user_wwq
3 years ago
| 2 | 2 | { |
| 3 | 3 | "cell_type": "code", |
| 4 | 4 | "execution_count": 3, |
| 5 | "id": "db0b0a11", | |
| 5 | "id": "769c9afd", | |
| 6 | 6 | "metadata": {}, |
| 7 | 7 | "outputs": [ |
| 8 | 8 | { |
| 39 | 39 | { |
| 40 | 40 | "cell_type": "code", |
| 41 | 41 | "execution_count": 4, |
| 42 | "id": "377355df", | |
| 42 | "id": "227c58c5", | |
| 43 | 43 | "metadata": {}, |
| 44 | 44 | "outputs": [], |
| 45 | 45 | "source": [ |
| 48 | 48 | }, |
| 49 | 49 | { |
| 50 | 50 | "cell_type": "code", |
| 51 | "execution_count": 9, | |
| 52 | "id": "dc65195f", | |
| 53 | "metadata": {}, | |
| 54 | "outputs": [], | |
| 55 | "source": [ | |
| 56 | "import time\n", | |
| 57 | "def run_time(func):\n", | |
| 58 | " def inner(model, image, question):\n", | |
| 59 | " back = func(model, image, question)\n", | |
| 60 | " print(\"Runned time: {} s\".format(round((time.time() - t)/10, 3)))\n", | |
| 61 | " return back\n", | |
| 62 | " t = time.time()\n", | |
| 63 | " return inner" | |
| 64 | ] | |
| 65 | }, | |
| 66 | { | |
| 67 | "cell_type": "code", | |
| 51 | 68 | "execution_count": 8, |
| 52 | "id": "fd4ec8ee", | |
| 69 | "id": "bcbe551f", | |
| 53 | 70 | "metadata": {}, |
| 54 | 71 | "outputs": [], |
| 55 | 72 | "source": [ |
| 62 | 79 | { |
| 63 | 80 | "cell_type": "code", |
| 64 | 81 | "execution_count": 6, |
| 65 | "id": "cc76d61f", | |
| 82 | "id": "36fcb0d9", | |
| 66 | 83 | "metadata": {}, |
| 67 | 84 | "outputs": [], |
| 68 | 85 | "source": [ |
| 86 | "@run_time\n", | |
| 69 | 87 | "def inference(model_PATH, img_p):\n", |
| 70 | 88 | " G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')\n", |
| 71 | 89 | " G_sample = model.generator(G_Z)\n", |
| 83 | 101 | { |
| 84 | 102 | "cell_type": "code", |
| 85 | 103 | "execution_count": null, |
| 86 | "id": "d93edf59", | |
| 104 | "id": "b79deb86", | |
| 87 | 105 | "metadata": {}, |
| 88 | 106 | "outputs": [], |
| 89 | 107 | "source": [ |
| 2 | 2 | { |
| 3 | 3 | "cell_type": "code", |
| 4 | 4 | "execution_count": 3, |
| 5 | "id": "ea42a489", | |
| 5 | "id": "3c86ef8a", | |
| 6 | 6 | "metadata": {}, |
| 7 | 7 | "outputs": [ |
| 8 | 8 | { |
| 39 | 39 | { |
| 40 | 40 | "cell_type": "code", |
| 41 | 41 | "execution_count": 4, |
| 42 | "id": "24a28be9", | |
| 42 | "id": "87b58a5a", | |
| 43 | 43 | "metadata": {}, |
| 44 | 44 | "outputs": [], |
| 45 | 45 | "source": [ |
| 48 | 48 | }, |
| 49 | 49 | { |
| 50 | 50 | "cell_type": "code", |
| 51 | "execution_count": 9, | |
| 52 | "id": "4e963aed", | |
| 53 | "metadata": {}, | |
| 54 | "outputs": [], | |
| 55 | "source": [ | |
| 56 | "import time\n", | |
| 57 | "def run_time(func):\n", | |
| 58 | " def inner(model, image, question):\n", | |
| 59 | " back = func(model, image, question)\n", | |
| 60 | " print(\"Runned time: {} s\".format(round((time.time() - t)/10, 3)))\n", | |
| 61 | " return back\n", | |
| 62 | " t = time.time()\n", | |
| 63 | " return inner" | |
| 64 | ] | |
| 65 | }, | |
| 66 | { | |
| 67 | "cell_type": "code", | |
| 51 | 68 | "execution_count": 8, |
| 52 | "id": "2f04ed3d", | |
| 69 | "id": "aba8e440", | |
| 53 | 70 | "metadata": {}, |
| 54 | 71 | "outputs": [], |
| 55 | 72 | "source": [ |
| 62 | 79 | { |
| 63 | 80 | "cell_type": "code", |
| 64 | 81 | "execution_count": 6, |
| 65 | "id": "4cc717e4", | |
| 82 | "id": "bc8c0bf4", | |
| 66 | 83 | "metadata": {}, |
| 67 | 84 | "outputs": [], |
| 68 | 85 | "source": [ |
| 86 | "@run_time\n", | |
| 69 | 87 | "def inference(model_PATH, img_p):\n", |
| 70 | 88 | " G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')\n", |
| 71 | 89 | " G_sample = model.generator(G_Z)\n", |
| 83 | 101 | { |
| 84 | 102 | "cell_type": "code", |
| 85 | 103 | "execution_count": null, |
| 86 | "id": "9ce2fc25", | |
| 104 | "id": "5c1557b3", | |
| 87 | 105 | "metadata": {}, |
| 88 | 106 | "outputs": [], |
| 89 | 107 | "source": [ |