pgn_segmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pgn segmentation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khushgrover/smart-match/blob/main/pgn_segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbqzZjUHFynX",
        "outputId": "05753ae7-d026-4e83-e1a8-3108c77b0479",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone \"https://github.com/Engineering-Course/CIHP_PGN.git\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'CIHP_PGN'...\n",
            "remote: Enumerating objects: 4363, done.\u001b[K\n",
            "remote: Total 4363 (delta 0), reused 0 (delta 0), pack-reused 4363\u001b[K\n",
            "Receiving objects: 100% (4363/4363), 830.35 KiB | 13.18 MiB/s, done.\n",
            "Resolving deltas: 100% (705/705), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOaQaR5yGJSs",
        "outputId": "01757bc9-2bb5-4a46-de60-a16402dd2f54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxfDl5OfMCfR"
      },
      "source": [
        "root_path = 'gdrive/My Drive/dataset/'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSO1DnlsN_7M"
      },
      "source": [
        "!mkdir CIHP_PGN/checkpoint"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AV8FcTQwMLIS"
      },
      "source": [
        "!cp -r \"gdrive/My Drive/dataset/CIHP_pgn.zip\" \"CIHP_PGN/checkpoint\""
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0ThOu_SLYi3",
        "outputId": "ae2d640a-3484-4850-c4cd-81548d5d76f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!cd '/content/CIHP_PGN/checkpoint'; unzip 'CIHP_pgn.zip'"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  CIHP_pgn.zip\n",
            "   creating: CIHP_pgn/\n",
            "  inflating: CIHP_pgn/checkpoint     \n",
            "  inflating: CIHP_pgn/model.ckpt-593292.data-00000-of-00001  \n",
            "  inflating: CIHP_pgn/model.ckpt-593292.index  \n",
            "  inflating: CIHP_pgn/model.ckpt-593292.meta  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FhuyASFLiqw"
      },
      "source": [
        "!cd '/content/CIHP_PGN/checkpoint'; rm 'CIHP_pgn.zip'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ai9hiuViMFtj"
      },
      "source": [
        "!cp \"gdrive/My Drive/dataset/test2.jpg\" \"CIHP_PGN/datasets/CIHP/images\""
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Go4u9iYkNA7P"
      },
      "source": [
        "import numpy as np\n",
        "import imageio as ii\n",
        "from PIL import Image\n",
        "nxb_img   = Image.open('CIHP_PGN/datasets/CIHP/images/test2.jpg')      # This is your image.\n",
        " \n",
        "# Reshape their label image to our size \n",
        "label_img = Image.open('CIHP_PGN/datasets/CIHP/labels/0005008.png')  # This is the label image from CIHP.\n",
        "nxb_label_img = label_img.resize(nxb_img.size, Image.NEAREST)\n",
        "nxb_label_img.save('CIHP_PGN/datasets/CIHP/labels/test2.png')\n",
        "\n",
        "# Reshape their edge image to our size \n",
        "edge_img  = Image.open('CIHP_PGN/datasets/CIHP/edges/0005008.png')\n",
        "nxb_edge_img  = edge_img.resize(nxb_img.size, Image.NEAREST)\n",
        "nxb_edge_img.save('CIHP_PGN/datasets/CIHP/edges/test2.png')\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_7My18KRKrh"
      },
      "source": [
        "# Reshape their label image to our size \n",
        "label_rev_img = Image.open('CIHP_PGN/datasets/CIHP/labels_rev/0005008.png')  # This is the label image from CIHP.\n",
        "nxb_label_rev_img = label_rev_img.resize(nxb_img.size, Image.NEAREST)\n",
        "nxb_label_img.save('CIHP_PGN/datasets/CIHP/labels_rev/test2.png')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b86ue3hEOfZE",
        "outputId": "a735aa89-5c0a-44fc-bdf2-0e905f8adba3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd CIHP_PGN"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/CIHP_PGN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yNG2yliQGgt",
        "outputId": "f73ff021-0c2d-43c2-a5a5-b384efd63d28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%pwd"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/CIHP_PGN'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gi7ll5rbT3hS",
        "outputId": "b5bdabc6-a094-4ccf-d3da-47d53d615469",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKMDKnAPRbgg",
        "outputId": "72f446f7-e7d7-41a0-ccda-27ec521af122",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!python test_pgn.py"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/utils/image_reader.py:235: slice_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(tuple(tensor_list)).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/input.py:373: range_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.range(limit).shuffle(limit).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/input.py:319: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/input.py:189: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/input.py:198: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/training/input.py:198: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/utils/image_reader.py:175: The name tf.read_file is deprecated. Please use tf.io.read_file instead.\n",
            "\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/util/dispatch.py:180: calling expand_dims (from tensorflow.python.ops.array_ops) with dim is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use the `axis` argument instead\n",
            "WARNING:tensorflow:From test_pgn.py:41: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From test_pgn.py:42: The name tf.image.resize_images is deprecated. Please use tf.image.resize instead.\n",
            "\n",
            "WARNING:tensorflow:From test_pgn.py:42: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From test_pgn.py:49: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/kaffe/tensorflow/network.py:45: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/kaffe/tensorflow/network.py:104: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/kaffe/tensorflow/network.py:205: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/kaffe/tensorflow/network.py:295: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/kaffe/tensorflow/network.py:302: The name tf.image.resize_bilinear is deprecated. Please use tf.compat.v1.image.resize_bilinear instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/CIHP_PGN/kaffe/tensorflow/network.py:283: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/metrics_impl.py:1177: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/metrics_impl.py:1178: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Deprecated in favor of operator or tf.math.divide.\n",
            "WARNING:tensorflow:From test_pgn.py:141: streaming_accuracy (from tensorflow.contrib.metrics.python.ops.metric_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.metrics.accuracy. Note that the order of the labels and predictions arguments has been switched.\n",
            "WARNING:tensorflow:From test_pgn.py:144: streaming_recall (from tensorflow.contrib.metrics.python.ops.metric_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.metrics.recall. Note that the order of the labels and predictions arguments has been switched.\n",
            "WARNING:tensorflow:From test_pgn.py:145: streaming_precision (from tensorflow.contrib.metrics.python.ops.metric_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please switch to tf.metrics.precision. Note that the order of the labels and predictions arguments has been switched.\n",
            "WARNING:tensorflow:From test_pgn.py:150: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From test_pgn.py:152: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From test_pgn.py:154: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "2020-11-02 06:03:01.273594: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
            "2020-11-02 06:03:01.273924: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x22672c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-02 06:03:01.273961: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-11-02 06:03:01.278585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-11-02 06:03:01.477308: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
            "2020-11-02 06:03:01.478034: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2267100 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-02 06:03:01.478064: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
            "2020-11-02 06:03:01.479151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
            "2020-11-02 06:03:01.479707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
            "name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-11-02 06:03:01.480024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-02 06:03:01.671605: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-02 06:03:01.787891: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-02 06:03:01.840694: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-02 06:03:02.083413: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-02 06:03:02.139390: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-02 06:03:02.601752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-02 06:03:02.601989: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
            "2020-11-02 06:03:02.602740: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
            "2020-11-02 06:03:02.603301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
            "2020-11-02 06:03:02.606151: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-02 06:03:02.607613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-02 06:03:02.607644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0 \n",
            "2020-11-02 06:03:02.607661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N \n",
            "2020-11-02 06:03:02.635267: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
            "2020-11-02 06:03:02.635893: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
            "2020-11-02 06:03:02.636509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14221 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From test_pgn.py:155: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From test_pgn.py:158: The name tf.local_variables_initializer is deprecated. Please use tf.compat.v1.local_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From test_pgn.py:161: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "Restored model parameters from model.ckpt-593292\n",
            " [*] Load SUCCESS\n",
            "WARNING:tensorflow:From test_pgn.py:169: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "2020-11-02 06:03:21.863503: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-02 06:03:27.256032: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-02 06:04:30.667643: W tensorflow/core/common_runtime/bfc_allocator.cc:305] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.\n",
            "2020-11-02 06:05:10.193876: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.78GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:10.194060: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.78GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:17.291379: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.62GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:17.291533: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.62GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:20.264666: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.37GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:20.264840: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.37GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:46.256669: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.24GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "2020-11-02 06:05:46.256754: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.24GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n",
            "step 0\n",
            "./datasets/CIHP/images/test2.jpg\n",
            "Mean IoU: 0.0348, Mean Acc: 0.3704\n",
            "Recall: 0.0471, Precision: 0.0817, F1 score: 0.0597\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtVb2-4eXThO",
        "outputId": "d65668d4-6f44-4bde-b9db-7578f69f586a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "\n",
        "imageOriginal = cv2.imread('/content/CIHP_PGN/datasets/CIHP/images/test2.jpg')\n",
        "imageOriginal = cv2.cvtColor(imageOriginal, cv2.COLOR_BGR2RGB)\n",
        "imageSegment = cv2.imread('/content/CIHP_PGN/output/cihp_parsing_maps/test2_vis.png')\n",
        "\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "ax1 = fig.add_subplot(1,2,1)\n",
        "ax1.set_title('Original') \n",
        "ax1.imshow(imageOriginal)\n",
        "ax2 = fig.add_subplot(1,2,2)\n",
        "ax2.set_title('Segmented') \n",
        "ax2.imshow(imageSegment)\n",
        "plt.tight_layout()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bd8duN3JgftW"
      },
      "source": [
        ""
      ],
      "execution_count": 21,
      "outputs": []
    }
  ]
}