torch_interpolate.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Untitled2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "si6zzdrPbINo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "37-X1dqVbT65",
        "colab_type": "code",
        "outputId": "33a286a4-24ba-4515-c87c-d0fc909f46b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "print(f\"pytorch version: {torch.__version__}\")\n",
        "print(f\"cv2 version: {cv2.__version__}\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pytorch version: 1.0.1.post2\n",
            "cv2 version: 3.4.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OYxjt4nYbMe8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8e23e655-9499-4f16-b149-61e84a175aa6"
      },
      "cell_type": "code",
      "source": [
        "x = torch.tensor([[[[64, 32], [64, 32]]]], dtype=torch.float32)\n",
        "x"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[64., 32.],\n",
              "          [64., 32.]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "k2JeeL49eYgi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "size = (4, 4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p1c63xCObSXM",
        "colab_type": "code",
        "outputId": "e3e1025d-4700-4e02-e8a3-8d703bc2337d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "cell_type": "code",
      "source": [
        "torch.nn.functional.interpolate(x, size=size, mode='bilinear')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2423: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
            "  \"See the documentation of nn.Upsample for details.\".format(mode))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[64., 56., 40., 32.],\n",
              "          [64., 56., 40., 32.],\n",
              "          [64., 56., 40., 32.],\n",
              "          [64., 56., 40., 32.]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "0Np82pf1djAD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "fdc51180-9571-47b8-f5a1-b01bcd5619b2"
      },
      "cell_type": "code",
      "source": [
        "cv2.resize(x.numpy()[0, 0], size)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[64., 56., 40., 32.],\n",
              "       [64., 56., 40., 32.],\n",
              "       [64., 56., 40., 32.],\n",
              "       [64., 56., 40., 32.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    }
  ]
}