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Reproduce Part 1: Baseline of "How to train your resnet"
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reproduce_original_how_to_train_your_resnet.ipynb
{
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"metadata": {
"colab": {
"name": "reproduce_original_how_to_train_your_resnet.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "monMVW8GPl4m",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"outputId": "d484ee41-92b8-433f-cb4e-1a1e1e040937"
},
"source": [
"!pip install pydot\n",
"!git clone https://github.com/davidcpage/cifar10-fast.git\n",
"import os\n",
"os.chdir('cifar10-fast')"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: pydot in /usr/local/lib/python3.6/dist-packages (1.3.0)\n",
"Requirement already satisfied: pyparsing>=2.1.4 in /usr/local/lib/python3.6/dist-packages (from pydot) (2.4.2)\n",
"Cloning into 'cifar10-fast'...\n",
"remote: Enumerating objects: 14, done.\u001b[K\n",
"remote: Counting objects: 100% (14/14), done.\u001b[K\n",
"remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
"remote: Total 156 (delta 4), reused 5 (delta 2), pack-reused 142\u001b[K\n",
"Receiving objects: 100% (156/156), 2.18 MiB | 2.01 MiB/s, done.\n",
"Resolving deltas: 100% (82/82), done.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5-5kYowAPejh",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"outputId": "85caa7f2-fb86-4035-c7a3-0e8dcc3d80e5"
},
"source": [
"from IPython.core.display import display, HTML\n",
"display(HTML(\"<style>.container {width:95% !important;}</style>\"))\n",
"\n",
"from core import *\n",
"from torch_backend import *"
],
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<style>.container {width:95% !important;}</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2RW24Wv4RCRd",
"colab_type": "text"
},
"source": [
"## Network definition"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CVacW6IPRDd8",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"outputId": "07d5b5ba-9323-46e8-aa60-5e11ccea2549"
},
"source": [
"def res_block(c_in, c_out, stride, **kw):\n",
" block = {\n",
" 'bn1': batch_norm(c_in, **kw),\n",
" 'relu1': nn.ReLU(True),\n",
" 'branch': {\n",
" 'conv1': nn.Conv2d(c_in, c_out, kernel_size=3, stride=stride, padding=1, bias=False),\n",
" 'bn2': batch_norm(c_out, **kw),\n",
" 'relu2': nn.ReLU(True),\n",
" 'conv2': nn.Conv2d(c_out, c_out, kernel_size=3, stride=1, padding=1, bias=False),\n",
" }\n",
" }\n",
" projection = (stride != 1) or (c_in != c_out) \n",
" if projection:\n",
" block['conv3'] = (nn.Conv2d(c_in, c_out, kernel_size=1, stride=stride, padding=0, bias=False), [rel_path('relu1')])\n",
" block['add'] = (Add(), [(rel_path('conv3') if projection else rel_path('relu1')), rel_path('branch', 'conv2')])\n",
" return block\n",
"\n",
"def DAWN_net(c=64, block=res_block, prep_bn_relu=False, concat_pool=True, **kw): \n",
" if isinstance(c, int):\n",
" c = [c, 2*c, 4*c, 4*c]\n",
" \n",
" classifier_pool = {\n",
" 'in': Identity(),\n",
" 'maxpool': nn.MaxPool2d(4),\n",
" 'avgpool': (nn.AvgPool2d(4), [rel_path('in')]),\n",
" 'concat': (Concat(), [rel_path('maxpool'), rel_path('avgpool')]),\n",
" } if concat_pool else {'pool': nn.MaxPool2d(4)}\n",
" \n",
" return {\n",
" 'prep': union({'conv': nn.Conv2d(3, c[0], kernel_size=3, stride=1, padding=1, bias=False)},\n",
" {'bn': batch_norm(c[0], **kw), 'relu': nn.ReLU(True)} if prep_bn_relu else {}),\n",
" 'layer1': {\n",
" 'block0': block(c[0], c[0], 1, **kw),\n",
" 'block1': block(c[0], c[0], 1, **kw),\n",
" },\n",
" 'layer2': {\n",
" 'block0': block(c[0], c[1], 2, **kw),\n",
" 'block1': block(c[1], c[1], 1, **kw),\n",
" },\n",
" 'layer3': {\n",
" 'block0': block(c[1], c[2], 2, **kw),\n",
" 'block1': block(c[2], c[2], 1, **kw),\n",
" },\n",
" 'layer4': {\n",
" 'block0': block(c[2], c[3], 2, **kw),\n",
" 'block1': block(c[3], c[3], 1, **kw),\n",
" },\n",
" 'final': union(classifier_pool, {\n",
" 'flatten': Flatten(),\n",
" 'linear': nn.Linear(2*c[3] if concat_pool else c[3], 10, bias=True),\n",
" }),\n",
" 'classifier': Identity(),\n",
" }\n",
"\n",
"\n",
"def conv_bn(c_in, c_out, bn_weight_init=1.0, **kw):\n",
" return {\n",
" 'conv': nn.Conv2d(c_in, c_out, kernel_size=3, stride=1, padding=1, bias=False), \n",
" 'bn': batch_norm(c_out, bn_weight_init=bn_weight_init, **kw), \n",
" 'relu': nn.ReLU(True)\n",
" }\n",
"\n",
"def basic_net(channels, weight, pool, **kw):\n",
" return {\n",
" 'prep': conv_bn(3, channels['prep'], **kw),\n",
" 'layer1': dict(conv_bn(channels['prep'], channels['layer1'], **kw), pool=pool),\n",
" 'layer2': dict(conv_bn(channels['layer1'], channels['layer2'], **kw), pool=pool),\n",
" 'layer3': dict(conv_bn(channels['layer2'], channels['layer3'], **kw), pool=pool),\n",
" 'pool': nn.MaxPool2d(4),\n",
" 'flatten': Flatten(),\n",
" 'linear': nn.Linear(channels['layer3'], 10, bias=False),\n",
" 'classifier': Mul(weight),\n",
" }\n",
"\n",
"def net(channels=None, weight=0.125, pool=nn.MaxPool2d(2), extra_layers=(), res_layers=('layer1', 'layer3'), **kw):\n",
" channels = channels or {'prep': 64, 'layer1': 128, 'layer2': 256, 'layer3': 512}\n",
" residual = lambda c, **kw: {'in': Identity(), 'res1': conv_bn(c, c, **kw), 'res2': conv_bn(c, c, **kw), \n",
" 'add': (Add(), [rel_path('in'), rel_path('res2', 'relu')])}\n",
" n = basic_net(channels, weight, pool, **kw)\n",
" for layer in res_layers:\n",
" n[layer]['residual'] = residual(channels[layer], **kw)\n",
" for layer in extra_layers:\n",
" n[layer]['extra'] = conv_bn(channels[layer], channels[layer], **kw) \n",
" return n\n",
"\n",
"\n",
"losses = {\n",
" 'loss': (nn.CrossEntropyLoss(reduce=False), [('classifier',), ('target',)]),\n",
" 'correct': (Correct(), [('classifier',), ('target',)]),\n",
"}\n",
"\n",
"remove_identity_nodes = lambda net: remove_by_type(net, Identity)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
" warnings.warn(warning.format(ret))\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cHn5fD2uRWJ1",
"colab_type": "text"
},
"source": [
"## Download and preprocess data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NV8jhUp2RXyL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "c54520be-2a8d-4914-fdc2-3afed6db15f4"
},
"source": [
"DATA_DIR = './data'\n",
"dataset = cifar10(DATA_DIR)\n",
"t = Timer()\n",
"print('Preprocessing training data')\n",
"train_set = list(zip(transpose(normalise(pad(dataset['train']['data'], 4))), dataset['train']['labels']))\n",
"print(f'Finished in {t():.2} seconds')\n",
"print('Preprocessing test data')\n",
"test_set = list(zip(transpose(normalise(dataset['test']['data'])), dataset['test']['labels']))\n",
"print(f'Finished in {t():.2} seconds')"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"\r0it [00:00, ?it/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
" 99%|█████████▉| 169123840/170498071 [00:11<00:00, 17697435.16it/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Preprocessing training data\n",
"Finished in 2.2 seconds\n",
"Preprocessing test data\n",
"Finished in 0.068 seconds\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J3QPMtFIRkhk",
"colab_type": "text"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D555ibr7RlvD",
"colab_type": "code",
"colab": {}
},
"source": [
"def train(model, lr_schedule, optimizer, train_set, test_set, batch_size, num_workers=0):\n",
" train_batches = Batches(train_set, batch_size, shuffle=True, set_random_choices=True, num_workers=num_workers)\n",
" test_batches = Batches(test_set, batch_size, shuffle=False, num_workers=num_workers)\n",
" optimizer.opt_params['lr'] = lambda step: lr_schedule(step/len(train_batches))/batch_size\n",
" table, timer = TableLogger(), Timer()\n",
" for epoch in range(lr_schedule.knots[-1]):\n",
" epoch_stats = train_epoch(model, train_batches, test_batches, optimizer.step, timer, test_time_in_total=True) \n",
" summary = union({'epoch': epoch+1, 'lr': lr_schedule(epoch+1)}, epoch_stats)\n",
" table.append(summary)\n",
" return summary"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "n2Gqd8K5QrFE",
"colab_type": "text"
},
"source": [
"## [Part 1: Baseline](https://www.myrtle.ai/2018/09/24/how_to_train_your_resnet_1/) - DAWNbench baseline + no initial bn-relu+ efficient dataloading/augmentation, 1 dataloader process (???s)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZTxAhCewTJtH",
"colab_type": "code",
"colab": {}
},
"source": [
"lr_schedule = PiecewiseLinear([0, 15, 30, 35], [0, 0.1, 0.005, 0])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2ttPMLWATKbT",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "cefb42cc-6183-414e-d654-45cd6d29d98e"
},
"source": [
"lr_schedule(1)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.006666666666666667"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Da4dECOpTcpZ",
"colab_type": "code",
"colab": {}
},
"source": [
"# lr_schedule = PiecewiseLinear([0, 1], [0, 0.006666666666666667])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DJzdXtRzTlvi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b4b8dc5d-6d3e-4287-aa45-f9e37f9942ef"
},
"source": [
"# lr_schedule.knots"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0, 1]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XLRglgfCT4hY",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"outputId": "4013fb83-48eb-44a7-d6e2-45640666389b"
},
"source": [
"batch_size = 128\n",
"\n",
"n = DAWN_net()\n",
"display(DotGraph(n))\n",
"model = Network(union(n, losses)).to(device)\n",
"#convert all children including batch norms to half precision (triggering slow codepath!)\n",
"for v in model.children(): \n",
" v.half()"
],
"execution_count": 20,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<core.DotGraph at 0x7f6bd1c2c6a0>"
],
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},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "z_GFFhZYQy4x",
"colab_type": "code",
"colab": {}
},
"source": [
"train_set_x = Transform(train_set, [Crop(32, 32), FlipLR()])\n",
"opt = SGD(trainable_params(model), momentum=0.9, weight_decay=5e-4*batch_size, nesterov=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "69vlFZWpRpOT",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 629
},
"outputId": "c509a192-a572-424e-e935-8ae24e78edc9"
},
"source": [
"summary=train(model, lr_schedule, opt, train_set_x, test_set, \n",
" batch_size=batch_size, num_workers=1)"
],
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": [
" epoch lr train time train loss train acc test time test loss test acc total time\n",
" 1 0.0067 69.9930 1.6669 0.3803 4.0853 1.3189 0.5241 74.0783\n",
" 2 0.0133 70.6333 1.0037 0.6422 4.0969 0.8593 0.7000 148.8085\n",
" 3 0.0200 70.7000 0.7628 0.7346 4.0890 1.2214 0.6107 223.5975\n",
" 4 0.0267 70.6757 0.6382 0.7789 4.0930 0.6554 0.7744 298.3662\n",
" 5 0.0333 70.6769 0.5598 0.8049 4.0847 0.6127 0.7907 373.1278\n",
" 6 0.0400 70.6171 0.5128 0.8222 4.0778 0.5017 0.8262 447.8227\n",
" 7 0.0467 70.5683 0.4688 0.8392 4.0815 0.5062 0.8255 522.4725\n",
" 8 0.0533 70.6264 0.4397 0.8489 4.0834 0.5103 0.8260 597.1823\n",
" 9 0.0600 70.6205 0.4151 0.8565 4.0881 0.4462 0.8499 671.8909\n",
" 10 0.0667 70.5929 0.4008 0.8624 4.0826 0.6079 0.8023 746.5664\n",
" 11 0.0733 70.6501 0.3878 0.8657 4.0806 0.6968 0.7826 821.2971\n",
" 12 0.0800 70.6610 0.3886 0.8671 4.0904 0.4868 0.8365 896.0485\n",
" 13 0.0867 70.6660 0.3900 0.8652 4.0853 0.6368 0.8000 970.7999\n",
" 14 0.0933 70.5665 0.3874 0.8665 4.0837 0.4599 0.8478 1045.4500\n",
" 15 0.1000 70.6026 0.3908 0.8660 4.0884 0.5594 0.8088 1120.1410\n",
" 16 0.0937 70.6209 0.3808 0.8693 4.0804 0.5009 0.8325 1194.8423\n",
" 17 0.0873 70.5951 0.3542 0.8778 4.0745 0.3985 0.8687 1269.5119\n",
" 18 0.0810 70.5654 0.3384 0.8826 4.0743 0.5677 0.8116 1344.1516\n",
" 19 0.0747 70.6022 0.3187 0.8896 4.0898 0.3896 0.8641 1418.8437\n",
" 20 0.0683 70.6093 0.2992 0.8975 4.0860 0.4497 0.8540 1493.5390\n",
" 21 0.0620 70.9694 0.2784 0.9051 4.1360 0.3841 0.8743 1568.6444\n",
" 22 0.0557 71.1082 0.2578 0.9113 4.0974 0.3875 0.8746 1643.8500\n",
" 23 0.0493 71.0915 0.2392 0.9176 4.1337 0.3785 0.8694 1719.0752\n",
" 24 0.0430 71.2790 0.2210 0.9244 4.1351 0.3260 0.8918 1794.4893\n",
" 25 0.0367 71.2745 0.1957 0.9314 4.1373 0.3002 0.8980 1869.9011\n",
" 26 0.0303 71.3423 0.1706 0.9406 4.1298 0.2942 0.9030 1945.3732\n",
" 27 0.0240 71.3367 0.1503 0.9474 4.1326 0.2554 0.9156 2020.8425\n",
" 28 0.0177 71.2604 0.1166 0.9590 4.1323 0.2513 0.9178 2096.2353\n",
" 29 0.0113 71.2467 0.0861 0.9696 4.1341 0.2401 0.9256 2171.6160\n",
" 30 0.0050 71.3229 0.0614 0.9794 4.1413 0.2067 0.9357 2247.0802\n",
" 31 0.0040 71.3311 0.0439 0.9859 4.1338 0.2030 0.9379 2322.5451\n",
" 32 0.0030 71.3209 0.0362 0.9891 4.1370 0.2016 0.9377 2398.0029\n",
" 33 0.0020 71.3418 0.0326 0.9902 4.1295 0.1999 0.9379 2473.4743\n",
" 34 0.0010 71.3481 0.0295 0.9912 4.1293 0.1990 0.9388 2548.9517\n",
" 35 0.0000 71.3413 0.0294 0.9919 4.1275 0.2005 0.9398 2624.4205\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "V7kAFvwCT7hi",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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