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https://github.com/sbrl/research-rainfallradar
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rr de: update to dataset_mono_predict
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1 changed files with 58 additions and 44 deletions
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@ -2,40 +2,33 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 12,
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"id": "1f6fdebf-69c5-46ab-a5a8-f9c91f000ff3",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-10-24 17:18:42.868504: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:42.868519: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",
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"import tensorflow as tf\n",
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"from datetime import datetime\n",
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"\n",
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"from lib.dataset.dataset_segmenter import dataset_predict"
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"from lib.dataset.dataset_mono import dataset_mono_predict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 13,
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"id": "4a6a94dd-5c4e-4481-bfae-eb5ccf6214db",
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"metadata": {},
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"outputs": [],
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"source": [
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"dirpath=\"/home/bryan-smithl/Documents/repos/PhD-Rainfall-Radar/aimodel/output/rainfallwater_records_embed_2022-10-06_contrast_embed_umap_d512e19_tfrecord\""
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"# dirpath=\"/home/bryan-smithl/Documents/repos/PhD-Rainfall-Radar/aimodel/output/rainfallwater_records_embed_2022-10-06_contrast_embed_umap_d512e19_tfrecord\"\n",
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"dirpath=\"/mnt/research-data/main/rainfallwater_records_tfrecord\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 14,
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"id": "d1aa931a-ecf2-4134-8e70-87db4ae60736",
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"metadata": {},
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"outputs": [
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@ -43,41 +36,33 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DEBUG:dataset ITEM rainfall:shape (512,) water:shape (94, 94, 1)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-10-24 17:18:44.582661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
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"2022-10-24 17:18:44.583031: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583152: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583257: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583353: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583438: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583519: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583598: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583675: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory\n",
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"2022-10-24 17:18:44.583688: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
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"Skipping registering GPU devices...\n",
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"2022-10-24 17:18:44.584534: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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"DEBUG DATASET:rainfall shape [7, 174, 105] / w 105 h 174\n",
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"DEBUG DATASET:water shape [348, 210]\n",
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"DEBUG DATASET:water_threshold 0.1\n",
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"DEBUG DATASET:water_bins 2\n",
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"DEBUG DATASET:output_size 100\n",
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"DEBUG DATASET:input_size 100\n",
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"DEBUG DATASET:water_offset x 55 y 124\n",
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"DEBUG DATASET:rainfall_offset x 3 y 37\n",
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"DEBUG:dataset BEFORE_SQUEEZE water (100, 100, 1)\n",
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"DEBUG:dataset AFTER_SQUEEZE water (100, 100)\n",
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"DEBUG DATASET_OUT:rainfall shape (100, 100, 7)\n",
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"DEBUG DATASET_OUT:water shape (100, 100)\n"
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]
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}
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],
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"source": [
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"dataset = dataset_predict(\n",
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"dataset = dataset_mono_predict(\n",
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"\tdirpath_input=dirpath,\n",
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"\twater_threshold=0.1,\n",
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"\tshape_water_desired=[94, 94],\n",
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"\t# shape_water_desired=[94, 94],\n",
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"\tparallel_reads_multiplier=1.5 # Mangles the ordering. For counting things this doesn't matter\n",
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").batch(64)"
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 15,
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"id": "456f7c8f-3f7d-4a2c-b361-900588c49612",
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"metadata": {},
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"outputs": [
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@ -93,9 +78,7 @@
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"output_type": "stream",
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"text": [
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"\n",
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"Complete. Counts:\n",
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"0: 10238285719\n",
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"1: 2854828393\n"
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"{msg}\n"
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]
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}
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],
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@ -103,13 +86,44 @@
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"i = 0\n",
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"counts = tf.constant([0, 0], dtype=tf.int64)\n",
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"for (items, label) in dataset:\n",
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"\tlabel = tf.cast(label, tf.int32)\n",
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"\tstep_counts = tf.math.bincount(tf.reshape(label, [-1]))\n",
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"\tcounts += tf.cast(step_counts, dtype=tf.int64)\n",
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"\t# print(\"STEP counts\", counts, \"step_counts\", step_counts)\n",
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"\ti += 1\n",
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"\tif i % 100 == 0:\n",
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"\t\tsys.stderr.write(f\"Processed {i} batches\\r\")\n",
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"print(\"\\nComplete. Counts:\\n\"+\"\\n\".join([ str(i)+\": \"+str(count) for i,count in enumerate(counts.numpy().tolist()) ]))"
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"\n",
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"msg = f\"Complete at {datetime.now()}. Counts:\\n\"+\"\\n\".join([ str(i)+\": \"+str(count) for i,count in enumerate(counts.numpy().tolist()) ])\n",
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"print(f\"\\n{msg}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "74a94efe",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Total 14817920000 cells, Percentages:\n",
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"0: 77.92587213320088%\n",
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"1: 22.074127866799117%\n"
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]
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}
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],
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"source": [
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"total = tf.math.reduce_sum(counts)\n",
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"\n",
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"percentages = (tf.cast(counts, tf.float64) / tf.cast(total, tf.float64)) * 100.0\n",
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"\n",
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"msg = f\"Total {total.numpy()} cells, Percentages:\\n\"+\"\\n\".join(\n",
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" [str(i)+\": \"+str(count)+\"%\" for i, count in enumerate(percentages.numpy().tolist())]\n",
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")\n",
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"print(f\"\\n{msg}\")"
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]
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}
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],
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@ -129,7 +143,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.10.12"
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},
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"vscode": {
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"interpreter": {
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