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"output_type": "stream", "text": [ "Collecting aicrowd-cli==0.1\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/45/232809ceb8b1462a2c7eec17335cef9e74bdd0fbde6d5d31a9fb570f1bcf/aicrowd-cli-0.1.0.tar.gz (40kB)\n", "\r\u001b[K |████████ | 10kB 22.0MB/s eta 0:00:01\r\u001b[K |████████████████▏ | 20kB 29.6MB/s eta 0:00:01\r\u001b[K |████████████████████████▎ | 30kB 21.7MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 40kB 2.3MB/s \n", "\u001b[?25hRequirement already satisfied: click<8,>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli==0.1) (7.1.2)\n", "Collecting gitpython\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n", "\u001b[K |████████████████████████████████| 163kB 17.9MB/s \n", "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli==0.1) 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(setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for aicrowd-cli: filename=aicrowd_cli-0.1.0-cp37-none-any.whl size=34929 sha256=4b79e7fd91a34e7d84e022bb6056132d92548bb51cdbfd6b0552678eec3ba179\n", " Stored in directory: /root/.cache/pip/wheels/7e/ec/cc/a38391f23ad9bd044c91cd5e6e0a30f2a4265a1eb5adedf9f5\n", "Successfully built aicrowd-cli\n", "Installing collected packages: smmap, gitdb, gitpython, requests-toolbelt, commonmark, colorama, rich, aicrowd-cli\n", "Successfully installed aicrowd-cli-0.1.0 colorama-0.4.4 commonmark-0.9.1 gitdb-4.0.7 gitpython-3.1.14 requests-toolbelt-0.9.1 rich-10.1.0 smmap-4.0.0\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "njcKvdYJQdXz" }, "source": [ "###RESTART RUNTIME TO USE NEW PACKAGES" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yAkfR_x8Qcwr", "outputId": "d645346b-f994-4170-9edc-4af867d921b4" }, "source": [ "API_KEY = \"\" # Please enter your API Key from [https://www.aicrowd.com/participants/me]\n", "!aicrowd login --api-key $API_KEY" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "\u001b[32mAPI Key valid\u001b[0m\n", "\u001b[32mSaved API Key successfully!\u001b[0m\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TVK75eXQQcrk", "outputId": "52cb6639-d2ac-4115-e219-b155f165b98e" }, "source": [ "!aicrowd dataset download --challenge stage-prediction\n", "\n", "!rm -rf data\n", "!mkdir data\n", "\n", "!unzip -q test.zip -d data\n", "import os\n", "os.rename('data/test', 'data/train')\n", "!mv sample_submission.csv data/sample_submission.csv" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "sample_submission.csv: 100% 13.3k/13.3k [00:00<00:00, 796kB/s]\n", "test.zip: 100% 27.7M/27.7M [00:02<00:00, 10.6MB/s]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "MBgLkllqQcpu", "outputId": "7202b025-013c-43e2-bb53-9d9508bf345e" }, "source": [ "import shutil\n", "\n", "shutil.copy('AIcrowd-AIBlitz7-Solution/challenge4/model.py','model.py')\n", "shutil.copy('AIcrowd-AIBlitz7-Solution/challenge4/dataset.py','dataset.py')" ], "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'dataset.py'" ] }, "metadata": { "tags": [] }, "execution_count": 15 } ] }, { "cell_type": "code", "metadata": { "id": "98MRyNkqQWC8" }, "source": [ "import pandas as pd\n", "baseline = []\n", "images_list = [598,1048,1198,1498,1798]\n", "last = 0\n", "for u, i in enumerate(images_list):\n", " baseline.extend([u]*(i-last))\n", " last = i\n", "baseline = [0]+baseline\n", "\n", "df = pd.read_csv('data/sample_submission.csv')\n", "df.label = baseline\n", "df.to_csv('data/labels.csv',index = False)" ], "execution_count": 16, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487, "referenced_widgets": [ "da5fa0bb9dc14e0b9d6022eb52055e5f", "9fbebcec992e45e7afa211c3d760dd98", "28a2f6f73f894bc79d53c47cbbcc5f81", "efb050903e484c249c7c93b884f17938", "620fe07f4682402a94ad13d5dc1b0ce9", "7a664c1870c64ebe9c04aa2028a6ebee", "b22e966626c7449c8754a7adbafa51a9", "aa83ed92fb244f63af33ffe46ac12f9d", "22f0755ebbd442e5ba4474240f1514c5", "42f54c4da6e24d7aa689f320d36dc7d0", "900dfcdf05454b8ab062e60acee7802d", "c48e6cf6f1144a018344d287db73020e", "8cfb043f86834ab7ba7d6cf013e315bc", "f9c0c41f0ab54e6d8463372b885002e1", "9ae3d57824894cc885940418d86ac243", "fb50191c2da04158b357c8305dd5aa4a" ] }, "id": "0hYmCZZ_QWC-", "outputId": "43179f69-bece-4e2c-eccb-ae38f8dd23ce" }, "source": [ "import model\n", "import dataset\n", "import torch\n", "import albumentations as A\n", "import pytorch_lightning as pl\n", "from pytorch_lightning import Trainer\n", "\n", "if __name__ == '__main__':\n", " trainer = Trainer(max_epochs = 30,gpus = 1, precision=16, amp_level='O1',deterministic=True)\n", " \n", " train_tr = A.Compose([\n", " A.CenterCrop(256,256,always_apply=True)\n", " ])\n", " \n", " val_tr = A.Compose([\n", " A.CenterCrop(256,256,always_apply=True)\n", " ])\n", " \n", " model = model.Classifier({'lr':3e-4,'batch_size':32,'train_tr':train_tr,'val_tr':val_tr})\n", " trainer.fit(model)\n", " trainer.test(model)\n", " out = trainer.predict(model)\n", " output = torch.tensor([])\n", "\n", " for i in range(len(out)):\n", " output = torch.cat((output,torch.tensor(out[i][0]).argmax(1)))\n", " output = output.int().tolist()\n", " dataset.writeSub(output)" ], "execution_count": 17, "outputs": [ { "output_type": "stream", "text": [ "GPU available: True, used: True\n", "TPU available: None, using: 0 TPU cores\n", "Using native 16bit precision.\n", "\n", " | Name | Type | Params\n", "----------------------------------------\n", "0 | resnet | EfficientNet | 10.7 M\n", "----------------------------------------\n", "10.7 M Trainable params\n", "0 Non-trainable params\n", "10.7 M Total params\n", "42.816 Total estimated model params size (MB)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Loaded pretrained weights for efficientnet-b3\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 6 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", " cpuset_checked))\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da5fa0bb9dc14e0b9d6022eb52055e5f", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "/content/model.py:23: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", " prob = F.softmax(x)\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:51: UserWarning: you defined a test_step but have no test_dataloader. Skipping test loop\n", " warnings.warn(*args, **kwargs)\n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "22f0755ebbd442e5ba4474240f1514c5", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Predicting', layout=Layout(flex='2'), m…" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "7uN87zVlYWou" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }