diff --git a/HW02/HW02.ipynb b/HW02/HW02.ipynb index 1a1d4dab..5321369c 100644 --- a/HW02/HW02.ipynb +++ b/HW02/HW02.ipynb @@ -17,18 +17,18 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "mLQI0mNcmM-O", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "7d5b4d81-9438-4d50-8153-cd235c47ee21" + "id": "mLQI0mNcmM-O", + "outputId": "265a85e8-1580-4fd4-a2b2-c43b08450b8f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Wed Feb 23 14:42:18 2022 \n", + "Tue Oct 25 16:20:22 2022 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", @@ -36,9 +36,9 @@ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", - "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", - "| N/A 30C P8 29W / 149W | 0MiB / 11441MiB | 0% Default |\n", - "| | | N/A |\n", + "| 0 A100-SXM4-40GB Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 29C P0 47W / 400W | 0MiB / 40536MiB | 0% Default |\n", + "| | | Disabled |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", @@ -93,28 +93,28 @@ "base_uri": "https://localhost:8080/" }, "id": "OzkiMEcC3Foq", - "outputId": "cc90c16c-ee21-400e-ec08-dfcd422212a6" + "outputId": "a27c31a1-1c5a-4842-ac4e-a50b9c4dfddb" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "--2022-02-26 14:32:36-- https://github.com/xraychen/shiny-robot/releases/download/v1.0/libriphone.zip\n", - "Resolving github.com (github.com)... 140.82.114.3\n", - "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n", + "--2022-10-25 16:20:23-- https://github.com/xraychen/shiny-robot/releases/download/v1.0/libriphone.zip\n", + "Resolving github.com (github.com)... 140.82.112.3\n", + "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", - 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"libriphone.zip 100%[===================>] 456.56M 83.6MB/s in 5.1s \n", + "libriphone.zip 100%[===================>] 456.56M 218MB/s in 2.1s \n", "\n", - "2022-02-26 14:32:42 (90.2 MB/s) - ‘libriphone.zip’ saved [478737370/478737370]\n", + "2022-10-25 16:20:25 (218 MB/s) - ‘libriphone.zip’ saved [478737370/478737370]\n", "\n", "feat test_split.txt train_labels.txt\ttrain_split.txt\n" ] @@ -169,9 +169,45 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "IJjLT8em-y9G" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IJjLT8em-y9G", + "outputId": "2dc2a5a1-8f3c-43bc-caf3-052f0425260c" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "3428it [00:01, 2885.84it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([2116368])\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(tensor([[-1.2992, -0.5816, -0.3350, ..., 1.2393, 0.8288, 0.7740],\n", + " [-1.3021, -0.4434, -0.2642, ..., 1.0158, 0.8109, 0.8794],\n", + " [-1.3545, -0.6166, -0.5883, ..., 0.1890, 0.3106, 0.7097],\n", + " ...,\n", + " [-2.5240, -0.1922, 0.6781, ..., -0.4569, -0.1138, 0.2691],\n", + " [-2.5024, -0.1648, 0.7813, ..., -0.4677, -0.2058, -0.0080],\n", + " [-2.4762, -0.2863, 0.6714, ..., 0.0311, 0.1020, 0.1824]]),\n", + " tensor([0, 0, 0, ..., 0, 0, 0]))" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], "source": [ "import os\n", "import random\n", @@ -184,16 +220,23 @@ " return feat\n", "\n", "def shift(x, n):\n", + " #print('x：', x)\n", + " #print('n：', n)\n", " if n < 0:\n", " left = x[0].repeat(-n, 1)\n", " right = x[:n]\n", + " #print('ifleft：', left)\n", + " #print('ifright：', right)\n", "\n", " elif n > 0:\n", " right = x[-1].repeat(n, 1)\n", " left = x[n:]\n", + " #print('elifleft：', left)\n", + " #print('elifright：', right)\n", " else:\n", + " #print('else', x)\n", " return x\n", - "\n", + " #print(torch.cat((left, right), dim=0))\n", " return torch.cat((left, right), dim=0)\n", "\n", "def concat_feat(x, concat_n):\n", @@ -211,6 +254,10 @@ " return x.permute(1, 0, 2).view(seq_len, concat_n * feature_dim)\n", "\n", "def preprocess_data(split, feat_dir, phone_path, concat_nframes, train_ratio=0.8, train_val_seed=1337):\n", + " # print(\"split\",split)\n", + " # print(\"feat_dir\",feat_dir)\n", + " # print(\"phone_path\",phone_path)\n", + " # print(\"concat_nframes\",concat_nframes)\n", " class_num = 41 # NOTE: pre-computed, should not need change\n", " mode = 'train' if (split == 'train' or split == 'val') else 'test'\n", "\n", @@ -235,7 +282,7 @@ " raise ValueError('Invalid \\'split\\' argument for dataset: PhoneDataset!')\n", "\n", " usage_list = [line.strip('\\n') for line in usage_list]\n", - " print('[Dataset] - # phone classes: ' + str(class_num) + ', number of utterances for ' + split + ': ' + str(len(usage_list)))\n", + " #print('[Dataset] - # phone classes: ' + str(class_num) + ', number of utterances for ' + split + ': ' + str(len(usage_list)))\n", "\n", " max_len = 3000000\n", " X = torch.empty(max_len, 39 * concat_nframes)\n", @@ -260,13 +307,15 @@ " if mode != 'test':\n", " y = y[:idx]\n", "\n", - " print(f'[INFO] {split} set')\n", - " print(X.shape)\n", + " #print(f'[INFO] {split} set')\n", + " #print(X.shape)\n", " if mode != 'test':\n", " print(y.shape)\n", " return X, y\n", " else:\n", - " return X\n" + " return X\n", + "\n", + "preprocess_data(split='train', feat_dir='./libriphone/feat', phone_path='./libriphone', concat_nframes=1, train_ratio=0.8)" ] }, { @@ -336,6 +385,7 @@ " self.block = nn.Sequential(\n", " nn.Linear(input_dim, output_dim),\n", " nn.ReLU(),\n", + " nn.BatchNorm1d(output_dim)\n", " )\n", "\n", " def forward(self, x):\n", @@ -360,135 +410,116 @@ }, { "cell_type": "markdown", - "source": [ - "## Hyper-parameters" - ], "metadata": { "id": "TlIq8JeqvvHC" - } + }, + "source": [ + "## Hyper-parameters" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iIHn79Iav1ri" + }, + "outputs": [], "source": [ "# data prarameters\n", - "concat_nframes = 1 # the number of frames to concat with, n must be odd (total 2k+1 = n frames)\n", + "concat_nframes = 21 # the number of frames to concat with, n must be odd (total 2k+1 = n frames)\n", "train_ratio = 0.8 # the ratio of data used for training, the rest will be used for validation\n", "\n", "# training parameters\n", "seed = 0 # random seed\n", - "batch_size = 512 # batch size\n", - "num_epoch = 5 # the number of training epoch\n", + "batch_size = 2048 # batch size\n", + "num_epoch = 7 # the number of training epoch\n", "learning_rate = 0.0001 # learning rate\n", "model_path = './model.ckpt' # the path where the checkpoint will be saved\n", "\n", "# model parameters\n", "input_dim = 39 * concat_nframes # the input dim of the model, you should not change the value\n", - "hidden_layers = 1 # the number of hidden layers\n", - "hidden_dim = 256 # the hidden dim" - ], - "metadata": { - "id": "iIHn79Iav1ri" - }, - "execution_count": null, - "outputs": [] + "hidden_layers = 3 # the number of hidden layers\n", + "hidden_dim = 1024 # the hidden dim" + ] }, { "cell_type": "markdown", - "source": [ - "## Prepare dataset and model" - ], "metadata": { "id": "IIUFRgG5yoDn" - } + }, + "source": [ + "## Prepare dataset and model" + ] }, { "cell_type": "code", - "source": [ - "import gc\n", - "\n", - "# preprocess data\n", - "train_X, train_y = preprocess_data(split='train', feat_dir='./libriphone/feat', phone_path='./libriphone', concat_nframes=concat_nframes, train_ratio=train_ratio)\n", - "val_X, val_y = preprocess_data(split='val', feat_dir='./libriphone/feat', phone_path='./libriphone', concat_nframes=concat_nframes, train_ratio=train_ratio)\n", - "\n", - "# get dataset\n", - "train_set = LibriDataset(train_X, train_y)\n", - "val_set = LibriDataset(val_X, val_y)\n", - "\n", - "# remove raw feature to save memory\n", - "del train_X, train_y, val_X, val_y\n", - "gc.collect()\n", - "\n", - "# get dataloader\n", - "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n", - "val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)" - ], + "execution_count": null, "metadata": { - "id": "c1zI3v5jyrDn", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "3ea2823a-83f3-42d9-ef05-2f2c002f9538" + "id": "c1zI3v5jyrDn", + "outputId": "07026afe-5e0a-497c-8dd5-ecf4de28d62d" }, - "execution_count": null, "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Dataset] - # phone classes: 41, number of utterances for train: 3428\n" - ] - }, { "output_type": "stream", "name": "stderr", "text": [ - "3428it [00:01, 2097.23it/s]\n" + "3428it [00:07, 481.26it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ - "[INFO] train set\n", - "torch.Size([2116368, 39])\n", - "torch.Size([2116368])\n", - "[Dataset] - # phone classes: 41, number of utterances for val: 858\n" + "torch.Size([2116368])\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ - "858it [00:00, 2087.91it/s]" + "858it [00:01, 470.15it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ - "[INFO] val set\n", - "torch.Size([527790, 39])\n", "torch.Size([527790])\n" ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] } + ], + "source": [ + "import gc\n", + "\n", + "# preprocess data\n", + "train_X, train_y = preprocess_data(split='train', feat_dir='./libriphone/feat', phone_path='./libriphone', concat_nframes=concat_nframes, train_ratio=train_ratio)\n", + "val_X, val_y = preprocess_data(split='val', feat_dir='./libriphone/feat', phone_path='./libriphone', concat_nframes=concat_nframes, train_ratio=train_ratio)\n", + "\n", + "# get dataset\n", + "train_set = LibriDataset(train_X, train_y)\n", + "val_set = LibriDataset(val_X, val_y)\n", + "\n", + "# remove raw feature to save memory\n", + "del train_X, train_y, val_X, val_y\n", + "gc.collect()\n", + "\n", + "# get dataloader\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n", + "val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "CfRUEgC0GxUV", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "f9804711-72b1-4717-896b-821a300cfe87" + "id": "CfRUEgC0GxUV", + "outputId": "d6b737a7-f87d-483a-f952-8a4686625725" }, "outputs": [ { @@ -544,140 +575,136 @@ }, { "cell_type": "markdown", - "source": [ - "## Training" - ], "metadata": { "id": "pwWH1KIqzxEr" - } + }, + "source": [ + "## Training" + ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "CdMWsBs7zzNs", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "17922ad2-a319-4253-8783-3e4939d0a7cf" + "id": "CdMWsBs7zzNs", + "outputId": "2bb1614d-8021-4440-818e-b521d7d2969b" }, "outputs": [ { - "metadata": { - "tags": null - }, - "name": "stderr", "output_type": "stream", + "name": "stderr", "text": [ - "100%|██████████| 4134/4134 [00:24<00:00, 168.54it/s]\n", - "100%|██████████| 1031/1031 [00:03<00:00, 276.80it/s]\n" + "100%|██████████| 1034/1034 [00:24<00:00, 41.61it/s]\n", + "100%|██████████| 258/258 [00:04<00:00, 56.13it/s]\n" ] }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "[001/005] Train Acc: 0.421913 Loss: 2.086623 | Val Acc: 0.440590 loss: 1.971902\n", - "saving model with acc 0.441\n" + "[001/007] Train Acc: 0.642713 Loss: 1.188205 | Val Acc: 0.676047 loss: 1.042712\n", + "saving model with acc 0.676\n" ] }, { - "metadata": { - "tags": null - }, - "name": "stderr", "output_type": "stream", + "name": "stderr", "text": [ - "100%|██████████| 4134/4134 [00:24<00:00, 166.62it/s]\n", - "100%|██████████| 1031/1031 [00:03<00:00, 267.27it/s]\n" + "100%|██████████| 1034/1034 [00:24<00:00, 42.43it/s]\n", + "100%|██████████| 258/258 [00:04<00:00, 56.20it/s]\n" ] }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "[002/005] Train Acc: 0.449009 Loss: 1.934465 | Val Acc: 0.449662 loss: 1.926844\n", - "saving model with acc 0.450\n" + "[002/007] Train Acc: 0.726286 Loss: 0.870105 | Val Acc: 0.694583 loss: 0.982762\n", + "saving model with acc 0.695\n" ] }, { - 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"metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "[004/005] Train Acc: 0.458398 Loss: 1.887975 | Val Acc: 0.456089 loss: 1.896490\n", - "saving model with acc 0.456\n" + "[004/007] Train Acc: 0.795546 Loss: 0.634672 | Val Acc: 0.700252 loss: 0.990889\n", + "saving model with acc 0.700\n" ] }, { - "metadata": { - "tags": null - }, - "name": "stderr", "output_type": "stream", + "name": "stderr", "text": [ - "100%|██████████| 4134/4134 [00:23<00:00, 178.26it/s]\n", - "100%|██████████| 1031/1031 [00:03<00:00, 275.13it/s]" + "100%|██████████| 1034/1034 [00:24<00:00, 41.43it/s]\n", + "100%|██████████| 258/258 [00:04<00:00, 54.75it/s]\n" ] }, { - "metadata": { - "tags": null - }, + "output_type": "stream", "name": "stdout", + "text": [ + "[005/007] Train Acc: 0.824015 Loss: 0.541611 | Val Acc: 0.697590 loss: 1.027288\n" + ] + }, + { "output_type": "stream", + "name": "stderr", "text": [ - "[005/005] Train Acc: 0.460776 Loss: 1.876422 | Val Acc: 0.457841 loss: 1.889746\n", - "saving model with acc 0.458\n" + "100%|██████████| 1034/1034 [00:24<00:00, 41.97it/s]\n", + "100%|██████████| 258/258 [00:04<00:00, 55.45it/s]\n" ] }, { - "metadata": { - "tags": null - }, + "output_type": "stream", + "name": "stdout", + "text": [ + "[006/007] Train Acc: 0.850195 Loss: 0.458949 | Val Acc: 0.695494 loss: 1.075581\n" + ] + }, + { + "output_type": "stream", "name": "stderr", + "text": [ + "100%|██████████| 1034/1034 [00:24<00:00, 42.03it/s]\n", + "100%|██████████| 258/258 [00:04<00:00, 56.62it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[007/007] Train Acc: 0.873643 Loss: 0.386278 | Val Acc: 0.690915 loss: 1.139174\n" + ] + }, + { "output_type": "stream", + "name": "stderr", "text": [ "\n" ] @@ -749,22 +776,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "ab33MxosWLmG", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "911e8c9b-fc0f-4591-b0f6-311a1231c5e2" + "id": "ab33MxosWLmG", + "outputId": "ae6fbd93-29d2-42d5-fea8-6b6e54aa0d44" }, "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ - "50" + "22" ] }, - "execution_count": null, "metadata": {}, - "output_type": "execute_result" + "execution_count": 175 } ], "source": [ @@ -786,52 +813,18 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "VOG1Ou0PGrhc", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "abaaa25b-a93c-49b0-d228-9eca1e2ab2e0" + "id": "VOG1Ou0PGrhc", + "outputId": "39de5655-e884-4682-f9c0-461666b96411" }, "outputs": [ { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", - "text": [ - "[Dataset] - # phone classes: 41, number of utterances for test: 1078\n" - ] - }, - { - "metadata": { - "tags": null - }, "name": "stderr", - "output_type": "stream", "text": [ - "1078it [00:00, 2496.92it/s]" - ] - }, - { - "metadata": { - "tags": null - }, - "name": "stdout", - "output_type": "stream", - "text": [ - "[INFO] test set\n", - "torch.Size([646268, 39])\n" - ] - }, - { - "metadata": { - "tags": null - }, - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "1078it [00:01, 572.64it/s]\n" ] } ], @@ -846,22 +839,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "ay0Fu8Ovkdad", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "e5b20aa7-4d8b-43a9-e068-f5c89706a360" + "id": "ay0Fu8Ovkdad", + "outputId": "9c1d93dc-715a-4b70-9b03-99e1d9445475" }, "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "" ] }, - "execution_count": null, "metadata": {}, - "output_type": "execute_result" + "execution_count": 177 } ], "source": [ @@ -883,21 +876,18 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "84HU5GGjPqR0", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "cebd6694-8f74-44ff-f922-96ca4385acb8" + "id": "84HU5GGjPqR0", + "outputId": "612182d0-7374-41f5-a1d6-9fa3374750db" }, "outputs": [ { - "metadata": { - "tags": null - }, - "name": "stderr", "output_type": "stream", + "name": "stderr", "text": [ - "100%|██████████| 1263/1263 [00:02<00:00, 439.04it/s]\n" + "100%|██████████| 316/316 [00:03<00:00, 99.21it/s]\n" ] } ], @@ -948,9 +938,10 @@ "accelerator": "GPU", "colab": { "collapsed_sections": [], - "name": "ML2022Spring - HW2.ipynb", + "machine_shape": "hm", "provenance": [] }, + "gpuClass": "premium", "kernelspec": { "display_name": "Python 3", "name": "python3" diff --git a/HW03/food11.zip b/HW03/food11.zip deleted file mode 100644 index 58c20c9d..00000000 --- a/HW03/food11.zip +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d62dca034fd9f04c38a11ce69b6c2ca02a25c3ce9fb6953644dcc410a0fe02f7 -size 1163077597 diff --git a/HW13/food11-hw13.tar.gz b/HW13/food11-hw13.tar.gz deleted file mode 100644 index 1514fb45..00000000 --- a/HW13/food11-hw13.tar.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:24e809ab42f5fad209d55e6996511ec7ddd08897551ee3766ac19a86c0bad16d -size 1203320552