From 993b30749acb5b849a0722b6bb3c458131b478dc Mon Sep 17 00:00:00 2001 From: MeneerTS Date: Mon, 22 Apr 2024 17:25:47 +0200 Subject: [PATCH] Changed to stride 1 max pool --- notebooks/simple_baseline.ipynb | 183 ++++++++++++++++---------------- 1 file changed, 91 insertions(+), 92 deletions(-) diff --git a/notebooks/simple_baseline.ipynb b/notebooks/simple_baseline.ipynb index 850bc5e..afbc2aa 100644 --- a/notebooks/simple_baseline.ipynb +++ b/notebooks/simple_baseline.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Fhf18v2MbqAs", - "outputId": "ee3ce256-b074-43ae-cbbd-47ed3b796cd6" + "outputId": "54c20458-f45b-4a3d-ad16-474b6a356f68" }, "outputs": [ { @@ -16,18 +16,18 @@ "name": "stdout", "text": [ "Cloning into 'dl2'...\n", - "remote: Enumerating objects: 200, done.\u001b[K\n", - "remote: Counting objects: 100% (200/200), done.\u001b[K\n", - "remote: Compressing objects: 100% (149/149), done.\u001b[K\n", - "remote: Total 200 (delta 73), reused 139 (delta 40), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (200/200), 92.30 KiB | 794.00 KiB/s, done.\n", - "Resolving deltas: 100% (73/73), done.\n", + "remote: Enumerating objects: 204, done.\u001b[K\n", + "remote: Counting objects: 100% (204/204), done.\u001b[K\n", + "remote: Compressing objects: 100% (151/151), done.\u001b[K\n", + "remote: Total 204 (delta 76), reused 143 (delta 42), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (204/204), 102.34 KiB | 5.69 MiB/s, done.\n", + "Resolving deltas: 100% (76/76), done.\n", "\n", "Current Directory:\n", "/content/dl2\n", "Collecting git+https://github.com/AMLab-Amsterdam/lie_learn\n", - " Cloning https://github.com/AMLab-Amsterdam/lie_learn to /tmp/pip-req-build-15hr9y3b\n", - " Running command git clone --filter=blob:none --quiet https://github.com/AMLab-Amsterdam/lie_learn /tmp/pip-req-build-15hr9y3b\n", + " Cloning https://github.com/AMLab-Amsterdam/lie_learn to /tmp/pip-req-build-tdrbmkxs\n", + " Running command git clone --filter=blob:none --quiet https://github.com/AMLab-Amsterdam/lie_learn /tmp/pip-req-build-tdrbmkxs\n", " Resolved https://github.com/AMLab-Amsterdam/lie_learn to commit 1ccc2106e402d517a29de5438c9367c959e67338\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", @@ -36,13 +36,13 @@ "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.17.1+cu121)\n", "Collecting escnn\n", " Downloading escnn-1.0.11-py3-none-any.whl (373 kB)\n", - 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" Created wheel for lie_learn: filename=lie_learn-0.0.1.post1-cp310-cp310-linux_x86_64.whl size=16176503 sha256=97b7f8124f8f39cba65132367a64aca4a228a5470007436c22d8ea72627c63a8\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-yli50_nk/wheels/3f/33/85/b8725ee77011bc42d77e4e35aeca2088482c3094f5c0a650a6\n", + " Created wheel for lie_learn: filename=lie_learn-0.0.1.post1-cp310-cp310-linux_x86_64.whl size=16176483 sha256=99e6dde556f08f73ecdd7bcfb79bb5b4e766fe4509bd320f0dcde65db6c17c20\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-q_pv1j7k/wheels/3f/33/85/b8725ee77011bc42d77e4e35aeca2088482c3094f5c0a650a6\n", " Building wheel for py3nj (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for py3nj: filename=py3nj-0.2.1-cp310-cp310-linux_x86_64.whl size=44135 sha256=58ec982ac2c9ed3467543121239c834c6ffad56d6baa833bcae4ed3f0e54ec3e\n", + " Created wheel for py3nj: filename=py3nj-0.2.1-cp310-cp310-linux_x86_64.whl size=44135 sha256=c13092c74ccdd15d5f273f8e57985145ce15622b0f919617e3b066c1c868e8b3\n", " Stored in directory: /root/.cache/pip/wheels/71/e9/70/30a34ed6dbc8b54ce93f25c091be4cf7a24319e27d953a882b\n", "Successfully built lie_learn py3nj\n", "Installing collected packages: smmap, setproctitle, sentry-sdk, scipy, py3nj, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lightning-utilities, docker-pycreds, pymanopt, nvidia-cusparse-cu12, nvidia-cudnn-cu12, lie_learn, gitdb, nvidia-cusolver-cu12, GitPython, wandb, torchmetrics, escnn, pytorch-lightning, lightning\n", @@ -190,19 +190,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5ScCJVP4uqkw", - "outputId": "924b47a9-0aca-449a-dacc-4ce3b5d90d0a" + "outputId": "18b965d7-f442-42b6-95df-9fa786c82ef2" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" @@ -216,7 +217,7 @@ ] }, "metadata": {}, - "execution_count": 5 + "execution_count": 4 } ], "source": [ @@ -226,14 +227,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "ZSXvCwp31174", - "outputId": "27563636-b451-4fbd-9642-762d5bc13a4d" + "outputId": "c4465598-a2dd-4e33-ba4f-b3f83ebf0d66" }, "outputs": [ { @@ -262,7 +263,7 @@ "" ], "text/html": [ - "Run data is saved locally in /content/dl2/wandb/run-20240422_124520-x7yyz9hn" + "Run data is saved locally in /content/dl2/wandb/run-20240422_151206-fimkvcce" ] }, "metadata": {} @@ -274,7 +275,7 @@ "" ], "text/html": [ - "Syncing run cosmic-puddle-15 to Weights & Biases (docs)
" + "Syncing run young-pond-18 to Weights & Biases (docs)
" ] }, "metadata": {} @@ -298,7 +299,7 @@ "" ], "text/html": [ - " View run at https://wandb.ai/uva-dl2/dl2/runs/x7yyz9hn" + " View run at https://wandb.ai/uva-dl2/dl2/runs/fimkvcce" ] }, "metadata": {} @@ -307,14 +308,14 @@ "output_type": "execute_result", "data": { "text/html": [ - "" + "" ], "text/plain": [ - "" + "" ] }, "metadata": {}, - "execution_count": 6 + "execution_count": 5 } ], "source": [ @@ -323,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "HCsuIxAbad22" }, @@ -373,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "avGSx5qejQTL" }, @@ -483,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "id": "tnN6MbeKad3A" }, @@ -632,13 +633,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "gHfxBg2yad3C", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "22e5637d-9dc9-4cf8-c574-3cf42f91b357" + "outputId": "43396ff7-4a6b-447f-b1ef-cd8a423f74a0" }, "outputs": [ { @@ -1059,7 +1060,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "oFzoZPKwlSL0" }, @@ -1183,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "id": "iZ_GNEKpw4CU" }, @@ -1266,7 +1267,7 @@ " self.backbone_channels = backbone_channels\n", " self.residual_channels = residual_channels\n", " self.blocks = self._make_blocks()\n", - " self.max_pool = torch.nn.MaxPool2d(kernel_size=3)\n", + " self.max_pool = torch.nn.MaxPool2d(kernel_size=3,stride=1) #This stride is sus\n", "\n", " # Fully Connected\n", " self.fully_net = torch.nn.Sequential(\n", @@ -1285,7 +1286,7 @@ " if i < len(self.backbone_channels) - 1:\n", " Cout = self.backbone_channels[i + 1]\n", " else:\n", - " Cout = 128 #this is also very vague from the og paper\n", + " Cout = 32 #We put this to 32, so it is 128/4, since we have a 2x2 output\n", "\n", " #print('hello', i)\n", " if i == 0:\n", @@ -1318,35 +1319,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "id": "pCCwo8ui2wuI", "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ - "0ab9297d153e4dee8c41f373745a6191", - "2319f4e7f8404d5f8f56fc5591d6a93b", - "7845c960312f45f88ce2f61ff57f822d", - "e99f5edbf6044329bb86c11ee1d39404", - "a7ee2e60605c4c92a2aad68bb6a6d01d", - "5bf84f74e70d46faaab5ad42e9797259", - "5d1d7f56186a4b23b2267ae20f049a84", - "dd73916afd8b498b97b09281f75aef09", - "743aeb459cb144cca177831701ff1ee7", - "4ab39a46a7f84c64acfa8756ccdf4ec3", - "ebaac37de1c440b88cdea95753dbf71b" + "0594bbdc401240c0b3d316a4562f4f62", + "9c489cbb2e6e4f68a892581f373d97fa", + "e74e0a997d61400c8b01b7c0afe8efa0", + "f1fb1ececd3b42d58152eb4638c93ff6", + "e00870aaee084ddfa86f2402a3751974", + "4d793574e83941a69c98861bb0ffded4", + "6a6682dcf94a478db926d71cb1789299", + "fe70f420f5314a2d9ad3d0958b16298f", + "c2546cd3e3204742898b1fa79953f0fe", + "c6361aecae5149708f525da6b156e815", + "36db033d7b334b4287c41795831406aa" ] }, - "outputId": "63376236-084e-43e8-dd2c-78ad131a5696" + "outputId": "187dbecd-51d3-46ff-e38b-ee3bd136feca" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 7 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", - " warnings.warn(_create_warning_msg(\n", "INFO: GPU available: True (cuda), used: True\n", "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n", "INFO: TPU available: False, using: 0 TPU cores\n", @@ -1364,7 +1363,7 @@ "INFO: \n", " | Name | Type | Params\n", "----------------------------------------------------\n", - "0 | net | CNN | 3.7 M \n", + "0 | net | CNN | 3.6 M \n", "1 | train_acc | MulticlassAccuracy | 0 \n", "2 | val_acc | MulticlassAccuracy | 0 \n", "3 | test_acc | MulticlassAccuracy | 0 \n", @@ -1373,14 +1372,14 @@ "6 | test_loss | MeanMetric | 0 \n", "7 | val_acc_best | MaxMetric | 0 \n", "----------------------------------------------------\n", - "3.7 M Trainable params\n", + "3.6 M Trainable params\n", "0 Non-trainable params\n", - "3.7 M Total params\n", - "14.661 Total estimated model params size (MB)\n", + "3.6 M Total params\n", + "14.596 Total estimated model params size (MB)\n", "INFO:lightning.pytorch.callbacks.model_summary:\n", " | Name | Type | Params\n", "----------------------------------------------------\n", - "0 | net | CNN | 3.7 M \n", + "0 | net | CNN | 3.6 M \n", "1 | train_acc | MulticlassAccuracy | 0 \n", "2 | val_acc | MulticlassAccuracy | 0 \n", "3 | test_acc | MulticlassAccuracy | 0 \n", @@ -1389,10 +1388,10 @@ "6 | test_loss | MeanMetric | 0 \n", "7 | val_acc_best | MaxMetric | 0 \n", "----------------------------------------------------\n", - "3.7 M Trainable params\n", + "3.6 M Trainable params\n", "0 Non-trainable params\n", - "3.7 M Total params\n", - "14.661 Total estimated model params size (MB)\n", + "3.6 M Total params\n", + "14.596 Total estimated model params size (MB)\n", "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", " self.pid = os.fork()\n", "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" @@ -1407,7 +1406,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0ab9297d153e4dee8c41f373745a6191" + "model_id": "0594bbdc401240c0b3d316a4562f4f62" } }, "metadata": {} @@ -1470,7 +1469,7 @@ "afteronebyone\n", "torch.Size([64, 168, 4, 4])\n", "torch.Size([64, 168, 4, 4])\n", - "torch.Size([64, 128, 1, 1])\n" + "torch.Size([64, 32, 2, 2])\n" ] }, { @@ -1539,7 +1538,7 @@ "afteronebyone\n", "torch.Size([64, 168, 4, 4])\n", "torch.Size([64, 168, 4, 4])\n", - "torch.Size([64, 128, 1, 1])\n" + "torch.Size([64, 32, 2, 2])\n" ] }, { @@ -3545,7 +3544,7 @@ "width": null } }, - "0ab9297d153e4dee8c41f373745a6191": { + "0594bbdc401240c0b3d316a4562f4f62": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -3560,14 +3559,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_2319f4e7f8404d5f8f56fc5591d6a93b", - "IPY_MODEL_7845c960312f45f88ce2f61ff57f822d", - "IPY_MODEL_e99f5edbf6044329bb86c11ee1d39404" + "IPY_MODEL_9c489cbb2e6e4f68a892581f373d97fa", + "IPY_MODEL_e74e0a997d61400c8b01b7c0afe8efa0", + "IPY_MODEL_f1fb1ececd3b42d58152eb4638c93ff6" ], - "layout": "IPY_MODEL_a7ee2e60605c4c92a2aad68bb6a6d01d" + "layout": "IPY_MODEL_e00870aaee084ddfa86f2402a3751974" } }, - "2319f4e7f8404d5f8f56fc5591d6a93b": { + "9c489cbb2e6e4f68a892581f373d97fa": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -3582,13 +3581,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_5bf84f74e70d46faaab5ad42e9797259", + "layout": "IPY_MODEL_4d793574e83941a69c98861bb0ffded4", "placeholder": "​", - "style": "IPY_MODEL_5d1d7f56186a4b23b2267ae20f049a84", + "style": "IPY_MODEL_6a6682dcf94a478db926d71cb1789299", "value": "Epoch 1: 100%" } }, - "7845c960312f45f88ce2f61ff57f822d": { + "e74e0a997d61400c8b01b7c0afe8efa0": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -3604,15 +3603,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_dd73916afd8b498b97b09281f75aef09", + "layout": "IPY_MODEL_fe70f420f5314a2d9ad3d0958b16298f", "max": 1, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_743aeb459cb144cca177831701ff1ee7", + "style": "IPY_MODEL_c2546cd3e3204742898b1fa79953f0fe", "value": 1 } }, - "e99f5edbf6044329bb86c11ee1d39404": { + "f1fb1ececd3b42d58152eb4638c93ff6": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -3627,13 +3626,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_4ab39a46a7f84c64acfa8756ccdf4ec3", + "layout": "IPY_MODEL_c6361aecae5149708f525da6b156e815", "placeholder": "​", - "style": "IPY_MODEL_ebaac37de1c440b88cdea95753dbf71b", - "value": " 1/1 [00:01<00:00,  0.69it/s, v_num=z9hn, train/loss=2.320, train/acc=0.109]" + "style": "IPY_MODEL_36db033d7b334b4287c41795831406aa", + "value": " 1/1 [00:01<00:00,  0.65it/s, v_num=vcce, train/loss=2.220, train/acc=0.219]" } }, - "a7ee2e60605c4c92a2aad68bb6a6d01d": { + "e00870aaee084ddfa86f2402a3751974": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3685,7 +3684,7 @@ "width": "100%" } }, - "5bf84f74e70d46faaab5ad42e9797259": { + "4d793574e83941a69c98861bb0ffded4": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3737,7 +3736,7 @@ "width": null } }, - "5d1d7f56186a4b23b2267ae20f049a84": { + "6a6682dcf94a478db926d71cb1789299": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -3752,7 +3751,7 @@ "description_width": "" } }, - "dd73916afd8b498b97b09281f75aef09": { + "fe70f420f5314a2d9ad3d0958b16298f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3804,7 +3803,7 @@ "width": null } }, - "743aeb459cb144cca177831701ff1ee7": { + "c2546cd3e3204742898b1fa79953f0fe": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -3820,7 +3819,7 @@ "description_width": "" } }, - "4ab39a46a7f84c64acfa8756ccdf4ec3": { + "c6361aecae5149708f525da6b156e815": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3872,7 +3871,7 @@ "width": null } }, - "ebaac37de1c440b88cdea95753dbf71b": { + "36db033d7b334b4287c41795831406aa": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0",