book/chapter-1/images/image_2021mixer.json...\n", "Copying gs://dl-book/chapter-1/prediction/prediction_dnn_v1.TFRecord...\n", "Copying gs://dl-book/chapter-1/prediction/prediction_unet_v1.TFRecord...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/testing/testing-00000-of-00038.tfrecord.gz...\n", @@ -385,8 +386,8 @@ "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00007-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00008-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00009-of-00038.tfrecord.gz...\n", - "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00010-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00011-of-00038.tfrecord.gz...\n", + "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00010-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00012-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00013-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/training/training-00014-of-00038.tfrecord.gz...\n", @@ -433,8 +434,8 @@ "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00017-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00018-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00019-of-00038.tfrecord.gz...\n", - "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00020-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00021-of-00038.tfrecord.gz...\n", + "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00020-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00022-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00023-of-00038.tfrecord.gz...\n", "Copying gs://dl-book/chapter-1/unet_256x256_planet_wo_indices/validation/validation-00024-of-00038.tfrecord.gz...\n", @@ -689,7 +690,7 @@ "base_uri": "https://localhost:8080/" }, "id": "zwQxS9BasW65", - "outputId": "90592638-0e76-4e1e-ea5a-763e40588a64" + "outputId": "785bf015-112e-4fb9-ba62-7f305adc8dbc" }, "execution_count": 12, "outputs": [ @@ -724,7 +725,7 @@ "base_uri": "https://localhost:8080/" }, "id": "0dgS1btomEqO", - "outputId": "b6c14620-7e94-493c-a0c8-94bff47fa069" + "outputId": "9834683e-0ef7-4c76-fedc-78730b8eb111" }, "execution_count": 13, "outputs": [ @@ -772,7 +773,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "d5c3d81c-e916-432b-9add-edd8ba1169ea" + "outputId": "d1a457d8-7e59-49d0-96cb-20983021bc6a" }, "execution_count": 14, "outputs": [ @@ -2054,10 +2055,22 @@ "print(f\"File copied from {config_file} to {drive_config_file}\")" ], "metadata": { - "id": "Xrn6AkXNvgPK" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xrn6AkXNvgPK", + "outputId": "ecbfa41f-f383-4e6f-d537-3e76942d1c7c" }, - "execution_count": null, - "outputs": [] + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "File copied from servir-aces/config.env to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/config.env\n" + ] + } + ] }, { "cell_type": "markdown", @@ -2085,7 +2098,7 @@ "metadata": { "id": "Ft9Wz2VHB72_" }, - "execution_count": 15, + "execution_count": 16, "outputs": [] }, { @@ -2096,7 +2109,7 @@ "metadata": { "id": "moBqLF7ToVYZ" }, - "execution_count": 16, + "execution_count": 17, "outputs": [] }, { @@ -2109,9 +2122,9 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "750a9db6-c585-40c3-eb75-3d2ea9074db5" + "outputId": "06779770-ef36-4de4-9d2c-7386f9d6715f" }, - "execution_count": 17, + "execution_count": 18, "outputs": [ { "output_type": "stream", @@ -3692,60 +3705,13 @@ " geometries = True\n", ")\n", "\n", - "print(\"predictionOutputUnet\", prediction_unet.getInfo())\n" + "# print(\"predictionOutputUnet\", prediction_unet.getInfo())\n" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54 - }, - "id": "eraJj8255FpI", - "outputId": "933cda22-1d79-44ac-ea60-1a25d6bf33dc" + "id": "eraJj8255FpI" }, - "execution_count": 42, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "predictionOutputUnet {'type': 'FeatureCollection', 'columns': {}, 'properties': {'band_order': ['remapped']}, 'features': [{'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.32299212961209, 27.523302327081193]}, 'id': '00000000000000000000_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '168.9 secs', 'center_lat': 27.52334724, 'center_lon': 89.32303705, 'collection': '4/3/23 19:36', 'common_sec': '', 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 168, 'plotid': 168, 'remapped': 1, 'rice': 1, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.4424680624, 27.381907501360782]}, 'id': '00000000000000000001_0', 'properties': 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15:55', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1976, 'plotid': 1976, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.46268015629268, 27.338968030779867]}, 'id': '0000000000000000066d_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.7 secs', 'center_lat': 27.33901295, 'center_lon': 89.46263524, 'collection': '4/6/23 15:55', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1977, 'plotid': 1977, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.3516483871755, 27.463833855272483]}, 'id': '0000000000000000066e_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 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'id': '00000000000000000673_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '11.9 secs', 'center_lat': 27.29616331, 'center_lon': 89.53998019, 'collection': '4/6/23 15:57', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1984, 'plotid': 1984, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.52681986757881, 27.33447645435927]}, 'id': '00000000000000000676_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '12.9 secs', 'center_lat': 27.33443154, 'center_lon': 89.52677495, 'collection': '4/6/23 15:57', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1987, 'plotid': 1987, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.49852293612905, 27.33447645435927]}, 'id': '00000000000000000677_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '9.4 secs', 'center_lat': 27.33443154, 'center_lon': 89.49847802, 'collection': '4/6/23 15:57', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1988, 'plotid': 1988, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.45423599262196, 27.3850516048552]}, 'id': '00000000000000000678_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '36.2 secs', 'center_lat': 27.38509652, 'center_lon': 89.45428091, 'collection': '4/6/23 15:58', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1989, 'plotid': 1989, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.46699206965646, 27.355227537422433]}, 'id': '00000000000000000679_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '5.9 secs', 'center_lat': 27.35518262, 'center_lon': 89.46694715, 'collection': '4/6/23 15:58', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1990, 'plotid': 1990, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.52331643797075, 27.280218211198452]}, 'id': '0000000000000000067b_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '23.1 secs', 'center_lat': 27.28026313, 'center_lon': 89.52327152, 'collection': '4/6/23 15:58', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1992, 'plotid': 1992, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.41713557138782, 27.46329486610201]}, 'id': '0000000000000000067c_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.0 secs', 'center_lat': 27.46324995, 'center_lon': 89.41709066, 'collection': '4/6/23 15:58', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1993, 'plotid': 1993, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.54128274365314, 27.302586261773026]}, 'id': '0000000000000000067d_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '12.0 secs', 'center_lat': 27.30263118, 'center_lon': 89.54132766, 'collection': '4/6/23 15:59', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1994, 'plotid': 1994, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.53697083028936, 27.280847031897334]}, 'id': '0000000000000000067e_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.7 secs', 'center_lat': 27.28080212, 'center_lon': 89.53701575, 'collection': '4/6/23 15:59', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1996, 'plotid': 1996, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.47211246677594, 27.34498674318347]}, 'id': '0000000000000000067f_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '8.8 secs', 'center_lat': 27.34494183, 'center_lon': 89.47206755, 'collection': '4/6/23 15:59', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1997, 'plotid': 1997, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.54424718409074, 27.319654252171297]}, 'id': '00000000000000000681_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '4.6 secs', 'center_lat': 27.31960934, 'center_lon': 89.5442921, 'collection': '4/6/23 15:59', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 1999, 'plotid': 1999, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.46474628144615, 27.32261869260889]}, 'id': '00000000000000000682_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.5 secs', 'center_lat': 27.32257378, 'center_lon': 89.4647912, 'collection': '4/6/23 15:59', 'common_sec': '', 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'pl_samplei': 2000, 'plotid': 2000, 'remapped': 0, 'rice': 0, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0}}]}\n" - ] - } - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -3887,60 +3853,13 @@ " .rename([\"prediction_class\", \"others_prob\", \"cropland_prob\", \"urban_prob\", \"forest_prob\", \"rice_prob\"]) \\\n", " .sampleRegions(collection=ceo_final_data, scale=10, geometries=True)\n", "\n", - "print(\"prob_output_unet\", prob_output_unet.getInfo())\n" + "# print(\"prob_output_unet\", prob_output_unet.getInfo())\n" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54 - }, - "id": "XhmgZTSa64qE", - "outputId": "f4978b40-6488-4be7-cb4a-e4f7917a5ca4" + "id": "XhmgZTSa64qE" }, - "execution_count": 45, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "prob_output_unet {'type': 'FeatureCollection', 'columns': {}, 'properties': {'band_order': ['prediction_class', 'others_prob', 'cropland_prob', 'urban_prob', 'forest_prob', 'rice_prob']}, 'features': [{'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.32299212961209, 27.523302327081193]}, 'id': '00000000000000000000_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '168.9 secs', 'center_lat': 27.52334724, 'center_lon': 89.32303705, 'collection': '4/3/23 19:36', 'common_sec': '', 'cropland_prob': 0.011368797160685062, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.053602952510118484, 'others_prob': 0.08749040216207504, 'pl_samplei': 168, 'plotid': 168, 'prediction_class': 1, 'rice': 1, 'rice_prob': 0.7508510947227478, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.09668676555156708}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.4424680624, 27.381907501360782]}, 'id': '00000000000000000001_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '149.6 secs', 'center_lat': 27.38186259, 'center_lon': 89.44242315, 'collection': '4/3/23 19:46', 'common_sec': '', 'cropland_prob': 0.51027512550354, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.1280757337808609, 'others_prob': 0.019250910729169846, 'pl_samplei': 172, 'plotid': 172, 'prediction_class': 0, 'rice': 0, 'rice_prob': 0.14964987337589264, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.19274835288524628}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.35434333302787, 27.443801424436614]}, 'id': '00000000000000000003_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '112.4 secs', 'center_lat': 27.44384634, 'center_lon': 89.35429842, 'collection': '4/3/23 20:33', 'common_sec': '', 'cropland_prob': 0.08032839000225067, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.00989092793315649, 'others_prob': 0.005895791109651327, 'pl_samplei': 176, 'plotid': 176, 'prediction_class': 1, 'rice': 1, 'rice_prob': 0.8960435390472412, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.007841365411877632}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.3516483871755, 27.458982952738236]}, 'id': '00000000000000000004_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '141.1 secs', 'center_lat': 27.45893804, 'center_lon': 89.35160347, 'collection': '4/3/23 20:38', 'common_sec': '', 'cropland_prob': 0.1118401288986206, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.007906298153102398, 'others_prob': 0.0014393763849511743, 'pl_samplei': 178, 'plotid': 178, 'prediction_class': 1, 'rice': 1, 'rice_prob': 0.8739610910415649, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.004853059072047472}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.49097708774245, 27.199998656326578]}, 'id': '00000000000000000005_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '185.9 secs', 'center_lat': 27.19995374, 'center_lon': 89.49093217, 'collection': '4/3/23 21:08', 'common_sec': '', 'cropland_prob': 0.9978918433189392, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 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'4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.6490793228149414, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.08540594577789307, 'others_prob': 0.11597605049610138, 'pl_samplei': 1996, 'plotid': 1996, 'prediction_class': 0, 'rice': 0, 'rice_prob': 0.08404353260993958, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.06549512594938278}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.47211246677594, 27.34498674318347]}, 'id': '0000000000000000067f_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '8.8 secs', 'center_lat': 27.34494183, 'center_lon': 89.47206755, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.9142755270004272, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.014445432461798191, 'others_prob': 0.0018039706628769636, 'pl_samplei': 1997, 'plotid': 1997, 'prediction_class': 0, 'rice': 0, 'rice_prob': 0.06697956472635269, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.0024954602122306824}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.54424718409074, 27.319654252171297]}, 'id': '00000000000000000681_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '4.6 secs', 'center_lat': 27.31960934, 'center_lon': 89.5442921, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.5310989618301392, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.05961405113339424, 'others_prob': 0.0014492308255285025, 'pl_samplei': 1999, 'plotid': 1999, 'prediction_class': 0, 'rice': 0, 'rice_prob': 0.4007010757923126, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.007136702071875334}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.46474628144615, 27.32261869260889]}, 'id': '00000000000000000682_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.5 secs', 'center_lat': 27.32257378, 'center_lon': 89.4647912, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.8229706883430481, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.052602894604206085, 'others_prob': 0.015829671174287796, 'pl_samplei': 2000, 'plotid': 2000, 'prediction_class': 0, 'rice': 0, 'rice_prob': 0.09212346374988556, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.016473280265927315}}]}\n" - ] - } - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -4435,60 +4354,13 @@ " .rename([\"prediction_class\", \"others_prob\", \"cropland_prob\", \"urban_prob\", \"forest_prob\", \"rice_prob\"]) \\\n", " .sampleRegions(collection=ceo_final_data, scale=10, geometries=True)\n", "\n", - "print(\"prob_output_dnn\", prob_output_dnn.getInfo())\n" + "# print(\"prob_output_dnn\", prob_output_dnn.getInfo())\n" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54 - }, - "id": "-eOLJa6D-grI", - "outputId": "05a038c6-0d66-4e92-c4d1-57d88d19abad" + "id": "-eOLJa6D-grI" }, - "execution_count": 52, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "prob_output_dnn {'type': 'FeatureCollection', 'columns': {}, 'properties': {'band_order': ['prediction_class', 'others_prob', 'cropland_prob', 'urban_prob', 'forest_prob', 'rice_prob']}, 'features': [{'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.32299212961209, 27.523302327081193]}, 'id': '00000000000000000000_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '168.9 secs', 'center_lat': 27.52334724, 'center_lon': 89.32303705, 'collection': '4/3/23 19:36', 'common_sec': '', 'cropland_prob': 0.006229014601558447, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.04852087050676346, 'others_prob': 0.18775418400764465, 'pl_samplei': 168, 'plotid': 168, 'prediction_class': 1, 'rice': 1, 'rice_prob': 0.6276366710662842, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.12985916435718536}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.4424680624, 27.381907501360782]}, 'id': '00000000000000000001_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '149.6 secs', 'center_lat': 27.38186259, 'center_lon': 89.44242315, 'collection': '4/3/23 19:46', 'common_sec': '', 'cropland_prob': 0.47396495938301086, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.10078249871730804, 'others_prob': 0.002512848237529397, 'pl_samplei': 172, 'plotid': 172, 'prediction_class': 0, 'rice': 0, 'rice_prob': 0.4158878028392792, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.0068519581109285355}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.35434333302787, 27.443801424436614]}, 'id': '00000000000000000003_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 0, 'Rice?_Yes': 100, 'analysis_d': '112.4 secs', 'center_lat': 27.44384634, 'center_lon': 89.35429842, 'collection': '4/3/23 20:33', 'common_sec': '', 'cropland_prob': 0.014808082953095436, 'email': 'mrm0065@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.02243768610060215, 'others_prob': 0.02335420809686184, 'pl_samplei': 176, 'plotid': 176, 'prediction_class': 1, 'rice': 1, 'rice_prob': 0.9093149900436401, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.030085159465670586}}, {'type': 'Feature', 'geometry': 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'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '5.9 secs', 'center_lat': 27.35518262, 'center_lon': 89.46694715, 'collection': '4/6/23 15:58', 'common_sec': '', 'cropland_prob': 0.34956127405166626, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.2128581702709198, 'others_prob': 0.030999865382909775, 'pl_samplei': 1990, 'plotid': 1990, 'prediction_class': 1, 'rice': 0, 'rice_prob': 0.3650481402873993, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.041532501578330994}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.52331643797075, 27.280218211198452]}, 'id': '0000000000000000067b_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '23.1 secs', 'center_lat': 27.28026313, 'center_lon': 89.52327152, 'collection': '4/6/23 15:58', 'common_sec': '', 'cropland_prob': 0.2046402245759964, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.5330834984779358, 'others_prob': 0.02136734314262867, 'pl_samplei': 1992, 'plotid': 1992, 'prediction_class': 2, 'rice': 0, 'rice_prob': 0.2149171382188797, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.025991812348365784}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.41713557138782, 27.46329486610201]}, 'id': '0000000000000000067c_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.0 secs', 'center_lat': 27.46324995, 'center_lon': 89.41709066, 'collection': '4/6/23 15:58', 'common_sec': '', 'cropland_prob': 0.015641503036022186, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.2076742947101593, 'others_prob': 0.4474293291568756, 'pl_samplei': 1993, 'plotid': 1993, 'prediction_class': 4, 'rice': 0, 'rice_prob': 0.10100354254245758, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.2282513529062271}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.54128274365314, 27.302586261773026]}, 'id': '0000000000000000067d_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '12.0 secs', 'center_lat': 27.30263118, 'center_lon': 89.54132766, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.014665151946246624, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.2047700583934784, 'others_prob': 0.41448867321014404, 'pl_samplei': 1994, 'plotid': 1994, 'prediction_class': 4, 'rice': 0, 'rice_prob': 0.10372135788202286, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.2623547613620758}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.53697083028936, 27.280847031897334]}, 'id': '0000000000000000067e_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.7 secs', 'center_lat': 27.28080212, 'center_lon': 89.53701575, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.05532778799533844, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.3432096242904663, 'others_prob': 0.27431780099868774, 'pl_samplei': 1996, 'plotid': 1996, 'prediction_class': 2, 'rice': 0, 'rice_prob': 0.13309337198734283, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.19405141472816467}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.47211246677594, 27.34498674318347]}, 'id': '0000000000000000067f_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '8.8 secs', 'center_lat': 27.34494183, 'center_lon': 89.47206755, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.04056183621287346, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.35230621695518494, 'others_prob': 0.26623380184173584, 'pl_samplei': 1997, 'plotid': 1997, 'prediction_class': 2, 'rice': 0, 'rice_prob': 0.19419308006763458, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.14670507609844208}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.54424718409074, 27.319654252171297]}, 'id': '00000000000000000681_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '4.6 secs', 'center_lat': 27.31960934, 'center_lon': 89.5442921, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.09148725867271423, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.7661265134811401, 'others_prob': 0.01085097435861826, 'pl_samplei': 1999, 'plotid': 1999, 'prediction_class': 2, 'rice': 0, 'rice_prob': 0.11945263296365738, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.012082703411579132}}, {'type': 'Feature', 'geometry': {'geodesic': False, 'type': 'Point', 'coordinates': [89.46474628144615, 27.32261869260889]}, 'id': '00000000000000000682_0', 'properties': {'Rice?_Mixe': 0, 'Rice?_No': 100, 'Rice?_Yes': 0, 'analysis_d': '6.5 secs', 'center_lat': 27.32257378, 'center_lon': 89.4647912, 'collection': '4/6/23 15:59', 'common_sec': '', 'cropland_prob': 0.17078129947185516, 'email': 'bb0134@uah.edu', 'flagged': 'FALSE', 'flagged_re': '', 'forest_prob': 0.5393715500831604, 'others_prob': 0.04450351744890213, 'pl_samplei': 2000, 'plotid': 2000, 'prediction_class': 2, 'rice': 0, 'rice_prob': 0.19760733842849731, 'sample_poi': 9, 'shape': 'square', 'size_m': 30, 'total_secu': 0, 'urban_prob': 0.04773624986410141}}]}\n" - ] - } - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", @@ -4779,7 +4651,7 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 627 + "height": 932 }, "id": "Gr4YettfF1hP", "outputId": "85fab9eb-ecf4-4f84-ee0d-57aa94387929" @@ -4872,7 +4744,7 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 320 + "height": 471 }, "id": "fq1de1KRTCdZ", "outputId": "685fa095-9ebf-427d-8382-4a1e4000122b" diff --git a/notebook/count_sample_size.ipynb b/notebook/count_sample_size.ipynb index b70a428..d99ea51 100644 --- a/notebook/count_sample_size.ipynb +++ b/notebook/count_sample_size.ipynb @@ -31,6 +31,8 @@ "source": [ "# Rice mapping in Bhutan with U-Net using high resolution satellite imagery\n", "\n", + "### This notebook shows an example of counting the sample size from the `tfrecords`\n", + "\n", "\n", "
\n", " \n", @@ -45,9 +47,7 @@ "
\n", "
\n", "
\n", - "
\n", - "\n", - "### This notebook shows an example of counting the sample size from the `tfrecords`" + "
" ] }, { diff --git a/notebook/prediction_dnn.ipynb b/notebook/prediction_dnn.ipynb index 37fda1f..865c46b 100644 --- a/notebook/prediction_dnn.ipynb +++ b/notebook/prediction_dnn.ipynb @@ -31,6 +31,8 @@ "source": [ "# Rice mapping in Bhutan with U-Net using high resolution satellite imagery\n", "\n", + "### This notebook shows an example of using the TFRecord images for prediction using saved DNN Model.\n", + "\n", "\n", "
\n", " \n", @@ -45,9 +47,7 @@ "
\n", "
\n", "
\n", - "
\n", - "\n", - "### This notebook shows an example of using the TFRecord images for prediction using saved DNN Model." + "
" ] }, { diff --git a/notebook/prediction_unet.ipynb b/notebook/prediction_unet.ipynb index f130c98..1e21706 100644 --- a/notebook/prediction_unet.ipynb +++ b/notebook/prediction_unet.ipynb @@ -31,6 +31,8 @@ "source": [ "# Rice mapping in Bhutan with U-Net using high resolution satellite imagery\n", "\n", + "### This notebook shows an example of using the TFRecord images for prediction using saved U-Net Model.\n", + "\n", "\n", "
\n", " \n", @@ -45,9 +47,7 @@ "
\n", "
\n", "
\n", - "
\n", - "\n", - "### This notebook shows an example of using the TFRecord images for prediction using saved U-Net Model." + "
" ] }, { @@ -61,30 +61,27 @@ }, { "cell_type": "markdown", - "source": [ - "## Setup environment" - ], "metadata": { "id": "EuQkhSxRmiIA" - } + }, + "source": [ + "## Setup environment" + ] }, { "cell_type": "code", - "source": [ - "!pip install servir-aces" - ], + "execution_count": null, "metadata": { - "id": "FuDztFucmhpB", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "FuDztFucmhpB", "outputId": "f1f29ee6-077b-40c0-c63d-361cccf96478" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Collecting servir-aces\n", " Downloading servir_aces-0.0.6-py2.py3-none-any.whl (31 kB)\n", @@ -228,13 +225,14 @@ "Successfully installed apache-beam-2.55.1 crcmod-1.7 dill-0.3.1.1 dnspython-2.6.1 docopt-0.6.2 fastavro-1.9.4 fasteners-0.19 hdfs-2.7.3 js2py-0.74 objsize-0.7.0 orjson-3.10.1 pyjsparser-2.7.1 pymongo-4.6.3 python-dotenv-1.0.1 servir-aces-0.0.6 zstandard-0.22.0\n" ] } + ], + "source": [ + "!pip install servir-aces" ] }, { "cell_type": "code", - "source": [ - "!git clone https://github.com/SERVIR/servir-aces" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -242,11 +240,10 @@ "id": "6PvoP9Sgqz4e", "outputId": "12f146c4-61fb-4db6-913c-3e742778ca4a" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'servir-aces'...\n", "remote: Enumerating objects: 680, done.\u001b[K\n", @@ -257,38 +254,38 @@ "Resolving deltas: 100% (384/384), done.\n" ] } + ], + "source": [ + "!git clone https://github.com/SERVIR/servir-aces" ] }, { "cell_type": "markdown", + "metadata": { + "id": "RukLD6eeq7Fl" + }, "source": [ "Now the repo is downloaded. We will create an environment file file to place point to our training data and customize parameters for the model. To do this, we make a copy of the `.env.example` file provided.\n", "\n", "Under the hood, all the configuration provided via the environment file are parsed as a config object and can be accessed programatically.\n", "\n", "Note current version does not expose all the model intracacies through the environment file but future version may include those depending on the need." - ], - "metadata": { - "id": "RukLD6eeq7Fl" - } + ] }, { "cell_type": "code", - "source": [ - "!cp servir-aces/.env.example servir-aces/config.env" - ], + "execution_count": null, "metadata": { "id": "jpnfjXRYsYMk" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "!cp servir-aces/.env.example servir-aces/config.env" + ] }, { "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount(\"/content/drive\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -296,28 +293,34 @@ "id": "mgk98jsYF5xU", "outputId": "03e7e478-0482-46d4-fe3d-1149ee928e7b" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount(\"/content/drive\")" ] }, { "cell_type": "markdown", - "source": [ - "## Setup config file variables" - ], "metadata": { "id": "9wn3sMH9IXKK" - } + }, + "source": [ + "## Setup config file variables" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "ooFqBUY3gS5e" + }, "source": [ "Okay, now we have the `config.env` file, we will use this to provide our environments and parameters.\n", "\n", @@ -327,13 +330,13 @@ "BASEDIR = \"/content/\"\n", "OUTPUT_DIR = \"/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output\"\n", "```" - ], - "metadata": { - "id": "ooFqBUY3gS5e" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "CEDUshfUi1OY" + }, "source": [ "For the prediction, we are using growing season and pre-growing season information. Thus, we have 8 optical bands, namely `red_before`, `green_before`, `blue_before`, `nir_before`, `red_during`, `green_during`, `blue_during`, and `nir_during`. In adidition, you can use `USE_ELEVATION` and `USE_S1` config to include the topographic and radar information. Since currently we are not including these, so we won't be settting these config values.\n", "\n", @@ -352,13 +355,13 @@ "\n", "KERNEL_BUFFER = 0\n", "```" - ], - "metadata": { - "id": "CEDUshfUi1OY" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "stwnTiy94pzU" + }, "source": [ "Next, we will specify the `MODEL_DIR_NAME`. The `MODEL_DIR` is then constructed as\n", "MODEL_DIR = OUTPUT_DIR / MODEL_DIR_NAME. The `MODEL_DIR_NAME` in my case is `unet_v1`, so we will use that. Similarly, you can specify your output of the file from the prediction using `OUTPUT_NAME` variable. Other config to change are `GCS_PROJECT`, `GCS_BUCKET`, `GCS_IMAGE_DIR`, and `GCS_IMAGE_PREFIX` (for prediction image direction, see this [notebook](https://colab.research.google.com/drive/1MZexam3GZKsQySQO9Jk_RPNyyMLmciEq?usp=drive_link)). For exporting our prediction to the GEE Asset, we will use `EE_OUTPUT_ASSET` to update it.\n", @@ -378,31 +381,33 @@ "# where the prediction output will be stored\n", "EE_OUTPUT_ASSET = \"projects/servir-ee/assets/dl-book/chapter-1/prediction\"\n", "```" - ], - "metadata": { - "id": "stwnTiy94pzU" - } + ] }, { "cell_type": "markdown", - "source": [ - "## Update the config file programtically" - ], "metadata": { "id": "0p6d8MQcqtO3" - } + }, + "source": [ + "## Update the config file programtically" + ] }, { "cell_type": "markdown", - "source": [ - "Let's make a dictionary so we can change these config settings programatically." - ], "metadata": { "id": "RMA7PH-jvvVr" - } + }, + "source": [ + "Let's make a dictionary so we can change these config settings programatically." + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cFhU_kNQqu2b" + }, + "outputs": [], "source": [ "BASEDIR = \"/content/\" # @param {type:\"string\"}\n", "OUTPUT_DIR = \"/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output\" # @param {type:\"string\"}\n", @@ -429,15 +434,15 @@ "# where the prediction output will be stored\n", "EE_OUTPUT_ASSET = \"projects/servir-ee/assets/dl-book/chapter-1/prediction\" # @param {type:\"string\"}\n", "\n" - ], - "metadata": { - "id": "cFhU_kNQqu2b" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j3_k_jmBv0xE" + }, + "outputs": [], "source": [ "config_settings = {\n", " \"BASEDIR\" : BASEDIR,\n", @@ -455,15 +460,15 @@ " \"GCS_IMAGE_PREFIX\": GCS_IMAGE_PREFIX,\n", " \"EE_OUTPUT_ASSET\": EE_OUTPUT_ASSET,\n", "}\n" - ], - "metadata": { - "id": "j3_k_jmBv0xE" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xMAmTIBvvRjW" + }, + "outputs": [], "source": [ "import dotenv\n", "\n", @@ -474,24 +479,24 @@ " key_to_set=config_key,\n", " value_to_set=config_settings[config_key]\n", " )\n" - ], - "metadata": { - "id": "xMAmTIBvvRjW" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Load config file variables" - ], "metadata": { "id": "lZvJKXATIf9v" - } + }, + "source": [ + "## Load config file variables" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "id1pU4aCFjR-" + }, + "outputs": [], "source": [ "from aces import Config\n", "\n", @@ -500,18 +505,11 @@ "import tensorflow as tf\n", "import numpy as np\n", "import subprocess\n" - ], - "metadata": { - "id": "id1pU4aCFjR-" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "config = Config(config_file)" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -519,11 +517,10 @@ "id": "R5_vh3SFFe3n", "outputId": "46874201-8cd9-41a7-9ab7-67c48a2af33e" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "BASEDIR: /content\n", "DATADIR: /content/data\n", @@ -531,14 +528,14 @@ "using labels: ['class']\n" ] } + ], + "source": [ + "config = Config(config_file)" ] }, { "cell_type": "code", - "source": [ - "OUTPUT_IMAGE_FILE = str(config.MODEL_DIR / \"prediction\" / f\"{config.OUTPUT_NAME}.TFRecord\")\n", - "print(f\"OUTPUT_IMAGE_FILE: {OUTPUT_IMAGE_FILE}\")\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -546,36 +543,32 @@ "id": "M26Ze2ZNDP7R", "outputId": "f1787c99-0ef8-4c01-f88b-d158c4808507" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "OUTPUT_IMAGE_FILE: /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/prediction/prediction_unet_v1.TFRecord\n" ] } + ], + "source": [ + "OUTPUT_IMAGE_FILE = str(config.MODEL_DIR / \"prediction\" / f\"{config.OUTPUT_NAME}.TFRecord\")\n", + "print(f\"OUTPUT_IMAGE_FILE: {OUTPUT_IMAGE_FILE}\")\n" ] }, { "cell_type": "markdown", - "source": [ - "## Get files for export" - ], "metadata": { "id": "GzrIzedTIv9v" - } + }, + "source": [ + "## Get files for export" + ] }, { "cell_type": "code", - "source": [ - "ls = f\"gsutil ls gs://{config.GCS_BUCKET}/{config.GCS_IMAGE_DIR}\"\n", - "print(f\"ls >> : {ls}\")\n", - "\n", - "files_list = subprocess.check_output(ls, shell=True)\n", - "files_list = files_list.decode(\"utf-8\")\n", - "files_list = files_list.split(\"\\n\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -583,22 +576,27 @@ "id": "HDJRxeeHDP91", "outputId": "af42f53c-cf98-43d6-aad5-91f70078eb12" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "ls >> : gsutil ls gs://dl-book/chapter-1/images\n" ] } + ], + "source": [ + "ls = f\"gsutil ls gs://{config.GCS_BUCKET}/{config.GCS_IMAGE_DIR}\"\n", + "print(f\"ls >> : {ls}\")\n", + "\n", + "files_list = subprocess.check_output(ls, shell=True)\n", + "files_list = files_list.decode(\"utf-8\")\n", + "files_list = files_list.split(\"\\n\")" ] }, { "cell_type": "code", - "source": [ - "files_list" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -606,10 +604,8 @@ "id": "0aCleCW6G1-L", "outputId": "459c2003-1753-430c-994f-92a21a4b8293" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "['gs://dl-book/chapter-1/images/',\n", @@ -623,19 +619,18 @@ " '']" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } + ], + "source": [ + "files_list" ] }, { "cell_type": "code", - "source": [ - "# Get only the files generated by the image export.\n", - "exported_files_list = [s for s in files_list if config.GCS_IMAGE_PREFIX in s]\n", - "\n", - "print(f\"exported_files_list: {exported_files_list}\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -643,28 +638,38 @@ "id": "12n86MsSIFip", "outputId": "17845cee-67ec-40fa-e454-5bc8b64fbb18" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "exported_files_list: ['gs://dl-book/chapter-1/images/image_202100000.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100001.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100002.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100003.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100004.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100005.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_2021mixer.json']\n" ] } + ], + "source": [ + "# Get only the files generated by the image export.\n", + "exported_files_list = [s for s in files_list if config.GCS_IMAGE_PREFIX in s]\n", + "\n", + "print(f\"exported_files_list: {exported_files_list}\")" ] }, { "cell_type": "markdown", - "source": [ - "## Get the list of image files and the JSON mixer file." - ], "metadata": { "id": "2XDPadw0ILJ7" - } + }, + "source": [ + "## Get the list of image files and the JSON mixer file." + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WET_70R1DQA2" + }, + "outputs": [], "source": [ "image_files_list = []\n", "json_file = None\n", @@ -673,32 +678,23 @@ " image_files_list.append(f)\n", " elif f.endswith(\".json\"):\n", " json_file = f" - ], - "metadata": { - "id": "WET_70R1DQA2" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "# Make sure the files are in the right order.\n", - "image_files_list.sort()" - ], + "execution_count": null, "metadata": { "id": "k3GNaHnsG7oV" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "# Make sure the files are in the right order.\n", + "image_files_list.sort()" + ] }, { "cell_type": "code", - "source": [ - "print(f\"image_files_list: {image_files_list}\")\n", - "\n", - "print(f\"json_file: {json_file}\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -706,24 +702,25 @@ "id": "PNcSm43FDQDp", "outputId": "8885177e-0166-4bb9-8063-317d4d77676e" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "image_files_list: ['gs://dl-book/chapter-1/images/image_202100000.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100001.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100002.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100003.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100004.tfrecord.gz', 'gs://dl-book/chapter-1/images/image_202100005.tfrecord.gz']\n", "json_file: gs://dl-book/chapter-1/images/image_2021mixer.json\n" ] } + ], + "source": [ + "print(f\"image_files_list: {image_files_list}\")\n", + "\n", + "print(f\"json_file: {json_file}\")" ] }, { "cell_type": "code", - "source": [ - "print(f\"Loading model from {str(config.MODEL_DIR)}/trained-model\")\n", - "this_model = tf.keras.models.load_model(f\"{str(config.MODEL_DIR)}/trained-model\")\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -731,22 +728,23 @@ "id": "tkrQCptGDQGd", "outputId": "8a193144-256f-4100-e769-84aeacb0db46" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Loading model from /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/trained-model\n" ] } + ], + "source": [ + "print(f\"Loading model from {str(config.MODEL_DIR)}/trained-model\")\n", + "this_model = tf.keras.models.load_model(f\"{str(config.MODEL_DIR)}/trained-model\")\n" ] }, { "cell_type": "code", - "source": [ - "this_model.summary()" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -754,11 +752,10 @@ "id": "ftVjQntTDQJD", "outputId": "509bafda-2263-46b7-eabb-7529eb7165f3" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Model: \"unet\"\n", "__________________________________________________________________________________________________\n", @@ -969,19 +966,27 @@ "__________________________________________________________________________________________________\n" ] } + ], + "source": [ + "this_model.summary()" ] }, { "cell_type": "markdown", - "source": [ - "## Get relevant info from the JSON mixer file." - ], "metadata": { "id": "Ac6sL0faJmLu" - } + }, + "source": [ + "## Get relevant info from the JSON mixer file." + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "o-Ns212ZDQLn" + }, + "outputs": [], "source": [ "cat = f\"gsutil cat {json_file}\"\n", "read_t = subprocess.check_output(cat, shell=True)\n", @@ -995,24 +1000,36 @@ "patch_height = mixer[\"patchDimensions\"][1]\n", "patches = mixer[\"totalPatches\"]\n", "patch_dimensions_flat = [patch_width * patch_height, 1]" - ], - "metadata": { - "id": "o-Ns212ZDQLn" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Load the buffer size for prediction" - ], "metadata": { "id": "-tFJcYnpJ04y" - } + }, + "source": [ + "## Load the buffer size for prediction" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ieIGfMCrJ5ka", + "outputId": "f8a1b0b1-b9c2-4887-f17b-d0278f1172cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "buffered_shape: (256, 256)\n" + ] + } + ], "source": [ "if config.KERNEL_BUFFER:\n", " x_buffer = config.KERNEL_BUFFER[0] // 2\n", @@ -1028,47 +1045,20 @@ " buffered_shape = config.PATCH_SHAPE\n", "\n", "print(f\"buffered_shape: {buffered_shape}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ieIGfMCrJ5ka", - "outputId": "f8a1b0b1-b9c2-4887-f17b-d0278f1172cb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "buffered_shape: (256, 256)\n" - ] - } ] }, { "cell_type": "markdown", - "source": [ - "## Setup features" - ], "metadata": { "id": "3bXJaIDlKD3M" - } + }, + "source": [ + "## Setup features" + ] }, { "cell_type": "code", - "source": [ - "if config.USE_ELEVATION:\n", - " config.FEATURES.extend([\"elevation\", \"slope\"])\n", - "\n", - "\n", - "if config.USE_S1:\n", - " config.FEATURES.extend([\"vv_asc_before\", \"vh_asc_before\", \"vv_asc_during\", \"vh_asc_during\",\n", - " \"vv_desc_before\", \"vh_desc_before\", \"vv_desc_during\", \"vh_desc_during\"])\n", - "\n", - "print(f\"Config.FEATURES: {config.FEATURES}\")\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1076,28 +1066,43 @@ "id": "AUHbFYADKIhH", "outputId": "813a79bc-0b31-4caf-f600-8d84ab4654ea" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Config.FEATURES: ['red_before', 'green_before', 'blue_before', 'nir_before', 'red_during', 'green_during', 'blue_during', 'nir_during']\n" ] } + ], + "source": [ + "if config.USE_ELEVATION:\n", + " config.FEATURES.extend([\"elevation\", \"slope\"])\n", + "\n", + "\n", + "if config.USE_S1:\n", + " config.FEATURES.extend([\"vv_asc_before\", \"vh_asc_before\", \"vv_asc_during\", \"vh_asc_during\",\n", + " \"vv_desc_before\", \"vh_desc_before\", \"vv_desc_during\", \"vh_desc_during\"])\n", + "\n", + "print(f\"Config.FEATURES: {config.FEATURES}\")\n" ] }, { "cell_type": "markdown", - "source": [ - "### Some useful functions" - ], "metadata": { "id": "WJv7lBJyKgSB" - } + }, + "source": [ + "### Some useful functions" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ovVaxx9YKjdP" + }, + "outputs": [], "source": [ "def parse_image(example_proto):\n", " columns = [\n", @@ -1111,51 +1116,43 @@ " stacked = tf.stack(inputs_list, axis=0)\n", " stacked = tf.transpose(stacked, [1, 2, 0])\n", " return stacked\n" - ], - "metadata": { - "id": "ovVaxx9YKjdP" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Create a dataset" - ], "metadata": { "id": "OZOpx1oWz7sz" - } + }, + "source": [ + "## Create a dataset" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bdk8mvK7z_re" + }, + "outputs": [], "source": [ "# Create a dataset from the TFRecord file(s) in Cloud Storage.\n", "image_dataset = tf.data.TFRecordDataset(image_files_list, compression_type=\"GZIP\")\n", "image_dataset = image_dataset.map(parse_image, num_parallel_calls=5)\n", "image_dataset = image_dataset.map(to_tuple_image).batch(1)\n" - ], - "metadata": { - "id": "bdk8mvK7z_re" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Perform Inference" - ], "metadata": { "id": "bk6J6dtV0HKV" - } + }, + "source": [ + "## Perform Inference" + ] }, { "cell_type": "code", - "source": [ - "predictions = this_model.predict(image_dataset, steps=patches, verbose=1)\n", - "print(f\"predictions shape: {predictions.shape}\")\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1163,43 +1160,71 @@ "id": "ngBve5IK0I-j", "outputId": "e1d67088-6756-44c3-e340-7801a5a64a36" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "252/252 [==============================] - 45s 153ms/step\n", "predictions shape: (252, 256, 256, 5)\n" ] } + ], + "source": [ + "predictions = this_model.predict(image_dataset, steps=patches, verbose=1)\n", + "print(f\"predictions shape: {predictions.shape}\")\n" ] }, { "cell_type": "markdown", - "source": [ - "## Write Predictions" - ], "metadata": { "id": "b0XXHcwW0SZ4" - } + }, + "source": [ + "## Write Predictions" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Rs8yfBE3wW5o" + }, + "outputs": [], "source": [ "from pathlib import Path\n", "\n", "# Create the target directory if it doesn't exist\n", "Path(OUTPUT_IMAGE_FILE).parent.mkdir(parents=True, exist_ok=True)" - ], - "metadata": { - "id": "Rs8yfBE3wW5o" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7CSi_UFn0UzA", + "outputId": "11cf30c3-6396-49ea-9926-721c5a052f86" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing predictions to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/prediction/prediction_unet_v1.TFRecord ...\n", + "Starting with patch 0...\n", + "predictionPatch: (256, 256, 5)\n", + "Writing patch 0...\n", + "Writing patch 50...\n", + "Writing patch 100...\n", + "Writing patch 150...\n", + "Writing patch 200...\n", + "Writing patch 250...\n" + ] + } + ], "source": [ "print(f\"Writing predictions to {OUTPUT_IMAGE_FILE} ...\")\n", "writer = tf.io.TFRecordWriter(OUTPUT_IMAGE_FILE)\n", @@ -1248,79 +1273,51 @@ " writer.write(example.SerializeToString())\n", "\n", "writer.close()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7CSi_UFn0UzA", - "outputId": "11cf30c3-6396-49ea-9926-721c5a052f86" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Writing predictions to /content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/prediction/prediction_unet_v1.TFRecord ...\n", - "Starting with patch 0...\n", - "predictionPatch: (256, 256, 5)\n", - "Writing patch 0...\n", - "Writing patch 50...\n", - "Writing patch 100...\n", - "Writing patch 150...\n", - "Writing patch 200...\n", - "Writing patch 250...\n" - ] - } ] }, { "cell_type": "markdown", - "source": [ - "## Upload to Google Earth Engine (GEE)" - ], "metadata": { "id": "menoRbWc1WZI" - } + }, + "source": [ + "## Upload to Google Earth Engine (GEE)" + ] }, { "cell_type": "markdown", - "source": [ - "Now we have write the prediction to the `OUTPUT_IMAGE_FILE`. You can upload this to GEE for visualization. To do this, you will need to upload to GCP and then to GEE." - ], "metadata": { "id": "kVmtfYTe0w5Z" - } + }, + "source": [ + "Now we have write the prediction to the `OUTPUT_IMAGE_FILE`. You can upload this to GEE for visualization. To do this, you will need to upload to GCP and then to GEE." + ] }, { "cell_type": "markdown", - "source": [ - "Make sure you have proper permission" - ], "metadata": { "id": "ek37oamcCbGt" - } + }, + "source": [ + "Make sure you have proper permission" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rKnzfd0NXC1E" + }, + "outputs": [], "source": [ "# Cloud authentication.\n", "from google.colab import auth\n", "auth.authenticate_user()" - ], - "metadata": { - "id": "rKnzfd0NXC1E" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "OUTPUT_GCS_PATH = f\"gs://{config.GCS_BUCKET}/chapter-1/prediction/{config.OUTPUT_NAME}.TFRecord\"\n", - "print(f\"OUTPUT_GCS_PATH: {OUTPUT_GCS_PATH}\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1328,26 +1325,23 @@ "id": "8Eyow_AL9R6e", "outputId": "241dffc8-8bd9-4f8d-8ea4-9014d187ce94" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "OUTPUT_GCS_PATH: gs://dl-book/chapter-1/prediction/prediction_unet_v1.TFRecord\n" ] } + ], + "source": [ + "OUTPUT_GCS_PATH = f\"gs://{config.GCS_BUCKET}/chapter-1/prediction/{config.OUTPUT_NAME}.TFRecord\"\n", + "print(f\"OUTPUT_GCS_PATH: {OUTPUT_GCS_PATH}\")" ] }, { "cell_type": "code", - "source": [ - "# upload to gcp\n", - "upload_to_gcp = f'gsutil cp \"{OUTPUT_IMAGE_FILE}\" \"{OUTPUT_GCS_PATH}\"'\n", - "print(f\"upload_to_gcp: {upload_to_gcp}\")\n", - "result = subprocess.check_output(upload_to_gcp, shell=True)\n", - "print(f\"uploading classified image to gcp: {result}\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1355,32 +1349,36 @@ "id": "RDoyR9hS0U1_", "outputId": "ad796653-52cd-491a-9d7e-64c863b2c886" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "upload_to_gcp: gsutil cp \"/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/prediction/prediction_unet_v1.TFRecord\" \"gs://dl-book/chapter-1/prediction/prediction_unet_v1.TFRecord\"\n", "uploading classified image to gcp: b''\n" ] } + ], + "source": [ + "# upload to gcp\n", + "upload_to_gcp = f'gsutil cp \"{OUTPUT_IMAGE_FILE}\" \"{OUTPUT_GCS_PATH}\"'\n", + "print(f\"upload_to_gcp: {upload_to_gcp}\")\n", + "result = subprocess.check_output(upload_to_gcp, shell=True)\n", + "print(f\"uploading classified image to gcp: {result}\")" ] }, { "cell_type": "markdown", - "source": [ - "Next we will upload this to the GEE asset." - ], "metadata": { "id": "QdeLBXDfDyWc" - } + }, + "source": [ + "Next we will upload this to the GEE asset." + ] }, { "cell_type": "code", - "source": [ - "config.EE_OUTPUT_ASSET, OUTPUT_GCS_PATH" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1388,49 +1386,49 @@ "id": "zaabEpmuD5q2", "outputId": "ab264c5f-b479-4065-d362-43cfc920f857" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "('projects/servir-ee/assets/dl-book/chapter-1/prediction',\n", " 'gs://dl-book/chapter-1/prediction/prediction_unet_v1.TFRecord')" ] }, + "execution_count": 32, "metadata": {}, - "execution_count": 32 + "output_type": "execute_result" } + ], + "source": [ + "config.EE_OUTPUT_ASSET, OUTPUT_GCS_PATH" ] }, { "cell_type": "markdown", - "source": [ - "Make sure you have proper permission." - ], "metadata": { "id": "F3vVU9lUHZ6S" - } + }, + "source": [ + "Make sure you have proper permission." + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UCxT0FLqYboq" + }, + "outputs": [], "source": [ "# Import, authenticate and initialize the Earth Engine library.\n", "import ee\n", "ee.Authenticate()\n", "ee.Initialize(project=f\"{config.GCS_PROJECT}\")" - ], - "metadata": { - "id": "UCxT0FLqYboq" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "!earthengine set_project {config.GCS_PROJECT}" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1438,24 +1436,22 @@ "id": "p8w9iCirYp5L", "outputId": "e57a8a38-d5bb-4b1c-8b88-0dc8a3a46fdf" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Successfully saved project id\n" ] } + ], + "source": [ + "!earthengine set_project {config.GCS_PROJECT}" ] }, { "cell_type": "code", - "source": [ - "upload_image = f\"earthengine upload image --asset_id={config.EE_OUTPUT_ASSET}/{config.OUTPUT_NAME} --pyramiding_policy=mode {OUTPUT_GCS_PATH} {json_file}\"\n", - "result = subprocess.check_output(upload_image, shell=True)\n", - "print(f\"uploading classified image to earth engine: {result}\")\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1463,163 +1459,37 @@ "id": "ZLJOT49o0U7U", "outputId": "3f76540f-30df-4435-be4a-f70e8fdb1b40" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "uploading classified image to earth engine: b'Started upload task with ID: XLMWAIQS2LKVZYR3VTUUUXS3\\n'\n" ] } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Test (Do not run)" - ], - "metadata": { - "id": "j_OniN5mDpCF" - } - }, - { - "cell_type": "code", - "source": [ - "# Get relevant info from the JSON mixer file.\n", - "affine_transform = mixer[\"projection\"][\"affine\"][\"doubleMatrix\"]\n", - "patch_dims = mixer[\"patchDimensions\"]\n", - "patches_per_row = mixer[\"patchesPerRow\"]\n", - "total_patches = mixer[\"totalPatches\"]" ], - "metadata": { - "id": "9BWBMiBd0VA1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NI3DZXi8Po4j" - }, - "outputs": [], "source": [ - "# Path to your TFRecord file\n", - "tfrecord_file = '/content/drive/MyDrive/Colab Notebooks/DL_Book/Chapter_1/output/unet_v1/prediction/prediction_unet_v1.TFRecord'\n", - "\n", - "# Define the feature description for deserialization\n", - "feature_description = {\n", - " # Create a dictionary describing the features.\n", - " 'prediction': tf.io.FixedLenFeature([], tf.int64),\n", - " 'cropland_etc': tf.io.FixedLenFeature([], tf.float32),\n", - " 'rice': tf.io.FixedLenFeature([], tf.float32),\n", - " 'forest': tf.io.FixedLenFeature([], tf.float32),\n", - " 'urban': tf.io.FixedLenFeature([], tf.float32),\n", - " 'others_etc': tf.io.FixedLenFeature([], tf.float32),\n", - "}\n", - "\n", - "def _parse_function(proto):\n", - " return tf.io.parse_single_example(proto, feature_description)\n", - "\n", - "# Create a dataset from the TFRecord file\n", - "raw_dataset = tf.data.TFRecordDataset(tfrecord_file)\n", - "parsed_dataset = raw_dataset.map(_parse_function)\n" - ] - }, - { - "cell_type": "code", - "source": [ - "from osgeo import gdal, osr\n", - "import cv2\n", - "\n", - "# Initialize an empty array for the entire image\n", - "full_image = np.zeros((patch_dims[0] * (total_patches // patches_per_row),\n", - " patch_dims[1] * patches_per_row, 3), dtype=np.uint8)\n", - "\n", - "# Iterate over each image in the parsed dataset\n", - "for i, features in enumerate(parsed_dataset):\n", - " img = tf.image.decode_image(features['prediction']).numpy()\n", - " row = i // patches_per_row\n", - " col = i % patches_per_row\n", - " full_image[row * patch_dims[0]:(row + 1) * patch_dims[0],\n", - " col * patch_dims[1]:(col + 1) * patch_dims[1]] = img\n", - "\n", - "# Create a GeoTIFF file\n", - "driver = gdal.GetDriverByName('GTiff')\n", - "outRaster = driver.Create('output.tif', full_image.shape[1], full_image.shape[0], 3, gdal.GDT_Byte)\n", - "outRaster.SetGeoTransform([affine_transform[2], affine_transform[0], 0,\n", - " affine_transform[5], 0, affine_transform[4]])\n", - "\n", - "# Set the projection\n", - "outRasterSRS = osr.SpatialReference()\n", - "outRasterSRS.ImportFromEPSG(4326)\n", - "outRaster.SetProjection(outRasterSRS.ExportToWkt())\n", - "\n", - "# Write the data\n", - "outband = outRaster.GetRasterBand(1)\n", - "outband.WriteArray(full_image[:,:,0])\n", - "outband = outRaster.GetRasterBand(2)\n", - "outband.WriteArray(full_image[:,:,1])\n", - "outband = outRaster.GetRasterBand(3)\n", - "outband.WriteArray(full_image[:,:,2])\n", - "\n", - "# Flush data\n", - "outRaster.FlushCache()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 373 - }, - "id": "x5OYxU-j3o8f", - "outputId": "5a6cd3ea-0a2a-42d6-e53e-a267e8248ea9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "error", - "ename": "InvalidArgumentError", - "evalue": "{{function_node __wrapped__IteratorGetNext_output_types_6_device_/job:localhost/replica:0/task:0/device:CPU:0}} Key: urban. Can't parse serialized Example.\n\t [[{{node ParseSingleExample/ParseExample/ParseExampleV2}}]] [Op:IteratorGetNext] name: ", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Iterate over each image in the parsed dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'prediction'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mpatches_per_row\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 808\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 810\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 811\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 812\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m_next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[0;31m# to communicate that there is no more data to iterate over.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 772\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSYNC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m ret = gen_dataset_ops.iterator_get_next(\n\u001b[0m\u001b[1;32m 774\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 775\u001b[0m \u001b[0moutput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flat_output_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/gen_dataset_ops.py\u001b[0m in \u001b[0;36miterator_get_next\u001b[0;34m(iterator, output_types, output_shapes, name)\u001b[0m\n\u001b[1;32m 3027\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3028\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3029\u001b[0;31m \u001b[0m_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3030\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3031\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mInvalidArgumentError\u001b[0m: {{function_node __wrapped__IteratorGetNext_output_types_6_device_/job:localhost/replica:0/task:0/device:CPU:0}} Key: urban. Can't parse serialized Example.\n\t [[{{node ParseSingleExample/ParseExample/ParseExampleV2}}]] [Op:IteratorGetNext] name: " - ] - } + "upload_image = f\"earthengine upload image --asset_id={config.EE_OUTPUT_ASSET}/{config.OUTPUT_NAME} --pyramiding_policy=mode {OUTPUT_GCS_PATH} {json_file}\"\n", + "result = subprocess.check_output(upload_image, shell=True)\n", + "print(f\"uploading classified image to earth engine: {result}\")\n" ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "9r1Necj03u4K" - }, - "execution_count": null, - "outputs": [] } ], "metadata": { + "accelerator": "GPU", "colab": { - "provenance": [], - "gpuType": "T4", "collapsed_sections": [ "j_OniN5mDpCF" ], + "gpuType": "T4", + "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" - }, - "accelerator": "GPU" + } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +}