From ef251b798467998dfcb1be6c55bf5089fa33e127 Mon Sep 17 00:00:00 2001 From: clementchadebec Date: Tue, 11 Oct 2022 16:39:44 +0200 Subject: [PATCH 1/9] add contextual iaf/maf --- README.md | 2 +- docs/source/pipelines/pythae.pipelines.rst | 2 +- .../normalizing_flows_training.ipynb | 507 ++++++++------- .../models_training/vae_iaf_training.ipynb | 16 +- examples/scripts/reproducibility/sync.ipynb | 300 +++++++++ examples/scripts/reproducibility/vae_nf.py | 583 +++++++++++++++++ examples/test.ipynb | 603 ++++++++++++++++++ src/pythae/models/hvae/hvae_model.py | 19 +- src/pythae/models/info_vae/info_vae_model.py | 6 +- src/pythae/models/iwae/iwae_model.py | 2 +- .../models/msssim_vae/msssim_vae_utils.py | 6 +- .../models/nn/benchmarks/celeba/convnets.py | 7 + .../models/nn/benchmarks/celeba/resnets.py | 7 + .../models/nn/benchmarks/cifar/convnets.py | 7 + .../models/nn/benchmarks/cifar/resnets.py | 7 + .../models/nn/benchmarks/mnist/convnets.py | 7 + .../models/nn/benchmarks/mnist/resnets.py | 7 + src/pythae/models/nn/default_architectures.py | 8 + .../normalizing_flows/iaf/iaf_config.py | 12 +- .../models/normalizing_flows/iaf/iaf_model.py | 18 +- src/pythae/models/normalizing_flows/layers.py | 13 +- .../normalizing_flows/made/made_config.py | 2 + .../normalizing_flows/made/made_model.py | 37 +- .../normalizing_flows/maf/maf_config.py | 10 +- .../models/normalizing_flows/maf/maf_model.py | 19 +- .../planar_flow/planar_flow_model.py | 2 +- .../radial_flow/radial_flow_model.py | 2 +- src/pythae/models/pvae/pvae_utils.py | 22 +- src/pythae/models/rhvae/rhvae_model.py | 23 +- src/pythae/models/rhvae/rhvae_utils.py | 4 +- src/pythae/models/svae/svae_model.py | 4 +- src/pythae/models/vae/vae_model.py | 2 +- src/pythae/models/vae_iaf/vae_iaf_config.py | 12 +- src/pythae/models/vae_iaf/vae_iaf_model.py | 24 +- .../models/vae_lin_nf/vae_lin_nf_model.py | 2 +- src/pythae/models/vamp/vamp_model.py | 16 +- src/pythae/models/vq_vae/vq_vae_utils.py | 4 +- src/pythae/models/wae_mmd/wae_mmd_model.py | 6 +- .../samplers/iaf_sampler/iaf_sampler.py | 2 +- .../iaf_sampler/iaf_sampler_config.py | 6 +- .../samplers/maf_sampler/maf_sampler.py | 2 +- .../maf_sampler/maf_sampler_config.py | 6 +- .../manifold_sampler/rhvae_sampler.py | 4 +- src/pythae/trainers/training_callbacks.py | 9 +- tests/data/custom_architectures.py | 7 + tests/test_IAF.py | 8 +- tests/test_MAF.py | 6 +- tests/test_VAE_IAF.py | 15 +- tests/test_iaf_sampler.py | 2 +- tests/test_maf_sampler.py | 2 +- tests/test_nn_benchmark.py | 49 +- 51 files changed, 2094 insertions(+), 354 deletions(-) create mode 100644 examples/scripts/reproducibility/sync.ipynb create mode 100644 examples/scripts/reproducibility/vae_nf.py create mode 100644 examples/test.ipynb diff --git a/README.md b/README.md index 8c563372..c0a835f5 100644 --- a/README.md +++ b/README.md @@ -188,7 +188,7 @@ The easiest way to launch a data generation from a trained model consists in usi ... 'path/to/your/trained/model' ... ) >>> my_sampler_config = MAFSamplerConfig( -... n_made_blocks=2, +... n_blocks=2, ... n_hidden_in_made=3, ... hidden_size=128 ... ) diff --git a/docs/source/pipelines/pythae.pipelines.rst b/docs/source/pipelines/pythae.pipelines.rst index 32372275..177916bf 100644 --- a/docs/source/pipelines/pythae.pipelines.rst +++ b/docs/source/pipelines/pythae.pipelines.rst @@ -75,7 +75,7 @@ provided in Pythae you only need 1) a trained model, 2) the sampler's configurat ... 'path/to/your/trained/model' ... ) >>> my_sampler_config = MAFSamplerConfig( - ... n_made_blocks: int = 2 + ... n_blocks: int = 2 ... n_hidden_in_made: int = 3 ... hidden_size: int = 128 ... ) diff --git a/examples/notebooks/models_training/normalizing_flows_training.ipynb b/examples/notebooks/models_training/normalizing_flows_training.ipynb index a564e93d..56a71a7e 100644 --- a/examples/notebooks/models_training/normalizing_flows_training.ipynb +++ b/examples/notebooks/models_training/normalizing_flows_training.ipynb @@ -46,17 +46,30 @@ "cell_type": "code", "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n" + ] + } + ], "source": [ "from pythae.models.normalizing_flows import MAFConfig, MAF, NFModel, IAF, IAFConfig\n", "\n", "prior = MultivariateNormal(torch.zeros(2).to(device), torch.eye(2).to(device))\n", "\n", - "conf = MAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_made_blocks=4, include_batch_norm=False)\n", - "flow = MAF(conf)\n", + "#conf = MAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_blocks=4, include_batch_norm=False)\n", + "#flow = MAF(conf)\n", "\n", - "# conf = IAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_made_blocks=2, include_batch_norm=False)\n", - "# flow = IAF(conf)\n", + "conf = IAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_blocks=2, include_batch_norm=False)\n", + "flow = IAF(conf)\n", "\n", "\n", "contained_flow = NFModel(prior, flow)" @@ -86,13 +99,15 @@ "output_type": "stream", "text": [ "Preprocessing train data...\n", + "Checking train dataset...\n", "Preprocessing eval data...\n", "\n", + "Checking eval dataset...\n", "Using Base Trainer\n", "\n", "Model passed sanity check !\n", "\n", - "Created dummy_output_dir/IAF_training_2022-06-13_20-17-16. \n", + "Created dummy_output_dir/IAF_training_2022-10-11_15-06-47. \n", "Training config, checkpoints and final model will be saved here.\n", "\n", "Successfully launched training !\n", @@ -102,7 +117,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d5b4acaa8b324d8ea7bbcd4220c7a22f", + "model_id": "d2fe2a9dcda54285913667536f1934ee", "version_major": 2, "version_minor": 0 }, @@ -116,7 +131,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f7da1ea5cd7420daf99b72a95bdefb0", + "model_id": "a38c0939391d47698cdd132cc4a80723", "version_major": 2, "version_minor": 0 }, @@ -132,15 +147,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 196.502\n", - "Eval loss: 170.852\n", + "Train loss: 199.4044\n", + "Eval loss: 176.467\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0a41a183c9b04113b2cb80ece717c3bc", + "model_id": "388b9bf3761f41beab1b4c60a8b02b58", "version_major": 2, "version_minor": 0 }, @@ -154,7 +169,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca5eba93763c455ba52cbfccf071bc98", + "model_id": "92feeb41477047f9986683d640b7227b", "version_major": 2, "version_minor": 0 }, @@ -170,15 +185,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 160.8794\n", - "Eval loss: 149.645\n", + "Train loss: 164.1447\n", + "Eval loss: 152.2655\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e1896e06c544bbfbc65be01695c0930", + "model_id": "a035325e88054dfaaeca69485f80cca4", "version_major": 2, "version_minor": 0 }, @@ -192,7 +207,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aabbd38611b84bb8ba350f9a89ef0ae9", + "model_id": "88223fe76c7a4639ac10f363086c0087", "version_major": 2, "version_minor": 0 }, @@ -208,15 +223,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 143.3026\n", - "Eval loss: 136.63\n", + "Train loss: 142.708\n", + "Eval loss: 134.5548\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06c7c90850ff41a895e17fe37c905b86", + "model_id": "94f727a49ebd44548b1911cd2dc4eb12", "version_major": 2, "version_minor": 0 }, @@ -230,7 +245,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40052dd95f8c47f99f41b9ce040b676a", + "model_id": "967fc0ff091040ba896b586bfc4598a1", "version_major": 2, "version_minor": 0 }, @@ -246,15 +261,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 134.135\n", - "Eval loss: 128.9466\n", + "Train loss: 132.302\n", + "Eval loss: 126.5011\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e976d434e2047f6bcad4ded805310cd", + "model_id": "1b03b75260d94369a5469092dddd8fc6", "version_major": 2, "version_minor": 0 }, @@ -268,7 +283,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80781e4b476740609b5d6ccc26fc8cb2", + "model_id": "1f87e808ba9f4e09836058afba0f1599", "version_major": 2, "version_minor": 0 }, @@ -284,15 +299,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 128.7787\n", - "Eval loss: 125.3792\n", + "Train loss: 124.4396\n", + "Eval loss: 119.2079\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "380762e321c3484f96897aae58433e1d", + "model_id": "f40fc1cda96d4d1586c51feafe546916", "version_major": 2, "version_minor": 0 }, @@ -306,7 +321,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d36e852ac94421f937918ceb3920208", + "model_id": "4df3fe2fa3bd4464a3048f7607138b9d", "version_major": 2, "version_minor": 0 }, @@ -322,15 +337,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 124.8889\n", - "Eval loss: 131.5444\n", + "Train loss: 118.2179\n", + "Eval loss: 114.9426\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bccd0ab269d143818e2480abb6896cab", + "model_id": "a292f721db194b22b6d47e64bb15be3a", "version_major": 2, "version_minor": 0 }, @@ -344,7 +359,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a19e3d4d2ae44875b24c256ec2b699d9", + "model_id": "62fda9e50348436abf27dc092dc62414", "version_major": 2, "version_minor": 0 }, @@ -360,15 +375,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 132.9134\n", - "Eval loss: 120.4659\n", + "Train loss: 109.7212\n", + "Eval loss: 101.282\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "757a7776313e402eaccfbab683c7dd46", + "model_id": "ed07549ebc7146608e5b4eb08f417d99", "version_major": 2, "version_minor": 0 }, @@ -382,7 +397,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f0ad9e907fa4f49b57e0a9c72b3df8b", + "model_id": "c5b927ddc9c54986a9f39b02b95e11e9", "version_major": 2, "version_minor": 0 }, @@ -398,15 +413,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 115.3224\n", - "Eval loss: 116.0495\n", + "Train loss: 97.6262\n", + "Eval loss: 96.3474\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "70417b1bd7714f2e93755b803d72c3c4", + "model_id": "4501d6d63f5c49afb11a05fac860248d", "version_major": 2, "version_minor": 0 }, @@ -420,7 +435,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61f6655fa472444892c58afdd35dd741", + "model_id": "d58d86e5a82d482ca8988df8f9fb44ba", "version_major": 2, "version_minor": 0 }, @@ -436,15 +451,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 113.405\n", - "Eval loss: 109.1003\n", + "Train loss: 89.5439\n", + "Eval loss: 92.045\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4c8ed7c985bf43b29bb5c892f5b2ef4a", + "model_id": "5ab9ebc6250b48278f475b3041f81c88", "version_major": 2, "version_minor": 0 }, @@ -458,7 +473,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c0a270b88f2b4f0491eefbfad7202035", + "model_id": "540d52509b824f1a9ff30e307eba892d", "version_major": 2, "version_minor": 0 }, @@ -474,15 +489,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 108.749\n", - "Eval loss: 107.0812\n", + "Train loss: 82.0804\n", + "Eval loss: 85.5623\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c20737ccb4ff4234abae047cdf619e17", + "model_id": "c1496c0dbc394352981b00fe42bddce7", "version_major": 2, "version_minor": 0 }, @@ -496,7 +511,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2a206580b2e4154ac17a921422a4b22", + "model_id": "a6a126ac7dc8444ca332936a5ad20578", "version_major": 2, "version_minor": 0 }, @@ -512,15 +527,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 106.9947\n", - "Eval loss: 103.0454\n", + "Train loss: 76.5315\n", + "Eval loss: 73.6278\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc7cfaa7052b4fa097721e6b88f6023f", + "model_id": "0336b942af414b9c8b57e53cae7b5838", "version_major": 2, "version_minor": 0 }, @@ -534,7 +549,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d323205cb5c4b53b84eba5ab13782d2", + "model_id": "d6066f7107a04dc99f1c2b558563083e", "version_major": 2, "version_minor": 0 }, @@ -550,15 +565,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 99.1485\n", - "Eval loss: 93.6906\n", + "Train loss: 71.9745\n", + "Eval loss: 81.3258\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "94b94ce3c93944fba5f0eb4ddc179bed", + "model_id": "c8b3fdd0ff5b4c02a34dbb7f5b222e3c", "version_major": 2, "version_minor": 0 }, @@ -572,7 +587,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50267d3e762a4d7f9aa32907b9605498", + "model_id": "0599c7ac077144288404925cb02d4b29", "version_major": 2, "version_minor": 0 }, @@ -588,15 +603,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 85.6769\n", - "Eval loss: 82.0904\n", + "Train loss: 69.2364\n", + "Eval loss: 65.1621\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a91431c788d49e0953565352542d426", + "model_id": "e1cfcf26385a4723a70ce77ed8da9729", "version_major": 2, "version_minor": 0 }, @@ -610,7 +625,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0565360eb7904723b2bc24ff09b28d82", + "model_id": "b84cfdb733804993aa0c45b95af42c3d", "version_major": 2, "version_minor": 0 }, @@ -626,15 +641,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 74.6891\n", - "Eval loss: 75.3146\n", + "Train loss: 68.1645\n", + "Eval loss: 69.9009\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eea31a6788e04bfa88276760683a615b", + "model_id": "de6cc0d009e34c898059c018195bea12", "version_major": 2, "version_minor": 0 }, @@ -648,7 +663,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5af4f21b9b5a4151aa8c30cc387ab45c", + "model_id": "a8bfcc701d8e46838c227086bb442964", "version_major": 2, "version_minor": 0 }, @@ -664,15 +679,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 73.2037\n", - "Eval loss: 75.3926\n", + "Train loss: 66.6123\n", + "Eval loss: 66.6524\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0072d6b8740b4044a2f0c8305be898f8", + "model_id": "4c75268957294e108b8b02b9195e32bd", "version_major": 2, "version_minor": 0 }, @@ -686,7 +701,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40a1f0a1f05f407f8cb421a04dc65245", + "model_id": "c967fbe92cbb48069fb558c5b54a87e0", "version_major": 2, "version_minor": 0 }, @@ -702,15 +717,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 69.9102\n", - "Eval loss: 73.4069\n", + "Train loss: 65.1784\n", + "Eval loss: 67.2206\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9071768cbc28448cad4d23a3088626ec", + "model_id": "27ff1c78c04b42ffa736282109e1de0e", "version_major": 2, "version_minor": 0 }, @@ -724,7 +739,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c7eabed3d4e44388c83764c071a78c4", + "model_id": "b8626c240f9344f790e23e2c4e268d97", "version_major": 2, "version_minor": 0 }, @@ -740,15 +755,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 67.7733\n", - "Eval loss: 68.4136\n", + "Train loss: 64.5969\n", + "Eval loss: 64.0229\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d14576255d224cb08850a062426a422c", + "model_id": "c298cc812a564a2b8a3d65fb4b07b5c4", "version_major": 2, "version_minor": 0 }, @@ -762,7 +777,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "09173c98513a478cb261de66695a16c4", + "model_id": "1395078de47e4b3bbf2d47fa0183b79b", "version_major": 2, "version_minor": 0 }, @@ -778,15 +793,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 67.2819\n", - "Eval loss: 71.3228\n", + "Train loss: 62.1612\n", + "Eval loss: 63.5125\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83234a9b9f5d49fb81399c1bf1ed2f76", + "model_id": "2915e53fb15244e5a5dbe94d667d3ccd", "version_major": 2, "version_minor": 0 }, @@ -800,7 +815,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f5d1016e6d8d4cf18bab788b3b9b816d", + "model_id": "4341612afa5e48968710bd24151e70be", "version_major": 2, "version_minor": 0 }, @@ -816,15 +831,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 67.9069\n", - "Eval loss: 68.826\n", + "Train loss: 62.9392\n", + "Eval loss: 64.693\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c355901907864553a3521a795e53636f", + "model_id": "dd130c7341d441c090f484270ea889b0", "version_major": 2, "version_minor": 0 }, @@ -838,7 +853,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "404180ef20a64af0a4e32ffe5f32d346", + "model_id": "5ddf00a186dc4529a0345f8a78f17f46", "version_major": 2, "version_minor": 0 }, @@ -854,15 +869,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 65.4511\n", - "Eval loss: 65.7339\n", + "Train loss: 62.7221\n", + "Eval loss: 70.1017\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c92aeeefbe54d7bb0ab81231e2bd7fb", + "model_id": "7a083b49e9214dd5b090009ff0d351ab", "version_major": 2, "version_minor": 0 }, @@ -876,7 +891,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "be94925ea59145cdbc5e37ca3de47417", + "model_id": "f92980523a734c8aba230df34ff6d43d", "version_major": 2, "version_minor": 0 }, @@ -892,15 +907,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 65.7002\n", - "Eval loss: 64.076\n", + "Train loss: 60.6886\n", + "Eval loss: 61.0657\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "11af2d040e4047868335f79dae50e742", + "model_id": "182d9bacf1ea464db26d14e67beb3a2d", "version_major": 2, "version_minor": 0 }, @@ -914,7 +929,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebe2d2f5555f4dd7a732fbb6a8db05cf", + "model_id": "0824368506f44e52a758d0c30336edda", "version_major": 2, "version_minor": 0 }, @@ -930,15 +945,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.9603\n", - "Eval loss: 67.954\n", + "Train loss: 62.3967\n", + "Eval loss: 60.0661\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abd2f574d97f42af9b398a07f63cbb65", + "model_id": "32f19d876f424213a47f6aa3eed2d840", "version_major": 2, "version_minor": 0 }, @@ -952,7 +967,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30aa0478cd874637992b7fa5649f3d29", + "model_id": "9b00bf2985244702974e256c9e386a34", "version_major": 2, "version_minor": 0 }, @@ -968,15 +983,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.3227\n", - "Eval loss: 64.0264\n", + "Train loss: 60.6978\n", + "Eval loss: 61.6838\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67bae0c9d34846958f7e4a56b20ccdc7", + "model_id": "f80fc83827ad4d539137f8b127636512", "version_major": 2, "version_minor": 0 }, @@ -990,7 +1005,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7fa8755b18949268f0394dc9c7b0439", + "model_id": "1ef8955acdaa482f9976185b7b04ae00", "version_major": 2, "version_minor": 0 }, @@ -1006,15 +1021,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.8905\n", - "Eval loss: 66.8284\n", + "Train loss: 59.372\n", + "Eval loss: 59.5102\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e034722d61d4439db1ab8d75af961d55", + "model_id": "37ccfa6466ea4ab5bcedf7f8f3fd0c80", "version_major": 2, "version_minor": 0 }, @@ -1028,7 +1043,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce01689368f3418b859d3114cf0aebe3", + "model_id": "c04802373da146918261f1e281e53784", "version_major": 2, "version_minor": 0 }, @@ -1044,15 +1059,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.0264\n", - "Eval loss: 64.7744\n", + "Train loss: 60.7134\n", + "Eval loss: 60.2775\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "253a6c951d254300a99e01726243ded3", + "model_id": "06ec963f7f16456a8ac45648b42145e1", "version_major": 2, "version_minor": 0 }, @@ -1066,7 +1081,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3362b09bcf745c6b772a539ff1b4bfb", + "model_id": "e3090e88a30b4d0ba398eb6cc668a0fc", "version_major": 2, "version_minor": 0 }, @@ -1082,15 +1097,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 62.6988\n", - "Eval loss: 65.8102\n", + "Train loss: 59.2085\n", + "Eval loss: 61.0245\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85bc433a6a7a4a979e3719d35354d0b1", + "model_id": "b0a1a8e9f70b4365b8f15d59dfa23319", "version_major": 2, "version_minor": 0 }, @@ -1104,7 +1119,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "003c72487617416bb8404182b762362d", + "model_id": "ad8ab9ac712b4950b7681582b3dc8cbf", "version_major": 2, "version_minor": 0 }, @@ -1120,15 +1135,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 64.4531\n", - "Eval loss: 69.3875\n", + "Train loss: 60.1679\n", + "Eval loss: 62.2964\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae5a2f203b7a4c0a9e83a83a37682702", + "model_id": "3089a9f6505d409babf9887eb876f30e", "version_major": 2, "version_minor": 0 }, @@ -1142,7 +1157,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d25e999f9e1f4f0fb96f28220c14195b", + "model_id": "c72e51948fd04fcabac267078410f55b", "version_major": 2, "version_minor": 0 }, @@ -1158,15 +1173,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 64.0672\n", - "Eval loss: 70.9859\n", + "Train loss: 59.6164\n", + "Eval loss: 59.9354\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a53ac86969141d7baa266180a3c73d6", + "model_id": "f689243239a049278632ef6a235f0ecc", "version_major": 2, "version_minor": 0 }, @@ -1180,7 +1195,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f7d910e3bdc4dae9a0110a721046bc3", + "model_id": "ec00593cf81640e696f0dc8ec36d82b3", "version_major": 2, "version_minor": 0 }, @@ -1196,15 +1211,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 62.9348\n", - "Eval loss: 63.0601\n", + "Train loss: 58.9498\n", + "Eval loss: 65.5969\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4be7dfbac6c48579a23372061faa324", + "model_id": "577b34d36fa5407eb3fb846293cc40e9", "version_major": 2, "version_minor": 0 }, @@ -1218,7 +1233,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78bb9d67acfe4a70ac21bc7e615e788b", + "model_id": "f01273b960cd439fb17b5a5d26d1cc9b", "version_major": 2, "version_minor": 0 }, @@ -1234,15 +1249,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.0094\n", - "Eval loss: 63.3553\n", + "Train loss: 58.3376\n", + "Eval loss: 61.5284\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "86a843d3ca1d404fb3f9c24ca96601f1", + "model_id": "bcf2419d537d476e99cc2fdba3169d80", "version_major": 2, "version_minor": 0 }, @@ -1256,7 +1271,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "075907fe16c14b3cbe87624de4c12feb", + "model_id": "87c6f60702dd496a84cbcacc3fea43ef", "version_major": 2, "version_minor": 0 }, @@ -1272,15 +1287,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.8406\n", - "Eval loss: 65.506\n", + "Train loss: 58.9406\n", + "Eval loss: 59.4494\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea9f829c08f64155a8ea050c83ce0d0f", + "model_id": "e9b4a75ac60d4332a841e32754bebad8", "version_major": 2, "version_minor": 0 }, @@ -1294,7 +1309,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34c687ee18824dc987fdbb52ca032200", + "model_id": "32ee6941fdbf469e8582cc2ef6038ec7", "version_major": 2, "version_minor": 0 }, @@ -1310,15 +1325,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 63.0173\n", - "Eval loss: 60.3433\n", + "Train loss: 58.7428\n", + "Eval loss: 63.984\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a60c614b5084c13b11fc50ad4e89f9c", + "model_id": "52bfb8eecfd44944814294201839c9f9", "version_major": 2, "version_minor": 0 }, @@ -1332,7 +1347,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1a4e09db5584286994e7f7dace14860", + "model_id": "6ef0486165954ac8832c92b1a0a7bf7f", "version_major": 2, "version_minor": 0 }, @@ -1348,15 +1363,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 61.2818\n", - "Eval loss: 60.9274\n", + "Train loss: 58.7539\n", + "Eval loss: 59.8462\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02e4f336ae7244aca0618f3e16b4f4e6", + "model_id": "4bc4c94cfcdc403b8761025d35790dd8", "version_major": 2, "version_minor": 0 }, @@ -1370,7 +1385,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a06e8ea8db1e48daabc021d435793101", + "model_id": "032018ae485a4fbfada04d845793fa24", "version_major": 2, "version_minor": 0 }, @@ -1386,15 +1401,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 61.5882\n", - "Eval loss: 63.5523\n", + "Train loss: 58.4324\n", + "Eval loss: 62.1807\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4154c03809d442d4895669d36b459d7d", + "model_id": "82c6f27f42ae4f45960b7d7c807a6001", "version_major": 2, "version_minor": 0 }, @@ -1408,7 +1423,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d66aa3b03e2a430ca21176cb34875d83", + "model_id": "7950e46810cd4ba99e5e54cfb1e11bdf", "version_major": 2, "version_minor": 0 }, @@ -1424,15 +1439,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 61.0341\n", - "Eval loss: 65.3404\n", + "Train loss: 57.8983\n", + "Eval loss: 58.6377\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c0a2df80bab479aba69a4616df7157f", + "model_id": "db88c15122ad40a38a6f1c2ba0616d99", "version_major": 2, "version_minor": 0 }, @@ -1446,7 +1461,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d0d6ed393d1b4552938e7826414a6f45", + "model_id": "027f7c93668240f18ffb2e66474f0a33", "version_major": 2, "version_minor": 0 }, @@ -1462,15 +1477,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.7392\n", - "Eval loss: 61.1621\n", + "Train loss: 57.4275\n", + "Eval loss: 64.1673\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33fe2cec712a4f1f8d25b1468e64f5a6", + "model_id": "d336f721407343dabd917d499f17b22a", "version_major": 2, "version_minor": 0 }, @@ -1484,7 +1499,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6cfed3b1989740129c04ec7ab756891c", + "model_id": "f3c847345fca4e89892407b5dc066a88", "version_major": 2, "version_minor": 0 }, @@ -1500,15 +1515,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 61.3899\n", - "Eval loss: 64.2951\n", + "Train loss: 57.8796\n", + "Eval loss: 58.3574\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "612c0e22d0ac4a4da4014cfe4670ba52", + "model_id": "8f38922f879f4274abf4ba00731ee073", "version_major": 2, "version_minor": 0 }, @@ -1522,7 +1537,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d0f032802fd43a7bb8eb73ade26ed90", + "model_id": "d31046bc0eea423ea6e859fcccf2cbc6", "version_major": 2, "version_minor": 0 }, @@ -1538,15 +1553,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 61.0669\n", - "Eval loss: 62.3087\n", + "Train loss: 59.2392\n", + "Eval loss: 58.4726\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ece6aa3fee674cb696540cfd5cbb4a87", + "model_id": "e209930aed554135867c778cf587d8f2", "version_major": 2, "version_minor": 0 }, @@ -1560,7 +1575,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b34a64d5d2246debc2d742a2ecf757d", + "model_id": "dde3663b5ff1461d9757e8ce6a82c0d0", "version_major": 2, "version_minor": 0 }, @@ -1576,15 +1591,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.5069\n", - "Eval loss: 62.6113\n", + "Train loss: 58.9304\n", + "Eval loss: 60.2344\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d3e2d4697d5489d9172914edc348fe3", + "model_id": "0c79de023ad54c1fad7e75bba2ccda66", "version_major": 2, "version_minor": 0 }, @@ -1598,7 +1613,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebadc53006cf4e5f90a716a2273474bf", + "model_id": "0bd41071d806465da784a51b76017bc5", "version_major": 2, "version_minor": 0 }, @@ -1614,15 +1629,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.4862\n", - "Eval loss: 63.8374\n", + "Train loss: 59.4971\n", + "Eval loss: 58.9068\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef55830298ca41e1942c44368ee25f4e", + "model_id": "6c7c53fddeff41f28bc5e17f3f0d22d2", "version_major": 2, "version_minor": 0 }, @@ -1636,7 +1651,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e32c41a1d1d449d19c27a53d5223ce9e", + "model_id": "439ff9cae0b64b45b0f9c4d3af5df335", "version_major": 2, "version_minor": 0 }, @@ -1652,15 +1667,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 61.2231\n", - "Eval loss: 61.2632\n", + "Train loss: 56.7857\n", + "Eval loss: 58.7656\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "429b0713f14d4f3db0891aa60c1f6e36", + "model_id": "1a0e93f3e9414f5e959f1f6ec2518c7b", "version_major": 2, "version_minor": 0 }, @@ -1674,7 +1689,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1bfb612b8e1d4fe29ea3a0684fa603bd", + "model_id": "ebc32db6a32f4f4eb9d4d06dec3c10c4", "version_major": 2, "version_minor": 0 }, @@ -1690,15 +1705,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.7346\n", - "Eval loss: 62.7917\n", + "Train loss: 58.0746\n", + "Eval loss: 59.5478\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3aca37c6da2a430fbffb654b5aea2d0d", + "model_id": "b96e6d76fee2496d898bb7679b027897", "version_major": 2, "version_minor": 0 }, @@ -1712,7 +1727,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f969328ea9ef4b7bb238d28b9ab007c9", + "model_id": "b719bd70d5e544299909bbaa7dc6e5b4", "version_major": 2, "version_minor": 0 }, @@ -1728,22 +1743,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 59.5126\n", - "Eval loss: 60.9537\n", + "Train loss: 57.3255\n", + "Eval loss: 58.4688\n", "--------------------------------------------------------------------------\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 43: reducing learning rate of group 0 to 5.0000e-04.\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de7bb71b5e964b74b73b501ccb11aeae", + "model_id": "8abb425564f640caa0cacc68f3124f22", "version_major": 2, "version_minor": 0 }, @@ -1757,7 +1765,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf9d34cf69174afaa6579f2bedfd5b7d", + "model_id": "f04b8b953e294b42a25faeb5efa851e4", "version_major": 2, "version_minor": 0 }, @@ -1773,15 +1781,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.7464\n", - "Eval loss: 58.6463\n", + "Train loss: 58.7062\n", + "Eval loss: 61.1852\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f89e1baadba64c5a8745673a53e72707", + "model_id": "da168e4da76b4fc9b42c3d3d0d4b3458", "version_major": 2, "version_minor": 0 }, @@ -1795,7 +1803,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bcb60e650a474f458d70c3dbcdc7e898", + "model_id": "aeb79ab8c1654a9fb3f19616fe02eb49", "version_major": 2, "version_minor": 0 }, @@ -1811,15 +1819,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.969\n", - "Eval loss: 58.2375\n", + "Train loss: 57.2106\n", + "Eval loss: 68.4626\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15160c531ed044568d8e5ed1cf54b386", + "model_id": "49c0c088fec54644adbfc18f5451dcb6", "version_major": 2, "version_minor": 0 }, @@ -1833,7 +1841,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "557b803034534e43adb13fdbf7f1d126", + "model_id": "637b6e9aa58a47e5a41a133379c01667", "version_major": 2, "version_minor": 0 }, @@ -1849,15 +1857,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.8539\n", - "Eval loss: 58.1819\n", + "Train loss: 58.3036\n", + "Eval loss: 58.5033\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f726916c58249d08fb1d117f84be2eb", + "model_id": "2ff2db91e9b74216878206cb869ba202", "version_major": 2, "version_minor": 0 }, @@ -1871,7 +1879,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b6ee7735727416bb57ec7592d183ff8", + "model_id": "9a0ca3b1055c4d7b996804587f89fb5d", "version_major": 2, "version_minor": 0 }, @@ -1887,15 +1895,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.3385\n", - "Eval loss: 59.312\n", + "Train loss: 58.1165\n", + "Eval loss: 65.9938\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c6eda59fe1664e6eaac9475b6c0ed434", + "model_id": "252ad7068a7e42d6bf1a1484ec972bbe", "version_major": 2, "version_minor": 0 }, @@ -1909,7 +1917,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "175761b2eb2b4e4eb620798e0772d71a", + "model_id": "945c835668794d03aeeb532d3a5184cc", "version_major": 2, "version_minor": 0 }, @@ -1925,15 +1933,22 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.5298\n", - "Eval loss: 58.8087\n", + "Train loss: 58.2602\n", + "Eval loss: 62.1577\n", "--------------------------------------------------------------------------\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 00048: reducing learning rate of group 0 to 5.0000e-04.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76d1200fe09f403c9a4579ce45460dba", + "model_id": "9ae42b6ef8854dd981d404d8108b1ef3", "version_major": 2, "version_minor": 0 }, @@ -1947,7 +1962,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "005abfe2d46549cf8d31f32cbc20d39b", + "model_id": "51400a916bd943b0abe00be488f913e4", "version_major": 2, "version_minor": 0 }, @@ -1963,15 +1978,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.6616\n", - "Eval loss: 57.7834\n", + "Train loss: 55.8915\n", + "Eval loss: 60.544\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24e3862b5f6442c8a9d0dc3db02ea5fe", + "model_id": "05825657e4e9442b9c606bc2933dc650", "version_major": 2, "version_minor": 0 }, @@ -1985,7 +2000,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a42a37facf44abfa4fda8a64e2c2144", + "model_id": "021ebb4f9cde4e019dc4788f5df903bb", "version_major": 2, "version_minor": 0 }, @@ -2001,11 +2016,11 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.3789\n", - "Eval loss: 58.8588\n", + "Train loss: 55.9214\n", + "Eval loss: 57.527\n", "--------------------------------------------------------------------------\n", "Training ended!\n", - "Saved final model in dummy_output_dir/IAF_training_2022-06-13_20-17-16/final_model\n" + "Saved final model in dummy_output_dir/IAF_training_2022-10-11_15-06-47/final_model\n" ] } ], @@ -2018,7 +2033,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -2028,22 +2043,34 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -2064,6 +2091,48 @@ "plt.scatter(z[:,0], z[:,1], c='r', s=20, alpha=0.5, label='transformed gaussian')\n", "plt.legend()" ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "z = maf_rec(train_moons.cuda()[:1000]).out.detach().cpu()\n", + "plt.scatter(z_sample.cpu()[:,0], z_sample.cpu()[:,1], c='b', s=20, alpha=0.5, label='prior distribution')\n", + "plt.scatter(z[:,0], z[:,1], c='r', s=20, alpha=0.5, label='transformed two moons')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/notebooks/models_training/vae_iaf_training.ipynb b/examples/notebooks/models_training/vae_iaf_training.ipynb index 6c6f0c19..aedd85df 100644 --- a/examples/notebooks/models_training/vae_iaf_training.ipynb +++ b/examples/notebooks/models_training/vae_iaf_training.ipynb @@ -65,9 +65,10 @@ "model_config = VAE_IAF_Config(\n", " input_dim=(1, 28, 28),\n", " latent_dim=16,\n", - " n_made_blocks=2,\n", + " n_blocks=2,\n", " n_hidden_in_made=3,\n", - " hidden_size=128\n", + " hidden_size=128,\n", + " context_dim=25\n", ")\n", "\n", "model = VAE_IAF(\n", @@ -118,7 +119,16 @@ "outputs": [], "source": [ "last_training = sorted(os.listdir('my_model'))[-1]\n", - "trained_model = AutoModel.load_from_folder(os.path.join('my_model', last_training, 'final_model'))" + "trained_model = AutoModel.load_from_folder(os.path.join('my_model/VAE_IAF_training_2022-10-11_16-30-05', 'final_model'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trained_model.encoder" ] }, { diff --git a/examples/scripts/reproducibility/sync.ipynb b/examples/scripts/reproducibility/sync.ipynb new file mode 100644 index 00000000..7b8f5603 --- /dev/null +++ b/examples/scripts/reproducibility/sync.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/clement/anaconda3/envs/pythae_37/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import torch\n", + "import os\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = torch.tensor(\n", + " np.load(os.path.join(\"data/mnist\", \"test_data.npz\"))[\"data\"]\n", + " / 255.0\n", + " ).type(torch.float)#.clamp(1e-5, 1-1e-5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = np.loadtxt(os.path.join(\"data/binary_mnist\", \"binarized_mnist_test.amat\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pythae.models import AutoModel" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoModel.load_from_folder('reproducibility/good_models/mnist/PIWAE_training_2022-09-06_19-10-31/final_model').eval().cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading PIWAE model to clementchadebec/reproduced_piwae repo in HF hub...\n", + "Creating reproduced_piwae in the HF hub since it does not exist...\n", + "Successfully created reproduced_piwae in the HF hub!\n" + ] + } + ], + "source": [ + "model.push_to_hf_hub('clementchadebec/reproduced_piwae')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading config file ...\n", + "Downloading PIWAE files for rebuilding...\n", + "Successfully downloaded PIWAE model!\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pythae.models import AutoModel\n", + "model = AutoModel.load_from_hf_hub('clementchadebec/reproduced_piwae', allow_pickle=True).eval().cuda()\n", + "model.training" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/clement/anaconda3/envs/pythae_37/lib/python3.7/site-packages/ipykernel_launcher.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " \"\"\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-84.57302521800995]\n", + "[-84.57302521800995, -84.57829508743286]\n", + "[-84.57302521800995, -84.57829508743286, -84.577232771492]\n", + "[-84.57302521800995, -84.57829508743286, -84.577232771492, -84.57393670692444]\n", + "[-84.57302521800995, -84.57829508743286, -84.577232771492, -84.57393670692444, -84.58700823421478]\n" + ] + }, + { + "data": { + "text/plain": [ + "(-84.57789960361481, 0.004960492095356239)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nll = []\n", + "#with torch.no_grad():\n", + "test_data = (test_data > torch.distributions.Uniform(0, 1).sample(test_data.shape).to(test_data.device)).float()\n", + "for i in range(5):\n", + " nll_i = model.get_nll(torch.tensor(test_data).float().cuda(), n_samples=5000, batch_size=5000)\n", + " nll.append(nll_i)\n", + " print(nll)\n", + "np.mean(nll), np.std(nll)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PIWAE(\n", + " (decoder): Decoder(\n", + " (fc1): Linear(in_features=50, out_features=200, bias=True)\n", + " (fc2): Linear(in_features=200, out_features=200, bias=True)\n", + " (fc3): Linear(in_features=200, out_features=784, bias=True)\n", + " )\n", + " (encoder): Encoder(\n", + " (fc1): Linear(in_features=784, out_features=200, bias=True)\n", + " (fc2): Linear(in_features=200, out_features=200, bias=True)\n", + " (embedding): Linear(in_features=200, out_features=50, bias=True)\n", + " (log_var): Linear(in_features=200, out_features=50, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pythae.models import AutoModel\n", + "model = AutoModel.load_from_hf_hub('clementchadebec/reproduced_wrapped_poincare_vae', allow_pickle=True).train().cuda()\n", + "model.training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nll = []\n", + "#with torch.no_grad():\n", + "for i in range(1):\n", + " nll_i = model.get_nll(torch.tensor(test_data).float().cuda(), n_samples=5000, batch_size=5000)\n", + " nll.append(nll_i)\n", + " print(nll)\n", + "np.mean(nll), np.std(nll)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.load_from_folder('/home/clement/Documents/benchmark_VAE/examples/scripts/reproducibility/reproducibility/mnist/PoincareVAE_training_2022-09-03_10-31-48/final_model').training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.posterior" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.push_to_hf_hub('clementchadebec/reproduced_wrapped_poincare_vae')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "5e51c5ac46389dd7ba2bd8215d251ab84152720d3cad2ff91113d77594821aef" + }, + "kernelspec": { + "display_name": "Python 3.8.12 ('pythae')", + "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": "3.7.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/scripts/reproducibility/vae_nf.py b/examples/scripts/reproducibility/vae_nf.py new file mode 100644 index 00000000..800720b3 --- /dev/null +++ b/examples/scripts/reproducibility/vae_nf.py @@ -0,0 +1,583 @@ +import argparse +import datetime +import logging +import os +from time import time +from typing import List + +import numpy as np +import torch + +from pythae.data.preprocessors import DataProcessor +from pythae.models import AutoModel +from pythae.trainers import (AdversarialTrainerConfig, BaseTrainerConfig, + CoupledOptimizerTrainerConfig, BaseTrainer) + +from pythae.models.nn import BaseEncoder, BaseDecoder +import torch.nn as nn +from pythae.models.base.base_utils import ModelOutput + + +logger = logging.getLogger(__name__) +console = logging.StreamHandler() +logger.addHandler(console) +logger.setLevel(logging.INFO) + +PATH = os.path.dirname(os.path.abspath(__file__)) + +ap = argparse.ArgumentParser() + +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +ap.add_argument( + "--model_config", + help="path to model config file (expected json file)", + default=None, +) +ap.add_argument( + "--training_config", + help="path to training config_file (expected json file)", + default=os.path.join(PATH, "configs/base_training_config.json"), +) + + +args = ap.parse_args() + +### Define paper encoder network +class Encoder(BaseEncoder): + def __init__(self, args: dict): + BaseEncoder.__init__(self) + self.input_dim = args.input_dim + self.latent_dim = args.latent_dim + + layers = nn.ModuleList() + + layers.append( + nn.Sequential( + nn.Linear(np.prod(args.input_dim), 400), + nn.ReLU(), + #nn.Linear(400, 400), + #nn.ReLU() + ) + ) + + self.layers = layers + self.depth = len(layers) + + self.embedding = nn.Linear(400, self.latent_dim) + self.log_var = nn.Linear(400, self.latent_dim) + + + def forward(self, x, output_layer_levels: List[int] = None): + output = ModelOutput() + + max_depth = self.depth + + if output_layer_levels is not None: + + assert all( + self.depth >= levels > 0 or levels == -1 + for levels in output_layer_levels + ), ( + f"Cannot output layer deeper than depth ({self.depth}). Got ({output_layer_levels})." + ) + + if -1 in output_layer_levels: + max_depth = self.depth + else: + max_depth = max(output_layer_levels) + + out = x.reshape(-1, np.prod(self.input_dim)) + + for i in range(max_depth): + out = self.layers[i](out) + + if output_layer_levels is not None: + if i + 1 in output_layer_levels: + output[f"embedding_layer_{i+1}"] = out + if i + 1 == self.depth: + output["embedding"] = self.embedding(out) + output["log_covariance"] = self.log_var(out) + + return output + +### Define paper decoder network +class Decoder(BaseDecoder): + def __init__(self, args: dict): + BaseDecoder.__init__(self) + + self.input_dim = args.input_dim + + layers = nn.ModuleList() + + layers.append( + nn.Sequential( + nn.Linear(args.latent_dim, 400), + nn.ReLU(), + #nn.Linear(400, 400), + #nn.ReLU() + ) + ) + + layers.append( + nn.Sequential( + nn.Linear(400, np.prod(args.input_dim)), + nn.Sigmoid() + ) + ) + + self.layers = layers + self.depth = len(layers) + + def forward(self, z: torch.Tensor, output_layer_levels: List[int] = None): + + output = ModelOutput() + + max_depth = self.depth + + if output_layer_levels is not None: + + assert all( + self.depth >= levels > 0 or levels == -1 + for levels in output_layer_levels + ), ( + f"Cannot output layer deeper than depth ({self.depth}). " + f"Got ({output_layer_levels})." + ) + + if -1 in output_layer_levels: + max_depth = self.depth + else: + max_depth = max(output_layer_levels) + + out = z + + for i in range(max_depth): + out = self.layers[i](out) + + if output_layer_levels is not None: + if i + 1 in output_layer_levels: + output[f"reconstruction_layer_{i+1}"] = out + if i + 1 == self.depth: + output["reconstruction"] = out.reshape((z.shape[0],) + self.input_dim) + + return output + + + +def main(args): + + + try: + if args.dataset == "mnist": + logger.info(f"\nLoading {args.dataset} data...\n") + train_data = ( + np.load(os.path.join(PATH, f"data/{args.dataset}", "train_data.npz"))[ + "data" + ] + / 255.0 + ) + eval_data = ( + np.load(os.path.join(PATH, f"data/{args.dataset}", "eval_data.npz"))["data"] + / 255.0 + ) + test_data = ( + np.load(os.path.join(PATH, f"data/{args.dataset}", "test_data.npz"))["data"] + / 255.0 + ) + + else: + logger.info(f"\nLoading {args.dataset} data...\n") + train_data = np.loadtxt(os.path.join(PATH, f"data/{args.dataset}", "binarized_mnist_train.amat")) + eval_data = np.loadtxt(os.path.join(PATH, f"data/{args.dataset}", "binarized_mnist_valid.amat")) + test_data = np.loadtxt(os.path.join(PATH, f"data/{args.dataset}", "binarized_mnist_test.amat")) + #train_data = torch.cat((torch.tensor(train_data), torch.tensor(eval_data))) + + except Exception as e: + raise FileNotFoundError( + f"Unable to load the data from 'data/{args.dataset}' folder. Please check that both a " + "'train_data.npz' and 'eval_data.npz' are present in the folder.\n Data must be " + " under the key 'data', in the range [0-255] and shaped with channel in first " + "position\n" + f"Exception raised: {type(e)} with message: " + str(e) + ) from e + + logger.info("Successfully loaded data !\n") + logger.info("------------------------------------------------------------") + logger.info("Dataset \t \t Shape \t \t \t Range") + logger.info( + f"{args.dataset.upper()} train data: \t {train_data.shape} \t [{train_data.min()}-{train_data.max()}] " + ) + logger.info( + f"{args.dataset.upper()} eval data: \t {eval_data.shape} \t [{eval_data.min()}-{eval_data.max()}]" + ) + logger.info("------------------------------------------------------------\n") + + data_input_dim = tuple(train_data.shape[1:]) + + + if args.model_name == "vae": + from pythae.models import VAE, VAEConfig + + if args.model_config is not None: + model_config = VAEConfig.from_json_file(args.model_config) + + else: + model_config = VAEConfig() + + model_config.input_dim = data_input_dim + + model = VAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "iwae": + from pythae.models import IWAE, IWAEConfig + + if args.model_config is not None: + model_config = IWAEConfig.from_json_file(args.model_config) + + else: + model_config = IWAEConfig() + + model_config.input_dim = data_input_dim + + model = IWAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "info_vae": + from pythae.models import INFOVAE_MMD, INFOVAE_MMD_Config + + if args.model_config is not None: + model_config = INFOVAE_MMD_Config.from_json_file(args.model_config) + + else: + model_config = INFOVAE_MMD_Config() + + model_config.input_dim = data_input_dim + + model = INFOVAE_MMD( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "wae": + from pythae.models import WAE_MMD, WAE_MMD_Config + + if args.model_config is not None: + model_config = WAE_MMD_Config.from_json_file(args.model_config) + + else: + model_config = WAE_MMD_Config() + + model_config.input_dim = data_input_dim + + model = WAE_MMD( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "vamp": + from pythae.models import VAMP, VAMPConfig + + if args.model_config is not None: + model_config = VAMPConfig.from_json_file(args.model_config) + + else: + model_config = VAMPConfig() + + model_config.input_dim = data_input_dim + + model = VAMP( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "beta_vae": + from pythae.models import BetaVAE, BetaVAEConfig + + if args.model_config is not None: + model_config = BetaVAEConfig.from_json_file(args.model_config) + + else: + model_config = BetaVAEConfig() + + model_config.input_dim = data_input_dim + + model = BetaVAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "hvae": + from pythae.models import HVAE, HVAEConfig + + if args.model_config is not None: + model_config = HVAEConfig.from_json_file(args.model_config) + + else: + model_config = HVAEConfig() + + model_config.input_dim = data_input_dim + + model = HVAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "rhvae": + from pythae.models import RHVAE, RHVAEConfig + + if args.model_config is not None: + model_config = RHVAEConfig.from_json_file(args.model_config) + + else: + model_config = RHVAEConfig() + + model_config.input_dim = data_input_dim + + model = RHVAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "aae": + from pythae.models import Adversarial_AE, Adversarial_AE_Config + + if args.model_config is not None: + model_config = Adversarial_AE_Config.from_json_file(args.model_config) + + else: + model_config = Adversarial_AE_Config() + + model_config.input_dim = data_input_dim + + model = Adversarial_AE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + + elif args.model_name == "msssim_vae": + from pythae.models import MSSSIM_VAE, MSSSIM_VAEConfig + + if args.model_config is not None: + model_config = MSSSIM_VAEConfig.from_json_file(args.model_config) + + else: + model_config = MSSSIM_VAEConfig() + + model_config.input_dim = data_input_dim + + model = MSSSIM_VAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "factor_vae": + from pythae.models import FactorVAE, FactorVAEConfig + + if args.model_config is not None: + model_config = FactorVAEConfig.from_json_file(args.model_config) + + else: + model_config = FactorVAEConfig() + + model_config.input_dim = data_input_dim + + model = FactorVAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "beta_tc_vae": + from pythae.models import BetaTCVAE, BetaTCVAEConfig + + if args.model_config is not None: + model_config = BetaTCVAEConfig.from_json_file(args.model_config) + + else: + model_config = BetaTCVAEConfig() + + model_config.input_dim = data_input_dim + + model = BetaTCVAE( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "vae_iaf": + from pythae.models import VAE_IAF, VAE_IAF_Config + + if args.model_config is not None: + model_config = VAE_IAF_Config.from_json_file(args.model_config) + + else: + model_config = VAE_IAF_Config() + + model_config.input_dim = data_input_dim + + model = VAE_IAF( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + elif args.model_name == "vae_lin_nf": + from pythae.models import VAE_LinNF, VAE_LinNF_Config + + if args.model_config is not None: + model_config = VAE_LinNF_Config.from_json_file(args.model_config) + + else: + model_config = VAE_LinNF_Config() + + model_config.input_dim = data_input_dim + + model = VAE_LinNF( + model_config=model_config, + encoder=Encoder(model_config), + decoder=Decoder(model_config), + ) + + print(model) + + logger.info(f"Successfully build {args.model_name.upper()} model !\n") + + encoder_num_param = sum( + p.numel() for p in model.encoder.parameters() if p.requires_grad + ) + decoder_num_param = sum( + p.numel() for p in model.decoder.parameters() if p.requires_grad + ) + total_num_param = sum(p.numel() for p in model.parameters() if p.requires_grad) + logger.info( + "----------------------------------------------------------------------" + ) + logger.info("Model \t Encoder params \t Decoder params \t Total params") + logger.info( + f"{args.model_name.upper()} \t {encoder_num_param} \t \t {decoder_num_param}" + f" \t \t {total_num_param}" + ) + logger.info( + "----------------------------------------------------------------------\n" + ) + + logger.info(f"Model config of {args.model_name.upper()}: {model_config}\n") + + if model.model_name == "RAE_L2": + training_config = CoupledOptimizerTrainerConfig.from_json_file( + args.training_config + ) + + elif model.model_name == "Adversarial_AE" or model.model_name == "FactorVAE": + training_config = AdversarialTrainerConfig.from_json_file(args.training_config) + + elif model.model_name == "VAEGAN": + from pythae.trainers import (CoupledOptimizerAdversarialTrainer, + CoupledOptimizerAdversarialTrainerConfig) + + training_config = CoupledOptimizerAdversarialTrainerConfig.from_json_file( + args.training_config + ) + + else: + training_config = BaseTrainerConfig.from_json_file(args.training_config) + + # reset the `output_dir` to allow job-arrays + slurm_array_task_id = 0#int(os.environ["SLURM_ARRAY_TASK_ID"]) + + training_config.output_dir = os.path.join( + f"reproducibility/{args.dataset}",f"{model.model_name}".lower(), f"{slurm_array_task_id}" + ) + + logger.info(f"Training config: {training_config}\n") + + callbacks = [] + + if args.use_wandb: + from pythae.trainers.training_callbacks import WandbCallback + + wandb_cb = WandbCallback() + wandb_cb.setup( + training_config, + model_config=model_config, + project_name=args.wandb_project, + entity_name=args.wandb_entity, + ) + + callbacks.append(wandb_cb) + + ### Process data + data_processor = DataProcessor() + logger.info("Preprocessing train data...") + train_data = data_processor.process_data(train_data) + train_dataset = data_processor.to_dataset(train_data) + + logger.info("Preprocessing eval data...\n") + eval_data = data_processor.process_data(eval_data) + eval_dataset = data_processor.to_dataset(eval_data) + + ### Optimizer + optimizer = torch.optim.RMSprop(model.parameters(), lr=training_config.learning_rate) + + ### Scheduler + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, milestones=[2000000], gamma=10**(-1/5), verbose=True + ) + + seed = 123 + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + logger.info("Using Base Trainer\n") + trainer = BaseTrainer( + model=model, + train_dataset=train_dataset, + eval_dataset=None,#eval_dataset, + training_config=training_config, + optimizer=optimizer, + scheduler=scheduler, + callbacks=callbacks, + ) + + print(trainer.scheduler) + + trainer.train() + + trained_model = AutoModel.load_from_folder(os.path.join(training_config.output_dir, f'{trainer.model.model_name}_training_{trainer._training_signature}', 'final_model')).to(device) + + test_data = torch.tensor(test_data).to(device).type(torch.float) + + with torch.no_grad(): + nll = [] + for i in range(5): + nll_i = trained_model.get_nll(test_data[:10], n_samples=200) + logger.info(f"Round {i+1} nll: {nll_i}") + nll.append(nll_i) + + logger.info( + f'\nmean_nll: {np.mean(nll)}' + ) + logger.info( + f'\std_nll: {np.std(nll)}' + ) + +if __name__ == "__main__": + + main(args) diff --git a/examples/test.ipynb b/examples/test.ipynb new file mode 100644 index 00000000..7e75eb76 --- /dev/null +++ b/examples/test.ipynb @@ -0,0 +1,603 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "from pythae.models.normalizing_flows import MADE, MADEConfig\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "c = MADEConfig(input_dim=(10,), hidden_sizes=[20, 10, 3], output_dim=(10, ))#, context_dim=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n" + ] + } + ], + "source": [ + "made = MADE(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "x = torch.randn(3, 10)\n", + "h = torch.randn(3, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MADE(\n", + " (net): Sequential(\n", + " (0): MaskedLinear(in_features=10, out_features=20, bias=True)\n", + " (1): MaskedLinear(in_features=20, out_features=10, bias=True)\n", + " (2): ReLU()\n", + " (3): MaskedLinear(in_features=10, out_features=3, bias=True)\n", + " (4): ReLU()\n", + " (5): MaskedLinear(in_features=3, out_features=20, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "made" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([3, 10])" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "made(x, h).mu.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n" + ] + } + ], + "source": [ + "masks = made._make_mask()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]]),\n", + " tensor([[1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1.]]),\n", + " tensor([[1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]]),\n", + " tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "masks" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "def create_input_order(input_size, input_order=\"left-to-right\"):\n", + " \"\"\"Returns a degree vectors for the input.\"\"\"\n", + " if input_order == \"left-to-right\":\n", + " return np.arange(start=1, stop=input_size + 1)\n", + " elif input_order == \"right-to-left\":\n", + " return np.arange(start=input_size, stop=0, step=-1)\n", + " elif input_order == \"random\":\n", + " ret = np.arange(start=1, stop=input_size + 1)\n", + " np.random.shuffle(ret)\n", + " return ret\n", + "\n", + "\n", + "def create_degrees(\n", + " input_size, hidden_units, input_order=\"left-to-right\", hidden_degrees=\"equal\"\n", + "):\n", + " input_order = create_input_order(input_size, input_order)\n", + " degrees = [input_order]\n", + " for units in hidden_units:\n", + " if hidden_degrees == \"random\":\n", + " # samples from: [low, high)\n", + " degrees.append(\n", + " np.random.randint(\n", + " low=min(np.min(degrees[-1]), input_size - 1),\n", + " high=input_size,\n", + " size=units,\n", + " )\n", + " )\n", + " elif hidden_degrees == \"equal\":\n", + " min_degree = min(np.min(degrees[-1]), input_size - 1)\n", + " degrees.append(\n", + " np.maximum(\n", + " min_degree,\n", + " # Evenly divide the range `[1, input_size - 1]` in to `units + 1`\n", + " # segments, and pick the boundaries between the segments as degrees.\n", + " np.ceil(\n", + " np.arange(1, units + 1) * (input_size - 1) / float(units + 1)\n", + " ).astype(np.int32),\n", + " )\n", + " )\n", + " return degrees\n", + "\n", + "\n", + "def create_masks(degrees):\n", + " \"\"\"Returns a list of binary mask matrices enforcing autoregressivity.\"\"\"\n", + " return [\n", + " # Create input->hidden and hidden->hidden masks.\n", + " (inp[:, np.newaxis] <= out).astype(np.int)\n", + " for inp, out in zip(degrees[:-1], degrees[1:])\n", + " ] + [\n", + " # Create hidden->output mask.\n", + " (degrees[-1][:, np.newaxis]\n", + " < degrees[0]).astype(np.int)\n", + " ]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "deg = create_degrees(10, [20, 10, 3])\n", + "msks = create_masks(deg)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),\n", + " array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]),\n", + " array([[1, 1, 1],\n", + " [1, 1, 1],\n", + " [1, 1, 1],\n", + " [0, 1, 1],\n", + " [0, 1, 1],\n", + " [0, 1, 1],\n", + " [0, 0, 1],\n", + " [0, 0, 1],\n", + " [0, 0, 0],\n", + " [0, 0, 0]]),\n", + " array([[0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]])]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "msks" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n" + ] + }, + { + "data": { + "text/plain": [ + "[tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 0.]]),\n", + " tensor([[1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", + " 1., 1.]]),\n", + " tensor([[1., 1., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [1., 1., 1., 1., 1., 1., 1., 1., 0., 0.]]),\n", + " tensor([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 0., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 0.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])]" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "made._make_mask()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 0 0 0 0 0 0 0]\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + "[0 0 0 0 0 0 0 0 0 0]\n", + "[0 0 0]\n" + ] + } + ], + "source": [ + "for i in range(len(deg)):\n", + " print(deg[i]- made.m[i-1].numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1)\n", + "tensor(1, dtype=torch.int32)\n", + "tensor(1, dtype=torch.int32)\n", + "0.0\n", + "0.0\n", + "0.0\n", + "0.0\n" + ] + } + ], + "source": [ + "made_masks = made._make_mask()\n", + "\n", + "for i in range(len(deg)):\n", + " print(abs(msks[i].T- made_masks[i].numpy()).sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "x = torch.rand(3, 10)\n", + "h = torch.randn(3, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "forward() got an unexpected keyword argument 'h'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_18124/4069216790.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/envs/pythae/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1128\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1129\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1131\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1132\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/benchmark_VAE/src/pythae/models/normalizing_flows/made/made_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, h, **kwargs)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0mModelOutput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAn\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mof\u001b[0m \u001b[0mModelOutput\u001b[0m \u001b[0mcontaining\u001b[0m \u001b[0mall\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrelevant\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \"\"\"\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mnet_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1131\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1132\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: forward() got an unexpected keyword argument 'h'" + ] + } + ], + "source": [ + "made(x, h)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([6, 8, 5, 9, 2, 4, 3, 7, 1])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = torch.arange(1, 10)\n", + "idx = torch.randperm(len(a))\n", + "a[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1, 2, 3, 4, 3, 6, 3, 5, 9, 1])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "a = torch.randn(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(-1.7427)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.min(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "5e51c5ac46389dd7ba2bd8215d251ab84152720d3cad2ff91113d77594821aef" + }, + "kernelspec": { + "display_name": "Python 3.8.12 ('pythae')", + "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": "3.8.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/pythae/models/hvae/hvae_model.py b/src/pythae/models/hvae/hvae_model.py index f6d0afd4..b1530b90 100644 --- a/src/pythae/models/hvae/hvae_model.py +++ b/src/pythae/models/hvae/hvae_model.py @@ -164,14 +164,11 @@ def _log_p_x_given_z(self, recon_x, x): if self.model_config.reconstruction_loss == "mse": # sigma is taken as I_D - logp_x_given_z = ( - -0.5 - * F.mse_loss( - recon_x.reshape(x.shape[0], -1), - x.reshape(x.shape[0], -1), - reduction="none", - ).sum(dim=-1) - ) + logp_x_given_z = -0.5 * F.mse_loss( + recon_x.reshape(x.shape[0], -1), + x.reshape(x.shape[0], -1), + reduction="none", + ).sum(dim=-1) # - torch.log(torch.tensor([2 * np.pi]).to(x.device)) \ # * np.prod(self.input_dim) / 2 @@ -244,10 +241,10 @@ def get_nll(self, data, n_samples=1, batch_size=100): log_q_z0_given_x = -0.5 * ( log_var + (z0 - mu) ** 2 / torch.exp(log_var) ).sum(dim=-1) - log_p_z = -0.5 * (z ** 2).sum(dim=-1) - log_p_rho = -0.5 * (rho ** 2).sum(dim=-1) + log_p_z = -0.5 * (z**2).sum(dim=-1) + log_p_rho = -0.5 * (rho**2).sum(dim=-1) - log_p_rho0 = -0.5 * (rho ** 2).sum(dim=-1) * self.beta_zero_sqrt + log_p_rho0 = -0.5 * (rho**2).sum(dim=-1) * self.beta_zero_sqrt recon_x = self.decoder(z)["reconstruction"] diff --git a/src/pythae/models/info_vae/info_vae_model.py b/src/pythae/models/info_vae/info_vae_model.py index 95599b05..3eb31bed 100644 --- a/src/pythae/models/info_vae/info_vae_model.py +++ b/src/pythae/models/info_vae/info_vae_model.py @@ -120,7 +120,7 @@ def loss_function(self, recon_x, x, z, z_prior, mu, log_var): mmd_z = (k_z - k_z.diag().diag()).sum() / ((N - 1) * N) mmd_z_prior = (k_z_prior - k_z_prior.diag().diag()).sum() / ((N - 1) * N) - mmd_cross = k_cross.sum() / (N ** 2) + mmd_cross = k_cross.sum() / (N**2) mmd_loss = mmd_z + mmd_z_prior - 2 * mmd_cross @@ -145,7 +145,7 @@ def imq_kernel(self, z1, z2): """Returns a matrix of shape [batch x batch] containing the pairwise kernel computation""" Cbase = ( - 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth ** 2 + 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth**2 ) k = 0 @@ -159,7 +159,7 @@ def imq_kernel(self, z1, z2): def rbf_kernel(self, z1, z2): """Returns a matrix of shape [batch x batch] containing the pairwise kernel computation""" - C = 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth ** 2 + C = 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth**2 k = torch.exp(-torch.norm(z1.unsqueeze(1) - z2.unsqueeze(0), dim=-1) ** 2 / C) diff --git a/src/pythae/models/iwae/iwae_model.py b/src/pythae/models/iwae/iwae_model.py index 6bef4bf9..58daef15 100644 --- a/src/pythae/models/iwae/iwae_model.py +++ b/src/pythae/models/iwae/iwae_model.py @@ -113,7 +113,7 @@ def loss_function(self, recon_x, x, mu, log_var, z): ).sum(dim=-1) log_q_z = (-0.5 * (log_var + torch.pow(z - mu, 2) / log_var.exp())).sum(dim=-1) - log_p_z = -0.5 * (z ** 2).sum(dim=-1) + log_p_z = -0.5 * (z**2).sum(dim=-1) KLD = -(log_p_z - log_q_z) diff --git a/src/pythae/models/msssim_vae/msssim_vae_utils.py b/src/pythae/models/msssim_vae/msssim_vae_utils.py index 829030af..a2e9fa51 100644 --- a/src/pythae/models/msssim_vae/msssim_vae_utils.py +++ b/src/pythae/models/msssim_vae/msssim_vae_utils.py @@ -13,7 +13,7 @@ def __init__(self, window_size=11): def _gaussian(self, sigma): gauss = torch.Tensor( [ - np.exp(-((x - self.window_size // 2) ** 2) / float(2 * sigma ** 2)) + np.exp(-((x - self.window_size // 2) ** 2) / float(2 * sigma**2)) for x in range(self.window_size) ] ) @@ -97,8 +97,8 @@ def forward(self, img1, img2): mssim = (mssim + 1) / 2 mcs = (mcs + 1) / 2 - pow1 = mcs ** weights - pow2 = mssim ** weights + pow1 = mcs**weights + pow2 = mssim**weights output = torch.prod(pow1[:-1] * pow2[-1]) return 1 - output diff --git a/src/pythae/models/nn/benchmarks/celeba/convnets.py b/src/pythae/models/nn/benchmarks/celeba/convnets.py index 650de4e1..63b5f477 100644 --- a/src/pythae/models/nn/benchmarks/celeba/convnets.py +++ b/src/pythae/models/nn/benchmarks/celeba/convnets.py @@ -235,6 +235,7 @@ def __init__(self, args: BaseAEConfig): self.input_dim = (3, 64, 64) self.latent_dim = args.latent_dim self.n_channels = 3 + self.outputs_context = False layers = nn.ModuleList() @@ -270,6 +271,10 @@ def __init__(self, args: BaseAEConfig): self.embedding = nn.Linear(1024 * 4 * 4, args.latent_dim) self.log_var = nn.Linear(1024 * 4 * 4, args.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(1024 * 4 * 4, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): """Forward method @@ -315,6 +320,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/src/pythae/models/nn/benchmarks/celeba/resnets.py b/src/pythae/models/nn/benchmarks/celeba/resnets.py index a661853b..0d420c86 100644 --- a/src/pythae/models/nn/benchmarks/celeba/resnets.py +++ b/src/pythae/models/nn/benchmarks/celeba/resnets.py @@ -235,6 +235,7 @@ def __init__(self, args: BaseAEConfig): self.input_dim = (3, 64, 64) self.latent_dim = args.latent_dim self.n_channels = 3 + self.outputs_context = False layers = nn.ModuleList() @@ -259,6 +260,10 @@ def __init__(self, args: BaseAEConfig): self.embedding = nn.Linear(128 * 4 * 4, args.latent_dim) self.log_var = nn.Linear(128 * 4 * 4, args.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(128 * 4 * 4, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): """Forward method @@ -301,6 +306,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/src/pythae/models/nn/benchmarks/cifar/convnets.py b/src/pythae/models/nn/benchmarks/cifar/convnets.py index 3a59d75d..fd8212c4 100644 --- a/src/pythae/models/nn/benchmarks/cifar/convnets.py +++ b/src/pythae/models/nn/benchmarks/cifar/convnets.py @@ -230,6 +230,7 @@ def __init__(self, args: BaseAEConfig): self.input_dim = (3, 32, 32) self.latent_dim = args.latent_dim self.n_channels = 3 + self.outputs_context = False layers = nn.ModuleList() @@ -265,6 +266,10 @@ def __init__(self, args: BaseAEConfig): self.embedding = nn.Linear(1024 * 2 * 2, args.latent_dim) self.log_var = nn.Linear(1024 * 2 * 2, args.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(1024 * 2 * 2, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): """Forward method @@ -310,6 +315,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/src/pythae/models/nn/benchmarks/cifar/resnets.py b/src/pythae/models/nn/benchmarks/cifar/resnets.py index 84ecedaf..30acc6e1 100644 --- a/src/pythae/models/nn/benchmarks/cifar/resnets.py +++ b/src/pythae/models/nn/benchmarks/cifar/resnets.py @@ -228,6 +228,7 @@ def __init__(self, args: BaseAEConfig): self.input_dim = (3, 32, 32) self.latent_dim = args.latent_dim self.n_channels = 3 + self.outputs_context = False layers = nn.ModuleList() @@ -250,6 +251,10 @@ def __init__(self, args: BaseAEConfig): self.embedding = nn.Linear(128 * 8 * 8, args.latent_dim) self.log_var = nn.Linear(128 * 8 * 8, args.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(128 * 8 * 8, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): """Forward method @@ -292,6 +297,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/src/pythae/models/nn/benchmarks/mnist/convnets.py b/src/pythae/models/nn/benchmarks/mnist/convnets.py index 560616bd..1c7e0e22 100644 --- a/src/pythae/models/nn/benchmarks/mnist/convnets.py +++ b/src/pythae/models/nn/benchmarks/mnist/convnets.py @@ -234,6 +234,7 @@ def __init__(self, args: BaseAEConfig): self.input_dim = (1, 28, 28) self.latent_dim = args.latent_dim self.n_channels = 1 + self.outputs_context = False layers = nn.ModuleList() @@ -269,6 +270,10 @@ def __init__(self, args: BaseAEConfig): self.embedding = nn.Linear(1024, args.latent_dim) self.log_var = nn.Linear(1024, args.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(1024, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): """Forward method @@ -313,6 +318,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/src/pythae/models/nn/benchmarks/mnist/resnets.py b/src/pythae/models/nn/benchmarks/mnist/resnets.py index 7a937dde..223bad3e 100644 --- a/src/pythae/models/nn/benchmarks/mnist/resnets.py +++ b/src/pythae/models/nn/benchmarks/mnist/resnets.py @@ -229,6 +229,7 @@ def __init__(self, args: BaseAEConfig): self.input_dim = (1, 28, 28) self.latent_dim = args.latent_dim self.n_channels = 1 + self.outputs_context = False layers = nn.ModuleList() @@ -251,6 +252,10 @@ def __init__(self, args: BaseAEConfig): self.embedding = nn.Linear(128 * 4 * 4, args.latent_dim) self.log_var = nn.Linear(128 * 4 * 4, args.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(128 * 4 * 4, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): """Forward method @@ -293,6 +298,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/src/pythae/models/nn/default_architectures.py b/src/pythae/models/nn/default_architectures.py index b410bb5e..58d7b67d 100644 --- a/src/pythae/models/nn/default_architectures.py +++ b/src/pythae/models/nn/default_architectures.py @@ -3,6 +3,7 @@ import numpy as np import torch import torch.nn as nn +from attr import has from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder, BaseMetric @@ -70,10 +71,15 @@ def __init__(self, args: dict): self.layers = layers self.depth = len(layers) + self.outputs_context = False self.embedding = nn.Linear(512, self.latent_dim) self.log_var = nn.Linear(512, self.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(512, args.context_dim) + def forward(self, x, output_layer_levels: List[int] = None): output = ModelOutput() @@ -105,6 +111,8 @@ def forward(self, x, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out) output["log_covariance"] = self.log_var(out) + if self.outputs_context: + output["context"] = self.context_layer(out) return output diff --git a/src/pythae/models/normalizing_flows/iaf/iaf_config.py b/src/pythae/models/normalizing_flows/iaf/iaf_config.py index 4c58a455..9f646419 100644 --- a/src/pythae/models/normalizing_flows/iaf/iaf_config.py +++ b/src/pythae/models/normalizing_flows/iaf/iaf_config.py @@ -1,3 +1,5 @@ +from typing import Union + from pydantic.dataclasses import dataclass from ..base import BaseNFConfig @@ -5,19 +7,21 @@ @dataclass class IAFConfig(BaseNFConfig): - """This is the MADE model configuration instance. + """This is the Inverse Autoregressive Flow model configuration instance. Parameters: input_dim (tuple): The input data dimension. Default: None. - n_made_blocks (int): The number of MADE model to consider in the IAF. Default: 2. + n_blocks (int): The number of IAF blocks to consider in the flow. Default: 2. n_hidden_in_made (int): The number of hidden layers in the MADE models. Default: 3. hidden_size (list): The number of unit in each hidder layer. The same number of units is - used across the `n_hidden_in_made` and `n_made_blocks`. Default: 128. + used across the `n_hidden_in_made` and `n_blocks`. Default: 128. + context_dim (int): The dimension of the context. Default: None. include_batch_norm (bool): Whether to include batch normalization after each :class:`~pythae.models.normalizing_flows.MADE` layers. Default: False. """ - n_made_blocks: int = 2 + n_blocks: int = 2 n_hidden_in_made: int = 3 + context_dim: Union[int, None] = None hidden_size: int = 128 include_batch_norm: bool = False diff --git a/src/pythae/models/normalizing_flows/iaf/iaf_model.py b/src/pythae/models/normalizing_flows/iaf/iaf_model.py index 6366a652..0f978aba 100644 --- a/src/pythae/models/normalizing_flows/iaf/iaf_model.py +++ b/src/pythae/models/normalizing_flows/iaf/iaf_model.py @@ -29,6 +29,7 @@ def __init__(self, model_config: IAFConfig): self.model_config = model_config self.input_dim = np.prod(model_config.input_dim) self.hidden_size = model_config.hidden_size + self.context_dim = np.prod(model_config.context_dim) self.model_name = "IAF" made_config = MADEConfig( @@ -36,20 +37,22 @@ def __init__(self, model_config: IAFConfig): output_dim=(self.input_dim,), hidden_sizes=[self.hidden_size] * self.model_config.n_hidden_in_made, degrees_ordering="sequential", + context_dim=(self.context_dim,) if self.context_dim is not None else None, ) - for i in range(model_config.n_made_blocks): + for i in range(model_config.n_blocks): self.net.extend([MADE(made_config)]) if self.model_config.include_batch_norm: self.net.extend([BatchNorm(self.input_dim)]) self.net = nn.ModuleList(self.net) - def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: + def forward(self, x: torch.Tensor, h: torch.Tensor = None, **kwargs) -> ModelOutput: """The input data is transformed toward the prior (f^{-1}) Args: - inputs (torch.Tensor): An input tensor + x (torch.Tensor): An input tensor. + h (torch.Tensor): The context tensor. Default: None. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters @@ -61,7 +64,7 @@ def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: if layer.__class__.__name__ == "MADE": y = torch.zeros_like(x) for i in range(self.input_dim): - layer_out = layer(y.clone()) + layer_out = layer(y.clone(), h=h) m, s = layer_out.mu, layer_out.log_var @@ -79,11 +82,12 @@ def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: return ModelOutput(out=x, log_abs_det_jac=sum_log_abs_det_jac) - def inverse(self, y: torch.Tensor, **kwargs) -> ModelOutput: + def inverse(self, y: torch.Tensor, h: torch.Tensor = None, **kwargs) -> ModelOutput: """The prior is transformed toward the input data (f) Args: - inputs (torch.Tensor): An input tensor + x (torch.Tensor): An input tensor. + h (torch.Tensor): The context tensor. Default: None. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters @@ -94,7 +98,7 @@ def inverse(self, y: torch.Tensor, **kwargs) -> ModelOutput: for layer in self.net[::-1]: y = y.flip(dims=(1,)) if layer.__class__.__name__ == "MADE": - layer_out = layer(y) + layer_out = layer(y, h=h) m, s = layer_out.mu, layer_out.log_var y = y * s.exp() + m sum_log_abs_det_jac += s.sum(dim=-1) diff --git a/src/pythae/models/normalizing_flows/layers.py b/src/pythae/models/normalizing_flows/layers.py index aa410c0d..6ea0060d 100644 --- a/src/pythae/models/normalizing_flows/layers.py +++ b/src/pythae/models/normalizing_flows/layers.py @@ -17,13 +17,20 @@ class MaskedLinear(nn.Linear): only a selection of weight. """ - def __init__(self, in_features, out_features, mask): + def __init__(self, in_features, out_features, mask, context_features=None): nn.Linear.__init__(self, in_features=in_features, out_features=out_features) self.register_buffer("mask", mask) - def forward(self, x): - return F.linear(x, self.mask * self.weight, self.bias) + if context_features is not None: + self.context_linear = nn.Linear(context_features, out_features, bias=False) + + def forward(self, x, h=None): + out = F.linear(x, self.mask * self.weight, self.bias) + if h is None: + return out + else: + return out + self.context_linear(h) class BatchNorm(nn.Module): diff --git a/src/pythae/models/normalizing_flows/made/made_config.py b/src/pythae/models/normalizing_flows/made/made_config.py index 9ab312c2..d6bcecb7 100644 --- a/src/pythae/models/normalizing_flows/made/made_config.py +++ b/src/pythae/models/normalizing_flows/made/made_config.py @@ -18,8 +18,10 @@ class MADEConfig(BaseNFConfig): Default: [128]. degrees_ordering (str): The ordering to use for the mask creation. Can be either `sequential` or `random`. Default: `sequential`. + context_dim (tuple): The dimension of the context. Default: None. """ output_dim: Union[Tuple[int, ...], None] = None hidden_sizes: List[int] = field(default_factory=lambda: [128]) degrees_ordering: Literal["sequential", "random"] = "sequential" + context_dim: Union[Tuple[int, ...], None] = None diff --git a/src/pythae/models/normalizing_flows/made/made_model.py b/src/pythae/models/normalizing_flows/made/made_model.py index 191e703d..1798e255 100644 --- a/src/pythae/models/normalizing_flows/made/made_model.py +++ b/src/pythae/models/normalizing_flows/made/made_model.py @@ -29,6 +29,7 @@ def __init__(self, model_config: MADEConfig): self.input_dim = np.prod(model_config.input_dim) self.output_dim = np.prod(model_config.output_dim) self.hidden_sizes = model_config.hidden_sizes + self.context_dim = np.prod(model_config.context_dim) self.model_name = "MADE" if model_config.input_dim is None: @@ -47,11 +48,22 @@ def __init__(self, model_config: MADEConfig): "automatically" ) - hidden_sizes = [self.input_dim] + model_config.hidden_sizes + [self.output_dim] + hidden_sizes = model_config.hidden_sizes + [self.output_dim] masks = self._make_mask(ordering=self.model_config.degrees_ordering) - for inp, out, mask in zip(hidden_sizes[:-1], hidden_sizes[1:-1], masks[:-1]): + self.net.extend( + [ + MaskedLinear( + self.input_dim, + hidden_sizes[0], + masks[0], + context_features=self.context_dim, + ) + ] + ) + + for inp, out, mask in zip(hidden_sizes[:-1], hidden_sizes[1:-1], masks[1:-1]): self.net.extend([MaskedLinear(inp, out, mask), nn.ReLU()]) @@ -69,14 +81,22 @@ def __init__(self, model_config: MADEConfig): def _make_mask(self, ordering="sequential"): # Get degrees for mask creation - + self.m[-1] = torch.arange(1, self.input_dim + 1) if ordering == "sequential": - self.m[-1] = torch.arange(self.input_dim) for i in range(len(self.hidden_sizes)): - self.m[i] = torch.arange(self.hidden_sizes[i]) % (self.input_dim - 1) + min_deg = min(torch.min(self.m[i - 1]), self.input_dim - 1) + self.m[i] = np.maximum( + min_deg, + np.ceil( + np.arange(1, self.hidden_sizes[i] + 1) + * (self.input_dim - 1) + / float(self.hidden_sizes[i] + 1) + ).astype(np.int32), + ) else: - self.m[-1] = torch.randperm(self.input_dim) + idx = torch.randperm(self.input_dim) + self.m[-1] = self.m[-1][idx] for i in range(len(self.hidden_sizes)): self.m[i] = torch.randint( self.m[-1].min(), self.input_dim - 1, (self.hidden_sizes[i],) @@ -95,11 +115,12 @@ def _make_mask(self, ordering="sequential"): return masks - def forward(self, x: torch.tensor, **kwargs) -> ModelOutput: + def forward(self, x: torch.tensor, h=None, **kwargs) -> ModelOutput: """The input data is transformed toward the prior Args: - inputs (torch.Tensor): An input tensor + x (torch.Tensor): An input tensor. + h (torch.Tensor): The context tensor. Default: None. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters diff --git a/src/pythae/models/normalizing_flows/maf/maf_config.py b/src/pythae/models/normalizing_flows/maf/maf_config.py index 1203e8af..0309d61d 100644 --- a/src/pythae/models/normalizing_flows/maf/maf_config.py +++ b/src/pythae/models/normalizing_flows/maf/maf_config.py @@ -1,3 +1,5 @@ +from typing import Union + from pydantic.dataclasses import dataclass from ..base import BaseNFConfig @@ -9,15 +11,17 @@ class MAFConfig(BaseNFConfig): Parameters: input_dim (tuple): The input data dimension. Default: None. - n_made_blocks (int): The number of MADE model to consider in the MAF. Default: 2. + n_blocks (int): The number of MAF blocks to consider in the flow. Default: 2. n_hidden_in_made (int): The number of hidden layers in the MADE models. Default: 3. hidden_size (list): The number of unit in each hidder layer. The same number of units is - used across the `n_hidden_in_made` and `n_made_blocks` + used across the `n_hidden_in_made` and `n_blocks` + context_dim (int): The dimension of the context. Default: None. include_batch_norm (bool): Whether to include batch normalization after each :class:`~pythae.models.normalizing_flows.MADE` layers. Default: False. """ - n_made_blocks: int = 2 + n_blocks: int = 2 n_hidden_in_made: int = 3 + context_dim: Union[int, None] = None hidden_size: int = 128 include_batch_norm: bool = False diff --git a/src/pythae/models/normalizing_flows/maf/maf_model.py b/src/pythae/models/normalizing_flows/maf/maf_model.py index 1b2f11f2..0ad19024 100644 --- a/src/pythae/models/normalizing_flows/maf/maf_model.py +++ b/src/pythae/models/normalizing_flows/maf/maf_model.py @@ -26,6 +26,7 @@ def __init__(self, model_config: MAFConfig): self.m = {} self.model_config = model_config self.hidden_size = model_config.hidden_size + self.context_dim = model_config.context_dim self.model_name = "MAF" @@ -34,20 +35,22 @@ def __init__(self, model_config: MAFConfig): output_dim=(self.input_dim,), hidden_sizes=[self.hidden_size] * self.model_config.n_hidden_in_made, degrees_ordering="sequential", + context_dim=(self.context_dim,) if self.context_dim is not None else None, ) - for i in range(model_config.n_made_blocks): + for i in range(model_config.n_blocks): self.net.extend([MADE(made_config)]) if self.model_config.include_batch_norm: self.net.extend([BatchNorm(self.input_dim)]) self.net = nn.ModuleList(self.net) - def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: + def forward(self, x: torch.Tensor, h: torch.Tensor = None, **kwargs) -> ModelOutput: """The input data is transformed toward the prior Args: - inputs (torch.Tensor): An input tensor + x (torch.Tensor): An input tensor. + h (torch.Tensor): The context tensor. Default: None. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters @@ -57,14 +60,15 @@ def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: sum_log_abs_det_jac = torch.zeros(x.shape[0]).to(x.device) for layer in self.net: - layer_out = layer(x) if layer.__class__.__name__ == "MADE": + layer_out = layer(x, h) mu, log_var = layer_out.mu, layer_out.log_var x = (x - mu) * (-log_var).exp() sum_log_abs_det_jac += -log_var.sum(dim=-1) # - alpha else: + layer_out = layer(x) x = layer_out.out sum_log_abs_det_jac += layer_out.log_abs_det_jac @@ -72,11 +76,12 @@ def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: return ModelOutput(out=x, log_abs_det_jac=sum_log_abs_det_jac) - def inverse(self, y: torch.Tensor, **kwargs) -> ModelOutput: + def inverse(self, y: torch.Tensor, h: torch.Tensor = None, **kwargs) -> ModelOutput: """The prior is transformed toward the input data Args: - inputs (torch.Tensor): An input tensor + x (torch.Tensor): An input tensor. + h (torch.Tensor): The context tensor. Default: None. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters @@ -90,7 +95,7 @@ def inverse(self, y: torch.Tensor, **kwargs) -> ModelOutput: if layer.__class__.__name__ == "MADE": x = torch.zeros_like(y) for i in range(self.input_dim): - layer_out = layer(x.clone()) + layer_out = layer(x.clone(), h=h) mu, log_var = layer_out.mu, layer_out.log_var diff --git a/src/pythae/models/normalizing_flows/planar_flow/planar_flow_model.py b/src/pythae/models/normalizing_flows/planar_flow/planar_flow_model.py index 4ef15611..e821ec99 100644 --- a/src/pythae/models/normalizing_flows/planar_flow/planar_flow_model.py +++ b/src/pythae/models/normalizing_flows/planar_flow/planar_flow_model.py @@ -44,7 +44,7 @@ def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: """The input data is transformed toward the prior Args: - inputs (torch.Tensor): An input tensor + x (torch.Tensor): An input tensor. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters diff --git a/src/pythae/models/normalizing_flows/radial_flow/radial_flow_model.py b/src/pythae/models/normalizing_flows/radial_flow/radial_flow_model.py index 4f2b9e5a..2339e172 100644 --- a/src/pythae/models/normalizing_flows/radial_flow/radial_flow_model.py +++ b/src/pythae/models/normalizing_flows/radial_flow/radial_flow_model.py @@ -33,7 +33,7 @@ def forward(self, x: torch.Tensor, **kwargs) -> ModelOutput: """The input data is transformed toward the prior Args: - inputs (torch.Tensor): An input tensor + inputs (torch.Tensor): An input tensor. Returns: ModelOutput: An instance of ModelOutput containing all the relevant parameters diff --git a/src/pythae/models/pvae/pvae_utils.py b/src/pythae/models/pvae/pvae_utils.py index a836da72..c486612b 100644 --- a/src/pythae/models/pvae/pvae_utils.py +++ b/src/pythae/models/pvae/pvae_utils.py @@ -59,13 +59,13 @@ def _mobius_add(x, y, c, dim=-1): ## OK y2 = y.pow(2).sum(dim=dim, keepdim=True) xy = (x * y).sum(dim=dim, keepdim=True) num = (1 + 2 * c * xy + c * y2) * x + (1 - c * x2) * y - denom = 1 + 2 * c * xy + c ** 2 * x2 * y2 + denom = 1 + 2 * c * xy + c**2 * x2 * y2 return num / denom.clamp_min(MIN_NORM) def _mobius_scalar_mul(r, x, c, dim: int = -1): ## OK x_norm = x.norm(dim=dim, keepdim=True, p=2).clamp_min(MIN_NORM) - sqrt_c = c ** 0.5 + sqrt_c = c**0.5 res_c = tanh(r * artanh(sqrt_c * x_norm)) * x / (x_norm * sqrt_c) return res_c @@ -74,7 +74,7 @@ def _project(x, c, dim: int = -1, eps: float = None): ## OK norm = x.norm(dim=dim, keepdim=True, p=2).clamp_min(MIN_NORM) if eps is None: eps = BALL_EPS[x.dtype] - maxnorm = (1 - eps) / (c ** 0.5) + maxnorm = (1 - eps) / (c**0.5) cond = norm > maxnorm projected = x / norm * maxnorm return torch.where(cond, projected, x) @@ -86,7 +86,7 @@ def _gyration(u, v, w, c, dim: int = -1): ## OK uv = (u * v).sum(dim=dim, keepdim=True) uw = (u * w).sum(dim=dim, keepdim=True) vw = (v * w).sum(dim=dim, keepdim=True) - c2 = c ** 2 + c2 = c**2 a = -c2 * uw * v2 + c * vw + 2 * c2 * uv * vw b = -c2 * vw * u2 - c * uw d = 1 + 2 * c * uv + c2 * u2 * v2 @@ -115,7 +115,7 @@ def zero(self): def dist( ## OK self, x: torch.Tensor, y: torch.Tensor, *, keepdim=False, dim=-1 ) -> torch.Tensor: ## OK - sqrt_c = self.c ** 0.5 + sqrt_c = self.c**0.5 dist_c = artanh( sqrt_c * _mobius_add(-x, y, self.c, dim=dim).norm(dim=dim, p=2, keepdim=keepdim) @@ -137,7 +137,7 @@ def mobius_add( return res def logmap0(self, x: torch.Tensor, y: torch.Tensor, *, dim=-1) -> torch.Tensor: - sqrt_c = self.c ** 0.5 + sqrt_c = self.c**0.5 y_norm = y.norm(dim=dim, p=2, keepdim=True).clamp_min(MIN_NORM) return y / y_norm / sqrt_c * artanh(sqrt_c * y_norm) @@ -147,7 +147,7 @@ def logmap( sub = _mobius_add(-x, y, self.c, dim=dim) sub_norm = sub.norm(dim=dim, p=2, keepdim=True).clamp_min(MIN_NORM) lam = _lambda_x(x, self.c, keepdim=True, dim=dim) - sqrt_c = self.c ** 0.5 + sqrt_c = self.c**0.5 return 2 / sqrt_c / lam * artanh(sqrt_c * sub_norm) * sub / sub_norm def transp0(self, y: torch.Tensor, v: torch.Tensor, *, dim=-1) -> torch.Tensor: @@ -175,13 +175,13 @@ def logdetexp(self, x, y, is_vector=False, keepdim=False): ## OK ).log() def expmap0(self, u, dim: int = -1): - sqrt_c = self.c ** 0.5 + sqrt_c = self.c**0.5 u_norm = u.norm(dim=dim, p=2, keepdim=True).clamp_min(MIN_NORM) gamma_1 = tanh(sqrt_c * u_norm) * u / (sqrt_c * u_norm) return gamma_1 def expmap(self, x, u, dim: int = -1): - sqrt_c = self.c ** 0.5 + sqrt_c = self.c**0.5 u_norm = u.norm(dim=dim, p=2, keepdim=True).clamp_min(MIN_NORM) second_term = ( tanh(sqrt_c / 2 * _lambda_x(x, self.c, keepdim=True, dim=dim) * u_norm) @@ -192,7 +192,7 @@ def expmap(self, x, u, dim: int = -1): return gamma_1 def expmap_polar(self, x, u, r, dim: int = -1): ## OK - sqrt_c = self.c ** 0.5 + sqrt_c = self.c**0.5 u_norm = u.norm(dim=dim, p=2, keepdim=True).clamp_min(MIN_NORM) second_term = ( tanh(torch.tensor([sqrt_c]).to(x.device) / 2 * r) * u / (sqrt_c * u_norm) @@ -217,7 +217,7 @@ def normdist2plane( norm: bool = False, ): c = self.c - sqrt_c = c ** 0.5 + sqrt_c = c**0.5 diff = self.mobius_add(-p, x, dim=dim) diff_norm2 = diff.pow(2).sum(dim=dim, keepdim=keepdim).clamp_min(MIN_NORM) sc_diff_a = (diff * a).sum(dim=dim, keepdim=keepdim) diff --git a/src/pythae/models/rhvae/rhvae_model.py b/src/pythae/models/rhvae/rhvae_model.py index 9b3cbfad..3408879a 100644 --- a/src/pythae/models/rhvae/rhvae_model.py +++ b/src/pythae/models/rhvae/rhvae_model.py @@ -182,7 +182,7 @@ def forward(self, inputs: BaseDataset, **kwargs) -> ModelOutput: M.unsqueeze(0) * torch.exp( -torch.norm(mu.unsqueeze(0) - z.unsqueeze(1), dim=-1) ** 2 - / (self.temperature ** 2) + / (self.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) @@ -222,7 +222,7 @@ def forward(self, inputs: BaseDataset, **kwargs) -> ModelOutput: M.unsqueeze(0) * torch.exp( -torch.norm(mu.unsqueeze(0) - z.unsqueeze(1), dim=-1) ** 2 - / (self.temperature ** 2) + / (self.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) @@ -332,7 +332,7 @@ def G(z): self.centroids_tens.unsqueeze(0) - z.unsqueeze(1), dim=-1 ) ** 2 - / (self.temperature ** 2) + / (self.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) @@ -348,7 +348,7 @@ def G_inv(z): self.centroids_tens.unsqueeze(0) - z.unsqueeze(1), dim=-1 ) ** 2 - / (self.temperature ** 2) + / (self.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) @@ -404,14 +404,11 @@ def _log_p_x_given_z(self, recon_x, x): if self.model_config.reconstruction_loss == "mse": # sigma is taken as I_D - recon_loss = ( - -0.5 - * F.mse_loss( - recon_x.reshape(x.shape[0], -1), - x.reshape(x.shape[0], -1), - reduction="none", - ).sum(dim=-1) - ) + recon_loss = -0.5 * F.mse_loss( + recon_x.reshape(x.shape[0], -1), + x.reshape(x.shape[0], -1), + reduction="none", + ).sum(dim=-1) -torch.log(torch.tensor([2 * np.pi]).to(x.device)) * np.prod( self.input_dim ) / 2 @@ -532,7 +529,7 @@ def get_nll(self, data, n_samples=1, batch_size=100): log_q_z0_given_x = -0.5 * ( log_var + (z0 - mu) ** 2 / torch.exp(log_var) ).sum(dim=-1) - log_p_z = -0.5 * (z ** 2).sum(dim=-1) + log_p_z = -0.5 * (z**2).sum(dim=-1) log_p_rho0 = normal.log_prob(gamma) - torch.logdet( L / self.beta_zero_sqrt diff --git a/src/pythae/models/rhvae/rhvae_utils.py b/src/pythae/models/rhvae/rhvae_utils.py index ad1a54c8..29c485ea 100644 --- a/src/pythae/models/rhvae/rhvae_utils.py +++ b/src/pythae/models/rhvae/rhvae_utils.py @@ -12,7 +12,7 @@ def G(z): dim=-1, ) ** 2 - / (model.temperature ** 2) + / (model.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) @@ -33,7 +33,7 @@ def G_inv(z): dim=-1, ) ** 2 - / (model.temperature ** 2) + / (model.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) diff --git a/src/pythae/models/svae/svae_model.py b/src/pythae/models/svae/svae_model.py index 2f7d9f0a..331ffc7b 100644 --- a/src/pythae/models/svae/svae_model.py +++ b/src/pythae/models/svae/svae_model.py @@ -147,7 +147,7 @@ def _sample_von_mises(self, loc, concentration): w = self._acc_rej_steps(m=loc.shape[-1], k=concentration) - z = torch.cat((w, (1 - w ** 2).sqrt() * v), dim=-1) + z = torch.cat((w, (1 - w**2).sqrt() * v), dim=-1) return self._householder_rotation(loc, z) @@ -163,7 +163,7 @@ def _acc_rej_steps(self, m: int, k: torch.Tensor, device: str = "cpu"): batch_size = k.shape[0] - c = torch.sqrt(4 * k ** 2 + (m - 1) ** 2) + c = torch.sqrt(4 * k**2 + (m - 1) ** 2) b = (-2 * k + c) / (m - 1) a = (m - 1 + 2 * k + c) / 4 diff --git a/src/pythae/models/vae/vae_model.py b/src/pythae/models/vae/vae_model.py index e89874ab..d60fe455 100644 --- a/src/pythae/models/vae/vae_model.py +++ b/src/pythae/models/vae/vae_model.py @@ -163,7 +163,7 @@ def get_nll(self, data, n_samples=1, batch_size=100): log_q_z_given_x = -0.5 * ( log_var + (z - mu) ** 2 / torch.exp(log_var) ).sum(dim=-1) - log_p_z = -0.5 * (z ** 2).sum(dim=-1) + log_p_z = -0.5 * (z**2).sum(dim=-1) recon_x = self.decoder(z)["reconstruction"] diff --git a/src/pythae/models/vae_iaf/vae_iaf_config.py b/src/pythae/models/vae_iaf/vae_iaf_config.py index 56046741..14cae7d2 100644 --- a/src/pythae/models/vae_iaf/vae_iaf_config.py +++ b/src/pythae/models/vae_iaf/vae_iaf_config.py @@ -1,3 +1,5 @@ +from typing import Union + from pydantic.dataclasses import dataclass from ..vae import VAEConfig @@ -11,16 +13,18 @@ class VAE_IAF_Config(VAEConfig): input_dim (int): The input_data dimension latent_dim (int): The latent space dimension. Default: None. reconstruction_loss (str): The reconstruction loss to use ['bce', 'mse']. Default: 'mse'. - n_made_blocks (int): The number of :class:`~pythae.models.normalizing_flows.MADE` models - to consider in the IAF used in the VAE. Default: 2. + n_blocks (int): The number of :class:`~pythae.models.normalizing_flows.IAF` blocks + to consider in the VAE. Default: 2. n_hidden_in_made (int): The number of hidden layers in the :class:`~pythae.models.normalizing_flows.MADE` models composing the IAF in the VAE. Default: 3. hidden_size (list): The number of unit in each hidder layerin the :class:`~pythae.models.normalizing_flows.MADE` model. The same number of - units is used across the `n_hidden_in_made` and `n_made_blocks`. Default: 128. + units is used across the `n_hidden_in_made` and `n_blocks`. Default: 128. + context_dim (tuple): The dimension of the context. Default: None. """ - n_made_blocks: int = 2 + n_blocks: int = 2 n_hidden_in_made: int = 3 hidden_size: int = 128 + context_dim: Union[int, None] = None diff --git a/src/pythae/models/vae_iaf/vae_iaf_model.py b/src/pythae/models/vae_iaf/vae_iaf_model.py index 37c646c8..99b1f865 100644 --- a/src/pythae/models/vae_iaf/vae_iaf_model.py +++ b/src/pythae/models/vae_iaf/vae_iaf_model.py @@ -49,9 +49,10 @@ def __init__( iaf_config = IAFConfig( input_dim=(model_config.latent_dim,), - n_made_blocks=model_config.n_made_blocks, + n_blocks=model_config.n_blocks, n_hidden_in_made=model_config.n_hidden_in_made, hidden_size=model_config.hidden_size, + context_dim=model_config.context_dim, include_batch_norm=False, ) @@ -80,8 +81,23 @@ def forward(self, inputs: BaseDataset, **kwargs): z0 = z - # Pass it through the Normalizing flows - flow_output = self.iaf_flow.inverse(z) # sampling + if self.iaf_flow.context_dim is not None: + try: + h = encoder_output.context + + except AttributeError as e: + raise AttributeError( + "Cannot get context from encoder outputs. If you set `context_dim` argument to " + "something different from None please ensure that the encoder actually outputs " + f"the context vector 'h'. Exception caught: {e}." + ) + + # Pass it through the Normalizing flows + flow_output = self.iaf_flow.inverse(z, h=h) # sampling + + else: + # Pass it through the Normalizing flows + flow_output = self.iaf_flow.inverse(z) # sampling z = flow_output.out log_abs_det_jac = flow_output.log_abs_det_jac @@ -188,7 +204,7 @@ def get_nll(self, data, n_samples=1, batch_size=100): log_q_z_given_x = ( -0.5 * (log_var + torch.pow(z0 - mu, 2) / torch.exp(log_var)) ).sum(dim=1) - log_abs_det_jac - log_p_z = -0.5 * (z ** 2).sum(dim=-1) + log_p_z = -0.5 * (z**2).sum(dim=-1) recon_x = self.decoder(z)["reconstruction"] diff --git a/src/pythae/models/vae_lin_nf/vae_lin_nf_model.py b/src/pythae/models/vae_lin_nf/vae_lin_nf_model.py index e4cd5164..77a96a61 100644 --- a/src/pythae/models/vae_lin_nf/vae_lin_nf_model.py +++ b/src/pythae/models/vae_lin_nf/vae_lin_nf_model.py @@ -202,7 +202,7 @@ def get_nll(self, data, n_samples=1, batch_size=100): log_q_z_given_x = ( -0.5 * (log_var + torch.pow(z0 - mu, 2) / torch.exp(log_var)) ).sum(dim=1) - log_abs_det_jac - log_p_z = -0.5 * (z ** 2).sum(dim=-1) + log_p_z = -0.5 * (z**2).sum(dim=-1) recon_x = self.decoder(z)["reconstruction"] diff --git a/src/pythae/models/vamp/vamp_model.py b/src/pythae/models/vamp/vamp_model.py index 8c582fcc..a36e467e 100644 --- a/src/pythae/models/vamp/vamp_model.py +++ b/src/pythae/models/vamp/vamp_model.py @@ -157,17 +157,11 @@ def _log_p_z(self, z): prior_mu = prior_mu.unsqueeze(0) prior_log_var = prior_log_var.unsqueeze(0) - log_p_z = ( - torch.sum( - -0.5 - * ( - prior_log_var - + (z_expand - prior_mu) ** 2 / torch.exp(prior_log_var) - ), - dim=2, - ) - - torch.log(torch.tensor(C).type(torch.float)) - ) + log_p_z = torch.sum( + -0.5 + * (prior_log_var + (z_expand - prior_mu) ** 2 / torch.exp(prior_log_var)), + dim=2, + ) - torch.log(torch.tensor(C).type(torch.float)) log_p_z = torch.logsumexp(log_p_z, dim=1) diff --git a/src/pythae/models/vq_vae/vq_vae_utils.py b/src/pythae/models/vq_vae/vq_vae_utils.py index 65667bc3..8e8ecb08 100644 --- a/src/pythae/models/vq_vae/vq_vae_utils.py +++ b/src/pythae/models/vq_vae/vq_vae_utils.py @@ -30,7 +30,7 @@ def forward(self, z: torch.Tensor): distances = ( (z.reshape(-1, self.embedding_dim) ** 2).sum(dim=-1, keepdim=True) - + (self.embeddings.weight ** 2).sum(dim=-1) + + (self.embeddings.weight**2).sum(dim=-1) - 2 * z.reshape(-1, self.embedding_dim) @ self.embeddings.weight.T ) @@ -107,7 +107,7 @@ def forward(self, z: torch.Tensor): distances = ( (z.reshape(-1, self.embedding_dim) ** 2).sum(dim=-1, keepdim=True) - + (self.embeddings.weight ** 2).sum(dim=-1) + + (self.embeddings.weight**2).sum(dim=-1) - 2 * z.reshape(-1, self.embedding_dim) @ self.embeddings.weight.T ) diff --git a/src/pythae/models/wae_mmd/wae_mmd_model.py b/src/pythae/models/wae_mmd/wae_mmd_model.py index 592d3b01..2e67eedc 100644 --- a/src/pythae/models/wae_mmd/wae_mmd_model.py +++ b/src/pythae/models/wae_mmd/wae_mmd_model.py @@ -94,7 +94,7 @@ def loss_function(self, recon_x, x, z, z_prior): mmd_z = (k_z - k_z.diag().diag()).sum() / ((N - 1) * N) mmd_z_prior = (k_z_prior - k_z_prior.diag().diag()).sum() / ((N - 1) * N) - mmd_cross = k_cross.sum() / (N ** 2) + mmd_cross = k_cross.sum() / (N**2) mmd_loss = mmd_z + mmd_z_prior - 2 * mmd_cross @@ -108,7 +108,7 @@ def imq_kernel(self, z1, z2): """Returns a matrix of shape [batch x batch] containing the pairwise kernel computation""" Cbase = ( - 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth ** 2 + 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth**2 ) k = 0 @@ -122,7 +122,7 @@ def imq_kernel(self, z1, z2): def rbf_kernel(self, z1, z2): """Returns a matrix of shape [batch x batch] containing the pairwise kernel computation""" - C = 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth ** 2 + C = 2.0 * self.model_config.latent_dim * self.model_config.kernel_bandwidth**2 k = torch.exp(-torch.norm(z1.unsqueeze(1) - z2.unsqueeze(0), dim=-1) ** 2 / C) diff --git a/src/pythae/samplers/iaf_sampler/iaf_sampler.py b/src/pythae/samplers/iaf_sampler/iaf_sampler.py index a544b5ae..a61f5dc7 100644 --- a/src/pythae/samplers/iaf_sampler/iaf_sampler.py +++ b/src/pythae/samplers/iaf_sampler/iaf_sampler.py @@ -44,7 +44,7 @@ def __init__(self, model: BaseAE, sampler_config: IAFSamplerConfig = None): iaf_config = IAFConfig( input_dim=(model.model_config.latent_dim,), - n_made_blocks=sampler_config.n_made_blocks, + n_blocks=sampler_config.n_blocks, n_hidden_in_made=sampler_config.n_hidden_in_made, hidden_size=sampler_config.hidden_size, include_batch_norm=sampler_config.include_batch_norm, diff --git a/src/pythae/samplers/iaf_sampler/iaf_sampler_config.py b/src/pythae/samplers/iaf_sampler/iaf_sampler_config.py index b915c64f..6af2d253 100644 --- a/src/pythae/samplers/iaf_sampler/iaf_sampler_config.py +++ b/src/pythae/samplers/iaf_sampler/iaf_sampler_config.py @@ -8,15 +8,15 @@ class IAFSamplerConfig(BaseSamplerConfig): """This is the IAF sampler model configuration instance. Parameters: - n_made_blocks (int): The number of MADE model to consider in the IAF. Default: 2. + n_blocks (int): The number of IAF blocks to consider in the flow. Default: 2. n_hidden_in_made (int): The number of hidden layers in the MADE models. Default: 3. hidden_size (list): The number of unit in each hidder layer. The same number of units is - used across the `n_hidden_in_made` and `n_made_blocks` + used across the `n_hidden_in_made` and `n_blocks` include_batch_norm (bool): Whether to include batch normalization after each :class:`~pythae.models.normalizing_flows.MADE` layers. Default: False. """ - n_made_blocks: int = 2 + n_blocks: int = 2 n_hidden_in_made: int = 3 hidden_size: int = 128 include_batch_norm: bool = False diff --git a/src/pythae/samplers/maf_sampler/maf_sampler.py b/src/pythae/samplers/maf_sampler/maf_sampler.py index bd303ba9..985f9b31 100644 --- a/src/pythae/samplers/maf_sampler/maf_sampler.py +++ b/src/pythae/samplers/maf_sampler/maf_sampler.py @@ -44,7 +44,7 @@ def __init__(self, model: BaseAE, sampler_config: MAFSamplerConfig = None): maf_config = MAFConfig( input_dim=(model.model_config.latent_dim,), - n_made_blocks=sampler_config.n_made_blocks, + n_blocks=sampler_config.n_blocks, n_hidden_in_made=sampler_config.n_hidden_in_made, hidden_size=sampler_config.hidden_size, include_batch_norm=sampler_config.include_batch_norm, diff --git a/src/pythae/samplers/maf_sampler/maf_sampler_config.py b/src/pythae/samplers/maf_sampler/maf_sampler_config.py index 64bab067..b057a749 100644 --- a/src/pythae/samplers/maf_sampler/maf_sampler_config.py +++ b/src/pythae/samplers/maf_sampler/maf_sampler_config.py @@ -8,15 +8,15 @@ class MAFSamplerConfig(BaseSamplerConfig): """This is the MAF sampler model configuration instance. Parameters: - n_made_blocks (int): The number of MADE model to consider in the MAF. Default: 2. + n_blocks (int): The number of MAF blocks to consider in the flow. Default: 2. n_hidden_in_made (int): The number of hidden layers in the MADE models. Default: 3. hidden_size (list): The number of unit in each hidder layer. The same number of units is - used across the `n_hidden_in_made` and `n_made_blocks` + used across the `n_hidden_in_made` and `n_blocks` include_batch_norm (bool): Whether to include batch normalization after each :class:`~pythae.models.normalizing_flows.MADE` layers. Default: False. """ - n_made_blocks: int = 2 + n_blocks: int = 2 n_hidden_in_made: int = 3 hidden_size: int = 128 include_batch_norm: bool = False diff --git a/src/pythae/samplers/manifold_sampler/rhvae_sampler.py b/src/pythae/samplers/manifold_sampler/rhvae_sampler.py index 85cc7494..5f66c964 100644 --- a/src/pythae/samplers/manifold_sampler/rhvae_sampler.py +++ b/src/pythae/samplers/manifold_sampler/rhvae_sampler.py @@ -171,7 +171,7 @@ def grad_log_sqrt_det_G_inv(z, model): @ torch.transpose( ( -2 - / (model.temperature ** 2) + / (model.temperature**2) * (model.centroids_tens.unsqueeze(0) - z.unsqueeze(1)).unsqueeze(2) @ ( model.M_tens.unsqueeze(0) @@ -181,7 +181,7 @@ def grad_log_sqrt_det_G_inv(z, model): dim=-1, ) ** 2 - / (model.temperature ** 2) + / (model.temperature**2) ) .unsqueeze(-1) .unsqueeze(-1) diff --git a/src/pythae/trainers/training_callbacks.py b/src/pythae/trainers/training_callbacks.py index 5bb70f60..359e6afb 100644 --- a/src/pythae/trainers/training_callbacks.py +++ b/src/pythae/trainers/training_callbacks.py @@ -15,9 +15,11 @@ def wandb_is_available(): return importlib.util.find_spec("wandb") is not None + def mlflow_is_available(): return importlib.util.find_spec("mlflow") is not None + def rename_logs(logs): train_prefix = "train_" eval_prefix = "eval_" @@ -356,6 +358,7 @@ def on_prediction_step(self, training_config, **kwargs): def on_train_end(self, training_config: BaseTrainerConfig, **kwargs): self.run.finish() + class MLFlowCallback(TrainingCallback): # pragma: no cover def __init__(self): if not mlflow_is_available(): @@ -378,7 +381,9 @@ def setup(self, training_config, **kwargs): self._mlflow.start_run(run_name=run_name) - logger.info(f"MLflow run started with run_id={self._mlflow.active_run().info.run_id}") + logger.info( + f"MLflow run started with run_id={self._mlflow.active_run().info.run_id}" + ) if model_config is not None: model_config_dict = model_config.to_dict() @@ -418,4 +423,4 @@ def __del__(self): callable(getattr(self._mlflow, "active_run", None)) and self._mlflow.active_run() is not None ): - self._mlflow.end_run() \ No newline at end of file + self._mlflow.end_run() diff --git a/tests/data/custom_architectures.py b/tests/data/custom_architectures.py index 0b086b1e..373319a2 100644 --- a/tests/data/custom_architectures.py +++ b/tests/data/custom_architectures.py @@ -96,6 +96,7 @@ def __init__(self, args): self.input_dim = args.input_dim self.latent_dim = args.latent_dim self.n_channels = 1 + self.outputs_context = False layers = nn.ModuleList() @@ -131,6 +132,10 @@ def __init__(self, args): self.embedding = nn.Linear(512, self.latent_dim) self.log_var = nn.Linear(512, self.latent_dim) + if hasattr(args, "context_dim") and args.context_dim is not None: + self.outputs_context = True + self.context_layer = nn.Linear(512, args.context_dim) + def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): output = ModelOutput() @@ -163,6 +168,8 @@ def forward(self, x: torch.Tensor, output_layer_levels: List[int] = None): if i + 1 == self.depth: output["embedding"] = self.embedding(out.reshape(x.shape[0], -1)) output["log_covariance"] = self.log_var(out.reshape(x.shape[0], -1)) + if self.outputs_context: + output["context"] = self.context_layer(out.reshape(x.shape[0], -1)) return output diff --git a/tests/test_IAF.py b/tests/test_IAF.py index 0116f458..2ac4132d 100644 --- a/tests/test_IAF.py +++ b/tests/test_IAF.py @@ -20,7 +20,7 @@ PATH = os.path.dirname(os.path.abspath(__file__)) -@pytest.fixture(params=[IAFConfig(n_hidden_in_made=10), IAFConfig(n_made_blocks=2)]) +@pytest.fixture(params=[IAFConfig(n_hidden_in_made=10), IAFConfig(n_blocks=2)]) def model_configs_no_input_output_dim(request): return request.param @@ -28,13 +28,13 @@ def model_configs_no_input_output_dim(request): @pytest.fixture( params=[ IAFConfig( - input_dim=(1, 8, 2), n_made_blocks=2, n_hidden_in_made=1, hidden_size=3 + input_dim=(1, 8, 2), n_blocks=2, n_hidden_in_made=1, hidden_size=3 ), IAFConfig( input_dim=(1, 2, 18), hidden_size=12, include_batch_norm=True, - n_made_blocks=1, + n_blocks=1, ), ] ) @@ -188,7 +188,7 @@ def training_configs(self, tmpdir, request): @pytest.fixture( params=[ IAFConfig( - input_dim=(12,), n_made_blocks=3, n_hidden_in_made=3, hidden_size=134 + input_dim=(12,), n_blocks=3, n_hidden_in_made=3, hidden_size=134 ), ] ) diff --git a/tests/test_MAF.py b/tests/test_MAF.py index 720dd671..54283aed 100644 --- a/tests/test_MAF.py +++ b/tests/test_MAF.py @@ -19,14 +19,14 @@ PATH = os.path.dirname(os.path.abspath(__file__)) -@pytest.fixture(params=[MAFConfig(n_hidden_in_made=10), MAFConfig(n_made_blocks=2)]) +@pytest.fixture(params=[MAFConfig(n_hidden_in_made=10), MAFConfig(n_blocks=2)]) def model_configs_no_input_output_dim(request): return request.param @pytest.fixture( params=[ - MAFConfig(input_dim=(1, 8, 2), n_made_blocks=2, n_hidden_in_made=1), + MAFConfig(input_dim=(1, 8, 2), n_blocks=2, n_hidden_in_made=1), MAFConfig(input_dim=(1, 2, 18), hidden_size=12, include_batch_norm=True), ] ) @@ -180,7 +180,7 @@ def training_configs(self, tmpdir, request): @pytest.fixture( params=[ MAFConfig( - input_dim=(784,), n_made_blocks=3, n_hidden_in_made=3, hidden_size=134 + input_dim=(784,), n_blocks=3, n_hidden_in_made=3, hidden_size=134 ), ] ) diff --git a/tests/test_VAE_IAF.py b/tests/test_VAE_IAF.py index 09eb68ca..ede10755 100644 --- a/tests/test_VAE_IAF.py +++ b/tests/test_VAE_IAF.py @@ -31,9 +31,10 @@ def model_configs_no_input_dim(request): VAE_IAF_Config( input_dim=(1, 28), latent_dim=5, - n_made_blocks=1, + n_blocks=1, n_hidden_in_made=2, hidden_size=142, + context_dim=15 ), ] ) @@ -280,6 +281,18 @@ def vae(self, model_configs, demo_data): model_configs.input_dim = tuple(demo_data["data"][0].shape) return VAE_IAF(model_configs) + def test_raises_no_context(self, model_configs, demo_data): + + # Set context to None to build wrong encoder + model_configs.context_dim = None + bad_enc = Encoder_VAE_Conv(model_configs) + model_configs.context_dim = 12 + model_configs.input_dim = tuple(demo_data["data"][0].shape) + vae = VAE_IAF(model_configs, encoder=bad_enc) + + with pytest.raises(AttributeError): + out = vae(demo_data) + def test_model_train_output(self, vae, demo_data): vae.train() diff --git a/tests/test_iaf_sampler.py b/tests/test_iaf_sampler.py index eff123de..4d4ded76 100644 --- a/tests/test_iaf_sampler.py +++ b/tests/test_iaf_sampler.py @@ -30,7 +30,7 @@ def model(request): @pytest.fixture( params=[ - IAFSamplerConfig(n_made_blocks=2, n_hidden_in_made=1), + IAFSamplerConfig(n_blocks=2, n_hidden_in_made=1), IAFSamplerConfig(hidden_size=12, include_batch_norm=True), None, ] diff --git a/tests/test_maf_sampler.py b/tests/test_maf_sampler.py index 03af31a5..39d614eb 100644 --- a/tests/test_maf_sampler.py +++ b/tests/test_maf_sampler.py @@ -30,7 +30,7 @@ def model(request): @pytest.fixture( params=[ - MAFSamplerConfig(n_made_blocks=2, n_hidden_in_made=1), + MAFSamplerConfig(n_blocks=2, n_hidden_in_made=1), MAFSamplerConfig(hidden_size=12, include_batch_norm=True), None, ] diff --git a/tests/test_nn_benchmark.py b/tests/test_nn_benchmark.py index e19c494a..4e263058 100644 --- a/tests/test_nn_benchmark.py +++ b/tests/test_nn_benchmark.py @@ -114,6 +114,7 @@ def test_ae_encoding_decoding_default( def test_vae_encoding_decoding_default( self, ae_mnist_config, mnist_like_data, recon_layers_default ): + ae_mnist_config.context_dim = 12 encoder = Encoder_VAE_MLP(ae_mnist_config).to(device) decoder = Decoder_AE_MLP(ae_mnist_config).to(device) @@ -129,6 +130,11 @@ def test_vae_encoding_decoding_default( ae_mnist_config.latent_dim, ) + assert output.context.shape == ( + mnist_like_data.shape[0], + ae_mnist_config.context_dim, + ) + reconstruction = decoder(embedding).reconstruction assert reconstruction.shape == mnist_like_data.shape @@ -352,6 +358,7 @@ def test_ae_encoding_decoding( def test_vae_encoding_decoding( self, ae_mnist_config, mnist_like_data, recon_layers ): + ae_mnist_config.context_dim = 12 encoder = Encoder_Conv_VAE_MNIST(ae_mnist_config).to(device) decoder = Decoder_Conv_AE_MNIST(ae_mnist_config).to(device) @@ -367,6 +374,11 @@ def test_vae_encoding_decoding( ae_mnist_config.latent_dim, ) + assert output.context.shape == ( + mnist_like_data.shape[0], + ae_mnist_config.context_dim, + ) + reconstruction = decoder(embedding).reconstruction assert reconstruction.shape == mnist_like_data.shape @@ -631,12 +643,21 @@ def test_ae_encoding_decoding( def test_vae_encoding_decoding( self, ae_mnist_config, mnist_like_data, recon_layers ): + ae_mnist_config.context_dim = 12 encoder = Encoder_ResNet_VAE_MNIST(ae_mnist_config).to(device) decoder = Decoder_ResNet_AE_MNIST(ae_mnist_config).to(device) - embedding = encoder(mnist_like_data).embedding + encoder_output = encoder(mnist_like_data) + + embedding, log_covariance = encoder_output.embedding, encoder_output.log_covariance assert embedding.shape == (mnist_like_data.shape[0], ae_mnist_config.latent_dim) + assert log_covariance.shape == (mnist_like_data.shape[0], ae_mnist_config.latent_dim) + + assert encoder_output.context.shape == ( + mnist_like_data.shape[0], + ae_mnist_config.context_dim, + ) reconstruction = decoder(embedding).reconstruction @@ -923,6 +944,7 @@ def test_ae_encoding_decoding(self, ae_cifar_config, cifar_like_data, recon_laye def test_vae_encoding_decoding( self, ae_cifar_config, cifar_like_data, recon_layers ): + ae_cifar_config.context_dim = 12 encoder = Encoder_Conv_VAE_CIFAR(ae_cifar_config).to(device) decoder = Decoder_Conv_AE_CIFAR(ae_cifar_config).to(device) @@ -937,6 +959,10 @@ def test_vae_encoding_decoding( cifar_like_data.shape[0], ae_cifar_config.latent_dim, ) + assert output.context.shape == ( + cifar_like_data.shape[0], + ae_cifar_config.context_dim, + ) reconstruction = decoder(embedding).reconstruction @@ -1201,12 +1227,21 @@ def test_ae_encoding_decoding( def test_vae_encoding_decoding( self, ae_cifar_config, cifar_like_data, recon_layers ): + ae_cifar_config.context_dim = 12 encoder = Encoder_ResNet_VAE_CIFAR(ae_cifar_config).to(device) decoder = Decoder_ResNet_AE_CIFAR(ae_cifar_config).to(device) - embedding = encoder(cifar_like_data).embedding + encoder_output = encoder(cifar_like_data) + + embedding, log_covariance = encoder_output.embedding, encoder_output.log_covariance assert embedding.shape == (cifar_like_data.shape[0], ae_cifar_config.latent_dim) + assert log_covariance.shape == (cifar_like_data.shape[0], ae_cifar_config.latent_dim) + + assert encoder_output.context.shape == ( + cifar_like_data.shape[0], + ae_cifar_config.context_dim, + ) reconstruction = decoder(embedding).reconstruction @@ -1491,6 +1526,7 @@ def test_ae_encoding_decoding( def test_vae_encoding_decoding( self, ae_celeba_config, celeba_like_data, recon_layers ): + ae_celeba_config.context_dim = 12 encoder = Encoder_Conv_VAE_CELEBA(ae_celeba_config).to(device) decoder = Decoder_Conv_AE_CELEBA(ae_celeba_config).to(device) @@ -1505,6 +1541,10 @@ def test_vae_encoding_decoding( celeba_like_data.shape[0], ae_celeba_config.latent_dim, ) + assert output.context.shape == ( + celeba_like_data.shape[0], + ae_celeba_config.context_dim, + ) reconstruction = decoder(embedding).reconstruction @@ -1775,6 +1815,7 @@ def test_ae_encoding_decoding( def test_vae_encoding_decoding( self, ae_celeba_config, celeba_like_data, recon_layers ): + ae_celeba_config.context_dim = 12 encoder = Encoder_ResNet_VAE_CELEBA(ae_celeba_config).to(device) decoder = Decoder_ResNet_AE_CELEBA(ae_celeba_config).to(device) @@ -1789,6 +1830,10 @@ def test_vae_encoding_decoding( celeba_like_data.shape[0], ae_celeba_config.latent_dim, ) + assert output.context.shape == ( + celeba_like_data.shape[0], + ae_celeba_config.context_dim, + ) reconstruction = decoder(embedding).reconstruction From 64572339dc775dcb3e31c16916274ad270b79914 Mon Sep 17 00:00:00 2001 From: clementchadebec Date: Tue, 11 Oct 2022 16:44:43 +0200 Subject: [PATCH 2/9] apply black and isort --- .../7674c3254fe54d91a1e0cdb1b5bb11da.zip | Bin 0 -> 340585 bytes .../d9db7e2432a84e27be7100763eaa3780.zip | Bin 0 -> 246570 bytes .../fcc5dd5bd9fb41b19c3dadf003f9a880.zip | Bin 0 -> 129923 bytes examples/scripts/reproducibility/factorvae.py | 594 ++++++++++++++++++ .../scripts/reproducibility/nll_comp.ipynb | 60 ++ src/pythae/models/hvae/hvae_model.py | 19 +- src/pythae/models/info_vae/info_vae_model.py | 6 +- src/pythae/models/iwae/iwae_model.py | 2 +- .../models/msssim_vae/msssim_vae_utils.py | 6 +- src/pythae/models/pvae/pvae_utils.py | 22 +- src/pythae/models/rhvae/rhvae_model.py | 23 +- src/pythae/models/rhvae/rhvae_utils.py | 4 +- src/pythae/models/svae/svae_model.py | 4 +- src/pythae/models/vae/vae_model.py | 2 +- src/pythae/models/vae_iaf/vae_iaf_model.py | 2 +- .../models/vae_lin_nf/vae_lin_nf_model.py | 2 +- src/pythae/models/vamp/vamp_model.py | 16 +- src/pythae/models/vq_vae/vq_vae_utils.py | 4 +- src/pythae/models/wae_mmd/wae_mmd_model.py | 6 +- .../manifold_sampler/rhvae_sampler.py | 4 +- 20 files changed, 721 insertions(+), 55 deletions(-) create mode 100644 examples/notebooks/my_offline_runs/7674c3254fe54d91a1e0cdb1b5bb11da.zip create mode 100644 examples/notebooks/my_offline_runs/d9db7e2432a84e27be7100763eaa3780.zip create mode 100644 examples/notebooks/my_offline_runs/fcc5dd5bd9fb41b19c3dadf003f9a880.zip create mode 100644 examples/scripts/reproducibility/factorvae.py create mode 100644 examples/scripts/reproducibility/nll_comp.ipynb diff --git a/examples/notebooks/my_offline_runs/7674c3254fe54d91a1e0cdb1b5bb11da.zip b/examples/notebooks/my_offline_runs/7674c3254fe54d91a1e0cdb1b5bb11da.zip new file mode 100644 index 0000000000000000000000000000000000000000..7cbf4dc619e8ee279d0558854179d9061520031a GIT binary patch literal 340585 zcmeFadpy+J_dh;FO(l(zrjo{`GIAStavc;Sl@Vzo#u$vbGUJ{uk|ZPzNs~%ViiT2j z5s@O7P>Q6eR8y%Ysf6&`uTkgJ`!mk_d>`+Bf6jTGW9GG9d+)XOTF}2R!({GIYVE 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\u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1131\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1132\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/benchmark_VAE/src/pythae/models/normalizing_flows/made/made_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, h, **kwargs)\u001b[0m\n\u001b[1;32m 117\u001b[0m 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\u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1131\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1132\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: forward() got an unexpected keyword argument 'h'" - ] - } - ], - "source": [ - "made(x, h)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([6, 8, 5, 9, 2, 4, 3, 7, 1])" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = torch.arange(1, 10)\n", - "idx = torch.randperm(len(a))\n", - "a[idx]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([1, 2, 3, 4, 3, 6, 3, 5, 9, 1])" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import numpy as np\n", - "a = torch.randn(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(-1.7427)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "torch.min(a)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "5e51c5ac46389dd7ba2bd8215d251ab84152720d3cad2ff91113d77594821aef" - }, - "kernelspec": { - "display_name": "Python 3.8.12 ('pythae')", - "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": "3.8.12" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 19976cce831edf93e6215ba236169696c5642673 Mon Sep 17 00:00:00 2001 From: clementchadebec Date: Tue, 11 Oct 2022 17:27:17 +0200 Subject: [PATCH 7/9] clean up example --- .../notebooks/models_training/vae_iaf_training.ipynb | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/examples/notebooks/models_training/vae_iaf_training.ipynb b/examples/notebooks/models_training/vae_iaf_training.ipynb index 70c45f47..b53bf381 100644 --- a/examples/notebooks/models_training/vae_iaf_training.ipynb +++ b/examples/notebooks/models_training/vae_iaf_training.ipynb @@ -119,16 +119,7 @@ "outputs": [], "source": [ "last_training = sorted(os.listdir('my_model'))[-1]\n", - "trained_model = AutoModel.load_from_folder(os.path.join('my_model/VAE_IAF_training_2022-10-11_16-30-05', 'final_model'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trained_model.encoder" + "trained_model = AutoModel.load_from_folder(os.path.join('my_model', last_training, 'final_model'))" ] }, { From 716eaa30384e193737e0c73b67b68e48158d4af0 Mon Sep 17 00:00:00 2001 From: clementchadebec Date: Tue, 11 Oct 2022 17:32:07 +0200 Subject: [PATCH 8/9] clean up example --- .../normalizing_flows_training.ipynb | 505 +++++++++--------- 1 file changed, 241 insertions(+), 264 deletions(-) diff --git a/examples/notebooks/models_training/normalizing_flows_training.ipynb b/examples/notebooks/models_training/normalizing_flows_training.ipynb index 56a71a7e..08f31817 100644 --- a/examples/notebooks/models_training/normalizing_flows_training.ipynb +++ b/examples/notebooks/models_training/normalizing_flows_training.ipynb @@ -12,9 +12,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -31,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -44,32 +53,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1)\n", - "tensor(1, dtype=torch.int32)\n", - "tensor(1, dtype=torch.int32)\n", - "tensor(1)\n", - "tensor(1, dtype=torch.int32)\n", - "tensor(1, dtype=torch.int32)\n" - ] - } - ], + "outputs": [], "source": [ "from pythae.models.normalizing_flows import MAFConfig, MAF, NFModel, IAF, IAFConfig\n", "\n", "prior = MultivariateNormal(torch.zeros(2).to(device), torch.eye(2).to(device))\n", "\n", - "#conf = MAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_blocks=4, include_batch_norm=False)\n", - "#flow = MAF(conf)\n", + "conf = MAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_blocks=4, include_batch_norm=False)\n", + "flow = MAF(conf)\n", "\n", - "conf = IAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_blocks=2, include_batch_norm=False)\n", - "flow = IAF(conf)\n", + "#conf = IAFConfig(input_dim=(2,), n_hidden_in_made=3, hidden_size=24, n_blocks=2, include_batch_norm=False)\n", + "#flow = IAF(conf)\n", "\n", "\n", "contained_flow = NFModel(prior, flow)" @@ -77,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -107,7 +103,7 @@ "\n", "Model passed sanity check !\n", "\n", - "Created dummy_output_dir/IAF_training_2022-10-11_15-06-47. \n", + "Created dummy_output_dir/MAF_training_2022-10-11_17-30-56. \n", "Training config, checkpoints and final model will be saved here.\n", "\n", "Successfully launched training !\n", @@ -117,7 +113,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2fe2a9dcda54285913667536f1934ee", + "model_id": "35d4e586f3d04d30bfdee4dcd39b47ec", "version_major": 2, "version_minor": 0 }, @@ -131,7 +127,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a38c0939391d47698cdd132cc4a80723", + "model_id": "2e56fab1926d4a1e9b8ab5c5d358bbc7", "version_major": 2, "version_minor": 0 }, @@ -147,15 +143,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 199.4044\n", - "Eval loss: 176.467\n", + "Train loss: 184.328\n", + "Eval loss: 161.8518\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "388b9bf3761f41beab1b4c60a8b02b58", + "model_id": "a69d940f4dc34ee8aec0fbaaaa965f49", "version_major": 2, "version_minor": 0 }, @@ -169,7 +165,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "92feeb41477047f9986683d640b7227b", + "model_id": "c5710de309a846e5be5af97ff8f875fc", "version_major": 2, "version_minor": 0 }, @@ -185,15 +181,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 164.1447\n", - "Eval loss: 152.2655\n", + "Train loss: 152.1601\n", + "Eval loss: 146.8646\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a035325e88054dfaaeca69485f80cca4", + "model_id": "fa1506b62f7547c3a699abe9bcaabb90", "version_major": 2, "version_minor": 0 }, @@ -207,7 +203,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88223fe76c7a4639ac10f363086c0087", + "model_id": "a1718634cf304de28f2232c03efd8002", "version_major": 2, "version_minor": 0 }, @@ -223,15 +219,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 142.708\n", - "Eval loss: 134.5548\n", + "Train loss: 132.7612\n", + "Eval loss: 123.2201\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "94f727a49ebd44548b1911cd2dc4eb12", + "model_id": "c17a4c2a79444b6c917818eae2fa7463", "version_major": 2, "version_minor": 0 }, @@ -245,7 +241,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "967fc0ff091040ba896b586bfc4598a1", + "model_id": "ab48ccdd2e824497bf2ac6e96ad4554b", "version_major": 2, "version_minor": 0 }, @@ -261,15 +257,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 132.302\n", - "Eval loss: 126.5011\n", + "Train loss: 114.7159\n", + "Eval loss: 100.2041\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b03b75260d94369a5469092dddd8fc6", + "model_id": "cb4bfba783924c8a96d7033d01c53bca", "version_major": 2, "version_minor": 0 }, @@ -283,7 +279,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1f87e808ba9f4e09836058afba0f1599", + "model_id": "1e2e99acba5e40749ef08e0b2c2cebda", "version_major": 2, "version_minor": 0 }, @@ -299,15 +295,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 124.4396\n", - "Eval loss: 119.2079\n", + "Train loss: 110.5958\n", + "Eval loss: 101.2492\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f40fc1cda96d4d1586c51feafe546916", + "model_id": "29c8647ba38b42c0bf7ca05460ae6846", "version_major": 2, "version_minor": 0 }, @@ -321,7 +317,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4df3fe2fa3bd4464a3048f7607138b9d", + "model_id": "f00fd8484491471c9d5bf814f0df3576", "version_major": 2, "version_minor": 0 }, @@ -337,15 +333,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 118.2179\n", - "Eval loss: 114.9426\n", + "Train loss: 89.4724\n", + "Eval loss: 78.5732\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a292f721db194b22b6d47e64bb15be3a", + "model_id": "0f5158edc9164449b5a87682868c1bef", "version_major": 2, "version_minor": 0 }, @@ -359,7 +355,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62fda9e50348436abf27dc092dc62414", + "model_id": "3e3f8307b13246479ff5dc355f327492", "version_major": 2, "version_minor": 0 }, @@ -375,15 +371,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 109.7212\n", - "Eval loss: 101.282\n", + "Train loss: 73.8978\n", + "Eval loss: 60.217\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed07549ebc7146608e5b4eb08f417d99", + "model_id": "558ec75df84245e088237ea2d21ee291", "version_major": 2, "version_minor": 0 }, @@ -397,7 +393,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c5b927ddc9c54986a9f39b02b95e11e9", + "model_id": "f22eab113d8a459d82479ee0c3ec0c93", "version_major": 2, "version_minor": 0 }, @@ -413,15 +409,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 97.6262\n", - "Eval loss: 96.3474\n", + "Train loss: 64.3036\n", + "Eval loss: 59.2912\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4501d6d63f5c49afb11a05fac860248d", + "model_id": "c54c1453786d48d68213ce0fb50ba8f0", "version_major": 2, "version_minor": 0 }, @@ -435,7 +431,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d58d86e5a82d482ca8988df8f9fb44ba", + "model_id": "29572661c7394b63b380b1429952e9de", "version_major": 2, "version_minor": 0 }, @@ -451,15 +447,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 89.5439\n", - "Eval loss: 92.045\n", + "Train loss: 58.2209\n", + "Eval loss: 53.6635\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5ab9ebc6250b48278f475b3041f81c88", + "model_id": "cad47a11594b4a5e8803dd4ffd099048", "version_major": 2, "version_minor": 0 }, @@ -473,7 +469,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "540d52509b824f1a9ff30e307eba892d", + "model_id": "6695951f75534b359964a98deacb1a5d", "version_major": 2, "version_minor": 0 }, @@ -489,15 +485,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 82.0804\n", - "Eval loss: 85.5623\n", + "Train loss: 57.1512\n", + "Eval loss: 56.1623\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c1496c0dbc394352981b00fe42bddce7", + "model_id": "d460714d9be8415daff0f166a4b1fa9f", "version_major": 2, "version_minor": 0 }, @@ -511,7 +507,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6a126ac7dc8444ca332936a5ad20578", + "model_id": "1090dd186add44c78dccfdf35f6ca526", "version_major": 2, "version_minor": 0 }, @@ -527,15 +523,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 76.5315\n", - "Eval loss: 73.6278\n", + "Train loss: 56.963\n", + "Eval loss: 56.4587\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0336b942af414b9c8b57e53cae7b5838", + "model_id": "95a336f683cb468cb0c030b6abd16289", "version_major": 2, "version_minor": 0 }, @@ -549,7 +545,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d6066f7107a04dc99f1c2b558563083e", + "model_id": "833c80b1d948408a95680dc9a11114d6", "version_major": 2, "version_minor": 0 }, @@ -565,15 +561,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 71.9745\n", - "Eval loss: 81.3258\n", + "Train loss: 54.9752\n", + "Eval loss: 50.3529\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8b3fdd0ff5b4c02a34dbb7f5b222e3c", + "model_id": "e6001303199545bf9307d492e9777894", "version_major": 2, "version_minor": 0 }, @@ -587,7 +583,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0599c7ac077144288404925cb02d4b29", + "model_id": "dc531aadb338442e9e0d5357a79e4e32", "version_major": 2, "version_minor": 0 }, @@ -603,15 +599,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 69.2364\n", - "Eval loss: 65.1621\n", + "Train loss: 50.3636\n", + "Eval loss: 44.0703\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1cfcf26385a4723a70ce77ed8da9729", + "model_id": "bd784ca3ecbf44a19d855ba4d8c23e7a", "version_major": 2, "version_minor": 0 }, @@ -625,7 +621,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b84cfdb733804993aa0c45b95af42c3d", + "model_id": "f803f4ec04984e0c9d46e0b1a84715cd", "version_major": 2, "version_minor": 0 }, @@ -641,15 +637,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 68.1645\n", - "Eval loss: 69.9009\n", + "Train loss: 49.1614\n", + "Eval loss: 46.4413\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de6cc0d009e34c898059c018195bea12", + "model_id": "ea4317c004484e2dbc51bdd7c8422084", "version_major": 2, "version_minor": 0 }, @@ -663,7 +659,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8bfcc701d8e46838c227086bb442964", + "model_id": "c0f3aaf626e0443e961b412025355cae", "version_major": 2, "version_minor": 0 }, @@ -679,15 +675,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 66.6123\n", - "Eval loss: 66.6524\n", + "Train loss: 47.4819\n", + "Eval loss: 44.7632\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4c75268957294e108b8b02b9195e32bd", + "model_id": "ee35897cd2fd4af198b8ecb5731418ef", "version_major": 2, "version_minor": 0 }, @@ -701,7 +697,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c967fbe92cbb48069fb558c5b54a87e0", + "model_id": "9197fa62a15e47e893ed8e173ea365f2", "version_major": 2, "version_minor": 0 }, @@ -717,15 +713,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 65.1784\n", - "Eval loss: 67.2206\n", + "Train loss: 47.0086\n", + "Eval loss: 44.9806\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "27ff1c78c04b42ffa736282109e1de0e", + "model_id": "12829758c3864403a794966b4849785f", "version_major": 2, "version_minor": 0 }, @@ -739,7 +735,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8626c240f9344f790e23e2c4e268d97", + "model_id": "2e651b1b0437419e87bbfc7148c1902b", "version_major": 2, "version_minor": 0 }, @@ -755,15 +751,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 64.5969\n", - "Eval loss: 64.0229\n", + "Train loss: 46.9667\n", + "Eval loss: 44.1112\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c298cc812a564a2b8a3d65fb4b07b5c4", + "model_id": "ed2ce98073d04c258ebb6f8368bcc18a", "version_major": 2, "version_minor": 0 }, @@ -777,7 +773,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1395078de47e4b3bbf2d47fa0183b79b", + "model_id": "63689ae4e99447deb9ec395d62ff7d9e", "version_major": 2, "version_minor": 0 }, @@ -793,15 +789,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 62.1612\n", - "Eval loss: 63.5125\n", + "Train loss: 45.0875\n", + "Eval loss: 38.6945\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2915e53fb15244e5a5dbe94d667d3ccd", + "model_id": "0c137ff4672045d9b2b821914eed3dd7", "version_major": 2, "version_minor": 0 }, @@ -815,7 +811,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4341612afa5e48968710bd24151e70be", + "model_id": "2816ec6bcf084714bbd9bfe2e54bad35", "version_major": 2, "version_minor": 0 }, @@ -831,15 +827,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 62.9392\n", - "Eval loss: 64.693\n", + "Train loss: 44.0051\n", + "Eval loss: 38.5322\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd130c7341d441c090f484270ea889b0", + "model_id": "7763c1f75ce5423881955c4872627e1b", "version_major": 2, "version_minor": 0 }, @@ -853,7 +849,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5ddf00a186dc4529a0345f8a78f17f46", + "model_id": "f6589e54f715487b9a03f48a417b0e8b", "version_major": 2, "version_minor": 0 }, @@ -869,15 +865,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 62.7221\n", - "Eval loss: 70.1017\n", + "Train loss: 44.6618\n", + "Eval loss: 42.3022\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a083b49e9214dd5b090009ff0d351ab", + "model_id": "6ef26d0e53fa4e21b88b4150eb0c6138", "version_major": 2, "version_minor": 0 }, @@ -891,7 +887,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f92980523a734c8aba230df34ff6d43d", + "model_id": "8d2f2ad7850840aaa4ab8e964f143dd5", "version_major": 2, "version_minor": 0 }, @@ -907,15 +903,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.6886\n", - "Eval loss: 61.0657\n", + "Train loss: 43.1473\n", + "Eval loss: 40.0984\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "182d9bacf1ea464db26d14e67beb3a2d", + "model_id": "640fe5e144a945d7a7eeb39e19b2fd8d", "version_major": 2, "version_minor": 0 }, @@ -929,7 +925,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0824368506f44e52a758d0c30336edda", + "model_id": "86deacc7961146828cdab7f69d8f7c65", "version_major": 2, "version_minor": 0 }, @@ -945,15 +941,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 62.3967\n", - "Eval loss: 60.0661\n", + "Train loss: 44.6584\n", + "Eval loss: 39.8881\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "32f19d876f424213a47f6aa3eed2d840", + "model_id": "17f4c46492374dc69bc3b4e88036d233", "version_major": 2, "version_minor": 0 }, @@ -967,7 +963,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b00bf2985244702974e256c9e386a34", + "model_id": "cb363f2558144ebf8ca6377b220eaca6", "version_major": 2, "version_minor": 0 }, @@ -983,15 +979,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.6978\n", - "Eval loss: 61.6838\n", + "Train loss: 45.5855\n", + "Eval loss: 37.6275\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f80fc83827ad4d539137f8b127636512", + "model_id": "0640e09e33be4373a0dc299c2e0fd9c4", "version_major": 2, "version_minor": 0 }, @@ -1005,7 +1001,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ef8955acdaa482f9976185b7b04ae00", + "model_id": "2758b9f04e7340878b003b88dc95ce94", "version_major": 2, "version_minor": 0 }, @@ -1021,15 +1017,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 59.372\n", - "Eval loss: 59.5102\n", + "Train loss: 44.3301\n", + "Eval loss: 42.4146\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37ccfa6466ea4ab5bcedf7f8f3fd0c80", + "model_id": "1dca223973b8481cabe986e461153db5", "version_major": 2, "version_minor": 0 }, @@ -1043,7 +1039,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c04802373da146918261f1e281e53784", + "model_id": "7a388a41de5e44548cf661e4ffeffa67", "version_major": 2, "version_minor": 0 }, @@ -1059,15 +1055,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.7134\n", - "Eval loss: 60.2775\n", + "Train loss: 43.6667\n", + "Eval loss: 40.0082\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06ec963f7f16456a8ac45648b42145e1", + "model_id": "a1775308900c47dda2110bdb42c891e9", "version_major": 2, "version_minor": 0 }, @@ -1081,7 +1077,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3090e88a30b4d0ba398eb6cc668a0fc", + "model_id": "9175f4f8d6b941029b817d667e6977b0", "version_major": 2, "version_minor": 0 }, @@ -1097,15 +1093,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 59.2085\n", - "Eval loss: 61.0245\n", + "Train loss: 42.445\n", + "Eval loss: 42.7074\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0a1a8e9f70b4365b8f15d59dfa23319", + "model_id": "e772c96f4cb84a83a1bd13fde8c6cd73", "version_major": 2, "version_minor": 0 }, @@ -1119,7 +1115,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ad8ab9ac712b4950b7681582b3dc8cbf", + "model_id": "53a48baa204a47cba852bce7bb92ef13", "version_major": 2, "version_minor": 0 }, @@ -1135,15 +1131,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 60.1679\n", - "Eval loss: 62.2964\n", + "Train loss: 44.2301\n", + "Eval loss: 37.0219\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3089a9f6505d409babf9887eb876f30e", + "model_id": "2c24edf4c2134a578851be03cfb2909f", "version_major": 2, "version_minor": 0 }, @@ -1157,7 +1153,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c72e51948fd04fcabac267078410f55b", + "model_id": "a679f7c74852413893974a907c77a219", "version_major": 2, "version_minor": 0 }, @@ -1173,15 +1169,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 59.6164\n", - "Eval loss: 59.9354\n", + "Train loss: 43.2677\n", + "Eval loss: 42.9303\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f689243239a049278632ef6a235f0ecc", + "model_id": "1f4af8d4ce8e4ec597117e3e60162313", "version_major": 2, "version_minor": 0 }, @@ -1195,7 +1191,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec00593cf81640e696f0dc8ec36d82b3", + "model_id": "2e2ea9b74ba04ca3a03552092cca848e", "version_major": 2, "version_minor": 0 }, @@ -1211,15 +1207,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.9498\n", - "Eval loss: 65.5969\n", + "Train loss: 42.2071\n", + "Eval loss: 40.9839\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "577b34d36fa5407eb3fb846293cc40e9", + "model_id": "96af73fec53a43929e71392cce9525ac", "version_major": 2, "version_minor": 0 }, @@ -1233,7 +1229,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f01273b960cd439fb17b5a5d26d1cc9b", + "model_id": "1485cb2105d740f385e98ea2f82e6045", "version_major": 2, "version_minor": 0 }, @@ -1249,15 +1245,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.3376\n", - "Eval loss: 61.5284\n", + "Train loss: 42.8744\n", + "Eval loss: 43.2838\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bcf2419d537d476e99cc2fdba3169d80", + "model_id": "fef22c6e5b464b0b97f615e375a0c7e8", "version_major": 2, "version_minor": 0 }, @@ -1271,7 +1267,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "87c6f60702dd496a84cbcacc3fea43ef", + "model_id": "56c12153fbed42e3b6a4b120e9319a97", "version_major": 2, "version_minor": 0 }, @@ -1287,15 +1283,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.9406\n", - "Eval loss: 59.4494\n", + "Train loss: 42.7862\n", + "Eval loss: 37.937\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e9b4a75ac60d4332a841e32754bebad8", + "model_id": "7c089546df00478693ebe5bb9431e33c", "version_major": 2, "version_minor": 0 }, @@ -1309,7 +1305,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "32ee6941fdbf469e8582cc2ef6038ec7", + "model_id": "f7de402d89b74086a8d74a402a1cf291", "version_major": 2, "version_minor": 0 }, @@ -1325,15 +1321,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.7428\n", - "Eval loss: 63.984\n", + "Train loss: 43.1582\n", + "Eval loss: 40.082\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52bfb8eecfd44944814294201839c9f9", + "model_id": "66f0cc51af2a4a34aec2af0ecd95319b", "version_major": 2, "version_minor": 0 }, @@ -1347,7 +1343,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ef0486165954ac8832c92b1a0a7bf7f", + "model_id": "27ad054517f94b4fb94d77bdbed25d7f", "version_major": 2, "version_minor": 0 }, @@ -1363,15 +1359,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.7539\n", - "Eval loss: 59.8462\n", + "Train loss: 42.2759\n", + "Eval loss: 42.197\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4bc4c94cfcdc403b8761025d35790dd8", + "model_id": "0b14c53161f24c76a366939f939c199d", "version_major": 2, "version_minor": 0 }, @@ -1385,7 +1381,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "032018ae485a4fbfada04d845793fa24", + "model_id": "b8071d2b324541e588282122a392ce08", "version_major": 2, "version_minor": 0 }, @@ -1401,15 +1397,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.4324\n", - "Eval loss: 62.1807\n", + "Train loss: 43.974\n", + "Eval loss: 36.066\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82c6f27f42ae4f45960b7d7c807a6001", + "model_id": "5f726ab20fa14c80a95691834d0169ee", "version_major": 2, "version_minor": 0 }, @@ -1423,7 +1419,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7950e46810cd4ba99e5e54cfb1e11bdf", + "model_id": "a02c754ce4a64fdeb1381917209947a4", "version_major": 2, "version_minor": 0 }, @@ -1439,15 +1435,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.8983\n", - "Eval loss: 58.6377\n", + "Train loss: 40.9478\n", + "Eval loss: 41.9381\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db88c15122ad40a38a6f1c2ba0616d99", + "model_id": "a1c629b4497f41f68cd8f76f0fdb1bfe", "version_major": 2, "version_minor": 0 }, @@ -1461,7 +1457,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "027f7c93668240f18ffb2e66474f0a33", + "model_id": "382bded151e44bd1b0707ae143500911", "version_major": 2, "version_minor": 0 }, @@ -1477,15 +1473,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.4275\n", - "Eval loss: 64.1673\n", + "Train loss: 43.7921\n", + "Eval loss: 39.4629\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d336f721407343dabd917d499f17b22a", + "model_id": "fbf08b7717384f30bc9d86320ad798d1", "version_major": 2, "version_minor": 0 }, @@ -1499,7 +1495,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3c847345fca4e89892407b5dc066a88", + "model_id": "ee30190c2b624f6a9b64fa857bc4107f", "version_major": 2, "version_minor": 0 }, @@ -1515,15 +1511,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.8796\n", - "Eval loss: 58.3574\n", + "Train loss: 40.0904\n", + "Eval loss: 38.101\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f38922f879f4274abf4ba00731ee073", + "model_id": "de97038172f643a19995c3834d5834dc", "version_major": 2, "version_minor": 0 }, @@ -1537,7 +1533,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d31046bc0eea423ea6e859fcccf2cbc6", + "model_id": "6b18ab89b54d41acb490a5dfff50640d", "version_major": 2, "version_minor": 0 }, @@ -1553,15 +1549,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 59.2392\n", - "Eval loss: 58.4726\n", + "Train loss: 42.0358\n", + "Eval loss: 34.0293\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e209930aed554135867c778cf587d8f2", + "model_id": "b0c31968d693473aa0f650427e20b77e", "version_major": 2, "version_minor": 0 }, @@ -1575,7 +1571,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dde3663b5ff1461d9757e8ce6a82c0d0", + "model_id": "be3d1cbac09c484f9b061557c6a67ba5", "version_major": 2, "version_minor": 0 }, @@ -1591,15 +1587,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.9304\n", - "Eval loss: 60.2344\n", + "Train loss: 39.7718\n", + "Eval loss: 36.4644\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c79de023ad54c1fad7e75bba2ccda66", + "model_id": "22bed6bf5f92482abf9fabc6738e78fa", "version_major": 2, "version_minor": 0 }, @@ -1613,7 +1609,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0bd41071d806465da784a51b76017bc5", + "model_id": "ba122aa1972545c3aecf867dfc5fc96e", "version_major": 2, "version_minor": 0 }, @@ -1629,15 +1625,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 59.4971\n", - "Eval loss: 58.9068\n", + "Train loss: 40.3231\n", + "Eval loss: 36.3404\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c7c53fddeff41f28bc5e17f3f0d22d2", + "model_id": "a96e8b4090804bc2aacd6f617e1dc5e6", "version_major": 2, "version_minor": 0 }, @@ -1651,7 +1647,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "439ff9cae0b64b45b0f9c4d3af5df335", + "model_id": "843a483505bd46a695fe1c9194eb193a", "version_major": 2, "version_minor": 0 }, @@ -1667,15 +1663,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 56.7857\n", - "Eval loss: 58.7656\n", + "Train loss: 45.2427\n", + "Eval loss: 49.6391\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a0e93f3e9414f5e959f1f6ec2518c7b", + "model_id": "4d2a40a7cdb94e11975bb91fcc1d10e8", "version_major": 2, "version_minor": 0 }, @@ -1689,7 +1685,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebc32db6a32f4f4eb9d4d06dec3c10c4", + "model_id": "195602853d174573bee0f22c050d4ae4", "version_major": 2, "version_minor": 0 }, @@ -1705,15 +1701,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.0746\n", - "Eval loss: 59.5478\n", + "Train loss: 42.101\n", + "Eval loss: 41.5577\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b96e6d76fee2496d898bb7679b027897", + "model_id": "72bff0fbd73c445a9699414540e73886", "version_major": 2, "version_minor": 0 }, @@ -1727,7 +1723,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b719bd70d5e544299909bbaa7dc6e5b4", + "model_id": "8d1e69edda584c2ca9c17f1444ef8b16", "version_major": 2, "version_minor": 0 }, @@ -1743,15 +1739,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.3255\n", - "Eval loss: 58.4688\n", + "Train loss: 40.5898\n", + "Eval loss: 41.6189\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8abb425564f640caa0cacc68f3124f22", + "model_id": "0eafafa127fb430c925b406956149b49", "version_major": 2, "version_minor": 0 }, @@ -1765,7 +1761,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f04b8b953e294b42a25faeb5efa851e4", + "model_id": "88cbeda02ce845bcb513a415f3a15a03", "version_major": 2, "version_minor": 0 }, @@ -1781,15 +1777,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.7062\n", - "Eval loss: 61.1852\n", + "Train loss: 39.3039\n", + "Eval loss: 42.3499\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da168e4da76b4fc9b42c3d3d0d4b3458", + "model_id": "5fa1f611253d4f348bd15b67e655fef7", "version_major": 2, "version_minor": 0 }, @@ -1803,7 +1799,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aeb79ab8c1654a9fb3f19616fe02eb49", + "model_id": "a69635b1e47d4148963e0bbe9b11689e", "version_major": 2, "version_minor": 0 }, @@ -1819,15 +1815,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 57.2106\n", - "Eval loss: 68.4626\n", + "Train loss: 40.2525\n", + "Eval loss: 40.2899\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "49c0c088fec54644adbfc18f5451dcb6", + "model_id": "9a659a91a7b640aeb2ee9d82e13f2cbb", "version_major": 2, "version_minor": 0 }, @@ -1841,7 +1837,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "637b6e9aa58a47e5a41a133379c01667", + "model_id": "43054d890dd54d698991c7ffb21969db", "version_major": 2, "version_minor": 0 }, @@ -1857,15 +1853,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.3036\n", - "Eval loss: 58.5033\n", + "Train loss: 38.8709\n", + "Eval loss: 34.4457\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ff2db91e9b74216878206cb869ba202", + "model_id": "36f6db455988459c94c066e896b85143", "version_major": 2, "version_minor": 0 }, @@ -1879,7 +1875,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a0ca3b1055c4d7b996804587f89fb5d", + "model_id": "85e6e48f19cd4ec39e534bd61b77fdd7", "version_major": 2, "version_minor": 0 }, @@ -1895,15 +1891,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.1165\n", - "Eval loss: 65.9938\n", + "Train loss: 42.3591\n", + "Eval loss: 36.3393\n", "--------------------------------------------------------------------------\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "252ad7068a7e42d6bf1a1484ec972bbe", + "model_id": "235673171e644eee8d489d74d760fc88", "version_major": 2, "version_minor": 0 }, @@ -1917,7 +1913,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "945c835668794d03aeeb532d3a5184cc", + "model_id": "56664177932f4e6f9beb5c2dfe25970c", "version_major": 2, "version_minor": 0 }, @@ -1933,22 +1929,15 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 58.2602\n", - "Eval loss: 62.1577\n", + "Train loss: 39.2903\n", + "Eval loss: 38.7344\n", "--------------------------------------------------------------------------\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 00048: reducing learning rate of group 0 to 5.0000e-04.\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ae42b6ef8854dd981d404d8108b1ef3", + "model_id": "4fa3657ff6de4cefb3a958b528e3635c", "version_major": 2, "version_minor": 0 }, @@ -1962,7 +1951,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51400a916bd943b0abe00be488f913e4", + "model_id": "cfe6e521b7cf4ce99ff48f8e379cab6c", "version_major": 2, "version_minor": 0 }, @@ -1978,15 +1967,22 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 55.8915\n", - "Eval loss: 60.544\n", + "Train loss: 41.2091\n", + "Eval loss: 38.0018\n", "--------------------------------------------------------------------------\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 00049: reducing learning rate of group 0 to 5.0000e-04.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05825657e4e9442b9c606bc2933dc650", + "model_id": "5303558161764293b03d6677a2003c7e", "version_major": 2, "version_minor": 0 }, @@ -2000,7 +1996,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "021ebb4f9cde4e019dc4788f5df903bb", + "model_id": "b7e410c296c24eb3becb13635d258e2f", "version_major": 2, "version_minor": 0 }, @@ -2016,11 +2012,11 @@ "output_type": "stream", "text": [ "--------------------------------------------------------------------------\n", - "Train loss: 55.9214\n", - "Eval loss: 57.527\n", + "Train loss: 37.3718\n", + "Eval loss: 33.6677\n", "--------------------------------------------------------------------------\n", "Training ended!\n", - "Saved final model in dummy_output_dir/IAF_training_2022-10-11_15-06-47/final_model\n" + "Saved final model in dummy_output_dir/MAF_training_2022-10-11_17-30-56/final_model\n" ] } ], @@ -2033,7 +2029,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -2043,34 +2039,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(1)\n", - "tensor(1, dtype=torch.int32)\n", - "tensor(1, dtype=torch.int32)\n", - "tensor(1)\n", - "tensor(1, dtype=torch.int32)\n", - "tensor(1, dtype=torch.int32)\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2082,11 +2066,11 @@ } ], "source": [ - "maf_rec = AutoModel.load_from_folder(os.path.join(pipeline.trainer.training_dir, 'final_model')).to(device).eval()\n", + "nf_rec = AutoModel.load_from_folder(os.path.join(pipeline.trainer.training_dir, 'final_model')).to(device).eval()\n", "\n", "z_sample = prior.sample((128*8,))\n", "\n", - "z = maf_rec.inverse(z_sample).out.detach().cpu()\n", + "z = nf_rec.inverse(z_sample).out.detach().cpu()\n", "plt.scatter(train_moons[:,0], train_moons[:,1], c='b', s=20, alpha=0.5, label='true distribution')\n", "plt.scatter(z[:,0], z[:,1], c='r', s=20, alpha=0.5, label='transformed gaussian')\n", "plt.legend()" @@ -2094,22 +2078,22 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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YNvz613/g6ad/g6qqFBeXUVPz3wfJuo5Gfb1roTsbzIcrVsAjj7iFNPz+84dVMYs3Jy5oGtsB5bHN4oKHabrl5DTtyM/Ky4/8bMuWLVRVjeX2212rXB+7YDzumlDOdCu9+irceKMrxH0+GD/eFeavt+3sNs+iD29KGtssEUUWh+PwIhaKcuIiFv25WSIR9/Mz2VKG4R4UFcUtbG2asGmTK8xfT9tn89SQxZsHF6aNPIsLBrbtCrM3orSborhFKxzH7dNx3Pf9CfKzVR0oGnUfHl6vOwZNg1TKHcvptp1NSsriVHFeCfJzYebJ4uwhkYCmJtfc0dTkvj/b6CtiUV7uvh5diahvj/W5WSIR2LrVfR2I8MJQyO1z/HhXkHd2ukL8S186/bazSUlZnCrOG0Hu8/no7OzMCvPzAAOhRdu2W95Nll2tVJbd92+UZt5fEQshBJ2dnfh8vt73A9dnX4QKuA8Evx/GjYPJk2HxYpg+/agLT0GtPp9rimZxfuG8cXZalkVTU9PB2Owszg1MEw6PwszJOdJReKrIZKC7+0hhatuQlweec+iZ8fl8DB48GNNUB8zZ2Z8du7q6Hwfl6zB4n0lN0ayT9M2H897ZqarqEVmUWbzxMAwGTLgNZFtnA21tA+PsPNyO3TfP2lp3nkcUST7RhSfo8PVWCco6Sd9aOG9MK1mce5yKTfZULQPne4r70WYLM2JQbDYTUnsndooTPWU79vEubGk5aT+nm5SUdZK+9XDeaORZnHucrHL76Wp553PNycNzygIN9bxzWx0TRlvo96owdy4sWXJKEz3lavf9XRiLwX33uX8fQLX5bIVWZnH+IquRZ3EQJ9KiX6+WdzJt8kzS2M80BT4chkULDO4K1nHJ3CAFkyrduMF77nFfT2Gip3zyOPrCXjpe8vNPW20+2byzTtK3HrIaeRZH4Hha9NnQ8ja8avC72ijdUggR0E9LIR0oG7BuRUGzIL93Yoriemr7vLSnMNFTPXkY1WG6b19EiCgBEq42fpoLeirzPgsMFlmc58gK8iyOQX8Js6dsQjhFJFbUE7mxjvcrFrJX5YXxNdTWhk/JGfo6/Yb94+iJ2bYbVtMXJ3mKEz1ZkvEhAayjqjo3zzOYcJoLejrzPp/NWlkMPLKmlSxOCQPqvDQMnEfqSMhBkoWVmFqQyzbVIiWMU/H9DWyizNETS6dh4UL3dYC8tP2ZpR5ZrJO48fQW9HTnfSbMjVlcWMhq5FmcMgZMy4tG8coWGV8Q0wS0IJ6eCE5XlPvucxs9wmxwVED0aZ8OThZQffTEAEaMcJ2Ro0dDScnrnOjB6fZrlopUhAmcxoIO9KkoizcPsoI8i+OiP/k3IDxloRCqX2XG+DgrNgVxYnFERsVQQ5TlH2k2+NG8egKLjzQK6+EwN94IDz/sZov20cT2O64+m0Yi4aZy3nwzzJhx7HV9E6uvdzXyDRvcz8NhuPfeM4omOaEAPo0Fzdq+szgezpvMzizOL5z1hJLelEUraZF2VDo/UsOCp8NUVh66pGmbwXfN2wkNDqLlH8oq2nDDImof108qmw9mJSWTLgVhOu3avh9//LCc+aOuv+UWWL36kHQ0DJg6FR566Iwk5plkaPY3zLNl+85mg57fOO8zO99qOJc3zMn6HlBn4vH6rg6jL1qEGo2ihkJI6Kh/dzVNRYHWVmheHWWLsOjZG2TaNCgtDWK1R/hdbZRgqU5ZmTu2xYthwoR+xhaNupr45s0uz0AwCB0drir/058e+4Vo1B1gH0EMuA+BvkG/zskbBhQUwFe+4pJoVVW51prXuwf6VeIHYEOdMtVAFucdsoL8HOBcpk+fSt9nK6Hk2L51wmG3QR03D+drX3NpX6NRmDsrRKBNRRBn1aogV8+OYzoq3VKI0lMZWyjkquzptHuhabqVHmT5yC/0CUFVdT/LZFzCGY/HpS7U9ddtiO6bc2srbNvmmtxLS08r5+gYHCOzT/Cjnqp87+/hvWABlHvaKDD20ROq4tN3lmTT/M9TnHHUiiRJlZIkLZUkabMkSZskSbp1IAb2ZsW5TJ8+lb4N4xDd7EAmlJysb8OAJ590+9E0l9N70x6d5ZNr0ImTF2vE7Iojf6kGEdBPbWy67tpdbNvVxE3zYLkeQw3R3OyGQXL77a4d/N57XcpCw4CdO13JW1oK8+f3LwXb2lwzTG+N1OPNOUc2SDU0U+A1aGx0nyOnkXN0BOoPG+7tt8PGFcdf2Pp611J0++3ua5/Zvz8c/fDWNCh76Sm+8q/LqXn5Y9z+j8t56bY/ZdP8z1MMhEaeAW4XQqyVJCkHWCNJ0hIhxOYBaPtNh3OZPn2yvg9X7GIx919u7sA41U7Wd0uLK2gKClyh3N0NBw7ATn+Y1tmLcLqiXLQoRKBEp8Z/Gg6/GTNcm/jDDx8soLltbg0/vldHShhcv7qOsdOCFA/rHdAvfwnvfKdr3+l7olVXH9vuU0+50jSTcTX3hQvhgx88Zs4lrfW8o6GOWc1uvPxvcmrIZMKnm3ME9K81//aRKAtlC7XsyIVNtERZuFBn+3Z32o7jDvcHP3C52k+WJ5BubOOWznuxCwLEfUE8Zpz3rrqH2M456JPOLIoni4HHGQtyIcR+YH/v/3skSdoCVABZQd4P3tAQstMI2+tPSEQicPfd/d/4p4vTmbeiQFkZ7N3rCnOrUKfmLh295JC9ecGCQ5YEy3I/P+4Yp093beLRKIYa4sf36gSDMCgQxatYrNgU5OoK0PqyOvvML+m0O9CjJWxbmysVA4FDk7nnHpgz54hQxZBqcNW2OkzVi+EJoGRsPtpVy0tiER6Pfro5R/0+DFvbQqQdFfWohW1JhA4+GDXN/S2XLoWvfhWKio415RwdEVPVvY+AliGlBlGAtBJEFzHyYvuAC1+Qv9mcugNqI5ckaSgwBVjRz98+B3wOoKqqaiC7vaBwNkLI+t2U/dhN9XD4uH03N/evMQcCh7hWzmTjHz1vHYNbr4+iEwJ0ystdwbJ9u+tfBLjiCvjud10Oc8tyixo//vihKZ2WnbnXQxg9bJ4pM4TsVfGk4qRSQbS+rM6tW2HVKleoA1xzDXz844fa2rfP/dvhixWLuZ8fJsh1K8qkslaiGxqpMm2SpkIsrxJfytWWlyxxc4FOdQ/09zAUAR35xhp4rNZ9wPSG8YjAocZs230gyjIMGuSadPpzXh8RTm9WIV/pobUnTkoJ4rPj5BV68I++8O/dNyPF74CFH0qSFAReAL4jhPjzia7Nhh8OnEbQ76asNk5IBt5f3yfiD29oGLiNbxgQf6WegifqUDmywQ0b4IEHDmnX8+e7cqmuzhXuq1bBtGkwbJj7MFiyxBXm+fmnznd+9DyDuzdw8YpaLgpbBHJV1FnT4I47DpUZ6lOTn3/+kJBua4PLLz9SI08kjrzmsOtsX4CMN4iUdK+z/v08+rCSU98Dh124YZfefxjjihXwyCMHzUeJG2v49P1htm93BXlLiyvE3/c+d1qNje6p5gjO9KPxpz9hf+0eHDODrHlQvnOs+ehCw/nOk38ynNXwQ0mSVOBPwO9OJsSzcDEQiTXHCxP80e1RAicwSPfX9/FOCnBqoYinKpR0DPSn6lySqqMaDId1HnroyATLvpsuEHBl1KZNrvB5HdxW/c4zlgizbuwi/q5EsQjxJX0LIwsL3arNHo/bQUvLQW3bMCBqlVDw9YX4v32Pq4n32ciPjim0LBg9GqWxESXVDR4Fxo9G06yDYznpHjjqSR2uqWHRovCRa20YbgxmaenBNQ08Vss37ljEjx/VicXctZo61RXip2zO++AHUebMQdm371DM5AWONyvF7xkLckmSJOCXwBYhxI/OfEhZnCqOuykJEXgdhvj+UvCPZ3I5fOOfVvzxSe6kw9kWE4lDl/ZFDqbTbnji6+S2OmKeLS0uAWF+vk4w6EbC/OyZKr7v0VAsy7VBxONuR1VVR83zg3ypbg7jg4cJuaMXYt48V7hWVbkPBNt2J3CqDpHjPKn1RYvQKw5b1OOs6YTBUR56SCcadYtfL158eqYcwJ3Xm0CA9+HNSnMwEBr5bOCTwAZJktb1fvbfQohnBqDtLE6A423KvPLXb4g/Wks82cY/Xvxxbq7792NMMX0N9mX+2PYRDR4uC8FVePv6Hj3aNa/s3+/azRcu5LTtzIfPMxBw/3+4/GuMlBC5bSFFDxypbbdRwv33u87DvkSkh58sYdGikkNa8dELsXgx3HgjPPbYkU+5owd5vOPMqaqPquo+6SKRQ3am3jXt+z0rKtykqTeTg+/14M1KczAQUSsvAdIAjCWL08QJN+WZMlz1Chc9FKKmRj/uxu8v/njDBrjsMtcme4Qphl6BNWcOfOc7R4bu9druj5aFsZjbb0PDoYQaj8eVj9Onu5GCLS1u3+Xlpze9ZIdByIhiRkJo+fpB+ef/+AfhujmuOaWqivoDJdx/O7zyiiv8p0+HwYOPkqnHE7oVFSf+Hfo5zhjVvaYTNYR+9FMU3KNKnyOh7/vxOKxZcyjjqB/pNCA8OW8CvBkpfrOZnRc4TrgpX++deyp22V4crbF3drqfFxa6r33yLP5KvWsbTyTcJJrJk13V1rZdtfqd7yQa1Y+Rhbm58NnPwv33u/7FkhK3r8cec3N7TuqI7UfbffVV+Mu367lqex3vtixsSeWlSTXES8OH5J9ectAmXlfnxmF3d7tN/eUvcPXVkJNz2JH8REeX4/0O/Ty5OhbUsiB3EQYub/ltc2sYtaTPoB9zv3fffYdMN4sXu98tK3NNOF1d7pHoTWQOORt4sz3UsnzkbwL0HZ0HZGMeJwVTx+i3j75TQR+tdibjClLTdP8ej7uaeMETvW0WFbkmle3bXaN3fv5BUu3+SpTFYvDgg66Wv3Klm+rex8Pd0nKSTNWj0yA3bGDFCqi5wWDy8jpauoPEcitxAkFu89WyaIHRL11BIuGeBsrL3RNHJuOaeObNO2w9jl6IXn5xA/34/OpHafGmFmTrBosiT5TSUjdi5/vPhDEWLHID+nNzYciQw0jNH3HDefqefPn57gD77FJZvGWQ1cizOFJpfR1u/aNPBbt2HTL3ANwwNwr/PMxr6fW60rGjw+WgPcyee7ipqA8lJYds7qtWwezZ7lcApITBoECUlBkiGNQx2gxiW6LoVar7BPB4XFODaWI9UMvPE4vIE1GCmsUBNUh7OxQXB1FFxC37xpFzDIVAswzy4lHU4hA+n04y6dqbBw8+8ULUN+jU3e4up47BTddHGTszdGgdj9Li051xLFQ67RAv/597WEkm4ZV6navG9mPQb2tzjwpvNs9dFqeNrCB/i+NoE+3N80L9lyBTVTeE5ThGxcOPqn3y7JVX4Ikn4H+eCSFWq4wl7qbCDxoEL70Ezz3napC9NvLDv9unCbtRJW78+KpVrobe1QV33QUVXfVcv7oOb2+5uPUlc5mzcwmhjAWxVjdWMRRyTwDTpmEaFkE7iuxVkW2TPCVCWyYfzYyj6f0LQL2hnu8YD7K6xSDZqvPHQfPxTQvj95+A3+Uoe3+1Uc/k5XV0vmxhzVZR5x8W1tNnHolE8KGydGwNz686FL3jOO4azvxeP/byQOCQM/XN5LnL4rSRFeRvYfTnXHxksc6Pbqwh8FitW7XBcVyP4r33njwj6HDVHp2nnnKFcDCos1KqgVW1hKw21IYGePe7XU35MBt5nwDqeygYxiGFtbTU1cS7ulxBX6IbcLvLk7K8PojTHuGy+nvYMnguz28p4PL4SrzdMaxQCR5FQVm+HG3SVMp6mnhvajHCjDMytoZd2mgmjS11hWt/0SQLF1K6dzuXD5LpaHOojC/gd57H+WyNfkJ52XewydcMpj1fh6kH6ZSDpDxx1AULsPy5mCZouor1yRvpDlaQWxXibfU6f7jZjZlXFJg1y32gbdmnM67vdzlcaIfDrrPgzeS5y+K0kRXkbwUcJ7zthCXIbrjBtXFkMm6EyeEplT/4Qa80LXFNGfui5MUbCTz5+EFhH/9QDVKimiI1SjQdwqwI84RYxOiPbqFUfdhtqw+7d8OWLTB27BHj6y8q5667ev14ze7gnUAQx4GubgUPGQJBBVVOsb/dg08Uk9mTBEWhJCeN78PX8dlfL2aNP4gxtIw9yUFcXNFK4aKvHDmevvXq6DjI5KXna/hCJuWdGwjf0kIgPPKES95nNbE7oyi2hUEQRQFvrkbq/zawwn8ZUe8gMOL4lz7G05cuQgR0brwRrphuUGi2EMqDbd3lrFrnJkoFAmFunreICYOP+i3fbJ67LE4bWUH+ZscJiCVCqkGxeWz4XUg1XFKT0lJXI9+40TVTqCqsW+faN26/nd2Tr6Pxl0tQzCTDOleRumQKBWNdLbugbgEfWptLdwxsWeXp0I3kjKkgOLbKtYv3mQh27HBtJpblGsKPyiYKh/Vjo3J6uXatDKxfHcfjCeJTbYTiof2ATckwHSuZweuXaB51BbaRYlcqw4TiMRTl/i+XXxPEbGrFv2EVSnPMZZL6r/+CmTOPDINJJNzso14oCigeUAMnX/a+h9DPHwjRnVTBiTNtVhCpq5NIBDIlhQQ12N4SpNSKMCQvSodX59/317OwayHWmg0IB9YTpviye9GqwwdPTIsW9X8aeLMRQWVx6sgK8oHE+XYnnajUT0MDel0dd8UtNq5R+c/ow8LvrOiRKZV9zsmVK12bdm4upi8H77fvQRo2FykvgNphoj7/f9hNg1AUGbm9E6FeTataSbm5mwWNN9BoTUX7dgDePdc1p2zb5trKCwrcKJbx4w9lE1nWQQIofcaMfsnArM4Y+bEYUm4uaVQerVzIxV1LCDXtQs9043ckhm7/NxsJ80DOvQx6sJzvZ1QKgxGUDavI2ICiomzahP2FL5IePQlVzqBWDnKlttfrjiEadf/vOFjjwrRTTp5x8ryecBi+95BO/JUaCp6oRTUjGAloDIXxKSbxjEbAiWPLKlFC5GsGl65/kFzPdpTRBSSTMK5xOyXND/Cv8EMEg/px/c5vRiKoLE4dWUE+UDjRnfR6BfyJvtf7N0MNEbX0g5ecUgTKYXF7BWVBLqmKc1FXLdKCReglOhihI2Oix493PZem6UaBjB+PmVGQ7QyKV0FICgGzG1sIbBuU5n1IPXFG5qwit1Ki7MAmNI+CGSwi5ZFRlyxx657dcYcb01dc7LZdX+9OYNIk19ySTrvOwL4am0c9mDzBOOmdEV6aejf7pXKeX6WzTyvgh92fw/IHMbxetmRGYCi5dBdWM6JEp7alhpu2/QB7V4y0J4hqC/x5Mp7WLhKta8lLHgCfB1XhUGUhjweGDKHTV06tMp+99+nH/MT19fCLBw1UI4qlh/jc52DCYDehSr8qDDN7jxVqiH/V7OKq7bUUEqHbUXmysIbROTp2ZzN+20Dxyyh+DU0BISUhbpDT00LUDKATIhTSj9kKZ6s0XxYXBrKCfCBwojtp48YjWOlOVVVKrKjHeaQOr2yh+vuRGnV1dLVabNzmatNtpeFjaF2PG4ECRwh4LT+IFo9AX/jd0cZpvx9+/nM3K2fvXti4EX86Q4IMdtpG8UJCy8OXiuI50OwKvkAAGRjcshJFZDCVAI7mw1uoQWvEzRzSdbdt03Q1/a4u1ya/cyf4/dj+IHZ7B9KPH0b9xU+PMepr+UFGjYzwh2iAqK4ztsrgMx2PYXt8xNVC5IzJoMRu9gbHc+mEKL58nYZomG+wiBsrbkfSVEqb1pBpOoDiVbByClDju8kkNYTPgyTL7m977bVYKZvvad+DkhIqj/qJAf6ysJ6Pb32QgDAQqTSppR6sOblH/na6jg68/94wP39gEaoRpbU6hOxxY81VM8SQ8TrK7gyJth4OdHiQMg6d+9OM/et9+P0wNqyi7zpyDw0kEdT5dqjM4tSQTQgaCPR3J1mWG143bx689pprY04mT6mm14ZXDVbPq+Ol14L8e1Ml7cnDMl16HxqGHOT5hkosX5D3NNaSIxvHlA97ZLFO4sZjk1QoLz8286Y3/tgw3ChDo7o3DnDBAvd16lRX6MrullFUGd+44UiJOKK9E0vSsGfNQS4fBMOGIQ+uIL9YQU4lsNMWjXnjuegSDc3s7auqyg2fGz8eO2WSaukkbYI1pBqSSeIphT3bTZo7fKxeI7PlleiRcdemScfG/WzfAUlvyI3VtqLkhiTkgBdddR8Oub4URYUOhEJEIr1h1/4SVl52F0gSXsvAIyyiOZV4MmmE7EESIPqoFTUNNA0TDWFax/zELS2wba3B1SsXUtW2muL2zQxvfpGKjvWkckv7yVI6ZHK5bVEFP/utzu23u+MyVZ0/J99NsstA7NxJWfc2nMIiiko9mIF8prynksIhx7bXXyLV6wkn7yd/KosLBFmNfCDQX3o2wO9+5wqDwkJX69y06VCo2HHUHcOA39VGeb9i4RQGMU1YsSnIO8ZHUKNREgmI7LV4pTHI/v3Q4Q0yPjdCMBMlk9GPoHWNNhvs6ChgxFcWoGvWkWpWPyQt9Q36EeaBz84/VByZ5mbXdn3NNa4D0Ocjt7WVi265m24rQJ7RRODXj0JLBlIpOsa/jfWbNbzFXSwZ8hnm+Z6kINEIoldDLSmBmho6FtSyoXMc3RHBi/538uFtTzGt5wCYbejBMvYPnobAzy9/rbJgSJTAvHnwwx9ir9+A1QbBkjBT8nax3gmzck0IJxigo3AUJW0b8aRtAgHBneaXWPdvHY/HLe68bBns0sNE3vUQhRuWMvffd+DLpBEeBVP2Y8seJF0B2UEWAoRwwwQJHfETx2K9ce4dLdy0fwPxvAJ8XoEjKQTNLnxWj/vb96MeHx5i2edXztcMJq1fwos570HKcSj0J1DSCWxUTDXoMj3mHqtuDwQRVNY8c2HjrSXIz9a5sb876frr4de/dlXkPtNBLOaqXidQlaJR6JbcyjWqGQctiBOLk3ZUtjaGqKuDt72iklTiQBBfJs7+TpWICB1B62qtqeeql+qIrrJYo6mULaxh1AcrDnV0eOaNqpLotnjmrlf56K7H8ckWKUfl6QU1VD8edpeq72Flmq5A71X7AiPKCei9dvXyu2HbNqw/PM3WlSZB1WT5uE/z773T+Gt6Gm/PizLviyEmhF0JlvAVsNCzgM26hVqs8tnGe9lmDSdRXMjozuV4yeAoHvaNmMs1K+9F3GO5iZeZDMnpl7GmvpD8oMm0VbW0Xr6IpKyzpmAuH1p/D1LGBEfmD6O+waDJ0xncS7S44jmDz78/yuKnQ2zP6Dihaxn69WJCv3kEW0gERAmD4jvJJFMEzU7s/HzUl19GXbiQTxbCbx5upkkLHazAk58PlQHw++BAHGzJQ66Q0DUHj8pJE6oOP8z5YlF8skWrtwwhwHAKUVt349gOESVOT0+QXLl/dftMiaBOZJ7p+3vW3HL+4q0jyM+2W//oOwncAr3jx7uaeE+PK8S/9KUT3g2hkFu+64XxNVy2qRZPTwTZUUl/pobax3WEH36XV8OH2mrJsyPYssrPQjWUpfSDtK5N2wyueqmO3EFBREkQEY9z4J5aKub0OjP7oOsHw+1EZ5JPLl1F25BpdOcNQzXjzNlQy/6di/AX6YRCOvpx1L4j7PkeiL79Ola3CGa3/4Vhrz7BV5SneCp3Hh7PYB59FL6RrMf7yzrslMW1r6k0KzWgFuAnQY8I0CGXEKu4jiHOLlZP+SwXv1JLt1qAWl2G1bofNmxBumYsaBoGGrrZRnHTWq4ZnMuEfc+wrnguskdh5HCb0VuWEVPnojgW+dHtjH7pMUZ1S3zNDvCAVcOenDC/2zGDzz40gcHBKA/WqVSzi6vXfo+IVkJCymHmDJueB58kceAZPuhARlbRPj+P/101mHwtRFwrp2twmML92ynIk9ECeUhSxn1w+/1uGaPjJFSFQm76vrw/ip2jknJUdOKMuijIc3+Lows//yibxw3OYnYvi1DSlxl6kuza08XxOL8aG93E22w0zPmNASv1djp4w0u9nav6Ths2uIIvkTgYSseMGaf8NSlhkCeifOLmEPmDdb79VYNB/ii//JOrfefYLv9H1NL5v/9zc1oMA3a+0Ez0lnsRgysPtqm0NDLifxYwaOphWvlh62KlHVp/938I1cfuUVeTsjWU/Y08M2MBUb3iyDJyh6lnG141iNx4Owk5iN8xmJ5ZjuYkaeuQ2VU8gw09wxic2c1YYxXREdPojkLI6aQjbzhJbz5EI9DZxX9KP8at+24nbSk4qpfWkvEEnASy18PQzjXkDs4lMXoym7Z6GNP4f+wov4KCSZVE1+1mxP5lOLKH/FwHj5Qhefm70ErzyXT10PDMVsycQnTJoHLPS/R4C8kfEuLV+Hgsj59vBhfRnXELIf/kpnqc2jqKlChlrevYM/QKGuKlaJhMbPgz6/RZhKpC5JsHKN2/joaCaShBP/WX1pBIwJQXHiAoG6Q8OsvHf47ragYzdqLqCvHj7b36ejoX1rFlg4WFys7yOXwo8C9kVWLF+gCrZ9QQHxZGx6BrV5TbF4YoH3l29mzfvusT2n3Z/2f1tuk7JfdV0c6q/SfEWS31dt7jXNR3Orrc+2ls0EPKfa8mrLtRLNevrkN2LEZmVH7q1LBJDlPphQmjXcsNuF2MmBpijaYiDlOvhEcltyp0ZEeHrYuqmRSU+eg6kCbZlUJIJoqAYj1BQalBxNR7bab6weo0hgGPPxDl/Y6FN6RR3fA8LegMLbQJhTKURzaxxSlmWHITXr+MbQkqW1dRTgvFVjMt2jBy2nbit+OMa1jBRjGGKqkFnRRju1fQmjeK6otyydmhQ8qAv/+RqUgo2Ize/ywtqTDT1B1Q7EEuLkLBhp07CS5fQsr2IJpbGZ0xaG6vQPOq2CjkSAn2NBeSH9/EJmkc/uFRpEKdeKtB+sd1OIEgbXoBRR1bGLRnOSsz11AgOvFkUoyKr0PbblNOC52ZXDryC+lukan+Vy1/mbOIdVMeYlRJFKUwRMTUefAp+NGQ5kNl90zTPZUlk+7aGwbcfz+F5QVMH1lGZssO3rbhW8SqJ7Npq5dHMzeyY3OYWSEwdR0jpJN3HM71gbAaHn2oPOu3Td8pubX1ENl8H5d6Vu0/Lbw1BPkbXd+pPzPOCavcHosjjsmGQWDxIV6RhBzni9SyZK5rKjm6epheolO2sIYD99QixSIIj2sjP8KsAkeui6YRGD8Mb2YD+WM7sONJtm2Dq9feh62orJpWwxrL5STvKxDx6poQz60NcUmXitTRyXBshOxmcvrzVar0NO+q6ERaEcfCS2nretKaDzPpwY7EmJh5hpTkR1WhKxNiOHvYWHwFtlCoTtRTHd1MYLOO0tOFc6AVbyaDLWt051Vhe7xYtkzPsDHkRfai+HufZEVFOM0t2KaMo2hk5ABFUhyRtrE9GumeDFFkAiKBLQl2N6sUHtjBoMwB4iLBsEsL6GxOsSM4mREHXmKIsx0nEEDIMjYysYyXYkeQJ8coKpUJlAbxdUb40oda+OX/BFAK3R9ikNPMzkToUNm93bux6zdhJ1MoTgblz3+Gl1+GtWshNxetqgrtpWU4Vgajaytq2RRu8T7GXc54li7VuWK6wa2fiqIT4miGxoG0Gh5tnjlrt02fd9Xrde03gYD7WlWV9bK+Drw1BPnpuPX7UW1OS9t5Pe7/k3XQqxoVDwvyrgpoagrS/GqEPBHFSOv9TmXUB8NUzFlEbF+U3KrQsUL88HVZsOBgrJkyYRz6Df8P649PE9mfD/4gOnEmL69l69RF5Dc1wMI6rKSFvEKlmhr+NWgeH2l5ADtuoOoO0sxZYCZQli2jYtcyHDpwvEEkI04kqaJgU5w5gEoahIRjeyh1msnBj9SxlK1qmAKziYxPwtO+H3piSBkLW/KQ8ObhteJ0CT9thkp6t0pV3KEA010DRcEOFRKNB7D0EPnRPciOhe0IYlIufhEhQDcy8HfxDr4Tq2GKvAHV4+CzE/BygAmlMqK9nagdgIzF09Z78QV6KEk24ncSgISl56HKDrlSHDUTo+I39/H+DRBcG8OjgiHnMs1WCUVqSHz0RpxP30AkpiA5Ao/IUPTf9+CpqnD3ouPA88+DJCG8fkzbS2Xna7SVTOC6y6N07mjga1YdoScseOpISf16o01OZU8PRDTMcdGn7gcCrhc6L8+t3KEoB/nps4L81PHWEORwam79flSbehE+PW3ndM+jp6JOHaY5a8Eg1SVxKueoDL87RF75CW7EEr1/AX44qqshNxdr9mWkAoV4ZRPt979HlSQmzQqyfDl0poKU2xG+eF0LgcWu1EgGgqQ9cb6aXEDj/lzSQmOXMooJ4zRUpzcDdNIkKClBTqfhL39FpJIoikYHpVQ4u7HQMNAJOVEkHPykCDhxJluraJCGM1be6/oWhEACFJHBl+7BlH0kbYsePY8/a5/ii7778ezejqdIRh4/FiQJ64UGohFIOsWUWk30iFxSaZl1XEI3IX7Of/Epfs0ottPjKUBVbEpEK07KQGqXMVMOuhRnpLSDrxpfZ6s0lvrc2QRzFbppZXjPawSMDlJCdXO9ioOMnuKQ/vdKhBC0VVxBeIpM4v5aFonbmdY1FcOTxxzPSziKj1R3BL/mQ0knXZOLZblrpqkUduzD45h4Mily2nYya98TBEYHIf9YSf16zB+no8Ef77Y5Y1POQVYx2xXe8Xi/NVyzODW8dQQ5nNitf7RqE4lgffcH/FpdRLCk5NS1ndMx4xzep6a52Y4PPAAPPXRkB7qb2OM8XIu3PYLqdyMXztTpZRgQ2xJFaYOV+wf13lMaswa1UxByiDbHaW4O4rXitEgqyT0wxrIwtSBWD2QUjcrIBuwhl9EmV2KliymojrjVbMANsh40iPaGGCl5MB7NIGb60X02sg1RrZTcVBsIB4FEs1SJrfpo1oZRlC8h9Siky6vxbluPpGrIEmiOhWxbNPlms7783bw78gR7UoUcsCfRkjuHuDOGq6/ViKytZVC7e8pY5X8bP7M+Q4tSSUcywH7KCRElj240LDJ2GlW2cVAw/IVoIUgnexC2g+HJw+fE8SoWFYEIk0aYSDk6dZnHaXQqCJDgnvhdaCtfpjiVwrGbEEJQ7iyFzT7qo5XER0O3FUDKmMRMCBZ4cJCwTQfF43HL3nV0QE8PSnMjOUBKCbItdxqTVv2C0WMktPwywK0iZEUjSC1RAiP107Yavh4N/ujbZkBMOYer+5WVh2zk6XSWU/11YEAEuSRJjwHXAm1CiAkD0eYbjsNVm9ZWl5GvK8b7pdtZf/VdtAXDx9d2jlZPTvU82tenYbhH64MlYV6Bq646eFl9PdQ9FkaSFpHnRPnEvN5Y7MNxmipS382Y6Q5x1UsquYPiBEvcO3tjg5/h35nH2nmLGSRFwKvyRH4Nu2vLCY9S2bQ6TlIO4uvpxDShJV2I7IUpc4KoZsQ9LvdKGDMSZ/VGHxNlCfQgO4fMJdHUxRWeKHomhgcbGUEGmRBRVMskV0vSpoxDSRt07bEIOT4kkcHnJBGSjCk8LHUuZ8SeJbTIQTxpiXeJ57mq/i9kNvuIPV/Ec1MW8so133VD+OOdfOCfj+OYFglUHqGGAAmmsYoKWpBtgW1LSEh4CvNQFLdcmqN48XksJEdCWDZeyWLXHigZbGFZMV7ZPx4dlUTHNnyDA+ihXOR0CiQJQrmk4ybFXdvY2JRHfaCGL6QfwEcSogZJXx7Blr0gHDfT1nFcegJAliRUj0O7p5z8TBsNOxwojuMEgqxfHkdNqzx9X4jPzncF6Lx5LgtEW5u79H3brb8tcaYOzAFNHDoqlyEbtfL6MVAa+ePAI8CvB6i9Nx59qk0k4gpxQA7lkrIKmLy8lqXXLCJi6sdqO8dTT04lO6OvoeXLD11zsCTMTAx0WlrcimX5+RAs04nHdR5ZDIsmHNbsaapIh9+MTkDn94U1fLy9lhI1giTDc1XXs2X3BL5uumXRoqkQktDxeuH+RA3/j1qKiGBIGbY7I1BNA9urISfi4FePeKAlvluLt9uivWAUwoFST4R4sQR5Ybq272OQsRtTaEhIBEQPMvAv+1Ji0WIqhAdF8jDJb5JvNNOj5JHxuNwsn3PqWJW5mIRQmcVLFNOGJTyYSpBApof31y/k0Ykvog3SueRvi9juDaJXBjH2xfly4gGKaCdEBLn3QSIhY6Kh9XTgiDyCxInYHkpTe4k6uVzCq+wMvJukmkvyleV8mpuZmjebJUXXs43RDG9rpIoYis/nCvLubjxeP/tzRyNZFkZ1mP/e9xDTpef4TvpOAoUacp5bbDSTspCSKaT8QuSebjIOOCkLtbWRdm+IDVPncfmKxXicCAGvyvJpNUhBN4qor8CQLLtb58Yb3Z9+xYr+KX7O1O8/4JEsWS71AcGACHIhxIuSJA0diLbOGfo06R/8wE3kyM1FmTGNCeSzY2kjXbuiGKGjHIvHU09ONeRQ190M0JdfBlnGlhSSE2fhzZjsWB7loT+5NtBtaw3eMSOKqoWOpTI9VRXpMPXs8Gr1punSqn5fW8S8Ea9w8fYnuGTXEyRrn2KwVcN2LYyigNpjoCejdOZWs2L8Vxi65Rm0vS/hVVQujiyhndFsWVWKXFdDKqqjGrD+QJjfs4iNVpTmaIjBBQaj4tsoCyWRun7HXmU0lyn/xO8Y+EgiBCRzSuiRCglVBEntL+B/Ku9mSucSvpy6G0fWkBB05w/BG9nPJbxKAh+ltCFjY+ElaXkIqA4FQZNg8zYSTX7kdIKxU8tYuxZsb5DBzh4ususRtoYp/AgkFJHB9nihrBgx5wpW/rODiW3P0iaVk5JUorLEoOhmhJBoRUeSQBNp3tn+a2KeAjaHqgiEHYpYiqJIcPnl2LZMye40kYYQmTSEKnRCFRfRunUGQ6YJ2LqB5M5m4k0RgkmbtA2BQB5OJIqMwK/ZPFFew9b2MI0jhhHo3MdWo4rOjSUoilsx75FH3CjXPp/hY4+5aQtf/KIrxH0+Nyft5w8Y3Hd3FL08RE2N/rodmG90AFgWp4Y3zEYuSdLngM8BVFVVvVHdnh76NOnbb3fvjvx8SuNxCmarVPfnWOxPPWlocL+vaadmQJw5E2bPpjPm4ZVthYhVJrojeCwWomw4jM3U84HOOjz/sKgYqrIsXIPqDx+6cfqpxH64DRWA+nqsB+swDQtNV8n/fA2qGj6CpXbnKwbhNb8i4i2gemo+29bGucNXy62ZRQy1Gvi8XUdZrkXV8n1UJHeiJzsxbYVVwSvYNWwuutnFLysW4PyyhHjcLfiTSkFOjo6jwLTMcm7YU8d4ZQvsdcgJZOhmJl0ihOPk4pdNFEXgVzxYko9SLUIME8OTx7Kcd/GZ7h9hKj565Hy0WDuFTgfNDKKQLhTJwSNs4mggXAHo1Rzm67/AtEBjNat2S1RXD8OXiePtVDGbPQRVCWGCI0BGgCNIqzl4CouI5ASJdBayRb+IpmQRl/IypVacjOQhgE2x2UIm4grzB5XbmGWtZesaC90Zy4jh4DQlDjJTDpugk0i4ay0HQoyZ6EHduBLbo7G/y4PPl4MpWahWknSPBLKPbf4JfL+yFlPVmXhgCR/veJJIBC7XVP4xpIYNhNm6FUaOdNe6z2c4aBA8/PCRFD/pVfVcl6lDGBaEVMI1NSxaFH5dzsqzGsmSxevGGybIhRCPAo+Cm9n5RvV72igpceuJ1da6ca3qCRyLR6snkYjrtJk717WFnKInKfH5+az5aC1mtBVbVvl5Tg0b1ul8arjBrHV1dJUH2dYcRInHmb6qlncuXoTe195hY2g1XBuqNwn/uSfBR75sMGECdCysY+32IEk5SNCKMKX1B3zpjkU8/GQJkQiMSNTzjZL78dWvIKXmsmfNNAypFL8aYdbgFj52oI40XgIhGNuyBittIzsWqmPxtvg/2WT+P5K2xu7tFkOLYdcu17rQ0wNTvfVct/9B5kjLyBGd9PgH05nS0aUOJiRWsUcaQoXUyHZpLJLmoTgIU+wtVO3dxoGc0dy6owbbho5gFaOM1/BmYvgy3XR4Smn3DKUjnaRI6UIXPQRJ4JdiePLyYdQo1PJi1GAQAxj6r1eIWB1EPIXUqvP5oPghU+zXKCSNlzQWHlJ6AdvtMKPTGoX2XorkCBdJ6xgf9LMrOYgCcxua6GGY0s1upxLD0ZFwuMxZSl3hd3nnuzRissuyGDCjeOe6lZdydsPmzTB1rEHAjJIcNQme+xNICkUJiZaqmXQzFFv1ke4xMdQQf6i8g+T6A3zB+CEzMi+jlwRYk3cpXWmdd++pZUflIopH6Ozc6VLflAYNpO4oezeHGHOxfpDiJ1cxeP+BOpKlQdTqIJjuntQXLTqY2HW6OFNelywGHm+tqJVTxanu1KPVE9N0Pe/5+e7fT9GA2Jwf5iueRVQOi2IG3KzAyDaIN0Ux4xa7IkEkCSwtyJhhEUoGRzmYFNI7BuuBWna8HKHEceOYr910H13zVCLf+hBbN1hQEKTcbKWsZRVmY4xRwdv50W13Ec2vJvTtOl6JFjDKn4umwMjoKhI5s5F8kN53gNxUMxXyfkKpGKQNVAkUj4KiKDhpi0G7X2J96HI65BAty6GpyR2WiBt80K4jhQdLeJCQCJlttDvDSCVslIyF8MDmwBSa536GVcpM7r3DoODbt7N271x6HJ2JXf8gVADi3dfQuHcMjc/vhIBCoXWA/KBJOuBHsbzsKpzK8nGf5YNzDjB0XBD+9KeDp5R4W5Kg0YpIpVCdALlF7+PZoo8ysXMDnaIAj0dm2YwvEx8znZEvLcbTtIvKjtdYoc2hNN2Cx04xlo3EvCWUGntQMgaDlUZSeiEmHmamVhDs/iobo19DG15BpBUi6IzJP0R6Odaq56aGOjQ7gfKvldgFRe4DeF8nlY0vkwiW8Icrf0qjNpJ3fyzEy7fCd6wbGMEmCqQomY44I+UX2DzqOgZJFm+/KMpu02V01Bvq+eCeOlTcE9eyRA3+8WE2bQIrHsXjWEy+NOhm/2oDk555MtN2ltf8jUVWkB8Pp+qECYcxFvQm3hSq6N+/93UZEJOyTodXR1PcY3F+PrSmQ+w7oOJX4xQNCxIkztYGFV0NHZnbFw7Tfvci/vHVFj606z4MXz6WFiTVEcf67RM4DuTYEcpbViEUSKq59Eg5eL//A0J33IFIW8TVMg5UTqO8ZRVeK0ZpvAFL8bOg+GHGN71IY2YQ++0iysRePMIiSRA9HUUWUOS0s69iBlKPTrTVtc0mkzDMH4VEknyPSUGqlQAJpJTDaDmJ107gIJHOeLHlNoZs+BmbZs1EsiyChRrFWj57l8ZIJGV6DNj6txSZuGCGuQdJONjIZDrSBHWFkhIH7YvXMvXln6A+vgEcB9vKkEpAp1xM8KVnMdHY6wxBdZLc0vY1tuROp2HEO2hqUagstxmZ2sTT+o1snL2ISR/ZQsPah9mWGsZ2awIFsd1cbf8D3YwgeVXScoBMRkJPdeBXVEwbqhObGLx6AU8WPH7wtBSPuw5IOWnw8VgdTlEQSw6QaVFxZFA72vB7JdJJaPKPYNL633DZrx7iQI9OkbmD0dYGIk4eITkKQJnUxvrmA8RLNKKE+NKX4Lc/M/h/e+qwhgRJeYIoqTjztVq+41nEuHE6mhXi0rRKaZ7LlvlGGLWzZefeeAxU+OETwOVAkSRJTcA3hBC/HIi2z3e4m1bHstyIltvm1jBqybEGxBNpKOXl7rG7dVuUuCdEXOiMHw81d+n8KVnDh9tqUe0ItqLyt9E1DLd0jn7E5JXrCH+AjAVWrstjLlSNHMlgffXHmbnvD3jTMZKeXJq91bBkJX4zxr7191E9JIPfiRPxlxIrnE26sQ0jriJLEtPV1xCOYCh7aRIOrXIZZU4LmtmDKWtE/GWYapBBe1YgvB+np0cnIFyyrwk525lhvEIFrag5EiKjICVNJNtAkiSSai4h26BF5FHa8hqBlp3c//MRfOhVlX0HIvhUB8nOoMgyB9pkrrCXExd+VjKdaazET5IViUm8Vvp53vPTxwimNyDn+0kJjZ7WCPa/V7AjM4KpIkNrYCQirZB2/JRJbRTRyaauavIHQdIHenQ3waYtfOyusaQKxiL8fioDcbY0aLzNXofTG9/iSyfxkEHFQbZsYnYBTfIQMmiUNG5AaW1h/tdGAof40gJmlOI8C9sfxEyaOB4vsumyYaoIPKSYoG5HdnZitr/Cj5++Cp+vLxJFYT9lVNKIKpkMyk3g3Pplvv5hnZISyIlF6XzZIuYJotgQnhYkGI9w7y1R0n2slbveOKN2ltf83GCgolauH4h2LjT0t2l/tCTMogWL3ALGvVL7ZBqK3lDPg546Nmcs4t3wpHQ96aEz+cUvdGI5Yf4yehGFSpRYSsXssdwq90eJcl2HD34mROxzKum2ODouE6F/a5qPjdNZPPwzzExDSs1hSOtKFA3sYC5mMJ/mHfuYEm5n644ITW0qzxV8hhuUXxNq2kSn5ccrB5HtNBYKL6rXMNTczii2I4dywetji28a7XtM7ECUMWYDX5TqCJDkyp5l+KUetHQcKSXcyJziIrqSQYLpLmyPD08myWBrD2rG4oM7v8e6kV9jQ+lcrt55Dz6RQiXFTnskpTShkeY5riBCPst4G2Uc4FvyAqY07GFY53NoTjtWi0LGo4Mvn4684fzB/hyVrTuxbdBFnAr2ogmTwYkddIgiJr9jGJX2bsSqVUz3PoT6qEry/dfzj9IbGffKY1RaURRsWimmiiZsFGwUMnjxk6A7bzA5veE/cgJunQ9Fvb9tn3Vu/84QiZtUkh1xMr4gOdNGoyzfiSNwE2ByclCNbigpwf7dE2jqTKovLWfH/jBD0+sppAtNtYl78vkZNyGtCBPY0LuPZoawZqukPHG67SCbV/XGmT/kxplXVHDQVJhoiRIlRF75sYrAQOFc8NNl8WYt9XawXplxVrs5XoW3qKW7d1CvJl5XBwVeg3F5zRR4jSMrdfVeUDgkyMWzNMYlVvN942bubL6FsRk3M7E1rtO9u5Mrn7+Xu+L3ot97ZB0uw3B5yH/6G51nR9fgNyNMN5bi9wNXXEFhdT63ljzJiAduZuroHoJ2FA2TaF4Vw9pWUhrdTsBjMuxr1/OvqxZR/oGZ5GgmBd44lqTRIRUjkPFKFjn0sND3HV7yzWVvxWy2D72a9piGTzYRpsnNch1xESQu5+JPRsihN/W6N9BZiUTIzRV0+8pwEkn8jltNqYXB7IyVMm35A8zoeoYGMZQ8ugmSZBLreYVpvMxs/CS4mv/jcpYynk0U2y28o/03+JwEGRSQJDSzh/zYXvJatzGn6y/8Vv8cfifOUHsHMg4byt9BU+VMprGKQfHtqK+tQps5DTVPh9WrUeffzNu3/4xfKfP4Ogt5kTnUM5k03t4EJpsOpYx2StDsND6zG78waAqF2WeVY7S5+0/HoKICpl6mM7XuRuaMbuUdo3ZTUCjTPWkOrxVciSEC9CQUMmkbJk/Gq0GeiKKGdHZcdyeBoExULaJNH8aL8uV82vsEowcbhyrIoaPOr8GXibPnpUaCxNlyRQ1avn7EPqtvcEvL3XOfflbLuA1U2bksTg9vPhv5G2igO5WY2mgUSlrreU9jHYptueaRyhqi0d7KO71PAxMNseJ50h4dTZFxJA+XbaqlYdwibr8Fyn5YhzY3iHYU30Z9g86DD7qh6F4vzJoVZkXJ3eQu/W8mzixDzc8BQDWilI4pIHHDxzCXvoww0wzdu4yYv5i0N5eCihJKX3yKvLyZ5LQ3oDhp8pIHyBFtpArKWBa/Go8m86P8RUTUEh5oDHDr3lpGBTYwO7aVLYxhoXQvOWqciFyGX0kjGRJ2RpBBQZMcJElCUlX8ik168giiyy0yeOnRiqi3JtJu6IyJt+I348yWVmAKFRONAHFu5ifU8Ag/5jZAwkJhPZP4L36BjyRR8igngWpbSDgk8bM1NBPTzmdEzyb+MO0HzNn8M7ZmRiBiCjHLRzIEDfH3cX25SX5xMTz/PAY6bV0ynSkPHzcX82UW8SDzuZUHaKcEB5mNTAQ9B28iQoYgwwtMIqbOj5z5jPlOA+/YVcfEEUmKC3sLifh8BJ58DHQJhEPi459h/W1PIuV76UlW4Vg2EQdKbI2ABp/4XIhHFkOjkc+B4gnowwexaV8OLe0aSqSRnsYoweGH5RP0+kievidKQXUIS9MJcmSFnzfK3JENTzw3eHMJ8jfYQHcqmzakGly1rQ4jEIQ8d0xXbaslpC4CdFBVulpNNr/UyuB2GyMJakAhGSjEG20lT0QpD0BAs1zSJA7Fiid3Rqn7iRu54Pe7/za8ajBjxFbKO9YjlmwAjzhEyHTbbQQ2bkTWBZmuNiTHJijaMd/1PrSSfGhs5IvXtdD2lToaleHsH1TIqM7lKEYGS/Hx7MT5xDtLkG3Y7g3zW/OjPNj6OUxFY0RgP68lc6h2ttGoVpH05hCRC/DbBgJBUnhxFA1Kq/BNHU/3R29jXmMhCyNfoiS5l7H2RkY79cT2FTJYaSBID47swXEEIKHSynRpLXvEEAbTDMAYttFKCTaQRzcRQijY5NJNCj+NVim5ksFM9WX8agzH2kK1tIOopSNLDl1GCRc3/oXU/o3YTZvBsTnQXYhHUegQhRQqrYScKJsIM5+HeJv6Cp+1aglTjxyHbVqYh6ybKcmvoKEzxLBhMH317fRYSQ7s24S3NIX+yidQRlS70UxlZRCP4/zuSV4YfiPv3P8Yrf6hFHWtR3YEkb+/TGxymGppFz+aJ3AeeBCfvJH9a3dQVj6LpKqTcVRe2hji4oJepUE1oDlKXl4II1SBZEJQO1KpeKPNHdnwxDceby5Bfg4MdCfbtLoVZcJoi+WNQexuUJQgs0ZHXBt6fQPWg3Xsq48yMrYZVWSQfCGWO7PI6zQBlU/cHCJQzjGx4mpa5Xf3hehMwdixrvWi2qjnA00PMmfnc+ipLjyttuttUxQYOhTWrsXp6MCTE0IJeJAsB6mikJyhh2pwjhkDw6dZxHKDLFkSpMF7HeXpXTzov5v1a0a6YZAWDC8z+JLxSxxLIx0swqeYTGY7+zLDqc7toicRZZd3DC2JMsKsx0am1S5lU/tkCur9BL84lopJ0PMfPyETFEwUHKoyO1mVCfMODqCIFAoycXT8pPiMeJQ8IkTlIjqcQnRizKKB15iCiomMjYOHJDoR8rF60oSV5Rh4aVeGkp9eT8jpxBY+VE0wON3AHt84tpZdQSHLUA60oCka+yovJbjPJB1XEYrKcLWZNjPEcmcmH+IJXpQuI5NbiEibzLN+xi933U352BBGc5QASYaam+jJaOw5ANWiCV/LfjytrXDJJVBairc9QlSv4LFxi2he1cIXgt/AsmW61EE4GwUjP/EA48dD4fB84pkZeP61nNHNS6D0Mh6W5tNm6HR1wT3X1aPf654+dVXltrk1/GhJuF+l4ngnx7NZxjYrwN84vLkE+TnKHz7hpg2FKChVuboqTkoJ4rPjaGnVHVddHWZ3krzEflB9kO4hlj+EMslk/FhB8Cs1BKb3NnxYrHjQq7Luihp0TWf5EpeLf/Zkg9lP12FnwGv14MkLIMcjboapJIGq4rR1YDky0YiMLWnkijSKkcHf2XmIkKO8HNWvIiVc5kM5YdKBTnscfD4DX6FOezukW6PEHRshyWhOkozHTw7d5BVpZOZdT6buCVo6Cgig4jCJclrwyA66lODFCXew/0mda2Y3w78yWKgERTeFRPCSZA7LiZJDEREcIIc4cQIU0IWKSZWzh3KaAId2itjGKEo4gJc0L3I5Jf44Y5KvUWbvQ3HSNAyaQ/fKbibLfto9g3lNzCCFl6ulf2OjYARL4Yr3Ita9xv5dedg9CXKTcf7mvINvyfdSqCZwMiYv576TIb4MqzsrsWJQSoSZ4mX0jntIrAjxt6J5OLaDlErgkWXKM3uxNY2kbROwbJRVq2D2bLpjkIwneH5biBHtexhtryUl+ZEzW9ngm4bPNmioh1yph8CLS8G0UdIZItMnUzY6zIwuWLTAoOT7RzJnjnrmARZ97yGiln6EYD7eybG3XGs2TPBNgDeXID8fDXS9Y9Jqa9Gsw8ZkWdDdjW/rJoTkp0suxmNJNHbqPFR0N9++pZyp0w8b92Gx4oMGQU9OOfmae2Lv6oISK8qgQosh4wIEl4Gs4gpwSQIhsGUF2xak8CLLINspMsC91jf55IevYMoVIbefaBTmzSP57cXkGxHypBgScDf3YaZUnsrU0CmHKc00MclZi19KEow14fj8+EWCHmUIuQ/cwXJrGi1yBe9w/gHAX3kP5Z5O/B4FpaKUnvXN1P7dZLG9jbjwopPE6eUklxB4kYkRwE+SGHmk8FNAFxomFhomXnTiFNHF5TyLF4cCOria/yOTVNnIeNIEaJYqGNyykgqgmCaiUgF2wIuUtpE1Ga/RQXhSAq3LgqpyAtd9gv3//RgBJc295rfYaY9gWHwfZezn6ug/MfV8ejTYZlYwS16OJXtpUqvRMLm6eTF/sq9gEX9Hth18pOnWqrDw4HUklK4uUhu2sWVLDlem7uNKQEu1kZZkkpKG4yiEM8tJlE8m0WPCs8+C34sW8pHqTnHxK7W8UPIR7rqrhBIt2i9zpl7/CvpVVx309/cJ9KOJBru7XZbh/PxsmOCbAW8uQQ7np4HuqDEZ6BjPraBw5WqUrg4qZD8N3cWkZR+OR2PMRQF+8YTO2KlHEvlbWxu4emMdgW0WslflhfE1lJaGWbDApaMtvk9FDcqwvcTlNBXC9YDisulZio8uisjYEsW0kZCCzE38hacfGcXo4k63aIRl0RFT+VH0Rv5OPnc59xGT8+mRggREnHmJWpKVC7hj9w/xkaTYbnUjrFM97Br2dqIlYwh07GOMvYlMfi5SxNXY385SFEeh1IpS8ccbqeooRRMm+6VBFKvteK0U/t5UeQelN8hPwUJFAjTSWKh4SaNioWIhIUjho5AIMhKNVKFikUFlI2FyJIPpzgoMdBw8JKQgxeIAV6T/idfrEKrQGdz8Z+TdaZddavp0qlvr6CoMMDSyldxkO5U0khABkrKOrqaQFJmp1gpsEUYTaZZrV6AFNaJRjSrRzttZyr+Zy2TWUUIbWryThpLLGCTtIJUWdK7axb7kTFq8lUz0bGQGy2gVJQwRu+gShXgk+J+cT9LT0cFFiWfIWCoBHfxjhqB2xfjvT+yjIFwCRsjdGP0wZ24IzqT2cf0YTVvXD2nh0SisWwdXXHEEBX82TPACxZsz/FA/FP533qB3TPUNOl+9xWDTnYtZJU0jZXtIx01KMy3s9o4mWOKneGQIb3cb8RdWQ1sb9fXw1VsMdt1ZR8wJsteupDMVZPqqWm6eZ1BSAuUj3TA00mlXTS8qgosvdh1sc+bA1Gn8ZtZPWBeYdVDrDQiDWcYSbl72YfjGNyAYxCytZO32IO/reswleZLBkIIoCiSkIHbSwrd3G+Mz69GcFB0UEiEfW0gEuprpMRRs1YeXNJIMAS1DMe2k8WPJGt5UDL19L/FAKT1qAaXOflYrM4goxaTwYqPSQ5AYeeynHMM/iHrvdDrkUrykEUAc1wciAD8JfKTxKILmwGjSkh9b8pCjprAlhQwelnI5awOXEpULaKGSVb45BAv8+PfvRc5kXEewZUFDA97NrzGkewPdKQ0he5Bx0EmieWxAJoHOgaIJLJ14C2sDs7F9OpkM+O04qsehWIsyStpFGj9dUgkGAYb11OMImdcCl+J4vIxjEwHiDDJ2YSOTkHPoyhuCqtissqfw8w0zWeedQdxfjBEoYqcYTlMTdMU8fPe3VW7oYB9zZjrtptGaJsyahZWB39VGCboMuYfCFA3334MPus/3ykr3Gb98ufvVPoYJVT2H9whvWOTwmw5vPo38PEZfUE25J0qe36JVG8Xf21Rm+lbiiDSyR+YnUg1z1/2Tea/cS9GqDJbs4bmyhYRGXkKe38LyB9FicS4ZF0OnB/Vw3pU+zb+lpTelMODWQrQs1FCI2bt0fnnLBCY9/xIZFHyShSIJAsn9yOtXwkVjSaUgKQcp8ES4+GJguUqOEychBxldESdXVjF2q3gwkbDJ4EcADjKJqEmTsPHnjmaCsgo92ckOcwjjpDhFeSaaYtOdLEJTPeRoKWJqPjsyoymy9uN1kgSlBLaskauYxNQCAmmLaP5Qxhh7MDNpugmB30eR1QoZV5Cn8OHHZL8opsEZRpW8AwGk8ZGjGthpj8s17ij41QxpT4C8iiABBBxIuU+q3qIGTiSKreeSTxc+y0QSbvoPZMjNdCEHA+iiBaVsBHt8F7HGCfGZWC25IoKJyqP2f1ErbsIQAWJSPrKqEvI77MsNw4yxdK6BwdoW/HKKPKsTzU7QRil+x6A82YGwLYq8PXw09C8uSy5hj1PFhJ7XcOwe5BydF69eiJVfwg9+4P7MJb3MmXg8B6kOjahgvxWiorcW9eGa9ubNbpiq3+/6v0ePdqN1166Fffvc9/fee+5s5dnU/tePrCB/A9EXVKOUhrAVlUDbbkqNTaQ8AlXA04GPsTdVyjtf/gh5FQGSgSCtu+Jcu/trLGxdTEKGSmMNFXtfxb8vgyIDL7wHPvaxQ50c7sECV2ubORN0nXAYvvtAAOdqUDtSZCQVIYEi2chGDxw4gK+sCow4uzpV1mXK2VdUw+esWvL0CLmFKssDc/nC7p+gYpFLDwo2FhoHKMFGZmhmB9XdDezLG83MSYKfRz5NUepvSD4/lsdH1dYlSJkMKUkmN7EP01FA86NcdhWG1UNy9QYkO0NjKExFfpIWMZqN+iUIcYBLMsvZWzadXGcl3q4DSKkUkhzAtiVUkWGIv40W3yj8fijpaEXzq9TmL2SWsQQpmUBg014+nkmX5qD8JwGZDA4SUjqNkGQyKZstyeEMy+zAI1Qcr59uLZe8eAuW7EVFpsMpYN1GD/Ua5A4N842mRXiTUbqcEPlSlM32aIbQSJ7UTW6OQlveWFIEUWwTwwqyJj2eKc4yptvLKKaDqFKELXlodMoRqgd5aBVf2nMPK3Pnstr/NqJlYxAHWll//Q/ZI4ax6mWXLv/22+Guu3TC8+e7KndrKx0xlYcyNazYouPdAbNmuYq72utbf/JJVwv3+92tsW0bXHSRq6H3cb2dLqX+QCGb2n9myAryE2GAY7MOFiEydVaOn8c7npwHjkmB3U2PJ4+7227nXxPvokzKIBUGaWgArwL5zn7mHbiPDIIh8edJCi+27CfpzaPgq9/EN3MmDBt25N1gGO65+eWXYfp0+NSnYOZMAiPKYcoonKXNqIrt+kN9uvudRAKpuRF/RuXnyo2074iyzalmjbaIuUNauGhMgsue/xYqDcTJIUgPXkw6KKSeKfxauZH5yi94Wb2cQp9gTNNybty/hq6C4WhGmpxYC7KdxmtbvCf5OwIkiUs5aLZNzo58PLqGWuqnqdOPjIOydxfTiw6wNT2EdNUgEumR6LJFrCVDsSIjjx+HJyXRekBQb47mj6VfonD2WEIheO7PUUZND1FarbMu+k6M5ihTP9zMpUsfQzWbQJIwPT5kK4UECCFIo1EsWkl4cvBmUkTsIB7Fh/ALnCuuZOOeIAk5h+TeVkJEaW3X6TF1ZFVH08AS0GWUYhZWofkUdL+NlE7z3NB5zH11MdL+CKbkYbt/EvvSJehem8u05YTizbT4h9NQOANvwIdPydAVVYgB250SRufEcdo6Wbu1BEfTyc116fJdQRdG702/X3CfS5t7RZn70y9d6irs8+cfeq7PmuUWwFJNgzwjyvuvDvGvZfoRhJ2nS6k/EMim9p8ZsoL8eDgL5zxdh5vnGfz2kSg7ugqo8k4m7NtIk1VIWvaTY3XyQd/f8agy6e44wvZTmNgLHg/7fKPIMfaTERLRgmr8agY9dgBnfxzr1jtQv/NN9+7uu2P7ztCpFKxeDStXwuzZJD4/n56ab1G0aQtKd+TQsXzSJHjoITq3dbNu91Y+/MrP+KANJir/l5zLFa8tYWxTB2M7nmMnQ2iljG7yKKKdGupIe3K5WapllLmBntQuckwTpdJDkdQFXZCTaKWhdCZ5l41GWbqEArGfjrwRZGwo6NxAap9JW2gUWjLKkHQDmwd/AE/MxrtrMxexAadFIREs4U/v/y0vNH+cmj13oFkWesBL4eUT8K3zE7hoLDFbx7N5E+/3LyfROYJmbToioDPvv3X0wRWYV49H3beFZEri360ml1n/6nWrZminCEn2YtoKJh58UgZ7ygwGNb1Mm8dPZ0zFTBiYKZUWKYTHY1BmR4naIVKOjqLr/Eyp4V5PLbkeC9NS+fvQGtSLwiwZP4FVS6IUiA7uzHwPES+kS2hsqXw34YanaSmbzPg5pax5NkIs4UFWbYJBmJi7m6Ftq+n518N8Lu3ncb2GQVeHyXfzt1xBV6ETCegYQEGvRnvNNS4v/N13u8UnDMPdxroOn5xcz+TldciKxcVLVbYkaojFD9Wk3bLFfQCUlrp281PRjM9U58lWHjozZAV5fzhb57z6eiYsrmOhbGEEYHdeDF8igxyUyZhJJJ+XosoAfPI2PD96gPxEF1LGYveIqykO6mQ6ClH2QRADf08EIWwykhc7rxS17zwci7nFm1tbXUOoZcGoUWCadHR72HxDLS8Mv4FrlIsYn7serwaMGQOf/zzs2kXhz3/OR9e+TFfGy6vMwkRjAffwrD2XTm0QPr/M4MQB4nYuDgomGlGllNv8P8H0FNBt5CJMkzznAMldCn6/THGBhLQ3yQRjJWJbEx12GgcZoajEYlAk+fBiUtFVj2KbKJKgevdS/IlOZBwEEo6koKZ6CK9ezG8uqaPn9l/h+8UjxHokNtb7+alcw99+r/M9+zZu4WE82LAZEoNHsfdbv+aRxdN7n8k6N88bS0HaT7MVoskzDFlkELYDOJgZP5s8k5nAOnyk2L0txbPB/+Lt//gFkzIZbNnDg4ULqepq4PPpOjxYWKg8Kmpot6vJeHws9t3ErFnQ2QGbN/uYdZGBlaNTLDfw4f0PUSWto1Rs4WVnFj09OnvyL2LcWIlcsxG/rPJI6ULmsgQts4vB+1ezWp2GXD0MpSXOraKWRVsXUeiDkBklpIYA/QhB2BtWjq67zJpwKDL35w8YTHy5jrQ3yKTLggT0ODV7a/lWZBGNEZ3WVtd/unatu32mTevlD4oef+sPhM5zPkYOX0jICvL+cDbOeYc9HNSyIKF4nOF7d6Osa0H07EcoHpzpl6DmBuDjH0e57jpiz2xj53d/QUwqJtDTylR5OWk1h2C0ERyHlOxnhe8KJnpLyLEaobsbKwMiI+FxHGTHcQ2ggI3CqoZCiqVm3rO3lmjRcJ6zx3LV0B2om9a7xR+3biU+OExa8pPAz8WsYgUzUcngSAopVSedX4YSP0CZL4KNwl4tTMAfoFxLYPgDbMpMZrp/DVrCQlgpUrYX3diL7NiAAK9KgdRCyoF02gEBiiJQrCSKLJMBPMIh2NOCLGxMVJAVugkRII1qRHnHjChf+8sMOhIT2LDcZfTrSOqMdDbxJR5BwjmoZetN2yi+5aOMfe8TxIdOwO6M8nityrwrP0TomV/TJpVQJfawW6lmiL2Ddc4o9tqD6ZF1hud3cVt0Abe1f5/nlLlYkgIZm+kdzzBbhS4pH8MXRI1H+K74KqJHZrS8HW1PGs9uk4yskbC9dD4RZuO77mBu+lH2Ox42F82humMlb8s8y57URTw7voacGyuwQvAHq5zXtutsUd7JsPQW3r3zYZo8w5iQC6oaJN0QoXjHcubs+ROlhRapGhX93hr0cJiaGvdZ3keIFQ67WnmfUA2H4b67o4h7LNTq3kITBCnMjXDf3VGa0Vm40K04pGmQycCyZTBjxvE144HUec7HyOELBW9pQX7c4+DZOOcdXVtT0vB3tZC58nKUPbvcuOn2Brj/noOpoiNuKKF8XAjx4wfwrVoGHg+dM6/ihRUmF6eWsyLvajyDK9i2IkLpeJOG1xIkXsuQH/PiswoplCNoThpiMZLTLsdeZWJlHIQmQTCI3WUiNm2Gjg7YsgWnrZ3UgY34fH60NMiWjZcEGWRCUjeRiI+2CdOwW1bQ6BuBpfj5n5IvUxbv4hLPakRGYT8yLfYg4sAYexOKbYFs4wR0SCQRZga1MA/KBzNkfztdlkMmE0CWHBTbRsXCRkGgIGOjYWILjXw5imV7KKvSWLxUxVsCWxt12i3Il6IYDkxljVt7E7lXk5eRcFAyKa59+W5Sa/PQ0nH0ribydlQQ1JrZmhrKVmckEoId0himK+soCyTpkEu5u/MuDElDcSy61DJMGzxeKMu04tOgJVPJyEArwxMrGObsRKCwX6ogx+4ihx56nBwOSCWE2rYz5S/foLB7F1V+HSIKLf7hjEzVU+bpZN6KL9C2cRi5c8qptOYTGx5m1SqdrZ1jmWX68ctxYrEgRb44bcAnvU9SPCIfUwuyZnucKx6oRX1oEaWlLu/OicwigfIQhFS35Jt2aG8HykMEou42Hz3ata87jptndMstxxeqA63zZFP7Xx/esoL88OMgHBHccXbOeUfV1tz2fCcj2xy2eCsZP30EpYUO1oEOOuQKco1DXYnxYRJXX4f84vN0xyG34RnGZgpAlii29pPX3UFJdBupstF0ff1HVLevp1MU0CENJpLWqc5pxxcOc2CvSdsuwY/UL/Ep+zHyUnHyfGk87ftBU6GwENEVJRhrZX3gSgozm1AwKaeZVsq4XH4er6nQuHcS/xl6J+P3PENawNtjP2NwXozEzGkEVi+joqeZSjZjodEt54MQ5Ms92AmLaKCCpsRIqsblkPnu/eTRjfHaAfZ9+yeUdW0kP96EKiSEkEgKH15kvKRBuBJFQrBppcE09V42XV7D8ITgVrsOVVikJZUl4rJeMZ5BOmzp/VaMUONLCEkCAabix7+5ldzSYQxu2o9XTpGRVZ4LvJcuqZqQ3cXX7AXspYQin4GVVFHTcSRFI8/qJOF4SaY95BBhVGwViieDk/GQEQolUjsZ3FOQhEDzyZCBwfFtEPRj6X6stM2Y9hcw80vJizXhT3Uy1OjAs7aM/+ddwNv3PE57QkeWdZZPruHj0VoieyOY+bDMO5eP5P2TdDCIBnQng5hGhB3Lo3zv1zpr1rga9bRpMDjfQESjdLeE0Ptqzp5gb4d612vbNqiudjVy04QVK+DjH+9/+2dt2+cH3pKC/HjBHX0e/nCYMzvn9afqH1Zbs2FZGyWpFnwkGXvgOdL/8NIxsZrtTTp/uy+BkWfw2fk6QsAvHjT4wAv/S1mHnzKpHVOo5EtR2p0S4nIOiQi0V8xl4vB8enbuJ2MJZMWmxG4mN9NFT0LHSip8Z9f1bB8ykWS3xW+S8/jYvsVMntzhql1lZQfLrstd+xi0fw2uu89hMuvwYZEJFiHLoEca+UjP98gIDwZ+dtnDqeio59//mcM7kt1YhFBwULAIOjG2e8bSToZKsYdgqp1B7fXULx3DC1/ZQ9vQ6dw8r5zw7FyijROIvxKn0GoCJAwtn6gpkDwKPpIUZFrxYDPc2UEklcOVS7/ObI/OLqWcVjvIUHbzLbGQPVQykt0HfwpHkvFqIKVsLHyowkTDJBOz6RYm5VYTmkcgJA9VUhO7qMZPHJG2sG3oTOk8ZNfwdRYw2V6PgsO+vDB/y/sEb+v8X7yJGFJQJ+ErpVDuQsYhFnWQEHgUB1Wy8AgLR1HZP2QWg1rXoTpJPCLDnuAoRre9hKX4kbBwVC/qlg1MHtLCquhIFAX+ujtM6ccWkbtpOZ/2Polv2zMM7VjNAR+068PwWnGStsrDvwlRUOAKcYDIC/V8XHE1leL7VJhfc6SNpZ9CE305Ri+/7IbXKwpceqkrzI+nYWdt2+cH3pKCPBoFKWFQpEZ59dUQuq4jyxAQBn/4bgvD78YN0zv6nHcqrvkTeX7CYSLXzSP4r28ypHMVCIEvGcUREtKyHYz0F/JJOUZMLeBfd17PrpKZDPJH8ftgm+8iSuPPABKKsFnLZLzdFpIMBQX5rFgB+zoKGWJ52W0P5hKxHAF47STdPTIfjvwMx8rFykA0rfIz+0Z+3ZzP93WbyZFN+JqbkWwbIST2UMlENqGQIZceIoTwJ7oAwSizAQdIomOjUEAHecSYnXoWnTgxcnAkBSH5MB2bXNGDoyg4tsxq5yIKEhHK2MPnXrmBv+TW8bNvVDChaTxv31qLZMvYFNPtLSGqVzAoth0hBKqdwkZBQqIw08o14i/YGQ+SBF5msVuqZrzYgARklCD7nKGUcgDVI5CtNCJlIAEeYSIQZDICCcjv2YtGBinjIAuJydYKGs18TK9KuxXC43FdDLupxkMGzech5XioSO/i074n+etHfkDRc/cyakYBOT4T+cVnkTvaEJof20yi2UkqE9toyLuYrtxiUkqIhuFX4+k8wOCUTVzKxbFBYBMIyBimByFcYawah3zVra3wzpY/kXt5PoMKgzS8LDFs7yq689zTxX09NSzdoHPFFa4mvuFVg/c119FdHmTqFUFU/VjDdX2D3lui8Mht2k+OEUKcWMPO2rbPPd6Sgjy/qZ7rV9chOxbDO1T+UlFDxhJ8dvlCivZvQF0FTAm7aW59QvhUXPMn8/y0tZH/6x9jx3eRkb04ioo/2YUlB5AQWP5cCju2USArVO59mW1Fs9n59s8hK7ganVKOnYGM5KGbAoJSHCFg/8o45SODBFSTLdI4JjjrkXAwJS9JfwHFrRsYY6ZYY17BnvQgPJk4n5Ee45eFi3jAuJsfdXyS0qI8LKES6U4wmXokwIuJjEOIKI6pIJB6OVBAJYYAQkRopwhTuNmeBXQRFXlkhIaXFB47ibAlNkvjCBEjJfsRUpDcRCMf+8fHiac18pxOuihkC6MZzk4Gp3eRa0fZTzmDxT400mhY2HjwiR4sFNeKLkyuZIn7UBESrZQgOyalotV1eFq4MeJIOICMjej9JEoeBURxDTYSji0oSDYzMr+NRb7/JmHoCNsVYoNoYTxbsPQCEmkN2TYZ1L4BjQxLp97FqHQta9dZZOQ5rJv8Lj4c/QWZXdsJpCIIx6HU3MuTpZ9lbMcyMC0icY0lw7/DZcYzKKEgvlQP7VYxqf0p6kWYHUY55eWwd69row5molQOsli6MohtA7nDGDJE8ELwS6SHjUXTdbz73ZPlNdfA2y+KMqjHYtq1wV6heqTh+mTb9LAco1PWsLO27XOLC0uQD0SCjmEQWFzH2GlBltcHiTlx3t/4AIOKLQq7tpP0FSAXAtu3wwMPwEMPud87Fdf8iTw/DQ1w//2oa1ZSTDcJW8LBjdrweR1ME9JykJKe7XQGhyBUD5LmYfqLP0RTbGZkNpNx0thIrBOT8Mlp6tT5KAp81qpFaoqQ41H559AvYjU+xlRWkeN0UyA6UZuTlAZzOJApJJkEIYKEpAjxpihk2twMRws8Pe3kCQsvKQSQ6RXbEo5LRiVpIPqEowsJl+/EQCdBkAAGIaJIQBoveVKMiMhjrNhIhygmJuVTxR5yRRQl46Dix0uKMlKU0oJBAAuVzkwOOXRiyR5kRSbmhPAJAwF4sLFQkHrJtbwiAUgU04ok5N6RO8g4vWMVSMiIXpKtl5jFH/kwi7iTFH4syUvQa6JlEtSpt7An6D6gewN+eikIwOgBVXdtx07GZRCcfUOYb/5hEd5wlNU7Q+QdaOG9u3dRETTocYIoMuTa3VzS8Xe+LD2IZVu0yiGCaZ1/+t/J1aWvcNm+X5PuMUl5dOqK5rOjxS2sXFgIt90G180NsfUalWAgfrA4yfamAI0Xj2Vwvrv/Zs1yHZS7dkGJHiI8RUUXceCQ4dpQQ0SbXfaGEzkoT0fDPt4tebZ4zrPoHxeOIB+oBJ1eYVs8LMi7KqCpKUj7S02oyQS2kCmt1FD8gJk8tBvh1Fzzx/P89HKPU1AAeXl4enoIKg5CyiDZDrIH7KISPN1xbAcyeCgrtfFPy8H+ywtsLbuMvcM/wMTBnezamKJOfJ1NkXK6MzqOA3dKixiZE4VQCFWFd3Y/gZaWUBI2cjxBRth4fYLJxXtYq4zCicWxJZVyYzvfytyJX0RQOlKgB13bqJNBQhxMv/dINhI2tseLYplIcIQz0UeSwbSQwYOFBwkJDxYKAlUYqFiYyJTSSrFoRyONjN373RQOMgoODhJ5dJNBZRQ7MVGJO37i+FDJkO59BUCWEU4GgUQajRS+XuFtoWK71LE4vSNU6CKEisWzvJ2b+BkhuumkEJ0EXslEEoJOUUhTsogdba7roA/7KWcDYUab23GsJFXlDomKMO1qOb/9Laxdq6OqOnl5bkCILNkkEgLhUZBlG2yZeDSDmm+xLV2B3w9W1CC/JMrv987kj/JMRg6PYgZCJGydkjb4wQ9c+3RJCTQ36/xndA3vaaxF6Y5gKypPj64h7dEPbjVdd00it9wCgYCOP1IDj9VCezs4Djsu/wy/vD1KtwSmqhOLndhBeSoa9vFuySxnyhuPARHkkiS9E3gQUIBfCCHuG4h2D2Igg1UPE7ZaMEh1SZzKS3XMpIZv614UzQQT907WdZd21gBhqmiR+KGamf255o/n+bEs919ZmVsl5oUXkJubXZVr/FgoLMSTgZJNW5ACOQwJpVDmzCLg9GCXQHBuIbJPI5UaRHWqkTI7wAsvuXb9YBByc3W2R3RmToQ9e2D1yOuZvHYZeR6wPEGiwUqUZIzhXasQxR52GgEe40a+kHkY4VGJeCvJNXcgG3ECikOnt5JQqgWBhFdx6NQGoTkpNCeNI6nIwg31EdBrbhFkcKM0AJAVJMcV6wAqbrWjHoL4SJIgQB4uz4mCTRovGimUg22Cg4SGSTflKNis5hK6pTxGSdu53FkKTgYZgYUHgUIGjWZKyaEbCdBJoBDDI9kIIePBposCfkYNXXIJpqSzxp7KRM9WFEUghMR2bQw7jPKDmngvlTtJSec74l6+LD1AUDbYEtH5rTyfppTOtGmuPXn/fvc5vd8uZ7dvHNNSLyFnDCQhE/cWECOXlC+EJMEYs55P9NShRS1Stsqjnhp254UpzAFst71w2BXifVu2rTTM01WLKFSidNohutI6N8+DxYsPbbV3vxt+8pM+ARrm9jk3MPJftVgdUfQ7vsjHCkeTDpXywvgaVuBWEnq9Dsrj3ZILFmQ5U84FzliQS5KkALXAXKAJWCVJ0l+FEJvPtO2DGMhg1X6ErXrnfFQ4Npti/vxepxCUxGu4ak0tE0ZHKCg9wc7v71zalx8dj7sBvnPnunzhd98NI0awYYNLPZqcoTK0p55PaU9QaJoAKJPCpGImS5/V6DkQJ+CoxKaGuOgitzlZdpM3xo93mWirqqBp20wOfPJicuKvkQgUIzI2CVmmJWcUL0y4hWcCY9GtKMFWCVXyEksqxOQQmp3A65UpGFkMO7uRPAoygpyQzr7EEPLiTcjo5JsHeg0VbqJRG2VulR96yKXbFZ5+P3LS6BXMCmlPEG/G5WXppoDBNKHTg4zLMGgQxCBAgAR+0ggEabysZzL5RFAli5CawHByeZWZjHB2kEu0l39coYMi1nAROcQYxxaSBFAkgTqoCE1XWdl9EZG4ymZ1Jv4MWI5Onf9ebow9QJ5kkJY1/pr3KbQeIOn+lAdNKwI2SmHu0B4iT0RJKiHygzoh3eX0nj4d/vd/XZtyWDTgC/lIdeaQR5Qo+ezKmcSv1PkUDdFpaTH4aHcdXU6QtCdIUIrz+Uwtd2xbRGqITiDgbqG+jMwjt6xOq6UfoeVOmHCoWMS99x4SoGbEYP93HmfI5QWwfgtpJUBhopF9hVUHi3p/4RbX2VlV1fvQOA17yPFuyX37spwp5wIDoZFPB3YKIXYBSJL0JPA+YOAE+UAHqx7PCPj44y4FLEB5OQY6dbe7XcplYf5VtYhnuqLcsyCEXnKCXXn0ubQ/Tf2//xvCYdra4Ae1UFDgEhc1x6/iW5GZ3Hd3lEB5iMSmXWz8VC2etggFqsqfymrY1eregG9/u1sPwbbdtOqxY/u61nloypcZ9uIN0B0lYnjZJMaTSvj5zaqx2D6dgAAtL8D66Hgm65uwHTdhJyryyE8kUAaVumuTTOK/+mqKX91G6rUWUhkvGkHSgTwyeOlOeLAljY3aFKbZr5JxvBSXyqjYOFYahExS0rFsBYN8dkhjGcR+2kUhEg5xKURS0dmUGc1IdS/LrMsYxyYkHDTSTGIdfkzWeGbwd+39vJ+nWWcOYbU8m0qtlZLUPtqtEEkCpIWfOr5ICe18UvodIambka076QhVYiQFv5A/T7BUJ0dy7cTVV4XZmvMQBdteoWLZE1zd+QSX2U/xoFPDFjmMEEcK8660jhnU0VT3s3TaFVper8si2L7H4IMddWxRhyBdMpJM0wHkdILfTnuI695Vwg9/CENDUfwJizYpiJ2BpDdIIRGKlShpU2fmTLjzziMLikSjblx3f1u2b6s1Nx8pQAuVKFLGIp0JEJBsLC0PNdONkBWchIXTFeWhh9xGVBVum1vPqCWnbg853i1ZVZWNKz8XGAhBXgE0Hva+CZhx9EWSJH0O+BxAVVXV6fVwNoJV+zMC6rrLMNSL6FE3h5av0xjXiVoHGcBPHf08POrr4f773YSLviSO0lJojLgkSAEdIhVhHp+wiJbNUTLBEBmvjhxzrTSR3pKcjgOf+cwhc76uw7ULpvOTuxYz7j+PIBSJjOzn174akkm4bkYzQlUZPOtDdNU+QSwwjhiCtVM+Q2drhi/m/Bp9w0qXcGv2bMyEhVHfwH67jLCzBoUMwUScLdpkdmnVBDLdXGy9iuM4NIQm88uxd/C+T+cz8XdfgcZGNFOirVmiPj2G7zt38DkexY+BhcYfvJ+iu2oiiahFtbeZd7Q8RqtdysWsQSONgkOnXEpYbKAg81OUglySsQBIaaLeUoSAb4m7iTsBSu0mbmQxKhaacG3vW5zhjOrcSafi4ZPqY/zVgFFXVPDPV0KEQjoh2eDtTb9iLQW0S/kE5Dg1Ti13OotISC6rod1r7hAClJTBOL2F/ESCluYAhsgjVWJRnhfi0iuiDF9h0eEPsqYeqqurKDUbKcq1+Mc/XO25ujTEsP+A2bCfdqeQkMfEFiopX4iLpsA997jaeHMzNDXB7x41UI0olh7is/P148rWowVrpx1CeFS8IoUibCpyu+noVIh326iO6/gsyz+kvR+4p46hc4NoZadmDzneLVlS8jpv1ax39Izwhjk7hRCPAo8CTJ06VZzk8mNxDoJVBzxr7bCHR5+N8fAkjt7avICrMRqG21fM/v/tvXl8HPV9///8zOzMHjOSVitZp+VDvvCxmMMHGAg4wYRAjub6NrRpEpM0aSNIaEgIaYrTmiahIaJcUo4mNm1DIf2RgyQkKSYhBDAY2WDLNsb3IVm2zl2tdvaY2Zn5/TGSJcuSLV9Yhn09Hn7IOnbnPbMzr8/7836/3u+3xhuHodJtw7ah3a0iXalRUOCRixDw+c97pdXl5YPOVPwfF/N32+YxJRyn+UCYqsxu7s58gWlrDlIr9hGOzybhi/DSzE/QM/NyYqZGMgisvByaX4LHHgPTJN2VZL9VSdR6lQQF2EJFdVMU2938Zuq9fCz1CH9un0q6oASRMZn68uP89Z56Vv3TvSx88X7iew3W7tP4d+k2momy0zePoBmnlzAZS0Nv9/p2HfZV8+3WCfwbX+YZlvIe1iAEVCpdVGgZZvi6cXKwIG2Qc2T6MmG2++eTiVQR8MHnOu6mx9IxUbmU9ciug4WPXoqodA+RtAv55qFP0fXiAi6KhUi+sYzFB55gwt51SJkwzYHF7M+UUy1iREQcrdSb/jMw+nRmtpmvW99gfud6CtwEKRHCDQQ5sG0+Sa2c3e9YjhRQCDlJHEcnkEuSNBXWNimkEwfpk8PMmLMbzU5wtbuZnAtvmFEeKFyBGvY+z+5u7zZPpaD3hWa+6GukMGhhWAr/X08dtT+JjlqYM9BZs70jjBvSmPTpZag/vgsMAy2RQL3wYkomZen+yzr8v9CO8d4zslctOtZ4yGiP5Ek/qvns6GnjTBD5QaBmyPcT+3925vEmi1XPZtXaQIyxosLzxJuavMaFu3d73WfvuQc0DJZ/MM4Fva3cnP4uMzKbcfHitc9VruD5A1Ecx/MWQyGvren0SoNHvxPnrvows2ZpuCGNVlcjkzG4I3s3F7hbqc4cwnEl/LscJs27ltzGx/hlejLZkipuqQPNinuVIZdfDvE46Ud+z/xf/wMhN0kImYSr4yIocnq4re+bFPTspYvFJMwSLEul1Imj7N/F5/+plG98/Z94saWbP1RP4kBfGaIX4pZGrH9PI+MtRocOeUnAb/nu5TJ7PSEnQYgUjisjhIIsAz2d2IqK3zYJ4aBbfYT8NhfKW8kGi6kKxunMRQi4GVyk/omfWTopp8hJMMvZjIXMfqOUSxfaRJ7/ChkCqOkEE+kjkM2SVq/FzSnkQmH8fi98UloKE0IG9d3/zNzO51BJg4AgGUwnREVqD28UTGJe02peW3gzc9atosqOIWcUfpVext/1rcDnWhQWQfZPCQ5OnMzBohlMDHbjGDn6JtSiCm9X9cgjHo9OoINFPffSKkVI+ysI5JLMbmug6bcruWaJdSxDDumsmXUUpI/dTOjxNV4+RpYhk0Hp60OpX4nQylB+c6z3HrCPliuOxWMZ7ZEc86M6HiZKvAV2A2eCyJuAGUKIqXgE/jHgr47/kvMHZ2sjMNTbLy/3PPGODi9UMmEC1Bpez+i+Z1PUdb6C5TjEAqXkLJjnbKV4wz+xc+L9HPRNxTShJmIwo/WP3PSH/yFtq7i3Bym7o467745y551Q6+5ikbOWAgxCTsorkdnfh3L4KWZYDv8v0UXA71Czz4UpESwU4jfcRCg6jQn/eS/tgVKC6RQCmzBxcvjAFRR37ySSa+cD/JyDdiUWKiomjzkfpePQBCq+2I3PP5v5ajlPVtexqTzKjh1euCLiN6gMxkn6wkgZ+Ku+RlK+AkI5TxfuIhA4CDMLpgqui5AkJOEiXAcZh1Cqi7vML9AamMrszKtMdjfxMosJYjCBLlwEEWLECZNDIUuAvh6L3W+Y1GS6SYSm06pMoYYWKsxWwm47/yZ9ndAEjenT4TOfgSefhIq+NqbuayYQlLBTsjcGzjZRTUGJ08Kcyhj7DipsT1azZVE97741zk9+p3D1H1aQCuhEJusUuIe4oHMz4rIZXCSrbNxYSUm8hSVz4vz1HRoTJ3qLe63RzGUv3EskvY5YrpBdxQtJBMupzu7G/9UvkL4Q7KCG9A+3EVoUPaazphKLwUP/5gXvZ870brjCwiNB/WMdFI2Ku+tQ1zRAy5tcZ3+uJ0q8RXYDp03kruvmhBC3AP+H52Ctcl1362lbNo5wNjYCI3n7n/40/Oy/DGqybSxe9wAZrRjDCuG6UJTrIRkqpTDbxUR3PzWpFr6/5zruL7+HttAM/q75GyxMPovsk+gNVeB3FkJDAx+ur2dxaDedn/wyVZlD/fTYL/NzbSKZg57+OuZSYndiHfKT3FtNMmHjPPkiB0IVzOxtZUJIx7RC+HNJAAQCU/gpznXSSxGFJKiiDQONBIWU0MUk9pN2dHyWwzq5lL8++B0OF6/EVFRK7RY+bT2CbHr9vH/GR0CySMlF9KFTxmG8SaACIcke87suTjqDcO3+3zioTpp55gZKcodBSJTTxQf5BUl0OinFRWICnfjxeqyESHF17hnUgzau66AGfcRzQfY7UygQ3fxIu42cEqFa7SCz2+Idl4Z5z3s0+l6F0i0Scq9AMl3cjImLwHZcHFdCvLYet+xy3vXhMDMu0ujdaTAjsR7NTtDqVBBIQXeghIkSlEjdqBMrKdOSmD0KC+rDaGUeH2sYXLS2kawWIRcqxOmF2ckmtrCIC9mE0alx+M8+ZBw61q0kuKqBufoBLxZTUQHt7dgvN+HE4kiyQFYUb3rUMC/7WAclCtef3dDliI7vuey6NR52A2cIZyRG7rrub4Hfnon3ejth+MMktjSjrm9Ed+LUdG1kZ/VS8BdTVBmE3SbVxnYK3TguYIgCckGdr8S/zi7nEqrT27F9KjmfnwlSN8rrzXDhhaR2tVH8Xw8Q8e9noB7TZbCgR8bFRqE414WNQLJNUq3t+P0KaX0Cxak27IyFUHMEfDlEzvUqPhUV1TaRXa+KMiNpSI5JihCVHMZBQiGHcA0KrATFuU4c1c+C7ItsdWdR49vP2txC9jEVjSSfDDyGkKAo20ORG8dAQwoGCOQML5tbXo7VZ+B2dCLDkapNGxcJm0InhqsEOOifgZY6jIvg97wHjTQpglwovc5sZxPFxAFB1vGTFQGUXIqygEkqkeOQqOITxvcIiwQzerfTEZ5FaEU52h11aJfUwsXzYdMm5GwWN+viuiAJFx8WJZk2flu0mO9+XeOzJU/wF6+u4G9UEy3bzR9TfTTHLiUiGUyZMp3SVNpLAisK6h110K+A0jT4/F+0kfhTnA63lnjRQqqNJjQnQZl5ECfn0OMUUFCgIssmU7qa2POZL2ItUlE2rgfbJvXadjo6wXXDtIdnMv/5JoLgxeuGednHOChnwmMZJUwxquN7LrtunevdwBnE+VPZ+RbFkWfHMKC/dcD65gilzjZqWtcy/b3LKExMwWrZgWU5CNfBlgIU6BCeq0NbLyWTuvCFfYguH5IPJFtAOk1Pt8O/3wPvXWcwNWYS4OiKTBfRr+KWkfpLa7x+3inS2QCZXIaUEyTtllPZ244rBD4hYQk/Dgqy8IZFhInhOl4P8CApbG/i5hEJIUDITeIze3EKwiwwXqHLLmIeW2mjGgMdJ9NBJ8V8mtWE8Jpcub4CMPtLLDs6yAUKsFFwUQjiNcDyaFwQwMR0ZGbIe0ji4CfL+/k1faIIn5tlgtNJIXFsFCQBDj6yUoiD+gVku/qodPZQ7bRQqvSgyg5pESJitCACkwa9tBUrvLYN3d2IjRvJ5VxaMxMI+GxIpylofoEK5WL+396vkKCAmFOG7qi8Q7yAnxyTnf3s3DeLw67CpC9+kNp31Hj6UcMATSO1rpma/3kAVWzENrex1r+EQ7VXcKCjh0fVm/nX9s8gfDa9vSCHbDQzTooQnXoNEy4Gad1L9HU4yMEwh2sWklDLea3Lx8WfuZXgJbPPPjmNwtYndHzPVdett1AP3jyRnw7OZJJkSOuAd1WD2bKE4HO/Q37xl9jpLJIsUEqKEZ0Wih5CUn2Q7IWASnBiKfS2Q8UEnEOHsbMWjg4/UG7FLqtCLfBTcKDryKEGQisOMpbwk3NldLLk8OEKEK5NIX3E7QgVtNFLIS3qdGwhEZYNInIv2SzE/BUUZDpQnRQ5FEwCBMggAWp/Kf1A9aeEi+u6ZEQQ0zYJEydDkAAZKjnIAl6mmkP4MPsXFhe5L9E/6A3cZBJ/MkkGP1uZzUx2omKSJEQnFdSyx0vWSWEOUcUk9hOhB9uVKKWLAvoQeEW7OXwE3DQJt5DvJf+Gv5JW0VN+AQWde8k4KuX2fnqDsymUk5g5GfosRFucUDTq9d5pa4MXX0Ss/BZqoo8Co4cOp4R3mM+w1HqaiNtNCSqtTo5OqYyAk8FF8EJgGd12MVdYOym88/PYZQqy8EbY7/zIVzlU/zgpqZigupRLMmu5oPtZDtVeweYr/oKPvvgTFMlhcm4XXblSzJRMLFDM9ng5sXWgKFO5ZJLFQStLrmIi2WAxmpkkK4WIVcwmeJY87aN+Pwpbx+PaiR3fc9F16y3UgzdP5KeK4yVJxkDwx/zJsNYBakSFUIDuGYtIrd1EoZ2BHhO1fBLBjgOQ83s9Rr/5TS+htXIlqVc2E08XcbhwJr+cfhebWcT8Ytg656PMfv1n/clDFyQJAUhlEzAmzsd+fQdW2o8sbLLBMFqqk6zrkhZBLNeHX1jIwsIVfl7XFzMz0wwBh9cKrmZu2xoidLGHWkwpyHRnOyY+dNL4sHCBNCH8Ug7ZgXTCxJYUEo5GiCS17GAmu9hDLVM4gIWKjI2P7JGSfxcQ/ZU5fmFh+gr4vfVuZGymsg8/WXqIIOOgFU+gKJvBzXjLVSmddFCO1l+uqWB6enBy+LD5fPybXOS8RjoZQHEtWqlGVmBCoJeMrbJ+nY3PUfjFPWH+9javcpPGRnoOprG6HBQzyx53Mq4NVewlg4YtFHBdapz9ZPDhIMhJKrGsxgSnlbkt/0eB24toFSAJ3NaDRJ7fQqbsIpxJl5JLq6zjHRQVH+bP0S9w2cbvsV0U84xyPZc7z6NYGTZwCUo2R2mFSXGxCskk29sKeeHC5Szdt5qg0YIlFJ6P1rGo6jSJaSwJweOEKcJhbfw6vm+RHrx5Ij8VHG+vuHv3CW/6kZ+LYd6BaWLNnM1r+0q5QJZIRiZTFNtHT1KloroG+ctfgo985EhDjlc+/wgrXm1DqoCEXsWkiMb2jV6lXXfpLHq1ClzboUzp9Qgyl8P81+/w739ain6RybXPrWBfdwHkclziewGfFmCjuIKedIDJzj5aIhdyfdejTI1twB/IESuZTqjjIMVuNwKXiaKNw04ZSXQi9NApynFdL7Wq+lyEa2H5Aki2S8aRKaWrfy7oemxU+ijovzpeBD+HhNrf9EoM+Y2Ew2XKazw0+VsU7XmNHqePmWzHQkLHQPT1EhIOISmL3R+VmUAnKYK4QiLkGv3hJInXtUVc3PccFjI+YZMSGhPdFnpyZWCadASnEZSzbFhSR1bW+K+Ve/mW+S+YxeW8uj3EfByKrXZCvhQ9FGH7AtioHKKUilwrqmRSLnWxNXcB0+03mMIudJ+JZNuAA453fVyfQsBMUBV7HUcroLR7B046Q3Ghw8sdB8ilLC/8FNR51nwPU8UOvhe8E8uS+FKiAT0Ww1UUfj27jonvifLF++YRMuOk1DD/+DHt9LjpePc6DBLgccIU497xfQv04M0T+algNO+jre2EWfDjxguHegeKQvb2FTg5G+GTcYFEeBK7Ci4iPN9GW7bsqPd8aJXGwdAMSkrAMb0uvNOmQU8PlMZ7EI5DiYgj5XKecDsSwXnmWdTMfFI1Uf68+A6WbGwgG89SXgBbQ1FKrByX9P2OkJtibscm9k9YSF+onLJSG6kvRsZn8bTvRi50NlLltHAB28jiRwgoKJLY4n8XFWmvZW28dBpmTuZQp8y8zDqCZAn1e8kugoW8QiclTKDTm65DjhxS//8HPXNbUpF8Mpf3/Ia9IsJU9lBIrxcWwiHQ3UoQyKEcCd3oJHleWkq500aIFNuYjeSTSCklSLjEKSbsxnGQsGU/j0/5GpsnvofePd10ZCfR82IZ1yWe4PPtd2LL7diuygy7iMOBCQSVXlJaGY7pw83G8WHTLZXjBEL403E2qgvpKJjM7t5ZXOU+R4l9uH8mqQRODtcBbAd8Cu2ByVS1NmHJARxfAP+Vc/k7+ZekCqAim6TpTwYXp58nJGX4EvXcp3yF75TV86FL2hACOnNVHFwHV75bQ5Y1bBvWrIHrrz81njIMSGyLU5q2UCqG3etr18LPfna0N3Ictn6LOL7jFnkiPxWM5n3ACbPgJ0qUG2jE8RoySbfWEfhUA92hGip6t3O4aBZarg81h1cx1P+wxCNRb1pOf7RFVb3ionAY6lcahFasxv++qxDNr2HvPwCyRHrJMkRxmCt/38C3dqzETkf4UWwlEd3ixotb+Zz4Idorv8eREiTlMJFcF+XqRp4v/AB9skpRpp1MGvZIMzhMJcucp6ikjcO+GibKhwkanURSe3ijYgm5eB+vVH6a93T/N7UT9xPZ1Ysgh4sPuT+WPpkD5FD7W94qpFFwCJBDoohEf5MuiTQh1OIyJhzcwy67lCwBVCx8/SMiHDwNLHh90gFkbGY5r9PEIr4vfZ5NzoX8S24FwYyEIyQUYWOIQnrkUkQuxzMHZ/PJ7n9Dsi36Mgr/43yMLzp3oLsJHNtGwaCCPhJOhMNuBapQ8QsDEZ1Hdnsrk5R2HOHjW7l/5DJnHWg6vVmdp3M38P+UX/CGO5MrUk8fuZ1cF4SVY3+yhF2UQ0klF1wcIBFRsfe18Kh7E5fueIx3WX/ET4KYKGVudhPfKlrJw+kvs/jF1fgliynTFX6o1CHVDO4A9+6FbdsG+/CMFQO7RpEKc9N6hdkkmTC1/14HePxxKC4+1hs5Dlu/BRzfcYs8kZ8KRtsrVlWdMAt+vET5sSGXKJHV9fzk4TjpnEKh28tt2XtQph39AIVX1hMKacydC1u3Ql+fp9i79VYoU+OgWrSrU9nqFFLLs2TSMtubCqFApyS5my/13E4io5ITCj8vrqOrdjEP7Q9QZ/yJbPF0UFXcriTi8CGmz+nj9d1+4l0aOQUKzRgBN41MDkkIyu3DqLk+fFhMlXZgyxP5zbV38c7Nq7nk4hxK0/4jw5EHmt+K/k6KPixM/P2JWIUewhjo9BKmmoMcppyENIGkOYWJ8mbilODmvHCLhIuMiYvcT/k55P5BEgYFJNBIoNMkX05a0fgPp46/NxvYJke50HmNPlGIm3O4R1nBTdJP6croJGwd1UpyG/dSSidJr7cjYRL4MAllevij/G6kjMqUoh5+UlGPVQJXTTnAPnsST/5S49LUqwRySXRdx4rDTv0S0klwJRXXyRwZfCHJ0H3tX1L52u84dNjgD38IoLhJgrbC6++9HPWdE5j9+J/oCEzHUYL4XJPp6U08HL2f4KQy/KEQZsbm6uca+H1lPVqZxt69sGWdwX8bcdyi4/dqGYqjdo0VGq+IOmhqIEwMJah4gz0fe2xkb6S6+tyz9VugUvNkkSfyU8Voe8UTBANH6olRV+f9bmjIxYwNltrf9f1q4nEoTlmE7uGYB0iz4tTVaTQ0wJw5nod3yy1ee1WMMBYKm9YmIViAackICXr7ZKaIFiri28jOejet7cWElSR/k2hgg1NPPBPCchWQQHJyJAJlFMRb2PnnQyTlUhq5jcr0Dla6d6FgUkInkmujkkHq15pITo6uTpuX34iwKJNGea0JYnFcpCM6cPpJXOr/vydbtAmQpYQeHHxsZR7/ptzFO8WfqFS7mJXciJ0zeY/7FCoGKuaR62siMCUNn5NBwYst72cyMjaaZDK1OE5XDjJ2hP8oXklAttD8Jnq6mwNMQnYs3nHgDxhCJeR69D+XrQTw3s/pPzsbia7ARPy2iSy5fE+/g9f3lnHoELgTypg8GQIl8B+5Or5oNlDuxghVKZTfu4LnHkmy9On/wwgUknN8ONksmmQi59LYsQQLU5tRc7AzEOVfWEFqm8asRSHwKfgkmaopINvgiznIbids2kk6adPVLRNSamhaE6dwtobY0sy9vkbCuy0yjsIvVtZR+8jIvVqGYviuMTk1ymNuPTNujVM5O+z98IknRvRGzjmHvkUqNU8WeSI/HYy0VxytH/nA97t3H90T4+Y6QtHoUW1Iy9qbWdjUSCZh4d6uoN9R58XPjbB3jEOHjkzGTeUU3tgTZuKsUXa1mkbPTXUoLzYQsi32qjMplnq5svfX6JkcWSeHmzERAvocnTI3RufOOOt3VfFRu4Zo+6v4fGDl4DVxKT+e9m3aqGLzZvgbGnhWXkbGlpnMHt7N0yhYuAhMAphCYXpuJ4qdwoiZCHGYhK3h8xWi5eJHZJADycyBsXHePCKFQ3INL7EE24GfyR8jXTaN+7o/SchK4LgOExxvNmeuP0QjAAWHhK8QxZSQsdnPZHKo+DHoQ6M008ItmbsJSBbmYYX/La2jqTdKIDAVvx9IG+hugovd9YDLRFrppZA2KqimrX/3IJFGpTR3mB9PuIPfZ5cyQdcoK/BCW01NXv+buXMhXRvlyUA9k3xtfPKTMGtxETcl4yT+WIyUySC7GUJuChyZdz3/L7zgXsFv/R9iSlE3PjdHS66WUBq61CoORqKUdO/An00j48C8ObBjB3ZIpyVRREBKMs3azpJrFPZ1GCyXGvGX6iRUHcVMctXmBnrb6tFm9N8co7DuSLtGN6RROFsbbPs5gsMy0Lv/nHHoW6hS82SRJ/Kz4UIMJfihHgJ4wevJk72eGMmkN45rbv0RiZYZM1jY1IiBTrJQR40MU8QkEkeGX7QUR6lrX8EbL2n4fHD33fDhDx97bvqFtfziinrCxFkrKdS1fY39oano1QXM2LOG6gNr2akuQ870kVLgdy+FmTABfCVhOrqqwHFwJImUP4JRVIWZ0ohIB1EdizYqsIE9TCfOBiBOSmgIn4zs5DBtif3tIf4/9a+5JvE7hMhi24IcypFxb5Kn3TgyB1QCkgTYoi1CVouo8XezmF38g/h3grkEpqKDsAhl+nBwsVAw8aNgkcUPskyTeiWYGaZKLTgObPNFebL0c3wqtQojoHMoo1Id6ubz1v18s/JButIaX/0q3PWl/m6HLoRIomEg49BFCUlChMjSRwFt6lQCIsMNyZ/yW3kpoZBH4uGw1wn5+uvhz78zkNw4eryVWwtXU7WqHb66ncmTpxETAgIB/LkEOTS6nGIUV2WGuZX2UDU9SiVl2RYmF8XpRONQQuNX81dQZ9+PrHoFRNx0EzQ0YO9uQc32IvtlusKzKCmwSMXjR3YkKmCgEyJGmDigHddzHZPKZJjDMrR3//CJQZb1Jnnob6FKzZPF25vIT3EbNmbuH+4hHDrkkfCMGd5T7ziQTkM8jlbthVge/U6cTMIiWaizcCHeaLmWIYqYyZNhxgxSrd2s+1WOnqm1VBV5D85dd8FVV/UrEgfOLZ1Gcxxuvf5W6p9fxOSqg1h74aBUidQKXdISrvP9ng9Lv6BPKLwhoiwq3YNTFKE7FsSZfQ2JBKSVAkRHOwV9bQgnxGFJwZUUwr4ksm1wGWvJ9dNx0E2Ss/yktRJ2yPOxJlTxXE8Vz/uWMiWzjXLacRCkKCBEGgkLV/GT6493W0JlX/hSFspbke0M/mSKf1HbqY5tR7HS5IRKsMCHlAXXlZD6Qx0uEm7VRMSMOfzC921eO1xFcbqN7h7YZVRR1henNGKxcKYBTX/CztqoqTTR0EscmHktU8sMLgtvQ7hB/mAsY5n7e3opQhZgqYW42W6yBEhEpiIyMtlcAK1AokaOE497i+n06d6ttO1/m/no1kaM7hSzk+tpUi/mmgn70EM+3J27sPBRmGrHwaVLLWdHxZVcpW8kYmbYJ2fo6DJJOgrVc8Pc8VmvzW9VVRSNBwdvPvBCHJWT2PWCjCrbBOUs3XYYpxBmX6iwaXeS3rRO0EkyO6oQqgqPyXMdk8pkiMMyvHe/rnt+x+23e8n3N8VDfwtVap4s3r5EforbsLFy/4jSrZIS7+vOnbB3L3Yqg51zyO1qJVRdTTQKd9WHcW9XUCPD5oPCUU9KPFCJ67RQEYjThddbOpHwRm2Vaf3n1tcHmzaBaTKjqYn7fvCftIXnsfP9CoV9STKyjpFSSbp+Dk9awraeCoK2wad7vsPT0l8xO7ke/z4Zx+cnuHAuu/pSfHSPN45VLlZ42l3G0sxvuUQ8jyNkNrGAOc5mKmjHkAvZnJvLQ+EVtMY0Kirh2y0r+VrmK1TTRhY/EjaO5AMnhwgGUAIBcr4g1qTplG5uIZ1TyAmVgG1SZrdQXB1COhyg0EogHA1b8pG1ffiEjYwJwSBvBC7GaCtgHVVEajTWb5oBMsiFcNFl4N8BUw6t5UBAwxewUTIZboj9N4++riN/6T+4LZOgMrUZSZ5HLqdwmEqq3TZyWYc0QQrpo6xvN67sY8eEy7niXUEeXB7mX77rST43b4a5Uwwu29bIwYyOX1GQhMO85DpsI0bS70O3ezHcWlL+KfhFhhw+9qfLeMeSuYQ2reP6C1roFYU8f/Ft+Hdr/PKXw+oNht6fdXWoDQ1cMs1iy3aFX8+qoyerUXcblFPH0vsbMI0Yqqag3FY38jihUTzXk1GZDOfQWAy2b/e66BYXv0lRjnEvWD97ePsS+Slsw47L/Qy66QOxwqHSraJqnWy3SWDmHJTNm0nnFA7HA+zX5+J+bjUT/nMe8xZr3gi5O/pvxpbRFTFFviS2rHA4E8bn937s83kFQMTj3sP6wgvelB8hwOcjdO/d8M2f0ijquFlpQCFGSpi87sxGaJMo6GhnXqqJYinO7N3reENcwOR0GzVlGcJ71iHNmE334WLSko4mJVlRu4ZDc99NwUPPYat+rog9T7tcxQGmsq8wipHzo8yqZdJBgwo5TnO6nB65nG67FJ0kAoeg20eutAJ/eTHplEMsBo8FvsAU/j96g6UEZYvFiacJZ9uRcmFkWUBOYAdDbBHz6cgWEnL6qDb3stOZQzBUwNrZdRgbNQ69YlDixglUhFl4jcbUMng9uYwlB56nOpTFPNRNp1PCfPESswN70dO9ZLISCIcFbCCNQoogv+QD+ITLEl7kJekdzHO3UqiYzFF2EKz7JyZP8RbSK6/0GhD62uMYay18ksF8ax3hbCcaSTJuEJDJ2YJSuYvDopqN6tVcmnmBOf49YNgwZw7JlMzre2DVa3CgCJYs8W7JEYmw33WOxONcqoSZZmlDPOgoyoP1KMPd6rPguQ7nUNP0Bp0UF4/58TozeJsK1t9+RD4QF1GUk76ZR+P+5EvNaE94brqFwi8TdeiTo0ekW5nnGwj6YuQkhY3TP8/Ha/+Ll3eWclgKYGdUyhItPH53nPt+qh3VRCjVFidOmKIi0OJxuPlmWLXKU6oAVV+4ifRPoc/gSIy8rAzoULC3bkOk0ghVRTi292S9/jpyexs7A1EeKq+nyI3THlP49P4VlMdjLHCasFXoTYfwKTZz9Db2Tl5Ke87myskt7HldJlSqUiIlSDsBWl9PcKH5KN1qEBMVP4Iy5zCH5So6A5MoMNq5fclaDj38M2TbwrRMInKcnKSiCJecIyMCftSQD2vOhWzaKLPuhlvpjMxl4qankGWJCdV+yrd34jgCO6AhSzGQJIzp87l791301M4lIsWJJRUO7rO4blGYtZs0FgWbeZ/SiJSzcHoUrJ5l1DatgXQKSTjIZhqruopcLEBY9FLRs4mu0gswpSCSGiRodvGj4NeZ2/NnpJyLKkzeYDbpQAQr66V0bdPi9XUJvv0QvPqqdz9cMstgdlGKSIFFZdd6DEnDEeXUukmE5CK5NjlJxefm2OmfR9IX5lXpCl6a/QUuK3kQM1zMiy/qKIEkn+hp4Hvl9TQ1aVx3nXfvDSVCwxgYMatRVeXdO8fQ1khu9VnyXIfVs7FixTmKcoxjwfrZUvW8vYh8eFxk2TJYswarM+YpSG6tI3ScqzuSI6NhEHmsEYo9Nz1zyFMHrJ1Rj4VGT3WUr/rquW5RnMJJnjxr41NP0N4tIekqBSQRrsIrO8K0tQ2ODPW8eo3qg+tYtv1hZs6SiFQHYflyL2Ty2GO8o/Uxnl/8BHveU0fZ4lpPM26EeX2ThZOZwsxcK+QsfCLndTXs6KCiYyPR6Aw2bdJo7tFwHHiyeDnfL/sOxZk4bakwG9RFXOhspNDJIAubVE6iTypEzvUwZ99TICR8loHIZvHlFCJFKj1dHkEK12F3MIqwTIojsGT/43TeUMzaTTq5vR3MT/0BnSSWHED1OUgTq+GiKPH3fpzH5AUUzyhDMuHnFXV86HADVbF9yLaFQELZtc3bXQQCqAd2c3PsXr5b+Qhd/mqSFrQJaOsFxTT40OFGErJOUY1O4kCMdz5zF7tqrmHuNJfsOgN/rB2/6KCcEPvKF1PIIVwrh+yDyjLo2y/xf31L+JH0EYqUOFlHYaX9NS5Pv0DWVenJ6qQyGbb+02Mo11+OJGmEdjdzxdZGikIWRf4ephYnOJyVSDl+WplKKAh7Jl2NkuihsmszCIFOkscm3EZpIAJARtaxbQgW6ShtMfRcnJij0d19NBE2N3sLd3/em2jUI84xx6DPkuc6lEPfplGOUXE2lZFvCSIf0yo3UlxkzRq2/OVKfrLKoleEcVdp1AVHv7gjOTJfvCmO8tigm+4v0VGIYXfHodJ7ANOShjxJw1K9xM/vptZxaUcDhdkYjk/hV2XLKUjFESkA7YipsxMvc9PLN5NzJfpeDlBw3VyUH/7QM6a/qi6UTDLvZythjTf400LhyZ7lXF08mXhyD2GjjZwt8MkukiQR/Ne7+MYdk/nI5kWUlMCFNPP37mr2H/YjEhLdFbPoS05ku2ExJ9FEz85uslKQA7d+DnfzvTgOSJJDMNVNUikmqxbSFZMQPpM9kQVUpvcQiQhK1BiTP7MMZctvKZ2sc63TjhR7lmCq3UtOOlmEpiNnDGhuRudx3r/xVzRJdTAjSmBhlAdfWsl3zS/SpU8hUiohDmzHFRK2P4Skh7i4/TUisV1s6Z1PZ6f3EezcaHja+ESKZGEF6cNQVSJTkUoSSf8Z54+HEGYKCxlT0REu6N37SfiKkXImNZFegpJDbnGU9OtV+CyNhK0Rj8NLLOYjzs+8EntL4uXgUlwXtq+L09sDt6caibs6va6OHNCZ79vFxOsuodUq52DTQaZ2NUEqTWR6Cf9xwX8RmlGNYSnMdy3aD5qk4yYBLYYsFyOnk0TKFTrMMGkLcjm47bZBNesDD0DLGwYzNW/C0htvaHzzm15zxv72Oyd+btDoTUE4FSdUxRln2bdplGNEnG1l5HlP5GNe5UaIi1idMX6yyiJXXk35GC/uMTcnYXhi0E1XTU8d8L+5MEbL4GsGSueTSeidFOXXgXq6d8cpt1r50MEfUqYZTL5Pgy/f5pXcpwyu3tKAI2RMrYRcysTdvBVmTfVmMNb0j0lVVS+huWgRZuUkeg6ZzH91Nd8LfI7PuO1c7HQSECauXgjTayGTYepTD3HVJd+npAQ++GIjuYDOwUQF2YjCnFgTVlhme3eI5awmIU3EVxrmsqfi/Ou8Qta33EjQ6GJaziVSKtPszGWK8yrhdCuhQIr9JVFmffoKSnaug42/o+/F9WyWbap6X6fE6EBSVdB1fMk+XBzseC8dkxbzys5aHCfJlN828MRl9QRLNerv6iX6hItavBhl/cs4DuQcl+60TmhHCyVShi+Z93C3/Y8oVVH+3+xm3rn5AWJGgtlsZktO0CZPxe9kkIw+YmmNIjztuoxAsVL4hEOlZNH6gduotA/iZgyssEbf+z9LxVfjVFZC0tXo2GtwRXwd7UoNqZyK6fiYlNrOerGAzUaYUl+ckM8i4aoEMgkO9mps1Gczrcdkank7E68O8upFj/DzF6tJSGFe3KjxQaWZd+9pxGxpZ3JmOy2RCmpDG1hSPY3XD4Z5euatzK3WuOkmb3zqwD0Zj0PkYDO3H2ok5LNI2wr3m3W8YkS5/Xa4444Te3rNzfDk3c1cubkRBYvZUYWSFWdeVjKOoxxvKs62MvK8JvKTWuVGiItkHYVeEab8JC/u0TfnsW56yYo6vl2rHSH7PXuO9uJvu817XeO98Ilnb6OKHZRpEspGB1auJNzwCEVuHMsW2D6vgYrjU5FzicFJywPnsWMHdHSQfqWZjtgb7ChaSPchi5bJ1dwXXcUXt/4tU9LbmDirHFn2dNuqbDM1uw0pVYTiWvSioyjQWzyVAxq8MPdWHuycTdanHRn5+MoOUC5VeNc7TGLJUoLPCmzToSdYzlRZIqFXs3PmDRg9FnNW30/XwmW8sqMYOwaXZF8ipJgIBIak45cDZCSJjBUga8n8avMM/KrJxFKHSFGa9u1xlrCb3DcfQM5uRCn0k5q7gMTeFLoVozDXg4NEt6+CmfNDfHnz/bz0zm/zF8/cTbhrB2FHIuh3WOI8T0uZSzrlkpo4k6JduwnkkggsHGQMoaPILu2Usf+Fg9wc/DYX1FrM6Gtl+f+s5huORfdehZ+W1JF1IxQELd7wXURNYisqJgEpyy/8N2FkNQpdgyKznYukV7DxoQqHVMlMvhWpZ+WdFm5RmNUrNPSJUIXBu3p2Mv+5B9gj6Sy0WlCKQgTcBOuzUaZvehVRspCr96yi7BNBotceTa5hxeCGfY3sFzpxWcdMJPmM28AP9HoiEe2EzohhwI8eMPjYjkbciI6BzoYdSZbe34Dy4Fu/eOZc4GwrI89rIj+pVW6EuIh0ax3uKu3kL+7wWM4Ie0iNQRtG22LW396GsnYzUkURsk/ydOWbN6P1tvHxW6roWR5ib3AuNYmt1BT1edV8//AP3tiuhgbo7IQtW7BLy2jr1ZEVmN27lh7/AvbGwoRDGo9U/iN3ty7H6oohFwaQa6pQtmzk76Y9yOsvKtjZBLIVY8klMmQz7N1g0t6t4Lpe2wxV9aTufY5G+4fr8P2wgQ0vWfjSHsNLSiuOYnFw5lI6DZ14e4KYlePpp2W0SugJTWW7ahEwE1wQ2kXG9lGU6ERxTUw0dgXmUmwcZK6xFX93CkXkmFLSzHv2PUk2VMw6aSkLUmuJ/+5lNnMRBb4+ZontOIEAds5H8c5Xmd2dZs+G31HatpleKULMVjFzQYrdLp6q+ltEKMDiDX9Jq7+SrKimIrsXnSSyY9MhV7B/8tWYPSmm6gfY3zaJ5e5qNkk6c6/V2fxSkr/LNPDHCz7G4uZXEDb0Sir71FnE7EK2FlzOItHMl8R3udDaRMgx6JHKCAT95Pze/dnb6+0C0mkobm0m+nIjRblupqdfY1fxIor9NmawCDnVw4SubdiaH1FRgWxJxP61AeNar3fKkVvZijN/tkXndp1DhyBl65QHYiycESesQLI9Tm9beLCCc4TnRjHiBCSLhOoVDCWTKlZnHGVooiaPM4azrYw8r4n8pFe5YYwa0ryY+Eld3NFiOSfYQ47061AIsLNwYJ+XxHPd/h/CvMUaqUfqcB5qwG/PQZGHNlDBO49t28B1Sbsa0u+aUIWNz8nybMVN6KrGZVGDXNdE/tFo5O/dH6P02Vy4ZSPBqxYSmTqVRbVJ7NcOoB7ejPxcGmIxIiGN+Xtf40NalH9Pr2BrNkpHh2fWnY9G2bq1HtUXxywPY9tQmGzja9xDMKlxoB2KXJu05SNr2RgdoIskku11NJSsLCWZw2SlALZawE4xFyH7uMx+HoREIb30OkWs6LyFdqOWPQULCRTqxP03MqF4D//Z81e8P/sEVdYuSq0uMgU1ZF2VjJml7KVf0uc6xAC9EIQFrgXhpt/zGfVRpN4OqrI2nXIlbf6phHPdbItcSWJCLVXOQUqMJj7e8yCW6VJYkqS7oIKCAnjne1R6Xu6mbPc9+GQHPdtDkeRQ5kvyxarHqNDhrh3fYHp2CzpJHGRCqsX+C95HQccubtpwO6UPqfQkFIwNNzOjfRU5kaaaXYSdHhZ0/Z5sURk+WcbOgeza4A+S8wXAryISMRIH4kcRuWEq+CWTG6+I0ScX88Lvk9gJhZ7mFq78092UFFpMuEeB2wZDJUN9j3AYLC1MxlFQzCTCMJh/cC3+TNbrqnnbbW+L/iRvNs5mzkC4rnvivxrtxUJ8FPhnYDawyHXd9WN53YIFC9z168f0pyfE5s0eEZ9OJtjoMEgc8FQlQx+YY//Q8ErVhsZykslTz1h0dMBll3kqFLl/UnxBAbz88mDG6niZ3H57TL/OH/6sUmR345dzfH/2g7Q+t5uP9zVipy18QYUD1y7HH5JZ/MqDLPhILaqKF7j/+c+9xWHDBmhv9+yYMoV0LMPLzgI+m3qQ7oxGYWG/1DLpeenhsNc2t68PPjZ3M7cHGtjwsnesV8LLmHVgDVLOwh8EJZ2gVZpM1WSVq6fuhxdf4I3J17EtVk04c4iLY3/AkgIYrobsmkxlD4pwOOSfwjrlSkKlGvTGcF3oyBYTNtu53n6KoN8mZSp0uqXIwqFTrkC3e/GpEiUBAzWbQMv0IEsOuJ7cESFornkvPr9E3ClEdnJMjzfxirOQNv9UCuwYS3NreKPiGt5xcRI2vkpPSwrd7CZRPIW0rxAnlULP9XL//NUYuw7zrx1/i4tAd/uOVJhuqVpGVWo3yg3LKJpczB9/lUSJtdPTaTPD2UEWlQlFJqWJPWQVDUvRaC+YwYTenXRVRElUzMBJm0ipJBc8VY+levrw1t81076iES3ZTmXfdgLzZ7H+QDmPSDfzwfgqDKETmqDzF8uSqFnv3hypBwrAL1Zu5upN9zOz40WKyvyE3tUvWD+dezqPswohxAbXdRcM//npeuRbgA8BPzjN9zllnPYq19yM1tiINpaV4HixnIHfn4wRlgXz58O+fR6pyrJX+NPbO0jkw1354cTeX9m3eIpX2ff7abfh88Hf00i7pNPj09FzSaY8u5pdn1hJVgqR7U5CiY55oJugA3JR0ZGiIQAkiaAmcVGklznbt9E9eTZ72jUCAe8ZDzoGem8cuyAMaPRNiWJ8vp6H/jqOrzSME9R4UbqK7M4DlIUVlvsepLRUp5h2Ii2bcJ1uatteIl1wGe1uEEWykeQc/qBJVeIN79KgolpJLss9S1PHFTzOTSwPPIYvrJO0VOJmDT5jP4d8NaScICEMukUp+6QpFEkpwnYzOcnvjbcTAhxvsLTjQvmh1/jKxMexZs7lHz+4jTe+9SB7e6eSikGMYvZQwezWp3EPdJK1fbwuLeJiex2hxGEyJR75l7pd3OG7j6C8GUey8QkH21WRHRPHhblOM77L56PMKGbPHth1WKfG7kR1UyhOhqSkE0+Bq02m7PJptP/NXUxROhHfb0TesJmyjs20lUSJ3bKCFf/mzbxULYMPvtCIGtHpK6vA6J2EdKCHX166kgsrLCY9a5Es1kkmPRmjasVItcVpbNRGzCPd/kiUvlfvpOTBu1Bm1HorNBwbnxxae/GmNU7J42RwWkTuuu42wHtQziFONjN+hAsVA+1kNEGjxXJaWjxR78luC8JhKC/3yjF7emDjRo/U77mH1OduI1YdPfqZGS2sM6yyL771INue9BYcnw0ZdHLpGB2tFk9Pr2PmoQb2PhfDdkDLRZncIzFBUbwdgdQfqzcMQr1b+FTiIexskIedOnaHoizwN/NZqxFhWTiWwksX13HbV6LU1mqUX6KxYwfMjDfz4Y5GCFiU+cCyE6RjMWriz5ELduB3clQ6LVT0HsSpqMJUHIzeBCHjEABZtRAEpNwiWgLTee2dd/Lqc1V8oO8JVJGksErncM9MJhj7UUSOkJNkk38RruPSWHwnvlQvX7X/jWm5NyiSO3BdByHL2DlBWoTY6FtAc3c1fVs1Hi6czV9KIS6YmKSlQyViHaY61ca+qiUk92/AF9ap6DtMj1JGcbYDu6OLSK6DA3IZrx+YyMXGDsJCQjgWruzDcX3EtSqKL78URQtgxpJs3qyjiyS2EuTH/pu5N/X3FLvduPjZGbiQA3uD/PLXlXx40/eYvbCWkksuwDzUTdjJcefWWvT+9vPpXXES3RbF1Toy4BQV4/Yl8TkWCSmMFFCQ00lkWfcGUSsKccKj+h7V1aBdUgUl4aNlVcMF642N3m5t+3avXLO8/G3THvZ8gfRmHUgI8VkhxHohxPrOzs4367DHoLnZi46sWAF33x6np33YXT5QPjcSBjIWyaRH3smkV235yCPea2tqvK8NDd5qcSIMfb+mJo9Ely6lM1fM+k818K9fM7j99v6ij6ESneHH0TSorkYr06iuBn95GEsohNwkBQUQsJNkbIU/vhpmi4jy/h31PBFdyQsfeZA/XbWCba9lsWqmeGGdYNB70nM5pMsXY02cSp+r80mjAS3VwT/4GymZopMorCFSo/PxRAMiZaBp3jVdMt/gs3YjhZU6vupyOmM+fCJHud1GhXOQtCVjT54KpolIG+SMLLGUn1xO4NoOOUnFysEBpwbXsijQXYKVRVz6Do2fltRRWZBkQraF2osLOTDhUkJ+G1kRRLNN6FYPPh9ccN0kIlMLCQQlWt1qHNvFyXlDLDaFLqfDitCeDZNIwAuvafx7to6y9H7e1ftzFqefJeAYOK7Awo/PzVEQyNGiTKeTUg4otSR8xbxecBk9hh9bDZJQSzB9ISxUsoqO78rLUMpK4NOfxmptp7RvL7VlSX5SVMfL/qu5VV9NZ/VFpGvnEMsE+fO8OipLLSTHYm2zDoqKPr0S1/GSkgO3Z6DC+1xzvf1TepJJhKrwN7eGaU9q/LS0DjeZZElNixdWqaujqGpw8HH/S47OI410Tw8kigbuOb/f+10o5H31+8d+j+fxpuCEHrkQ4hmgYoRffd113SfHeiDXdX8I/BC8GPmYLTyDGC5XNGNhtmxQuGzSsAZVx5OtDI/lnKJA9MiuoDaKduedXuvC2lpMVNY1wQQ5xvTSOIckT0523+1xQmM8TuV0jaaFdVz2agMTiJH1K/yooI5LrtIoKoK9ezVe2Ktx3Qyw+ocGTPzbOMFChSJ6CSUOw49+hDJ1KlcXwssv6/g7YszxHSDiWuyM61RXgxLQUY0YP3k4zl3zvOkz9XfFce+yUHWD+Jo/cbDXJkSahsA/MDf4GqlgCa4kIbs6EjkOdaj0SWF0f4CsUwBZk4xaQLk/TsDoprW7limrVrBGqeN1OcruL9XzyQ/ECRUp+D9eR9+GLgKyRIFpMDm4iUjgbgIHQmyfeQNGaw/TpGZ6/eUI12FTZjYdUiWNoo6MrOFa3tq1uaOWRKiQruA8Jmd3UuG0cPH+X9JjhymJx1DsDP5ggg2BS4hd9E6mrN/N7OyrZNIBuksrmZBNwYLL8LfsglmzKJhc6FUNP/44ql8gC4f185czrTrKtt9Ah28xz757HpnDcZ62wlwa1njjBYNpXQoJJ0lrq05tWRLhV+gywsQOeD1cTLzP9X0tDchtMVyfQsXddWQi3uffGo7yv4vqqfhEnMjl4SPqqeOqJQwDIpGR+80O3NuhkLdbKyrywn6yfGy/gDzOKU5I5K7rXvtmGPJmYDjnqsUaz8yq45KeBtTkSWiChsdyTlIgOjxCcsvyKuaFw2CaZBwVXyaJFFDIBMLoaj9fEyY0xuNoGiy/L8qD364n1xWnKxdm1x6N7v5ybseBbNbrp2Wa0JXW+OYjWv+plHn2BIOQTFJervOedyQxexQuuXMSrFDoXpdk716vNWq3UNheqpDYdhBtdthrk6qBvW4tXX0aWQlk1+FyZy3NIsoFuT30tbtEhEM6WEwuo5DLgR1QeT20iDnWC7T6p1Iiv86WSTew7sAMinxJPuc08GB1PQ+t0vjwJzRC1kHCNYWE5t+I2dVH8NUXIJcjK5diIzF3y+Psp4Cd2iXUFPeRykgkD/vIpSwsGwKSQWUwTtAJM6kwDjmLWvaRQKNTn8Kkvq2UB+JkbYV4qBxT1aGokmu2fY/XQpcyKbMdycxS0LGbr5V/jy99ZToXL1IGP9QVK0DXUSoqmKYkMZtW84ZvHvPm9ZNuTAO/xpS53mZM0zSerK7j/a0NHHw5hjYLvt97E037vbYD4TAsXAgr7otSW15/JDmPph3JvxcVgW1rPPCERv3lg31XRs0jjRSqq64evJEGQom27ZF3MjmYlH+btIc9X3Beyw9PFiOFuJPlUcTKerDip5bEOUmB6MCuIOI3KAnF6bbDPLxa476b6witaiCYjhFyFJ6bW4elakf4uqjq5I7juuAENZJFGts3eeHPYHDQBseBrq7BDrlD5+gOtYdYDFVRUO+oQ59bRutn6kj/poFqKYarKPxBLON9TSvQvmNBpN+mm24i9+yLuIaErsg0iSX4HJMGt47vRh8j1mrQVzYN1xFoB14n58LmwBJMn8Y6+Qp+X/wpbg3+B4d8tfh8Xk/2iBNjanGc5m7Na9U72/swVddELVawLQvbF2D2xQGam2HG4c28IV2NXT6R4sNPUNbXjSImoir7uc/5EgesSSgu+PYpbJh/M4vLXOTNWYjokMzia9WRisPIQkKNVODLJimYkcNakyNXHOEF43K0kEmF3E3lpVX84DfV1C/t/zj628Saqk4mAUXVOpcTY8at3gg2GCTVl17yVKWSBG8oUUo+WE/VvrWYWx8nGn+MC5Un+N+JdWxyogSDUFsLoOFUaqB5TbP27fMEUK47WPQ73Fk+Jo80lmq6ofd2Tc1gjDybzTdOGWc4LSIXQnwQeAiYADwlhNjouu67z4hlZwGjcm6Zxgh948aOk5DOxOPeKLf3tTQi2xa2rPDrmjpi1VFC9V7L0UhrmF2rNayWYXw9xuMMPKPFxZ6X1i83J532fi9J8N3vwsUXQyrlSYeHR2wG7Bl+rMMTonynrJ6QGSfrKtzV92UUySGlVlCoc2QsTHL+FbzW6cMsKMEvTFTTZX/R5cT++XIebYjjLw9TpBiEXv4jNVt+izBN5JzLr2fdxqa+WvYeCpFwkti2jhVP0iMr7I2FB1v1AnzkI/DYY/R05MgcctgTmUt8q8qimha09hz+UAHG4T6Uvh5sV0ItDmFmHBbSRAs1dKiTKBJJrmtZxcFPfJrpWz8Pif7VrcC7ELIQyL1xKCmhJJTBLjK51vcnYgGLIquTrFRAZP89PMltxOP98zDDYboSCq+uT5KWvJ3LpTMVb97lEI4Er/T+iis8wVBJCWDAggM/I+krpkPVCStJPtbTwL7SekxT46WXvFkSluX1n89kPLWqongzR8Dj24EF+rg34lhCdcNbGuZVK+MSp6ta+QXwizNky5uCsybKH6N0JqwYXLu9ESOkQ5HnCV27vYGwUn/kPeZVQ/28UWwcw3GGPqOmCYGA50RdeaX3fS4HS5cO5rPgqDGgg7tmTcOgv9VA/6EnTQKpQIOAxsdjP+CG7l9hI1P4OwnetRQKCnh9k8Xj3MYsGlC62xEBhcfK6yifotHwCHSmNJxfNPOJZCN+yaKoVEH/25vQr72c3zVqXKbAy3vrmP9CAzXEMC2FBqmO3Yc17r4b9L3NsLoRUimslMWj1l/hvOfzLNmxmknxTeR2bsPI5VhsraGjag7qbod0TsHn96EpKXyWixzSmD4VdF1Hj8f40Z9msKLxEZQfPISZtgnEX0YuLfVkmYcPe1722rXITg6/m6bQjHsNvFQNw9W9z9BcCQctOsww/56u40angVIpRgaFBuq4He0Yd0HTvPqbhgZobYWwEWfOdItXDum4vd6INi3ndUEM2PDzh+IUVIdRyjXWr/c+57IyL3S9b5/nOM+aNThZcPQbMTz2kGC+Ycq4x9sqtDKAc3lfalacebMs1rbo2L0gyzpLZsXQrDhDdwWnY+PwZ3Rufxy2t9fLWw100YNjxoAeaYeqaaOrHe++Gxq/upebWu/Fcn0EwkFc2cZ55g/Y19/I9x8Po06r5pWSejY/H6fTChNdoJHo9Pp7BWyDz/U0kgroVFyg4je6yTzyGB0VF7LvxTgJKcy2A1ECTj0TlDhadZiErVEiwfrnDC5ueIC5ExMUd+/CTef40OGv8tRHHuHZa1Zy/e+/wLbIEhAyc4xXmNi+ATNYRNZSkDNJbMcioZRQFJYoKgLF9AjsUDrMU53V/B/fpzq3jXcrDzLtHRMpD/bBn/+MncqQvngJwa3rkYWLUlVGS6oExUpi2jILK9rRVtxOT1Jl81aF5kwdz5fUc+W8OAU1Yfa3a6PmBqNRrzvxww9D2h9m5zaFC6YnyWZ1+g4nSToKs7RWbnjlboyYhfArbLisDkmKIsvebqumxvPOL7lkjOHrs10znsebirclkZ9ThMNEyhWum+SNWpMzSdw+BcNU0A4exFDCxC3tyIN4KjuH4c9oMOgpJKurj36vgRBM/xhQurs9b7229vgh1A/PaOaGC/4F5VAHjs9PNpUhKWQUO8fuqTdgHNaI6ICucdmHNfbsgU99Cr7wBU8gUZqN4xcWdjrOtO2vkM1C0Opjxxdu5hZfOSlL4SG3jk1EMdEo7PN2ChUVcJXyEjPbn8e3rwenUEaurkGQ5Mo//DNbL7qJ8raNFIkgjpDZX7kQzU2ybsbHuWDbL5hRZbDtgMaG8huo2roGfX8LBBTus+toekXjyWegtFSjomA2VxNiU5PJNe9QsA2Hjt4CXt9WwpwOP2UFKYK6j9pwGjsnU7U4g/ridswLlrF2WzFBPcnyWAP3U88z26q5IgIaBsWpOBjHfpiGAatXe/JsXddYK+pY2NTA9RfFcOYrdNy4nIp/XY1TotNq6QTtJJe81MBTRfW0xTUiEdi7Fwq9TsZj5+MTbU/P1hSEPM448kT+ZmNINWayPcbG7QqbK5YRvXEFNZUWLYcUnpt2M61uNYYSxh/RTqn1wFhCSMPDpJWVnkx4QEafSnFkOryue60M+l5tQ3vkAYKTynGCfgxDQsiCTKiUrJ3jx3veCaHBUv7ubu/Y5eWDx037wxTaPSzJ/Rm3T6ZAgM/OMsnez87AheRMk79zGrhDqkcENTIZz+tcusjg4s2P4coyjithSyrKnp2Up7MUGweZ3PoCKTVM6fQIODb+1rXsKVlAc2QpF/1gKf/+ozhSNEzM1Piv9uvJtsfpFV5bhkDAO99kEowCjUZRx+fTDaRbUxjdNh2VcwmW6HTYc1EONeG7YArKvl3Is2ZBpg9mzSITKPYEHkU6lSUxCuw4HYY3cOLLwUZC9wzb2gz5HNJp71q3tsLG7VEec+q52o7z2dvDlMhxfI6FXahTJUFbm44/HSOQiVNS4k0GCgS8Bfnb3x57P/Ij9+NIN8fZnIKQxxlHnsjPBaJRjJX1fOf2OP5rFN77ygoSAZ3fNussKN3LZ1/6FC/bC8hKIQ59sI4WPXpKTehPFJ4JKwYTzDhmLIymgd0dRyNMOKyxZQusX++pIPx+eHdlMzftbqQkHYfNG2HpUjKXL0WseRY1l8V0TJ659tskQ2XcdBP84AdHh2uyWe/rjh39vcFUCynnYiHhc21kn8BG4HczuIWFhDMxquU4drlXnj5nDlQE4mRS8IJzBdfkniQdSyNbSeTCAvyqH9fKEXT6kNJd0N1NMGdRXDOZSz63h1h1lE5Vo1yDphe9BLfPB5F0HEv1hn8AWHED24rzslOLu7iee78U59HcQa4/tIrC3hbsUJAfX7maz3xjIpWTjpYbBmyvqpJkElVXmLcoTHXM4C61kdCEUdQhhsHhDXG2rAuTljQ6O2HCBAiFNfzl8JOH43zpTgXX58XKdF1nWkWSXFxh7oIwldO9kEog4BVfnjA2Phac7SkIeZxx5In8HCFuaXSqGnMCB5Fti1xAR7JNKnu2YiOT8JXiILHglQaS760nZo0eYz0l9PeYuSNpsefFBI4DZqCQ2VEFsbWO1aujLFwIW7eCSBlEX2hk+g06yuQI9rZt2M+txV12IxtqS4lY7fzxhu9yUJmKkoQLL/S2+cuWGFRrXsx71SqNL38ZfvhDUDvjBFIl+MRkYt0SWdtHReINBCYdVoDqCd5g6QsuCxMs9eR5oRA03hvmwGGFrKKzbeZfMOvgMwStXlw1gDyxyktKGobnWjsOkqoQ7GyBe1eS/M4jmKZGe7sng641mvlAVyM+1yKbUHg8UkdR1uVTuUaChoXlKmyy6tBnReksC/PMhNvRNDgsVdGT1SicDQb9Ox4NxM1ep8rFVTG27fKm2fc5Grd/+iChIROkjlKH7N6N9UAj2edTPJBzaRC38PvcYjo74YuXv8x7X2jASAlC94SY9JllHPjRGkQihuRTmHB3Hf7nNUzTu9ZntL/12Z6CkMcZR57IzxEGEpLddhhbVvBlkwRxkHMZHDVAxg6QEyp+4Y2NUxTtzNVfDPG4IhGVop3rsR3ghhtRMUk+1IAQ9Uyt7S/574pT+UcLvVynPQZvOEuYcfBZWn+zh0nTwjxa9M/sT049sgO3LKjuOVZiOXFilAcfhN62MBP+1YeSmo60bjOZlg5kHAJkuCS0ja5MOQseqeOqRdpRYaEv3aXxg446PtrRQC7l0OS7nAulF7GsEiZIheiBuNejPZUCnw97Si05fyHOK5u57yttdCZnsGcP+LIG7082UjTRG6qQak3y2fT9dGXALCimT/UqK6ccbMBd9ym+kXiEbZu9wdrxaB11K6Ls3j0YeUgkAKKUBusJq21c+0X4xGVVVE7n6AlSA7Em8D78xkbM3jSVPa8TlLLcZy+HyCM4tsunX7wZB5lC/EjmXKbsWMOEp1aS6LaOdOmsu/As5SrP9hSEPM448kR+jjCYkNT4dU0d125v4JoL04hNDntDcynyq/hzSfqyCl25MHW3nUFnaKjHlUggKxIygJOBwkL8nTGKnDjJpNc1LxsMI1RP9dHUpKOHNA5Nu4I/XHQnh0QV//Rt7Yi8GODQLoOl2xox9GESS3MlWtxC626BRA9s2oTa3UWXXMpr4XeSVTTCTg8Pait5uKaM6dVHm11VBckpUZ6orWfLC3H6Joe5rPf33B6/C2NHgpAUQ1qyBDZvxpRUUvtjdIV06IUN66Gr2AsTzSiOU95tEZd0FBne/SGdgrZWtm6FrpKa/op0nUBXJ5FHG1Aml7Nohk62O8mVuQbM8npuX+FdG1X1QlAAc6btZu5zjcTWWEiVCs4365j54Sipm+tg5Ur8OzYjS3gxpuZmSKUI7HkdR1YxVB0908UXnH+nuweyrkSHXUJBwKTzj1spuGwOEdVCWzB4UcaSBzmlfGX/zWnd34DZGkPVFJTbzr2iJZ97HR15Ij+HGHwQo4SVejQrTmpXKxf9aDWLpRbwKcQ+eDOLZ/UPxz2doqWhGOpxBQJemScw0KdWCSp8fHmYh1cPeHsaFXfX4f6ygaJEjEChwrrFt5Etn4HRMlgj8tJL8Nhjnhb6hqxFt6Tjtz2J5VWVu9FW3O5VIz3/vFcBEwggyxKW8NPjKwNFRUkmUYU1WPQzAMNAi8e5ZXmYf3tYY3d/j3Tj3R/mv7Wr8G1az+f0n6DNnoLd3kl6fzc+N0vI7uNlouxKVTGxxgur7O0KM/diBX8kib/Em7OKozFThb49SZJJr4hn1kwHRRXeLFZArdShJUbXgTiWpQ2sg0iS12J27nONWH6dPkWnQE1y+K4Gekvq+e//ruXDOwrBdzUXLCmhPGx6F8o0ka0s5TU67S0mKTeAKnIsvAiMrQFKVRPJr6Ik+9jxhktUCY+oQx+N1E4nX9nsRvkR9SjEsQjzt2icy1RnPvd6fOSJ/Bxj8EH0qktD4TCE/95jiFyOsp+ugl+M7e4d0WMZ6YdD9YmWxZHBnK2tXibylluYt1gbVpQUxbiqnqdujyNFwqjFg+0DWls97fmLL3oe7zULw/h1hYJsjEsvlQkHPHke85Z5i8ZAleC0aUjd3Ux02pFSffT2+AGFoovCtLf3qy8MA9auhccfB2CeonD/p+u4jSiRiFe92pEsw5x0NSL3K/q6TeyLr6av/QVCpDhQvoD7nK9goJHLefU9aUnD+GQdpb9pgPbBQaolwNL7GzCNfi/0c7fCqlXHhBgKJ4WPWQeD2Tg+16JP1pEAX5GOeyjGfz8Up7QUCjToClbStBGuu05FJQYf/jC8+iq63UGwKkRm+lwmhnxkTdjZNpepqa34jARCOKy54BZqrGMLika9FzoM/ufeOJFIGLVCO6l85ZHIW7GGXnNyrz0byOdeT4w8kY8nNDd71TabN3vskMvBVVfB1KknvHtH9Fjc47gxw/flW7Z4FSmS5ImaQyG0aPTofh1lGn99h9eNcaB9wPLl3p/7fJ5ePRiENWs1ZGMZf3/oLnKHc7jFwLSJHut2d3vHcF3vX1kZ6r79zC1sIVFewWtL6pg1ob/j4/JmQj98YHCFWOJNsCl5vIHb/mol//kji9aOML4ijWU3aHz38TquWteAmkvhanN5Y+Ffs2fSO2n7tUYu7hGCEN6ply6NwtJj4xLKg16bhCM/G5iPOhCIXr4czYpzy3KvJ83AOihnwmSf98JP5ZN15HQSFwg4KQIFRdiygkaStO2FaNREAn7/e68Mc/t25OlVaCVBL6SRBudTDRzyz8UnOzwXvZWDBYvGHqJubsa9t5Eb11kEChWaFtZBeXTM+crxluscb/aMR+SJfLzAMOCBBzx9XiTi6fX27vVIvbr6uHfvSB7Lf9xvUE8jSvEJmiIN1OkPVqQcd9EYrYtveflgY7xku8FlrGF9eBnl1TLCzPCOtj8hx2Je39hIxCP0ri7o6cEuLMZWg+y++maSE6PoeB6l83AjhIasEE1NcN11xHe0Y33hdj7squQkhcKv1fHjNVH0yVF2Fi3nkrUPk7T8TNz4FE++NgVXRCks9GLs1dVDK1uPbkEAEI9rhMPa4GkPPeHWVu86WRbzFIX7bq47MvwDNJpW1WE90IAaj6E5CaZMBd+2e5D2KOyetYxJ29dQko0R6O95Q3GxV5JZW+sNFlm5EsrKCAGR1fX85GFP5+6GtLEnMvtvBjWi01uoY5NkYVMD/7vI69Vywh4sHJvrNGMGYSNOuiuMMfTavEnI515PjDyRjxfE495DKEleBk2Wvf+nUoP9Zvvv3uHRkpE8lr7WOCYWSs0Y3JiTdHlG6uJrml6b1eeeg5AZR1UtfJMqkHTo7S0kWz6LUE+P9xTOn++d07ZtXhXS5VeReEVjXtMqOsvmEjM1QmYc27YwK8pRZdk7kG1jtraTem075pRluEXFyMkkiW83YC+opzgCF29cTTZcTkzR8WWTfFlq4I831BO3NHp6ji6YGbqL8ZQnnpTvmCjWwMnefffgahmLEXroO15jMa2M5mZ4fGsUe1E9RUYbX3HvoXRmMTNm6mxam6S8eQ0/X7SST3/CQpk8rFNZcbF3XYaIwOct1rhrnnbkcwav3csJE339n6VaobNwITQ16VidMZrWxCmep7FixYnjy0Mjb6HdzVyzqRHZtWj5S4X/idbxwRXRNzU+ne8mcGLkiXy8YOAJdRyPFcHzXMHzXEMhqKsbcZBube2xHoulhVEZoxtzGi7P8HD7ggVgTApTelBBqEkM00scKtXl8O3B4QWpXW24K+5CmVmLqqnMXwI7n43Rt6ONvkyInFB4daeCtcPk0lkLKdm+FrJZrN4Uhwpm4RQVewboOr54jGA2jt0Nsm1hoOO6kPPrFEgxCp04oj+mP8CVQ3cxQ5UnN97oXf5jNiRDF7v2dm93kEjA7beTuvUOGldFPY6v0JAOhdj5HBTP0ynX4Zobdaw9MRbfZRGaUe0dfAzXe2DBPKlE35DPsrxcZ+miJC8/o7Dw2jBa2djjy9Eo1K80sL7YyMshHQp1AiS5dkcD/3F/Pd9+8NQ981NRn5zNCfRvBbxpo97yOAEG2uDNnOlts3t6PM/1v/7LcyPr6zFqoyNOe4Njp3X97W2aJxkbaYQX3sN08GB/98MBNh7lb0+EgYds5Ur43vfgn+/V+OMFdZg9SUI9LVw6M+nZUlYG1dU079a488EqXtke5k9Pm7S3Q7mWZPGcBHeKe/j4jhV8+sAK2i9cRpETY/erMay5F3kx/PsfwNDLj5pdJlSFT3whTFcuTG9aASPJ4sWgkSTjeAM6hnPlUF7OZLzNjyR5/x9x4t8AQcZiHomD575HIjgPeePuBhxsuSSMheINuQZUM4kWVryhGwOf9Riv9/Em/I16Hw15b7cvyXNz6/pbNZ94muFRb2XFUVyLrKKjqmCpOgHJQjHiY3r9SBg6avHIGMMxQtP654zmSfwY5D3yc4QRvZJo1Otu1dbmfV9VddRdGz/oPYTFqkEgEUcJhI9UfI7ssYzsxozs4Q37WxjjXt7D0HBLNAq1j0TpbasnTLx/atDgAjKgiNi2tI6L1jaw89kYkcWgyCBHiunRdEqDSeZteRxLCWI6kHV9KOXlaFPLqLi7jsN3NSASg+POZl6tMXsBJF+qI/JYA4oTIzDTax+7v107Zjt+AgXmsQ7yAEF+5zueJ15Y6MWSiovxx1uO0t3HTI3no3VcmWuAllFiAWN0MU8p0TfkvYUSpmOFhn4q8eVwGFVTCDpJTFM/sjBaWviU4tN59cnZQ57IzwGOu1XWNK8V4QgIh2FKopml6xsJSBYZR8GeWUc47L24P3VHf/fwwfcb8pQc/2E6lb38yNA00GZonlWGtwgNH3HaoUd59sZ6evbEmf6ZFBWP3IO/REeWIWOrzOzYzM6qq+mJ1KKWDRo688NRqi9ZSXLbAfTZk9Cmlg0e89ooXO6RWEk4zO1ox3DlwCJ6882eunCoArO9fZS5lvG4F8Oqr/dcyQHt44i6e/jkiihK7QmIegy9ik856tX/3hqnEV/WvF3dpSsb2LY5honC89E6/va2Uwur5NUnZw95In+TcTpeiYbB52lkAzqd6ARJUkcDGvXQvHtM5Dt86ITjePnUIw/TGXabhq8Jn7lpsFGXWqwRMzWMsEbBLC9urJpJFi7UeeO5biwLeuUSFi70xr2xt9NLkNo22urVaKOd6xCCHBriBo4qrR+QT06ceHTI5YRzLe+4w7smLYMjnOZFh+vu+49+muWWZyLRd1rx5WiUkkfquaQtTpwwi6pGJvGxnFJefXL2kCfyNxmn5ZXE45QUWlxzo97f8U5HbY95oZgRyNdYWX+kt/nwkMLevV5DrGzWkwwePNg/d/c03aahDzQcbZa+t5nU3zfypWkWb2xQeGZWHcnyKLcsN7zBGv2i9HIrRmQBWLVR3jXNRC1WPYObmjxG2rTJC2ucpL5etQycnjgl0waLZFavPlaRedTJPPCAJ5IvLz86CzoCM47BwT6l3c6ZSPSNybbjvDg0QyM0yq/Hekp59cnZQ57I32ScllfS/2LVTKIWDnkxHEO+PbtjfOf2OJ3q0f3MNc3jy+XLveReIOBNEFq1yvuqnYaBwx/oj3xkMKavd7dx5eYH6JSKCczQuaw2ySU9DUgfW06oX5uNonjxjupqlHAYZc8e76nf2+mR+MKF3jy6LVu8VWgM+vofPWBQ5YtTIVqZtX41nQctatoUNiyuO3GRzNq1XjFSMAiyjHXxQsw+C9EW99QnJ8tAp7HbOS0iPos42VPKq0/ODvKqlTcZpyUQGe3FVVWD5AuYsSRbtitIkfCISoeJEz1OvO4679/UqUOUDKdooFcSfpByqYM5RQeJ+A0ee8zrgrj0qdt55x/vYuK+Fwk6BoGAFyrRRYrQjx8+WpKxatXgEz7w1N96q6drnDrVW3n8fk9ekskctdAcpcQBki8188EXb+f9L32Nv3hyObqc5rBSQ8zUmf9iA0aHMfoaZRheWwC/H4JBDEvl8M/XsuFVuPOe8Mhqi+EGDMdIu52xSkjGKU7llPLqkzOP0/LIhRD3Au8DTGA3sNx13fgZsOstjdONWY744iF7VtP0whZqsfe74U5rOOw5mQO1R8c43SdrYH9J+Eefa6cmvZ2uklkYejm/r7yZ5e4qXkenR44QFttYmFuLyo2QNL0SfUk6fhhH02D2bE9HP3wIaXe3dyIj6OtvWW4w6zGviVWvL0SFkCnr2EqkoJpdh3XKzRhN6Ti33jNK4m6AiZYswV7XRPdBG7+bZc/im1CLtWO9zuPEF46Em0wFzTS9cxwoAjrPg8T5uPf4wOl65GuAea7rXgjsAL52+ia9PXBaXslILx4i5hb19XSUR4dKrQEvqWkYY3S6x2rgQEl4gZ/SdAsZKURRooWM42fZ9ocoL0xxzY0677hWpeKDSwhJWdizxzvoLbd4RDzU0JFYYLjBwaAX3P7Wt0bV1//k4TjkLOYv0Uk5AdKOH8wMBUqG2ZOSVE1RWLgszJo1ozjQAwylaRhXXMfukkW0Tr6CgzWXH+t1HkfsPaCb/sEtzWy4cQW9B5OwZo0X5x9tt3Miz34c4TRLEPI4Qzgtj9x13aeHfPsy8JHTMyeP08IokrOB8vN77jnaWTwjscqBkvCiEGUlNi2JIvzZXrI5mbmzJBTZgYGYvqTBFVfAnXcOauRDobFlv45j8IC+fqhj394RJusolGtJ3vUeHWvHXHwbm4g43ahSkKYrvCKZnpZRYuRDMnPBtIUioGn6TcAI680oCeJUW5zGRo2I3+B9LY0YIZ3nExVcd80k1L7B3ipH4Tzs15qPe597nMlk583AT8/g++VxGhh4uNraPAIvLh45GTXqQzdWidyA52rbBHWZaaEkdk6m8kob1QkOtkccIOrbbjtaJ38yLDCKwSNt792QhnRzHaxqQLViqKVBUj9eza9/NPGYNryjhgH6bVNeeonZfY/B5sewNj/B89E6PrliSGfIUeILccJYFpSE4si2BUU6di9kAsWo2aN7qxy55udpxcx4Tca+XXBCIhdCPANUjPCrr7uu+2T/33wdyAGPHud9Pgt8FmDSMVMD3joYT1NMBhxeOIGacLjRo3iFI57bUE1ZTQ3y9u3eZHknO+hNzjtGYH2soSP8/JjjjXJxB0y4/36vQaGmeetFKBqFuYOLREjT+OsIR7XhHUsYwHrsCdQJxVz8Ph0n4U0JUmrrOaroagRdXVGVdtQ4P5LecOaAPcoKEo978a+BfIDjQDqdr5jJ44Q4IZG7rnvt8X4vhPgU8F7gXa7rusd5nx8CPwRYsGDBqH93PmM87opPmIwabvRAueMwr3Dzp+ppeEQb+dyGetWKMjgyaGjCclh16YkWu+FmfWlZMzPXjH5xR73zhh37ZMMAr6+N0/OiRXfQqzhduFCngBE0iyO88WCIa3Cc35JZMdTsKCtIa6vXvcs0obfXawPg93s/r64+vqF5vK0hjsO9J36xENcD9wFXu67bOdbXLViwwF0/0G7uLQLD8JJaQ/kvmTx6V3yuvPXNmwe7Ex7FgYYBX/iCV/BSUuIRSHu7N32htvbI6629LdzlrCRXXj3quY0VY1nsBq5lxG9QIsdJZBTe9acVXLZM9yo8hxkwlmt/KjAM+NoXDD62/nZczRvUjJFk6YIkyoNjf/Mjn7vSX/g00g0wcBJ9ffD0097KJAQsW+YR+ngOr4ynbehbHEKIDa7rLhj+89ONkT8M+IE1QgiAl13X/bvTfM/zEicqiDyX3vqoXuhLLx1V8MLChZ4c0HGOcuGzjkKvCFN+mj0yRhuAcc+dRzfWisehrL2Z97U0ItsWkm1iG0kycgXqCAYc99ozMsmMhXt62wwUI86WhTdz8cZVBO0YvVmFnpvqKD+JEx/cFGgw2rC2oRM6qqq88EoqBRUVnnd+uuGVs0W243Eb+jbE6apWpp8pQ853HC+EMR5yWMeEoQ3DGwDcX/ACeJWMCxbAZz97VIJSurUOd5V22lrh4YRbazQz+8VG3LssCA+SQFgxuHa7p/KgSEfqjTE9sYFAZhIUHqu/Hu3aF7c2w93HksyYuKe5mQkPNPLBjRaSX+G1hcs5LE+kKxdm0eVn4UMbkjQ+Ep4a+v3pCLPPFtmOhxs7DyBf2XnGcDw97bgs6BtS8IJpekm1bBZuugkWLx5sMF5fT2hR9IxohYcSrmIaXLTWK9hRao/WXmtWnHmzLJLo9PZCQi5Gu3iWJ9kbwYCRrv0tyw1Cq4/VdhsdBo2NXthmoAL1mP7e/QSlFOvMWFpDEp0ZL6ymKxc+5c5/J8TASWSznr2plPc1mz09YfZJNzQ/CYzLG/vtiXyvlTOA4V1Oh+9gx2X125CCF667zquSzOXg8su93w9x4Q3D69q6cuWxecyTwVBxhxuP42Qt5i/1hhagDomHhMNEyhWum5Qk06/yULPlxzXgmPDRKPGWxIE4Ze27j4RtbFnh1zV1xOND5IRDXnvUhJ8744RmnEVP80RJ4xNhpPDJ2ewdOy5v7Lcn8kR+mhjLrnUUddq53X0ONWrA+MGpxEcw0vmdjoBigKt628JMuEdB0ZLAMBLot01t8DTgRw48vHhmhFMaND88IskUlihHhW1IJrl2ewNhZYiccBhBqWYSNazAwISfs4lTFWSPdiOeTbIdlzf22xOnpVo5VbxVVCsnq5YYl8n94xh1ttQgRzCqnObEtp3y+0ci9NyygrUtNdi2l+NdUtNC5OGVR69QJ7JtPOFEH9TZPpdxeWO/NXG2VCtva5zsrnVcVr8dx6izPtHleKLuM0EOI72/YRwJ2/Q5OiKVpIARPNTzqe58tA+qrc1Tv4wW8ztTGJc39tsLeSI/DbwlQ4RDCDQc1s7++Y1EAmdSZTH8/fvDAYmVDbyxOYbVP77sg3u0Yw/xJhHUaa9ZI92IiYTXmwFO/hrmPezzDvnQymnifNqBnxAjEOhmoqd0fqfMBWOM55wO1wwU+pT64sglYWKmdmZDRieBM7ZmDb0RwSPyyZNPPiaW14WPa+RDK2cJZ3wHfq68oVE0wdH6eurrjx1gfDycFheMIZ5zulwTj4OBRqRSwwF09dwMAT6jMuyhN2Iq5XnjJxsTy+vCz1vkdeRnAJp2hiaeDDSvXrHC+zriGJqzhONogk/m/E5Jtjy0//bQMAEcE885E7LoExziTcMZl2EPfFDDJkaN+QTzuvDzFnkiHy84m4UbY8EpstvwGQgnzQXDF689e447qeBMcM3xirfeTJy1BeVUT3C8rHB5nDTyoZXxgrMuETkBTkETPFKIo7b2JBLAx9vKjxKvOlGCeayRqfEgSjmrMuxTOcG8Lvy8RZ7IxwvGgwTmJB7+43HwmLngeIvXKLGc43HNycbOx4Nq7qwuKKdyguNhhcvjpJEn8vGC88wbOh4Hj5kLTnbx6ne3o7XhYxKw53OebjwsKEdh3BmUx4mQJ/LxhHPtDZ2ES3siDh4TF5zM4jXMNq2uDm2Ibec6MpVHHucS+WTneMMZk8CcJE4y2XrGEoYDi1d/p8URF44x2JbP0+XxdkbeI8/Dwym4tGdsA3Ei930Mto3FuT9TEv184WMe4w15Is/DwykmW9+UcOoYbTvewnKmChbzhY95jEfkQyt5eBgv4urTtG2kyNSZkuifa6l/HnmMhrxHnscgznWy9Xg4DdvOVCI0n1DNY7wiT+R5HI3xLD07RdvOlER/PEj988hjJORDK3m85XGmokbjOfqUx9sbp9XGVghxN/ABwAE6gE+5rtt2ote9ldrY5nH+IK9ayeN8x2htbE/XI7/Xdd0LXde9CPgNsOI03y+PPM4azpRE/1xJ/fPIYzScFpG7rpsY8q0GvPlTKvLII4883uY47WSnEOKbwCeAXmDpcf7us8BnASZNmnS6h80jjzzyyKMfJ4yRCyGeASpG+NXXXdd9csjffQ0IuK77jRMdNB8jzyOPPPI4eZzyqDfXda8d4zEeBX4LnJDI88gjjzzyOHM4rRi5EGLGkG8/ALxxeubk8XbG8GlDeeSRx9hwujHye4QQs/Dkh/uBvzt9k/J4O+J86GGSlx3mMV5xWkTuuu6Hz5Qhebx9cT4MhTgfFpo83r7IV3bmcc4x3oe355tl5THekSfyPM45xvtQiPG+0OSRR57I8zjnGO89TMb7QpNHHvnuh3mMC4znDrrn2VzsPN6GyBN5HuMG47mD7nheaPLII0/keeQxRoznhSaPtzfyMfI88sgjj/MceSLPI4888jjPkSfyPPLII4/zHHkizyOPPPI4z5En8jzyyCOP8xynNbPzlA8qRCdek60zgVKg6wy919lE3s4zi/PFTjh/bM3beWZxNuyc7LruhOE/PCdEfiYhhFg/UqP18Ya8nWcW54udcP7YmrfzzOLNtDMfWskjjzzyOM+RJ/I88sgjj/McbwUi/+G5NmCMyNt5ZnG+2Annj615O88s3jQ7z/sYeR555JHH2x1vBY88jzzyyONtjTyR55FHHnmc53hLELkQ4m4hRLMQYqMQ4mkhRNW5tmkkCCHuFUK80W/rL4QQ4XNt00gQQnxUCLFVCOEIIcadzEsIcb0QYrsQYpcQ4s5zbc9IEEKsEkJ0CCG2nGtbjgchRI0Q4lkhxOv9n/kXz7VNo0EIERBCvCKE2NRv67+ca5tGgxBCFkK8JoT4zZtxvLcEkQP3uq57oeu6FwG/AVacY3tGwxpgnuu6FwI7gK+dY3tGwxbgQ8Cfz7UhwyGEkIEG4D3AHOAmIcScc2vViHgEuP5cGzEG5IDbXdedA1wG1I3T6wmQBd7puu584CLgeiHEZefWpFHxRWDbm3WwtwSRu66bGPKtBozLDK7ruk+7rpvr//ZlYOK5tGc0uK67zXXd7efajlGwCNjluu4e13VN4HHgA+fYpmPguu6fgZ5zbceJ4LruIdd1X+3/fx8e+VSfW6tGhuuhf+AeSv+/cfesCyEmAjcCP3qzjvmWIHIAIcQ3hRAtwF8zfj3yobgZ+N25NuI8RDXQMuT7VsYp8ZxvEEJMAS4G1p1jU0ZFf8hiI9ABrHFddzzaej9wB+C8WQc8b4hcCPGMEGLLCP8+AOC67tdd160BHgVuGa929v/N1/G2tI+OZzvzePtACKEDPwNuG7bDHVdwXdfuD6FOBBYJIeadY5OOghDivUCH67ob3szjnjej3lzXvXaMf/oo8FvgG2fRnFFxIjuFEJ8C3gu8yz2HIv6TuJ7jDQeBmiHfT+z/WR6nCCGEgkfij7qu+/Nzbc9Y4LpuXAjxLF4eYjwllK8A3i+EuAEIAIV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" ] @@ -2121,18 +2105,11 @@ } ], "source": [ - "z = maf_rec(train_moons.cuda()[:1000]).out.detach().cpu()\n", + "z = nf_rec(train_moons.cuda()[:1000]).out.detach().cpu()\n", "plt.scatter(z_sample.cpu()[:,0], z_sample.cpu()[:,1], c='b', s=20, alpha=0.5, label='prior distribution')\n", "plt.scatter(z[:,0], z[:,1], c='r', s=20, alpha=0.5, label='transformed two moons')\n", "plt.legend()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 790661632cc629d8be068d1dffd59ed0dc799b64 Mon Sep 17 00:00:00 2001 From: clementchadebec Date: Tue, 11 Oct 2022 17:33:39 +0200 Subject: [PATCH 9/9] apply black --- src/pythae/models/normalizing_flows/made/made_model.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/pythae/models/normalizing_flows/made/made_model.py b/src/pythae/models/normalizing_flows/made/made_model.py index 1fdd5938..6a8f5eec 100644 --- a/src/pythae/models/normalizing_flows/made/made_model.py +++ b/src/pythae/models/normalizing_flows/made/made_model.py @@ -53,11 +53,11 @@ def __init__(self, model_config: MADEConfig): masks = self._make_mask(ordering=self.model_config.degrees_ordering) self.context_input_layer = MaskedLinear( - self.input_dim, - hidden_sizes[0], - masks[0], - context_features=self.context_dim, - ) + self.input_dim, + hidden_sizes[0], + masks[0], + context_features=self.context_dim, + ) for inp, out, mask in zip(hidden_sizes[:-1], hidden_sizes[1:-1], masks[1:-1]):