From 0678f206b59b8ecb5daa356cb9f1e0ce1ef12274 Mon Sep 17 00:00:00 2001 From: Faried Abu Zaid Date: Sat, 2 Aug 2025 21:03:54 +0200 Subject: [PATCH] Delete Notebook --- notebooks/Untitled.ipynb | 15339 ------------------------------------- 1 file changed, 15339 deletions(-) delete mode 100755 notebooks/Untitled.ipynb diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb deleted file mode 100755 index b298344..0000000 --- a/notebooks/Untitled.ipynb +++ /dev/null @@ -1,15339 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "858bca21-b72a-4a5c-861d-809f3c53e59f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "batch: torch.Size([2, 2, 2])\n", - "event: torch.Size([])\n" - ] - } - ], - "source": [ - "from pyro import distributions as dist\n", - "import torch\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "N = dist.Normal(torch.zeros(2,2,2), torch.ones(2,2,2))\n", - "print(f\"batch: {N.batch_shape}\\nevent: {N.event_shape}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d9cc0fd1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/home/faried/temp/session_latest/artifacts/2025-07-14_20-43-50/_trial_2025-07-14_20-43-50/driver_artifacts/_trial_7bef6_00000_0_batch_size=32,c_hidden=16,gating=True,normalize_layers=True,num_layers=3,coupling_blocks=2,nonlinearity=ref_p_2025-07-14_20-43-50/params.pkl']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from glob import glob\n", - "glob(\"/home/faried/temp/session_latest/**/*.pkl\", recursive=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "511b31a2-8654-4349-9c5d-813cd032e7cb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/faried/Projects/USFlows/src/explib/config_parser.py:242: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " state_dict = torch.load(state_dict)\n" - ] - } - ], - "source": [ - "from src.explib.config_parser import from_checkpoint\n", - "\n", - "\n", - "config = glob(\"/home/faried/temp/session_latest/**/*.pkl\", recursive=True)[0]\n", - "weights = glob(\"/home/faried/temp/session_latest/**/*.pt\", recursive=True)[0]\n", - "\n", - "model = from_checkpoint(\n", - " config,\n", - " weights\\\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a4f80e5", - "metadata": {}, - "outputs": [], - "source": [ - "model = model.simplify()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7cdccc38-c6dd-4eeb-a80e-99ec4b59d5f9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(nrows=5, ncols=5, figsize=(5, 5))\n", - "\n", - "for ax in axes.flatten():\n", - " sample = model.sample(context=0.0).detach().squeeze(0)\n", - " sample[sample < 0] = 0\n", - " sample[sample > 1] = 1\n", - " sample = (sample * 255).to(torch.int32)\n", - " sample = (\n", - " sample.reshape(4, 4, 7, 7) # sample.reshape(4, 4, 7, 7) # Split spatial dims into (n, k) blocks\n", - " .permute(2, 0, 3, 1) # Reorder axes to (c, n, n, k, k)\n", - " .reshape(28, 28) # Combine channels and blocks into (k²c, n, n)\n", - " )\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.imshow(sample, cmap=\"grey\")\n", - "plt.tight_layout()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2beef001", - "metadata": {}, - "outputs": [], - "source": [ - "from src.explib.datasets import MnistDequantized\n", - "mnist0 = MnistDequantized(dataloc=\"/home/faried/Projects/USFlows/data/mnist\", space_to_depth_factor=4, digit=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bea617d3", - "metadata": {}, - "outputs": [], - "source": [ - "calbration_set = mnist0[:1000][0]\n", - "latent_representations = model.backward(calbration_set)\n", - "logprobs = model.base_distribution.log_prob(latent_representations)\n", - "latent_norms = (latent_representations - model.base_distribution.loc).reshape(-1, 784).norm(p=1, dim=-1)\n", - "profiles = model.base_distribution.r_profile(latent_norms)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "062fe7a1", - "metadata": {}, - "outputs": [], - "source": [ - "expected = model.base_distribution.norm_distribution.sample([10000])\n", - "expected_tail = expected[expected >= latent_norms.min()]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "09ceb6f1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([0])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "expected_tail.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e582286c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bw = .25\n", - "sns.kdeplot(latent_norms.detach(), bw_adjust=bw, alpha=0.7, label=\"Data\")\n", - "sns.kdeplot(expected.detach().reshape(-1), bw_adjust=bw, alpha=0.7, label=\"Expected\")\n", - "#sns.kdeplot(expected_tail.detach().reshape(-1), bw_adjust=bw, alpha=0.7, label=\"Expected Tail\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "73b3db7a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([ 3.6459e+01, -7.5776e+01, -1.3246e+02, -3.3859e+01, -1.3449e+02,\n", - " -3.5288e+02, -3.6463e+02, -5.3363e+02, -1.6644e+02, 1.1129e+01,\n", - " -4.5846e+01, -6.5443e+01, 2.5461e+01, -5.1702e+02, -1.2541e+02,\n", - " 3.2425e+00, 6.6869e+00, -4.6837e+02, -1.0631e+01, -3.1263e+01,\n", - " -9.6028e+01, -7.5852e+01, -1.2363e+02, -3.8187e+01, -1.5659e+02,\n", - " -2.5160e+02, -1.1417e+02, 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-4443.4688, -4487.8154, -4745.6377,\n", - " -4399.5664, -4524.5366, -4915.2568, -4384.0093, -4422.9956, -4771.2017,\n", - " -4463.3687, -4337.2861, -4576.1733, -4416.9180, -4396.9185, -4502.2881,\n", - " -4454.9033, -4441.1904, -4439.1338, -4490.3081, -4392.2510, -4669.1953,\n", - " -4399.9829, -4499.9839, -4645.5713, -4956.3379, -4413.1309, -4425.0073,\n", - " -4445.1655, -4473.5483, -4439.9570, -4675.1562, -4441.1040, -4384.2764,\n", - " -4542.8940, -4444.7314, -4656.8862, -4481.9595, -5136.6392, -4570.4580,\n", - " -4749.5620, -4524.1167, -4548.3379, -4514.0098, -4492.0283, -4532.6880,\n", - " -4575.0728, -4559.5474, -4375.1826, -4580.5386, -4383.7402, -4400.1431,\n", - " -4686.5713, -4348.8359, -4538.0317, -4512.0527, -4573.2373, -4555.9692,\n", - " -4567.0454, -4539.1021, -4429.8896, -4461.9268, -4408.8770, -4382.8296,\n", - " -4367.7212, -4358.8179, -4507.7749, -4516.5796, -4426.6860, -4678.2046,\n", - " -4478.2012, -4458.3467, -4400.0942, -4465.8076, -4672.1416, -4763.9663,\n", - " -4586.8032, -4919.8994, -4641.2109, -4531.1660, -4473.5322, -4414.2275,\n", - " -4392.3384, -4414.7876, -4431.8154, -4325.1035, -4685.6021, -4532.9419,\n", - " -4355.1377, -4355.6753, -4358.1582, -4442.3521, -4424.1880, -4378.6309,\n", - " -4594.8057, -4394.7432, -4573.5361, -4481.5376, -4536.2549, -4722.8711,\n", - " -4397.1035, -4646.6382, -4787.3506, -4487.2373, -4498.5439, -4641.5869,\n", - " -4302.9619, -4465.5527, -4553.4727, -4972.6401, -4508.3794, -4504.6147,\n", - " -4765.7305, -4448.7407, -4985.6006, -4648.0088, -4857.3667, -6037.7476,\n", - " -4519.2852, -4445.8271, -5001.6221, -4431.4360, -4586.7388, -6156.7134,\n", - " -4647.8022, -5433.6958, -4678.9429, -4720.5386, -6110.9312, -4510.7158,\n", - " -4682.0913, -4925.6680, -4387.4219, -4334.7642, -4726.7622, -4734.5693,\n", - " -4367.7769, -4617.2812, -4560.3511, -4757.0391, -4491.7217, -4632.1318,\n", - " -4462.5073, -4317.0869, -5580.6572, -4313.7349, -4374.4561, -4521.4658,\n", - " -4384.3413, -4386.1763, -4501.5718, 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- " -4549.7012, -4364.6670, -4486.7773, -4473.7808, -4552.9229, -4555.3164,\n", - " -4476.3320, -4374.5596, -4619.9927, -4459.7324, -5026.0215, -4534.0723,\n", - " -4678.3701, -4706.5029, -4507.1943, -4346.8301, -4819.5283, -4444.8242,\n", - " -4690.4775, -4894.9614, -4428.0073, -4400.1274, -4602.8384, -4738.0068,\n", - " -4558.7256, -4708.6870, -4465.6108, -4689.1172, -4454.0283, -4325.1426,\n", - " -4647.1870, -4403.6021, -4464.3716, -4699.0386, -4344.0254, -4841.5103,\n", - " -4560.2734, -4468.5479, -4506.9097, -4324.9692, -4589.0854, -4513.8496,\n", - " -4431.0562, -4572.2139, -4565.6743, -4402.8384, -4441.6680, -4534.9238,\n", - " -4291.0669, -4299.7930, -4502.9897, -4450.9668, -4462.0308, -4411.5332,\n", - " -4826.1172, -4690.8149, -4509.6968, -4318.0503, -4352.7529, -4840.7129,\n", - " -4362.2031, -6045.8442, -4835.5347, -4673.8813, -4923.0986, -4740.5596,\n", - " -4679.0732, -4635.4253, -4620.9385, -4590.4478, -4495.9282, -4520.3677,\n", - " -4602.5952, -4598.5195, -4618.5752, -4585.9595, -4835.2637, -4634.8955,\n", - " -4461.5869, -4384.9834, -4450.8848, -4503.8042, -4612.0156, -4440.2715,\n", - " -4488.4873, -5039.0498, -4503.0864, -4820.0840, -4552.8799, -4548.2617,\n", - " -4337.8477, -4732.4536, -4393.6333, -4454.4692, -4463.5718, -4417.6992,\n", - " -4559.8994, -4618.7222, -4671.3574, -4781.0410, -4567.1006, -4535.1294,\n", - " -4488.4873, -4688.6025, -4410.1226, -5642.1201, -4528.0654, -4512.1445,\n", - " -4359.9111, -4502.6421, -4356.7900, -5284.8994, -4394.1118, -4419.3984,\n", - " -4415.8052, -4437.3086, -4938.7603, -5550.6069, -4461.9595, -4471.9282,\n", - " -4450.0107, -4606.3281, -4627.7568, -5192.2725, -4434.7656, -4373.3843,\n", - " -4614.0918, -4723.5532, -4703.6006, -4534.8647, -6054.4795, -6336.9590,\n", - " -4533.5293, -4465.9155, -4570.9111, -4551.5371, -4554.6343, -4525.0015,\n", - " -4520.0522, -4762.8081, -4771.1528, -4626.7754, -4593.3735, -5768.5044,\n", - " -4403.8105, -4553.9497, -4709.1914, -4981.4907, -4815.8584, -4619.9170,\n", - " -4506.0889, -4542.5327, -4889.5474, -4552.6387, -4682.8325, -4478.7583,\n", - " -4389.3184, -4471.5435, -4871.2598, -4460.3940, -5168.3481, -5900.1357,\n", - " -4441.7080, -5696.2783, -4571.3521, -4461.2158, -4517.1538, -4799.4678,\n", - " -4413.9326, -4648.4258, -4393.5811, -4768.8735, -5669.6504, -4516.7817,\n", - " -4760.0869, -4691.1685, -4563.0200, -6284.7793, -4539.6064, -4600.6899,\n", - " -4618.4570, -5008.0771, -4845.1914, -4490.7417, -4349.0298, -4389.1484,\n", - " -4532.1772, -4490.8525, -4568.4502, -4596.8501, -4497.2739, -4511.4888,\n", - " -4556.2275, -4434.8389, -4582.4639, -4453.7134, -4476.9336, -4428.3042,\n", - " -4355.4102, -4372.4321, -4545.4512, -4545.7515, -4611.1582, -4552.6919,\n", - " -4599.6167, -4501.5278, -4462.7622, -4483.9302, -4731.1978, -4569.1060,\n", - " -4549.2803, -4509.7661, -5106.0796, -4788.4468],\n", - " grad_fn=)\n" - ] - } - ], - "source": [ - "i = 100\n", - "rs = latent_representations.reshape(latent_representations.shape[0], -1).norm(1, dim=-1).sort()[0].detach() #torch.linspace(10, 3000, 500)\n", - "ps = model.base_distribution.r_profile(rs).detach()\n", - "#plt.plot(rs, ps)\n", - "#plt.show()\n", - "\n", - "\n", - "event_dims = tuple(\n", - " range(latent_representations.dim() - len(model.base_distribution.event_shape),\n", - " latent_representations.dim()\n", - " )\n", - ")\n", - "norm = (latent_representations - model.base_distribution.loc).norm(p=1, dim=event_dims)\n", - "#norm = latent_representations[0].flatten().norm(p=1)\n", - "\n", - "print(model.base_distribution.log_prob(latent_representations[:100]))\n", - "print(model.base_distribution.r_profile(norm))\n", - "print(model.base_distribution.norm_distribution.log_prob(norm) - model.base_distribution.log_delta_volume(1, norm))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2a0fd755", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([ 72.1478, 83.2674, 89.5192, 78.9270, 89.7512, 118.6238,\n", - " 120.4174, 149.4253, 93.4896, 74.5199, 80.1446, 82.1757,\n", - " 73.1684, 146.2891, 88.7169, 75.2743, 74.9439, 137.4772,\n", - " 76.6199, 78.6657, 85.4492, 83.2754, 88.5153, 79.3644,\n", - " 92.3205, 104.2313, 87.4523, 79.9673, 154.6376, 97.6262,\n", - " 93.3055, 81.2312, 79.5160, 110.8487, 71.8545, 73.5721,\n", - " 66.7177, 68.9474, 78.0023, 75.7370, 78.9032, 74.5902,\n", - " 192.5165, 1100.8661, 73.2147, 88.7445, 99.9019, 205.9033,\n", - " 84.6182, 94.4084, 83.6637, 78.1391, 98.4256, 162.1396,\n", - " 87.2163, 76.3277, 97.1700, 120.8750, 89.3177, 67.1609,\n", - " 89.2158, 68.9861, 72.0228, 71.1451, 82.6173, 98.3822,\n", - " 91.2317, 73.4773, 80.8572, 82.6433, 82.9154, 117.3899,\n", - " 84.0294, 106.6756, 136.3762, 77.5079, 115.4267, 77.6317,\n", - " 78.9581, 72.7156, 142.4030, 88.5172, 120.4769, 66.2886,\n", - " 84.0192, 449.8506, 121.5188, 132.8557, 73.8172, 113.8867,\n", - " 72.7416, 76.0161, 120.5390, 95.3881, 78.6779, 142.8781,\n", - " 87.3027, 75.1907, 292.7859, 77.8709, 107.4825, 399.9462,\n", - " 115.8797, 103.2171, 594.9361, 85.0316, 73.9780, 71.2877,\n", - " 86.5412, 104.8344, 79.8008, 81.7188, 204.1585, 65.9663,\n", - " 78.2746, 86.6716, 81.5926, 162.1558, 70.4324, 74.5314,\n", - " 95.5377, 108.3032, 84.9391, 88.2917, 108.4610, 96.6145,\n", - " 77.4876, 86.7867, 83.6079, 103.4379, 72.9424, 111.4608,\n", - " 75.0866, 107.5289, 86.4331, 104.7712, 80.2722, 101.9549,\n", - " 122.1112, 105.1309, 130.5500, 72.3392, 131.0250, 85.6564,\n", - " 74.4029, 81.8872, 98.7681, 79.0929, 83.0654, 91.1292,\n", - " 98.9908, 66.2433, 75.5458, 76.7760, 83.9959, 81.4498,\n", - " 89.4939, 86.7032, 99.5626, 77.7031, 69.5235, 75.6270,\n", - " 83.8200, 74.5960, 94.5598, 76.3504, 68.9269, 78.0331,\n", - " 74.7716, 70.7749, 79.1338, 122.7806, 84.9894, 79.8717,\n", - " 70.1383, 96.0218, 73.7774, 100.2509, 71.0841, 70.7582,\n", - " 79.7533, 77.4601, 95.8844, 85.6128, 78.4192, 97.8299,\n", - " 91.9388, 117.9524, 80.1501, 94.7191, 113.7457, 97.1718,\n", - " 83.9782, 83.2983, 77.4145, 98.6103, 80.0349, 66.6630,\n", - " 74.7613, 70.1860, 89.5435, 84.4030, 79.1935, 75.2518,\n", - " 85.3073, 85.5127, 81.4997, 81.5150, 100.9493, 81.7976,\n", - " 75.3141, 73.3919, 96.7062, 74.4689, 89.6198, 77.7568,\n", - " 88.8828, 85.3179, 73.6106, 67.8135, 81.5011, 105.3752,\n", - " 80.4934, 161.5629, 146.1001, 79.4155, 117.3185, 83.8499,\n", - " 81.4287, 74.6090, 77.8846, 117.5635, 96.4732, 78.9176,\n", - " 108.4999, 81.9101, 82.7343, 96.8276, 250.3919, 75.1626,\n", - " 115.7462, 77.7711, 82.3080, 68.4124, 85.0824, 72.1403,\n", - " 91.5677, 88.6888, 80.3248, 63.8241, 68.3791, 69.9894,\n", - " 70.8262, 78.1393, 71.2473, 73.6016, 79.6067, 101.1932,\n", - " 119.1272, 94.0082, 80.1581, 130.6313, 64.4497, 84.0460,\n", - " 72.9520, 108.6294, 76.8176, 91.1589, 72.8646, 66.4092,\n", - " 94.4896, 84.3693, 68.7344, 81.0824, 87.2097, 83.5226,\n", - " 76.8482, 85.2418, 102.0014, 66.4585, 86.8594, 73.1159,\n", - " 73.6440, 82.6867, 69.1788, 74.5206, 110.2710, 99.9529,\n", - " 172.9194, 228.7941, 66.9642, 83.9545, 77.2769, 86.6850,\n", - " 156.5493, 74.2424, 185.0971, 74.3184, 71.2955, 71.0041,\n", - " 63.7122, 67.3344, 73.3555, 107.5876, 72.9595, 71.6172,\n", - " 84.2683, 245.8306, 70.2269, 74.3565, 84.3358, 71.1793,\n", - " 71.9113, 80.5084, 77.3896, 75.8649, 101.3012, 73.2502,\n", - " 90.1694, 67.2345, 86.0759, 83.5816, 79.4347, 74.0049,\n", - " 66.8522, 68.6627, 74.5997, 78.6963, 77.4973, 89.1937,\n", - " 96.4829, 117.3800, 95.0836, 70.9458, 73.2488, 77.5438,\n", - " 87.8867, 89.3929, 84.9856, 104.8704, 95.1578, 504.7673,\n", - " 113.4291, 74.4003, 137.5874, 87.3280, 96.6296, 87.5999,\n", - " 75.0648, 89.2529, 103.8380, 462.5834, 108.3111, 126.0523,\n", - " 71.8908, 92.1199, 89.0254, 603.9030, 109.0834, 83.1705,\n", - " 96.7730, 103.8984, 84.2644, 75.0569, 104.7182, 85.0386,\n", - " 77.2118, 89.3420, 81.5444, 81.6736, 97.1098, 87.3234,\n", - " 89.3335, 94.8769, 94.5629, 77.0676, 117.7128, 86.0925,\n", - " 72.6746, 82.6328, 69.2123, 70.3662, 80.6044, 66.1642,\n", - " 73.6477, 67.5505, 71.8813, 76.1331, 82.2437, 72.4610,\n", - " 240.9329, 78.1667, 118.5749, 78.2611, 64.2688, 74.0809,\n", - " 127.7762, 83.1358, 443.1578, 122.8724, 70.8575, 80.8169,\n", - " 100.5388, 120.4249, 114.6004, 115.8745, 82.7447, 100.9755,\n", - " 101.4341, 81.4103, 88.5828, 78.8087, 160.8203, 86.6059,\n", - " 70.0968, 97.1021, 82.0760, 76.0688, 76.6639, 72.7683,\n", - " 85.5297, 90.6661, 78.8146, 107.3196, 102.9003, 78.8764,\n", - " 122.6108, 97.3475, 128.5701, 77.7184, 101.2970, 93.2642,\n", - " 81.5624, 77.7508, 68.0923, 69.5105, 72.3728, 78.5665,\n", - " 87.3682, 86.9976, 91.5707, 73.8550, 94.0865, 102.3181,\n", - " 92.6942, 100.2765, 92.4193, 94.7093, 107.4188, 301.1100,\n", - " 108.6913, 68.7325, 839.6890, 123.0297, 85.7970, 74.7283,\n", - " 98.2796, 73.9024, 168.8571, 73.2732, 423.8907, 86.1783,\n", - " 87.9972, 74.0873, 84.8550, 84.5717, 82.2578, 90.7336,\n", - " 105.9696, 89.9584, 77.8228, 88.5568, 88.6258, 66.9286,\n", - " 75.3157, 79.6057, 63.4427, 84.2777, 96.0738, 73.7045,\n", - " 70.6626, 72.2800, 93.0346, 75.2092, 115.6072, 69.8694,\n", - " 87.1190, 84.9038, 99.7605, 85.7063, 77.5517, 85.2252,\n", - " 72.2757, 84.9546, 84.2531, 76.2996, 79.6893, 84.3098,\n", - " 98.7629, 107.9857, 92.5186, 130.9090, 121.5686, 102.1011,\n", - " 76.7776, 91.2005, 81.4688, 69.4708, 91.7722, 73.2707,\n", - " 79.7883, 139.5016, 87.6068, 68.9403, 70.0609, 70.7952,\n", - " 76.5578, 130.1484, 69.3786, 97.4268, 98.7577, 70.4830,\n", - " 76.5185, 81.4121, 65.9381, 117.0727, 93.0347, 131.0933,\n", - " 97.0902, 90.0552, 78.4225, 84.6644, 78.5052, 75.7179,\n", - " 86.3595, 108.7450, 90.3061, 84.4574, 76.9483, 88.3539,\n", - " 70.4288, 104.3495, 82.9358, 98.2751, 74.5983, 87.2582,\n", - " 107.5212, 109.4184, 77.7884, 82.4740, 74.1977, 79.8899,\n", - " 81.1452, 84.2420, 77.5932, 74.4729, 84.1070, 72.4271,\n", - " 85.7057, 90.1386, 97.0151, 76.9318, 81.4147, 113.1632,\n", - " 72.7370, 85.3238, 140.5353, 71.3061, 74.9463, 116.9188,\n", - " 78.9121, 67.1755, 91.1404, 74.3668, 72.4914, 82.9336,\n", - " 78.0635, 76.7083, 76.5070, 81.6743, 72.0605, 102.6375,\n", - " 72.7757, 82.6898, 99.5871, 148.1054, 74.0080, 75.1391,\n", - " 77.0987, 79.9447, 76.5875, 103.4218, 76.6998, 71.3304,\n", - " 87.3479, 77.0560, 101.0366, 80.8081, 186.4558, 90.4776,\n", - " 113.7318, 85.2781, 87.9573, 84.1844, 81.8539, 86.2167,\n", - " 91.0124, 89.2256, 70.5067, 91.6500, 71.2816, 72.7906,\n", - " 104.9407, 68.1738, 86.8072, 83.9743, 90.7993, 88.8188,\n", - " 90.0841, 86.9259, 75.6091, 78.7669, 73.6070, 71.1987,\n", - " 69.8381, 69.0484, 83.5168, 84.4612, 75.3004, 103.8253,\n", - " 80.4212, 78.4076, 72.7860, 79.1582, 103.0245, 115.8434,\n", - " 92.3862, 141.3710, 99.0340, 86.0493, 79.9431, 74.1117,\n", - " 72.0686, 74.1648, 75.7953, 66.1385, 104.8108, 86.2447,\n", - " 68.7247, 68.7719, 68.9903, 76.8222, 75.0605, 70.8179,\n", - " 93.3353, 72.2903, 90.8340, 80.7646, 86.6104, 109.9202,\n", - " 72.5085, 99.7229, 119.3552, 81.3547, 82.5379, 99.0816,\n", - " 64.2944, 79.1325, 88.5360, 151.2213, 83.5813, 83.1803,\n", - " 116.1047, 77.4515, 153.7452, 99.8976, 130.5198, 589.3589,\n", - " 84.7535, 77.1639, 156.9236, 75.7586, 92.3786, 686.0644,\n", - " 99.8713, 272.4833, 103.9232, 109.5932, 647.1000, 83.8310,\n", - " 104.3419, 142.4164, 71.6175, 66.9595, 110.4678, 111.5748,\n", - " 69.8430, 96.0532, 89.3172, 114.8230, 81.8219, 97.8924,\n", - " 78.8253, 65.4647, 328.7401, 65.1851, 70.4413, 84.9899,\n", - " 71.3363, 71.5036, 82.8577, 72.8567, 109.5866, 109.6278,\n", - " 127.6077, 128.5153, 100.4798, 369.5361, 95.4297, 92.2342,\n", - " 240.1330, 140.4996, 171.8199, 127.7707, 77.2369, 91.7638,\n", - " 75.4459, 79.0773, 132.4259, 71.9650, 150.8931, 78.5894,\n", - " 90.2752, 100.5418, 72.2377, 122.8305, 79.3397, 84.9528,\n", - " 95.5919, 91.6746, 80.3178, 96.2397, 71.8855, 67.3971,\n", - " 96.9601, 86.3693, 73.9924, 87.0735, 77.8415, 80.5187,\n", - " 209.3069, 92.0294, 79.7221, 106.3754, 110.1519, 78.3681,\n", - " 114.2309, 78.9934, 90.5128, 95.9358, 84.8321, 73.2351,\n", - " 92.6145, 64.6984, 80.0933, 77.6931, 71.3182, 193.4463,\n", - " 123.9094, 84.2230, 119.3505, 92.6841, 103.8531, 110.2839,\n", - " 75.0955, 85.3833, 68.4676, 69.5051, 109.1305, 74.1958,\n", - " 87.4004, 80.9617, 74.2056, 110.5395, 107.4481, 114.6261,\n", - " 88.1106, 69.5662, 81.3068, 79.9684, 88.4739, 88.7448,\n", - " 80.2294, 70.4507, 96.3864, 78.5465, 161.8905, 86.3693,\n", - " 103.8472, 107.6463, 83.4548, 67.9993, 124.3624, 77.0651,\n", - " 105.4655, 136.9394, 75.4276, 72.7891, 94.2977, 112.0657,\n", - " 89.1320, 107.9469, 79.1384, 105.2824, 77.9763, 66.1418,\n", - " 99.7927, 73.1128, 79.0132, 106.6249, 67.7562, 127.9032,\n", - " 89.3084, 79.4358, 83.4245, 66.1271, 92.6558, 84.1672,\n", - " 75.7218, 90.6807, 89.9265, 73.0416, 76.7551, 86.4633,\n", - " 63.3250, 64.0347, 83.0079, 77.6720, 78.7774, 73.8572,\n", - " 125.4133, 105.5110, 83.7220, 65.5454, 68.5157, 127.7730,\n", - " 69.3476, 595.4847, 126.9307, 103.2536, 141.9498, 112.4316,\n", - " 103.9405, 98.3049, 96.5029, 92.8172, 82.2626, 84.8707,\n", - " 94.2684, 93.7790, 96.2120, 92.2867, 126.8869, 98.2385,\n", - " 78.7327, 71.3948, 77.6639, 83.0943, 95.4094, 76.6183,\n", - " 81.4846, 164.6067, 83.0181, 124.4507, 88.4690, 87.9488,\n", - " 67.2237, 111.2738, 72.1879, 78.0203, 78.9326, 74.4411,\n", - " 89.2657, 96.2301, 102.9213, 118.3973, 90.0904, 86.4860,\n", - " 81.4846, 105.2132, 73.7243, 355.5849, 85.7092, 83.9841,\n", - " 69.1449, 82.9710, 68.8698, 225.3249, 72.2320, 74.6028,\n", - " 74.2612, 76.3289, 144.8176, 316.3625, 78.7702, 79.7794,\n", - " 77.5773, 94.7189, 97.3468, 200.1857, 76.0815, 70.3450,\n", - " 95.6627, 110.0160, 107.2480, 86.4567, 602.0886, 863.6520,\n", - " 86.3095, 79.1692, 90.5299, 88.3174, 88.6674, 85.3745,\n", - " 84.8366, 115.6722, 116.9115, 97.2250, 93.1646, 417.8717,\n", - " 73.1323, 88.5900, 108.0165, 152.9404, 123.7808, 96.3771,\n", - " 83.3371, 87.3076, 135.9957, 88.4418, 104.4408, 80.4784,\n", - " 71.7912, 79.7402, 132.8563, 78.6128, 194.1618, 494.3711,\n", - " 76.7590, 381.0504, 90.5810, 78.6954, 84.5231, 121.2166,\n", - " 74.0839, 99.9507, 72.1831, 116.5717, 368.3096, 84.4830,\n", - " 115.2709, 105.5586, 89.6221, 807.9736, 86.9819, 94.0393,\n", - " 96.1975, 158.2225, 128.5060, 81.7196, 68.1906, 71.7756,\n", - " 86.1606, 81.7311, 90.2459, 93.5793, 82.4042, 83.9138,\n", - " 88.8481, 76.0885, 91.8756, 77.9450, 80.2911, 75.4562,\n", - " 68.7486, 70.2595, 87.6336, 87.6673, 95.3050, 88.4478,\n", - " 93.9104, 82.8531, 78.8510, 81.0118, 111.0954, 90.3215,\n", - " 88.0632, 83.7294, 179.3188, 119.5224],\n", - " grad_fn=)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "norms_reshape = (latent_representations - model.base_distribution.loc).reshape(-1, 784).norm(p=1, dim=-1)\n", - "event_dims = tuple(\n", - " range(latent_representations.dim() - len(model.base_distribution.event_shape),\n", - " latent_representations.dim()\n", - " )\n", - ")\n", - "norms_event = (latent_representations - model.base_distribution.loc).norm(p=1, dim=event_dims)\n", - "norms_event " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2e99cef5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "logprob 340.8048095703125\n" - ] - }, - { - "data": { - "text/plain": [ - "tensor([[1.0000e-20, 6.3006e+01]])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "calbration_set = mnist0[:1000][0]\n", - "model.calibrated_latent_radial_udl_profile(0.5, calbration_set)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "a171c146", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1000])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.base_distribution.norm_distribution.log_prob(norm.unsqueeze(-1)).shape" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "001ae22b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([ 72.1478, 83.2674, 89.5192, 78.9270, 89.7512, 118.6238,\n", - " 120.4174, 149.4253, 93.4896, 74.5199, 80.1446, 82.1757,\n", - " 73.1684, 146.2891, 88.7169, 75.2743, 74.9439, 137.4772,\n", - " 76.6199, 78.6657, 85.4492, 83.2754, 88.5153, 79.3644,\n", - " 92.3205, 104.2313, 87.4523, 79.9673, 154.6376, 97.6262,\n", - " 93.3055, 81.2312, 79.5160, 110.8487, 71.8545, 73.5721,\n", - " 66.7177, 68.9474, 78.0023, 75.7370, 78.9032, 74.5902,\n", - " 192.5165, 1100.8661, 73.2147, 88.7445, 99.9019, 205.9033,\n", - " 84.6182, 94.4084, 83.6637, 78.1391, 98.4256, 162.1396,\n", - " 87.2163, 76.3277, 97.1700, 120.8750, 89.3177, 67.1609,\n", - " 89.2158, 68.9861, 72.0228, 71.1451, 82.6173, 98.3822,\n", - " 91.2317, 73.4773, 80.8572, 82.6433, 82.9154, 117.3899,\n", - " 84.0294, 106.6756, 136.3762, 77.5079, 115.4267, 77.6317,\n", - " 78.9581, 72.7156, 142.4030, 88.5172, 120.4769, 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73.1323, 88.5900, 108.0165, 152.9404, 123.7808, 96.3771,\n", - " 83.3371, 87.3076, 135.9957, 88.4418, 104.4408, 80.4784,\n", - " 71.7912, 79.7402, 132.8563, 78.6128, 194.1618, 494.3711,\n", - " 76.7590, 381.0504, 90.5810, 78.6954, 84.5231, 121.2166,\n", - " 74.0839, 99.9507, 72.1831, 116.5717, 368.3096, 84.4830,\n", - " 115.2709, 105.5586, 89.6221, 807.9736, 86.9819, 94.0393,\n", - " 96.1975, 158.2225, 128.5060, 81.7196, 68.1906, 71.7756,\n", - " 86.1606, 81.7311, 90.2459, 93.5793, 82.4042, 83.9138,\n", - " 88.8481, 76.0885, 91.8756, 77.9450, 80.2911, 75.4562,\n", - " 68.7486, 70.2595, 87.6336, 87.6673, 95.3050, 88.4478,\n", - " 93.9104, 82.8531, 78.8510, 81.0118, 111.0954, 90.3215,\n", - " 88.0632, 83.7294, 179.3188, 119.5224],\n", - " grad_fn=)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "norm" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "5cca1c4f", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "samples = mnist0[:1000]\n", - "\n", - "if isinstance(samples, torch.Tensor):\n", - " samples = samples.detach().numpy()\n", - "\n", - "# Create the histogram\n", - "plt.figure(figsize=(8, 6))\n", - "plt.hist(samples, bins=100, edgecolor='black', alpha=0.7)\n", - "plt.title('Histogram of Samples from Norm Distribution of the Base distribution')\n", - "plt.xlabel('Value')\n", - "plt.ylabel('Frequency')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5515d238", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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I6gUDDxlwsVVi7hNdAACfx2Ugo7Bc4oqIiIgenGSBJzs7GxMnToSPjw+sra3Rrl07zJ07F9XV1QZtBEG4bTl27JjBvjZu3Ag/Pz9YWVmha9eu2LlzZ2Mfjll5MrAVBndyRbVWhxkbz6CWt7aIiKiJkyzwpKWlQafT4euvv0Zqaio+/fRTLFu2DO+8885tbffu3Yv8/Hz9EhwcrN929OhRjBo1ChMnTkRiYiIiIyMRGRmJlJSUxjwcsyIIAj56uivsrCyQnFuKpQd4a4uIiJo2QTShWSMXLlyIpUuXIjMzE0DdFR4fHx8kJiYiMDDwju8ZOXIkKioqsH37dv26Pn36IDAwEMuWLbunz9VoNHBwcEBZWRns7e0f+DjMxebTlxG9IRkWMgFbp/RFgKeD1CURERHpGfP7bVJ9eMrKyuDk5HTb+uHDh8PNzQ39+vXDtm3bDLbFx8cjPDzcYF1ERATi4+P/8nOqqqqg0WgMFrrdU0GeiOiiQq1OxIwNyaiq5YCERETUNJlM4MnIyMAXX3yB1157Tb/O1tYWixYtwsaNG7Fjxw7069cPkZGRBqFHrVZDpVIZ7EulUkGtVv/lZ82fPx8ODg76xdvbu/4PyAwIgoCPnuoK5xYKpBeU4z+xv0tdEhER0X2p98Aza9asO3Y0/vOSlmY4SWVeXh6GDh2KESNG4JVXXtGvd3FxQXR0NEJCQtCrVy8sWLAAY8aMwcKFCx+oxpiYGJSVlemX3NzcB9qfOXO2VeKjp+sGJPzmUCZOZZdIXBEREZHxLOp7hzNmzMD48ePv2sbX11f/z1euXMHgwYMRFhaGb7755m/3HxISgtjYWP1rd3d3FBQUGLQpKCiAu7v7X+5DqVRCqVT+7WdRnYgu7ni6hyc2n87DjI3J2Plmf7RQ1vu/OkRERA2m3n+1XF1d4erqek9t8/LyMHjwYAQHB2PlypWQyf7+glNSUhI8PDz0r0NDQxEXF4eoqCj9utjYWISGhhpdO/21uU90QfzFq7h09Qbm7zqPf/0xDQUREVFTINn/Tc/Ly8OgQYPQpk0bfPLJJygqKtJvu3V1ZvXq1VAoFAgKCgIAbN68GStWrMB3332nbztt2jQMHDgQixYtwrBhw7Bu3TqcOnXqnq4W0b1zsLbEwme7Y8zy41hzLAeDO7nh4c6qv38jERGRCZAs8MTGxiIjIwMZGRnw8vIy2PbnJ+U//PBDXLp0CRYWFvDz88P69evx7LPP6reHhYXhhx9+wHvvvYd33nkHHTp0wNatWxEQENBox9Jc9Ovggpf6+mDFkSy8tekMdk/rDzd7K6nLIiIi+lsmNQ6PVDgOz72rqtUi8sujOJ+vQb/2LvjvS70hkwlSl0VERM1Qkx2Hh0yf0kKOL0YFwspShsMZxfjucKbUJREREf0tBh4yWns3O/0Eowv3pOPs5TKJKyIiIro7Bh66L8/38sajAe6o0Yp4c10iKqpqpS6JiIjoLzHw0H0RBAHzn+4KDwcrZBVX4P1tqVKXRERE9JcYeOi+OdoosHhkIAQB2JhwGT8n5UldEhER0R0x8NADCfF1xhuD2wMAYjafRUbhdYkrIiIiuh0DDz2waeEdEerrjBvVWkxZexqV1ZxVnYiITAsDDz0wuUzAZ6MC4WKrRHpBOeb8nCJ1SURERAYYeKheuNlZ4fNRgZD90Z9n4ynOQE9ERKaDgYfqTVg7F0Q/0hEAMPvnFKSryyWuiIiIqA4DD9Wr1we1x4COrrhZo8PktQkcn4eIiEwCAw/VK5lMwKfPdYe7vRUyiyowa/NZcLo2IiKSGgMP1TtnWyWWvBAEC5mAX5KvYPnhLKlLIiKiZo6BhxpEz7ZOmP24PwDgo53ncTSjWOKKiIioOWPgoQYzNrQNnu7hCZ0ITP0xEZev3ZC6JCIiaqYYeKjBCIKAj57qigBPe5RUVGPSmgTcrOGghERE1PgYeKhBWVnKsWxMMJxaKJCSp8E7W9iJmYiIGh8DDzU4r5Y2WDIqCDIB2Hw6D6uPZktdEhERNTMMPNQowtq74J3HOgMA/rXjPI5lXpW4IiIiak4YeKjRTOzng+HdW6FWJ2LymgTkXGUnZiIiahwMPNRoBEHAv5/phq6eDrh2owYTV59E+c0aqcsiIqJmgIGHGpW1Qo5vx/aEm50SFwqv440fE6HVsRMzERE1LAYeanTuDlb4blxPKC1kOJBehI92npe6JCIiMnMMPCSJbl6OWPRcdwDA8sNZ+PFEjsQVERGROWPgIck83q0Vpod3BADM3pqC+It8couIiBoGAw9J6s2H2+OJW09urU1AdnGF1CUREZEZYuAhSQmCgIXPdkN3LweU3qjBhFUnUVJRLXVZRERkZhh4SHJWlnJ8O64nPB2tkVVcgZdXn+ScW0REVK8YeMgkuNlZYdWEXrC3ssDpnFJErUvi4+pERFRvGHjIZHRQ2eGbsT2hkMuwO1WNeTv4uDoREdUPBh4yKX18nfHJH4+rrziSheWHsySuiIiIzAEDD5mc4d1bYdajfgCAf+04h11n8yWuiIiImjoGHjJJrw3wxZg+rSGKQNT6JCRcKpG6JCIiasIYeMgkCYKA95/ogvDObqiq1WHi6lO4UFAudVlERNRESRp42rZtC0EQDJYFCxYYtDlz5gz69+8PKysreHt74+OPP75tPxs3boSfnx+srKzQtWtX7Ny5s7EOgRqQhVyGz0cFIdDbEaU3avDi8hO4fO2G1GUREVETJPkVng8++AD5+fn65Y033tBv02g0GDJkCNq0aYOEhAQsXLgQ77//Pr755ht9m6NHj2LUqFGYOHEiEhMTERkZicjISKSkpEhxOFTPbBQWWDm+Fzq42UKtuYmxy0/g6vUqqcsiIqImRhBFUbLBTtq2bYuoqChERUXdcfvSpUvx7rvvQq1WQ6FQAABmzZqFrVu3Ii0tDQAwcuRIVFRUYPv27fr39enTB4GBgVi2bNk91aHRaODg4ICysjLY29s/2EFRg8gvq8SzS+ORV1qJrp4O+OGVENhZWUpdFhERSciY32/Jr/AsWLAAzs7OCAoKwsKFC1FbW6vfFh8fjwEDBujDDgBEREQgPT0d165d07cJDw832GdERATi4+Mb5wCoUXg4WOP7ib3h3EKBs3llePW/CRyNmYiI7pmkgefNN9/EunXrsH//frz22mv46KOP8Pbbb+u3q9VqqFQqg/fceq1Wq+/a5tb2O6mqqoJGozFYyPT5utpi1YTesFVaID7zKqatS0StVid1WURE1ATUe+CZNWvWbR2R//9y63ZUdHQ0Bg0ahG7dumHSpElYtGgRvvjiC1RVNWwfjfnz58PBwUG/eHt7N+jnUf3p6uWAb8YGQyGXYU9qAd7dkgIJ78oSEVETYVHfO5wxYwbGjx9/1za+vr53XB8SEoLa2lpkZ2ejU6dOcHd3R0FBgUGbW6/d3d31/3unNre230lMTAyio6P1rzUaDUNPExLWzgWfjwrC62sTsP5ULmyUcsx53B+CIEhdGhERmah6Dzyurq5wdXW9r/cmJSVBJpPBzc0NABAaGop3330XNTU1sLSs66AaGxuLTp06oWXLlvo2cXFxBh2fY2NjERoa+pefo1QqoVQq76tGMg1DA9zx72e64a1NZ7DySDasLOV4O6ITQw8REd2RZH144uPjsXjxYiQnJyMzMxNr167F9OnTMWbMGH2YeeGFF6BQKDBx4kSkpqZi/fr1+OyzzwyuzkybNg27d+/GokWLkJaWhvfffx+nTp3C1KlTpTo0aiQjenrjw8gAAMDSAxfxxb4MiSsiIiJTJdlj6adPn8brr7+OtLQ0VFVVwcfHBy+++CKio6MNrr6cOXMGU6ZMwcmTJ+Hi4oI33ngDM2fONNjXxo0b8d577yE7OxsdOnTAxx9/jMcee+yea+Fj6U3bd79l4l9/zKz+zmN+eHVAO4krIiKixmDM77ek4/CYCgaepm/Jvgv45NffAQD/HN4F48LaSlsQERE1uCY1Dg9RfZj6UAdMHdweADB3WyrWnciRuCIiIjIlDDxkNmYM6YiX+/kAAGK2nMWWxMsSV0RERKaCgYfMhiAIeHdYZ7zYpw1EEZixIZmhh4iIADDwkJkRBAH/HN4Fo3p7QycC0RuS8VMCQw8RUXPHwENmRyYTMC+yK14IaQ1RBP6xKRkbT+VKXRYREUmIgYfMkkwm4F9PBmBMn7rQ8/ZPZ7DhJEMPEVFzxcBDZksmE/DhkwEYF9pGH3p+5NNbRETNEgMPmTVBEPD+8C6Y0LctACBm81msPX5J2qKIiKjRMfCQ2RMEAXMe98fEPx5Zf3dLCv4bny1tUURE1KgYeKhZEAQB7w3rjFcH+AIA5vyciq8OcO4tIqLmgoGHmg1BEBDzqB/efKhuROaPd6fj491p4OwqRETmj4GHmhVBEBA9pBNiHvUDAHx14CLmbkuFTsfQQ0Rkzhh4qFl6bWA7zHsqAIIA/Df+Ev6xKRm1Wp3UZRERUQNh4KFma3RIG3z6XCDkMgGbT+dh6g+JqKrVSl0WERE1AAYeatYigzyxdHQPKOQy7E5V45X/JqCymqGHiMjcMPBQszekiztWjO8Fa0s5Dv1ehBeXH0fZjRqpyyIionrEwEMEoF8HF6x5uTfsrCxw6tI1jPj6KPLLKqUui4iI6gkDD9Efgts4YeOkUKjslfi94Dqe+eooMgrLpS6LiIjqAQMP0Z/4udvjp8lh8HVtgStlN/HssngkXLomdVlERPSAGHiI/h+vljbYNCkMgd6OKL1Rg9HfHUPc+QKpyyIiogfAwEN0B04tFPjhlRAM6uSKmzU6vPp9AjacypW6LCIiuk8MPER/wUZhgW/H9sQzPbyg1Yl4e9MZfLk/g1NREBE1QQw8RHdhKZfhkxHdMGlgOwDAwj3peHdrCkdlJiJqYhh4iP6GIAiY9agf5j7hD0EAfjieg5dWn0L5TY7VQ0TUVDDwEN2jCX198PWYYFhZynDo9yKMWBbPsXqIiJoIBh4iIwzp4o4Nr4XCxVaJNHU5Ir88gtQrZVKXRUREf4OBh8hI3bwcsXVKGDqqbFGgqcJzy+KxP61Q6rKIiOguGHiI7oNXSxtsnBSGvu2dUVGtxcTVJ/H9sUtSl0VERH+BgYfoPjlYW2Ll+N4YEewFnQjM3pqCf20/B62Oj60TEZkaBh6iB6CwkOHjZ7vhH0M6AgC+O5yFiatPQsMnuIiITAoDD9EDEgQBUx/qgC9f6AErSxkOpBfhqS+PIKu4QurSiIjoDww8RPVkWDcPbJoUBg8HK1wsqkDkl0dwJKNY6rKIiAgMPET1KsDTAT9P7Yug1o4oq6zB2BUn8N/4bE5HQUQkMQYeonrmZmeFH1/pg6eDPKHViZjzcyre3ZqCGk5HQUQkGQYeogZgZSnHoue6I+ZRP/10FGO+O46SimqpSyMiapYkCzwHDhyAIAh3XE6ePAkAyM7OvuP2Y8eOGexr48aN8PPzg5WVFbp27YqdO3dKcUhEBgRBwGsD2+G7sT1hq7TA8awSDF9yGCl5HJmZiKixSRZ4wsLCkJ+fb7C8/PLL8PHxQc+ePQ3a7t2716BdcHCwftvRo0cxatQoTJw4EYmJiYiMjERkZCRSUlIa+5CI7ujhzipsfj0MbZxtcPlaJZ5ZehRbEi9LXRYRUbMiiCbSm7Kmpgaenp544403MHv2bAB1V3h8fHyQmJiIwMDAO75v5MiRqKiowPbt2/Xr+vTpg8DAQCxbtuyePluj0cDBwQFlZWWwt7d/4GMhupOyGzWYtj4RB9KLAADjw9ri3WGdYSnnnWUiovthzO+3yfyl3bZtG65evYoJEybctm348OFwc3NDv379sG3bNoNt8fHxCA8PN1gXERGB+Pj4Bq2XyFgONpZYPq4X3nyoPQBg1dFsvPDtMRSW35S4MiIi82cygWf58uWIiIiAl5eXfp2trS0WLVqEjRs3YseOHejXrx8iIyMNQo9arYZKpTLYl0qlglqt/svPqqqqgkajMViIGoNcJiB6SCd8O7Yn7JQWOJl9DU98cRgJl65JXRoRkVmr98Aza9asv+yMfGtJS0szeM/ly5exZ88eTJw40WC9i4sLoqOjERISgl69emHBggUYM2YMFi5c+EA1zp8/Hw4ODvrF29v7gfZHZKxH/FXYOrUvOrjVzbj+/DfxWHPsEsfrISJqIBb1vcMZM2Zg/Pjxd23j6+tr8HrlypVwdnbG8OHD/3b/ISEhiI2N1b92d3dHQUGBQZuCggK4u7v/5T5iYmIQHR2tf63RaBh6qNG1c7XFlil98famZOw8q8Z7W1OQlFuKD58MgLVCLnV5RERmpd4Dj6urK1xdXe+5vSiKWLlyJcaOHQtLS8u/bZ+UlAQPDw/969DQUMTFxSEqKkq/LjY2FqGhoX+5D6VSCaVSec81EjUUW6UFvnyhB745lIl/707DpoTLSMkrw5eje6Cdq63U5RERmY16DzzG2rdvH7KysvDyyy/ftm316tVQKBQICgoCAGzevBkrVqzAd999p28zbdo0DBw4EIsWLcKwYcOwbt06nDp1Ct98802jHQPRg7g1Xk9XTwe8uS4JaepyDP/iMD56uiueDPSUujwiIrMgeafl5cuXIywsDH5+fnfc/uGHHyI4OBghISH4+eefsX79eoMnucLCwvDDDz/gm2++Qffu3bFp0yZs3boVAQEBjXUIRPUirL0Ldr7ZD318nVBRrcW0dUl4d8tZ3KzRSl0aEVGTZzLj8EiJ4/CQKanV6vBZ3AUs2Z8BUQS6tLLHV6N7oI1zC6lLIyIyKU1yHB4iqmMhl2HGkE5YNaE3nFookHpFg8c/P4xdZ/OlLo2IqMli4CEyUQM7umLHm/3Qs01LlFfVYvLa03h/WyqqaznrOhGRsRh4iEyYh4M1fny1D14bWDeUw6qj2Xhm6VFkFVdIXBkRUdPCwENk4izlMsQ82hnfje0JRxtLnM0rw7DPf8NPCZc5UCER0T1i4CFqIsL9Vdg1rT9CfJxwo1qLGRuTEbU+CeU3a6QujYjI5DHwEDUhHg7W+OGVPvjHkI6QywT8nHQFj33+GxJzOBcXEdHdMPAQNTFymYCpD3XAhtdC4elojdySSoxYFo+vDmRAp+MtLiKiO2HgIWqigtu0xM5p/fF4Nw/U6kR8vDsdY5YfR4HmptSlERGZHAYeoibMwdoSX4wKwsfPdIO1pRxHL17F0MWHsDuFY/YQEf0ZAw9REycIAp7r5Y3tb/ZDl1b2uHajBpPWnMaMDcnQsEMzEREABh4is9HO1RZbXu+L1we1g0wAfjp9GY8u/g3HMq9KXRoRkeQYeIjMiMJChreH+mH9a6HwdrJGXmklRn17DB/tPI+qWk5CSkTNFwMPkRnq1dYJu6YNwMie3hBF4JtDmXhyyRGcz9dIXRoRkSQYeIjMlK3SAv9+thu+HdsTzi0USFOX48klR/D1wYvQ8vF1ImpmGHiIzNwj/irsmT4A4Z1VqNbqMH9XGkZ9cwyXrnI+LiJqPhh4iJoBF1slvh0bjI+f6YYWCjlOZJdg6OLfsOpIFgcrJKJmgYGHqJm49fj67qgB6OPrhMoaLd7/5Rye/5ZXe4jI/DHwEDUz3k42+OHlPvjwyS6wUchxIqvuas9KXu0hIjPGwEPUDMlkAl4MbYs9UQMQ6uuMyhot/vnLOTz/zTFkF/NqDxGZHwYeombM28kGa18OwYeRAXVXe7JLMPSzQ7zaQ0Rmh4GHqJmTyQS82KeN/mrPzRqd/mpPFq/2EJGZYOAhIgD/u9rzr8iAPz3JdQhfHchAjVYndXlERA+EgYeI9GQyAWP6tMHuqAHo38EFVbU6fLw7HcOXHEFybqnU5RER3TcGHiK6jbeTDf77Um8sGtEdjjaWOJ+vwVNfHcEHv5xDRVWt1OURERmNgYeI7kgQBDwT7IW46IGIDGwFnQisOJKFIZ8ewoH0QqnLIyIyCgMPEd2Vs60Si58PwqoJveDpWDcD+/iVJzFtXSKuXq+SujwionvCwENE92RQJzf8On0AJvbzgUwAfk66gvD/HMRPCZchinyEnYhMGwMPEd2zFkoLzH7cH1te74vOHva4dqMGMzYmY8zy47hYdF3q8oiI/hIDDxEZrbu3I7ZN7YuZQ/2gtJDhSMZVPLr4Nyz6NR03a7RSl0dEdBsGHiK6L5ZyGSYPaofY6QMxuJMrqrU6fLEvA498ehD70gqkLo+IyAADDxE9kNbONlgxvheWjQmGh4MVcksq8dKqU3jt+1O4UlopdXlERAAYeIioHgiCgKEB7tgbPRCvDfCFhUzAntQChP/nIL4+eJEjNROR5ASRj1dAo9HAwcEBZWVlsLe3l7ocoiYvXV2O2VtTcCK7BADQUWWLf0V2RW8fJ4krIyJzYszvN6/wEFG96+Ruh/Wv9cHCZ7vBqYUCvxdcx3NfxyN6fRIKNTelLo+ImiEGHiJqEIIgYERPb+ybMRAvhLSGIACbE/Mw+JMD+PrgRVTX8jYXETWeBgs88+bNQ1hYGGxsbODo6HjHNjk5ORg2bBhsbGzg5uaGt956C7W1hvP0HDhwAD169IBSqUT79u2xatWq2/bz5Zdfom3btrCyskJISAhOnDjRAEdERPfD0UaBj57qiq2v90WgtyMqqrWYvysNQxcfwn5OUUFEjaTBAk91dTVGjBiByZMn33G7VqvFsGHDUF1djaNHj2L16tVYtWoV5syZo2+TlZWFYcOGYfDgwUhKSkJUVBRefvll7NmzR99m/fr1iI6Oxty5c3H69Gl0794dERERKCzkH1IiU9Ld2xGbJ4dh0YjucLFVIrO4AhNWnsTEVSeRXVwhdXlEZOYavNPyqlWrEBUVhdLSUoP1u3btwuOPP44rV65ApVIBAJYtW4aZM2eiqKgICoUCM2fOxI4dO5CSkqJ/3/PPP4/S0lLs3r0bABASEoJevXphyZIlAACdTgdvb2+88cYbmDVr1j3VyE7LRI2r/GYNvtiXgRWHs1CrE6GQy/Byfx9MGdweLZQWUpdHRE1Ek+i0HB8fj65du+rDDgBERERAo9EgNTVV3yY8PNzgfREREYiPjwdQdxUpISHBoI1MJkN4eLi+DRGZHjsrS7zzWGfsjhqAAR3rBi386sBFPLzoIH5OyuPcXERU7yQLPGq12iDsANC/VqvVd22j0WhQWVmJ4uJiaLXaO7a5tY87qaqqgkajMViIqPG1d7PF6gm98O3YnmjtZAO15iamrUvCiGXxSM4tlbo8IjIjRgWeWbNmQRCEuy5paWkNVWu9mT9/PhwcHPSLt7e31CURNVuCIOARfxV+nT4Ab0V0grWlHKcuXcOTXx7B9PVJHK2ZiOqFUTfLZ8yYgfHjx9+1ja+v7z3ty93d/banqQoKCvTbbv3vrXV/bmNvbw9ra2vI5XLI5fI7trm1jzuJiYlBdHS0/rVGo2HoIZKYlaUcUwa3xzM9vLBwTzp+On0ZWxLzsCslH6/298VrA9uxfw8R3Tej/nq4urrC1dW1Xj44NDQU8+bNQ2FhIdzc3AAAsbGxsLe3h7+/v77Nzp07Dd4XGxuL0NBQAIBCoUBwcDDi4uIQGRkJoK7TclxcHKZOnfqXn61UKqFUKuvlOIiofrk7WGHRc90xPqwtPtxxDieySvD5vgz8eDIXbw3phGeCvSCXCVKXSURNTIP14cnJyUFSUhJycnKg1WqRlJSEpKQkXL9+HQAwZMgQ+Pv748UXX0RycjL27NmD9957D1OmTNGHkUmTJiEzMxNvv/020tLS8NVXX2HDhg2YPn26/nOio6Px7bffYvXq1Th//jwmT56MiooKTJgwoaEOjYgaQVcvB6x/tQ+WjQlGG2cbFJVX4e2fzuDxLw7jaEax1OURURPTYI+ljx8/HqtXr75t/f79+zFo0CAAwKVLlzB58mQcOHAALVq0wLhx47BgwQJYWPzvwtOBAwcwffp0nDt3Dl5eXpg9e/Ztt9WWLFmChQsXQq1WIzAwEJ9//jlCQkLuuVY+lk5k2qprdfhvfDY+i7uA8pt1g5OGd3ZDzGOd0c7VVuLqiEgqxvx+c/JQMPAQNRUlFdX4PO4Cvj92CVqdCAuZgNEhrfHGwx3gYsvb1ETNDQOPkRh4iJqWjMLrWLDrPPaerxtRvYVCjlcHtMPL/X3YsZmoGWHgMRIDD1HTdDSjGAt2p+HM5TIAgIutEtPCO+D5Xt6wlHNuZCJzx8BjJAYeoqZLpxOxMyUfC/ek49LVGwAAH5cWeCuiEx4NcIcg8IkuInPFwGMkBh6ipq+6Vod1J3Pw2d4LuFpRDaBuwtJZQ/0Q2s5Z4uqIqCEw8BiJgYfIfFyvqsW3hzLx7W+ZuFGtBQAM6uSKmUP90NmD/30TmRMGHiMx8BCZn6LyKnyx7wJ+OJ6DWp0IQQCeCvREVHhHtHa2kbo8IqoHDDxGYuAhMl9ZxRX45Nd07DiTDwCwkAkY2csbbzzUAe4OVhJXR0QPgoHHSAw8ROYvObcUn/yajt8u1I3SrLCQ4cU+bfD6oHZw5hg+RE0SA4+RGHiImo/jmVfxya/pOJl9DQBgo5Djpb4+eGWALxysLSWujoiMwcBjJAYeouZFFEUculCMT/ak42xe3Rg+9lYWeHWALyb05eCFRE0FA4+RGHiImidRFLEntQD/iU3H7wV1Exs7t1Bg8qB2GNOnDaws5RJXSER3w8BjJAYeouZNqxOx/cwVfBr7O7L/GLzQ3d4KUx5qj+d6ekFpweBDZIoYeIzEwENEAFCj1eGnhMv4PO4CrpTdBAB4OFjh9UHt8FwvbwYfIhPDwGMkBh4i+rOqWi1+PJ6DpQcvokBTBaAu+Ewe1A7P9fTmrS4iE8HAYyQGHiK6k5s1Wmw4lYuv9l+EWlN3xcfd3gqvD2bwITIFDDxGYuAhoru5WaPFxlO5+PJPwUdlr8Trg9pjZC8GHyKpMPAYiYGHiO5FVa0WG05dxlf7M5Bf9r/gM3lgOzzfuzWDD1EjY+AxEgMPERmjqlaLjX8En1udm93slJg0sB1G9W4NawWDD1FjYOAxEgMPEd2PqlotNiVcxlf7LyKvtBIA4NRCgYn9fDCmTxuO3EzUwBh4jMTAQ0QPorpWh00Jl7Hs4EXklNSN42OntMDYsDZ4qa8P5+oiaiAMPEZi4CGi+lCr1WH7mXx8dSBDP3KzlaUMo3q3xiv9fdHK0VriConMCwOPkRh4iKg+6XQiYs8X4Mv9GThzuW6uLku5gKeDvDBpUDv4uLSQuEIi88DAYyQGHiJqCKIo4kjGVSzZfwHHMksAADIBGNatFV4f1A6dPfj3huhBMPAYiYGHiBpawqUSfLn/IvalFerXhXd2w+RB7RDcxknCyoiaLgYeIzHwEFFjSb1ShqUHLmLH2Xzc+usb3KYlXhvgi/DOKshkgrQFEjUhDDxGYuAhosaWWXQdXx/MxJbEPFRrdQAAX9cWeLW/LyKDPDmIIdE9YOAxEgMPEUmlUHMTK49mY82xSyi/WQsAcLFVYkLfthgT0gYONhzLh+ivMPAYiYGHiKR2vaoW607kYPnhLP20FTYKOZ7v1RoT+/vAk4+0E92GgcdIDDxEZCpqtDpsP3MFXx/MRJq6HAAglwkY3r0VXh3gyye7iP6EgcdIDDxEZGpEUcShC8X4+uBFHL14Vb++fwcXvDrAF/3au0AQ2MGZmjcGHiMx8BCRKTt7uQxfH7qInWfzofvjL3YnlR1e6tcWTwaygzM1Xww8RmLgIaKmILfkBpYfzsKGU7m4Ua0FADi3UGB0nzZ4sU8buNpxzi5qXhh4jMTAQ0RNSVllDTaczMWqo9n6WdoVchme6N4KE/v5wL8V/45R88DAYyQGHiJqimq1OuxJLcDyw5k4nVOqXx/q64yJ/XzwkJ8bBzIks8bAYyQGHiJq6hJzrmH54SzsSlFD+0dHHx+XFpjQty2e6eGFFkoLiSskqn/G/H7LGqqIefPmISwsDDY2NnB0dLxte3JyMkaNGgVvb29YW1ujc+fO+OyzzwzaHDhwAIIg3Lao1WqDdl9++SXatm0LKysrhISE4MSJEw11WEREJimodUsseaEHDr09GK8N8IWdlQWyiisw5+dUhM6Pw/yd53H52g2pyySSTIMFnurqaowYMQKTJ0++4/aEhAS4ublhzZo1SE1NxbvvvouYmBgsWbLktrbp6enIz8/XL25ubvpt69evR3R0NObOnYvTp0+je/fuiIiIQGFh4W37ISIyd56O1oh5rDOOxTyMD57sgrbONtDcrMXXhzIx4OP9ePW/p3Akoxi8uE/NTYPf0lq1ahWioqJQWlr6t22nTJmC8+fPY9++fQDqrvAMHjwY165du+NVIgAICQlBr1699EFJp9PB29sbb7zxBmbNmnVPNfKWFhGZK51OxL60Qqw6mo3DGcX69e3dbDEutA2e6uEFW97uoibKJG5p3Y+ysjI4OTndtj4wMBAeHh545JFHcOTIEf366upqJCQkIDw8XL9OJpMhPDwc8fHxf/k5VVVV0Gg0BgsRkTmSyQSE+6uw5uUQ7I0egLGhbdBCIUdG4XXM/jkVoR/F4f1tqcgsui51qUQNymQCz9GjR7F+/Xq8+uqr+nUeHh5YtmwZfvrpJ/z000/w9vbGoEGDcPr0aQBAcXExtFotVCqVwb5UKtVt/Xz+bP78+XBwcNAv3t7eDXNQREQmpL2bHT54MgDH3nkY7z/hD1+XFiivqsWqo9l4aNFBjF1xAnHnC/SdnonMiVGBZ9asWXfsRPznJS0tzegiUlJS8OSTT2Lu3LkYMmSIfn2nTp3w2muvITg4GGFhYVixYgXCwsLw6aefGv0ZfxYTE4OysjL9kpub+0D7IyJqSuysLDG+rw/2Rg/E9xN7I7yzGwQBOPR7ESauPoXBnxzAt4cyUXajRupSieqNUTduZ8yYgfHjx9+1ja+vr1EFnDt3Dg8//DBeffVVvPfee3/bvnfv3jh8+DAAwMXFBXK5HAUFBQZtCgoK4O7u/pf7UCqVUCo5IikRNW8ymYD+HVzRv4Mrcktu4Ptjl7D+ZC5ySm5g3s7zWBSbjqeCPDE6pA0CPB2kLpfogRgVeFxdXeHq6lpvH56amoqHHnoI48aNw7x58+7pPUlJSfDw8AAAKBQKBAcHIy4uDpGRkQDqOi3HxcVh6tSp9VYnEZG583aywTuPdcb08I74OSkPq+Mv4Xy+Bj+eyMWPJ3LR3dsRo0Na44lurWCt4Nxd1PQ0WNf8nJwclJSUICcnB1qtFklJSQCA9u3bw9bWFikpKXjooYcQERGB6OhofZ8buVyuD1WLFy+Gj48PunTpgps3b+K7777Dvn378Ouvv+o/Jzo6GuPGjUPPnj3Ru3dvLF68GBUVFZgwYUJDHRoRkdmyVsjxfO/WGNnLGyezr2HNsUvYlZKP5NxSJOeW4sPt5/BMDy+MDmmNDio7qcslumcNFnjmzJmD1atX618HBQUBAPbv349BgwZh06ZNKCoqwpo1a7BmzRp9uzZt2iA7OxtA3VNYM2bMQF5eHmxsbNCtWzfs3bsXgwcP1rcfOXIkioqKMGfOHKjVagQGBmL37t23dWQmIqJ7JwgCevs4obePE4qv+2NTwmX8cDwHOSU3sOpoNlYdzUZvHyeMDmmNoQHuUFrwqg+ZNk4tAY7DQ0R0L3Q6Eb9lFGPtsUvYe74Atx7mcm6hwIie3nihd2u0draRtkhqVjiXlpEYeIiIjJNfVon1J3Ox7kQu1Jqb+vUDOrpidEhrPOznBgu5yYx8QmaKgcdIDDxERPenVqtDXFoh1h7PwaHfi/TrVfZKjOzVGs/19IJXS171oYbBwGMkBh4iogd36WoFfjyRiw2nclFSUQ0AEASgfwdXPN/LG+GdVVBY8KoP1R8GHiMx8BAR1Z+qWi12p6ix/mQujl68ql/v3EKBp3t4YmQvb7R34xNe9OAYeIzEwENE1DCyiyuw4VQuNiZcRlF5lX59zzYtMbKXN4Z184CNgpOX0v1h4DESAw8RUcOq1eqwP70I60/mYH96kX6+LjulBYYHtsLzvVojwNMegiBIXCk1JQw8RmLgISJqPAWam9iUcFk/jcUt/h72eL63N57s7gkHG0sJK6SmgoHHSAw8RESNT6cTcSzzKtadzMXuFDWqtToAgNJChse6emBEsBf6+DpDJuNVH7ozBh4jMfAQEUnrWkU1tiblYf3JXKSpy/XrPR2t8UywF57p4Yk2zi0krJBMEQOPkRh4iIhMgyiKOHO5DOtO5mL7mSsov1mr39bbxwnPBnvhsa4esFWyozMx8BiNgYeIyPTcrNHi13MF2JRwGb9dKMKtXytrSzke7eqOZ4O90MeHt7yaMwYeIzHwEBGZtvyySmxJzMOmU5eRWVyhX3/rltezPbw4j1czxMBjJAYeIqKmQRRFJOaWYlPCZfySbHjLK+RPt7xa8JZXs8DAYyQGHiKipudmjRZ7UtXYlHAZhzOK9be8bBRyDA1wx9NBXght5ww5b3mZLQYeIzHwEBE1bfllldh8Og8/JRje8lLZK/FkoCeeCvJEZw/+fTc3DDxGYuAhIjIPoijidM41bD6dh+1n8lFWWaPf5uduh8ggTzwZ2AoeDtYSVkn1hYHHSAw8RETmp6pWiwPpRdiamIe484X6gQ0FAQj1dUZkkCceDXCHnRVHdW6qGHiMxMBDRGTeym7UYGdKPrYk5uFEVol+vdJChkf8VXgqyBMDOrrCUi6TsEoyFgOPkRh4iIiaj9ySG9iWfAWbT1/GxaL/9fdxaqHAE908EBnkiUBvR05k2gQw8BiJgYeIqPkRRREpeRpsSczDtuQrKL5epd/m49ICT3RvheHdW6G9m62EVdLdMPAYiYGHiKh5q9XqcDijGFsT87AntQCVNVr9Nn8PewwPbIXHu3nAqyUHNzQlDDxGYuAhIqJbKqpq8es5NbYlXcFvF4pRq/vfz2Rwm5YY3r0VHuvqAVc7pYRVEsDAYzQGHiIiupNrFdXYlaLGtuQ8HM8q0Q9uKBOAvu1d8ES3VogIcIeDNZ/0kgIDj5EYeIiI6O8UaG5i+5l8bEu+guTcUv16hVyGgZ1cMbx7K4R3VsFaIZeuyGaGgcdIDDxERGSMS1cr6sJP0hWkF5Tr19so5HjEX4UnurXCgI6uUFjwMfeGxMBjJAYeIiK6X+nqcmxLzsMvyfnIKbmhX+9gbYmILioM69YKYe2cOcZPA2DgMRIDDxERPShRFJF8uQzbkq5g+5krKCz/32PujjaWiPB3x2PdPBh+6hEDj5EYeIiIqD5pdSJOZJVg59l87EpRG4zxw/BTfxh4jMTAQ0REDYXhp+Ew8BiJgYeIiBoDw0/9YuAxEgMPERE1NoafB8fAYyQGHiIiktLdwo+DtSXCO6swNMAd/Tu4wMqS4/zcwsBjJAYeIiIyFXcLPzYKOQZ3ckNEgDsGd3KFnVXzHuGZgcdIDDxERGSKtDoRCZeuYXeKGntS1cgrrdRvU8hl6NfBBUO7uCPcXwWnFgoJK5UGA4+RGHiIiMjUiaKIlDwNdqfWXfnJLKrQb5MJQIiPM4YGuCOiizvcHawkrLTxGPP73WC9oObNm4ewsDDY2NjA0dHxjm0EQbhtWbdunUGbAwcOoEePHlAqlWjfvj1WrVp1236+/PJLtG3bFlZWVggJCcGJEyca4IiIiIikIwgCuno54K0IP+ybMQh7owfgH0M6IsDTHjoRiM+8irnbUtFnfhye+uoIvj54EdnFFX+/42aiwa7wzJ07F46Ojrh8+TKWL1+O0tLS2z9cELBy5UoMHTpUv87R0RFWVnXJNCsrCwEBAZg0aRJefvllxMXFISoqCjt27EBERAQAYP369Rg7diyWLVuGkJAQLF68GBs3bkR6ejrc3NzuqVZe4SEioqYst+QG9qSqsTtFjYSca/jzL7ufu53+yo+fux0EQZCu0HpmUre0Vq1ahaioqL8MPFu2bEFkZOQd3ztz5kzs2LEDKSkp+nXPP/88SktLsXv3bgBASEgIevXqhSVLlgAAdDodvL298cYbb2DWrFn3VCMDDxERmYtCzU38eq4Ae1LViL94FbW6//3MeztZI7yzCo/4q9C7rRMsmvjj7iZxS+teTZkyBS4uLujduzdWrFiBP+ev+Ph4hIeHG7SPiIhAfHw8AKC6uhoJCQkGbWQyGcLDw/Vt7qSqqgoajcZgISIiMgdu9lYY06cNvp8YglPvhWPRiO54xF8FpYUMuSWVWHkkGy98exzB/9qL6euTsPNsPq5X1UpddoOzkPLDP/jgAzz00EOwsbHBr7/+itdffx3Xr1/Hm2++CQBQq9VQqVQG71GpVNBoNKisrMS1a9eg1Wrv2CYtLe0vP3f+/Pn45z//Wf8HREREZEIcbRR4JtgLzwR74UZ1LX67UIzYcwXYl1aIkopqbEnMw5bEPCjkMoS2c8Yj/nVXf1T25tfp2ajAM2vWLPz73/++a5vz58/Dz8/vnvY3e/Zs/T8HBQWhoqICCxcu1AeehhITE4Po6Gj9a41GA29v7wb9TCIiIinZKCwQ0aWuL8+tx933ni9A7LkCZBVX4ODvRTj4exHe25qC7l4OeMRfhXB/FTqpzKPfj1GBZ8aMGRg/fvxd2/j6+t53MSEhIfjwww9RVVUFpVIJd3d3FBQUGLQpKCiAvb09rK2tIZfLIZfL79jG3d39Lz9HqVRCqVTed51ERERNmVwmoLePE3r7OCHmUT9cLLqOX8/VhZ+k3FIkXy5D8uUyfPLr7/B2ssYjnd3xiL8Kvdq2bLL9fowKPK6urnB1dW2oWpCUlISWLVvqw0hoaCh27txp0CY2NhahoaEAAIVCgeDgYMTFxek7Put0OsTFxWHq1KkNVicREZG5EAQB7d3s0N7NDq8Pao/C8puIO1+I2HMFOJxRjNySSqw4koUVR7LgaGOJhzq5IdxfhQEdXWGrlLRnjFEarNKcnByUlJQgJycHWq0WSUlJAID27dvD1tYWv/zyCwoKCtCnTx9YWVkhNjYWH330Ef7xj3/o9zFp0iQsWbIEb7/9Nl566SXs27cPGzZswI4dO/RtoqOjMW7cOPTs2RO9e/fG4sWLUVFRgQkTJjTUoREREZktNzsrjOrdGqN6t0ZFVS1+u1CE2HOF2JdWgGs3arA5MQ+b/+j3E+LrhIf93PBwZxW8nWykLv2uGuyx9PHjx2P16tW3rd+/fz8GDRqE3bt3IyYmBhkZGRBFEe3bt8fkyZPxyiuvQCb73+WyAwcOYPr06Th37hy8vLwwe/bs226rLVmyBAsXLoRarUZgYCA+//xzhISE3HOtfCydiIjo7mq1OiRcuobYcwWIPV+AS1dvGGzvqLLFQ34qhHd2Q1DrlpDLGr7fj0mNw9MUMPAQERHdO1EUcbGoAvvSCrD3fCESLl2D9k/j/bS0scTgTm54qLMbBnR0hX0DTXLKwGMkBh4iIqL7V3qjGgd/L0Lc+UIcSC+E5ub/xvWx+KOD9EN+bni+d+t67ffDwGMkBh4iIqL6cevWV1xaIeLOF+DiH5OcKixkSJrzCGwU0gSeptO9moiIiEyehVyGEF9nhPg6453HOiO7uEI/0GF9hh2j65Lsk4mIiMjstXVpgZf6+UhdhvRzaRERERE1NAYeIiIiMnsMPERERGT2GHiIiIjI7DHwEBERkdlj4CEiIiKzx8BDREREZo+Bh4iIiMweAw8RERGZPQYeIiIiMnsMPERERGT2GHiIiIjI7DHwEBERkdnjbOkARFEEAGg0GokrISIiont163f71u/43TDwACgvLwcAeHt7S1wJERERGau8vBwODg53bSOI9xKLzJxOp8OVK1dgZ2cHQRCkLsckaTQaeHt7Izc3F/b29lKX0+zxfJgenhPTwvNhWhrqfIiiiPLycrRq1Qoy2d176fAKDwCZTAYvLy+py2gS7O3t+cfDhPB8mB6eE9PC82FaGuJ8/N2VnVvYaZmIiIjMHgMPERERmT0GHronSqUSc+fOhVKplLoUAs+HKeI5MS08H6bFFM4HOy0TERGR2eMVHiIiIjJ7DDxERERk9hh4iIiIyOwx8BAREZHZY+BpxubPn49evXrBzs4Obm5uiIyMRHp6ukGbmzdvYsqUKXB2doatrS2eeeYZFBQUGLTJycnBsGHDYGNjAzc3N7z11luora1tzEMxSwsWLIAgCIiKitKv4/loXHl5eRgzZgycnZ1hbW2Nrl274tSpU/rtoihizpw58PDwgLW1NcLDw3HhwgWDfZSUlGD06NGwt7eHo6MjJk6ciOvXrzf2oZgFrVaL2bNnw8fHB9bW1mjXrh0+/PBDg3mUeE4azqFDh/DEE0+gVatWEAQBW7duNdheX9/9mTNn0L9/f1hZWcHb2xsff/xx/RyASM1WRESEuHLlSjElJUVMSkoSH3vsMbF169bi9evX9W0mTZokent7i3FxceKpU6fEPn36iGFhYfrttbW1YkBAgBgeHi4mJiaKO3fuFF1cXMSYmBgpDslsnDhxQmzbtq3YrVs3cdq0afr1PB+Np6SkRGzTpo04fvx48fjx42JmZqa4Z88eMSMjQ99mwYIFooODg7h161YxOTlZHD58uOjj4yNWVlbq2wwdOlTs3r27eOzYMfG3334T27dvL44aNUqKQ2ry5s2bJzo7O4vbt28Xs7KyxI0bN4q2trbiZ599pm/Dc9Jwdu7cKb777rvi5s2bRQDili1bDLbXx3dfVlYmqlQqcfTo0WJKSor4448/itbW1uLXX3/9wPUz8JBeYWGhCEA8ePCgKIqiWFpaKlpaWoobN27Utzl//rwIQIyPjxdFse4/AJlMJqrVan2bpUuXivb29mJVVVXjHoCZKC8vFzt06CDGxsaKAwcO1Aceno/GNXPmTLFfv35/uV2n04nu7u7iwoUL9etKS0tFpVIp/vjjj6IoiuK5c+dEAOLJkyf1bXbt2iUKgiDm5eU1XPFmatiwYeJLL71ksO7pp58WR48eLYoiz0lj+v+Bp76++6+++kps2bKlwd+rmTNnip06dXrgmnlLi/TKysoAAE5OTgCAhIQE1NTUIDw8XN/Gz88PrVu3Rnx8PAAgPj4eXbt2hUql0reJiIiARqNBampqI1ZvPqZMmYJhw4YZfO8Az0dj27ZtG3r27IkRI0bAzc0NQUFB+Pbbb/Xbs7KyoFarDc6Hg4MDQkJCDM6Ho6MjevbsqW8THh4OmUyG48ePN97BmImwsDDExcXh999/BwAkJyfj8OHDePTRRwHwnEipvr77+Ph4DBgwAAqFQt8mIiIC6enpuHbt2gPVyMlDCUDdjPFRUVHo27cvAgICAABqtRoKhQKOjo4GbVUqFdRqtb7Nn39cb22/tY2Ms27dOpw+fRonT568bRvPR+PKzMzE0qVLER0djXfeeQcnT57Em2++CYVCgXHjxum/zzt9338+H25ubgbbLSws4OTkxPNxH2bNmgWNRgM/Pz/I5XJotVrMmzcPo0ePBgCeEwnV13evVqvh4+Nz2z5ubWvZsuV918jAQwDqriqkpKTg8OHDUpfSbOXm5mLatGmIjY2FlZWV1OU0ezqdDj179sRHH30EAAgKCkJKSgqWLVuGcePGSVxd87RhwwasXbsWP/zwA7p06YKkpCRERUWhVatWPCf0t3hLizB16lRs374d+/fvh5eXl369u7s7qqurUVpaatC+oKAA7u7u+jb//ymhW69vtaF7k5CQgMLCQvTo0QMWFhawsLDAwYMH8fnnn8PCwgIqlYrnoxF5eHjA39/fYF3nzp2Rk5MD4H/f552+7z+fj8LCQoPttbW1KCkp4fm4D2+99RZmzZqF559/Hl27dsWLL76I6dOnY/78+QB4TqRUX999Q/4NY+BpxkRRxNSpU7Flyxbs27fvtsuIwcHBsLS0RFxcnH5deno6cnJyEBoaCgAIDQ3F2bNnDf4ljo2Nhb29/W0/FnR3Dz/8MM6ePYukpCT90rNnT4wePVr/zzwfjadv3763DdPw+++/o02bNgAAHx8fuLu7G5wPjUaD48ePG5yP0tJSJCQk6Nvs27cPOp0OISEhjXAU5uXGjRuQyQx/tuRyOXQ6HQC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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "samples = torch.linspace(100, 1000, 1000)\n", - "profiles = model.base_distribution.r_profile(samples)\n", - "plt.plot(samples.detach(), profiles.detach())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b6fa4970", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[1.0000e-20, 1.6002e+02]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.base_distribution.radial_udl_profile(threshold=-100)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "716cd488", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[167.1230, 172.3712],\n", - " [172.4038, 174.0072],\n", - " [174.0226, 174.2758],\n", - " [174.2769, 174.3353],\n", - " [174.3484, 174.3529],\n", - " [174.3648, 174.3717],\n", - " [174.4054, 174.5983],\n", - " 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182.9111],\n", - " [183.1184, 183.1184],\n", - " [183.1562, 183.1562],\n", - " [183.3358, 183.3358],\n", - " [183.5568, 183.5568],\n", - " [183.5972, 183.5972],\n", - " [183.6628, 183.6628],\n", - " [183.7583, 183.7583],\n", - " [183.8157, 183.8157],\n", - " [183.9798, 183.9798],\n", - " [184.2502, 184.2502],\n", - " [184.3623, 184.3623],\n", - " [184.4102, 184.4102],\n", - " [184.9834, 184.9834]], grad_fn=)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n_samples = 10000\n", - "q = .1\n", - "\n", - "samples = torch.stack([d.sample() for _ in range(n_samples)])\n", - "logproborg = d.log_prob(samples)\n", - "r = samples.reshape(-1, 784).norm(dim=-1, p=1)\n", - "\n", - "logprob, indices = torch.sort(logproborg)\n", - "threshold_idx = int(n_samples * (1-q))\n", - "threshold = logprob[threshold_idx]\n", - "\n", - "indices = torch.arange(n_samples)\n", - "r, ridxs = torch.sort(r)\n", - "indices = indices[logproborg[ridxs] > threshold]\n", - "\n", - "def merge_intervals(intervals: torch.Tensor) -> torch.Tensor:\n", - " \"\"\"returns start and end of consecutive intervals of indices\"\"\"\n", - " if len(intervals) == 1:\n", - " return torch.tensor([[intervals[0], intervals[0]]])\n", - "\n", - " intervals = intervals.sort().values\n", - " merged = []\n", - " start = intervals[0]\n", - " end = intervals[0]\n", - " for i in range(1, len(intervals)):\n", - " if intervals[i] == end + 1:\n", - " end = intervals[i]\n", - " else:\n", - " merged.append([start, end])\n", - " start = intervals[i]\n", - " end = intervals[i]\n", - " merged.append([start, end])\n", - " return torch.tensor(merged)\n", - "\n", - "r[merge_intervals(indices)]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "c3747927", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(r.detach().reshape(-1, 500)[:, 49], logproborg[ridxs].detach().reshape(-1, 500).mean(dim=-1))" - ] - }, - { - "cell_type": "markdown", - "id": "c07aa9e8-5c18-450d-bd38-082918e84f11", - "metadata": {}, - "source": [ - "# RadialDistribution loc norms" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "32900eb7-ebac-49c6-91a1-32a554d3fec5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([-2114.0122], grad_fn=)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d.norm_distribution.log_prob(10*torch.ones(1)) + d.log_delta_volume(1, 10*torch.ones(1))" - ] - }, - { - "cell_type": "markdown", - "id": "e03a7477-164c-4df2-bf7e-ea14a08d7a62", - "metadata": {}, - "source": [ - "# Random loc distances" - ] - }, - { - "cell_type": "code", - "execution_count": 343, - "id": "38eb78a2-e2e0-48d5-baa8-aebc50a63220", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(1.3728, grad_fn=)" - ] - }, - "execution_count": 343, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "perm1 = np.random.choice(len(locs), 500, replace=True)\n", - "perm2 = np.random.choice(len(locs), 500, replace=True)\n", - "diffs = locs[perm1] - locs[perm2]\n", - "diffs.norm(dim=-1).max()" - ] - }, - { - "cell_type": "markdown", - "id": "3304b9f2-1d87-4ae0-a507-a3360adbe210", - "metadata": {}, - "source": [ - "# Normdistribution (lognormal) loc" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5a5b8f0d-45c8-4c57-92c9-3fdd977fa789", - "metadata": {}, - "outputs": [], - "source": [ - "model.log_prob(model.sample())\n", - "model.base_distribution.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2b476468-afd4-4d19-adc5-2e16142bda82", - "metadata": {}, - "outputs": [], - "source": [ - "from src.usflows.distributions import RadialDistribution, LogNormal\n", - "dist = RadialDistribution(\n", - " p = 1.0,\n", - " loc = torch.zeros([16, 7, 7]),\n", - " norm_distribution = LogNormal(\n", - " loc = torch.ones([1]) * 4.5,\n", - " scale = torch.ones([1]) * .35\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "97a7ac6c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dist.batch_shape" - ] - }, - { - "cell_type": "markdown", - "id": "5d4b62ed-2901-4130-87bf-7b291dce0ada", - "metadata": {}, - "source": [ - "# Mixture component probs" - ] - }, - { - "cell_type": "code", - "execution_count": 346, - "id": "7ee06c65-5580-4a8f-99d1-47bd4e298303", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([0.0530, 0.0463, 0.0366, 0.0534, 0.0400, 0.0367, 0.0499, 0.0650, 0.0568,\n", - " 0.0529, 0.0645, 0.0380, 0.0555, 0.0431, 0.0572, 0.0597, 0.0388, 0.0501,\n", - " 0.0529, 0.0497], grad_fn=)" - ] - }, - "execution_count": 346, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.base_distribution.mixture_distribution.logits.softmax(-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "id": "4661edd7-8dc1-45bb-aaf2-51bf69c6bd1f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([20, 16, 7, 7])\n", - "torch.Size([20, 1, 1, 1])\n", - "torch.Size([20, 1, 1, 1])\n", - "torch.Size([20])\n", - "torch.Size([20])\n" - ] - } - ], - "source": [ - "for p in model.base_distribution.parameters():\n", - " print(p.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "45cffdbe-6559-48e7-ae14-c12b28813e04", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Parameter containing:\n", - "tensor([[[[-0.3206]]],\n", - "\n", - "\n", - " [[[-0.3206]]],\n", - "\n", - "\n", - " [[[-0.3206]]],\n", - "\n", - "\n", - " [[[-0.3206]]],\n", - 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"text": [ - "/Users/fariedabuzaid/Projects/veriflow/src/veriflow/distributions.py:391: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).\n", - " dims = tuple(\n", - "/Users/fariedabuzaid/Projects/veriflow/src/veriflow/distributions.py:391: TracerWarning: Using len to get tensor shape might cause the trace to be incorrect. Recommended usage would be tensor.shape[0]. Passing a tensor of different shape might lead to errors or silently give incorrect results.\n", - " dims = tuple(\n" - ] - } - ], - "source": [ - "model.simplify().to_onnx(\"model0_1404_forward.onnx\", export_mode=\"forward\")\n", - "model.simplify().to_onnx(\"model0_1404_backward.onnx\", export_mode=\"backward\")\n", - "model.simplify().to_onnx(\"model0_1404_log_prob.onnx\", export_mode=\"log_prob\")\n", - "#model.simplify().to_onnx(\"model0_1404_sample.onnx\", export_mode=\"sample\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "309a92dc-ad52-459e-8ce7-36f6cd8da2c9", - "metadata": {}, - "outputs": [], - "source": [ - "sample = model.base_distribution.sample()\n", - "loss = model.base_distribution.log_prob(sample)\n", - "loss.backward()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "587977e4-13e2-4cde-9f4f-92668c3cb7b7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n", - "None\n", - 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" 1.1257e-02, -4.8226e-03]],\n", - "\n", - " [[ 1.6627e-03, -3.6081e-03, 4.4779e-04, ..., -5.4617e-03,\n", - " -2.6595e-03, 2.4164e-02],\n", - " [-1.3162e-02, 1.4034e-02, 1.3727e-02, ..., 3.3396e-03,\n", - " 1.0951e-02, -8.4353e-03],\n", - " [-1.1062e-02, 6.5243e-04, 8.8010e-03, ..., -1.0260e-02,\n", - " 3.8541e-03, 7.8189e-03],\n", - " ...,\n", - " [ 1.4212e-03, 1.2829e-03, 2.7409e-03, ..., -1.1482e-02,\n", - " -1.5948e-02, 1.3652e-02],\n", - " [ 2.4768e-03, -5.8657e-03, 6.8741e-03, ..., -6.6147e-03,\n", - " -8.7475e-04, 1.9121e-03],\n", - " [ 3.1853e-03, -4.0766e-03, 5.0747e-03, ..., -1.4362e-02,\n", - " 4.6509e-03, 4.7457e-03]],\n", - "\n", - " [[ 1.1384e-02, -8.1871e-03, -1.2986e-02, ..., 1.5902e-03,\n", - " 7.0505e-03, -1.0841e-02],\n", - " [ 1.6939e-02, 1.5843e-02, 1.5487e-03, ..., -6.2750e-03,\n", - " -5.5676e-03, 1.1551e-02],\n", - " [-5.1778e-03, -6.7483e-03, -3.5694e-03, ..., -5.3172e-03,\n", - " -1.3839e-02, 4.7693e-03],\n", - " ...,\n", - " [ 1.6102e-03, 6.4417e-03, -8.0928e-04, ..., -6.9359e-03,\n", - " 8.6959e-03, 7.3302e-03],\n", - " [ 6.0504e-03, -1.3849e-03, 1.9678e-04, ..., 1.9612e-02,\n", - " 2.4154e-03, -1.3510e-02],\n", - " [-1.7019e-02, -4.7464e-03, -6.7026e-04, ..., -4.3538e-03,\n", - " 8.0470e-03, -1.8950e-03]]],\n", - "\n", - "\n", - " ...,\n", - "\n", - "\n", - " [[[-1.7246e-02, 3.8177e-03, 1.1049e-03, ..., -6.3992e-03,\n", - " -1.8513e-02, -7.4197e-03],\n", - " [ 7.5961e-03, 1.0076e-03, -7.1612e-03, ..., 4.7206e-03,\n", - " -3.5594e-03, -3.6004e-03],\n", - " [ 1.0294e-02, -1.5142e-02, -2.2511e-02, ..., 7.1647e-03,\n", - " 3.0340e-03, 7.9123e-03],\n", - " ...,\n", - " [ 1.1099e-03, 3.4507e-03, 9.1461e-03, ..., 1.9311e-03,\n", - " -4.4299e-03, 1.2821e-02],\n", - " [ 4.1982e-03, -8.7993e-03, 1.8996e-02, ..., 2.3680e-02,\n", - " 8.4614e-03, -1.8324e-03],\n", - " [-5.8293e-03, 7.0428e-03, -3.1313e-03, ..., 5.9371e-03,\n", - " -1.1141e-02, -9.4322e-03]],\n", - "\n", - " [[ 2.1823e-02, -2.9451e-03, 2.3693e-03, ..., 1.5034e-02,\n", - " 2.9666e-03, 3.0530e-03],\n", - " [-7.3394e-03, -1.2130e-02, -6.9688e-03, ..., 1.2966e-02,\n", - " 3.7910e-03, -1.2901e-02],\n", - " [-1.3474e-03, 4.1075e-03, -1.5020e-03, ..., 1.4168e-02,\n", - " -1.5871e-02, -1.6609e-03],\n", - " ...,\n", - " [-1.8073e-02, -1.0295e-02, 2.1627e-02, ..., 4.7789e-03,\n", - " -7.4957e-03, -1.8470e-03],\n", - " [ 1.1154e-03, 3.0017e-03, -1.5637e-02, ..., 1.8501e-02,\n", - " 1.3742e-02, 5.8809e-03],\n", - " [-4.7776e-04, 2.1683e-02, 1.7376e-02, ..., -1.0037e-02,\n", - " -1.5900e-02, 2.1495e-02]],\n", - "\n", - " [[-2.0724e-02, -1.8366e-03, -5.7150e-03, ..., -1.8190e-02,\n", - " 7.9776e-03, -7.6301e-03],\n", - " [-9.8904e-03, 4.8468e-03, 1.6259e-02, ..., 3.5040e-03,\n", - " 7.3128e-03, 1.8142e-02],\n", - " [-2.2402e-02, 4.4997e-03, 1.4155e-02, ..., -6.1447e-03,\n", - " 1.4765e-02, -5.7531e-03],\n", - " ...,\n", - " [-1.1617e-02, -6.5931e-03, 7.5177e-03, ..., -1.0182e-02,\n", - " -7.8468e-03, 8.0238e-03],\n", - " [-2.0392e-02, -1.8559e-02, -4.3337e-03, ..., -9.1631e-03,\n", - " 9.5347e-04, -1.1704e-02],\n", - " [-1.6072e-03, -2.6249e-03, 1.4152e-02, ..., -6.1586e-03,\n", - " 5.6471e-03, -1.2915e-02]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.1317e-02, 1.5939e-03, 1.6868e-02, ..., 9.0945e-03,\n", - " 1.6539e-02, 7.2441e-03],\n", - " [-1.8387e-02, 1.3313e-02, -1.0042e-02, ..., 6.8572e-03,\n", - " 1.1903e-02, 9.7627e-04],\n", - " [ 7.3444e-03, 7.2827e-03, 1.7701e-02, ..., -2.6214e-02,\n", - " -1.0754e-02, 1.5172e-03],\n", - " ...,\n", - " [-3.6115e-03, 5.5973e-03, 1.7137e-02, ..., -1.8621e-02,\n", - " -9.3553e-03, 1.0467e-02],\n", - " [ 1.2329e-03, -6.5424e-03, -3.8942e-03, ..., 1.0699e-02,\n", - " 1.5045e-02, -2.2271e-02],\n", - " [ 7.2763e-03, 4.1604e-03, 1.6468e-02, ..., 3.7435e-03,\n", - " -2.1653e-03, 3.9925e-03]],\n", - "\n", - " [[ 1.8048e-03, 9.2925e-03, -1.8853e-02, ..., 8.2253e-03,\n", - " 1.0745e-03, 3.0388e-03],\n", - " [ 1.2395e-03, 1.0829e-03, -9.9304e-03, ..., 6.4600e-03,\n", - " -1.1291e-02, 1.5622e-03],\n", - " [ 8.1813e-03, 8.4817e-03, 6.0989e-03, ..., -1.9589e-04,\n", - " -3.9908e-04, 1.8657e-02],\n", - " ...,\n", - " [ 1.1565e-02, 1.1888e-02, 1.8871e-02, ..., 2.2661e-02,\n", - " -1.2974e-04, -1.3141e-02],\n", - " [-2.4476e-02, 1.4277e-02, -7.0780e-03, ..., 5.6487e-03,\n", - " 5.2245e-03, 5.9000e-03],\n", - " [-1.1433e-03, 1.0040e-02, -1.6114e-02, ..., 1.7087e-02,\n", - " -1.2446e-02, 6.0406e-03]],\n", - "\n", - " [[-6.2881e-03, -4.1633e-03, -6.1897e-03, ..., -2.5103e-02,\n", - " -1.8375e-03, 2.2743e-02],\n", - " [-2.2482e-02, -2.5944e-03, -2.0108e-02, ..., 5.0397e-03,\n", - " -1.8061e-02, 2.3630e-02],\n", - " [-5.6300e-03, -6.3634e-04, -5.4901e-03, ..., 3.1692e-03,\n", - " -1.0174e-02, -2.8583e-03],\n", - " ...,\n", - " [-1.1289e-02, 1.0276e-02, 1.0901e-02, ..., -1.3141e-03,\n", - " -1.0675e-02, 2.4756e-02],\n", - " [ 4.7085e-03, -2.3740e-03, -1.5175e-02, ..., 3.4709e-03,\n", - " 1.9787e-02, 8.6226e-03],\n", - " [ 1.1231e-02, 7.6633e-03, 6.7459e-03, ..., 8.3394e-03,\n", - " -1.1403e-02, 7.0895e-04]]],\n", - "\n", - "\n", - " [[[-5.9913e-03, 2.1223e-02, -9.1457e-03, ..., 3.3283e-05,\n", - " 5.7435e-03, -4.6126e-03],\n", - " [ 1.2811e-02, 5.5203e-03, 1.5202e-02, ..., 7.0270e-04,\n", - " 6.4929e-04, -1.4688e-02],\n", - " [-3.8179e-03, 5.9050e-03, 1.8219e-02, ..., -3.4971e-03,\n", - " -2.1958e-03, 1.4328e-03],\n", - " ...,\n", - " [ 1.4420e-03, -1.6977e-02, -2.4560e-03, ..., -1.7158e-02,\n", - " 2.1484e-02, -1.8448e-03],\n", - " [ 1.0686e-02, -3.8691e-03, -7.9818e-03, ..., 2.6626e-02,\n", - " -5.4487e-03, -2.2226e-02],\n", - " [ 1.5867e-03, 9.1928e-03, 1.5666e-02, ..., -1.3196e-02,\n", - " 8.1916e-03, -3.2531e-03]],\n", - "\n", - " [[ 7.8532e-04, 6.9034e-03, 1.7616e-02, ..., 6.3200e-03,\n", - " 1.2658e-02, -5.5336e-03],\n", - " [ 1.0760e-02, 1.3843e-02, 1.1123e-02, ..., -9.6189e-03,\n", - " 2.1582e-03, -6.4268e-03],\n", - " [-5.0936e-03, -1.2249e-02, 5.8693e-03, ..., -2.1250e-03,\n", - " 6.1009e-03, 1.2308e-03],\n", - " ...,\n", - " [ 1.3122e-02, 1.8367e-04, -2.0598e-03, ..., 2.4995e-03,\n", - " 1.0209e-03, 1.4471e-03],\n", - " [ 3.4445e-03, -7.6418e-03, -1.6826e-02, ..., 8.5121e-03,\n", - " 5.6866e-03, 1.1220e-02],\n", - " [-1.0219e-03, -9.6666e-03, -2.2229e-02, ..., -6.1835e-03,\n", - " 8.8125e-03, 2.4587e-04]],\n", - "\n", - " [[ 5.3727e-03, 5.3528e-03, -1.5723e-03, ..., -1.5450e-02,\n", - " -3.1185e-03, -4.2437e-03],\n", - " [-1.1866e-02, 2.9138e-03, 7.2187e-03, ..., -2.0208e-03,\n", - " -1.0609e-02, -1.2269e-02],\n", - " [ 7.7918e-03, 1.0443e-02, 1.5918e-02, ..., -4.4863e-03,\n", - " 1.4096e-02, -1.0997e-02],\n", - " ...,\n", - " [ 1.1733e-02, -6.9946e-04, 1.7284e-02, ..., -8.7113e-03,\n", - " 1.0302e-02, 5.9363e-03],\n", - " [-4.7722e-03, 8.4847e-04, 1.0779e-02, ..., -2.8126e-03,\n", - " 1.0940e-03, 4.8717e-03],\n", - " [ 6.6479e-03, 2.7740e-03, -1.8492e-03, ..., -2.1423e-02,\n", - " -4.6197e-03, 9.0869e-03]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.4647e-02, 2.1519e-02, 6.4437e-03, ..., 5.6818e-03,\n", - " -1.2872e-02, -1.5029e-04],\n", - " [ 1.4757e-03, -4.6369e-03, -1.2767e-02, ..., 4.8025e-03,\n", - " -1.8693e-03, 1.0898e-02],\n", - " [ 1.5124e-02, 1.3781e-02, 1.9828e-03, ..., -9.4338e-03,\n", - " -2.8079e-03, -1.2457e-02],\n", - " ...,\n", - " [ 1.4762e-02, 1.7234e-03, 7.9889e-05, ..., 7.2472e-03,\n", - " -5.8335e-03, 9.6403e-03],\n", - " [-3.7711e-03, 7.2476e-03, -7.0787e-03, ..., 1.3103e-02,\n", - " 3.4502e-03, -1.3744e-02],\n", - " [-5.6655e-03, -2.7039e-04, 2.7027e-03, ..., 7.4666e-03,\n", - " 4.1614e-03, 3.6583e-04]],\n", - "\n", - " [[ 1.5173e-02, 2.6936e-03, 2.1758e-02, ..., -2.0725e-03,\n", - " -9.6602e-03, 4.9264e-04],\n", - " [ 1.4665e-03, 1.2504e-02, 5.6109e-03, ..., 1.2709e-02,\n", - " -6.8653e-03, -3.1042e-03],\n", - " [-2.5092e-03, 1.3068e-02, -1.1713e-02, ..., -7.2434e-03,\n", - " 2.4461e-03, -4.8537e-03],\n", - " ...,\n", - " [ 5.9768e-03, 1.8805e-02, 1.3270e-02, ..., -2.5086e-03,\n", - " 7.1440e-03, 5.3079e-03],\n", - " [-1.5324e-02, -2.6361e-03, 8.6180e-03, ..., 1.7935e-02,\n", - " -3.9405e-03, 8.3035e-03],\n", - " [ 9.2596e-03, -1.5454e-02, -2.0323e-02, ..., -6.1912e-03,\n", - " 6.4717e-03, -1.2650e-02]],\n", - "\n", - " [[-1.5168e-02, 8.5897e-03, 2.1240e-03, ..., 1.1857e-02,\n", - " 2.0351e-03, 7.3588e-03],\n", - " [ 3.1706e-03, 1.1555e-02, 3.5530e-03, ..., 6.7071e-03,\n", - " -8.6887e-03, 5.0796e-03],\n", - " [-3.3712e-03, 5.5126e-03, -5.1951e-03, ..., -6.0295e-03,\n", - " -5.5660e-03, 1.8340e-03],\n", - " ...,\n", - " [-1.3542e-03, 8.3469e-03, -3.2795e-03, ..., 2.9315e-03,\n", - " -1.3540e-03, 1.1163e-02],\n", - " [-9.9491e-03, 4.3437e-03, -4.4070e-03, ..., 8.6747e-03,\n", - " -5.1391e-03, 4.8599e-03],\n", - " [-6.5959e-03, -4.3679e-03, -1.7301e-02, ..., -3.1554e-03,\n", - " 1.3501e-02, 1.3358e-02]]],\n", - "\n", - "\n", - " [[[-3.0818e-03, -4.6108e-03, -4.3402e-03, ..., 3.0884e-03,\n", - " -2.1152e-03, 4.2934e-03],\n", - " [ 2.4470e-03, -1.7472e-02, 1.1801e-03, ..., -7.3641e-03,\n", - " 9.4429e-04, -1.2448e-03],\n", - " [-4.5321e-03, 3.1483e-02, -6.3536e-04, ..., 1.5372e-02,\n", - " 1.6405e-02, -1.2238e-02],\n", - " ...,\n", - " [-1.8043e-02, 1.5560e-02, 1.2345e-02, ..., -1.7685e-02,\n", - " 4.7318e-03, -4.0560e-04],\n", - " [ 4.8353e-04, 4.5140e-03, 1.8633e-02, ..., 8.9691e-03,\n", - " 1.1212e-02, 1.6369e-02],\n", - " [-6.5000e-03, 1.6386e-03, 3.1902e-03, ..., -2.8749e-02,\n", - " -3.8878e-03, -8.9072e-04]],\n", - "\n", - " [[ 6.0005e-03, -3.1217e-04, -1.7765e-02, ..., 3.3028e-03,\n", - " 3.4868e-03, 2.0822e-02],\n", - " [-7.3848e-03, -8.5399e-03, -2.2512e-02, ..., 1.0536e-02,\n", - " 8.6598e-03, -7.6989e-03],\n", - " [ 1.7275e-03, -1.1478e-02, 1.0279e-02, ..., -2.7799e-03,\n", - " 6.8832e-03, -1.9635e-02],\n", - " ...,\n", - " [-1.7792e-02, -1.3235e-02, -2.1194e-02, ..., 1.1038e-02,\n", - " -1.6426e-03, 9.0488e-03],\n", - " [ 4.8562e-03, 7.5388e-03, -8.0145e-03, ..., 3.2724e-03,\n", - " 8.5612e-03, 1.1272e-02],\n", - " [-6.1222e-03, 2.5271e-03, -1.5463e-03, ..., -6.1233e-03,\n", - " -2.0987e-02, -7.0225e-03]],\n", - "\n", - " [[-2.1141e-02, 1.2273e-02, -8.6205e-03, ..., 2.3955e-04,\n", - " -1.2370e-02, 5.6845e-03],\n", - " [-5.9005e-03, 1.1892e-03, 2.0396e-02, ..., -8.7245e-04,\n", - " 1.1476e-03, 1.0408e-02],\n", - " [ 1.1074e-03, -4.4871e-03, 1.1154e-02, ..., -4.3471e-03,\n", - " -1.0282e-02, 1.7899e-02],\n", - " ...,\n", - " [-4.2702e-03, -3.8162e-03, 1.3428e-02, ..., 8.2925e-03,\n", - " -4.7022e-03, -2.5126e-03],\n", - " [-2.1067e-03, -1.8812e-02, -1.0063e-03, ..., 8.1609e-03,\n", - " -5.4450e-03, -2.1151e-04],\n", - " [-1.7847e-02, -1.1836e-02, -1.0723e-02, ..., 6.5797e-03,\n", - " -1.6565e-02, -1.1299e-02]],\n", - "\n", - " ...,\n", - "\n", - " [[-7.7906e-03, -3.8375e-03, -4.0833e-03, ..., 2.9368e-03,\n", - " -1.3612e-02, 1.4860e-02],\n", - " [-1.3946e-02, 8.2240e-04, 2.0762e-02, ..., 2.9295e-03,\n", - " 8.3887e-04, 5.7813e-03],\n", - " [-1.2437e-02, -6.1601e-03, -3.4674e-03, ..., -3.7435e-03,\n", - " -9.5855e-03, 2.5360e-03],\n", - " ...,\n", - " [ 1.5513e-04, -1.2863e-02, 1.7997e-02, ..., -3.5625e-03,\n", - " -2.2545e-02, 9.3039e-05],\n", - " [ 1.4292e-02, -1.1181e-02, -2.5577e-03, ..., 1.0377e-02,\n", - " 5.7628e-03, 5.5486e-03],\n", - " [ 2.8162e-03, 3.3893e-04, 3.8256e-03, ..., -1.9160e-02,\n", - " 1.3259e-03, -4.3779e-03]],\n", - "\n", - " [[ 3.0367e-03, -3.0934e-03, 3.0502e-04, ..., -5.4446e-03,\n", - " -4.1964e-03, 4.4382e-04],\n", - " [-8.3244e-03, -8.2291e-03, -6.3795e-03, ..., -5.7214e-03,\n", - 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" 5.7334e-03, -1.0047e-02]]]], requires_grad=True)\n", - "Parameter containing:\n", - "tensor([[[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]],\n", - "\n", - "\n", - " [[[3.]]]], requires_grad=True)\n", - "Parameter containing:\n", - "tensor([[[[-0.4328]]],\n", - "\n", - "\n", - " [[[-0.4328]]],\n", - "\n", - "\n", - " [[[-0.4328]]],\n", - "\n", - "\n", - " [[[-0.4328]]],\n", - "\n", - "\n", - " [[[-0.4328]]],\n", - "\n", - 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] - } - ], - "source": [ - "for p in model.base_distribution.parameters():\n", - " print(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2f1fc3c2-4da8-4430-94bd-14efc18f32fe", - "metadata": {}, - "outputs": [], - "source": [ - "dist = Laplace(torch.zeros(2), torch.ones(2))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "5cf60f01-e869-4bc8-adb0-0fd78ddf3442", - "metadata": {}, - "outputs": [], - "source": [ - "sample = dist.sample()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "82b21307-d77f-49e8-8cdd-ad7d8394de51", - "metadata": {}, - "outputs": [], - "source": [ - "loss = dist.log_prob(sample)\n", - "loss.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "16985a02-7c60-4013-97e3-079c4558213d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([-0.4838, 0.1757])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dist.trainable_args[\"scale\"].grad" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7aa285a4-6521-42da-8b64-6d6034ed0192", - "metadata": {}, - "outputs": [], - "source": [ - "from src.explib.datasets import MnistSplit" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "115991d3-f297-4beb-9835-8bfa23b30ece", - "metadata": {}, - "outputs": [], - "source": [ - "data = MnistSplit(digit=0)\n", - "data = data.get_train()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "9fd311cf-1df8-40d5-be14-529bfb78ed7d", - "metadata": {}, - "outputs": [], - "source": [ - "idx = torch.randint(high=len(data), size=(25,))\n", - "samples = data[idx][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "22be5afc-fb3c-4ffb-bfd2-c5a9bc1b33a6", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter containing:\n", - "tensor([[ 1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n", - 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" 0.0500, 0.0500], requires_grad=True)\n", - "Parameter containing:\n", - "tensor([[[[3.0620]]],\n", - "\n", - "\n", - " [[[3.0298]]],\n", - "\n", - "\n", - " [[[3.0248]]],\n", - "\n", - "\n", - " [[[3.6585]]],\n", - "\n", - "\n", - " [[[3.0334]]],\n", - "\n", - "\n", - " [[[3.0424]]],\n", - "\n", - "\n", - " [[[3.0292]]],\n", - "\n", - "\n", - " [[[3.0188]]],\n", - "\n", - "\n", - " [[[3.0499]]],\n", - "\n", - "\n", - " [[[3.2501]]],\n", - "\n", - "\n", - " [[[3.1063]]],\n", - "\n", - "\n", - " [[[3.0185]]],\n", - "\n", - "\n", - " [[[3.0407]]],\n", - "\n", - "\n", - " [[[3.0376]]],\n", - "\n", - "\n", - " [[[3.2531]]],\n", - "\n", - "\n", - " [[[3.0304]]],\n", - "\n", - "\n", - " [[[3.0228]]],\n", - "\n", - "\n", - " [[[3.0277]]],\n", - "\n", - "\n", - " [[[3.0255]]],\n", - "\n", - "\n", - " [[[3.2256]]]], requires_grad=True)\n", - "Parameter containing:\n", - "tensor([[[[0.4588]]],\n", - "\n", - "\n", - " [[[0.5197]]],\n", - "\n", - "\n", - " [[[0.4948]]],\n", - "\n", - "\n", - " [[[0.0301]]],\n", - 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" [ 475, 4833, 2494, 2439, 4606]])" - ] - }, - "execution_count": 482, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abd6b043-91e4-465d-9e09-fb02742d2550", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26c0958a-1c7b-48f4-9f9b-f7378bc83a67", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "c98e2f5d-b9fb-4a26-b2e8-7c5d351f4d6d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fariedabuzaid/Library/Caches/pypoetry/virtualenvs/veriflow-75zEOOJt-py3.12/lib/python3.12/site-packages/torch/distributions/distribution.py:307: UserWarning: does not define `support` to enable sample validation. Please initialize the distribution with `validate_args=False` to turn off validation.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "model.log_prob(model.sample()).backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "385ade3d-b1f3-4991-a449-5bf6cf938468", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.trainable_layers.parameters()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "1cf37990-2ce3-4090-a346-3105fdd21ad9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.parameters()" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8878130-756a-4be6-8773-a608bfc3ca47", - "metadata": {}, - "outputs": [], - "source": [ - "from src.veriflow.distributions import LMM" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "10bab5e1-6c10-4328-8650-664557441668", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "LMM() missing 1 required positional argument: 'DistributionModule'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mLMM\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: LMM() missing 1 required positional argument: 'DistributionModule'" - ] - } - ], - "source": [ - "LMM(\n", - "\n", - " \n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "971f18c3-737d-4698-a607-a6e084c76dc9", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Parameter containing:\n", - 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" [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.]],\n", - "\n", - " [[0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0., 0.]]])" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample.reshape(16, 7, 7).round()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "962523ae-e9e2-43e3-b98b-2d6dc6181571", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "from src.veriflow.distributions import RadialMM, LogNormal\n", - "\n", - "norm_dists = LogNormal(loc=torch.ones(2,1), scale=torch.ones(2,1), n_batch_dims=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "6cc61a60-8380-4ddc-a0bf-14908024f646", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[12.8923],\n", - " [ 3.9039]],\n", - "\n", - " [[ 8.7049],\n", - " [ 2.8075]]])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample = norm_dists.sample([2])\n", - "sample" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "db49b0a0-f642-4bef-a6ed-3b61477f0a95", - "metadata": {}, - "outputs": [], - "source": [ - "rad = RadialMM(norm_distribution=norm_dists, loc=torch.zeros(2, 3), p=1.)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "5dd91eed-deb9-4b05-ba57-5092a2801481", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[-0.0652, 0.1423, 1.1670],\n", - " [ 0.0408, -9.3055, -6.6674]],\n", - "\n", - " [[ 1.1204, 1.0238, -1.0689],\n", - " [ 0.0512, 0.7589, -0.2022]]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample = rad.sample([2, 2])\n", - "sample" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "e3e9fec0-fc1f-47c2-9dbe-a8337174b704", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ -5.6541, -10.2364],\n", - " [ -7.3525, -4.7139]], grad_fn=)" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rad.log_prob(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "e56b078a-2cc4-41ae-865d-a9d5b55a55e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[0.3633],\n", - " [5.1998]],\n", - "\n", - " [[0.5285],\n", - " [0.0337]]])" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rad.distribution._component_distribution.norm_distribution.sample([2])" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "18c19a96-50c2-4174-9568-b745e3e501c6", - "metadata": {}, - "outputs": [], - "source": [ - "class Tree(object):\n", - " def __init__(self):\n", - " self.root = None\n", - " self.left_subtree = None\n", - " self.right_subtree = None\n", - " def insert(self, val: int):\n", - " if self.root is not None:\n", - " if self.root > val:\n", - " if self.left_subtree is None:\n", - " self.left_subtree = Tree()\n", - " self.left_subtree.insert(val)\n", - " else:\n", - " self.left_subtree.insert(val)\n", - " elif self.root < val:\n", - " if self.right_subtree is None:\n", - " self.right_subtree = Tree()\n", - " self.right_subtree.insert(val)\n", - " else:\n", - " self.right_subtree.insert(val)\n", - " else:\n", - " pass\n", - " else:\n", - " self.root = val\n", - "\n", - " def _left_attach(self, subtree):\n", - " if self.left_subtree is None:\n", - " self.left_subtree = subtree\n", - " else:\n", - " self.left_subtree._left_attach(subtree)\n", - " \n", - " def delete(self, val):\n", - " if self.root == val:\n", - " if self.left_subtree is not None:\n", - " self.root = self.left_subtree.root\n", - " self.left_subtree = self.left_subtree.left_subtree\n", - " if self.left_subtree.right_subtree is not None:\n", - " if self.right_subtree is None:\n", - " self.right_subtree = self.left_subtree.right_subtree\n", - " else:\n", - " self.right_subtree._left_attach(self.left_subtree.right_subtree)\n", - " elif self.right_subtree is not None:\n", - " self.root = self.right_subtree.root\n", - " self.right_subtree = self.right_subtree.right_subtree\n", - " self.left_subtree = self.right_subtree.left_subtree\n", - " else:\n", - " self.root = None\n", - "\n", - " def sorted(self):\n", - " result = []\n", - " left = self.left_subtree.sorted() if self.left_subtree is not None else []\n", - " right = self.right_subtree.sorted() if self.right_subtree is not None else []\n", - " return left + [self.root] + right\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "aaa02134-a273-4eef-85fa-ca565dbc25b1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[7, 80, 360, 368, 409, 579, 796, 871, 899, 957]" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "t = Tree()\n", - "for _ in range(10):\n", - " val = np.random.randint(1000)\n", - " t.insert(val)\n", - " \n", - "t.sorted()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "f7fd7296-e7ca-4dcb-a662-6d86bb4f789c", - "metadata": {}, - "outputs": [], - "source": [ - "from src.veriflow.distributions import RadialDistribution, RadialMM, LogNormal\n", - "d = RadialMM(\n", - " loc=torch.randn(20, 16, 7, 7),\n", - " p=2.0,\n", - " norm_distribution=LogNormal(\n", - " loc=torch.ones(20, 1, 1, 1),\n", - " scale=torch.ones(20, 1, 1, 1) * .5,\n", - " n_batch_dims=1\n", - " ),\n", - " mixture_weights=torch.ones(20)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "06f0be0b-9c8b-4bbe-bb6a-a715796636f5", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[[[-1.0641e-02, -1.8356e-02, -5.0627e-04, ..., 4.2425e-03,\n", - " 5.4559e-03, -9.3546e-03],\n", - " [ 2.3538e-03, -1.3041e-03, 4.5850e-03, ..., -7.9635e-03,\n", - " -8.3710e-03, -1.1148e-02],\n", - " [ 5.7637e-03, -4.4658e-03, 1.9638e-02, ..., 1.7102e-02,\n", - " 2.4164e-03, 1.8459e-02],\n", - " ...,\n", - " [ 6.0563e-03, -5.8210e-03, -2.5002e-02, ..., -9.5405e-03,\n", - " 3.0469e-03, 1.3430e-02],\n", - " [-3.5892e-03, 6.9478e-03, 2.5280e-03, ..., 1.5228e-03,\n", - " -1.1312e-02, -1.7733e-02],\n", - " [ 6.9326e-03, -6.9347e-03, -1.0718e-02, ..., -8.3332e-03,\n", - " 5.2878e-03, -1.0400e-02]],\n", - "\n", - " [[ 1.7683e-02, 1.1007e-02, -3.6408e-03, ..., 5.3899e-03,\n", - " -1.5014e-02, 1.1181e-02],\n", - 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" -2.7609e-02, -1.1474e-02],\n", - " [-1.4903e-02, 4.2087e-03, 1.5551e-04, ..., -6.1773e-03,\n", - " 1.6466e-02, -1.2364e-03]],\n", - "\n", - " [[ 1.2934e-02, 6.7094e-04, -3.2748e-03, ..., -1.0177e-03,\n", - " -3.6042e-02, 7.5899e-03],\n", - " [-1.7604e-03, -1.1913e-02, -3.1706e-03, ..., 6.1594e-03,\n", - " 4.3443e-03, 2.3927e-04],\n", - " [ 7.4346e-04, -1.3852e-03, 5.1133e-03, ..., -2.5205e-03,\n", - " -7.8671e-03, 1.2978e-02],\n", - " ...,\n", - " [ 9.2485e-03, -5.7591e-05, 1.6366e-03, ..., -9.6419e-03,\n", - " -9.6584e-03, -5.2533e-03],\n", - " [-1.3663e-02, 6.3429e-03, -1.2970e-03, ..., 1.2737e-02,\n", - " -4.3263e-03, -6.2805e-03],\n", - " [ 1.9246e-02, 1.1678e-02, -3.5866e-03, ..., -1.0740e-03,\n", - " -5.4685e-03, -2.1116e-02]],\n", - "\n", - " [[-3.4049e-03, -1.6717e-02, -1.2618e-02, ..., 4.2461e-03,\n", - " 1.1804e-02, -2.5840e-03],\n", - " [-1.1627e-02, 1.6910e-02, -1.5836e-02, ..., -2.0751e-02,\n", - " -9.8276e-03, -3.8139e-03],\n", - " [-1.5965e-02, -1.5505e-02, 1.5682e-03, ..., -5.4647e-05,\n", - " 1.1494e-02, -8.5778e-03],\n", - " ...,\n", - " [-1.4064e-02, 3.4178e-03, 9.1753e-03, ..., -9.7510e-03,\n", - " 5.0313e-03, -1.4325e-03],\n", - " [-1.2670e-02, -4.7068e-03, -1.2467e-02, ..., -1.0128e-02,\n", - " -1.0915e-02, -4.8354e-03],\n", - " [-1.5265e-02, -6.1876e-03, -1.5844e-02, ..., 1.1545e-02,\n", - " 2.1354e-02, -8.4958e-03]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.7043e-02, -3.3754e-03, 9.3646e-03, ..., -7.8351e-03,\n", - " 4.4370e-03, -2.2408e-02],\n", - " [-4.3515e-03, -1.2098e-02, 6.6907e-03, ..., 6.2725e-07,\n", - " 9.1828e-04, 5.5309e-03],\n", - " [-8.7187e-03, -7.1697e-03, -1.2980e-02, ..., -3.5322e-03,\n", - " -5.9631e-03, -1.4464e-02],\n", - " ...,\n", - " [-8.1544e-04, -1.1720e-02, 4.9078e-03, ..., -7.7263e-04,\n", - " 9.6800e-03, -4.8875e-03],\n", - " [-9.6212e-03, -4.1888e-03, 2.4466e-02, ..., 1.6271e-03,\n", - " 6.7066e-03, 1.2310e-02],\n", - " [-1.5993e-02, -4.7250e-03, -3.9586e-03, ..., -1.8490e-02,\n", - " 2.3422e-02, -5.3210e-03]],\n", - "\n", - " [[ 1.6098e-02, -1.8300e-02, -1.8275e-02, ..., -6.4157e-03,\n", - " 2.0559e-02, 5.7523e-03],\n", - " [-5.9618e-03, -4.8720e-03, 5.2287e-03, ..., 5.2535e-03,\n", - " 1.8737e-02, -6.3875e-03],\n", - " [ 4.7588e-05, -6.2873e-03, 2.3455e-02, ..., -1.8379e-02,\n", - " 7.8645e-03, -6.9133e-03],\n", - " ...,\n", - " [-6.2302e-03, -1.7784e-02, -1.3045e-02, ..., -7.3098e-03,\n", - " -2.0633e-02, 1.2547e-02],\n", - " [-3.5607e-03, 6.8879e-03, 2.0120e-02, ..., 1.0841e-03,\n", - " 9.9239e-03, 7.6227e-04],\n", - " [-1.5565e-02, -2.7434e-03, 9.4117e-04, ..., -5.3960e-03,\n", - " -5.3491e-03, 5.2168e-03]],\n", - "\n", - " [[-6.1777e-04, -1.1749e-02, -2.1079e-02, ..., -8.4615e-03,\n", - " -4.7960e-03, -1.1582e-02],\n", - " [ 1.0567e-02, -1.3577e-02, 6.2224e-03, ..., -3.5602e-03,\n", - " 1.1789e-02, 4.8470e-03],\n", - " [ 3.8631e-03, -6.0210e-03, 5.9773e-03, ..., 2.5608e-03,\n", - " -9.7245e-03, -1.7772e-03],\n", - " ...,\n", - " [-5.8558e-03, 1.3961e-02, 1.7912e-02, ..., -6.4973e-03,\n", - " 9.4491e-04, 1.7589e-03],\n", - " [ 1.0136e-02, -8.6381e-03, 9.3434e-04, ..., -1.0084e-02,\n", - " 2.7025e-03, 1.7461e-02],\n", - " [-2.1343e-03, -1.3379e-02, -1.1905e-03, ..., -1.5124e-03,\n", - " 3.5938e-03, 1.8656e-03]]],\n", - "\n", - "\n", - " ...,\n", - "\n", - "\n", - " [[[-5.5427e-03, 5.1092e-03, -1.1132e-02, ..., 4.7468e-03,\n", - " 2.2609e-02, 9.6245e-03],\n", - " [ 1.3732e-02, 1.1629e-02, -9.5406e-03, ..., 3.6257e-05,\n", - " -1.6056e-03, -6.6577e-03],\n", - " [-2.4846e-03, 7.0955e-03, 2.1065e-02, ..., -1.0258e-03,\n", - " -7.3519e-03, 4.8537e-03],\n", - " ...,\n", - " [ 4.0938e-03, -6.9510e-03, -2.3365e-02, ..., -1.7258e-02,\n", - " 5.9717e-04, -5.2333e-03],\n", - " [-2.2199e-03, 1.1353e-03, -7.3646e-03, ..., 3.3422e-03,\n", - " -1.8673e-02, -1.1137e-02],\n", - " [-1.3007e-03, -1.5811e-02, -1.0561e-02, ..., -1.2916e-02,\n", - " 2.6466e-02, 1.3849e-02]],\n", - "\n", - " [[ 1.5716e-03, 2.2112e-03, -1.3131e-02, ..., 2.5282e-02,\n", - " -2.3464e-02, 5.6856e-03],\n", - " [-5.4008e-03, -1.4771e-02, -4.6626e-03, ..., -1.0272e-04,\n", - " -2.7178e-03, 1.6614e-03],\n", - " [ 1.5707e-03, -7.0405e-03, 1.4007e-02, ..., -4.6683e-04,\n", - " -1.3308e-02, -9.2460e-04],\n", - " ...,\n", - " [ 1.4975e-03, -6.7669e-03, 2.9501e-03, ..., -7.5599e-03,\n", - " -4.6264e-03, -1.1943e-02],\n", - " [-1.1739e-02, -1.3377e-02, -3.4688e-03, ..., 1.0056e-02,\n", - " 4.3252e-03, 6.5338e-04],\n", - " [ 5.2604e-04, 5.9555e-03, 8.8684e-03, ..., 5.0308e-04,\n", - " -6.2439e-04, -1.6377e-02]],\n", - "\n", - " [[-1.0706e-02, -1.3968e-02, 4.9383e-03, ..., -4.9722e-03,\n", - " -6.8609e-03, -9.4605e-03],\n", - " [-8.3099e-03, -9.9432e-04, -1.4732e-02, ..., -1.0756e-03,\n", - " -7.1710e-04, -6.3000e-03],\n", - " [ 2.1441e-03, -6.7200e-03, 2.4562e-02, ..., -9.6651e-03,\n", - " 4.0319e-03, -9.3079e-04],\n", - " ...,\n", - " [ 2.1732e-03, 1.5165e-03, -9.9975e-04, ..., -1.6076e-02,\n", - " 1.2978e-02, 6.2204e-03],\n", - " [ 1.0522e-02, 6.7325e-03, -7.6841e-03, ..., -2.1066e-02,\n", - " -2.0944e-02, -1.2923e-02],\n", - " [-2.0520e-04, -1.3470e-03, -5.2936e-03, ..., 6.7464e-03,\n", - " 5.5055e-04, -2.1903e-03]],\n", - "\n", - " ...,\n", - "\n", - " [[ 5.2623e-04, -4.8632e-03, -2.5922e-03, ..., -1.0146e-02,\n", - " 1.5927e-02, -1.9344e-02],\n", - " [-1.4310e-02, 6.7192e-03, 2.1284e-02, ..., 1.4355e-02,\n", - " -1.7849e-02, 9.0169e-03],\n", - " [-4.1691e-03, -2.5892e-02, -4.2626e-03, ..., 6.7426e-03,\n", - " -4.1759e-03, 6.3792e-03],\n", - " ...,\n", - " [ 5.5908e-03, -2.3038e-02, -2.8989e-03, ..., -5.5277e-03,\n", - " 2.7558e-02, -8.7071e-03],\n", - " [ 3.2682e-03, 6.4616e-04, 3.2420e-03, ..., -4.1532e-03,\n", - " 1.4011e-02, 1.3985e-02],\n", - " [ 1.1575e-02, -2.1428e-02, 5.8270e-03, ..., -4.7007e-03,\n", - " 2.7809e-02, -7.7069e-03]],\n", - "\n", - " [[ 3.7069e-04, -7.9428e-03, -2.0492e-02, ..., -1.2124e-02,\n", - " 2.3127e-02, 8.0580e-03],\n", - " [-1.1172e-02, -1.0633e-02, -4.6670e-05, ..., -2.7054e-03,\n", - " 7.0815e-03, -2.7799e-03],\n", - " [ 6.8306e-04, 4.5178e-03, -2.1892e-03, ..., -1.7638e-02,\n", - " 5.6595e-03, 2.2640e-04],\n", - " ...,\n", - " [-1.2573e-02, -1.0793e-02, -3.1232e-03, ..., -2.9205e-03,\n", - " -1.3484e-02, 1.4475e-02],\n", - " [ 2.9310e-03, 1.5451e-02, 1.8877e-02, ..., 1.9308e-02,\n", - " 1.1131e-03, 7.1759e-03],\n", - " [-1.2669e-03, -1.0169e-02, 4.7439e-03, ..., 1.5222e-03,\n", - " -7.0718e-03, 7.2667e-04]],\n", - "\n", - " [[ 5.3464e-03, -1.3970e-02, -1.3702e-02, ..., -3.0763e-03,\n", - " -1.7175e-02, -1.3518e-02],\n", - " [-1.6171e-03, 1.0057e-03, -1.2812e-02, ..., -7.5024e-03,\n", - " -2.0334e-03, -7.7039e-04],\n", - " [ 7.9577e-03, 7.4820e-03, 2.1345e-02, ..., 5.0694e-03,\n", - " -1.1764e-03, -1.7517e-02],\n", - " ...,\n", - " [-2.5673e-03, 3.1513e-02, 1.1374e-03, ..., -1.4240e-02,\n", - " -6.7132e-03, -1.6280e-02],\n", - " [ 8.4792e-03, 1.7053e-03, -1.1228e-02, ..., 8.8721e-03,\n", - " -3.5256e-03, 7.3051e-03],\n", - " [ 1.0944e-03, -5.7138e-03, 4.2810e-03, ..., 7.2196e-03,\n", - " 1.8633e-02, -1.6215e-03]]],\n", - "\n", - "\n", - " [[[ 1.9708e-03, -2.5194e-03, 2.6638e-04, ..., -1.0616e-03,\n", - " 6.9695e-03, 8.2542e-04],\n", - " [-1.6664e-02, 1.3416e-02, 5.5320e-03, ..., 5.6322e-03,\n", - " -9.3146e-03, 2.7692e-03],\n", - " [ 5.8612e-03, -6.1997e-03, 2.8540e-02, ..., 1.2350e-02,\n", - " 7.3895e-03, 8.3802e-03],\n", - " ...,\n", - " [-5.8809e-03, -6.1566e-03, -2.5711e-02, ..., -1.8138e-02,\n", - " -4.7841e-03, -4.3649e-03],\n", - " [ 5.4427e-03, 1.9839e-03, -3.5416e-03, ..., 3.8511e-03,\n", - " -2.5073e-03, 5.3199e-03],\n", - " [ 8.2905e-03, -2.6792e-03, -3.4125e-03, ..., -6.6737e-03,\n", - " 5.6280e-03, -1.3035e-02]],\n", - "\n", - " [[ 9.7265e-03, 6.5723e-03, -2.5015e-02, ..., 1.2347e-03,\n", - " -2.4749e-02, 1.7890e-02],\n", - " [-2.8682e-02, -8.7245e-03, -1.2904e-02, ..., 1.8930e-03,\n", - " 1.0118e-02, 1.3147e-02],\n", - " [-1.0312e-02, -1.1205e-02, 2.2770e-03, ..., -1.7408e-02,\n", - " 5.5119e-03, 5.3866e-03],\n", - " ...,\n", - " [-3.8062e-03, 6.1746e-03, -9.1127e-03, ..., -2.3471e-02,\n", - " -7.3200e-03, -1.3286e-02],\n", - " [-7.4565e-03, -2.8471e-03, 8.0964e-04, ..., 6.9136e-03,\n", - " 7.9423e-03, -6.3285e-03],\n", - " [ 2.4525e-02, 9.3354e-03, -1.1701e-02, ..., 1.2247e-02,\n", - " -3.3789e-03, -4.5885e-03]],\n", - "\n", - " [[ 2.4680e-03, -1.1023e-02, 8.6417e-04, ..., -1.1559e-02,\n", - " 4.3652e-03, 5.6675e-04],\n", - " [-1.2035e-02, -8.2983e-03, 1.1995e-03, ..., -1.9785e-02,\n", - " 1.3152e-02, 1.1698e-02],\n", - " [-6.8171e-03, -3.8015e-03, -9.5799e-04, ..., -1.6084e-02,\n", - " -2.0474e-03, -2.7097e-03],\n", - " ...,\n", - " [-1.4614e-03, -7.4297e-03, 1.9127e-04, ..., -1.9639e-02,\n", - " 8.5179e-03, -1.0707e-02],\n", - " [-5.7776e-03, -3.6749e-04, -5.6664e-03, ..., -2.2098e-02,\n", - " -2.9163e-02, -5.2890e-03],\n", - " [-5.7136e-03, -1.3048e-02, 3.1626e-03, ..., 5.4737e-03,\n", - " 1.0441e-02, -1.2394e-03]],\n", - "\n", - " ...,\n", - "\n", - " [[-3.9985e-04, 9.9940e-03, 4.5917e-03, ..., -7.2317e-03,\n", - " 4.6821e-03, -1.2191e-02],\n", - " [-1.8463e-03, -5.4757e-03, 5.7816e-03, ..., 2.3376e-03,\n", - " -1.1617e-02, 6.9755e-03],\n", - " [-4.4578e-03, -2.6272e-02, -1.9093e-02, ..., -9.1514e-04,\n", - " -9.4862e-03, -1.0948e-02],\n", - " ...,\n", - " [ 2.6277e-03, -1.2193e-02, 5.7346e-03, ..., 1.5109e-03,\n", - " 1.3754e-02, -8.7912e-03],\n", - " [-2.3304e-04, 1.5912e-04, 1.5133e-02, ..., -4.3957e-03,\n", - 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" [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[203.5828]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]],\n", - "\n", - "\n", - " [[[ 0.0000]]]])\n", - "None\n" - ] - } - ], - "source": [ - "sample = d.sample()\n", - "loss = d.log_prob(sample)\n", - "loss.backward()\n", - "for p in d.parameters():\n", - " print(p.grad)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f0b61b35-7376-4d2e-b039-0f05a663d7c2", - "metadata": {}, - "outputs": [], - "source": [ - "d = model.base_distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9e4e1f1a-1426-4879-ba7f-a7e350d8be21", - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "d = deepcopy(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "9cc5b0b3-bb4a-4a07-ba23-b7f9d6cb7342", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RadialMM(\n", - " (params): ParameterDict()\n", - " (module_args): ModuleDict()\n", - " (radial_batch): RadialDistribution(\n", - " (norm_distribution): LogNormal(\n", - " (params): ParameterDict()\n", - " (module_args): ModuleDict()\n", - " )\n", - " )\n", - " (mixture_distribution): Categorical(logits: torch.Size([20]))\n", - ")" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "23cf7218-c2c1-40c4-86b0-7d8e43dcc078", - "metadata": {}, - "outputs": [], - "source": [ - "from src.explib.config_parser import read_config, parse_raw_config, create_objects_from_classes" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "cfa2bb66-a29e-4935-b8f0-0c82a44c5cc9", - "metadata": {}, - "outputs": [], - "source": [ - "cfg_file = \"/Users/fariedabuzaid/Projects/veriflow/experiments/mnist/mnist_usflow_cpu_gammamm.yaml\"\n", - "cfg = read_config(cfg_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "acd38968-680c-4792-9252-05595cf36005", - "metadata": {}, - "outputs": [], - "source": [ - "model_cls = cfg.experiments[0].trial_config['model_cfg']['type']\n", - "args = cfg.experiments[0].trial_config['model_cfg']['params']\n", - "args = create_objects_from_classes(args)\n", - "model = model_cls(**args)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "59a2d879-f81c-4152-9807-064645386408", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cfg" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "2056db88-730b-4468-8e64-a793d3cef65d", - "metadata": {}, - "outputs": [], - "source": [ - "basedist = model.base_distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "79ddb6da-3874-4757-9e34-112cef8ec697", - "metadata": {}, - "outputs": [], - "source": [ - "sample = basedist.sample()\n", - "logprob = basedist.log_prob(sample)\n", - "logprob.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "9c621326-5693-4cad-ac7f-a8bc21820752", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[[ 0.3666, -0.3666, -0.3666, ..., -0.3666, -0.3666, 0.3666],\n", - " [-0.3666, -0.3666, 0.3666, ..., -0.3666, -0.3666, -0.3666],\n", - " [-0.3666, -0.3666, 0.3666, ..., 0.3666, -0.3666, 0.3666],\n", - " ...,\n", - " [ 0.3666, -0.3666, 0.3666, ..., -0.3666, 0.3666, -0.3666],\n", - " [ 0.3666, -0.3666, -0.3666, ..., 0.3666, 0.3666, 0.3666],\n", - " [ 0.3666, -0.3666, 0.3666, ..., 0.3666, 0.3666, 0.3666]],\n", - "\n", - " [[ 0.3666, -0.3666, -0.3666, ..., -0.3666, -0.3666, -0.3666],\n", - 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"metadata": {}, - "source": [ - "# Sample Model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1807f1b3-677b-4a3b-955f-9fac32aff713", - "metadata": {}, - "outputs": [], - "source": [ - "cfg_file = \"/Users/fariedabuzaid/Projects/veriflow/experiments/mnist/mnist_usflow_cpu_gammamm.yaml\"\n", - "cfg = read_config(cfg_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1494d459-314e-40e8-931d-054443c3a232", - "metadata": {}, - "outputs": [], - "source": [ - "model_cls = cfg.experiments[0].trial_config['model_cfg']['type']\n", - "args = cfg.experiments[0].trial_config['model_cfg']['params']\n", - "model = model_cls(**args)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "187174e7-51d1-4441-bd4b-f4730da3fc1f", - "metadata": {}, - "outputs": [], - "source": [ - "basedist = model.base_distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "95ae65c2-760e-4bd1-8c19-859dadfe04e3", - "metadata": {}, - 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" 2.0637e+04, -9.9701e+03],\n", - " [ 8.9909e+03, -2.4160e+04, -2.6867e+04, ..., 1.2898e+04,\n", - " 2.2912e+04, -7.3386e+03],\n", - " ...,\n", - " [-8.0414e+03, 1.1842e+04, 2.8273e+04, ..., -2.4791e+04,\n", - " 2.0022e+04, 2.7668e+04],\n", - " [-2.3405e+04, 1.8932e+04, 1.5428e+04, ..., 1.0150e+04,\n", - " 1.7408e+04, 5.8362e+03],\n", - " [-2.0576e+03, 1.9143e+04, 3.7368e+03, ..., 1.3185e+04,\n", - " -3.5108e+03, -1.3449e+04]],\n", - "\n", - " ...,\n", - "\n", - " [[-1.3574e+04, -2.7380e+04, 1.1609e+04, ..., 3.8717e+02,\n", - " 3.8459e+03, -1.5957e+04],\n", - " [ 6.5773e+03, -2.3341e+04, 2.3131e+04, ..., -3.3702e+03,\n", - " -1.5609e+04, -1.3325e+04],\n", - " [ 2.0429e+04, -5.7544e+03, -1.2972e+04, ..., 2.4820e+04,\n", - " 2.1511e+03, -3.2658e+04],\n", - " ...,\n", - " [ 1.4963e+04, -3.1878e+04, 7.1381e+02, ..., -1.6395e+04,\n", - " 1.8721e+04, 1.3186e+04],\n", - " [-1.8880e+04, -2.4519e+04, -2.1001e+04, ..., 2.6239e+03,\n", - " 8.3300e+03, 3.6366e+04],\n", - " [ 9.0694e+03, 3.2036e+04, -1.2215e+03, ..., 2.1024e+04,\n", - " 2.2086e+04, -6.2186e+03]],\n", - "\n", - " [[ 8.0651e+03, -1.1950e+04, -9.3331e+03, ..., 1.3298e+04,\n", - " -1.8178e+04, 4.9952e+03],\n", - " [-1.6030e+04, -1.1273e+04, -2.0007e+04, ..., -2.8379e+04,\n", - " 1.4278e+04, -8.3251e+03],\n", - " [-8.4971e+03, -1.3468e+04, 2.4624e+04, ..., 4.5962e+03,\n", - " 2.5532e+04, -3.2474e+04],\n", - " ...,\n", - " [-5.0594e+03, -4.9768e+03, 3.4381e+04, ..., -1.1060e+04,\n", - " -2.8091e+04, -5.2372e+03],\n", - " [ 1.9291e+04, -1.4774e+04, -9.6330e+03, ..., 1.9388e+04,\n", - " 3.3025e+03, 2.3857e+04],\n", - " [-3.2380e+03, -1.2771e+04, 7.3151e+03, ..., -2.8797e+03,\n", - " -1.5373e+04, -1.2305e+04]],\n", - "\n", - " [[-2.1127e+04, -1.0285e+04, 3.3402e+04, ..., -3.2871e+04,\n", - " -3.4127e+04, -1.4596e+04],\n", - " [-4.8618e+03, -4.9809e+03, -1.9472e+04, ..., 5.2905e+03,\n", - " -7.4569e+03, -2.6750e+04],\n", - " [ 8.3521e+03, -1.0175e+04, 1.1103e+04, ..., -6.0388e+03,\n", - " -8.2178e+03, -2.3605e+04],\n", - " ...,\n", - " [ 1.6826e+04, -6.3906e+03, 1.3865e+04, ..., -2.3858e+04,\n", - " 4.2310e+02, 2.0211e+04],\n", - " [-3.1520e+04, -1.0984e+04, -2.0641e+03, ..., 6.6493e+03,\n", - " -1.6443e+03, 2.2563e+04],\n", - " [ 3.0155e+04, 3.2109e+04, 2.0357e+04, ..., 1.8394e+04,\n", - " -2.9489e+04, -1.0226e+04]]]])" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basedist.radial_batch.loc.grad" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7b566cdc-ce6a-426d-aaeb-675e290060fa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([4857.0884], grad_fn=)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logprob" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a8e01bc6-4766-43f6-adc4-19a78cd05c84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 16, 7, 7])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c6904616-8353-4af9-9ef9-d2de77c84d7a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'soft_training': False,\n", - " 'training_noise_prior': Uniform(low: 9.999999682655225e-21, high: 0.009999999776482582),\n", - " 'prior_scale': 5.0,\n", - " 'coupling_blocks': 1,\n", - " 'lu_transform': 1,\n", - " 'householder': 0,\n", - " 'conditioner_cls': src.veriflow.networks.ConvNet2D,\n", - " 'conditioner_args': {'c_in': 16,\n", - " 'c_hidden': 32,\n", - " 'num_layers': 1,\n", - " 'padding': 1,\n", - " 'dilation': 1,\n", - " 'stride': 1,\n", - " 'kernel_size': 3,\n", - " 'rescale_hidden': 1,\n", - " 'normalize_layers': False,\n", - " 'gating': False},\n", - " 'in_dims': [16, 7, 7],\n", - " 'affine_conjugation': True,\n", - " 'nonlinearity': ,\n", - " 'base_distribution': RadialMM(\n", - " (radial_batch): RadialDistribution(\n", - " (norm_distribution): LogNormal()\n", - " )\n", - " (mixture_distribution): Categorical()\n", - " (_mixture_distribution): Categorical()\n", - " (_component_distribution): RadialDistribution(\n", - " (norm_distribution): LogNormal()\n", - " )\n", - " )}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "args" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1e4229c7-7b07-42dd-8f74-807af1050b4a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-28 18:10:16,950\tINFO worker.py:1841 -- Started a local Ray instance.\n", - "/Users/fariedabuzaid/Library/Caches/pypoetry/virtualenvs/veriflow-75zEOOJt-py3.12/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:125: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0\n", - " _log_deprecation_warning(\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexperiments\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconduct\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Projects/veriflow/src/explib/hyperopt.py:216\u001b[0m, in \u001b[0;36mHyperoptExperiment.conduct\u001b[0;34m(self, report_dir, storage_path)\u001b[0m\n\u001b[1;32m 201\u001b[0m exptime \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(datetime\u001b[38;5;241m.\u001b[39mnow())\n\u001b[1;32m 202\u001b[0m tuner \u001b[38;5;241m=\u001b[39m tune\u001b[38;5;241m.\u001b[39mTuner(\n\u001b[1;32m 203\u001b[0m tune\u001b[38;5;241m.\u001b[39mwith_resources(\n\u001b[1;32m 204\u001b[0m tune\u001b[38;5;241m.\u001b[39mwith_parameters(HyperoptExperiment\u001b[38;5;241m.\u001b[39m_trial),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(tuner_config),\n\u001b[1;32m 215\u001b[0m )\n\u001b[0;32m--> 216\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;66;03m# TODO: hacky way to determine the last experiment\u001b[39;00m\n\u001b[1;32m 219\u001b[0m exppath \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 220\u001b[0m storage_path\n\u001b[1;32m 221\u001b[0m \u001b[38;5;241m+\u001b[39m [\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 225\u001b[0m ][\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 226\u001b[0m )\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/veriflow-75zEOOJt-py3.12/lib/python3.12/site-packages/ray/tune/tuner.py:345\u001b[0m, in \u001b[0;36mTuner.fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Executes hyperparameter tuning job as configured and returns result.\u001b[39;00m\n\u001b[1;32m 314\u001b[0m \n\u001b[1;32m 315\u001b[0m \u001b[38;5;124;03mFailure handling:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;124;03m RayTaskError: If user-provided trainable raises an exception\u001b[39;00m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ray_client:\n\u001b[0;32m--> 345\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_local_tuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 347\u001b[0m (\n\u001b[1;32m 348\u001b[0m progress_reporter,\n\u001b[1;32m 349\u001b[0m string_queue,\n\u001b[1;32m 350\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_remote_tuner_for_jupyter_progress_reporting()\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/veriflow-75zEOOJt-py3.12/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:502\u001b[0m, in \u001b[0;36mTunerInternal.fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 500\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResultGrid:\n\u001b[1;32m 501\u001b[0m trainable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverted_trainable\n\u001b[0;32m--> 502\u001b[0m param_space \u001b[38;5;241m=\u001b[39m \u001b[43mcopy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_space\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_restored:\n\u001b[1;32m 504\u001b[0m analysis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_internal(trainable, param_space)\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 221\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 221\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - " \u001b[0;31m[... skipping similar frames: _deepcopy_dict at line 221 (1 times), deepcopy at line 136 (1 times)]\u001b[0m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 160\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 162\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 259\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 261\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - " \u001b[0;31m[... skipping similar frames: _deepcopy_dict at line 221 (2 times), deepcopy at line 136 (2 times)]\u001b[0m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 160\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 162\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 259\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 261\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - " \u001b[0;31m[... skipping similar frames: _deepcopy_dict at line 221 (5 times), deepcopy at line 136 (5 times), _reconstruct at line 259 (2 times), deepcopy at line 162 (2 times)]\u001b[0m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 160\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 162\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 259\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 261\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 221\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:143\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 141\u001b[0m copier \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__deepcopy__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 145\u001b[0m reductor \u001b[38;5;241m=\u001b[39m dispatch_table\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/veriflow-75zEOOJt-py3.12/lib/python3.12/site-packages/torch/_tensor.py:114\u001b[0m, in \u001b[0;36mTensor.__deepcopy__\u001b[0;34m(self, memo)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(Tensor\u001b[38;5;241m.\u001b[39m__deepcopy__, (\u001b[38;5;28mself\u001b[39m,), \u001b[38;5;28mself\u001b[39m, memo)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_leaf:\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 115\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly Tensors created explicitly by the user \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(graph leaves) support the deepcopy protocol at the moment. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you were attempting to deepcopy a module, this may be because \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof a torch.nn.utils.weight_norm usage, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msee https://github.com/pytorch/pytorch/pull/103001\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 120\u001b[0m )\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m memo:\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m memo[\u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m)]\n", - "\u001b[0;31mRuntimeError\u001b[0m: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001" - ] - } - ], - "source": [ - "cfg.experiments[0].conduct('.')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "df5cd5fb-7cc1-4f0a-95cf-54cc014ccdd4", - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mcopy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m deepcopy\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexperiments\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrial_config\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 221\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 221\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - " \u001b[0;31m[... skipping similar frames: _deepcopy_dict at line 221 (1 times), deepcopy at line 136 (1 times)]\u001b[0m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 160\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 162\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 259\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 261\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - " \u001b[0;31m[... skipping similar frames: _deepcopy_dict at line 221 (2 times), deepcopy at line 136 (2 times)]\u001b[0m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 160\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 162\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 259\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 261\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - " \u001b[0;31m[... skipping similar frames: _deepcopy_dict at line 221 (5 times), deepcopy at line 136 (5 times), _reconstruct at line 259 (2 times), deepcopy at line 162 (2 times)]\u001b[0m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:162\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 160\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 162\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:259\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 259\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 261\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:136\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 134\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 136\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:221\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 219\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 221\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.12/3.12.7/Frameworks/Python.framework/Versions/3.12/lib/python3.12/copy.py:143\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 141\u001b[0m copier \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__deepcopy__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 145\u001b[0m reductor \u001b[38;5;241m=\u001b[39m dispatch_table\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n", - "File \u001b[0;32m~/Library/Caches/pypoetry/virtualenvs/veriflow-75zEOOJt-py3.12/lib/python3.12/site-packages/torch/_tensor.py:114\u001b[0m, in \u001b[0;36mTensor.__deepcopy__\u001b[0;34m(self, memo)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(Tensor\u001b[38;5;241m.\u001b[39m__deepcopy__, (\u001b[38;5;28mself\u001b[39m,), \u001b[38;5;28mself\u001b[39m, memo)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_leaf:\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 115\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly Tensors created explicitly by the user \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(graph leaves) support the deepcopy protocol at the moment. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you were attempting to deepcopy a module, this may be because \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof a torch.nn.utils.weight_norm usage, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msee https://github.com/pytorch/pytorch/pull/103001\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 120\u001b[0m )\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m memo:\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m memo[\u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m)]\n", - "\u001b[0;31mRuntimeError\u001b[0m: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001" - ] - } - ], - "source": [ - "from copy import deepcopy\n", - "deepcopy(cfg.experiments[0].trial_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0ba4697d-85d4-435f-8591-c275e72ccdc3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fariedabuzaid/Projects/veriflow/src/explib/datasets.py:356: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:209.)\n", - " torch.Tensor(dataset),\n", - "/Users/fariedabuzaid/Projects/veriflow/src/veriflow/flows.py:88: SyntaxWarning: invalid escape sequence '\\l'\n", - " \"\"\"Returns the log prior of the model parameters. The model is trained in maximum posterior fashion, i.e.\n", - "/Users/fariedabuzaid/Projects/veriflow/src/veriflow/flows.py:436: SyntaxWarning: invalid escape sequence '\\l'\n", - " \"\"\"Returns the log prior of the model parameters. The model is trained in maximum posterior fashion, i.e.\n", - "/Users/fariedabuzaid/Projects/veriflow/src/veriflow/flows.py:651: SyntaxWarning: invalid escape sequence '\\s'\n", - " \"\"\"Returns the log prior of the model parameters. If LU layers are used,\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'Categorical' object cannot be interpreted as an integer", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexplib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhyperopt\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m HyperoptExperiment\n\u001b[0;32m----> 3\u001b[0m \u001b[43mHyperoptExperiment\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexperiments\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrial_config\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Projects/veriflow/src/explib/hyperopt.py:107\u001b[0m, in \u001b[0;36mHyperoptExperiment._trial\u001b[0;34m(cls, config, device)\u001b[0m\n\u001b[1;32m 105\u001b[0m strikes \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mepochs\u001b[39m\u001b[38;5;124m\"\u001b[39m]):\n\u001b[0;32m--> 107\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptim_cfg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptim_cfg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparams\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 115\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(data_val), config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch_size\u001b[39m\u001b[38;5;124m\"\u001b[39m]):\n", - "File \u001b[0;32m~/Projects/veriflow/src/veriflow/flows.py:144\u001b[0m, in \u001b[0;36mFlow.fit\u001b[0;34m(self, data_train, optim, optim_params, batch_size, shuffle, gradient_clip, device, epochs)\u001b[0m\n\u001b[1;32m 141\u001b[0m perm \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mchoice(N, N, replace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 142\u001b[0m data_train_shuffle \u001b[38;5;241m=\u001b[39m data_train[perm][\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mN\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 145\u001b[0m end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(idx \u001b[38;5;241m+\u001b[39m batch_size, N)\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[0;31mTypeError\u001b[0m: 'Categorical' object cannot be interpreted as an integer" - ] - } - ], - "source": [ - "from src.explib.hyperopt import HyperoptExperiment\n", - "\n", - "HyperoptExperiment._trial(cfg.experiments[0].trial_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "56a8e450-c2f8-4d33-8d04-9d59530b581e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'epochs': 100000,\n", - " 'patience': 20,\n", - " 'logging': {'images': False, 'image_shape': [28, 28]},\n", - " 'dataset': {'type': src.explib.datasets.MnistSplit,\n", - " 'params': {'dataloc': '/Users/fariedabuzaid/Projects/veriflow/data',\n", - " 'space_to_depth_factor': 4,\n", - " 'device': 'cpu'}},\n", - " 'batch_size': ,\n", - " 'optim_cfg': {'optimizer': torch.optim.adam.Adam,\n", - " 'params': {'lr': 0.0001, 'weight_decay': 0.0}},\n", - " 'model_cfg': {'type': src.veriflow.flows.USFlow,\n", - " 'params': {'soft_training': False,\n", - " 'training_noise_prior': Uniform(low: 9.999999682655225e-21, high: 0.009999999776482582),\n", - " 'prior_scale': 5.0,\n", - " 'coupling_blocks': 1,\n", - " 'lu_transform': 1,\n", - " 'householder': 0,\n", - " 'conditioner_cls': src.veriflow.networks.ConvNet2D,\n", - " 'conditioner_args': {'c_in': 16,\n", - " 'c_hidden': 32,\n", - " 'num_layers': 1,\n", - " 'padding': 1,\n", - " 'dilation': 1,\n", - " 'stride': 1,\n", - " 'kernel_size': 3,\n", - " 'rescale_hidden': 1,\n", - " 'normalize_layers': False,\n", - " 'gating': False},\n", - " 'in_dims': [16, 7, 7],\n", - " 'affine_conjugation': True,\n", - " 'nonlinearity': ,\n", - " 'base_distribution': RadialMM(\n", - " (params): ParameterDict()\n", - " (module_args): ModuleDict()\n", - " (radial_batch): RadialDistribution(\n", - " (norm_distribution): LogNormal(\n", - " (params): ParameterDict()\n", - " (module_args): ModuleDict()\n", - " )\n", - " )\n", - " (mixture_distribution): Categorical(\n", - " (params): ParameterDict()\n", - " (module_args): ModuleDict()\n", - " )\n", - " )}},\n", - " 'device': 'cpu'}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cfg.experiments[0].trial_config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b2946f4-d07a-44c3-a0eb-25ba6942b6b6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}