diff --git a/Pipfile b/Pipfile index 0506499..c0851d3 100644 --- a/Pipfile +++ b/Pipfile @@ -6,7 +6,7 @@ name = "pypi" [packages] black = "==18.9b0" chainer = "==6.0.0b1" -comet-ml = "==1.0.42" +comet-ml = "==1.0.45" cupy-cuda92 = "==6.0.0b1" cython = "==0.29.2" descartes = "==1.1.0" @@ -20,6 +20,7 @@ matplotlib = "==3.0.2" netcdf4 = "==1.4.1" numpy = "==1.14.5" onnx_chainer = "==1.3.0a1" +optuna = "==0.7.0" packaging = "==19.0" pandas = "==0.24.1" pyproj = "==1.9.6" diff --git a/Pipfile.lock b/Pipfile.lock index 22a8e1a..90cc434 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "8fbccb5fe635a3f5f6a0870af05a41e334edc7204af4c5a6897b92e39a8a87d0" + "sha256": "04e0b399371759729ab1bbf6a1d9d4421e7d8365424f48e41eadfc2ad8fd00d6" }, "pipfile-spec": 6, "requires": { @@ -125,6 +125,13 @@ ], "version": "==1.0.4" }, + "cliff": { + "hashes": [ + "sha256:21a24dfee9f4e58c397e725bb1568e031d75a8925def92e4d3def2b755f816bc", + "sha256:3fffeb8ccb49847eeacdefaad68a0436867f1d1398c98c9b473b9701713db5b7" + ], + "version": "==2.14.0" + }, "cligj": { "hashes": [ "sha256:20f24ce9abfde3f758aec3399e6811b936b6772f360846c662c19bf5537b4f14", @@ -135,25 +142,53 @@ }, "cloudpickle": { "hashes": [ - "sha256:bf0b95dabf35645bc070a3f3d2f6e5c4ee8b247e2dfeb8022ad53bb2fe1bf03a", - "sha256:d894ba62b0a04c3ccd482f6bc720dd02d4febcf320f5916c33d258b85d8409b1" + "sha256:18d3a5dfc82f752b9f4c844cceb663213e26e130f4a2894a18ad1f11d57a30bc", + "sha256:b4a1889434c99c2425adad8ab294e6327740b9c38a9a19ec1bd1ae6d5e298fd6" ], - "version": "==0.7.0" + "version": "==0.8.0" + }, + "cmd2": { + "hashes": [ + "sha256:22c3461af56769e74225e3aeecab0e98ef86ab8d9b4ded29ba84722449fe7608", + "sha256:88a7fac6b630ed235f3d6a15dc85853ad7a0205322d7a64e8df0693631e4a857" + ], + "markers": "python_version >= '3.0'", + "version": "==0.9.8" + }, + "colorama": { + "hashes": [ + "sha256:05eed71e2e327246ad6b38c540c4a3117230b19679b875190486ddd2d721422d", + "sha256:f8ac84de7840f5b9c4e3347b3c1eaa50f7e49c2b07596221daec5edaabbd7c48" + ], + "version": "==0.4.1" + }, + "colorlog": { + "hashes": [ + "sha256:3cf31b25cbc8f86ec01fef582ef3b840950dea414084ed19ab922c8b493f9b42", + "sha256:450f52ea2a2b6ebb308f034ea9a9b15cea51e65650593dca1da3eb792e4e4981" + ], + "version": "==4.0.2" }, "comet-git-pure": { "hashes": [ - "sha256:07cafe60f82c118082e8927edcecb23f2aadde397484fa87f94ab797e98ff1ba", - "sha256:2ffd649c7127d69109000ea08087f7de08c9e880e08290c7349e719eacd34e9f" + "sha256:299167d42f8db46ffd3616fdd4200e776d3d22b6be077cfa53db81b90a2afceb", + "sha256:b3a739ef67e6bcd9f15a93ea557794eda63680859c987718f6ca423b01cb7919" ], - "version": "==0.19.10" + "version": "==0.19.11" }, "comet-ml": { "hashes": [ - "sha256:1ac23797400b8bae50610d1c2d2e79f41acac7a3c94230db0d40f6dae48598d3", - "sha256:dd9dd382695b3731115e4ae18889910958ee037e41e83c56a28035c8ab23c045" + "sha256:5459e18fa5d1da55be2d6c6ff73c30233ab2cec74f3844aff05e9c970c532787", + "sha256:99818a1415789f31e45cdf9d45ac5723884b8ad6c0fb02931a9ed12222a66715" ], "index": "pypi", - "version": "==1.0.42" + "version": "==1.0.45" + }, + "configobj": { + "hashes": [ + "sha256:a2f5650770e1c87fb335af19a9b7eb73fc05ccf22144eb68db7d00cd2bcb0902" + ], + "version": "==5.0.6" }, "cupy-cuda92": { "hashes": [ @@ -250,10 +285,14 @@ "version": "==0.3" }, "everett": { + "extras": [ + "ini" + ], "hashes": [ "sha256:35f69f6d8e45b2250a3d4b06b8e7f537d3cb296dae9a3ec4a4791258fe4de6eb", "sha256:860011cc71520fe27c7b9e2539b72cc6df2e235705489ad47935b8da83c9b855" ], + "markers": "python_version >= '3.0'", "version": "==1.0.1" }, "fastrlock": { @@ -660,6 +699,13 @@ "index": "pypi", "version": "==1.3.0a1" }, + "optuna": { + "hashes": [ + "sha256:34a7bdf7b7e5d937bab2133c33d9d4e4831d216c0b866219a2e2e04875859e3e" + ], + "index": "pypi", + "version": "==0.7.0" + }, "packaging": { "hashes": [ "sha256:0c98a5d0be38ed775798ece1b9727178c4469d9c3b4ada66e8e6b7849f8732af", @@ -702,10 +748,10 @@ }, "parso": { "hashes": [ - "sha256:6ecf7244be8e7283ec9009c72d074830e7e0e611c974f813d76db0390a4e0dd6", - "sha256:8162be7570ffb34ec0b8d215d7f3b6c5fab24f51eb3886d6dee362de96b6db94" + "sha256:4580328ae3f548b358f4901e38c0578229186835f0fa0846e47369796dd5bcc9", + "sha256:68406ebd7eafe17f8e40e15a84b56848eccbf27d7c1feb89e93d8fca395706db" ], - "version": "==0.3.3" + "version": "==0.3.4" }, "pbr": { "hashes": [ @@ -764,6 +810,14 @@ ], "version": "==5.4.1" }, + "prettytable": { + "hashes": [ + "sha256:2d5460dc9db74a32bcc8f9f67de68b2c4f4d2f01fa3bd518764c69156d9cacd9", + "sha256:853c116513625c738dc3ce1aee148b5b5757a86727e67eff6502c7ca59d43c36", + "sha256:a53da3b43d7a5c229b5e3ca2892ef982c46b7923b51e98f0db49956531211c4f" + ], + "version": "==0.7.2" + }, "prometheus-client": { "hashes": [ "sha256:e8c11ff5ca53de6c3d91e1510500611cafd1d247a937ec6c588a0a7cc3bef93c" @@ -840,6 +894,12 @@ ], "version": "==2.3.1" }, + "pyperclip": { + "hashes": [ + "sha256:979325468ccf682104d5dcaf753f869868100631301d3e72f47babdea5700d1c" + ], + "version": "==1.7.0" + }, "pyproj": { "hashes": [ "sha256:026074694f9e9a3110013802c5ceb2728070dbdde9f1038609f942845f4207d1", @@ -1079,6 +1139,19 @@ ], "version": "==1.4.2" }, + "sqlalchemy": { + "hashes": [ + "sha256:7dede29f121071da9873e7b8c98091874617858e790dc364ffaab4b09d81216c" + ], + "version": "==1.3.0b3" + }, + "stevedore": { + "hashes": [ + "sha256:b92bc7add1a53fb76c634a178978d113330aaf2006f9498d9e2414b31fbfc104", + "sha256:c58b7c231a9c4890cd3c2b5d2b23bd63fa807ff934d68579e3f6c3a1735e8a7c" + ], + "version": "==1.30.0" + }, "terminado": { "hashes": [ "sha256:55abf9ade563b8f9be1f34e4233c7b7bde726059947a593322e8a553cc4c067a", @@ -1331,11 +1404,10 @@ }, "more-itertools": { "hashes": [ - "sha256:38a936c0a6d98a38bcc2d03fdaaedaba9f412879461dd2ceff8d37564d6522e4", - "sha256:c0a5785b1109a6bd7fac76d6837fd1feca158e54e521ccd2ae8bfe393cc9d4fc", - "sha256:fe7a7cae1ccb57d33952113ff4fa1bc5f879963600ed74918f1236e212ee50b9" + "sha256:0125e8f60e9e031347105eb1682cef932f5e97d7b9a1a28d9bf00c22a5daef40", + "sha256:590044e3942351a1bdb1de960b739ff4ce277960f2425ad4509446dbace8d9d1" ], - "version": "==5.0.0" + "version": "==6.0.0" }, "nbformat": { "hashes": [ @@ -1367,10 +1439,10 @@ }, "parso": { "hashes": [ - "sha256:6ecf7244be8e7283ec9009c72d074830e7e0e611c974f813d76db0390a4e0dd6", - "sha256:8162be7570ffb34ec0b8d215d7f3b6c5fab24f51eb3886d6dee362de96b6db94" + "sha256:4580328ae3f548b358f4901e38c0578229186835f0fa0846e47369796dd5bcc9", + "sha256:68406ebd7eafe17f8e40e15a84b56848eccbf27d7c1feb89e93d8fca395706db" ], - "version": "==0.3.3" + "version": "==0.3.4" }, "pexpect": { "hashes": [ diff --git a/deepbedmap.ipynb b/deepbedmap.ipynb index b8915ed..8bc5e63 100644 --- a/deepbedmap.ipynb +++ b/deepbedmap.ipynb @@ -281,7 +281,8 @@ "outputs": [], "source": [ "def load_trained_model(\n", - " filepath: str = \"model/weights/srgan_generator_model_weights.npz\"\n", + " model=None,\n", + " model_weights_path: str = \"model/weights/srgan_generator_model_weights.npz\",\n", "):\n", " \"\"\"\n", " Builds the Generator component of the DeepBedMap neural network.\n", @@ -289,10 +290,11 @@ " \"\"\"\n", " srgan_train = _load_ipynb_modules(\"srgan_train.ipynb\")\n", "\n", - " model = srgan_train.GeneratorModel()\n", + " if model is None:\n", + " model = srgan_train.GeneratorModel()\n", "\n", " # Load trained neural network weights into model\n", - " chainer.serializers.load_npz(file=filepath, obj=model)\n", + " chainer.serializers.load_npz(file=model_weights_path, obj=model)\n", "\n", " return model" ] @@ -340,7 +342,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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"text/plain": [ "
" ] @@ -530,17 +532,17 @@ "text": [ "==> track_cubicbedmap.xyzi <==\n", "# x\ty\tz\n", - "-1593496.33\t-104797.8003\t-1074.669904\t-1154.73547141\n", - "-1593491.331\t-104797.7531\t-1074.68\t-1154.66860539\n", - "-1593486.331\t-104797.7058\t-1074.683558\t-1154.60182182\n", - "-1593481.331\t-104797.6599\t-1074.695031\t-1154.53424135\n", + "-1593496.33\t-104797.8003\t-1074.669904\t-1154.50380205\n", + "-1593491.331\t-104797.7531\t-1074.68\t-1154.42776538\n", + "-1593486.331\t-104797.7058\t-1074.683558\t-1154.35249079\n", + "-1593481.331\t-104797.6599\t-1074.695031\t-1154.27806665\n", "\n", "==> track_deepbedmap3.xyzi <==\n", "# x\ty\tz\n", - "-1593496.33\t-104797.8003\t-1074.669904\t-1243.02064091\n", - "-1593491.331\t-104797.7531\t-1074.68\t-1242.88752689\n", - "-1593486.331\t-104797.7058\t-1074.683558\t-1242.75766879\n", - "-1593481.331\t-104797.6599\t-1074.695031\t-1242.63093238\n", + "-1593496.33\t-104797.8003\t-1074.669904\t-1189.14516867\n", + "-1593491.331\t-104797.7531\t-1074.68\t-1188.99366118\n", + "-1593486.331\t-104797.7058\t-1074.683558\t-1188.84623769\n", + "-1593481.331\t-104797.6599\t-1074.695031\t-1188.70294894\n", "\n", "==> track_groundtruth.xyzi <==\n", "# x\ty\tz\n", @@ -571,15 +573,10 @@ "metadata": {}, "outputs": [], "source": [ - "df_groundtruth = pd.read_table(\n", - " \"track_groundtruth.xyzi\", header=1, names=[\"x\", \"y\", \"z\", \"z_interpolated\"]\n", - ")\n", - "df_deepbedmap3 = pd.read_table(\n", - " \"track_deepbedmap3.xyzi\", header=1, names=[\"x\", \"y\", \"z\", \"z_interpolated\"]\n", - ")\n", - "df_cubicbedmap = pd.read_table(\n", - " \"track_cubicbedmap.xyzi\", header=1, names=[\"x\", \"y\", \"z\", \"z_interpolated\"]\n", - ")" + "names = [\"x\", \"y\", \"z\", \"z_interpolated\"]\n", + "df_groundtruth = pd.read_csv(\"track_groundtruth.xyzi\", sep=\"\\t\", header=1, names=names)\n", + "df_deepbedmap3 = pd.read_csv(\"track_deepbedmap3.xyzi\", sep=\"\\t\", header=1, names=names)\n", + "df_cubicbedmap = pd.read_csv(\"track_cubicbedmap.xyzi\", sep=\"\\t\", header=1, names=names)" ] }, { @@ -753,56 +750,56 @@ " -1.582823e+06\n", " -127943.948452\n", " -1255.901352\n", - " -1321.530331\n", - " -65.628979\n", + " -1292.878085\n", + " -36.976733\n", " \n", " \n", " std\n", " 4.306205e+03\n", " 29434.912966\n", " 73.216368\n", - " 106.018069\n", - " 59.667879\n", + " 53.374756\n", + " 32.546586\n", " \n", " \n", " min\n", " -1.593587e+06\n", " -164048.233300\n", " -1390.940804\n", - " -1498.067524\n", - " -274.114707\n", + " -1382.551271\n", + " -243.874308\n", " \n", " \n", " 25%\n", " -1.585696e+06\n", " -160901.037700\n", " -1327.500988\n", - " -1417.397437\n", - " -119.243754\n", + " -1348.442196\n", + " -58.200732\n", " \n", " \n", " 50%\n", " -1.582073e+06\n", " -104396.422700\n", " -1250.925200\n", - " -1300.770379\n", - " -59.098591\n", + " -1273.693997\n", + " -30.277619\n", " \n", " \n", " 75%\n", " -1.579456e+06\n", " -101515.335350\n", " -1195.214216\n", - " -1219.812088\n", - " -18.455616\n", + " -1248.885375\n", + " -19.613619\n", " \n", " \n", " max\n", " -1.575591e+06\n", " -98049.505510\n", " -962.574500\n", - " -1154.733987\n", - " 95.064976\n", + " -1186.590148\n", + " 84.120595\n", " \n", " \n", "\n", @@ -811,13 +808,13 @@ "text/plain": [ " x y z z_interpolated error\n", "count 4.009500e+04 40095.000000 40095.000000 40095.000000 40095.000000\n", - "mean -1.582823e+06 -127943.948452 -1255.901352 -1321.530331 -65.628979\n", - "std 4.306205e+03 29434.912966 73.216368 106.018069 59.667879\n", - "min -1.593587e+06 -164048.233300 -1390.940804 -1498.067524 -274.114707\n", - "25% -1.585696e+06 -160901.037700 -1327.500988 -1417.397437 -119.243754\n", - "50% -1.582073e+06 -104396.422700 -1250.925200 -1300.770379 -59.098591\n", - "75% -1.579456e+06 -101515.335350 -1195.214216 -1219.812088 -18.455616\n", - "max -1.575591e+06 -98049.505510 -962.574500 -1154.733987 95.064976" + "mean -1.582823e+06 -127943.948452 -1255.901352 -1292.878085 -36.976733\n", + "std 4.306205e+03 29434.912966 73.216368 53.374756 32.546586\n", + "min -1.593587e+06 -164048.233300 -1390.940804 -1382.551271 -243.874308\n", + "25% -1.585696e+06 -160901.037700 -1327.500988 -1348.442196 -58.200732\n", + "50% -1.582073e+06 -104396.422700 -1250.925200 -1273.693997 -30.277619\n", + "75% -1.579456e+06 -101515.335350 -1195.214216 -1248.885375 -19.613619\n", + "max -1.575591e+06 -98049.505510 -962.574500 -1186.590148 84.120595" ] }, "execution_count": 20, @@ -866,67 +863,67 @@ " \n", " \n", " count\n", - " 4.137500e+04\n", - " 41375.000000\n", - " 41375.000000\n", - " 41375.000000\n", - " 41375.000000\n", + " 4.003100e+04\n", + " 40031.000000\n", + " 40031.000000\n", + " 40031.000000\n", + " 40031.000000\n", " \n", " \n", " mean\n", - " -1.582726e+06\n", - " -128055.788870\n", - " -1255.868749\n", - " -1300.852005\n", - " -44.983256\n", + " -1.582937e+06\n", + " -126647.148224\n", + " -1254.989995\n", + " -1299.432762\n", + " -44.442768\n", " \n", " \n", " std\n", - " 4.312147e+03\n", - " 29552.849624\n", - " 72.894995\n", - " 55.669080\n", - " 43.473380\n", + " 4.565391e+03\n", + " 29170.154478\n", + " 75.775107\n", + " 59.021202\n", + " 43.573560\n", " \n", " \n", " min\n", - " -1.593600e+06\n", - " -164173.784800\n", + " -1.593714e+06\n", + " -163928.941600\n", " -1390.940804\n", - " -1386.605258\n", - " -183.703256\n", + " -1388.976847\n", + " -179.727581\n", " \n", " \n", " 25%\n", - " -1.585415e+06\n", - " -160965.864200\n", - " -1327.230539\n", - " -1348.909886\n", - " -82.837216\n", + " -1.586045e+06\n", + " -160292.153750\n", + " -1327.932451\n", + " -1349.923849\n", + " -82.959801\n", " \n", " \n", " 50%\n", - " -1.581942e+06\n", - " -104397.196800\n", - " -1249.046190\n", - " -1291.897778\n", - " -47.909615\n", + " -1.582303e+06\n", + " -104305.511300\n", + " -1254.730000\n", + " -1291.975838\n", + " -47.598728\n", " \n", " \n", " 75%\n", - " -1.579410e+06\n", - " -101453.919900\n", - " -1195.530000\n", - " -1272.498885\n", - " -22.005799\n", + " -1.579429e+06\n", + " -101422.144400\n", + " -1194.651881\n", + " -1271.833734\n", + " -21.154374\n", " \n", " \n", " max\n", - " -1.575578e+06\n", - " -97923.920590\n", + " -1.575464e+06\n", + " -98167.517730\n", " -962.574500\n", - " -1137.576046\n", - " 77.049723\n", + " -1138.908560\n", + " 75.846243\n", " \n", " \n", "\n", @@ -934,14 +931,14 @@ ], "text/plain": [ " x y z z_interpolated error\n", - "count 4.137500e+04 41375.000000 41375.000000 41375.000000 41375.000000\n", - "mean -1.582726e+06 -128055.788870 -1255.868749 -1300.852005 -44.983256\n", - "std 4.312147e+03 29552.849624 72.894995 55.669080 43.473380\n", - "min -1.593600e+06 -164173.784800 -1390.940804 -1386.605258 -183.703256\n", - "25% -1.585415e+06 -160965.864200 -1327.230539 -1348.909886 -82.837216\n", - "50% -1.581942e+06 -104397.196800 -1249.046190 -1291.897778 -47.909615\n", - "75% -1.579410e+06 -101453.919900 -1195.530000 -1272.498885 -22.005799\n", - "max -1.575578e+06 -97923.920590 -962.574500 -1137.576046 77.049723" + "count 4.003100e+04 40031.000000 40031.000000 40031.000000 40031.000000\n", + "mean -1.582937e+06 -126647.148224 -1254.989995 -1299.432762 -44.442768\n", + "std 4.565391e+03 29170.154478 75.775107 59.021202 43.573560\n", + "min -1.593714e+06 -163928.941600 -1390.940804 -1388.976847 -179.727581\n", + "25% -1.586045e+06 -160292.153750 -1327.932451 -1349.923849 -82.959801\n", + "50% -1.582303e+06 -104305.511300 -1254.730000 -1291.975838 -47.598728\n", + "75% -1.579429e+06 -101422.144400 -1194.651881 -1271.833734 -21.154374\n", + "max -1.575464e+06 -98167.517730 -962.574500 -1138.908560 75.846243" ] }, "execution_count": 21, @@ -963,12 +960,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Difference : 26.14\n" + "Difference : -12.98\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1028,9 +1025,9 @@ "output_type": "stream", "text": [ "Groundtruth RMSE: 7.318264583382579\n", - "DeepBedMap3 RMSE: 88.69796997562734\n", - "CubicBedMap RMSE: 62.557033278894885\n", - "Difference : 26.14093669673246\n" + "DeepBedMap3 RMSE: 49.25984777384268\n", + "CubicBedMap RMSE: 62.23959615794133\n", + "Difference : -12.979748384098649\n" ] } ], diff --git a/deepbedmap.py b/deepbedmap.py index e0ff149..e61eeac 100644 --- a/deepbedmap.py +++ b/deepbedmap.py @@ -185,7 +185,8 @@ def plot_3d_view( # %% def load_trained_model( - filepath: str = "model/weights/srgan_generator_model_weights.npz" + model=None, + model_weights_path: str = "model/weights/srgan_generator_model_weights.npz", ): """ Builds the Generator component of the DeepBedMap neural network. @@ -193,10 +194,11 @@ def load_trained_model( """ srgan_train = _load_ipynb_modules("srgan_train.ipynb") - model = srgan_train.GeneratorModel() + if model is None: + model = srgan_train.GeneratorModel() # Load trained neural network weights into model - chainer.serializers.load_npz(file=filepath, obj=model) + chainer.serializers.load_npz(file=model_weights_path, obj=model) return model diff --git a/features/environment.py b/features/environment.py index acf7b58..2aa2fc2 100644 --- a/features/environment.py +++ b/features/environment.py @@ -10,7 +10,6 @@ import nbformat import pandas as pd import quilt -import requests def _load_ipynb_modules(ipynb_path: str): @@ -87,34 +86,32 @@ def _quick_download_lowres_misc_datasets(): def _download_deepbedmap_model_weights_from_comet(): """ Download latest neural network model weights from Comet.ML - Uses their REST API endpoint https://www.comet.ml/docs/rest-api/endpoints/ + Uses their Python REST API class at https://www.comet.ml/docs/python-sdk/API/ Requires the COMET_REST_API_KEY environment variable to be set in the .env file """ - authHeader = {"Authorization": base64.b64decode(s=os.environ["COMET_REST_API_KEY"])} - - # Get list of DeepBedMap experiments (projectId a7e4f47215b94cd98d6db8a092d78232) - r = requests.get( - url="https://www.comet.ml/api/rest/v1/experiments", - params={"projectId": "a7e4f47215b94cd98d6db8a092d78232"}, - headers=authHeader, + comet_api = comet_ml.API( + rest_api_key=base64.b64decode(s=os.environ["COMET_REST_API_KEY"]) ) - df = pd.io.json.json_normalize(r.json()["experiments"]) + + # Get list of DeepBedMap experiments + project = comet_api.get(workspace="weiji14", project="deepbedmap") + df = pd.io.json.json_normalize(data=project.data["experiments"].values()) # Get the key to the latest DeepBedMap experiment on Comet ML experiment_key = df.loc[df["start_server_timestamp"].idxmax()].experiment_key - - # Use key to access url to the experiment's asset which is the hdf5 weight file - r = requests.get( - url="https://www.comet.ml/api/rest/v1/asset/get-asset-list", - params={"experimentKey": experiment_key}, - headers=authHeader, + experiment = comet_api.get( + workspace="weiji14", project="deepbedmap", experiment=experiment_key ) - asset_url = r.json()[0]["link"] - # Download the neural network weight file (hdf5 format) to the right place! - r = requests.get(url=asset_url, headers=authHeader) + # Use key to access url to the experiment's asset which is the npz weight file + assets = experiment.asset_list + for asset in experiment.asset_list: + if asset["fileName"].endswith(".npz"): # make sure we pick the .npz file + asset_id = asset["assetId"] + break + # Download the neural network weight file (npz format) to the right place! open(file="model/weights/srgan_generator_model_weights.npz", mode="wb").write( - r.content + experiment.get_asset(asset_id=asset_id) ) @@ -125,6 +122,13 @@ def fixture_data_prep(context): return context.data_prep +@fixture +def fixture_srgan_train(context): + # set context.srgan_train to have all the module functions + context.srgan_train = _load_ipynb_modules(ipynb_path="srgan_train.ipynb") + return context.srgan_train + + @fixture def fixture_deepbedmap(context): # Quickly download all the neural network input datasets @@ -139,5 +143,7 @@ def fixture_deepbedmap(context): def before_tag(context, tag): if tag == "fixture.data_prep": use_fixture(fixture_func=fixture_data_prep, context=context) + elif tag == "fixture.srgan_train": + use_fixture(fixture_func=fixture_srgan_train, context=context) elif tag == "fixture.deepbedmap": use_fixture(fixture_func=fixture_deepbedmap, context=context) diff --git a/features/srgan_train.feature b/features/srgan_train.feature index 169a70a..b2fc543 100644 --- a/features/srgan_train.feature +++ b/features/srgan_train.feature @@ -1,2 +1,19 @@ # language: en -Feature: Super Resolution Model \ No newline at end of file +@fixture.srgan_train +Feature: Train Super Resolution Model + In order to have a well performing super resolution model + As a machine learning engineer, + We want to craft and teach the model to do well on a test area + + Background: Load the prepared data + Given a prepared collection of tiled raster data + + Scenario Outline: Train Super Resolution Model with fixed hyperparameters + Given some hyperparameter settings + And a compiled neural network model + When the model is trained for a while + Then we know how well the model performs on our test area + + Examples: Fixed hyperparameters + | num_residual_blocks | residual_scaling | learning_rate | + | 1 | 0.3 | 5e-4 | diff --git a/features/steps/test_srgan_train.py b/features/steps/test_srgan_train.py new file mode 100644 index 0000000..e2ff618 --- /dev/null +++ b/features/steps/test_srgan_train.py @@ -0,0 +1,67 @@ +from behave import given, when, then +import numpy as np +import optuna + + +@given("a prepared collection of tiled raster data") +def load_train_dev_datasets(context): + dataset, _ = context.srgan_train.load_data_into_memory() + _, _, context.test_iter, _ = context.srgan_train.get_train_dev_iterators( + dataset=dataset, first_size=len(dataset) - 1, batch_size=1, seed=42 + ) + + +@given( + "some hyperparameter settings {num_residual_blocks} {residual_scaling} {learning_rate}" +) +def get_neural_network_hyperparameters( + context, num_residual_blocks, residual_scaling, learning_rate +): + context.num_residual_blocks = int(num_residual_blocks) + context.residual_scaling = float(residual_scaling) + context.learning_rate = float(learning_rate) + + +@given("a compiled neural network model") +def compile_neural_network_model_with_hyperparameter_settings(context): + model = context.srgan_train.compile_srgan_model( + num_residual_blocks=context.num_residual_blocks, + residual_scaling=context.residual_scaling, + learning_rate=context.learning_rate, + ) + context.g_model, context.g_optimizer, context.d_model, context.d_optimizer = model + + +@when("the model is trained for a while") +def run_neural_network_model_training(context): + metric_names = [ + "discriminator_loss", + "discriminator_accu", + "generator_loss", + "generator_psnr", + ] + columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names] + + metrics_dict = context.srgan_train.trainer( + i=0, + columns=columns, + train_iter=context.test_iter, + dev_iter=context.test_iter, + g_model=context.g_model, + g_optimizer=context.g_optimizer, + d_model=context.d_model, + d_optimizer=context.d_optimizer, + ) + context.epoch_metrics = { + metric: np.mean(metrics_dict[metric]) for metric in columns + } + + +@then("we know how well the model performs on our test area") +def check_epoch_metrics_not_nan(context): + for metric in context.epoch_metrics.keys(): + try: + metric_val = context.epoch_metrics[metric] + assert not np.isnan(metric_val) + except AssertionError: + print(f"{metric} has value: {metric_val}") diff --git a/model/weights/srgan_generator_model_architecture.onnx.txt b/model/weights/srgan_generator_model_architecture.onnx.txt index 82b3885..88388af 100644 --- a/model/weights/srgan_generator_model_architecture.onnx.txt +++ b/model/weights/srgan_generator_model_architecture.onnx.txt @@ -1,4 +1,4 @@ -ir_version: 3 +ir_version: 4 producer_name: "Chainer" producer_version: "6.0.0b1" graph { @@ -3767,13 +3767,28 @@ graph { } } node { - input: "LeakyRelu_0" input: "Conv_64" - output: "Add_16" - op_type: "Add" + output: "LeakyRelu_49" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } } node { - input: "Add_16" + input: "Add_15" + input: "LeakyRelu_49" + output: "Concat_49" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_49" input: "Input_149" input: "Input_150" output: "Conv_65" @@ -3812,17 +3827,7 @@ graph { } node { input: "Conv_65" - output: "DepthToSpace_0" - op_type: "DepthToSpace" - attribute { - name: "blocksize" - i: 2 - type: INT - } - } - node { - input: "DepthToSpace_0" - output: "LeakyRelu_49" + output: "LeakyRelu_50" op_type: "LeakyRelu" attribute { name: "alpha" @@ -3831,7 +3836,19 @@ graph { } } node { + input: "Add_15" input: "LeakyRelu_49" + input: "LeakyRelu_50" + output: "Concat_50" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_50" input: "Input_151" input: "Input_152" output: "Conv_66" @@ -3870,17 +3887,7 @@ graph { } node { input: "Conv_66" - output: "DepthToSpace_1" - op_type: "DepthToSpace" - attribute { - name: "blocksize" - i: 2 - type: INT - } - } - node { - input: "DepthToSpace_1" - output: "LeakyRelu_50" + output: "LeakyRelu_51" op_type: "LeakyRelu" attribute { name: "alpha" @@ -3889,7 +3896,20 @@ graph { } } node { + input: "Add_15" + input: "LeakyRelu_49" input: "LeakyRelu_50" + input: "LeakyRelu_51" + output: "Concat_51" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_51" input: "Input_153" input: "Input_154" output: "Conv_67" @@ -3928,7 +3948,7 @@ graph { } node { input: "Conv_67" - output: "LeakyRelu_51" + output: "LeakyRelu_52" op_type: "LeakyRelu" attribute { name: "alpha" @@ -3937,7 +3957,21 @@ graph { } } node { + input: "Add_15" + input: "LeakyRelu_49" + input: "LeakyRelu_50" input: "LeakyRelu_51" + input: "LeakyRelu_52" + output: "Concat_52" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_52" input: "Input_155" input: "Input_156" output: "Conv_68" @@ -3974,158 +4008,8952 @@ graph { type: INTS } } - name: "Graph" - input { - name: "Input_153" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 64 - } - dim { - dim_value: 64 - } - dim { - dim_value: 3 - } - dim { - dim_value: 3 - } - } - } - } + node { + input: "Conv_68" + input: "Input_157" + output: "Mul_16" + op_type: "Mul" } - input { - name: "Input_154" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 64 - } - } - } - } + node { + input: "Mul_16" + input: "Add_15" + output: "Add_16" + op_type: "Add" } - input { - name: "Input_155" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 1 - } - dim { - dim_value: 64 - } - dim { - dim_value: 3 - } - dim { - dim_value: 3 - } - } - } + node { + input: "Add_16" + input: "Input_158" + input: "Input_159" + output: "Conv_69" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS } - } - input { - name: "Input_156" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 1 - } - } - } + attribute { + name: "group" + i: 1 + type: INT } - } - input { - name: "Input_4" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 32 - } - dim { - dim_value: 1 - } - dim { - dim_value: 30 - } - dim { - dim_value: 30 - } - } - } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS } - } - input { - name: "Input_5" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 32 - } - } - } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS } } - input { - name: "Input_1" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 32 - } - dim { - dim_value: 1 - } - dim { - dim_value: 6 - } - dim { - dim_value: 6 - } - } - } + node { + input: "Conv_69" + output: "LeakyRelu_53" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT } } - input { - name: "Input_2" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 32 - } - } - } + node { + input: "Add_16" + input: "LeakyRelu_53" + output: "Concat_53" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT } } - input { - name: "Input_7" - type { - tensor_type { - elem_type: FLOAT - shape { - dim { - dim_value: 32 + node { + input: "Concat_53" + input: "Input_160" + input: "Input_161" + output: "Conv_70" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_70" + output: "LeakyRelu_54" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_16" + input: "LeakyRelu_53" + input: "LeakyRelu_54" + output: "Concat_54" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_54" + input: "Input_162" + input: "Input_163" + output: "Conv_71" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_71" + output: "LeakyRelu_55" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_16" + input: "LeakyRelu_53" + input: "LeakyRelu_54" + input: "LeakyRelu_55" + output: "Concat_55" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_55" + input: "Input_164" + input: "Input_165" + output: "Conv_72" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_72" + output: "LeakyRelu_56" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_16" + input: "LeakyRelu_53" + input: "LeakyRelu_54" + input: "LeakyRelu_55" + input: "LeakyRelu_56" + output: "Concat_56" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_56" + input: "Input_166" + input: "Input_167" + output: "Conv_73" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_73" + input: "Input_168" + output: "Mul_17" + op_type: "Mul" + } + node { + input: "Mul_17" + input: "Add_16" + output: "Add_17" + op_type: "Add" + } + node { + input: "Add_17" + input: "Input_169" + input: "Input_170" + output: "Conv_74" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_74" + output: "LeakyRelu_57" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_17" + input: "LeakyRelu_57" + output: "Concat_57" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_57" + input: "Input_171" + input: "Input_172" + output: "Conv_75" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_75" + output: "LeakyRelu_58" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_17" + input: "LeakyRelu_57" + input: "LeakyRelu_58" + output: "Concat_58" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_58" + input: "Input_173" + input: "Input_174" + output: "Conv_76" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_76" + output: "LeakyRelu_59" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_17" + input: "LeakyRelu_57" + input: "LeakyRelu_58" + input: "LeakyRelu_59" + output: "Concat_59" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_59" + input: "Input_175" + input: "Input_176" + output: "Conv_77" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_77" + output: "LeakyRelu_60" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_17" + input: "LeakyRelu_57" + input: "LeakyRelu_58" + input: "LeakyRelu_59" + input: "LeakyRelu_60" + output: "Concat_60" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_60" + input: "Input_177" + input: "Input_178" + output: "Conv_78" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_78" + input: "Input_179" + output: "Mul_18" + op_type: "Mul" + } + node { + input: "Mul_18" + input: "Add_17" + output: "Add_18" + op_type: "Add" + } + node { + input: "Add_18" + input: "Input_180" + output: "Mul_19" + op_type: "Mul" + } + node { + input: "Mul_19" + input: "Add_15" + output: "Add_19" + op_type: "Add" + } + node { + input: "Add_19" + input: "Input_181" + input: "Input_182" + output: "Conv_79" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_79" + output: "LeakyRelu_61" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_19" + input: "LeakyRelu_61" + output: "Concat_61" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_61" + input: "Input_183" + input: "Input_184" + output: "Conv_80" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_80" + output: "LeakyRelu_62" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_19" + input: "LeakyRelu_61" + input: "LeakyRelu_62" + output: "Concat_62" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_62" + input: "Input_185" + input: "Input_186" + output: "Conv_81" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_81" + output: "LeakyRelu_63" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_19" + input: "LeakyRelu_61" + input: "LeakyRelu_62" + input: "LeakyRelu_63" + output: "Concat_63" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_63" + input: "Input_187" + input: "Input_188" + output: "Conv_82" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_82" + output: "LeakyRelu_64" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_19" + input: "LeakyRelu_61" + input: "LeakyRelu_62" + input: "LeakyRelu_63" + input: "LeakyRelu_64" + output: "Concat_64" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_64" + input: "Input_189" + input: "Input_190" + output: "Conv_83" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_83" + input: "Input_191" + output: "Mul_20" + op_type: "Mul" + } + node { + input: "Mul_20" + input: "Add_19" + output: "Add_20" + op_type: "Add" + } + node { + input: "Add_20" + input: "Input_192" + input: "Input_193" + output: "Conv_84" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_84" + output: "LeakyRelu_65" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_20" + input: "LeakyRelu_65" + output: "Concat_65" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_65" + input: "Input_194" + input: "Input_195" + output: "Conv_85" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_85" + output: "LeakyRelu_66" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_20" + input: "LeakyRelu_65" + input: "LeakyRelu_66" + output: "Concat_66" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_66" + input: "Input_196" + input: "Input_197" + output: "Conv_86" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_86" + output: "LeakyRelu_67" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_20" + input: "LeakyRelu_65" + input: "LeakyRelu_66" + input: "LeakyRelu_67" + output: "Concat_67" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_67" + input: "Input_198" + input: "Input_199" + output: "Conv_87" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_87" + output: "LeakyRelu_68" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_20" + input: "LeakyRelu_65" + input: "LeakyRelu_66" + input: "LeakyRelu_67" + input: "LeakyRelu_68" + output: "Concat_68" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_68" + input: "Input_200" + input: "Input_201" + output: "Conv_88" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_88" + input: "Input_202" + output: "Mul_21" + op_type: "Mul" + } + node { + input: "Mul_21" + input: "Add_20" + output: "Add_21" + op_type: "Add" + } + node { + input: "Add_21" + input: "Input_203" + input: "Input_204" + output: "Conv_89" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_89" + output: "LeakyRelu_69" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_21" + input: "LeakyRelu_69" + output: "Concat_69" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_69" + input: "Input_205" + input: "Input_206" + output: "Conv_90" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_90" + output: "LeakyRelu_70" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_21" + input: "LeakyRelu_69" + input: "LeakyRelu_70" + output: "Concat_70" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_70" + input: "Input_207" + input: "Input_208" + output: "Conv_91" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_91" + output: "LeakyRelu_71" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_21" + input: "LeakyRelu_69" + input: "LeakyRelu_70" + input: "LeakyRelu_71" + output: "Concat_71" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_71" + input: "Input_209" + input: "Input_210" + output: "Conv_92" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_92" + output: "LeakyRelu_72" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_21" + input: "LeakyRelu_69" + input: "LeakyRelu_70" + input: "LeakyRelu_71" + input: "LeakyRelu_72" + output: "Concat_72" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_72" + input: "Input_211" + input: "Input_212" + output: "Conv_93" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_93" + input: "Input_213" + output: "Mul_22" + op_type: "Mul" + } + node { + input: "Mul_22" + input: "Add_21" + output: "Add_22" + op_type: "Add" + } + node { + input: "Add_22" + input: "Input_214" + output: "Mul_23" + op_type: "Mul" + } + node { + input: "Mul_23" + input: "Add_19" + output: "Add_23" + op_type: "Add" + } + node { + input: "Add_23" + input: "Input_215" + input: "Input_216" + output: "Conv_94" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_94" + output: "LeakyRelu_73" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_23" + input: "LeakyRelu_73" + output: "Concat_73" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_73" + input: "Input_217" + input: "Input_218" + output: "Conv_95" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_95" + output: "LeakyRelu_74" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_23" + input: "LeakyRelu_73" + input: "LeakyRelu_74" + output: "Concat_74" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_74" + input: "Input_219" + input: "Input_220" + output: "Conv_96" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_96" + output: "LeakyRelu_75" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_23" + input: "LeakyRelu_73" + input: "LeakyRelu_74" + input: "LeakyRelu_75" + output: "Concat_75" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_75" + input: "Input_221" + input: "Input_222" + output: "Conv_97" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_97" + output: "LeakyRelu_76" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_23" + input: "LeakyRelu_73" + input: "LeakyRelu_74" + input: "LeakyRelu_75" + input: "LeakyRelu_76" + output: "Concat_76" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_76" + input: "Input_223" + input: "Input_224" 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1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_99" + output: "LeakyRelu_77" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_24" + input: "LeakyRelu_77" + output: "Concat_77" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_77" + input: "Input_228" + input: "Input_229" + output: "Conv_100" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_100" + output: "LeakyRelu_78" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_24" + input: "LeakyRelu_77" + input: "LeakyRelu_78" + output: "Concat_78" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_78" + input: "Input_230" + input: "Input_231" + output: "Conv_101" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_101" + output: "LeakyRelu_79" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_24" + input: "LeakyRelu_77" + input: "LeakyRelu_78" + input: "LeakyRelu_79" + output: "Concat_79" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_79" + input: "Input_232" + input: "Input_233" + output: "Conv_102" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_102" + output: "LeakyRelu_80" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_24" + input: "LeakyRelu_77" + input: "LeakyRelu_78" + input: "LeakyRelu_79" + input: "LeakyRelu_80" + output: "Concat_80" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_80" + input: "Input_234" + input: "Input_235" + output: "Conv_103" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { 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+ } + } + node { + input: "Concat_82" + input: "Input_241" + input: "Input_242" + output: "Conv_106" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_106" + output: "LeakyRelu_83" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_25" + input: "LeakyRelu_81" + input: "LeakyRelu_82" + input: "LeakyRelu_83" + output: "Concat_83" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_83" + input: "Input_243" + input: "Input_244" + output: "Conv_107" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_107" + output: "LeakyRelu_84" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_25" + input: "LeakyRelu_81" + input: "LeakyRelu_82" + input: "LeakyRelu_83" + input: "LeakyRelu_84" + output: "Concat_84" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_84" + input: "Input_245" + input: "Input_246" + output: "Conv_108" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_108" + input: "Input_247" + output: "Mul_26" + op_type: "Mul" + } + node { + input: "Mul_26" + input: "Add_25" + output: "Add_26" + op_type: "Add" + } + node { + input: "Add_26" + input: "Input_248" + output: "Mul_27" + op_type: "Mul" + } + node { + input: "Mul_27" + input: "Add_23" + output: "Add_27" + op_type: "Add" + } + node { + input: "Add_27" + input: "Input_249" + input: "Input_250" + output: "Conv_109" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_109" + output: "LeakyRelu_85" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_27" + input: "LeakyRelu_85" + output: "Concat_85" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_85" + input: "Input_251" + input: "Input_252" + output: "Conv_110" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_110" + output: "LeakyRelu_86" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_27" + input: "LeakyRelu_85" + input: "LeakyRelu_86" + output: "Concat_86" + op_type: 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attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_127" + output: "LeakyRelu_100" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_31" + input: "LeakyRelu_97" + input: "LeakyRelu_98" + input: "LeakyRelu_99" + input: "LeakyRelu_100" + output: "Concat_100" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_100" + input: "Input_291" + input: "Input_292" + output: "Conv_128" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } 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"Input_296" + input: "Input_297" + output: "Conv_130" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_130" + output: "LeakyRelu_102" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_32" + input: "LeakyRelu_101" + input: "LeakyRelu_102" + output: "Concat_102" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_102" + input: "Input_298" + input: "Input_299" + output: "Conv_131" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_131" + output: "LeakyRelu_103" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_32" + input: "LeakyRelu_101" + input: "LeakyRelu_102" + input: "LeakyRelu_103" + output: "Concat_103" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_103" + input: "Input_300" + input: "Input_301" + output: "Conv_132" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_132" + output: "LeakyRelu_104" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_32" + input: "LeakyRelu_101" + input: "LeakyRelu_102" + input: "LeakyRelu_103" + input: "LeakyRelu_104" + output: "Concat_104" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_104" + input: "Input_302" + input: "Input_303" + output: "Conv_133" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_133" + input: "Input_304" + output: "Mul_33" + op_type: "Mul" + } + node { + input: "Mul_33" + input: "Add_32" + output: "Add_33" + op_type: "Add" + } + node { + input: "Add_33" + input: "Input_305" + input: "Input_306" + output: "Conv_134" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_134" + output: "LeakyRelu_105" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_33" + input: "LeakyRelu_105" + output: "Concat_105" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_105" + input: "Input_307" + input: "Input_308" + output: "Conv_135" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_135" + output: "LeakyRelu_106" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_33" + input: "LeakyRelu_105" + input: "LeakyRelu_106" + output: "Concat_106" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_106" + input: "Input_309" + input: "Input_310" + output: "Conv_136" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_136" + output: "LeakyRelu_107" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_33" + input: "LeakyRelu_105" + input: "LeakyRelu_106" + input: "LeakyRelu_107" + output: "Concat_107" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_107" + input: "Input_311" + input: "Input_312" + output: "Conv_137" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_137" + output: "LeakyRelu_108" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_33" + input: "LeakyRelu_105" + input: "LeakyRelu_106" + input: "LeakyRelu_107" + input: "LeakyRelu_108" + output: "Concat_108" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_108" + input: "Input_313" + input: "Input_314" + output: "Conv_138" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_138" + input: "Input_315" + output: "Mul_34" + op_type: "Mul" + } + node { + input: "Mul_34" + input: "Add_33" + output: "Add_34" + op_type: "Add" + } + node { + input: "Add_34" + input: "Input_316" + output: "Mul_35" + op_type: "Mul" + } + node { + input: "Mul_35" + input: "Add_31" + output: "Add_35" + op_type: "Add" + } + node { + input: "Add_35" + input: "Input_317" + input: "Input_318" + output: "Conv_139" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_139" + output: "LeakyRelu_109" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_35" + input: "LeakyRelu_109" + output: "Concat_109" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_109" + input: "Input_319" + input: "Input_320" + output: "Conv_140" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_140" + output: "LeakyRelu_110" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_35" + input: "LeakyRelu_109" + input: "LeakyRelu_110" + output: "Concat_110" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_110" + input: "Input_321" + input: "Input_322" + output: "Conv_141" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: 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ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_142" + output: "LeakyRelu_112" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_35" + input: "LeakyRelu_109" + input: "LeakyRelu_110" + input: "LeakyRelu_111" + input: "LeakyRelu_112" + output: "Concat_112" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_112" + input: "Input_325" + input: "Input_326" + output: "Conv_143" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_143" + input: "Input_327" + output: "Mul_36" + op_type: "Mul" + } + node { + input: "Mul_36" + input: "Add_35" + output: "Add_36" + op_type: "Add" + } + node { + input: "Add_36" + input: "Input_328" + input: "Input_329" + output: "Conv_144" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_144" + output: "LeakyRelu_113" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_36" + input: "LeakyRelu_113" + output: "Concat_113" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_113" + input: "Input_330" + input: "Input_331" + output: "Conv_145" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_145" + output: "LeakyRelu_114" + op_type: "LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_36" + input: "LeakyRelu_113" + input: "LeakyRelu_114" + output: "Concat_114" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_114" + input: "Input_332" + input: "Input_333" + output: "Conv_146" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 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"LeakyRelu" + attribute { + name: "alpha" + f: 0.20000000298023224 + type: FLOAT + } + } + node { + input: "Add_36" + input: "LeakyRelu_113" + input: "LeakyRelu_114" + input: "LeakyRelu_115" + input: "LeakyRelu_116" + output: "Concat_116" + op_type: "Concat" + attribute { + name: "axis" + i: 1 + type: INT + } + } + node { + input: "Concat_116" + input: "Input_336" + input: "Input_337" + output: "Conv_148" + op_type: "Conv" + attribute { + name: "dilations" + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "group" + i: 1 + type: INT + } + attribute { + name: "kernel_shape" + ints: 3 + ints: 3 + type: INTS + } + attribute { + name: "pads" + ints: 1 + ints: 1 + ints: 1 + ints: 1 + type: INTS + } + attribute { + name: "strides" + ints: 1 + ints: 1 + type: INTS + } + } + node { + input: "Conv_148" + input: "Input_338" + output: "Mul_37" + op_type: "Mul" + } + node { + input: "Mul_37" + input: "Add_36" + output: "Add_37" + op_type: "Add" + } + node { + input: "Add_37" + input: 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name: "Input_212" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 64 + } + } + } + } + } + input { + name: "Input_147" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 64 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_148" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + } + } + } + } + input { + name: "Input_149" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 96 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_150" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + } + } + } + } + input { + name: "Input_151" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 128 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_152" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + } + } + } + } + input { + name: "Input_153" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 160 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_154" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + } + } + } + } + input { + name: "Input_155" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 64 + } + dim { + dim_value: 192 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_156" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 64 + } + } + } + } + } + input { + name: "Input_158" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 64 } dim { dim_value: 3 @@ -4138,10 +12966,10 @@ graph { } } input { - name: "Input_8" + name: "Input_159" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4151,16 +12979,16 @@ graph { } } input { - name: "Input_147" + name: "Input_160" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } dim { - dim_value: 64 + dim_value: 96 } dim { dim_value: 3 @@ -4173,29 +13001,29 @@ graph { } } input { - name: "Input_148" + name: "Input_161" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } } } } } input { - name: "Input_9" + name: "Input_162" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } dim { - dim_value: 96 + dim_value: 128 } dim { dim_value: 3 @@ -4208,29 +13036,29 @@ graph { } } input { - name: "Input_10" + name: "Input_163" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } } } } } input { - name: "Input_149" + name: "Input_164" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 256 + dim_value: 32 } dim { - dim_value: 64 + dim_value: 160 } dim { dim_value: 3 @@ -4243,29 +13071,29 @@ graph { } } input { - name: "Input_150" + name: "Input_165" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 256 + dim_value: 32 } } } } } input { - name: "Input_151" + name: "Input_166" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 256 + dim_value: 64 } dim { - dim_value: 64 + dim_value: 192 } dim { dim_value: 3 @@ -4278,23 +13106,23 @@ graph { } } input { - name: "Input_152" + name: "Input_167" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 256 + dim_value: 64 } } } } } input { - name: "Input_11" + name: "Input_169" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4313,10 +13141,10 @@ graph { } } input { - name: "Input_12" + name: "Input_170" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4326,10 +13154,10 @@ graph { } } input { - name: "Input_13" + name: "Input_171" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4348,10 +13176,10 @@ graph { } } input { - name: "Input_14" + name: "Input_172" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4361,10 +13189,10 @@ graph { } } input { - name: "Input_15" + name: "Input_173" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4383,10 +13211,10 @@ graph { } } input { - name: "Input_16" + name: "Input_174" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4396,10 +13224,10 @@ graph { } } input { - name: "Input_17" + name: "Input_175" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4418,10 +13246,10 @@ graph { } } input { - name: "Input_18" + name: "Input_176" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4431,10 +13259,10 @@ graph { } } input { - name: "Input_19" + name: "Input_177" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4453,10 +13281,10 @@ graph { } } input { - name: "Input_20" + name: "Input_178" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4466,10 +13294,10 @@ graph { } } input { - name: "Input_22" + name: "Input_113" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4488,10 +13316,10 @@ graph { } } input { - name: "Input_23" + name: "Input_114" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4501,10 +13329,10 @@ graph { } } input { - name: "Input_24" + name: "Input_115" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4523,10 +13351,10 @@ graph { } } input { - name: "Input_25" + name: "Input_116" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4536,10 +13364,10 @@ graph { } } input { - name: "Input_26" + name: "Input_117" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4558,10 +13386,10 @@ graph { } } input { - name: "Input_27" + name: "Input_118" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4571,10 +13399,10 @@ graph { } } input { - name: "Input_28" + name: "Input_119" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4593,10 +13421,10 @@ graph { } } input { - name: "Input_29" + name: "Input_120" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4606,10 +13434,10 @@ graph { } } input { - name: "Input_30" + name: "Input_121" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4628,10 +13456,10 @@ graph { } } input { - name: "Input_31" + name: "Input_122" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4641,10 +13469,10 @@ graph { } } input { - name: "Input_33" + name: "Input_124" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4663,10 +13491,10 @@ graph { } } input { - name: "Input_34" + name: "Input_125" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4676,10 +13504,10 @@ graph { } } input { - name: "Input_35" + name: "Input_126" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4698,10 +13526,10 @@ graph { } } input { - name: "Input_36" + name: "Input_127" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4711,10 +13539,10 @@ graph { } } input { - name: "Input_37" + name: "Input_128" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4733,10 +13561,10 @@ graph { } } input { - name: "Input_38" + name: "Input_129" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4746,10 +13574,10 @@ graph { } } input { - name: "Input_39" + name: "Input_130" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4768,10 +13596,10 @@ graph { } } input { - name: "Input_40" + name: "Input_131" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4781,10 +13609,10 @@ graph { } } input { - name: "Input_41" + name: "Input_132" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4803,10 +13631,10 @@ graph { } } input { - name: "Input_42" + name: "Input_133" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4816,10 +13644,10 @@ graph { } } input { - name: "Input_113" + name: "Input_135" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4838,10 +13666,10 @@ graph { } } input { - name: "Input_114" + name: "Input_136" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4851,10 +13679,10 @@ graph { } } input { - name: "Input_115" + name: "Input_137" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4873,10 +13701,10 @@ graph { } } input { - name: "Input_116" + name: "Input_138" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4886,10 +13714,10 @@ graph { } } input { - name: "Input_117" + name: "Input_139" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4908,10 +13736,10 @@ graph { } } input { - name: "Input_118" + name: "Input_140" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4921,10 +13749,10 @@ graph { } } input { - name: "Input_119" + name: "Input_141" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4943,10 +13771,10 @@ graph { } } input { - name: "Input_120" + name: "Input_142" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -4956,10 +13784,10 @@ graph { } } input { - name: "Input_121" + name: "Input_143" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4978,10 +13806,10 @@ graph { } } input { - name: "Input_122" + name: "Input_144" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -4991,10 +13819,10 @@ graph { } } input { - name: "Input_124" + name: "Input_79" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5013,10 +13841,10 @@ graph { } } input { - name: "Input_125" + name: "Input_80" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5026,10 +13854,10 @@ graph { } } input { - name: "Input_126" + name: "Input_81" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5048,10 +13876,10 @@ graph { } } input { - name: "Input_127" + name: "Input_82" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5061,10 +13889,10 @@ graph { } } input { - name: "Input_128" + name: "Input_83" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5083,10 +13911,10 @@ graph { } } input { - name: "Input_129" + name: "Input_84" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5096,10 +13924,10 @@ graph { } } input { - name: "Input_130" + name: "Input_85" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5118,10 +13946,10 @@ graph { } } input { - name: "Input_131" + name: "Input_86" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5131,10 +13959,10 @@ graph { } } input { - name: "Input_132" + name: "Input_87" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5153,10 +13981,10 @@ graph { } } input { - name: "Input_133" + name: "Input_88" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5166,10 +13994,10 @@ graph { } } input { - name: "Input_135" + name: "Input_90" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5188,10 +14016,10 @@ graph { } } input { - name: "Input_136" + name: "Input_91" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5201,10 +14029,10 @@ graph { } } input { - name: "Input_137" + name: "Input_92" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5223,10 +14051,10 @@ graph { } } input { - name: "Input_138" + name: "Input_93" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5236,10 +14064,10 @@ graph { } } input { - name: "Input_139" + name: "Input_94" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5258,10 +14086,10 @@ graph { } } input { - name: "Input_140" + name: "Input_95" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5271,10 +14099,10 @@ graph { } } input { - name: "Input_141" + name: "Input_96" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5293,10 +14121,10 @@ graph { } } input { - name: "Input_142" + name: "Input_97" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5306,10 +14134,10 @@ graph { } } input { - name: "Input_143" + name: "Input_98" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5328,10 +14156,10 @@ graph { } } input { - name: "Input_144" + name: "Input_99" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5341,10 +14169,10 @@ graph { } } input { - name: "Input_79" + name: "Input_101" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5363,10 +14191,10 @@ graph { } } input { - name: "Input_80" + name: "Input_102" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5376,10 +14204,10 @@ graph { } } input { - name: "Input_81" + name: "Input_103" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5398,10 +14226,10 @@ graph { } } input { - name: "Input_82" + name: "Input_104" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5411,10 +14239,10 @@ graph { } } input { - name: "Input_83" + name: "Input_105" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5433,10 +14261,10 @@ graph { } } input { - name: "Input_84" + name: "Input_106" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5446,10 +14274,10 @@ graph { } } input { - name: "Input_85" + name: "Input_107" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5468,10 +14296,10 @@ graph { } } input { - name: "Input_86" + name: "Input_108" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5481,10 +14309,10 @@ graph { } } input { - name: "Input_87" + name: "Input_109" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5503,10 +14331,10 @@ graph { } } input { - name: "Input_88" + name: "Input_110" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5516,10 +14344,10 @@ graph { } } input { - name: "Input_90" + name: "Input_45" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5538,10 +14366,10 @@ graph { } } input { - name: "Input_91" + name: "Input_46" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5551,10 +14379,10 @@ graph { } } input { - name: "Input_92" + name: "Input_47" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5573,10 +14401,10 @@ graph { } } input { - name: "Input_93" + name: "Input_48" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5586,10 +14414,10 @@ graph { } } input { - name: "Input_94" + name: "Input_49" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5608,10 +14436,10 @@ graph { } } input { - name: "Input_95" + name: "Input_50" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5621,10 +14449,10 @@ graph { } } input { - name: "Input_96" + name: "Input_51" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5643,10 +14471,10 @@ graph { } } input { - name: "Input_97" + name: "Input_52" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5656,10 +14484,10 @@ graph { } } input { - name: "Input_98" + name: "Input_53" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5678,10 +14506,10 @@ graph { } } input { - name: "Input_99" + name: "Input_54" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 64 @@ -5691,10 +14519,10 @@ graph { } } input { - name: "Input_101" + name: "Input_56" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5713,10 +14541,10 @@ graph { } } input { - name: "Input_102" + name: "Input_57" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5726,10 +14554,10 @@ graph { } } input { - name: "Input_103" + name: "Input_58" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5748,10 +14576,80 @@ graph { } } input { - name: "Input_104" + name: "Input_59" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + } + } + } + } + input { + name: "Input_60" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 128 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_61" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + } + } + } + } + input { + name: "Input_62" + type { + tensor_type { + elem_type: 1 + shape { + dim { + dim_value: 32 + } + dim { + dim_value: 160 + } + dim { + dim_value: 3 + } + dim { + dim_value: 3 + } + } + } + } + } + input { + name: "Input_63" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5761,16 +14659,16 @@ graph { } } input { - name: "Input_105" + name: "Input_64" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 32 + dim_value: 64 } dim { - dim_value: 128 + dim_value: 192 } dim { dim_value: 3 @@ -5783,29 +14681,29 @@ graph { } } input { - name: "Input_106" + name: "Input_65" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 32 + dim_value: 64 } } } } } input { - name: "Input_107" + name: "Input_67" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 160 + dim_value: 64 } dim { dim_value: 3 @@ -5818,10 +14716,10 @@ graph { } } input { - name: "Input_108" + name: "Input_68" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5831,16 +14729,16 @@ graph { } } input { - name: "Input_109" + name: "Input_69" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } dim { - dim_value: 192 + dim_value: 96 } dim { dim_value: 3 @@ -5853,29 +14751,29 @@ graph { } } input { - name: "Input_110" + name: "Input_70" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } } } } } input { - name: "Input_45" + name: "Input_71" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 64 + dim_value: 128 } dim { dim_value: 3 @@ -5888,10 +14786,10 @@ graph { } } input { - name: "Input_46" + name: "Input_72" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5901,16 +14799,16 @@ graph { } } input { - name: "Input_47" + name: "Input_73" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 96 + dim_value: 160 } dim { dim_value: 3 @@ -5923,10 +14821,10 @@ graph { } } input { - name: "Input_48" + name: "Input_74" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -5936,16 +14834,16 @@ graph { } } input { - name: "Input_49" + name: "Input_75" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 32 + dim_value: 64 } dim { - dim_value: 128 + dim_value: 192 } dim { dim_value: 3 @@ -5958,93 +14856,111 @@ graph { } } input { - name: "Input_50" + name: "Input_76" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 32 + dim_value: 64 } } } } } input { - name: "Input_51" + name: "Input_350" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 160 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_52" + name: "Input_349" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_53" + name: "Input_338" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } dim { - dim_value: 192 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_54" + name: "Input_327" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { + dim { + dim_value: 32 + } dim { dim_value: 64 } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_56" + name: "Input_316" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6053,173 +14969,218 @@ graph { dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_57" + name: "Input_315" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_58" + name: "Input_304" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 96 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_59" + name: "Input_293" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_60" + name: "Input_282" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 128 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_61" + name: "Input_281" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_62" + name: "Input_270" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 160 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_63" + name: "Input_259" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_64" + name: "Input_248" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } dim { - dim_value: 192 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_65" + name: "Input_247" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { + dim { + dim_value: 32 + } dim { dim_value: 64 } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_67" + name: "Input_236" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6228,164 +15189,209 @@ graph { dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_68" + name: "Input_225" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_69" + name: "Input_214" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 96 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_70" + name: "Input_213" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_71" + name: "Input_202" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 128 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_72" + name: "Input_191" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_73" + name: "Input_180" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } dim { - dim_value: 160 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_74" + name: "Input_179" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 } + dim { + dim_value: 64 + } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } } input { - name: "Input_75" + name: "Input_168" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { - dim_value: 64 + dim_value: 32 } dim { - dim_value: 192 + dim_value: 64 } dim { - dim_value: 3 + dim_value: 8 } dim { - dim_value: 3 + dim_value: 8 } } } } } input { - name: "Input_76" + name: "Input_157" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { + dim { + dim_value: 32 + } dim { dim_value: 64 } + dim { + dim_value: 8 + } + dim { + dim_value: 8 + } } } } @@ -6394,7 +15400,7 @@ graph { name: "Input_146" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6416,7 +15422,7 @@ graph { name: "Input_145" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6438,7 +15444,7 @@ graph { name: "Input_134" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6460,7 +15466,7 @@ graph { name: "Input_123" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6482,7 +15488,7 @@ graph { name: "Input_112" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6504,7 +15510,7 @@ graph { name: "Input_111" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6526,7 +15532,7 @@ graph { name: "Input_100" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6548,7 +15554,7 @@ graph { name: "Input_89" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6570,7 +15576,7 @@ graph { name: "Input_78" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6592,7 +15598,7 @@ graph { name: "Input_77" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6614,7 +15620,7 @@ graph { name: "Input_66" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6636,7 +15642,7 @@ graph { name: "Input_55" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6658,7 +15664,7 @@ graph { name: "Input_44" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6680,7 +15686,7 @@ graph { name: "Input_43" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6702,7 +15708,7 @@ graph { name: "Input_32" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6724,7 +15730,7 @@ graph { name: "Input_21" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6746,7 +15752,7 @@ graph { name: "Input_6" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6768,7 +15774,7 @@ graph { name: "Input_3" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6790,7 +15796,7 @@ graph { name: "Input_0" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6809,10 +15815,10 @@ graph { } } output { - name: "Conv_68" + name: "Conv_158" type { tensor_type { - elem_type: FLOAT + elem_type: 1 shape { dim { dim_value: 32 @@ -6833,6 +15839,6 @@ graph { } opset_import { domain: "" - version: 8 + version: 9 } diff --git a/srgan_train.ipynb b/srgan_train.ipynb index 81e4847..15ecd72 100644 --- a/srgan_train.ipynb +++ b/srgan_train.ipynb @@ -51,7 +51,10 @@ "import sys\n", "import typing\n", "\n", - "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", + "try: # check if CUDA_VISIBLE_DEVICES environment variable is set\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"]\n", + "except KeyError: # if not set, then set it to the first GPU\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", "\n", "import comet_ml\n", "import IPython.display\n", @@ -68,6 +71,7 @@ "import cupy\n", "import livelossplot\n", "import onnx_chainer\n", + "import optuna\n", "\n", "from features.environment import _load_ipynb_modules\n", "\n", @@ -78,192 +82,125 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "COMET INFO: old comet version (1.0.42) detected. current: 1.0.44 please update your comet lib with command: `pip install --no-cache-dir --upgrade comet_ml`\n", - "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/d64dd9dd8dc54b3397a36d26337080c3\n", - "\n" - ] - } - ], + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], "source": [ "# Set seed values\n", "seed = 42\n", "random.seed = seed\n", "np.random.seed(seed=seed)\n", - "# cupy.random.seed(seed=seed)\n", - "\n", - "# Start tracking experiment using Comet.ML\n", - "experiment = comet_ml.Experiment(\n", - " workspace=\"weiji14\", project_name=\"deepbedmap\", disabled=False\n", - ")" + "if cupy.is_available():\n", + " for c in range(cupy.cuda.runtime.getDeviceCount()):\n", + " with cupy.cuda.Device(c):\n", + " cupy.random.seed(seed=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# 1. Load data" + "# 1. Load data\n", + "- Download pre-packaged data from [Quilt](https://github.com/quiltdata/quilt)\n", + "- Convert arrays for Chainer, from Numpy (CPU) to CuPy (GPU) format (if available)" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading package metadata...\n", - "Fragments already downloaded\n" - ] - } - ], - "source": [ - "hash = \"1ccc9dc7f6344e1ec27b7aa972f2739d192d3e5adef8a64528b86bc799e2df60\"\n", - "quilt.install(package=\"weiji14/deepbedmap/model/train\", hash=hash, force=True)\n", - "pkg = quilt.load(pkginfo=\"weiji14/deepbedmap/model/train\", hash=hash)\n", - "experiment.log_parameter(name=\"dataset_hash\", value=hash)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2480, 100, 100, 1) (2480, 20, 20, 1) (2480, 10, 10, 1) (2480, 32, 32, 1)\n" - ] - } - ], - "source": [ - "W1_data = pkg.W1_data() # miscellaneous data REMA\n", - "W2_data = pkg.W2_data() # miscellaneous data MEASURES Ice Flow\n", - "X_data = pkg.X_data() # low resolution BEDMAP2\n", - "Y_data = pkg.Y_data() # high resolution groundtruth\n", - "# W1_data = np.load(file=\"model/train/W1_data.npy\")\n", - "# W2_data = np.load(file=\"model/train/W2_data.npy\")\n", - "# X_data = np.load(file=\"model/train/X_data.npy\")\n", - "# Y_data = np.load(file=\"model/train/Y_data.npy\")\n", - "print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.1 Convert arrays for Chainer\n", - "- From Numpy (CPU) to CuPy (GPU) format\n", - "- From NHWC format to NCHW format, where N=number of tiles, H=height, W=width, C=channels" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using GPU\n" - ] - } - ], - "source": [ - "# Detect if there is a CUDA GPU first\n", - "try:\n", - " cupy.cuda.get_device_id()\n", - " xp = cupy\n", - " print(\"Using GPU\")\n", - " experiment.log_parameter(name=\"use_gpu\", value=True)\n", - "\n", - " W1_data = chainer.backend.cuda.to_gpu(array=W1_data)\n", - " W2_data = chainer.backend.cuda.to_gpu(array=W2_data)\n", - " X_data = chainer.backend.cuda.to_gpu(array=X_data)\n", - " Y_data = chainer.backend.cuda.to_gpu(array=Y_data)\n", - "except: # CUDARuntimeError\n", - " xp = np\n", - " print(\"Using CPU only\")\n", - " experiment.log_parameter(name=\"use_gpu\", value=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2480, 1, 100, 100) (2480, 1, 20, 20) (2480, 1, 10, 10) (2480, 1, 32, 32)\n" - ] - } - ], + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], "source": [ - "W1_data = xp.rollaxis(a=W1_data, axis=3, start=1)\n", - "W2_data = xp.rollaxis(a=W2_data, axis=3, start=1)\n", - "X_data = xp.rollaxis(a=X_data, axis=3, start=1)\n", - "Y_data = xp.rollaxis(a=Y_data, axis=3, start=1)\n", - "print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)" + "def load_data_into_memory(\n", + " redownload: bool = True,\n", + " quilt_hash: str = \"07346a5773aad87a71a57f83624289f5af507ad12f7008aec29eee209f98c399\",\n", + ") -> (chainer.datasets.dict_dataset.DictDataset, str):\n", + " \"\"\"\n", + " Downloads the prepackaged tiled data from quilt based on a hash,\n", + " and loads it into CPU or GPU memory depending on what is available.\n", + " \"\"\"\n", + "\n", + " if redownload:\n", + " quilt.install(\n", + " package=\"weiji14/deepbedmap/model/train\", hash=quilt_hash, force=True\n", + " )\n", + " pkg = quilt.load(pkginfo=\"weiji14/deepbedmap/model/train\", hash=quilt_hash)\n", + "\n", + " W1_data = pkg.W1_data() # miscellaneous data REMA\n", + " W2_data = pkg.W2_data() # miscellaneous data MEASURES Ice Flow\n", + " X_data = pkg.X_data() # low resolution BEDMAP2\n", + " Y_data = pkg.Y_data() # high resolution groundtruth\n", + " # print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape)\n", + "\n", + " # Detect if there is a CUDA GPU first\n", + " if cupy.is_available():\n", + " print(\"Using GPU\")\n", + " W1_data = chainer.backend.cuda.to_gpu(array=W1_data, device=None)\n", + " W2_data = chainer.backend.cuda.to_gpu(array=W2_data, device=None)\n", + " X_data = chainer.backend.cuda.to_gpu(array=X_data, device=None)\n", + " Y_data = chainer.backend.cuda.to_gpu(array=Y_data, device=None)\n", + " else:\n", + " print(\"Using CPU only\")\n", + "\n", + " return (\n", + " chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data),\n", + " quilt_hash,\n", + " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1.2 Split dataset into training (train) and development (dev) sets" + "## 1.1 Split dataset into training (train) and development (dev) sets" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training dataset: 2356 tiles, Development dataset: 124 tiles\n" - ] - } - ], - "source": [ - "dataset = chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data)\n", - "train_set, dev_set = chainer.datasets.split_dataset_random(\n", - " dataset=dataset, first_size=int(len(X_data) * 0.95), seed=seed\n", - ")\n", - "experiment.log_parameters(\n", - " dic={\"train_set_samples\": len(train_set), \"dev_set_samples\": len(dev_set)}\n", - ")\n", - "print(\n", - " f\"Training dataset: {len(train_set)} tiles, Development dataset: {len(dev_set)} tiles\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 4, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ - "batch_size = 32\n", - "experiment.log_parameter(name=\"batch_size\", value=batch_size)\n", - "train_iter = chainer.iterators.SerialIterator(\n", - " dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True\n", - ")\n", - "dev_iter = chainer.iterators.SerialIterator(\n", - " dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False\n", - ")" + "def get_train_dev_iterators(\n", + " dataset: chainer.datasets.dict_dataset.DictDataset,\n", + " first_size: int, # size of training set\n", + " batch_size: int = 64,\n", + " seed: int = 42,\n", + ") -> (\n", + " chainer.iterators.serial_iterator.SerialIterator,\n", + " int,\n", + " chainer.iterators.serial_iterator.SerialIterator,\n", + " int,\n", + "):\n", + " \"\"\"\n", + " Create Chainer Dataset Iterators after splitting dataset into\n", + " training and development (validation) sets.\n", + " \"\"\"\n", + "\n", + " # Train/Dev split of the dataset\n", + " train_set, dev_set = chainer.datasets.split_dataset_random(\n", + " dataset=dataset, first_size=first_size, seed=seed\n", + " )\n", + "\n", + " # Create Chainer Dataset Iterators out of the split datasets\n", + " train_iter = chainer.iterators.SerialIterator(\n", + " dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True\n", + " )\n", + " dev_iter = chainer.iterators.SerialIterator(\n", + " dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False\n", + " )\n", + "\n", + " print(\n", + " f\"Training dataset: {len(train_set)} tiles,\",\n", + " f\"Development dataset: {len(dev_set)} tiles\",\n", + " )\n", + "\n", + " return train_iter, len(train_set), dev_iter, len(dev_set)" ] }, { @@ -311,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "lines_to_next_cell": 2 }, @@ -384,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": { "lines_to_next_cell": 2 }, @@ -396,8 +333,14 @@ " Final output has a residual scaling factor.\n", " \"\"\"\n", "\n", - " def __init__(self, in_out_channels: int = 64, inter_channels: int = 32):\n", + " def __init__(\n", + " self,\n", + " in_out_channels: int = 64,\n", + " inter_channels: int = 32,\n", + " residual_scaling: float = 0.3,\n", + " ):\n", " super().__init__()\n", + " self.residual_scaling = residual_scaling\n", " init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option=\"fan_in\")\n", "\n", " with self.init_scope():\n", @@ -442,7 +385,7 @@ " initialW=init_weights,\n", " )\n", "\n", - " def forward(self, x, residual_scaling: float = 0.2):\n", + " def forward(self, x):\n", " \"\"\"\n", " Forward computation, i.e. evaluate based on input x\n", " \"\"\"\n", @@ -468,14 +411,14 @@ " a5 = self.conv_layer5(a4_cat)\n", "\n", " # Final concatenation, with residual scaling of 0.2\n", - " a6 = F.add(a5 * residual_scaling, a0)\n", + " a6 = F.add(a5 * self.residual_scaling, a0)\n", "\n", " return a6" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": { "lines_to_next_cell": 2 }, @@ -493,15 +436,27 @@ "\n", " \"\"\"\n", "\n", - " def __init__(self, denseblock_class=ResidualDenseBlock, out_channels: int = 64):\n", + " def __init__(\n", + " self,\n", + " denseblock_class=ResidualDenseBlock,\n", + " out_channels: int = 64,\n", + " residual_scaling: float = 0.3,\n", + " ):\n", " super().__init__()\n", + " self.residual_scaling = residual_scaling\n", "\n", " with self.init_scope():\n", - " self.residual_dense_block1 = denseblock_class()\n", - " self.residual_dense_block2 = denseblock_class()\n", - " self.residual_dense_block3 = denseblock_class()\n", + " self.residual_dense_block1 = denseblock_class(\n", + " residual_scaling=residual_scaling\n", + " )\n", + " self.residual_dense_block2 = denseblock_class(\n", + " residual_scaling=residual_scaling\n", + " )\n", + " self.residual_dense_block3 = denseblock_class(\n", + " residual_scaling=residual_scaling\n", + " )\n", "\n", - " def forward(self, x, residual_scaling: float = 0.2):\n", + " def forward(self, x):\n", " \"\"\"\n", " Forward computation, i.e. evaluate based on input x\n", " \"\"\"\n", @@ -510,7 +465,7 @@ " a3 = self.residual_dense_block3(a2)\n", "\n", " # Final concatenation, with residual scaling of 0.2\n", - " a4 = F.add(a3 * residual_scaling, x)\n", + " a4 = F.add(a3 * self.residual_scaling, x)\n", "\n", " return a4" ] @@ -531,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": { "lines_to_next_cell": 2 }, @@ -565,18 +520,20 @@ " >>> y_pred.shape\n", " (1, 1, 32, 32)\n", " >>> generator_model.count_params()\n", - " 3333249\n", + " 7649793\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " inblock_class=DeepbedmapInputBlock,\n", " resblock_class=ResInResDenseBlock,\n", - " num_residual_blocks: int = 4,\n", + " num_residual_blocks: int = 10,\n", + " residual_scaling: float = 0.3,\n", " out_channels: int = 1,\n", " ):\n", " super().__init__()\n", " self.num_residual_blocks = num_residual_blocks\n", + " self.residual_scaling = residual_scaling\n", " init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option=\"fan_in\")\n", "\n", " with self.init_scope():\n", @@ -591,9 +548,9 @@ " pad=1, # 'same' padding\n", " initialW=init_weights,\n", " )\n", - " self.residual_network = resblock_class().repeat(\n", - " n_repeat=num_residual_blocks\n", - " )\n", + " self.residual_network = resblock_class(\n", + " residual_scaling=residual_scaling\n", + " ).repeat(n_repeat=num_residual_blocks)\n", " self.post_residual_conv_layer = L.Convolution2D(\n", " in_channels=None,\n", " out_channels=64,\n", @@ -701,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": { "lines_to_next_cell": 2 }, @@ -920,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": { "lines_to_next_cell": 2 }, @@ -972,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": { "lines_to_next_cell": 2 }, @@ -1005,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": { "lines_to_next_cell": 2 }, @@ -1065,50 +1022,55 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": 13, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "# Build the models\n", - "generator_model = GeneratorModel()\n", - "discriminator_model = DiscriminatorModel()\n", - "experiment.log_parameter(\n", - " name=\"num_residual_blocks\", value=generator_model.num_residual_blocks\n", - ")\n", - "\n", - "# Transfer models to GPU if available\n", - "if xp == cupy: # Check if CuPy was loaded, i.e. GPU is available\n", - " generator_model.to_gpu(device=0)\n", - " discriminator_model.to_gpu(device=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Setup optimizer, using Adam\n", - "generator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup(\n", - " link=generator_model\n", - ")\n", - "experiment.log_parameters(\n", - " dic={\n", - " \"generator_optimizer\": \"adam\",\n", - " \"generator_lr\": generator_optimizer.alpha, # learning rate\n", - " \"generator_epsilon\": generator_optimizer.eps,\n", - " }\n", - ")\n", - "discriminator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup(\n", - " link=discriminator_model\n", - ")\n", - "experiment.log_parameters(\n", - " dic={\n", - " \"discriminator_optimizer\": \"adam\",\n", - " \"discriminator_lr\": discriminator_optimizer.alpha, # learning rate\n", - " \"discriminator_adam_epsilon\": discriminator_optimizer.eps,\n", - " }\n", - ")" + "def compile_srgan_model(\n", + " num_residual_blocks: int = 10,\n", + " residual_scaling: float = 0.3,\n", + " learning_rate: float = 6.5e-4,\n", + "):\n", + " \"\"\"\n", + " Instantiate our Super Resolution Generative Adversarial Network (SRGAN) model here.\n", + " The Generator and Discriminator neural networks are created,\n", + " and an Adam loss optimization function is linked to the models.\n", + "\n", + " Returns:\n", + " 1) generator_model\n", + " 2) generator_optimizer\n", + " 3) discriminator_model\n", + " 4) discriminator_optimizer\n", + " \"\"\"\n", + "\n", + " # Instantiate our Generator and Discriminator Neural Network models\n", + " generator_model = GeneratorModel(\n", + " num_residual_blocks=num_residual_blocks, residual_scaling=residual_scaling\n", + " )\n", + " discriminator_model = DiscriminatorModel()\n", + "\n", + " # Transfer models to GPU if available\n", + " if cupy.is_available(): # Check if CuPy was loaded, i.e. GPU is available\n", + " generator_model.to_gpu(device=None)\n", + " discriminator_model.to_gpu(device=None)\n", + "\n", + " # Setup optimizer, using Adam\n", + " generator_optimizer = chainer.optimizers.Adam(alpha=learning_rate, eps=1e-8).setup(\n", + " link=generator_model\n", + " )\n", + " discriminator_optimizer = chainer.optimizers.Adam(\n", + " alpha=learning_rate, eps=1e-8\n", + " ).setup(link=discriminator_model)\n", + "\n", + " return (\n", + " generator_model,\n", + " generator_optimizer,\n", + " discriminator_model,\n", + " discriminator_optimizer,\n", + " )" ] }, { @@ -1142,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": { "lines_to_next_cell": 2 }, @@ -1232,7 +1194,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": { "lines_to_next_cell": 2 }, @@ -1326,54 +1288,28 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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1OUUM7dampYeilFI/aIfKXS09BKWUOi4FuD4/UUSGAQbYBdzic8hkINu5oerbzyvAC8aYlcDjdRyvlFLfa/uKy6mo8pCaHNdiYzCmcQ8EaQBeBfT1lv3c8MZKFt13El3bttw/bqVU8zPGXF7PfgPcfoyG0+wWbz/IVa8uY/YvJupNRqWUaoI9hWUtPQSllDouBbg+f7WO9l8D4/xsv9Hn+6tDMjillGoBt7y5ipyCMub/6kTio499SLuotIrfzFzXqGN1EVYVUEFpFQBFZVUtPBKllAqtXQdLANh/qLyFR6KUUj9snkZmASmllFJKKRWsjXuKWZNdyIHDFbyyMCMkfb69PIunPt9CQUllvW1XZeZz5nMLmbtxX71t/dEAvAqo0uWp8aqUUseLvUU28F5YqjcYlVKqKTT+rpRSSimlmts7K7KIighjUu92vLRgBwcOVzSpv6yDpfzxf+v5+5fbmfz4V/x93jZKKmqXVnR7DM9/tZ1LXlxKeJjw3s/HN+p8GoBXAVW5Pc6rfrJSSh1fcp0AvD7ho5RSTaMZ8EoppZRSKiSqymHFq/D+jVBZUr25rNLNB6t3c+agTvz5nIGUuzw8N29bk0715OdbCA8T3rxhDON6JvPU3K1MfvwrXluUQYXLDdia81e/uownPtvCmYNTmH3nRIY1ch05rQGvAtIMeKXU8Wpvsa1ZrAF4pZRqGgN4PIawMGnpoSillFJKKbCPKL57DRRkQMpQSBlmXzsOhKj4lh5dbZUlsOp1WPwcHN5rtw26CPqeAcDsdXs4VOHi8jGp9GjfisvHdGP6six+NqE73ds1/PdZv7uIj9bu4faTejKpd3sm9W7P6qwCnvh0Cw/O3sirizK4ZFQ33liyi9JKF49fOISLR3VFpPHXuxqAVwFVVmfAawBeKdVI5vs5f2gGvFJKhU6Fy0NsVHhLD0MppZRSSgHs/Ao2fQSdhsCWT+Db/9jtEgbt+kCXkXDawxCX1PRzlRyALx6A5J6QegJ0Hg4R0TWaGGPYV1zBzrzD7DhQws68w2Tnl/KzkclMyJ8JS/8JpQchfRKc8xy8cwVkL60OwL+9PIse7eMZ092O966T+zBz9W6e/GwLz185osFDfuyTzbSNi+SWKT2rt41Ibcv0m8ayePtBHv9sM898sZV+nRL4xxXj6NUhofHvj0MD8Cogb+Z7hWbAK6Ua69Delh5BLcaY6hrwRVoDXimlmqy8yq0BeKWUUkr9uOxeBR4PdBsdku4qXR48xhATGYJrqgVPQkJnuPELCI+C4j2Qu9Z+7VkNa96CrqNg1PVBd1lQUklURBjx0UeFkldPg2/fPPJzeDSm83AOJA3ni8M9WHAgnqKCg0S5DpFIKYlSQnJ4GSeHFTAkYyFQCr1Ohcn3Quo420fKUMhaBsCWvYdYnVXI78/qX52B3j4hmpsm9eBv87ZxY1YBw1PbBv17LNyWx6LtB/jD2QNIjImssU9EmNi7HRN6TWBNdiH9UxJD898DDcCrOngz4Cs1A14p1VgleXBwh70b/j1RXO6itNLWdNMMeKWUarpyp06mUkoppdSPxif3QcVhuH1pk7tav7uIn72+ghN6JPPc5cOb1tmuxZC5GM7465FM9NZd7Fe/M215mqf7Q8bCoAPw+4rLOf3ZBZRUuBidnsTkPu2Z0qc9/TolIBtmQpdRcPk75G9eSNaaeUTtWUGvrJe4XNxcDnYF0qiafXrCo/mscgh7h97Bzy46r+bObuNg5avgquTt5VlEhYdxwYiuNZrcNLkHby3L5NFPNvPfm8cFVR7G4zE89slmuraN5apxqQHbiUiDgvrB0AC8CqjKyXyv0gx4pVSjCXz2O7jinZYeSLV9xeXV32sAXimlmq68Sq8VlVJKKXWMGAMzb4IB50L/n7bcOPJ3QlkBVJZCVFyju/l6y35ue2s1pZVuvty8H5fbQ0R4WOPHteAJiG8PI67xv18Euk+GHV/a97KewLUxht/O/I6ySjdXjUtjyY6DPPbJZh77ZDOjWh3kPdd3LO19D3+bvoOlGdEYcyaj0q7koqHJnJWcS0LlAYhpAzGtIdZ5jU4kLDKGL95dy0ff7mbylMP0bN/qyElTx8HS56nIWc3M1UWcPqgTSfE1I/itoiO46+Te/OF/G/hy835O7t+x3rdm1ro9bNhTzLOXDiM64tg+vakBeBWQZsArpZosoRNs/QS2zYXep7b0aIAj9d/bJ0RTqAF4pZRqsvIqzYBXSiml1DGyfyN8NwP2rIG+Z0FYE4LVjVVebGuWA+zb0OgyNO+uyOb+D76jT8cELhnVlT/P2si63UWMaGz2dc5KW//91AcD3hRwewx724yiS8l/IW8zdOhfZ5fvr97NvM37+cPZA7hhYncA9haVs2BbHnFLnsJzULjru+7EJpfxy5P7cP7wLqQme8/dt86+fzO1H59v3MsDH21g2vVjjmSxO6Voti6fS3H5CC4f083v8ZeNSeW1xbv466ebObFvB8LDAt9MqHC5eeKzLQxISeScoZ3rHFedXJWNOkwD8CqgKl2EVSnVVPHtIbk9fPob6D4FIqICt3VVwJy77cIrQy8Lzfln3WUvQnwMLa3kk6hywiSR20vvDc15lFLqR0wD8EoppZQ6VjxbPiUM4OA2G2zudfKxH0RBxpHvc9c0OABvjOFv87bx7BfbmNS7Hf+8cgRVbsOfZ21kyY6DjQ/AL3gCYtv6LS3j9hhmr9vD37/cTnleFIuiweycj9QRgN9bVM6fZ21gdHpbfjY+vXp7p9YxXDKqGyxbikkdx/vnX0iXNrFBlYHx1T4hmrtP7cOfZ23k0/V7mTo4xe5o1QHadqdsxzekJ0/khB7Jfo+PDA/jV6f35ba3VvP+qhwuGe0/UA/w1tIscgrKmHb9YMLqCNTXy7fefQO0wG0i9UPhXXy1UkvQKKUaSwROfxQOboflLwZu56qEd6+1q7Nv+SR05//uPagsgbbp1V8HIjtzmBj6Vq6nQ3lGfT0opZSqh5agUUoppdSxUrR2Nps83TgU3haWv9Qyg8g/KgDfAFVuD795/zue/WIbF47oymvXjSYhJpKk+Cj6pyTyzY4DjRtT7lrY+imMuw2iE6o3u9weZq7O4dSn53PXO2sIF2Hk0KFke9qzdVngz97GGO6fuY4qt4cnLhpaO2i9fxPkbUIGXUjXtnENDr57XT0ujX6dEnho9kZKK13V24s7jKRH+XouH92tzr6nDurE0G5teHruVsoq/SeFFJdX8fcvtzGxVzsm92nfqHFW2/Z5ow7TALwKqMptAC1Bo5Rqoj6nQe/T4eu/wqF9tfe7XfD+DbZUTUQsVJWG7tyuchh4Plz2VvXXK10e4oXIawGI8JRr5qZSSjWRLsKqlFJKqToteBK2z2t6PyUHaX1wDZ97RvNZzBmw9bOawfBjxZsB33WMDXwHqaTCxU3TVvLfldnc+ZNePHnxECJ96r2P75nMyl0FjfuMuvApiE6EMTcDNtD/7spsTn56Pne/u5aoiDD+deUIPrlrEs9eOox97cbQMX8Fz3+51W93M1bl8NWWPO47ox/p7eJrN1g/EyTM1uJvgojwMB48dxB7isr551c7qrcvKO9JOynm4p51l40VEe6f2o+9xeVMevxLbp++mjeXZrJ9/yGMsXHNl+bvpKC0ivvO6NeksVJVBjvnN+pQDcCrgCqdD1OaAa+UarIzHrXB8HkP1tzuccMHN8Omj+CMx6DzcLuITSi4XeBxQWRsjc25ReXEt7IZAbFUUliqdeCVUqopKvRGplJKKaUCqTgMXz0Cy19ucld7V88iDA8LGcFrFSdBWDiseCUEg2yg/AyIawfpE2wmuKsiqMP++ulmFmzN49ELBnP3aX1rZXaP75lMhcvDt1mFDRvP/s2w8SMbfI9tQ4XLzUUvLOHX760jISaCl64eycd3TmLq4BTCwgQRYcTkc2gjJXw8dy6vL655EyO3qIyHZm1kTPckrj0hvfb5jIENMyF9oi0X00Rjuidx/vAuvLRgJxkHSiivcvPvLLuoatKB1fUeP65HMi9cNZJJvduzalcBf/hwPac8vYDRj8zj9umreXVRBucM7czgrq2bNtCMheAqa9ShGoBXAVVqCRqlVKgk94QTboM1/4GcVXabxwP/ux3Wvw+n/BnG/dwuFFNVEppzuuxiq0RE19i8t6icxIREAGKooEgXYlVKqSbREjRKKaWUCih3DRgP7F3X5K72r/qIA6Y1Q8acxMbDrXD1PdvW5K4M0WfIYBVkQFJ3SBlqk772baj3kF0HSnhn2S5uGRbD5QFqlY/unkSYwJKGlqFZ+BRExtnyM8Bz87axNruQJy4awqw7JnLawE61SsiE9ZgMwHWdM3lg1kZmrMwGbOmZ37z/HS6P4YmLhvivl773O1tmduAFDRtnHe6f2o+oiDD+PGsDn23Yy+qyDriiEiF7aVDHnzGoE89cOowl9/+E+b86kb9eOJiJvZJZtasAEbjntD5NH+TWTyHSz9MAQdAAvArIW4JGF2FVSoXE5F9Bq47wya9t5vvsu2Dt23DS72HiL22byLjQZcBXB+CPzoAvo01rJwAvVRqAV0qpJtJSXkoppZQKKGeFfS3eDSUHG91NcWkZaQVL2dl2AkOdRUp397kayotg3buhGGnw8ndB2+6QMsz+HEQZmic/38I1EXO5b9MF8PeRMPdPkLPSJqY5EmMiGdy1Dd/saMD7dHAHrH8PRl8P8cl8m1XAv77ewcUju3LxqDrqpyd2huReXNA2g0m923Hf++uYsy6Xd1dmM39rHr+Z2o+05ADB5g0zQcKh/znBj7MeHRJj+OUpvfl6Sx4Pz9lEt6RWhKeOg6xlDepHREhLjufS0ak8e9lwltz/E77946mBf5dgGWPrv/c8qVGHawBeBaQZ8EqpkIpOsJnuu1fCq6fB6mk2KD/lV0faRMWHrga8nwz4kgoXxeUu2raxj57Faga8Uko1mQbglVJKKRVQzkrACQLv+67R3SycN4fWUkKnUedUB1M3Rw6EToPtYqxOve9QM0f366qE4hybAd82HaJb1xuAX5tdyOx1uVyVtBkSUqBtGiz5B7xyMjwzEObca2uLu12M75nMmuxCSipcdfZZbdHTEBYJJ/yC8io398xYS6fEGP7w0wH1H5s+ifCsJbx45VBGprXlrne+5cFZGxnXI4mrx6UFekNs/fceJ0J8cnBjDNK149Pp07EVeYcquGxMNyRtHBzYAqX5je5TRIiOCG/64PZvhKJs6H1aow7XALwKyLv4aqW7eSYxpdSP0JBLoetoG4Qffyec9Lua+yPjQvf4YJUTgPepAb+32G5r17YN4K0BXxma8yml1I9UuSZrKKWUUsofY2wAvudP7M+5jStD4/EYitfNoooIUkedRVpSHABZ+WUw5hYbHN21KHAHbhd8eBv8+yzI3xn0eV+Yv4MhD3zO377YdiThoDDLltRp2x1EIGVInQF4YwyPfbKZDnFCWsk66P9TuPoD+NV2OP9F6DICvv0PTDsHPr6X8T2TcXkMK3YFEXQuzIK178DIayGhI09+toWdeSU8ftFQEmMi6z+++2SoPETcgfW8et1o+qckYoDHLxzqv/QMwO7VUJgJg0JXfsYrMjyMRy8YzPDUNlwyqht0G2d3ZC8P+bkabOun9lUD8CrUNANeKRVyYWFw0b/hglfg1AftBYuvqLhmzYDfW2S3dWhjA/AxUqkZ8Eop1USaAa+UUkopv4p3w+G90OcMSOxia4c3wvyteYysWEFh+1EQk0ibuEgSYiLIzC+BwRdBbBIsf9H/wW4XzLwJ1rwFe76FFybDuhn1nvPlBTt57JPNtE+I5pkvtnLyU/P5+LtcjDeAn9TdvnYeZmvAu/1/rlyw7QBLdh7kgRGlSFUpdJ9id8S2haGXwWVvwa93QL+zYdMsRqW2JTJcWBJMGZpVb9ibHOPvZHlGPq8uzuCqcalM7N2u/mMB0ifZ14wFJMZEMuPWE/jynhNJTY4LfMyGmTbjvt9ZwZ2jgUamJfHBbRNo1yra3pwIiwy6DnyDeNz2v9vh/cG13/q5LTmUmNKo02kAXgVUVZ0BrwF4pVQItekGQy6uHXwHu6CJq9z+MWwqPzXgc50AfEqbWExELLFUUKzB69LaAAAgAElEQVQBeKWUajRBF2FVSimlVADe+u9dR9pSMY1ciHXOgiX0CdtN0nBbc9zW+Y4j82CpfeJ5xDWweQ4UZtc80OOGD2+1QeNTH4Tbl0LHgTDzRpsRX3HY7/leW5TBIx9v4qwhKXz+f5N55+ZxJMZGcttbq3l9zle2UVsnAJ8yDNwVkLe5Vj8ej81+75YUy+mxW0HCIH1C7RNGxUOf06H0ALHFOxme2ja4OvAZC6DLCEpiU7h3xlq6tY3j/qn96z/Oq1V76DDA9gPERIbTqXVM4PYeD2z4AHqdYm8gNLfIWLvQbVYIAvDlRbB9Hnz1KEw7Dx5Lg3+Nh2nn1qjD71dpPuQst/+NGkkD8Cogb+C9SjPglVLHSpRzpz0UWfB+M+DLAOjUOgaJjCUxwqUZ8Eop1QQiQoVLM+CVUkop5UfOSgiPho6DodMQOLAVqsoa1MXOvMPEZ84DILzvGdXb05Liycp3PjeOvsG+rnz1yIEeN3z4c/huBpzyAEy4C9qkwnVzYPKvYc10eGlKrfIxby7ZxYOzN3LGwE48e+kwIsLDGNcjmdm/mMjD5w0isiiTUhPN7+buI7+k0gaIwW8Zmv+t3c2m3GLuPa0v4ZkLbNtAgevU8fY16xvG90xm/Z4iikrr+KxaWQJ7VkPaBB79ZBPZBaU8efFQ4qMjAh/jT/okG+B2BVGaNWe5faqhGcrPBJQ6zpa9cVUEbuOqgA9uhVdOPfL16mlHvp4fZwPu/7kAFjwOJQdgyCUw7jZbvmjLnLrHsP0LW3ZIA/CqOVSXoNEMeKXUsRLpBOArQxCAD1ADvm1cJDGR4RAZR2J4FYUagFdKqUYLE82AV0oppVQAOStt0DkiymbAG48NeDbAtCWZnBz+La62PSG5Z/X21OQ4dheU4XJ7bGC975m2JEtVmQ2+/+92WPdf+MkfYOL/HekwPAJ+8ju4dpYNYr9yCiz5JxjD28uz+MP/NnBK/w48d/lwIsOPhE3Dw4SrxqVxaU8Xh2K78M7KHE5+6mtmZERholrVCsBXuNw8+dlWBnZO5Kf9WtunAbzlZ/xJ7gnx7SHzG8b3bIcxsDSjjiz47OXgcbE+ajD/WZrF9RO6M6Z7UoPeW8DWgXeV2XXa6rN+JkTEQN+pDT9PY3Uba58wqGuh22Uvwtq3ITzKJvVFxdk4gPerbRqceD9c/SHclwk/XwRnPw2nPmSfZFjwZN2L+G79FOI7QMrwRv8aDbwton5MvCVoqjQAr5Q6VqLsavZUhWAh1gA14Du1dgLykTG0CtcMeKWUagoRoUJrwCullFLqaO4qyF0Do5zs9E6D7WvuOugyMqguDle4mLNqO78P30REv1tq7EtLisPlMewpLLc1y8feAptn24z3rKU2IHvS72Dyvf477z4Jbl1sA/Wf3c/KPeX8dmVfTuzbnuevHEFUhP+c5cjiTDqm9eeTn0zitzO/41fvr2dQYjrds1bhW7zlzSWZ7C4s47ELBxOWvRQ8LhvsDkQEUk+AzCUMO6cNMZFhLNlxkNMHdvLfPnMxRsK5a3EMPdrH86vT+9bxTtYhfQIgkLEQ0sYHbudxw8YPofepEJ3QuHM1RqqzEGvWUug2pvb+kgOw4Am7OOqV9df2ryE8wt6cmXUn7PgSep1cu43bZTPg+/3UrmnXSJoBrwKqcDLgK7QEjVLqWAllBnyAGvAp3pp2kbG0CtNFWJVSqinCBMq1BI1SSimljrZvPbjKMV1G8tqiDJYVJEB0YoMWYn1/VQ7DqtYQYapsgNWHd6HQzHwneSt9kq1nPuceu+DqlN/AlF/XfYL4ZLj8bUpjU9iz5nMm9mrHC1eNJDoi3H97jwcKdkFSd/p0TODdW07gkfMHsbIyFZP7Hc/N3UyFy01xeRX/+Go7k3q3Y1Lv9pDxtc3OTj2h7vGkjYeiLKJK9jA6PYlvdhwI3HbXYrJjepNxSHjq4qH2Ke/GiG1rb444deADyvwGDu+Dgcew/AxAqw42Sz17mf/9Xz9mn2Q47eHG9T/0crtA8MKn/O/PXmbrx/c5zf/+IGkAXgVUvQirBuCVUsdKdQZ8c9WALz+yqExkHHFhlXXX1VNKKVWnMBEtQaOUUkqp2nJsSZMZ+zrx4OyN3PTmKiraDQh6IVaPx/DGkl1cnLDBBu6PCl6nJdvPjpkHnc+OIjD2VnBX2hrvJ/4mqPN8tTWPhSVdGR2VyUtXj6o7kH14r/2c2TYdgLAw4cqxaZx7xlRipZKPvpzPmX9byG9nfkdhaRX3ndHPHpexALqOObLmWSDeDPTMJYzv2Y6t+w6Td8hP7fOqMjw5K/jkUE9+fmJPhqc2cUHU7pNtffe66vNvmGkT5ppQB73RUk+wGfBHl4nZvxlWvgajrof2jXwCICIKxt8JmYshc0nt/Vs/hbBI6HFS4/p3aABeBeQNvGsJGqXUMVOdAR+CEjTeiwenBnx5lZuDJZWkJB7JgI+lSjPglVKqCUTs/KqUUkopVUPOSipj2nH/l4WM75mMy2OYV9gJs2+DLWdSj0XbD7Az7zCTWA09T7KBUh+dEmOICg8jO98neWvENXDHSjjpt/YipR4b9xRzx1ur2RvXjxT3HmI9h+s+oGCXfU3qXmNzYo/RADw3xSYmzF6XyzlDOzOoS2sozbdld+oqP+PVcZC92eAsxAqwZGftOvCHti8lzFPF7tYjuPPk3vX3W5/uk+2Ni0BZ5rtXw7oZtva7N2nuWEodC6UHIH9nze1z/wBRrYK+2RLQiGsgrh0sfLL2vm2f2zI9MYlNOoUG4JVfLrcHj3NjSTPglVLHjDcjICQZ8E6mgJMBv7/Y/lydAR8RSwwVFJVVYepacKWpFj0Lc//UfP0rpVQLshnwGoBXSimlfgz2FpWzPCM/qETNqqwVLC7vTnpyPC9ePZIHzhnIl4UdkarS2oHUo+QdquCBjzYwIX4PsRV50OeMWm3Cw4SuSbFHMuDBBt3b9Q4q+J5bVMb1r68gISaSn04909lYx0KfAPkZ9rVtzQA8yb0hIpYBZDD37sk8cv4g/vTTAXbfrkWAgR51LMDqFRZu65xnLmFg50QSYiJY4qcMzcIvPsRjhMsvuiRwuZyGSD0BJNzWgT9azkqYdh7EJcEpDzT9XI3RzacOvNf2eTY4PvleiG/XtP6j4uCE22yt9z3fHtlesAvyNkPvpmf9awBe+VXlNj7fawBeKdU4hytcDTsg0rmbHpIa8E4GvFMDPrfI/pxSvQhrLNGmApfHUFrZjMGjFa/A5jnN179SSrWgMEFL0CilVDMQkddEZL+IrPfZ9oCI7BaRNc7Xmc72dBEp89n+QoA+k0Rkrohsc16bWLdC/aBVHIatn8POryF7OexdDwd3QHEulBdRVFrJom0HeP6r7dw8bSVj//IF4x6dxyUvLuGyl5ayuzBwuZLy4gNEFu5grenFS9eMIiEmkotHdiWpp118NWvD0oDHFpZWcvWry8gtKueJIXsAgV6n+m2blhRHZn7DPzsernBx/esrOVRexWvXjSapl7O45541dR9YkGED1W1Sa24Pj4BOgyB3LXFREVw5No3kVk4p1Iz59nNukAvPknoC5G0ioqKQsd2T+WZHzQz42ev20Gb/cg606kP/7qkBOmmgmEToPLx2HfispUeC7z/7uPbvfay06wMxbSDb+XfjccPnv4c2aXbx3VAYfSNEt4aFTx/ZtvVz+xqCsjsagFd++Wa9awa8UqqxGryIc3UGfAhK0LgqAIHwSAD2Ftua8J1aOxdCkXFEGbutsLnK0BRmQVE2VBxqnv6VUuooItJNRL4SkY0iskFE7vLTRkTkORHZLiLrRGSEz75rncDMNhG5Nojz6SKsSinVPF4Haqf9wjPGmGHO18c+23f4bL81QJ+/AeYZY3oD85yf1fdNxaHata6bw4InYPrFMO1cePVUeGEC/H0EPN0PHktlxl+u4apXl/HEZ1vYvv8w43u2408/HcDD5w1iy95DnPXcQr7YuK9Wt8YY3pjxPgCTT5pKz/atAHvNcNvFZ1FFBIsXf0lpZe1krcMVLq799wp25pXw8jWj6Lx/gQ1ct2rv91dIS44n62BJg55odrk93DF9NVv3HeL5K0cwoHOizaBu3Q1y6wnA52dA667VnzFrSBlqS814jvoMvHO+re3u7xh/vHXgs5YwvmcymQdLySmwNxn2HyrnwQ++ZWT4dtoN+klw/QWr+2TYs/rIZ9ddi+HNCyChow2+t+4a2vM1RFgYdBsLWU6JnNXTYP9GOPXBGmu+NUlMaxh7M2yaBXlb7Latn0JyL0ju2eTuNQCv/Kr0yXqvdB+DiV8pdVxq8A286hrwIciAryqz9d+dxw9zi7wBeG8GfAwRHluWptkWYs38xr5qAF4pdey4gHuMMQOAccDtIjLgqDZTgd7O183Av8BmRgJ/AsYCY4A/1ZcdGSZChWbAK6VUyBljFgD5Ie72XOAN5/s3gPNC3L9qqsN58FQ/u7Bkc9vysc22vu5juPJ9uGQanP8in6bfxxpPTy6KW8ObN4xh7R9P48t7T+SZS4fxswnduWpcGrN+MZEubWK5cdpKHp69scbnvtcW76IsYykGYcQJNYPEbRJaUdG2D53Lt/PwnE019pVVurnh9RWs313E81eOYGKKB3av8lt+xis1KY6SSrvWVzCMMfzpow18vSWPh84dxIl9OxzZmTI0uAz4o+q/Hzl+GFQeqllep3gPHNwWXPkZr84jIDwKMr9hfC+nDvyOgxhj+O3M9fSq2kI0lYSlTwy+z2B0nwQel8163zkf/nOhDbpfNwcSO4f2XI2ROhYObIGCTPjqEftvd8C5oT3H2J/bGMLCp+0TIrsW1vnvryE0AK/88gbg46LCqdSsJqVUIzU4AO9d0CVUGfA+d8P3FpWTEB1Bq+gIuyEylnC3Dco320KsuxbZ16oScDewHI9SSjWCMSbXGLPa+f4QsAnoclSzc4FpxloKtBGRFOB0YK4xJt8YUwDMxX/2ZTVdhFUppY65O5ynl1476iZpdxH5VkTmi8ikAMd2NMbkOt/vBTo271BVg239BCoPw9J/NW8W/MEdcGArDDjPLjDZ+xQYcC5fRp/ErZuHkt31bNpU5jKpfTmt42pnbndvF8/7Px/P1ePSeGVRBpe8uIScglK+2XGAv3y8iVMSsqFDf4hOqHVsq7QRjIrKZvqyTD7fsBewnxt//tYqlu/K5+lLhnLqgI6wba49oM9pAX+NtGSbwFWjDnwdXl64k7eWZXHLlB5cMfaociqdh0H+DigvCtxBfkbt+u9eKUPtq28WvbekS/cGBOAjY2zWf9YS+nRIIDk+iiU7DjJz9W6+2LSPu/vk2XbeTPlQ6TYOwiJh8d9g+iX2RsN1syGhU2jP01jeOvAzroWSPDj9kaBq/TdIfDKM/Bl8NwNWv2EXpu0d+N9fQ2gAXvlV5QTN4qMjamTDK6VUQzR4/giPsjX1QlUD3qn/DrYGfPUCrACRcU4A3jRfAN6bAQ82G0IppY4hEUkHhgPLjtrVBcj2+TnH2RZo+9H93iwiK0VkZUV5uQbglVLq2PkX0BMYBuQCTznbc4FUY8xw4G5guogk1tWRsTU7AkZ4fef6vLy8kAxeBWHzHEBs1vQuPwtihsrWz+yrT23r3YVl3P3uWvqnJHLa1AvsxqwlAbuIiQznofMG8fwVI9ix/zBn/m0ht721mu7JcQw025Cuo/wf2Gkw8a4CJqW4ue/9dewpLOOud77l6y15PHr+YM4d5lx6bPwQErtApyEBx+ANwGfl15/A9fmGvfzl482cNTiF+07vV7tBynD7mrvOfwflRVCWHzgDvn0/+3nWdyHXnfMhNgk6Dqp3fDWkjYfctYS5ShnXM5n5W/N4YNYGRqe3ZaTZaPuLS2pYn/WJioOuo+2/u+RecO0saNWh/uOOlS4j7A2CPd/CkEuDr6nfUON/YRfDnfsniE60mfYhEFQAXkTOEJEtTp3IgDXCRORCETEiEuD/MvVD4Q2atYqOqLEgq1JKNUSly9OgenyI2Cz4qlAE4GtnwNcMwNvgfDRVFJUF98higxzaazMo2jsXd1qGRil1DIlIK+B94JfGmOJQ9m2MeckYM8oYMyouLpZyXS9IKaWOCWPMPmOM2xjjAV7GlgvDGFNhjDnofL8K2AH08dPFPueJJ5zX/XWcq3qub9/ef/1tBbirYMUrkLe16X1VHIYdX5Hb6zI8MW2atwzN1k/t5xQnmFzl9vCL6atxuQ3/vHIE0V0G2wUpMxfX29VZQ1KYfedE0pLj8XgM/z6nLVJeaIO5/nQaDMBfx0NZlZsznl3AJ+v38oezB3DZGCcrvSgHtn8BQy+vM8u5a9s4RILLgH9h/g56dWjFU5cMJSzMT5+dh9nXQHXg8zPsa6AM+Igo6DDgSADeGJsB332SrWHeEKnjbTmYnBWM75nMwZJKXG7DkxcMQHKWQ9qEhvUXrDE3Qt+zbPA9vl3znKOxImPtf6OIGDj5j813nsQUGHYleKqg50n2v2sI1PsvQETCgeextSIHAJf7qSOJiCQAd1E7w0b9AHnLRrSKjsDtMbg9GoRXSjWcxxgKGlBf/fbpqykxUVAZghI03hrwjtyiclJ8A/BOdnwsFc2TAe+9WPXWjCsPafxLKaUCEpFIbPD9LWPMTD9NdgPdfH7u6mwLtD2gMBHcHkOVPjGplFLNzhs8d5wPrHe2t3diN4hID+waHztr98BHgHeB7WuB/zXfaH8ECrPh32fCnHvg1VMgM3C2eFB2zAN3Bb/c0IsPzRTMpllwOOA9kqDlHarA4xvTKS+yn1V8als/8dkWVmcV8tiFg+neLt5mAKeOq/lEbx3SkuP58PYJLP7NT+hW6tR27xIoA95mg3cu28Yfzx5IcbmLu0/tww0TfQLb3/7HBrBHXF3neWMiw+mUGENWPQH4sko363KKOKV/R2Iiw/03im8HiV0D14EvcALwgTLgwQaIc9fasefvhOKchpWf8eo2BiQMMr9hcu/2REWE8YezB5BWsdUmq6U3UwB+0IVw+fTQZ9eHyhmPwaVvNf+CsBN/CVEJMOiikHUZzC2YMcB2Y8xOY0wl8A62buTRHgL+CpSHbHSqxXgz4OOj7cSkH6qUUo2VlR9cNrvbY5i7YR+HPdEhz4CvcnvIO1xxZAFWqA7OtwqraqYA/DcQGQ/exXE0A14pdQyIiACvApuMMU8HaPYRcI1Y44AipybwZ8BpItLWqSt8mrOtjvPZVy1Do5RSoSUibwNLgL4ikiMiNwCPi8h3IrIOOAn4P6f5ZGCdiKwB3gNuNcbkO/284lOl4DHgVBHZBpzi/KwaY8sn8MJE2L8JznwS4jvAm+fBptmN7rL8u48oIIH85OG8VDIF8bgoWfZ6k4a5fncR4x6dx6nPzOfdldk22XLHlza72gnAf7FxHy8t2MnV49I4e4jPYptp422d+CBvAoSHCQkxkZCzwgYv2/f13zCmNbRNh73fccXYVJb/9mTuPLn3kf0eN6x+02Yft02v97ypSXFk1vOZ89usAlwew9ge9QSWOw+zJU78qc6Ar2NMKUOhvBAKMyFjvt3WmAB8TKItM5P5Dd2S4lj3p9NszXrvGmPNlQH/fdd1lF2voLm1TYf7dsGAc0LWZTAB+HprQYrICKCbMWZOXR1pDbEfDt8MeIAKfbRYKdVI2UEG4HcXlFHp9lBGdIhqwJdXZ7nnHarAGGpmwEfaeoHtYjwUBpulP+uXsPPr4NpmfmNXao911saq0Ax4pdQxMQG4GviJiKxxvs4UkVtF5FanzcfYzMjt2BIGtwE4wZqHgBXO14PeAE4gYU4EvrxKrxWVUiqUjDGXG2NSjDGRxpiuxphXjTFXG2MGG2OGGGPO8S6oaox53xgz0BgzzBgzwhgzy6efG40xK53vDxpjTjbG9DbGnFLfHK/8cFXCZ7+Dty+DNqlwy3wYcxNc/5kNmL57Naz8d4O7Na5K3Fs+5UvPCP51zVj+eN25LDEDKV70MnlFjf9s9PTcrcRHhRMVEc6v31vH5Me/YuvC9zAxbaHraHIKSrlnxloGdUnkd2f1r3mwN5EoyCz4ajkroctwm0UfSKchsNfWWu+QGFNz3/Z5NnN85HVBnS4tOa7eEjRLM/IJExiZ1rbOdnUuxFqQAXHt/C4sW616Ida1tv57YhdI7lnPbxBA2nj7Xroqj2TtZy62pYO+b+VhjkfhESHtrsmLsIpIGPA0cE99bbWG2A9HlfvIIqxwJCCvlFINFWwG/M4DhwFsCZqqEJSgcZVXZ8DnFtmHs/zVgG8X7Q4uA95dBav+DV//tf62pfmwf6PNTIh21r/SDHil1DFgjFlkjBEnODPM+frYGPOCMeYFp40xxtxujOnpBHJW+hz/mjGml/NVbwTB+2FCM+CVUkr9IHjc8PLJsOLVhh9bmAX/ngpL/gGjb4Ib5h4JrsYnw7UfQa9TYPYv7WeGBqyFtfCLj4j3HCZh2Ln06tCK8b3akTTl56SY/Tzzwr/Yf6jhxSZWZxXw5eb93DKlJx/fOZE3rh9Dj+QYknPnM7t8EE98sZ07pn+Lx2N4/ooRtUuzpAy1SUsNCcBXlcG+9YHrv3t1GmJLtPj7jLTqdYhvD32mBnXKtOR4DhyuoKTCFbDN8oyDDOicSGJMZN2d1bUQa35G3eVnADoMhLAIm0WfscBmv9dRw75OqSeAq+xITXm3C7KW/niz33/gggnA11cLMgEYBHwtIruAccBHuhDrD5s34O4NwGsJGqVUY0SESdAZ8BkHbND9kCdEGfBV5dVB9r1OAL5mBrz9PjnYALy3Ln3WN1Cwq+623ovUtAlHMiQ0A14pdRwS50NlhUsD8EoppX4AspbC7pWw4YOGHZex0JacObAVLn4Dznqy+vNEtah4uGw6DL0Cvv4LzLnbBvzrsaewjJwlM6ggmlPOuqx6e9/Jl1IZ045TSudw+UtL2V/csCD8M3O3khQfxXXj0xERpvRpz/SpESTLIfZ1PJF/fr2DNdmFPH7RENKS42t3EB5pa5E3JACfu9aWtwlU/93LWYiVfRtqbi/OtQvEDrsy6MUvU5Psk82BEr8qXG6+zSpkbPfk+jurayHWgl2BF2D1ioyB9v1h3Qwoy4fuk+s/ZyBp4+1rlvP+710LlYebr/67albBBOBXAL1FpLuIRAGXYetGAmCMKTLGtDPGpBtj0oGlwDm+mTTqh+foEjSaAa+UaoyoiLCgM+CrA/DuKExIasD7ZsCXAZCS6FsD3l6oJUW5KQ4mAO87pnXv1t028xu7OnuXEbZ+H+girEqp41JYdQ14vVZUSinVBMYcSXhpThs/tK+7V9knXIP11V8gurUtOTPwvMDtwiPhvH/CxP+Dla/BjGtt5nIAxhjue28tJ7ECd4+TCIv2CYRHRBE1+lpOCvsWinK47OWl7AsyCL88I5+F2w5w65Qe1YmVAGz9BMIiuPG6G5l39xTeuH4MUwenBO4obYLNaC8rCOq85Kywr13rCcCnDLGvR2ear/kPGDeMuCa482FL0AABy9B8l1NEhcvDmO5BLCwaaCFWVwUU5dSfAQ/2yYHiHPt9j0bUf/dq1QGSex1Z3HfXYvuaNrHxfaoWU28A3hjjAu7ALsC0CXjXGLNBRB4UkdBVo1ffK9WLsEZpBrxSqvGiwsPILmhYAP6wicKE4uLbpwb83qJyYiPDSYz1ufh0suPbRropDCoD3vk9JAzWvlP3I6WZi+1jlxHRNtAvYVqCRil1XNIMeKWUUiGx+Fn4S2d4eiBMvxTmPWSz1A9sDyqLPCgeN2z8n10EtKq0ugZ5varKbNb8wHMhqUf97UXglAfgtIdh0yxY/UbAptOXZ1GwYwUpcpC4IefWbjDiWsQYpg3fzL6ici5/aSkHD1fUO4Sn526hXatorh6XXnPH1s9saZPYNvRo34opfeopD502ATD2yYFg5Ky0tfFbdai7XUIKxCXX/G/g8cDqaTZrvAF109OS7E2LrHz/nyGXZdilDkanBxGAB5sFf3QGfGEWYOrPgIcjdeCTe0Ni57rb1if1BMhaYt+bzMU2IJ/QsWl9qhYRVA14p25kH6dO5CPOtj8aYz7y0/ZEzX7/4avOgI/RRViVUo0XFRHGnsLyoG7i7cwrIUygzESHMADvZMAXl5PSOqY6UARUB+dbR1QFWYLG1qinzxl2YZ6cAH/qyovshaT3kUERW4ZGA/BKqeOQZsArpZQKie3zoHUqpJ1gS30segZmXAf/GAmPdm3Uwqa1ZC2Bw/tgyn3Ozw0IKrsrG157+4Q7bLbyV49AWWHt4Rws5ZE5m7ix/UaMhNnPGUdrmwa9T6PLjhm8cd1wdheWcdO0lXWuvfLNjgMs3ZnPbSf2JDbKp657QaZdp8rfeQLpMhLCo2zwNxg5K+uv/w72M5LPQqwA7PzKBrpHXBv8+IDWcZG0jo0MmAG/LCOfvh0TSIoPrqQNKcPg4PaaTzDnZ9jXYDLgvWVsmlJ+xittPJQXwv4NNhNe67//YDV5EVZ1fKpy28zOVtF2sq7UDHilVCNEhYfh9hhyC+t+VLK8ys2eojL6dkqklGikGWrA11iAFar3tY5wUVxWhcdTzyJJ3hI0Qy+35WXWveO/XfZyMJ7qAHx2filVkQlaA14pdVwKc25s6iKsSimlGs1dZUvC9DsLLnwFbl8Gv90DN38N5/wD2veDeQ/aTPSm2PChTcIZeZ3N0g42AJ/5DSA2G7khROCMv0BpPix4osYuj8dw73trCRfhrMjVSNoEiAuQoT3qeji8l1HlS3n20mF8m13IPe+u9fv5xRjD059vpWNiNFeMTa25c+tn9rVvcIubArameZdRwdWBL861pVfqq//u1Wkw7N90pBTQ6jcgNgn6/zT48TnSkuP8lj51uT2s2pUfXPkZL28A3ffmQIETgA8mA77TELuA7PCrgj9nIN5/c8tfgooiSJ/U9D5Vi4iov4n6Map0HiOuXi8I+7gAACAASURBVIRVM+CVUo0QFRFGGXZBnFSnNp8/uw6WYAyMSG1DaV404iqzJV4au2I81MiA31tUztijL7qcGvCtwl14DByudJEYExm4P+9NgYRO9sPB+vfh9EdrLw6UuRjCIqDrGMqr3FzxylKmVUTQXTPglVLHIakOwOu1olJKqUbat94mu3Qbc2RbZAx0Hm6/knrA62fCuv/a4HljeMvP9DndLpbabRzs/LrGZ47s/FL+/uU2istcVLk9VLo9VLo8/O7AHOLDu3PPaxtoHRtJm7hI2sRG0jouitaxkSTFR3Jy/47+P0ukDLWB2GUv2kC6U1rltcUZLM/I559T2xD51WYYXcfv1ftUW5d85WtMveZc7p/aj798vJmuSbHcP7V/jaYLtx1gZWYBD507kJjI8Jr9bP3UljBpQHkXwCYWLXoGKg5DdKvA7XY7TwgHkwEPNlDtrrQL28a3h81zYOyt1Z/hGiI1KY51OUW1tm/YU0xJpbthAfgUJwC/Zw2kO/XWC3ZBZHz9pXXA/tu9IkCyVkO1Tbfleta8bX/WBVh/sDQDXvnlzYD3BuA1A14p1RhREfbPTH114DPybMmZEaltKTMxCKZpGS4eN3iqICIWt8ewrzhwBnxCeCUARaX1lKHxlqCJirdZ8GUFsO3z2u12LYbOIyAqjpf+n707D4/sLO+8/31qr9IutdSbpN5s9+a13e0FY/ACxjhsBkJsBibOgJ1kIMtMltd+X4KBIQmTyQDDjAlgcAxMMEOcEMNgGwLBC167sdt2d9tt9652u1v7Xns97x/nVKkkVWkpqVXV0u9zXX2VdOrUqUdtOH10131+92OH6OiN0psKYWOTLwhFRM50YxE06oAXEZESdTzrPLZdWvj5NW9yirVP/93Uc5imcvRJGOkcG6DafpnzvdvZ/MZAlJvvfpr/++IbHO4e4dRQjOF4Co9NsjH1Coci51MT8tE3muD5Y/38y+4T/M9/e43/8n/38Z/+zwt85JvPMBwvMmz1mr9wiso/+wsAfvHyKf7qwZe5bsty3un7tbPPxhuKr93jdT54OPQI9Bzk1ivX85HL2vn6o4f4h2eO5naz1vLf//VVVteH+dCOtvHHiA/BkcdnFz+TteZNzmDU489Ovd/BXzpxNSvOm9lx8wex7v4eZFKzjp/JLbEpwuv90UnRp8+6+e+TmrGmUt3sDmJ9fmxb72GnGD6XBrFSGPfOi0zS6b6fa6a8lI064KWgbMG9OqghrCJSOr/Xg99rCt4OmO+QO4D1wvZ6XsTteEiOQqB41/yUUm7kjS9Iz3CcVMayskgBvsrjFuCjSSZcpo6XjaDxR2D91VDVAi/cB5vfNbZPYhROPAeXf5KO3lHu+uUBIgEvA5kQ6eig/tEVkUUn1wGvIawiIlKqjmecgmfd6sLPGwOXfwJ++Ltw8N/grGtn/x57f+hcx599nfN9+2XO47Gn6fKv5t/d/QwDo0m+f9tlnN9aP/a6Y8/APXHe/s4P8PYt4z8gSGcsQ7EkvzrQzR99fze/+91d3HPLDoK+CZ3nNcvhyj+BX3yWg8/8hE/+Xy9bV9Xxpd+6EPMPn3IK1g1rpl7/to/Co1+AXfdg3vGXfObdW3m9L8qnH9jLqvowV29s4Zf7O3mho5+/fv95k9dw6BGn27yUAnzbpWC8TqPRhmsK79N3FJ7/Lpz/W04H+Ew0neVEAp180enOb38TNJ8z+/XhDGJNZywn+qOsaarKbX/mcC/rllXRUjvDNWVNHMTad9hZbzmseRPs/Wd1v5/h1AEvBWWHrkbcgR0JRdCISIlW14enLcAf7h5heW2Q1fVhRrMF+LkMYk26BXh/mDcGnK9X1IXH7+PxgjdAmLEC/JSy6wlUgdcH5/2mk6M42ju2z/GdTufG2jfz+Z/sw2MMt79zE8OESUeVAS8ii4+GsIqIyJx1PDs+fqaQre+H6uXw9Fdnf/xMGl7+0Vj8DEDzZgjWET/0JB/91jO8MRDj739nx/jiO8DRXzmP7nynfF6PoT4S4F3nr+K/fuB8njjQw3/+wQukC82Wuuw/kqptI/3QHbRUe7nnlh1UJXudHPpN75q8/0TZGMzd/wCpBD6vh//14W1sWlHDJ//hOfa8PsAX//VV2hrDfPDi1smvf/VhCNaNffAwG8FqpyA9VQ78L/8KjAeuumPmx/V4YflWp6mp91Dp8UKQizvNH8SayVh2HunlkrWz6H7Pyh/Emsk4ETQNa0te35yseytgin/4IWcEFeCloGQ6g99rcp+aJtIl3uYlIkteW2OEjhkU4NctqyLk95L0uN0JyTkMYs3rgM8W4Cd1wAP4w4SNU3ifVQEe4ILfcm4F3PvPY/scfQKMh1/FN/DTvaf4g2vPYsfaRoZsRENYRWRR0hBWERGZk4HXYaCjePxMli8AO26FAz+Hrv0AHOwa5je/9iQ/euHE1K89+gSMdMGW941t83hItu7g1N5HONQ9wjd/ezvbCxVqjzzhDIGtWjblW3zw4lbueOcmfvLiG3z2x3uxE6JyehMePp+4mXM4yj9eeojmmiDsfwiwTmF9Jrbe6MRgnnwJcCKD77llB7VhP7/19afY8/ogf3jN2fi9E0p9mQy8+jM4+23gnWLm1VTWvMnJeM82OuU7ucfJ57/094rfxVDMivOcnylUB1veU9racCJoAI7m/d65/9QQA9Ekl64voQCfP4h1+KTz+2XjDAawng7N58AfPud8CCVnLBXgpaBEKkPA6yHgnrjVAS8ipWqfcQHeGejjCboF7sR8FODDnBxwsuQnZcC7z4eIA9A/XQZ89gMBn9tJv+J8aNkCL+QN2Dn6JJnl5/EXDx9j3bIqPvbmdbQ1RhgijDc5XPrPIyJSoYxx/sRVgBcRkVJkc8XbZjC4c/vvgDcIT/8d1lo+9cM97DzSxx/e9zyf+N5z9I4kCr9uYvwMMJpI8Y+nVtOe7uCbv7mOK84qUGBPp5x4nDUzi/743bdu4La3rOc7Tx3lK784kNseTaT52Ld3ct/wRQwt307Ls38DsQFn6Gh9Oyw/d0bHp9W9S+D4ztym5bUh/v53dmCMYd2yKm68qEAB/MTzTt59KfEzWWuucCJsXv/15Od+8VkI1cKb/3j2x83mxV9wcy4itBTLa0IEfB6O9YzdRZ3Nf5/VANas/EGsvc6cABrKVIAHZxDxQufPy7xSAV4KSqYzBHye3ABFFeBFpFTtjRH6RpMMxgoXuPtHE/SOJFi/zCm8+4JOIZ7kHCJo8jvgB2MEvB4aI4HJ+/nDBKxTgJ9RB7w/Ah73n05jnIzD4zuh5yCk4nB8Jy/5zuVw9wh3vnsLQZ+X6qCPlL8aXyYOqSK/FIiInMGCPg8xXSuKiEgpOp51GlxWnD/9vlXLnLtQX/g+D+3cx1OHevjse7byZ+/YyM/2nuS6Lz3GL14+Nf416RS8/GOn+OzOl4ol09z6nV080NsOwFtChwu/38kXIDE8q+zt26/fxAe2tfKln7/K/376KOmM5Y++/zy7O/r5HzddRM17/xZGe+Dnn3Vy2Te9a+aF1brVULNqXAEeYNOKWh78wyv53q2X4pvY/Q7w6kNOPMxZb5vxzzFJ+2WAmRxDc/hxeO1nTsZ9uGH2x91wjdPUdMltpa8N8HgM7Y2RcRE0zx7uZXV9mNaGEuaKVTdD7WonB94d1Fu2DnhZFFSAl4ISqUxueCJoCKuIlK6t0bngKdYFnx3Aur7ZLcCH3AL8XDrg8zLgTw3EWF4XxOMpcGHrj+BNx/B7zcwK8IGq8dvO/xBgnC74E89DKsY3j63kui3LuWpjy9jbhOucL+JDpf9MIiIVKuT3KoJGRERK0/EMrN4282iUS38fUlEOPXwX57fW8ZHL1vCJq8/igU+8mWXVAT727V38+f0vMJRt/nHjZ6Ib38NTB3v4+qMH+fDdT/PkwR5uvvF94PE7OeyFHHnCeZxhBzw4heAvfOA8rtnUwl88sIdb/v5ZfrbvFHe+awvXn7sSVl0EF34Ydn0L0vGZx89ktW6fVIAHJwN95cSZV1mvPgxtl0GkhE7wrHCD06mfzcQHsBZ+fqdTqC61gN64Dv7jU9C0ofS1udobI7nZY9Zanjncw6WldL9nrbxwrAPeeKGubc5rlKVLBXgpKKEOeJElxRhzvTFmvzHmgDHm9gLPrzHG/MIY86Ix5hFjTIHJPoW1T1OAP9zlFODXuR3wgcg8d8APxFhZW+Ri1B/GJKPUhQMMRKfpTk+OOh3w+WpXwfqr4MXvw5HHAdiZOYe/eNeWcbuFatxhTsqBF5FFKORTAV5EREqQjMIbL0w/gDXf8i0crNnBB9MP8pfvPgev22SzZVUtD3zyCv7jVRu4/9fHuf7Lj/O1Rw/y5I+/SZQQF90HN9/9NH/90Ct0DsX5mw+cz/t2nOVkfRcrwB99Aho3OANQZ8Hv9XDXh7exrb2Bx1/r5ra3rOeWK/K6p6/5C/BXQbjRKYzPRusO6D8Kw50z23/whJMZf847Zvc+hax5k3PHQtr9cOPlHzuRNFfdMaf4mPmSLcBbaznUPUL3cKK0+JmsVe4g1pMvQV1r6fn5IoCv3AuQypTLgM8W4NUBL7JoGWO8wF3A24HjwE5jzI+stfvydvtb4DvW2m8bY64B/hr46EyOP9YBHy34/OHuEbwek9svFK5xnphTBrz7Xr4wJwdHuaC1vvB+/jAko9SFfaV1wANccBP88HdJPPV1DmXauPmqbbmfJauqthFOQSY6oE++RWTRCfo9xJK6VhQRkVk68TxkUtMPYM2z5/UBvth7Fff4d7Ji4FHgN3PPBX1e/vz6Tbxty3L+5Acv8N8e2svO0CO8WHU5v/fmrVzQVs/5q+toqg6OHbDtUnj2G84dtP68mVGZNBx9Cra+t6QfLRzw8ve/s4MnD/Rw3Zbl45+sXQnv/7pTyPbOsizX6mblH98Fm26Yfv9DjzqPZ107u/cpZM2b4NmvOx+arLzQyX5v3uTkt1eANU0RRhNpuobjc8t/z1p1EWDh0C+dn11kDlQHkIISKacD3u9RB7zIEnAJcMBae8hamwC+D0y80twC/Jv79S8LPF9UXdhPXdifux1wosPdI7Q3RvC7eYWhaqcAbxNz6YB3ct2tL+B0wBcawApuAX6UurC/9AL8pndh/VUEol3sC2zld9+6ftIudXVOHmJ/f++sfgwRkTOBOuBFRKQkHc84j60z64DPZCyf+pc9vBjcQbrxLHj6LicGZYJt7Q389I/fwq6PhGhkkEvf9TH++G3ncPXGlvHFd4D2y53hom/sHr/91F6ID8CaN5fykwFQG/Jz/bkrCkdhbn43nPv+2R905QXg8RWMoSno8GMQaYKWrbN/r4myReijT8Dz33W6w6/99Ow/RDhN1jQ5TVDHekZ55lAPy6qDubusS5IdxJpOlHcAqywKKsBLQdkhrB6Pwe816oAXWdxWAx153x93t+V7AcheId4I1BhjmgodzBhzmzFmlzFmV1dXFwBtjeGiBfhD3SPjLoyqqmoBSMXmUIBPOh3wAyk/iVSGFVMV4FOxmRXgC0XQAASrObHKGWi0/uJ3EPJ7J+3S0LgMgN7e7pn/DCIiZ4iQX0NYRUSkBB3PQtNZUFXw14pJfrCrg90d/dzxG1vxXv77Tgd9tog/QcDnoeHwTyBQPfXw0Wz3/bGnxm8/ms1/r7DO50DEyWKfSQHeWjj8KKy9EjzzUP6rboFl58CBn8MjX3D+7jbOoAt/gbQ3Or9THu0Z5ZnDvVy6vhEz0wG3hWQHsYIGsMqcqQAvBSXSmVw3qt/rIalfqkSWuj8F3mqMeR54K/A6ULDd0Vr7DWvtdmvt9ubmZsDJ4yuUAZ/JWI5MKMBXV9eQsYZYdA4DS90O+M5RpyOmaAe8z+mAr48E6B+drgN+tHAHPPDcit9id2Y97dsLX4A2L3MK8P19PTNYvIjImSWoIawiIjJb1jrFc7cA/syhHv6/H77Er4/2YQt0tfeOJPjCw69wydpG3r9ttRN7EqqHp+4qfPx0yskoP+f6qfPJq5udDwGOTSjkH30C6tuhvgIHb7bucON7pvm3t/cQDL4O694yf++95k1OV/3wSXjbZ2EuBe551tYYxhh44mA3bwzE5jaANSvbBa8OeJmjyrhPRCpOMmUJuAX4gM+jDniRxe11IP/KstXdlmOtPYHbAW+MqQY+YK3tn+kbtDVG+Pm+TjIZO+4WzJODMaLJ9LgCfH0kQJQAidG5FOCdDvhTo857ragrPoTVyYCfSQTNcNEC/Gu+s/nD5Oc50LS84PPLm1sAGB7om8HiRUTOLCG/l4HRaQZZi4iI5Os9BKM90HYJsWSaP73/BTp6o/zDM8fYuqqWj162hvdeuJpwwLm79G8efoWhWIr/8r5zna7mQBVcfAs8+RX4Px+B6uXunxbncegN5/hbb5x+Le2XwSs/gUzG6RS3Fo4+CWfPw+DS06F1B+y8G7pegeVTRMscdvPf1711/t57zRXw63vhnHfCmsvn77jzIOjzsrI2xEMvnQTmmP+etepC2P8TdcDLnKkALwXF0xnqAs6E54DXQ1IFeJHFbCdwtjFmHU7h/Sbgw/k7GGOWAb3W2gxwB3DPbN6gvTFCIp3h1FCMlXnF8MPdTszM+uaxwnZdxM8oQdKx4dJ+Gsh1wJ8YmaYD3h+BZIzasJ+hWIp0xuItlNEIxSNogN6ROPVhf9HXhqqdDPjosArwIrL4hHweOnW3pIiIzEY2OqbtUu598ggdvVG+/tGL6RqK892njnL7P7/EXz34Mh+8uI0L2ur4/s4ObnvLejauqBk7xuWfdHLIu1+DI7+C6IRr7UDNzIaPtl0Gz/9v6HkNmjc6he3RnsqLn8lq3e48Ht85dQH+0KNOhErThvl777Pe5sTOvP2/zN8x51F7U4QTA73UR/yc01Iz/Qumc8FNEBuA5s1zP5YsaSrAS0GJVCbXAe/3eojrlyqRRctamzLGfBL4KeAF7rHW7jXGfA7YZa39EXAV8NfGGAs8BnxiNu/R1jA2ECe/AH8oW4BfVp3b1hAJELVBvPOQAX9iGLwew7KJw5ayskNYQ84/h0OxJPWRQOF9p4ig6RtJ0lhV5HUAviBJfCRGBmb8I4iInClCiqAREZHZ6ngGgnV0h9dy1789xrWbWnjH1hUA/LtL29l5pI/vPn2U7zx1hNQTlhW1If7o2rPHH6O6GW76h7HvU3EY6YLhUzDcCTUrp46fyWp3O7mPPeUU4LP572uvmPvPeTo0rodwo1OAv/iWwvtkMnDkcTj7uvmNiYk0ws33zd/x5tmaxiqePtTLjrWNhYffzlZ9O7zjL+d+HFnyVICXgpwhrM7JKujzkFABXmRRs9Y+CDw4Ydun876+H7i/1OO3N7oF+N5RLl0/NmTpcNcIYb+X5bVjBfL6iJ8BQlQl5lCAT8UBw/HBNC01weJd7f4Q2DQNboN8/2iRAry1U0bQ9IzEpy7AG0PcGyETG5zdzyEicgYI+T3EkrpWFBGRWeh4Ftp28OVfHGA0meaOG8Y6jI0xXLKukUvWNdL5rs38y/Ovs31tI1XBaUpYviDUtTp/ZqNpA0SWOTnwF98CR56AmlWVm/ttjBNDc3xX8X069zld/POZ/34GaG9yfu+cl/x3kXmkIaxS0MQOeEXQiMhcrKoP4zHQ0Rcdt/1w9zDrllWNm05fHw4QJeh0nJcqFQVfiJNDMVYUi5+BXKRMo9/p3CyaA5+KAbZoBE3fSJKGYp3z2UP4avDEB0lnJg+VEhE5k4X8XmIpdcCLiMgMRfuh82W6Gy7ke88c4yOXtnNWS3XBXVtqQtz2lg1sa284fesxxsmBP/aUm//+hBM/U0EDRidp3eFE5USLjOXK5b8vrQL85pVO7MwVZy0r80pExlMBXgpyOuDzhrCqA15E5iDg87CyLkxH7/ii+uHuEdY1j+8qD/k9xAhiknPsgPcFeWMgVjz/HXK3pNYHpinAZz8MKNIB3zuaoKl66gK8DdYQIcobA9Ep9xMROdMogkZERGbl9V2A5VtHm6kK+vijt51T7hVB26XQd9gpwg+fqtz4maxsDvyJ5wo/f/gxaNww+7sBznBXb2zhl396FZtX1pZ7KSLjqAAvBSVSGfy5DnhDMq2OTRGZm7bGMMfyCvCJVIaOvijrl40vahtjSHrDeFJzKFQno2R8IY73RmlrLNy1DoDPKcDX+ZzCe3/RArw7ELZAAd5aS99IYtoOeE+ojhoT5XifCvAisriEfE4EjbW6XhQRkRnoeBZrPHzn2DL+6Nqzp45yXCjZHPjHv+g8rnlz+dYyE6u3AaZwDE065cToLLHud3B+l1y3rHDTlEg5qQAvBSXUAS8i86y9MTKuAH+sd5R0xha8QEr7InjTcyhUp+LErJ9EOsObp7r90O2Ar/U6hfeiHfBJd90FImgGYylSGTvtLw7+qlpqGJ10F4CIyJku6PcCENf1oojIvDHG3GOM6TTG7Mnb9hljzOvGmN3unxvc7W83xvzaGPOS+3hNkWMWfP1Cyxx7hoNmDcuamvjo5WvKsYTJVl4AvhAc+FeoaoZlZ0//mnIK1UHzJmcQ60QnnofEEKx/68KvS0QKUgFeCsrPgA/4vCSUAS8ic9TeGKFrKE404cQUHO52ImbWN0/Oe8z4wvjnVICPMpT2EfZ7uWSqATxuQb3amwJgsIQImt6RBMC0BfhgVT3VJjopB19E5EwXyhbgNYhVRGQ+3QtcX2D7l6y1F7p/HnS3dQPvttaeB/w28N0pjlvo9QsnkyZ97FmeTJzFHe/cRNDnXfAlFOQLwOqLna8rPf89q3W7U4CfeAdaNv997ZULvyYRKUgFeJnEWju+A95r1AEvInOWjYI53ucUsw93O7Eu65oK3CLojxDMxEp+L5uK05fw8KYNTVNf1Lsd8EEbJ+jzTJEBXzyCZqYFeG+4jjoTzf38IiKLRcjvXDNqEKuIyPyx1j4G9M5w3+ettSfcb/cCYWNM8LQtbg6GO17Enx6lf9lFvGPrinIvZ7z2y5zHSo+fyWrdDtE+6D00fvvhx2D5eVClQaQilUIFeJkknbFYS14HvEcd8CIyZ9kCfDaG5nD3CE1VAeoi/kn7mkAVQWKTuzlmKDY6zGDKx1Ubm6fe0S3Ak4xSH/HTP5oovN8UETQzLcATrKGaKMd7VIAXkdOnUGTBhOf/LC96YI8xJm2MaXSfO+LGF+w2xhQIlS0s5H7QqUGsIiIL4pPGmBfd831Dgec/ADxnrY2X+PrT6olfOk33173jPZhK6zI/+x3OjKizri33SmamdYfzmB9Dk4xBxzNLMv9dpJKpAC+TZIvtfl92CKuHpArwIjJH7RMK8Ie6RooOyPGGqvCRgXSRgvg0BoeHiVs/V21smXrHXAF+lLqwf4oOeCcup1AHfN8sCvA+UpzqG5h6PxGRubmXwpEFAFhr/1s2egC4A3jUWpvfYXm1+/z2mb5hNoImpggaEZHT7e+ADcCFwBvAf89/0hizFfivwO+W8voJx7rNGLPLGLOrq6trHpYOnUMxYoeeZNDXyKZN587LMedV+6Xw/56Apg3lXsnMNG+CQPX4AvzxZyEVUwFepMKoAC+TJFNOx2muA96rIawiMndNVQEiAS8dvU4G+uHuKQrwQScXPjY6VNJ7xUZH8AbCua77orIF+FSs5AJ8z4wL8LUAjA716ZwqIqfNbCILgJuB++b6nrkIGnXAi4icVtbaU9batLU2A9wNXJJ9zhjTCvwQ+PfW2oOzfX2Bfb9hrd1urd3e3DzNXaUz9PShXi7kVWzrpZWbse45g8pkHi+s3ja+AH/4MTBeJ8deRCrGGXRmkYUSTzu/PGU74AM+dcCLyNwZY2hvjHCsd5ShWJLOoTjrmgsX4P1hpwA/ODj7bvFoIk0qEaWutmb6nX0TO+BThfebIoKmbzRByO8hEvBN/V5uAb6KUU70axCriJSXMSaC0yn/T3mbLfAzY8yvjTG3zfRYQUXQiIgsCGPMyrxvbwT2uNvrgZ8At1trn5jt6xfK0f27WePppPosFYfnTesOOLkHEu7vK4cedYryodryrktExlEBXibJdmYGvWMRNHF1a4rIPGhtiNDRO8qRbucCcf2y6oL7Bd0C/PDQ7AvwTx/qIUiCZfV10++clwFfFw4wUCwDfqoO+OEEjZFput8hdxFcQ5QODWIVkfJ7N/DEhPiZN1trtwHvBD5hjCl4//rEWIKxIay6XhQRmS/GmPuAp4CNxpjjxpiPAX/jzup4Ebga+E/u7p8EzgI+nTfno8U9zjeNMdlYsWKvP/16DnLzK3/AsKnGu/U9C/a2i17rDrBpeGM3xIfg9V8rfkakAk3TrrfwrDtwr+KGcSwhybTz38Dvc/4bBH2KoBGR+dHeGOGJA90c6h4GYH2RDvhgldO9Pjw0OOv3eGR/J+eSpKF+Bl0f2Y72ZHT6CBqPD7yTC+19owkaq2dQgA86P1O1iXK8Tx3wIlJ2NzEhfsZa+7r72GmM+SFONMFjE19orf0G8A2A7du327EMeHXAi4jMF2vtzQU2f6vIvp8HPl/kuY/nff3R+VndLHW9iv32u/Gk4/zgvL/jPzSsLcsyFqXV7mcrx3c6BXibhnVvLe+aRGSSiuqAT6UzXPpXv+CB3SfKvZQlLVtsD3idX6Y0hFVE5kt7Y5hoMs3OI70YMzaYdaJIxCmeR0dKKMC/2kWVJ4kvME3+O4DX72QkugX4kUS68PkuOQr+qoJZlT0jCRpm0gHvFuDrTJSOXnXAi0j5GGPqgLcCD+RtqzLG1GS/Bq5jhtEEyoAXEZGiOl+Ge3+DVCrJTYlP0bq5aOy8lKK6GRrWOgX4w4+BNwht+jsWqTQV1QE/kkjTORTncPdIuZeypGWLT4G8DPiMdT4g8Xkr6jMbETnDtDc5RfFHX+1idX2YbNfkRJFqpwAfG5ndENbD3SMc7RklGE6CLzj9C4xxYmiSUepqnH8SB6NJr63exQAAIABJREFUmqonvDYxAkUK+n0jCdY1zaDY72bAt1el6FAHvIicJm5kwVXAMmPMceBOwA9grf2au9uNwM+stfkX3cuBH7p3ofqA71lrH57Je2Yz4ONJNWyIiEieky/Bd94LHj//fMHf8eqjcbataSj3qhaf1h1w+HHoPeIU37MxmyJSMSqqAJ/tmlH3THll8979XqfTM1uIT6YtvsK1MhGRGWlrcArVHb1Rrjx7WdH9qmudYnU8Ojyr4z+yvxMPGbw2OfMLT38YkqPUu13s/UUL8IXjcnpHEjRUzaQD3vmZVkdS7FQGvIicJkUiCybucy9w74Rth4ALSnnPXARNStfwIiLiOvE8fOd9zjX0b/+YRx7qp71xkGUTr7Nl7lp3wEv/CMMn4ZpPlXs1IlJARbUzRxMqwFeCiR3wfrfrXTnwIjJXrQ1jneLrlxUuaAOEwk5cSyI6uw74R/Z3sbHJLYbPpAMenAJ8KkZd2A9QOAc+OTqWF58nnkozHE/RNKMCvPMzrQgm6ehVB7yILB6KoBERkXGO/xq+/V7n+veWn2Ab1/PcsT4uaq8v98oWp9btY18r/12kIlVUAT7bNRPVxXtZjWXAj0XQAMTT+u8iInMTDnhpqXEK4+umKMAbt9s8FZt5JFksmebpQz1cfZZT6MY3ww54n9MBXztVAb5IB3zfiLPvjDrgfQHwhWj2x+gejuc+dBYROdNlO+AVQSMiIgD86A8gVAe/8yA0ruPEQIxTg3G2tSt+5rRYfp6T/R6ohlUXlXs1IlJARRXgxzrgdfFeTrkCfDYD3o2iSaZt2dYkIotHdvDq+ubq4ju5eevp2MwjaJ461EM8leHKddkC/Cw64N0hrAADozMvwPeOJABm1gEPEKyhwRcH4PV+xdCIyCIQ7cPv9eD1GEXQiIgIJKPQ9TJccBPUtwPw/LE+ABXgTxdfANZfBRvfCV5/uVcjIgVUVgE+qQ74SlBoCCsogkZE5kebW4CfqgM+G/diEzPvgH90fxchv4dtK0PuMWaaAR+BZJT6yOwjaLIF+IbITAvwtdQZJ35GMTQisigMngAg5POoiUZERKBrP9gMLN+S2/Tc0X5Cfg+bVtaUcWGL3M33wY1fL/cqRKSIiirAawhrZUiks0NYx2fAZwvzIiJzcUFrHctrg6yqn6JA7vGSMAGn8D1Dj+zv5PL1TQRxC+ildsAXjKAZLdwBP+oU4Btn0QFfhVuA1yBWEVkMrHOHZMjv1TW8iIhA5z7nsWVrbtNzx/o4f3V9rrYgp4HH6/wRkYpUUWe/bNeMLt7La1IGvIawisg8+veXr+WxP78ar8dMuV/SE8bMsAB/pHuEIz2jXLWxBVJuZ/lMM+DdArzf6yES8BYpwA8X7IDvG5l9AT6QHiHo83C8Tx3wIrIIWOf60CnA61pRRGTJO7XXySNvXA9APJVm34lBLlqjAawisnRVVAE+mwGvCJryShSLoFEHvIjMA4/HEPRN352R8obxpmZWpH701S4ArtrYDCknY312HfBOob8+7Ke/UAZ8snAHfM9IAmOgfqYRNKE6TGyQ1Q1hOnrVAS8ii4DNgLUE/R5lwIuIiNMB33wOeH0A7Hl9kEQ6w0Vtyn8XkaWrsgrwSQ1hrQRJdcCLSAVI+8IEbGxGd0U9sr+TdcuqWNNU5Qx+gllkwIchFQOgNuyf3AGfSTvPFyjA940kqA/7p+3mzwnWQHyItoaIImhEZJGwkIoR8nmJq4lGREQ6Xx4XP5MbwKoOeBFZwiqqAJ8tsmQ74aU8chnwGsIqImVk/REixAtHwuSJJdM8daiHt57T7GyYbQe8b6wDvi7sZ3Di+2UHwRYZwtow0/gZgGAtxAdpawwrgkZEFo/EKCG/hrCKiCx5o70w9Ma4AazPH+untSFMS02ojAsTESmviirAZwvvcd2+WlYTM+A1hFVEysIfIWzi9LmDTot55nAvsWTGiZ+BkjPgwSnATyr4Z3PoCw1hHUnQNKsCvNMB31ofpn80yVBs6g8XRETOCMkRDWEVEZGiA1gvalf8jIgsbRVVgM/mRqoDvrwSaQuA3+tEKqgDXkTKwROoIkKscCZ7nicOdBPwebhsfZOzYdYZ8BFIJyCTpj7ipz86oeCf7YAvUoBvmGn+OzgFeJtmba1zfu3oVRe8iCwCyahTgFcTjYjI0nbKLcC7HfBvDER5YyDGtnbFz4jI0lZRBfhowinwxlIZrLVlXs3SlUhlCHg9GDOhAK8OeBFZQJ5QFRHi0xbg958c4pzl1YT87mDXUjLg3dcV7ICfKoJmNEFT9SwK8KFaANqrUgAcVw68iCwGiRGCPkXQiIgseZ17IVQPNSsBeO5oPwDb1AEvIktcZRXg3dtW0xlLMq0CfLkk05lc0R00hFVEysMXqiZs4gxM7Eif4GDXMBuaq8c2zLoDfnwBPpbMjI9CKxJBY62lb9Yd8E4BflXYKcB3KAdeRBaDbAe8ImhERJa2U/tg+VZwm/meP9ZH0Odh88raMi9MRKS8KqoAn3/RrltYyyeRyuTiZ0Ad8CJSHoFQ9bQd8NFEmtf7oxMK8CVkwAMkR6kL+wHGd8EXiaAZjKVIZSyNsx3CCtR5olQFvHT0qgNeRBaBpIawiogsedZC58vQMjaA9bljfZy3um5cg5+IyFI0o7OgMeZ6Y8x+Y8wBY8ztBZ7/PWPMS8aY3caYXxljthQ6znTGFeCVA182idT4DvjcEFZ1wIvIAvK5Bfi+KQrwh7tHsJb56YBPxahzu9kHRgsU4CdE0PSOOJ35syvA1wBg4oO0NUY4rg54EVkMEiMEfV7i6oAXEVm6BjogMQQtmwGIp9LseX2QbWsUPyMiMm0B3hjjBe4C3glsAW4uUGD/nrX2PGvthcDfAF8sZTHRvIv2qC7gyyaZzuSK7qAOeBEpDxOoImBSDI0W7xI/2DUMwIaWvO70ZBR8odytr9PKFtaLdcAXiaCZSwGe+BCtDWFlwIvI4qAhrCIikhvAuhWAfScGSaQzXNSmAawiIjPpgL8EOGCtPWStTQDfB96bv4O1djDv2yqgpAD3aF7Xu25hLZ94kQx45fKLyIIKOIXx0eGhorsc7BrGGFjblFccT8Vn3v0OTrEechnwMLMImpIK8O4QVmKDtDY4HfAaOi4iZ7zkCCG/h2Taks7onCYisiR17nUe3Q745465A1jVAS8iMqMC/GqgI+/74+62cYwxnzDGHMTpgP/DQgcyxtxmjNlljNnV1dU16flYMp1rWFQHfPkkU5lc0R3I5cHHFUEjIgvJ7UyPjw4X3eVg1whtDRFCfu/YxlR05vnvee+TX4Dvn0EETd8cO+DbGiMMx1NTZtyLiJwREqO587AGsYqILFGn9kFdG4TqACf/fXV9mOW1oTIvTESk/OZtEoa19i5r7Qbg/wE+VWSfb1hrt1trtzc3N096PpbMUBvyu1/r4r1cEhM64I0xBLweEirAi8hCcjvOY6NTdMB3DrOheXxn+qw74HNDWKPUzyKCpqeUAnxgfAQNQIdiaETkTJeMEnKvHXUNLyKyRHXuGzeAdfexfi5qV/yMiAjMrAD/OtCW932ru62Y7wPvK2Ux0WSahog/97WURzI9vgMenC74pDLgRWQhuR3nqVjhDvhMxnKoe3j8AFZwMuD9s+mAHyvA1xaLoPGFwOMd97K+0QQhv4dIwDfz9/L6wF8F8UHaGpyfT4NYReSMZjxuBI1zjtQdkyIiS1AqAd2vwnKnAH9qMMbr/VEualf8jIgIzKwAvxM42xizzhgTAG4CfpS/gzHm7LxvfwN4rZTFRJNp6iNOJ2FcBfiySaTGD2EFZxCrOuBFZEEFpi7AnxiIEktm2NAyoQBfagd8KorXY6gJ+iYX4CfEzwD0DCdojMyi+z0rWAPxQdqbIngM7O7on/0xREQqhfHkhrCCOuBFRJakngOQSUGLM4D1uaN9AGxTB7yICDCDAry1NgV8Evgp8DLwA2vtXmPM54wx73F3+6QxZq8xZjfwn4HfLmUxsUQ6dyu/OuDLJ5EaH0ED4Pd61AEvIgvL70S+eNPRggWdg11ONvukDvg5ZMAD1Ib9kyNoAtWTXtY3mqBhNvEzWaFaiA9RHfRx/bkr+P6zxxiJp2Z/HBGRSmA8bgZ8NoJG14siIktO5z7n0e2Af76jn4DPw9ZVdWVclIhI5ZjRffPW2geBByds+3Te1380H4uJpdI0uN2Eungvn0TaTirAqwNeRBac2wEfIc5gNDl+0CpO/jtQJAN+FsOechE0ThZ7fcQ/uQM+UKADfiQxu/z3rGANxAYB+PiV63nwpZP8YFcHv3PFutkfS0Sk3NwImmC2Az6lJhoRkSXn1F7w+KDJCUd4/lgf566qnVRXEBFZqirmbJhMZ0im7VgGfEIX7+WSSKUnZcAHfB4S6oAXkYXkdsCHidM3mpz09MGuYeoj/slF8NlmwGeL9W4HfN3EDvgiETR9cynAx53BstvaG7h4TQPf+tVhUjrHisiZKBtB41MEjYjIfDLG3GOM6TTG7Mnb9hljzOvGmN3unxvynrvDGHPAGLPfGPOOIsdcZ4x5xt3v/7gxw3PXuc8pvvucwx3tGeXslpp5ObSIyGJQMQX47MV6gyJoyi5ZqAPeqw54EVlg2Q54E6d/NDHp6YNdzgBWY8z4J2abAW+ME1mTV4Af937JUQhUTXpZ6QX4WogP5r699cr1HO+L8tO9p2Z/LBGRcjMeSIzkImjiuotVRGS+3AtcX2D7l6y1F7p/HgQwxmzBmde31X3NV40x3gKv/a/u688C+oCPzctKT+3Lxc+k0hm6h+O01M7ielxEZJGrmAJ8tuBeG/JhjIawlpMzhHV8QUsd8CKy4Nyu8zAx+qOFOuBHJsfPwOwz4MHpmHcL8Kvqwxzvi5LOWOe5xMikAnw8lWYonipxCGttrgMe4O1blrOmKcI3Hj+EtXb2xxMRKSfjgeSohrCKiMwza+1jQO8Md38v8H1rbdxaexg4AFySv4NxulauAe53N30beN+cFxobhIFj0OIU4HtGEmQstNTOIhJSRGSRq5gCfLZbJhzwEfZ71QFfRom0hrCKSAVwi94R4gxMiKAZiCbpGopPHsAKs++Ah3EF+E0raoinMhztcYa8Foqg6XfX01hd+hDWLK/H8LE3r+OFjn52He2b/fFERMopG0GjDHgRkYXySWPMi25ETYO7bTXQkbfPcXdbviag31qbmmKf2et82XlcvtX5djDufFujDngRkayKKcBnC+4hv4eQ36shrGWUTGXwT8yAVwSNiCw0rx/r8RMxcfomRNAc6soOYC1QgE/GZpcBD87+qWwBvhaAV066RfICETQ9w856SuuAdzPgM2Pn1A9e3Ep9xM/djx2a/fFERMrJMz6CRtfwIiKn1d8BG4ALgTeA/3663sgYc5sxZpcxZldXV1fxHTv3OY9uB/ypwRgAy9UBLyKSUzkFeHfoatjvVQd8mcULdMA7ETSKRhCRBRaIUGXikyJoDnY53ekbWgp1wMfm1AF/9vJqPCavAJ+YXIDPfiBQ8hBWLCSGc5siAR8fuXQN//ryKQ53j8z+mCIi5eJ2wAc1hFVE5LSz1p6y1qattRngbsZiZl4H2vJ2bXW35esB6o0xvin2yX+vb1hrt1trtzc3NxdfVOc+CNRAfTsAp4ZUgBcRmahyCvDJsQJ80O/RxXuZWGtJpjMEvZMjaNQBLyILzfirqPelcpEvWQe7hvF7DW0NEzrdMxlIx0vIgI84ne5AyO9lbVMV+08OgnUL5RMiaHpG5lKAdzrs82NoAP79m9bg93j41q/UBS8ipXMjCTqNMXuKPH+VMWbAGLPb/fPpvOeuN8bsN8YcMMbcPrM3zGbAqwNeROR0M8aszPv2RiB7rv8RcJMxJmiMWQecDTyb/1rrDBv6JfBBd9NvAw/MeVGn9kHLZjDOHLlTg3GMgWWlRDWKiCxSFVOAzxbcQwGnA14F+PJIZSzWMimCJujzkFCmp4gstECEWm+Cgej4CJqDncOsbarCN+FcRdrJnMQ/y44bXyjXAQ+wcUUN+08OQToBNj25A35OBfga5zE+OG5zS02I9120ivt/fTx3fBGREtwLXD/NPo9bay90/3wOwBjjBe4C3glsAW42xmyZ9t2MG0HjzRbgdb0oIjIfjDH3AU8BG40xx40xHwP+xhjzkjHmReBq4D8BWGv3Aj8A9gEPA5+w1qbd4zxojFnlHvb/Af6zMeYATib8t+a0SGuhc69TgHd1DsZYVh2cfJ0uIrKE+abfZWHkCvA+rzLgyyg7aHXyEFZDUhE0IrLQ/BFqPImCHfBnt9RM3j9bRPfNsgDvj0B0bADqxhU1PLz3JKMjg0Rgcgb8SAJjoL6kDPjCHfAAH79yPT/YdZz//fRR/uDas2d/bBFZ8qy1jxlj1pbw0kuAA9baQwDGmO8D78Up5hRnPIDFk0kQ8Ho0hFVEZJ5Ya28usLlowdxa+5fAXxbYfkPe14cYi62Zu6GTzjW0O4AVnAz45bUawCoikq9iPpLMRdAElAFfTtmYmUlDWH2KoBGRMghUUeWJ05dXgE+mMxztGWVDS9Xk/VNuB/ysC/DhcR3wm1bUYC0cOdHpPj8+gqZvJEF92I/XY2b3PgAhtwAfG5z01DnLa3jrOc18+6kj6iIVkdPpcmPMC8aYh4wx2arJaqAjb5/j7rZJ8gfzDY848V0kRwn6PcTVRCMisnR07nUeW8ZumDo1GGd5jfLfRUTyVU4BPuFcrIf9Tgd8diirLKxskb3QENZsd7yIyILxR4iYOAOjY5Esx3pHSWUsG5oLDWAttQM+PCGCximSH32jy9kwoQO+dyRBQynxM1A0gibrtresp3s4wQO7i87EEhGZi+eANdbaC4D/CfzLbA+QP5ivusb9UDEx4t7Fqmt4EZEl45R7k1ReB3znUJwWdcCLiIxTMQX4WN4Q1pBft6+WS6JoBI064EUWs+kG7xlj2o0xvzTGPG+MedEYc0Oh48y7QISwjdMfHeuAP9g5DFCkAF9iBrw/PFa8B9obI4T9Xo539rjrmFyAbyq5AF88ggbgTRua2LyylrsfP4wzK0tEZP5YawettcPu1w8CfmPMMuB1oC1v11Z329SMe82YjDrX8CrAi4gsHZ0vQ/UKiDQCzp2qPSNxWtQBLyIyTsUU4KO5IaweZwirOuDLItcBXyCCJq4OeJFFaYaD9z4F/MBaexFwE/DVBVmcv4qgjTGaSBN3P5g92DUCwPrmAhE0JWfAj++A93oM5yyv5lR3t/v8+Aia3pEEDaXkv8O0HfDGGD58aTsHOoc51jta2nuIiBRhjFlhjDHu15fg/D7QA+wEzjbGrDPGBHDO9T+a/oDZAvwIIZ/mOImILCmde2H52K8N3cNxrIXltSrAi4jkq5gCfCyZxhin8Bvye4mp27ossoNWJ0XQeJ0IGnVjiixKucF71toEkB28l88Cbus2dcCJBVlZIEIgEwNgwM2BP9g1zPLaIDUh/+T9S86Aj0ByFPLOcRtX1NDV6w5mDYzvtu8dTdBUXWIBPlANmKId8ABnud39Hb3RovuIiBRijLkPeArYaIw5boz5mDHm94wxv+fu8kFgjzHmBeArwE3WkQI+CfwUeBnnQ9e9079hfge8V3exiogsFZk0dO2flP8OaAiriMgEvnIvICuaSBP2ezHGEA4oA75cig5h9XqwFlIZi99bwtBBEalkhQbvXTphn88APzPG/AFQBbyt2MGMMbcBtwG0t7fPbWX+CP6MU4TujyZpqQ1xsGu4cPwMlJ4Bn90/FXO64XFy4J97bgQCQGCsA95aS99cOuA9HqcLvsAQ1qy2RmcNHX3qgBeR2bHW3jzN8/8L+F9FnnsQeHBWb5gtwCdGCfkjGsIqIrJU9B5yrp3HFeCdxhl1wIuIjFc5HfAppwAPEPI5GfDqtl54ibTzwUehIayABrGKLF03A/daa1uBG4DvGmMK/huSP5yvubl5bu8aqMKbjmHI0D+axFrLwc6pCvClZsC7Bfa8GJpNK2qImNj454HBWIpUxtJYagY8OAX4KTrgV9aF8XkMHYUiaE7ugVPTN6WKiCyI/AgadcCLiCwd2evRvAiaTrcAryGsIiLjVVAHfIZQtgAf8GItxFNj22RhJFLOhx4Tu9yzHfGJVIZSmz7lzJRMJjl+/DixWKzcS1k0QqEQra2t+P0FIlTKYyaD9z4GXA9grX3KGBMClgGdp3VlbuE7TIL+0QTdwwkGYyk2FMp/h7llwOe/HieCJoJb0M+LoOkdSQDMsQBfWzQDHpwM+lX1YTr6CkTQPPAJ8Pjg1l+U/v4iE+hcP78q8Dx/+uRF0AR9XrqHE+Vdj4gUpPP8/FtS5/pCeg44j8s25jadGozj9RiaqlSAFykHnevn13ye5yumAB9Lpgn5nQv4kM8puseTKsAvtITb4R4s0gGfUAf8knP8+HFqampYu3Yt7sw2mQNrLT09PRw/fpx169aVezlZucF7OIX3m4APT9jnGHAtcK8xZjMQArpO+8oCTqE9Qpz+aJKDXcMAbGiZpgN+Hgrwy6qDLAumIMO4CJpsAb5hzh3wxQvw4MTQTOqAz6Sh82WnAJ/JOHE2IvNA5/r5U6Hn+dMnex5KjBDye4gn1QEvUol0np9fS+5cX0hswLnmzrtOPjUYo7k6iNej/42JlIPO9fNnvs/zFfObezSZJhxwiu3Zx6gu4Bdc0s2AD3jHf/ARyOuAl6UlFovR1NSkk/c8McbQ1NRUUZ9IFxu8Z4z5nDHmPe5ufwLc6g7tuw+4xS5ETphbGA+bGP2jibEC/HxnwGcL8KnxHedtVRkymHHHyxbgm05jBA1AW0OE4xMz4HsPQzoOyREYOFb6+4tMoHP9/KnE8/xpNXEIq67fRSqSzvPza8md6wuJDzrXtHk6h+IawCpSRjrXz5/5Ps9XTAd8dggrkHtUAX7hZTvc/b7x/2fNdcCrAL8k6eQ9vyrx77PQ4D1r7afzvt4HXLHQ68pG0NR4EvSPJjk5ECcS8LKi2GCnkjPgJ3fAA6wIZxgdDBG2kE3m6st2wM8ljytUCwMdU+7S1hihezjBaCJFJOD+c925b2yHzpehYW3paxCZoBLPTWeqJfV3mSvAjxLye4jpWlGkYi2pc9MCWPJ/n7FBJ1Yxz6nBGK0NkSIvEJGFsOTPTfNoPv8uK6YDPpZKj2XAu1E06qBZeNkhq9mO96xsBnwyrcG4IrKA3AiallA6F0GzblkVnmK3tZacAZ8dwjq+47w5lGaUIMfyomB6R90O+Oo5dsDHpo6gaW1wPhQ4np8D3/ky4P7s+cV4EZGyMeDxOxE0PnXAi4gsGfEhp6kkjzrgRUQKq5gCfDSRX4BXB3y5xN2uJb+3SAa8upqkDPr7+/nqV78669fdcMMN9Pf3T7nPpz/9aX7+85+XujQ53dzC+LJAioFRpwBfNH4GSs+Az+4/oQO+0Z9g1AbZf3KsWN47kiDo8+Tu1ipJsHb6CJpG52cflwPfuQ8a10Fdu1uMF1kcdJ4/wwUi4yJoFiKhTETOPDrXLzITImjiqTS9IwmWF7tTVUQWPZ3ni6uYAnwsORZBky3Aq4Nm4WUL7BrCKpWk2Ek8lUpN+boHH3yQ+vr6Kff53Oc+x9ve9rY5rU9OI3eoU1MgzRsDUV7vj05TgI+CNwizvVUs1wE/vgBf60kySohXTo4Vy3tHEjRVBeZ2O1qw1slxTxf/33BbQ6EC/MvQsgVaNqsAL4uKzvNnOH8VJEcI+jxkrO6YFJHCdK5fZOJD4yJouoacRhh1wIssXTrPF1cxBfhocnIGvArwCy8bQTOxA97vhh+rA17K4fbbb+fgwYNceOGF7NixgyuvvJL3vOc9bNmyBYD3ve99XHzxxWzdupVvfOMbudetXbuW7u5ujhw5wubNm7n11lvZunUr1113HdGoU2i95ZZbuP/++3P733nnnWzbto3zzjuPV155BYCuri7e/va3s3XrVj7+8Y+zZs0auru7F/hvYYnyOxE0TYEke04MYi1saKkqvn8qPvv8dyiaAe9NjZLxhXnljfEF+Ia5DGCFsW6hRPEu+GXVAcJ+Lx3ZCJpUHHoOOMX3lk3Q/Sqkk3Nbh0iF0Hn+DOcPQ2J0rIkmpWt4EZlM5/pFJjYIobrct6cGnQJ8izrgRZYsneeLq5ghrLFkhnBgYge8ir0LLVtgD0zogA+qA16Az/54L/tOTJ1bPVtbVtVy57u3TrnPF77wBfbs2cPu3bt55JFH+I3f+A327NnDunXrALjnnntobGwkGo2yY8cOPvCBD9DU1DTuGK+99hr33Xcfd999Nx/60If4p3/6Jz7ykY9Meq9ly5bx3HPP8dWvfpW//du/5Zvf/Caf/exnueaaa7jjjjt4+OGH+da3vjV/fwEyNbcDvt6Xyp2fpuyAT0ZnHz8DeQX48RnwJEbwBKvYf2p8Ab5xrgX4bF5mfAjCDQV3McbQ2hAe64Dvfg1s2inAp5OQTkDvIWjeOLe1iExQjnO9zvNnuFwEzdgcp9qQv8yLEpFidE2vc/28mBBB0zkYA2B5jQrwIpVA1/SVdZ6vqA74oHvRnu2AjybUPbPQckNYfUWGsKoDXirAJZdckjuBA3zlK1/hggsu4LLLLqOjo4PXXntt0mvWrVvHhRdeCMDFF1/MkSNHCh77/e9//6R9fvWrX3HTTTcBcP3119PQULhgKqeB2wFf53MGnxoD65ZN0wE/lwJ8KjZ+e3IUf7iGIz0juX+T5qUAn/1lZZpBrG2NkbEO+GzkTDaCBjSIVRYtnefPMNkIGvcaPq4mGhGZAZ3rz2CZzKQImlPZArwiaETEpfP8mIrogE9nLIlUJi8D3u2e0e2rCy7bYerzjM82Vga8ANN2tSyUqqqxAuwjjzzCz3/+c5566ikikQhXXXUVsVhs0muCwbELQa/Xm7uNqdh+Xq932pwyWQBuB3ytxynAtzaEc3dJFZQqsQP4i9gdAAAgAElEQVTeVziChsQIkdparIXXOoc4v7WevvkswE83iLUhzM7DvVhrMZ37wOOHxg1OJ7zxOEX5rTfObS0iE1TCuV7n+TOMPwyJYc1xEjlDVMJ5HnSuP6MlhgE7vgN+KI7fa2iIzPE6WUTmRSWc63WeH1MRHfDZi/RcAT6gDvhySaQtAZ9n0nDBQLYDXgV4KYOamhqGhgoXKgcGBmhoaCASifDKK6/w9NNPz/v7X3HFFfzgBz8A4Gc/+xl9fX3z/h5ShC8EGKq9TgF+yvgZKD0D3usDb6BgBE1NjZNt+crJIeKpNEPxFI1z/cUi6OZlTleAb4wwFE8xEE06xfams8AXcIpdjevVAS+Lhs7zZ7hAlZMB78tG0Oh6UUQm07l+EYm7d3GG8jvg47TUhPBMaOYTkaVD5/niKqIDPleAz2bA+9Q9Uy6JVCZXbM+XjaCJK4JGyqCpqYkrrriCc889l3A4zPLly3PPXX/99Xzta19j8+bNbNy4kcsuu2ze3//OO+/k5ptv5rvf/S6XX345K1asoKamZvoXytwZA4Eqqowz1GnaAnypGfDgdMFP7IBPjlJdU0fI72H/ySH6R52hp43V89UBP3UETWuDcwdAR2+U+s59sPrisSdbNo/F0oic4XSeP8P5w5Ac0RBWEZmSzvWLSLaJJC+CpnMoRnON4mdEljKd54uriAJ81C20Zwvvfq/B6zHqnimDRDo9Kf8dxoawqgNeyuV73/tewe3BYJCHHnqo4HPZHLBly5axZ8+e3PY//dM/zX197733TtofYPv27TzyyCMA1NXV8dOf/hSfz8dTTz3Fzp07x90WJaeZP0KYGRbgS82AB7eAlFeAz2QgOYonWMU5y2vYf3KInmGnE3/OHfC5IazTZcA70Tgnuro4r/8oXPTRsSdbtsArP3HWnM2wFzmD6Tx/BvNnh7CqiUZEpqZz/SKRnWM0IQN+yllNIrIk6DxfWEUU4LMX6dnoGWMMYb83V5iXhZNM2Sk74BPqgJcl6NixY3zoQx8ik8kQCAS4++67y72kpSUQocGfZNOKGi7f0DT1vqkohOpKe5+JBfhsHI0/wsblNfxyfxd9o24BfgGHsAKMHN/rbMgOX81+bTPQ/SqsvGBu6xFZ4nSen6NsBI1fETQiUrl0rp9H2Q74CRE0l62f5lpdROQ0quTzfEUU4KMJ5yI9nDdYL+T3qHumDBLpDH7f5My23BBWFeBlCTr77LN5/vnny72MpctfRcjGefiP3zL9vqVmwINbgM/LgM9+Hahi44oa/vHXx3ntlPPLxpwL8P4IGO+0GfC1IT91YT90vuhsGFeA3+I8dr6sArzIHOk8P0fZCJpcBryu4UWk8uhcP4/iA86j2wEfS6YZiCZZXlvidbiIyDyo5PN8ZQxhTY0fwgoQUgd8WSTShTPgA4qgEZFyCUQgMTKzfeeSAe8PQypvCnv2PQNVbFrh/HLx5MEeABrmWoA3xumCn6YAD04MTaT/NSejvmHt2BON653Bse4g1sFYkrd98VGePdw7t7WJiMyWPwI2Q8jjXLtrZpCIyCKXi6Bx7ursHHTiIluUAS8iUlBFFOCjCTeCxj+2nJDfS1y3ry64RCqTi5vJ53MnmasDXkQWnD8yvjN9KnPKgI+Mj6DJFuD9ETaucH65eOZwL8ZAfdhf2nvkC9ZOmwEP0NYQYdnoQWjeCJ6xD6rx+mHZOblBrL8+2seBzmF2HlkiBXhroe9IuVchIuBE0ABh63yIqQ54EZFFbkIEzakh5/yvDngRkcIqowCfzYDP64BXBnx5JFKZ3MDVfMYYAj4PibQtw6pEZElzs4VnJDXHDvgiETTNNUGaqgIMRJPUhf34CnxQOWsz7oCP0JY6is2Pn8lq2ZwrwO8+1g/Aif7o5P0Wo8f/Fv7HBfDcd8q9EhFxB0EHceZkqAAvIrLIxQcBA37nA9hsB7wK8CIihVVEAT57kR4OKAO+3JLpwh3wAAGvRx3wIrLw/BFIzjCCZi4Z8L5Q4Q54t7Nz00qnC37O+e9ZoVqIDUy724aqOMtNH8N150x+smUzDHRAbJDdHUuoAH/safjlXzuxPA/+OXTtL/eKRJY2twATwjn/KIJGRGTujDH3GGM6jTF7Cjz3J8YYa4xZ5n7/Z8aY3e6fPcaYtDGmscDr7jXGHM7b98KSFhcfcu7m9Di1g1OD2Q54RdCIiBRSWQV4ZcCXXSKVyeW9T+R0wOu/iVS+6upqAE6cOMEHP/jBgvtcddVV7Nq1a8rjfPnLX2Z0dKwj+oYbbqC/v3/+FiozE4jMrAPeWifD/TRE0ABsXO7cYtsYmacC/Aw74M/2HAfgjcC6yU+6g1ht58u8cDxbgI9N3m8xifbBP30c6tvg9x53/vdx/3+A5CL/uWUcnecrjNsB709HMUYd8CIyP3Su517g+okbjTFtwHXAsew2a+1/s9ZeaK29ELgDeNRaWyyX8M+y+1prd5e0sthgLn4GnAiagM9D3XzENIrIkrGUzvMVUYAfy4CfUIBP6OJ9oSXTxQvwfq8hmVIEjZw5Vq1axf3331/y6yeexB988EHq6+vnY2kyG/6qmWXAp5xbX+cWQZNXgM+LoAHYtGKeO+CDtTMqwLcmjwBw0NM2+Uk3lqbn8Av0jyapCfkWdwe8tfCjP4ChN+AD98Cys+F9X4NTe+Bnnyr36qQMdJ6vEAHng0qTjBHyeVWAF5F5tVTP9dbax4BCRfQvAX8OFPvl/GbgvtO1LsCJoHEHsIITQdNSE8QYc1rfVkQWp6Vwnq+MArw7bDU8IQNet68uvHiRIayQ7YDXfxNZeLfffjt33XVX7vvPfOYzfP7zn+faa69l27ZtnHfeeTzwwAOTXnfkyBHOPfdcAKLRKDfddBObN2/mxhtvJBodK1L+/u//Ptu3b2fr1q3ceeedAHzlK1/hxIkTXH311Vx99dUArF27lu7ubgC++MUvcu6553Luuefy5S9/Ofd+mzdv5tZbb2Xr1q1cd911495HShSION3odpoPAFPu3/V8d8C7BfiN816Ar5nRENbG4YMM2givjdZOfrKuHfxV9B99AYC3b17OUDzFYCw5P2usNLvugZd/DNd+Glovdradcx1c9gnYeTe88pPyrk9KpvP8Gc6NoCE54sZI6npRRCbTuX7ujDHvBV631r5Q5PkITtf8P01xmL80xrxojPmSMaa0zJj4oNNM4jo1GFP+u4joPD8F32k9+gxlo2byh3+G1QFfFlN1wAe8KsAveQ/dDidfmt9jrjgP3vmF/5+9O4+Pq673P/76zp5M9r37vqTQ0kJlEQoIiKAs6pVNBUGxgrjrdfvdq1fUe6/iFa/7BQXUiyAXLVQpAkoV2VuWlm5039u0SZqlyezz/f3xPTOZJDOZSTKZJf08H48+JnPOmXO+SeH05HM+5/0dcpNrrrmGz3zmM9x2220APPTQQzzxxBN86lOfoqKigtbWVs4880yuuOKKlF0XP/vZzygtLWXz5s2sX7+eU089Nb7u29/+NjU1NUQiES688ELWr1/Ppz71Kb7//e+zevVq6urq+u3rlVde4d577+Wll15Ca80ZZ5zBeeedR3V1Ndu2beOBBx7g7rvv5uqrr+b3v/89H/zgB0f5QzrBOUsBK17GijlIKtYBP9IMeKenr4gPgyJo5jaW47ApGrL1y0WGETSO1i3stE1l37EkFwQ2GzTMx3ZkM6WuyzlvXj1/eO0Ahzr8VDSNs0eAWzbBE1+FWRfCWZ/sv+6ir8OeZ+HR22DCYqiclJ8xjhd5ONfLeb7Ixc7NwV7cDq90wAtR6OSaHii+c71VXP8qJn4mlcuB54aIn/kKcBhwAXcBXwJuT3G85cBygKlTp/Zf6e+Csob425Yuf7xZRQhRIOSavqDO8wXRAR8IRfA4bdhsfT98j9OGPywX77kWjERxpeiAd8okrCJPlixZwpEjRzh48CDr1q2jurqapqYmvvrVr7Jo0SIuuugiDhw4QEtLS8p9PPPMM/GT6aJFi1i0aFF83UMPPcSpp57KkiVL2LhxI5s2bRpyPM8++yzvec978Hq9lJWV8d73vpd//OMfAMyYMYPFi81cRqeddhq7d+8e5XcvYh3oaXPgQ6PtgC+BaBgiVvf4gAiaEped333sTG566/SR7X8gT4W5qRAOpt5GaziyiRbPTPYdS/H9NzRT07ODhZMqmVxtbhaMuxiaYC88fJPptHrPz+MTfsU53PC+e83f3R8+ClG5fig2cp4vcrHzdMhnXcPL9aIQYjA514/aLGAGsE4ptRuYDLyqlGpK2OZahoif0Vof0kYAuBc4fYht79JaL9VaL62vr++/MtCdJIJGOuCFONHJeT61gumAT8x/B/C4pAM+H4Lh1AV4t0MK8Ce8NF0tY+mqq67i4Ycf5vDhw1xzzTXcf//9HD16lFdeeQWn08n06dPx+4c/CeOuXbv43ve+x5o1a6iurubGG28c0X5i3O6+pzjtdnvBPK5a1KwOdEI9QG3q7UadAR87Ti/YK00HvN0F9r5O8tOm1Yxs38nEHtsNdIMjxffVfRj8HXQ3zmZfe/L/lsJ186nS/8tbJ0SZVGW6UA92jrP/7p74ChzdAh/8Q79uq35qZ8G7/gtWfAyeuQPO/3Juxzie5OlcL+f5IpZwnvY4JQNeiIIn1/RpFeK5Xmv9BhC/ELKK8Eu11q3W+0rgPCBlC6dSaoLW+pAyrafvBjaMaDAJETS9wTDdgbBE0AhRaOSafki5Ps8XRAe8Lxjpl/8O4HGYDPhoVCb9zKVQRA8xCauNkETQiDy55pprePDBB3n44Ye56qqr6OzspKGhAafTyerVq9mzZ8+Qnz/33HP57W9/C8CGDRtYv349AF1dXXi9XiorK2lpaeHxxx+Pf6a8vJzu7sERIcuWLeORRx6ht7eXnp4eVqxYwbJly7L43Yp+Mu2AH20GfOxzsU76YE9fUWksxAvwQ+TAHzF39CN1zRzq9CU9B++2TQPgrLIj1Je7cdjU8DrgQ2m68PNt4wp45T44+9Mw+8Khtz3lWlh0Lfz9O7D7ueEfy3dsREMU2SHn+SIWi6AJ+XBLAV4IMQQ512dOKfUA8AIwTym1Xyn1kTQfeQ/wpNa6Z8B+VimlJlpv71dKvQG8AdQB3xrR4Px9k7Ae6TJNMI0VI4uTF0KML3KeT65gOuAHFuBLXOZ9IByNfy3GXjDdJKzSAS/y5KSTTqK7u5tJkyYxYcIEPvCBD3D55ZezcOFCli5dyvz584f8/K233spNN91Ec3Mzzc3NnHaamcDxlFNOYcmSJcyfP58pU6Zw9tlnxz+zfPlyLrnkEiZOnMjq1avjy0899VRuvPFGTj/dPLF58803s2TJkkJ5NHX86dcBP4RRZ8DHjmMVr0O9fcX/sRB7bHfIAvxmADwTTiKq93Oow8/U2v43Bdb6mpgNzLcdwG5TNFZ4ONgxjE6A314NVVPhyh8P8xvIgXAQHvs8TDoNLvjXzD7zru/B/pdhxS1w20tmEt9MrPkFPP4l+PR6yZDPEznPF7GEG6Ueh42ATMIqhEhBzvWZ01pfl2b99AHv7wPuS7LdOxO+vmDUAwsHIBIwcYqY/HdAOuCFEICc51NRWuenw3zp0qV67dq1ANz8q7Uc7PCx6tN9dyHue24X//bHTbz2r2+n2uvKyxhPRHP/5XE+fPYMvnzp4P8hbrz3ZY71BHn0E+fkYWQiXzZv3kxzc3O+hzHuJPu5KqVe0VovzdOQxkTiuX7Edv4Nfn0lfOhPMGOIu9U7VsNv3g03PQ7T3jr842x8BP7vQ3DrC9C4AB76kOlA/8SaEQ99SDv/Dr++Am58DKanOK8+chtse5Ln3/sC77/7Je6/+QzOnt1/YplPP/Aqt795OZWnvQ8u/2+u/vkLKAW/+9hZmY3jBwvB7oZPjvLvaSxs/wv87z/BdQ/CvEsz/9zuZ+G+d8H5X8ksiqanFX50Kvg74erfwIIrRj7mIiXn+uzL13leKXUPcBlwRGt9cpL1H8BMuqeAbuBWrfU6a91ua1kECGcy1vh5/vZaeOsn+dDed3KsN8hKuV4UoqDIeX5snJDX9D2tcMcsuPQOOGM5K9cd5FMPvMZfPncusxtkIlYh8knO9dmXrfN8QUTQ+EORQV3usfc+eYQ1Z7TWVgZ88pmInXYbAemAF0LkmjM2uV+6CJpsZcDnKILGk5ABn8qRTdDQzBRrctV97YN/Bq/v76TFMzPeLT+hyjO8DHh/F7TvMFE0hWbTo+Aqg5lvG97npp8DJ70Hnr0TOvam3/7pb5m/bxS0bBzRUIUoIPcBlwyxfhdwntZ6IfBN4K4B69+mtV487OKR09s3CatcvwshxPjl7zSv8Qgacw1ZL5OwCiFESgVRgDeTsPYfSmxSVinA504oYp6GSJUB73JIBrwQIg9iESLBdBE0o8yAj0XXhHMVQWMV4P0pImiiUTPxaMMCJlR6sNsU+471L8C39wTZ09ZLsGaeKcBrzcSqEg53+jObQ0VrcwNAR6F16yi/oSyLhGHLYzD3HSOLFXr7NwEFT6aJrjm0zmTMn74camdDy8jmIhOiUGitnwHah1j/vNY6NuHBi8DkrBzYVQrB2CSscr0ohBDjVqx5JCGCxuO0UeEpiIRjIYQoSBkV4JVSlyil3lRKbVdKDXqWWyn1OaXUJqXUeqXUX5VS04YziKSTsFrvpYMmd2LF9ZQZ8HYbQSnAn5DyFVU1XsnPc5jinekZdsBnKwM+2JObDHhfijpZxx7zPTc047DbmFjlYV97/872dfs6ACidvNBkyXcdYGJVCaGIpvV4IP0YQr2grX9nrQ76grH3eehtg+YRxsFUTYFln4NNj8CuZ5Jvo7XJfS+thfO+BI0nweE3Rj7mIifnpuwpop/lR4DHE95r4Eml1CtKqeWpPqSUWq6UWquUWnv06FGz0FkCoV48DpmEVYhCVUTnpqJwwv48Y/MXWdeyLV0BGis8KJX8SXohRG6dsOemMZDNn2XaArxSyg78BLgUWABcp5RaMGCz14ClWutFwMPAd4czCH8oEi+4x0gBPvdiE6ym7IC3yySsJyKPx0NbW5ucxLNEa01bWxsejzyimbGEyf2GFBptB3yJtR/rOGMdQVNSA9XT4e/fTR57EiuIN5h/cqdUlw7qgH9tXwc2BRPmnhr/zMRK8/0f6MgghiYx/ubIpuF+B2Nr00pwlMCct498H2/9pJlg9vEvm476gTb8Hva+ABd+DUqqoOlkc+Mj1VMJ45ic67OnWM7zSqm3YQrwX0pYfI7W+lTMdf9tSqlzk31Wa32X1nqp1nppfX29WSgRNEIUNDnPZ1exnOvHROz60d3XAd8o8TNCFAQ512dPts/zmTwjdDqwXWu9E0Ap9SBwJRD/TV1rvTph+xeBDw5nEP7Q4A74kngBXgq+uRLrgB86gkb+Jz7RTJ48mf379xPvcBOj5vF4mDw5O0/8nxDinenpImhGmQEf+1woRxE0dgdcvwLufZeZZPbGx6B+Xt/6WEHcWjalupS/bjnSbxfr9nUwt7GckoknxT8zceYZABzs8LNkapox9CvAF1AHfDQKm/8Isy8c3d+BswQu/jY8dD28ci+c/tG+dcEeE08zYTEssS5bGhea1yObYOqZIz9uEZJzfXYV+nleKbUI+AVwqda6LbZca33Aej2ilFqB+T0gxSMkAzhLTARNpR2/NGwIUXDkPJ99hX6uHzOxRgUrguZId4CTJlbkcUBCiBg512dXNs/zmRTgJwH7Et7vB84YYvuBj7LGWY+yLgeYOrWvKuBL0gEfK8D7gtJBkyuxCVZTRdA4pQP+hOR0OpkxY0a+hyFOZLECfLoO+FFnwOc4ggagZiZ8aCXc9y741RVw0yqonWXWHdkMlVPjv9xMqSmh9XjAxLa57GitWbe/g0tOaoLSGiifAC2bmHiq6eQ/mEkHfPwXqMrCKsDvXwPHD8OCK0e/r+bLYca5ZqLVk//J/KwA/vF96D4IV90HNusapOlk83r4jROuAC/n+hOHUmoq8Afgeq311oTlXsCmte62vr4YuD3jHbtKIdiL22knGI6itZY4AiEKiJznRdbEI2isAnyXnwvmN+RxQEKIGDnXF66sTsKqlPogsBS4I9n6pI+rYgrwJa6BETRmaP6wFOBzJdYB7x6iA14y4IUQOWezmSiSTDPgRx1Bk1CAH8sImpi6OXDDSoiG4VeXw7HdZvmRzdDQHN9sSo0Zy34rhmZ3Wy8dvSEWT6kyGzQ0w5FNVHgceF12DnZmEkFj/QI15Qzo3Fs40SubV4LdZSZgHS2l4JLvmG7/p79llrXvgud/BIuugakJPQUVk8BTld+JWOVxUTFKSqkHgBeAeUqp/UqpjyilblFK3WJt8jWgFvipUup1pdRaa3kj8KxSah3wMvCY1vrPGR/Y6TUZ8NY1fECaNoQQYnxKKMAfD4TpCUZorHDnd0xCCFHgMinAHwCmJLyfbC3rRyl1EfD/gCu01hnM/GZorfGHoikz4KUDPneCaSdhVfGOJiGEyClXqSmIDyXkM0Vb2wjvLSdO9hoJQTQ09h3wMQ3z4YZHzbHvuxzad0Lr1n4F+MnVZnyxHPjX9x0D4JR4AX4BHH0TpaNMrCrJrAM+FkEz5XTzevTN7Hw/o6G1yX+f+TbTmZ8NjQtM/Mwr98Kh9fDkv4DNARd9o/92SkHjyXA4TwX4zgPw3Rmw8+/5Ob4YF7TW12mtJ2itnVrryVrrX2qtf661/rm1/matdbXWerH1Z6m1fKfW+hTrz0la628P68CxCBqHzOMkhBDjmr/LNLw4XLR0+QForJAMeCGEGEomVYo1wByl1AyllAu4FliZuIFSagnwP5ji+5Ek+0gp1h0zMAM+PgmrdM/kTHwS1lQFeKszXnLghRA5Z3VWDikcMJ3yI2V3grJB2N9X7M9VAR5M/Mn1j0CgE37xdnMDoKFvzvMpNeZ729duCuuv7+2g1GVnbmO52aChGSIBaN9lFeD96Y+Z2AEPhTER68HXTDf+giuyu9/zv2y623/3QdjyJzj3C1AxYfB2TSebn0M0D9cf6x8E37H8duALMVKuUmsSVpnHSQghxrVAN7jN9WesAN8gk7AKIcSQ0hbgtdZh4BPAE8Bm4CGt9Ual1O1Kqdhvx3cAZcD/WY+yrkyxu0FiHe6xx1VjYpE0fumAz5l0k7DGOuNDEkMjhMi1TDrgwz5wjOLxV6VMF3zI13esXETQJJq4GD64oi9OJ6EDvr7MjcdpY197rAO+g4WTKrHbVP9tj2xiYlUJhzKKoLE64BtPNjc5CiEHfvNKUHaY987s7rekGi78GnTsgeoZcNZtybdrPNnc7Dm2K7vHT0drWPeg+bpHJk0SRWhABI10wAshxDgV6ErIfzfXrA0SQSOEEEPKZBJWtNargFUDln0t4euLRjoAn3VxPqgD3ioC++TiPWfSTcIaK8wHw1G88u+rECKXnKWZdcA7R9l947Sy5mPHymUHfMzk0+D6FfD6/f064JVSTK4uZd+xXvyhCJsOdfHhcxIm2KmaZl67DzOxspnW40H8SSY578efMIlWw/z8d8DH4mdmLOubLDWbTr3BZOw3X576Zk3iRKyxCXFz4eBrJnYIpAAvilM8gkbmcRJCiHHN3zWoA14iaIQQYmhZnYR1JOIF+AGTsDrsNpx2Jd0zORSLlknVAd8XQSMd8EKIHHN5IZimAB/yjXwC1hhHCYTyFEGTaMpb4PIfgL3/ffIp1SXsa/ex6VAXoYhmSSz/HeKdSPg7mVhl4moOdaaJoQl0ma5Vu8OaxDXPHfBHNkH7DmjOcvxMjM0Ob/8GTF6aepv6+SaKKNcxMOseBLsbqqdDT2tujy1ENrhKQUfw2My1u0TQCCHEOBXoBo+57mzpCuB12SlzZ9TbKYQQJ6y8F+BjBfZkHXoeh1064HMoXQZ8rDM+ILn8Qohcc5ZCKF0ETWD0BfhYB3y+ImjSmFJjOuBf39sBwOIp1X0rHS5zA8Hf0VeATzcRa0KGJw0LoOdIfou/m1YCCuZflr8xOEugdg60bMzdMSMh2PAwzLsUamZJB7woTtb5slQFAYmgEUKIcSshgqal2y/d70IIkYHCLsC77NI9k0PpMuDdsQga6YAXQuSaqzR9B3w4Cx3wzhLTSZ/PCJohTKkupdsf5pltR2mq8NBUOeD79VRCoIuJVWb5gbQF+K6EAnwsQz6PXfCbV8LUs6C8MX9jABNDcziHHfDb/wK9bXDKdeCtlwK8KE7xArzJA5YCvBBCjFP+vgL80a6A5L8LIUQG8l6A9wVNMXdgBnxsmVy8504wngGvkq6XSViFEHljTe43pKxkwJf274AvtAJ8jelsf3ZbK6dMqRy8gacS/J3xwvzBjnQRNH2PEMfz5vNVgG/dbiJoFoxR/MxwNJ4MnXvB15Gb4617EErrYPaF4K2TCBpRnKzzpUfHCvByvSiEEONSYgSNdMALIURG8l+ATzEJK4DHaZMCfA7FI2hSZcDbbf22E0KInMmkAz4bGfBOD4T9BRtBM7najCcc1f3jZ2KsArzbYae+3M3BdB3wCZNoUdYIJdX5m4h186Pmtfny/Bw/UaM1EWsufha+DnjzcVj4PrA7TQd84k0gIYqF09wg9GAK8AGZhFUIIcafaDQeQaO1pqVLCvBCCJGJvBfg/fFJWAcPpcQpGfC5FEwTQSOTsAoh8iZnGfClhR1BU9N3Q2Bx4gSsMVYBHmBipYeDncPIgFfKdMFn0gH/6m/g7gvNzzxbNj0Kk5ZC5eTs7XOkmqwCfC5iaDY9ApEALLrGvPfWm1eJoRHFxrph6cE8eSNNNEIIMQ6FegAN7nK6/GH8oSgN5RJBI4QQ6eS9AB8rsLsdgzvg3RJBk1MyCasQomC5vBANQziYepusZcAXbgRNZYmTCo8Dm4JFk1NH0ABMrCpJ3wEf6AZ3wn4amk0BXuuhP7fmbjiwFl6/f5jfQQrHdrGCsQAAACAASURBVMOhdYURPwNQPgFKaqDljbE/1roHoW4eTFxi3scL8BJDI4qMdb5061gBXq4XhRBi3PF3mVdPBUe6zPleOuCFECK9vBfg+zrgk2fA++TiPWfSTcLa1wGfpjAjhBDZFouCGaoLPhsZ8A4PhBIiaBwlo9vfGJhW62VuYzlet2PwykEFeD96qGJ64iSsYArwgU7oOpj6M7FiubLDs3dCJDSybyTmyBZ4+lvm6+YCKcArlZuJWNt3wd4X4JRrzDHBZMCDdMCL4mNF0Li0TMIqhBDjVsAqwLvLaeky53vpgBdCiPSS/PaeW75gmgz4Trl4z5W+SVglA14IUWBcVgE+2GtyypPJSga8NQlrqNd8bcv7fepBvnb5AmzJ58ruK8BrzYRKD75QhE5fiKpS1+Bto9H+k7BC/4lYKyclP8bmP5rXS/4DHv8irH8IlnxgeN9E2w7Y+AfYsAKObARlgyXXQ82M4e1nLDUuhLX3QDQCtsHXKFmx/iFAwcKr+5ZJBI0oVk7TAe+K+IBy6YAXQojxKNBtXt2VtEgHvBBCZCzvBfjYxbknSQG+xGnHLxM45UwwEkUpcKSo7MQ64KUAL4TIOauwE89mTyYrGfAlppAf7Cm4+JmYt0yvSb3SU2miekK9TKoy3agHOnzJC/DB48QyPOPq55vXI5tgzkXJj7FpJTQthNOXw2v/C//4Lzjl2vRF6kgIXr4b3ngIDr5mlk05Ey69AxZcCeWNQ38+1xpPMrFG7Tuhbk729681rH8Qpp8DVVP6lksHvChWVge8PezDYauQa3ghhBiPEiNoDlgd8BXSAS+EEOnkvbXPF4rgstuwJyn6epz2eIe8GHvBSBSn3YZSQxfgZRJWIUTOxTvgU0TQaJ2lDPhSMyFmoLsv9qaYeKw8d38nE60C/MEOf/Jt4x1MCQX40hooa0o9EWvnAdj/MjRfaSJTzv1naN8BG1ekH9vT34InvmK+vvhb8JkN8JEn4IzlhVd8h4SJWMcoB37/GlPcP+W6/sudJeAqlwx4UXxi5+mQD4/M4ySEEONTQgRNR28Qt8NGqSvvfZ1CCFHw8l6A94cieJzJhyEX77kVDEdxp4ifAXDaVXw7IYTIqXgGfIoO+Ig1OetoM+Bjn+9tK9gO+CElKcAf6kwxEWu8AF/Rf3lDs+mAT2bLn8zrgivN6/zLoL4ZnvmeibRJZftf4bkfwGk3wfK/wVs/2b/ruxDVzwebA1rGKAd+3YNmjoFkE89666QDXhSf+JNKPSZGUiJohBBi/IkX4CusmENnfscjhBBFIu8FeF8wknQCVogV4OXiPVdCkWjKCVghIYJGOuCFELkW69KOFY0HCllF5mx0wAP0thZpAd4qpvs7qfW6cNltHOhIVYDv+wWqn4YFcPRNk30+0KaVpjBdP9e8t9ng3C/A0c2w5Y/Jj3P8CKy4xRTqL/mP4X9P+eJwQ91caNmY/X2HA7Dh99B8Wf8nEGK89VKAF8XH7jSTMwd7cTvsBCSCRgghxh9/Ygd8iMoSKcALIUQm8l6A94cjSSdgBZMBH4xEiUR1jkd1YgqGoyknYAWZhFWI8UwpdYlS6k2l1Hal1JeTrL9TKfW69WerUqojpwOstLqlj+1Jvj5sMiizkgEP0NNWpBE0VebV34nNpphQ5RkigqbvF6h+GppNnM+x3f2XHz8Ke5+H5gEd2ye9B2pnwzN3mCigRNEorPiYOdZV9/b9fItF40lweAw64Lc9Cf4OWHRt8vXeeomgEcVHKXPjMuTD47QRkCYaIYQYfwLdgAJXGR2+IFUlSeYZEkIIMUjeC/C+YCTpBKwAJS4zPImhyY1QREsHvBAnIKWUHfgJcCmwALhOKbUgcRut9We11ou11ouBHwF/yOkgy5tMvEH7juTrw1nqgI99vmg74GMRNKa4PrGyhEMpO+Ctpwk8STrgYXAO/JY/gY4Ojkyx2WHZ501W+tYn+q974Uew42nT+d7QPMxvpgA0ngxd+6G3PTv762mDtffAX74BZY0w8/zk20kEjShWzlIrgkZiJIUQYlwKdJmnJ202On1hKiWCRgghMpL/AnwodQE+tlwu4HPDdMAnn4AViHfHh6QDXojx5nRgu9Z6p9Y6CDwIXDnE9tcBD+RkZDFKQe1MaEtVgLc64EedAW91vUeCRV6ANw8omA74FAV4f4oO+Pp55nVgAX7zSqieYYrSAy28Cqqm9u+C3/8K/PV20zF/2k0j+GYKQGwi1tHE0Pg64LX74Tfvhe/NgT991ix/13+BPcWkZbEO+KFy9YUoRM6SvklYJYJGCCFGRSl1j1LqiFJq0ON4SqnPK6W0UqrOen++Uqoz4YnVr6XY5wyl1EvWU6+/U0oNr4Xd3xW/duzsDUoEjRBCZCjvBXh/KHUETawA75MCfE4EwlFcjuR/FwAOm0Ip6YAXYhyaBOxLeL/fWjaIUmoaMAN4Ogfj6q92NrRtT74uaxnwCREpxRhB4+7LgAeYVFXC4S4/4WTn7VSTsLrLoGpa/4lYfcdg1zOm+10luVFrd8I5n4MDa2HnavPL2e8/DOUT4IofJv9MMWhcaF6HW4APHIc3HoYHrjNF90c/bv7bPftTcMuz8Ik10Hx56s9760FH4jdShCgaLi8Ee2USViGEyI77gEsGLlRKTQEuBvYOWPWP2BOrWuvbU+zzO8CdWuvZwDHgI8MaUaAr/vRkhy9ElRTghRAiIylar3LHH4pSX578pC0d8LmVbhJWpRQuu00K8EKc2K4FHtZapzwxK6WWA8sBpk6dmr0j18wyk4CGg+AY0KyTtQz4hKJ7MXbAOz3mZ2AV4CdWlRDV0NIdYFLVgPz1WAa8q2zwfhoW9O+Af/NxiIaheYgHIxa/33TA//0OqJgIHfvgplVQUj3KbyqPyhqgtA5a3ki/bcgH254yk6tufcLEIpVPgLd8FE7+J5h0auY3Irx15rXnKJTWjHz8QuSaswRCPbgddjp6Q/kejRBCFDWt9TNKqelJVt0JfBF4dDj7U0op4ALg/daiXwH/Bvws451YETTBcJTeYIQqiaARQoiM5L0AbyJokhd9S+IFeCn45kIwHMU1RAQNmIlYZRJWIcadA8CUhPeTrWXJXAvcNtTOtNZ3AXcBLF26NHuzaNfOMl3BHXugbk7/ddnKgE+MsCnGAjyYGBqrAD+h0nw/hzp8SQrw3eAqB1uSf4MbmmH7U303OzathIrJpoicisMNZ38aHv+ief+2f4GpZ2bjO8ofpUwMTaqJWLU2GffrfwdbVkGw2xTsl3zAFN2nnJn855uOt9689hztiwQSohg4S00EjccmDTRCCDEGlFJXAge01uvU4Bv7Zyml1gEHgS9orQc+wlcLdGitw9b7lE+9Wsca3FQTMNc6nT5zk1UiaIQQIjP5L8APMQlrrDAvETS5EYpEcae4GRLjckgBXohxaA0wRyk1A1N4v5a+zpg4pdR8oBp4IbfDs9TONq9tO5IU4LOcAT/w62KSUICPFd0PdPhYOnC7hEeIB2lYYDre27ZD5WRTZH7LR9J3cJ96Azz/I6iZCcs+N7rvo1A0ngwv3w2RcP/M9p42eOyzsOlR8zM/6d2m6D59Weps90wlFuCFKCYuL/S24ymzSwONEEJkmVKqFPgqJn5moFeBaVrr40qpdwKPAHOSbJexpE01/i6omUmnLwhAZenwIuSFEOJElfcC/FAZ8CUSQZNTwUiUMs/Q/0k47TZCEkEjxLiitQ4rpT4BPAHYgXu01huVUrcDa7XWK61NrwUe1Fpnr6t9OGpmmdf2JBOxjkUG/HjogLcK8Ac7/IO3S5hEa5CGZvN6ZJP5EwmYyVTTcZbArc+bn50t9ZwiRaVpofn+23f0daO/+WdY+UmTjX/h1+CsTw6ORRqNeAG+NXv7FCIXYhE0TjsBmYRVCCGybRZmLqZY9/tk4FWl1Ola68OxjbTWq5RSP1VK1WmtEy8m2oAqpZTD6oIf6qnX5ALm+jHWAS8Z8EIIkZm8F+B9mUzCGpQL+FwwETTSAS/EiUhrvQpYNWDZ1wa8/7dcjmmQ0hrwVCWfiDVbGfCOcVKA720HoMztoMLj4FCnb/B2ge7BE7DG1M0BZTc58K1boawRppyR4fFT7LNYNZ5sXg+/YTLdn/gKvPa/0HASXP8HU6DPttIaQEkHvCg+sQgamYRVCCGyTmv9BtAQe6+U2g0s1Vq3KqWagBattVZKnQ7YMAX3xM9rpdRq4H3Ag8CHGGaOfOz6MTbPh0TQCCFEZkYQTJo9WmvTAe8augDvl4JvTgQjUZxDTMIKpgAfiuSn+VUIcYJTyuTAtyXpgM9aBnxCAX4cRNCAmYj1YEeyAvwQHfAOt4n8OfAKbP8LzL9sZFnm40HdXLA5Yd2D8LOz4fXfwjmfg+Wrx6b4DubpgdJaKcCL4uPyQrAXj9MuT7AKIcQoKaUewEQ/zlNK7VdKfWSIzd8HbLAy4H8IXBt7alUptUopNdHa7kvA55RS2zGZ8L/MeEDhIIT94OkrwMskrEIIkZm8dsAHI1GimpQZ8LHCvF864HMiGI7iTtMB77TbCMgNESFEvtTMgr1JIujjGfAlg9cNh2N8TcIKJgf+QLIImkA3VE4ZvDymoRk2PWK+XpBB/Mx45XCZ6JntT5ls+w8/AVNOH/vjeuulAC+Kj7MEQr14HHbCUU04EsWR5tpSCCFEclrr69Ksn57w9Y+BH6fY7p0JX+8ERnYhE+g2r+6KhAgayYAXQohM5PWK2B80hdyUk7Ba3dh+yZDMiVAkiiujDngpwAsh8qR2NnTu68t8j4lnwLtHt3+brS+GplgL8O4KU4C3ovonVHlSR9AMFRfTsMC8ltTAtHPGYKBF5OzPwLLPwy3P5qb4DuCtkwx4UXycXoiGKLWba3d5ilUIIcaRgNXg4a6gwxdCKShPM4ecEEIII68FeJ/1aGrKSVhdkgGfS8FwFGe6DHi7kgx4IUT+1MYmYt3Vf3m2MuABnNY+ijmCJhqK35SYWFVCR2+InkC4/3b+rtQZ8NA3Eev8d4L9BP/latFVZrLVXN6UkQ54MUJKqXuUUkeUUhtSrFdKqR8qpbYrpdYrpU5NWPchpdQ268+Hhn1w6ykkry0IIDE0Qggxnvi7zKungs7eIBUeJzabyu+YhBCiSOS3Az5WgHclH4bHYRXg5eI9J0IRnVEHfFA64IUQ+RIrwA+ciDXsMzndtuQ3dIclVngv1g54T6V5DZhfkiZVmYJYvy74aARCPakz4MF0epdPgCXXj9VIxVCkAC9G7j7gkiHWXwrMsf4sB34GoJSqAb4OnIGJJ/i6Uqp6WEd2mfOnFOCFEGIcikfQlNPpC0n+uxBCDENBdMDHCu0D2WwKl8OGPyQF31zIrANeImiEEHlUE+uAHzARazgw+vz3mNh+irkDHuI58BOtAvzBxBx4qzg/ZAd8eRN8fgtMPXMsRinS8dabv8NwMN8jEUVGa/0M0D7EJlcCv9bGi0CVUmoC8A7gKa11u9b6GPAUQxfyB3OaG5deZZ5Kkmt4IYQYRxKuHzt8ISpLpAAvhBCZKowCvCt1x6LHYZPumRzQWhPMIAPeabdJBI0QIn88FaYw2TagAB/yjT7/PabYM+A9VebVKsBPqDSROgc7EjrgEzqYRIHy1pnXXsmBF1k3CdiX8H6/tSzV8sxZNzBLkA54IYQYd2IRNO5yOnqlAC+EEMOR50lYh86AB5MDLxfvYy8UMZP1uSWCRghR6GpnDy7AhwN9hfPRchZ7Ab5/B3xjhQebSlGAH2oSVpFf3nrzKjE0ogAppZYrpdYqpdYePZrw36gVQVOC6YAPhOUaXgghxo349WOlFUHjyu94hBCiiOS3AB9OX4D3OO2SAZ8DsaK60z70JCou6YAXQuRbzawkETRZ7IB3loCyg71If6kYUIB32m00Vng4kBhBk9DBVAi2HO7i3ud2sa+9N99DKRxSgBdj5wAwJeH9ZGtZquWDaK3v0lov1Vovra+v71vhjBXgzfkmIBE0QggxfgTMtWUsA76yxJHf8QghRBHJbwRN0FyUe4bqgHdKB3wuhKyiuitdBrxDCvBCiDyrnQXHW/qKyGBlwHuys39nKbjKQA19Q7JgxQvwHfFFEyo9/SdhjUfQ5L8DXmvNlx5ezzf+uIll313NlT95jruf2cmBxI79E1EsgqZHImhE1q0EblDGmUCn1voQ8ARwsVKq2pp89WJrWeasArw7FkEjHfBCCDF+BLrB7iZqc9HRG6SqpEibVYQQIg/yessy1tk+VAe822nHJ90zYy7eAZ9BBI1MwiqEyKva2ESsO2HiYvN1yAeObBXgPfEYhaI0oAMezESsGw70vc9oEtYceXVvB+v2d/KpC2ZT6nbw2PpDfHvVZr69ajNLplbxroUTeO+pk6nxnmC/5EkHvBghpdQDwPlAnVJqP/B1wAmgtf45sAp4J7Ad6AVusta1K6W+CayxdnW71nqoyVwHs6K7PNoHeGUSViGEGE/8XeCp4HgwTFRDValkwAshRKYKogDvcaUu+pY4ZRLWXAhm2AEvk7AKIfKuJlaA39FXgA8HsleAn/dOqJ6enX3lg9MDdne/AvykqhKe3NRCNKqx2VRCAT7/ETT3PLuLCo+Dj503C6/bwS3nzWJPWw+PvXGIx9Yf4luPbebOp7bykWUz+eiyGZR7TpBf9tzl5u9RCvBimLTW16VZr4HbUqy7B7hnxAe35tBwRU0GvFzDCyHEOBLoMvEzvSEAKmQSViGEyFheC/CBDDrgS5x22nqCuRrSCSvWAe/KqANe52JIQgiRXM1M85o4EWvYB6V12dn/oquzs5988lT2K8BPrS0lGI5ysNPH5OrShAia/Bbg9x/r5fENh/jospl43X2XJNNqvXz8/Nl8/PzZbG3p5gd/2coP/7qNX7+wm4+fP4sbzpo+ZHzduKCU6YKXCBpRTKwIGmfUZMBLB7wQQowjgW5wV9DpMwX4KinACyFExvKcAW91wKebhDUo3TNjbVgd8JEopnlKCCHywFUKFZMGFOCzmAE/HgwowM9vMlEzmw9ZhXd/FyhbPC4iX37zwh6UUtzw1ukpt5nbWM5PP3Aaf/zEOSyaXMW/r9rCeXes5v6X9oz/SDRvnXTAi+JinVNcUTOHg3TACyHEOGJF0HRYHfBVpSdYPKAQQoxC3iNoHDaFc4iib4nTLhM45UAoww54t7U+GInidozz7kMhROGqnWUiaGKymQE/Hngq+hXg5zWZTvcth7p4+4JGq4OpPK8TzfYEwvz25b1ccnITk6pK0m6/cHIlv/7w6by4s407nniT/7diAz95ejtzGsup9bqoLXNRW+aOf91Y4WFGnZdSV14vdUbHWy8FeFFc7C5QNhwRqwNeruGFEGL8CHSDd0a8A75SOuCFECJjeS/ADxU/A9YkrMFx3uFWAGId8EPdDDHrTbEmFNG4i7imIYQocjWzYNMjfe+zmQE/HngqTZeSpcztYFptKZsPW8sC3eCuzNPgjN+/up9uf5gPnz1jWJ87c2YtD99yFqvfPMJvX9rHkW4/248cp60nkDTuoskqxE+v8zKzzsuMOi9nzartF3lTsLz1cHRLvkchROaUAqcXR8SHUtAbkAK8EEKMG4EucFfQ4TMRwTIJqxBCZC6vv336Q1E8rqEL8CVOezwrXoydjDPgrQJ9MBwF95gPSwghkqudDb5j0NsOpTUmA14K8H08ldCxr9+i+U3lbIlF0FiTaOVLNKq597ndnDKlilOnVg3780opLpjfyAXzG+PLtNb0BiO09wRpPR7gYIef3W097Dzaw67W4/x5wyGOWY9MV5c6+ei5M7nhrOmUFXIh3ltrOuC1zuvTCkIMi7MEFeplYmUJ+4715ns0QgghsmVABI10wAshRObyXICP4HEOXfD1OG34pAA/5jLtgHdZsTPjPndXCFHYameZ17YdVgFeMuD7GZABD9A8oYInN7XQGwxTmucC/Oo3j7CrtYcfXrcElaXCslIKr9uB1+1gSk0pS6YO3uZYT5BNh7q465mdfPfPb3L3MzsLuxDvrYewH4LH8z5hrhAZc5VCyMfMei+7WnvyPRohhBDZYl0/dvlCuB22IefyE0II0V/eJ2FNF0FT4rQTjmop+I6xWAHenaYDPhZBE9teCCHyosYqwLfvMN3BkgHfX5IC/PymCrSGrS3H4x1M+XLPc7toqvBw6clNOT1utdfF2bPr+NWHT2fFx9/KKVOq+O6f3+Sc7zzNT1Zv53ggnNPxpOWtN6+SAy+KidMLoV5m1HnZebQHrXW+RySEEGK0dBTQJoKmNyTxM0IIMUwFnwEfu6vqD0XSdmeLkQtFzC9H6Tvg+yZhFUKIvKmeDspmOuAjIUBLAT6RpxIiAQj5408GLJhgCu5bDnWxONDd9xRBjm053MVz29v44iXz8vrv+pKp1dx30+m8vq+D//7LVu544k1+uno7zRMqmNNYzpyGMuY2ljO3sYz6cnfWOvWHJV6Ab4Wambk/vhAj4SyBYA8zp3g5Hghz9HiAhnI5PwshRFGLWqkE7nI6fEGqSlz5HY8QQhSZAoigSVOAd8UK8FHk2n3sBCPmH9RhZcALIUS+OFxQNRXatpv8d5ACfCKPNcGqvzNegJ9cXYLXZWfzoS5rEtb8RJrc8+wuPE4b7z89SUZMHiyeUsW9ViH+4Vf2sfXwcR7fcIgHrHxTMBmnEyo9OOwKu82G06Zw2BVOuw27TWGzivOJJXqloKrUxS3nzWR2wwh/1t468yod8KKYWBE0M+rLANh5tEcK8EIIUey0VYD3VNDpC0n+uxBCDFPeC/BVpUPfOS1J6IAXYycUNh3waQvwDinACyEKRO1sE0ETDpj3kgHfx2NNbOrvhHIzUanNppjXVM7mw91WhmfuI2hajwd45PWDXHXa5LT//ufa4ilVLJ5ifm5aa44eD7Ct5TjbWrrZeuQ4bccDhCOaUFQTjkQJRzU9gTDhqEZr0Gjrs337fHFnOyteO8D1Z07jMxfNyeh7jkQ1dptVypcIGlGMnF7wHWNmnReAXa09nDmzNs+DEkIIMSrxDvhKOnpDTKkpze94hBCiyOQ9gmZC2ggaW3xbMXYCkdgkrEM/Yh8rwEsmvxAi72pmwd4XTf47SAd8osQO+ATNEypYtW4v4M9LAf63L+0lGI5y09nTc37s4VBK0VDuoaHcw9mz60a8n9bjAb7/1FZ+/cJuHnn9AJ+9aC7vP2PqoOidI11+/rzxMKveOMTLu9r5xYeWcsH8RiiVDnhRhJwlEOxlUlUJLoeNnUeP53tEQgghRkv3RdB0+o6zUDrghRBiWPJegC9xpZ+EFaQDfqyFYpOw2of++3BKBI0QolDUzoLgcejYa95LAb5PigL8/AkVrHqpCzzkfBLWQDjCb17cw3lz60ceyVJk6src/Pt7FnL9mdP45p828fWVG/nNi3v418sWMKehjD9vOMzjGw6xds8xtIY5DWWUOO2seuOwKcA7PeZGSU9rvr8VITJnRdDYbIoZtV52tfbke0RCCCFGK2r9/u+poNN3TCJohBBimPIcQRNNnwFvrfcFpQA/lmKTqjodmXXAyySsQoi8i00i2rLRvEoBvk+8AN/Rb/GCCeWUKeuJgRxnwP/8bzs52h3g5qtn5PS4haB5QgX333wGT21q4durNvOhe16Or5vfVM5nL5rLpSc3MaexnNt++yr/2HYUrbWZ+NVbJx3worg4SyFkiu4z6728ebg7zwMSQggxalYHfMDhpTcYoapUCvBCCDEcGRXglVKXAP8N2IFfaK3/c8D6c4EfAIuAa7XWD2eyX38wEo+YSSVWgPdLx/WYinW0u+wyCasQokjUxArwG8yrZMD3SdEBP6+pgnJyX4Bft6+DHz69jXcvnsiyOfU5O24hUUpx8UlNnDevnofW7ON4IMI7TmpkpjVRZcy5c+p4bP0htrYcZ15TucmBlwK8KCbO0ng02Iw6L09taiEUiQ6KXhJCCFFErAz4rmgJAJUFNpePEEIUurQFeKWUHfgJ8HZgP7BGKbVSa70pYbO9wI3AF4ZzcF8oEo+YSSWeAT9EB/yxniAaqPHKPwIjFYpEsSlwpCvASwe8EKJQVE0Fm1M64JOJ5bsPKMCXuR3MqohCgJxlwPuCET770Os0lLv5xpUn5+SYhcztsHP9WdNTro/doPjHtqN9Bfj2nTkanRBZ4PJCJAiRMDPrywhHNfvaewfdbMo7rUEN/eSnEEIIi44Ais6IqblIBI0QQgxPJq0opwPbtdY7tdZB4EHgysQNtNa7tdbrgYyrshoIR3XaAnxsfSCcugD/uYde5xO/fTXTQ4skguHMOpNiHfAyCasQIu9sdqiZAUc2m/eOkvyOp5A4S8zNiUDXoFXzqrT5Ikcd8P/5+GZ2Hu3he1edIr+sZWBiVQmzG8r4+1ar610iaESxcVrn4lAPM+q8AOw8WmA58N0t8J1psO0v+R6JEEIUh2gU3OV0+Exdpkqu6YQQYlgyKcBPAvYlvN9vLRs2pdRypdRapdTao0fNhGLpJmHNJAN+86FuNhzoRGs9kmEJTEd7rLt9KE6HRNAIIQpIzSwIW5EqDnd+x1JIlDIxNAM64AHTAQ/4bd4xH8YzW4/yqxf2cNPZ0zl7dt2YH2+8WDanjpd3tZsJ6L310NsWf/RbiILnLDWvIR+z6s15puAmYt36Z3N+3Pt8vkcihBApKaXuUUodUUptSLLu80oprZSqs95/QCm1Xin1hlLqeaXUKSn2eZ9SapdS6nXrz+KMBqMj4K6gozcEIBnwQggxTDkNY9Ra36W1Xqq1XlpTWwuAO8MOeH8o+S+evmCEw11+uvxhWo8HszvgE0gwHE2b/w4JGfARudkhhCgAsYlYoa/rUhgpCvBTy8y/pzu6hz7n9wTC/L8Vb7DhwOB9ZKKjN8g/P7yO2Q1lfOmS+SPax4nq3Ln1BMJR1uxuNwV4HQXfsXwPS4jMuKybe8EeZvZJEgAAIABJREFUqkpd1Hhd7Gw9nt8xDbTtSfPaui2/4xBCiKHdB1wycKFSagpwMSYKOGYXcJ7WeiHwTeCuIfb7z1rrxdaf1zMaSTQCngo6faYAL081CiHE8GRSgD8ATEl4P9laNirRqCngps+AtzrgQ8k7rve298a/3n6kwC7ui0gwnFkHvEzCKoQoKIkFeOmA7y9FAX6ix/zitKlt6Bupf1x3kPtf2sv7736R1/d1DPvw//roRtqOB/nBNYvj/5aLzJwxowaX3cYzW4+aCBqQGBpRPOIRNH0TsRZUBE04ADtWm6+lAC+EKGBa62eA9iSr7gS+iEn2jW37vNY6drf+RUzdJouDiVgRNFYHfInMvyeEEMORSQF+DTBHKTVDKeUCrgVWjvbAVv09bQHebRWFfSk64He39V3Q7zgqBfiRCkUyzICXCBohRCGpnd33tWTA95eiAF9l8xPSdjYeGfqpsRWvHWBydQmVpU4++IuXeGVPst//knv09QP8cd1BPnPRHE6eVDnsoZ/oSl0Olk6v5h/bWk0HPEgBXhQPp9UBHzJNMjPrvOwspAiaPc9BqAfq50P7Dol3EkIUFaXUlcABrfW6ITb7CPD4EOu/bcXV3KmUStnBkhghHA4GwF1BZ28QpaDc4xjptyCEECektBVXrXUY+ATwBLAZeEhrvVEpdbtS6goApdRblFL7gauA/1FKbUy336iV117iGnoINpvC47QRSFGA32MV4F12m3TAj0LGGfB2BcgkrEKIAlEjHfAppSjAq2A3PpuXzYe7U370YIePl3a1c9VpU/jd8rOoL3dzwy9f5qWdbWkPe6jTx78+soElU6u45bxZabcXyZ07t54th7tp0xVmgRTgRbGIdcAHzTX6jHovR7sDdPtDeRxUgq1Pgt0NSz8CkSB07Mn3iIQQIiNKqVLgq8DXhtjmbZgC/JdSbPIVYD7wFqBmiO36RQg77LZ4BE2Fx4nNpkb8fQghxIkoowx4rfUqrfVcrfUsrfW3rWVf01qvtL5eo7WerLX2aq1rtdYnZbBPgIweS/c47UN0wPdSXepk/oRy6YAfhWBYZ5QB77DbsCnpgBdCFIjyCX2d7w5PfsdSaFIU4Al0E3Z62XyoK+Xk5SvXHQTgysUTmVhVwoPLz6Sp0sON967h+e2tST8TDEd5/I1D3PyrtYQimjuvXowjg39XRHLL5pjomecOWz/DnuQ/dyEKjqtvElaAmXVlQAFNxLrtSZixDCZY8xNKDI0QonjMAmYA65RSuzExM68qpZoAlFKLgF8AV2qtk3ZNaK0PaSMA3AucntGRo30RNDIBqxBCDF/efjOORdBkUoAvcdpTTsK6p62HabVeZtWXsUM64EcsGInizKADHkwMjXTACyEKgs1mcuBtDrDLo7D9pCrA+7vAXUGXP8yhTn/Sjz7y2gEWT6liep2Jkmis8PDg8rOYUlPCTfetMdnkljcPd/PNP23izP/4K7fe/yptx4N8/+pT4p8VI9PcVEFdmYun9wRB2aQDvtAc3gCt2/M9isI0MIKm3rwviBz4th0mdmbOO6BujlkmBXghRJHQWr+htW7QWk/XWk8H9gOnaq0PK6WmAn8Artdab021D6XUBOtVAe8GNmR28Ai4K+joDVElE7AKIcSw5a1aEY+gybgDPnnBd3drL2+ZXs3shjJWvHaAnkAYr1uKMMMVCkdxZ9ip6LTbCEgHvBCiUNTMhGMSITCIpxLCfgj5wZnwdECgG2epyWXffKiLiVX9s/PfPNzNlsPd/NvlC/otry9388BHz+SDv3yZm3+1llvOm8nft7Wybl8HTrvi7QsauWrpFM6dU49dHkseNZtNsWxOPc9sPYourUVJAb5w+DrgV5dDaQ3ctsbcCBR9BkTQTKstxaYojBz4rU+Y17kXm7+/0lpoTVmnEkKIvFJKPQCcD9RZkb9f11r/MsXmXwNqgZ+a2jphrfVSaz+rgJu11geB+5VS9YACXgduyWgwOmoy4H0hKqQAL4QQw5a3SrUeZgE+WQd8IBzhYKePabWTmWV11+w4epxFk6uyO9gTQDASxePM7BdIt3TACyEKycn/ZAopoj+PNflpoGtAAb4TT/lEALYc7ubC5sZ+H3vk9QPYbYrLTpk4aJe1ZW4e+OgZXP/Ll/nh09uZ21jGv7yrmfcsmURtmWTwZ9uyOXWseO0AgYoaPBJBUzievRN87ebPm6ug+bJ8j6iwuGId8CaCxu2wM7m6lJ2FEBW57QmomwfV0837urnQJk8yCCEKk9b6ujTrpyd8fTNwc4rt3pnw9QUjHpCVAT+lpnTEuxBCiBNVHjvgzWuJK5MCvC1pAX5fuw+tYXpdKbMbTL6kFOBHJhiOUpHhTOZOu00y4IUQheOkd5s/oj+P9W+hvxPKGvqWB7px1lcyubqETYe6+n0kGtWsfP0g58yuoy5FQb2q1MXvPnYme9t7mddYjtVlJcbAOVYOfKuuYHIxd8C3bITKyX03hYpZxz548Wdw8vvgwFp47gcw/10g/x/0ccYy4Ps63mfUefOfAR/oht3PwZkJzZ51c+DNx/M3JiGEKCbuCjp6gxJBI4QQI5DHDHhrElbHyDPg97TFHm31Mq3Wi8Om2C458CMSikRxDSMDPigd8EIIUdg8FebV37/ITqAb3OU0T6hgy4AC/No9xzjQ4ePdSwZ3vycqdTmY31Qhxfcx1lDuoXlCBXsD3uLNgO9pg7vOh9+8ByKhfI9m9Fb/u3m96Otw1idg/xrY+0J+x1RoHG5AxTvgweTA72rtSTnxc07s/BtEQyb/PaZ2jvl/y3csb8MSQohiEXWV0SmTsAohxIjkfxJWV/ohmAz4wQX43W1mcqfptV6cdhtTa0vZcaQA8iWLUDAcxZlhBrzLLhE0QghR8GLdxv6OvmVam4K8p4LmpnJ2tfb0u8H9yOsHKHHauXhBU44HK1I5d04d24570MVagN/4B4gE4cAr8PQ3s7PPcMD8t5xrh9+AdQ/AGR+Dqqmw+AMmQ/y5/879WAqZUiaGJtgbXzSzzktvMEJLV2Dk+03xdx6ORJM26gyy9QlwV8DUM/uW1c01rzKhrhBCpOWze4lqqJQOeCGEGLa8ZsDblCnmplPitOMLJu+AL/c4qLbuwM6uL2N7IeRLFqHgMDrgJYJGCCGKQLwA39m3LBwwHaDucpqrKohq2NrSzaLJVQTDUVa9cYi3L2iUycwLyLI59bzwXAUq0D14Qt1isO4BaFwIk08zheoZ58HsC0e+v542+PFp4C6Hk95r5oBoWpibCJinvmb+v1r2OfPeVQqnfwz+9u9wZDM0NI/9GIqFs6RfBM3MehMVufPocZoqh/nfcCQEa35B5G/fYX/jBfx5ymfZ1anZ297LvmO9HOzwU+K086sPn85p06qT70Nr2PYUzHob2BMKR3VzzGvrVpjyluGNSwghTjDdugTwSQFeCCFGIK8d8CVOe0aPr5tJWAcXfHe39TK91hvfx6yGMna39kh39ggEw9GMboZALIImj48QCyGESC9ZAT5gRc64K5g/wUTUbLZiaP6+9SgdvaG08TMit5ZOr6bLbv1d9hbZRKyt20zn+ynXwDv+A+qbYcUtcPzIyPf54k/A1wE1M+H5H8H/LIMfv8VEwxzZkr2xD7T9r7DjaTj3n6Ekoch7+kdN5vnzPxq7Yw+TUuoSpdSbSqntSqkvJ1l/p1LqdevPVqVUR8K6SMK6lSMehLO0XwTNjDozMeuO4ebA73oGfr4M/vxlNvuqmLL7D1zwzDXs2rQGXyjCqVOr+fj5s6gtc3HL/77CoU5f8v0cWgfHD/ePnwGomgY2pynACyGEGFJnxMzxUVXqyvNIhBCi+OQ1A97jTJ//DqknYd3T1sO02r4ZuGfXlxGOmo4YMTzD6YB32W0Ewxk86iuEECJ/khbgu82ru4JpNaWUOO1sPmSWPfL6AWq8LpbNqc/xQMVQPE47tQ2TzZtii6FZ9yAoGyy8ynSLv+8ecxNoxccgOoJmCd8xeOkuWHAl3PAofGEbXPYDKG+Cv38XfnoG3PU2OJ7ln1M0Ck993cTOnP7R/utKa2DJ9bD+Ieg8kN3jjoBSyg78BLgUWABcp5RakLiN1vqzWuvFWuvFwI+APySs9sXWaa2vGPFAXF4I9hXbmyo8lDjt7DqaYQG+8wD8303wq8sh1MOfFnyPywLf4s2Lf83s8jC/4yuseMsW/vuaxXz+4nncfcNSfMEIy3/9SvI4mm1Pmtc5b++/3O6A2lnQJhE0QgiRzrGoeYJJOuCFEGL4iqIAn2wS1lAkyv5jPqbXeuPLZjeYx1tlItbhCw0nA94hETRCCFHwnKVgc6TogC/HZlPMaypny+Euuv0h/rKphcsWTcj43wKROzOnTwOgtWV/nkcyDNGoKUrPfJspkAM0LoB3/LvpJH9hBB3jL90FwW449wvmvbcWlt4EN/4JPr8FLvlPaNkIj9w6sgJ/Kut/By1vwIVftyYYHeCs20BH4aWfZe+YI3c6sF1rvVNrHQQeBK4cYvvrgAeyPgpnCYT6GmJsNsWMOi87W9Nco4cD8I//gh8vhTdXwflfJXrrS3xn9yzeOquO5rOvQN36LEw/Bx77HDx0A/g6mNtYzg+uWcyGg5188eH1gyd73fYkTDwVyhoGH7N2tnTACyFEBtrD5t9AmYRVCCGGL2+/ZWsNJa5MO+DNJKyJF9MHjvmIRHW/DviZ9dbjrZIDP2zD6oB32AhJBI0QQhQ2pUwXfGIB3m8V4D0mfqZ5QgWbD3XzxMYWAuEoVy6elIeBinQWzZ0NwPZdu4f3wWAP/OP7sGM1RMLZH9hQ9j4PnXvhlOv6L1/6YWi+Av56O+x/JfP9+bvgxZ/CvHeazPeBypvgzFvhHd+G7U+ZqJpsCPng6W/BhMUmcz6Z6mlw0ntg7X0mHie/JgH7Et7vt5YNopSaBswAnk5Y7FFKrVVKvaiUeneqgyilllvbrT16NMkTBwMiaABm1HvZlS6CZtUXzH8bsy6A216G87/Ei/t62dfu4+qlU8w2ZQ3wgYfh7bebIv3Pl8G+l7loQSP//I55rFx3kJ//fWffPntaYf9amPuO5Mesmwvtu0zWfLHb/lfo2Jd+OyGEGC5loz1gon+rpANeCCGGLa8d8CWZdsC77EQ1/Yq+u9vMBfz0ur4O+HKPk6YKj3TAD5PWmlBEZ5wB77Qr6YAXQohiMLAAH4+gKQegeUI5nb4Qdz2zg6k1pZw6tSoPgxTpTJtqOuAPHtyb+YeCvfDba+Cv34DfvBu+Px8e+zzseT673eGprHsQXGUw/139lysFV/wQyprg9x/uuymUzppfgL+jr/s9lbfcDPMvg798Aw68OrKxJ3rpf6BrP1z8TbANcZ109qdMd/4r947+mLlzLfCw1jrxMdNpWuulwPuBHyilZiX7oNb6Lq31Uq310vr6JLFVztJ+ETQAs+q87GvvJTBUjOHu58zf37X3mxsbwENr91HucXDJyU1929lscPan4cNPgALuuQSeuYNbl03nilMm8t0ntvDXzS1m2+1/ATTMuTj5Mevmmsmpj+1Ba82TGw/zk9XbB3fRF7rjR+D+q0zEU7GNXQhR+JSdTp+5UVkhBXghhBi2/BXgoybbPRNuqzPblxBDs6fNPNaa2AEPMKvByw4pwA9L0Jq0NvMOeLtMdCuEEMVgUAG+L4IGYH6T6YTf2nKcKxdPzGhidJF7yl1OSLnoaj1EbzCDTvaQH373Adj9LFzxY7j6NzDtbHjtfrj3UrjzJPjzV+HwhrEZcMgHmx41We2u0sHrS6rhn34BHXvhT59JXywM9sALP4ZZF8Kk04beVim44kdQ1ggPD6PAn0xvu3mCYM47YMa5Q2874RQTt/Piz/4/e+cdH1WVv+HnzEx67ySQkA6EgPTepCNSBAuoiIioWLHv+lNx1d217Kq4VrCjCAiIiCJSpPdeAilAgIRAGunJJDNzf3+cJASSkEkIJIHzfJjPTe6ce+6ZyZDc+573vF8Zo9JwJAOBFb5vUbqvKiZwSfyMpmnJpdvjwDqgY51GYVu1A96iwenqajWVFELmcfCLLt+VXVjCikNnGdMhoOroyhZd4OGN8rO29k3Ed2N4Z4gn0QFuPDV/H/HnciFuJTj5ylUMVeEdAUBi7F7umr2Nh+bu5t2VsexPyq66fWPl0BLQzHBys3TCKxQKRX2i05FdWIK9jc7qKGGFQqFQXKBpZMCXRtUYKwjwiRn5ONrq8XG+OIsz3MeZY2n5Tc+10oCUrSyojQPeqBzwCoVC0fip1gEvC7S29ncpf0rFzzRihMDi6I2LOYtZa+Iv39ZULHOxj62FMR9Bp0kQNRru/BaeT4DxX0JAB9g5B2YPgNg/6n+8sb/LyZ72d1XfpmVPGPASHFoM6966fH+7v4GCDOj/gnXnd/SE8XMg66R0/df1mvCPv0lX++DXrGvf+ynIOycz4xuOnUCEECJECGGLFNmXXdpICNEa8AC2VtjnIYSwK/3aG+gNxNRpFDZOF2XAA4R6y1pNx6orxJoeB2jg26Z817J9yRhNFu7qElT9uRzcZYHfsZ/Cmb3Yf9GP73qexd5Gz8PfbseSsEa636tZwZBikEWO5/2+hmOpebw8sg22Bh1L9zZ8Ud1acWAB+LYFj2C58uVarHRRKBQ3DkJPVkEx7g62DT0ShUKhaJI0bAa8lQK8vUG2u9QB39LLqZJbL9zXmTyjiXM5Deo+alKUxcnY6K1zPtoZdOWueYVCoVA0Yqp1wEshzNXehkBPB6Kbu5YXMlc0Tuzc/GjrVsyXG08Qeza36kbmElg0BeJXwq3vQ8d7L+nEGdrdDhN/hGeOQrNoWDipNKKjHtk/H1xbQHDfy7fr+6wc4/q3YMN/qm5TUgSbP5R9BfWwfgwte0H/v8HBhbC/DjVG9/0oBc1+L8jisdYQOgCatZfjbSDxU9M0E/A4sBI4AizUNO2wEOJ1IcToCk0nAPO1ix0rbYBdQoj9wF/AW5qm1VGAd6gUQRNSWqup2hz41CNyW0GAX7griTb+rkQ3d738+YSADnfDIxvBMwSP5VNZEbqIkJyd6IzZ/CexJX9fcoAvNh5nXWwqSecLyCkq4Z0/jjLgo72kaW7cGpDHuucH8GDfUAa28mX5gRRMTeV6Nz0ezuyR78GAl+DsAYhZ2tCjUigU1xM6GUHjpuJnFAqFok4YGurEFk2zughrWbvCSxzwrfxcKrUN8ylz1+TRzM2+HkZ6/VMmwNsarPt52Oh1KoJGoVAomgJVFWHV24HhwuqxWRM64mLXYJcDCmtx8iHcfBbnAgOvLD3Egod7XGxCMJtgyTQ4uhxGvCOLnV62Py+4dwl8Nxrm3wN3L4TQ/lc+zrxUGX/R+8nLZ6aDfH7Uh3LiYO0b8nPZ64mL2+ydC3lnYdzs2o+l33NwYgP89hy06Abe4dYdl54gnfMte0O/560/nxDSBb94Khz5RRZmbQA0Tfsd+P2Sfa9e8v1rVRy3Baiiwm0dqCKCxtXeBm9nO46nVRMVmXoEdDbgGQpAzJkcDiZnM3NUlPXxWF5h8MCfsO5f+Gz6gDm2BkyagYO2nThw6CznCyoXWh3bIQDX3Ch8dKlgL4WlsR0D+OPwWbYez6BvRBUZ942NAwtB6CB6vCxSu/kD+OufsuCxXv1+VygU9YDQk1VQgpujEuAVCoWiLjRsBI2Vgm9ZVnxRiRR9zRaN05nSAX8pZQ4+VYjVekpqmwGv16kirArFdYYQYrgQIlYIkSCE+Fs1be4UQsQIIQ4LIeZd6zEq6oCd6wXXO8gIGvuLnaSdgjyIqGJCW9HIcPLBUJjB30e0ZkdiJot2J114zmKGXx6Fwz/D0Deh+8PW9enoCZN+AY8Q+HGCLNB6pRxcJHOo20+wrr1OD2M+gaix8OfLsuhpGaZi2PQBBHavOYO9ur7HzQaDrVwZYE02u8ko2xpsYdyc2ouXUWNlDMifr1YSoG8obJzAbJSfzQqE+jhxvLoImrSjsiCqXoo7C3edxlavY2xt47EMtjI26L6l6Jy8MUQO49tHB7P31aHsfnkwCx7qwT9vi+bRAWH88lhvPpjQEbtmraWLvJQBrXxxsTOwdO+Z2p27IdA0uVojpD+4+svP/cBXICMB9v3Q0KNTKBTXC6UOeHflgFcoFIo60WCWCE3Dagd8WVZ8YbG8iD+TVUiJWSPYq3JhLx8XO1zsDEqArwXGWkbQ2BqUA16huJ4QQuiBj4EhQBKwUwixrGL0gBAiAvg70FvTtPNCCN+GGa2iVti7yxxmU7EUpYw55QVYFU0MJ2/IPcudR2cQ6ZpFwXITJYc8sdHrZD56yj4pul3qIK+xXy+YvAy+GQk/3AGTfobAbnUf54H5stilb2vrj9EbZFFWiwlWvAB6W+gyRfaVkwSjZkl3eV1way4F/vkT4den4NYPwOYyKyRXvybjOybMk8fWFr0BRrwN394KW/5nfW799YaNg9wW51806Rfq7cSqmHNVH5MaAy26AmA0mVm6L5khbf3wcKpj3nDoAJhxELQL16xeznZ4OdvRPdTr4rbeEVCYCfkZ4OSFvY2e4dHNWHHoLP8siW7cBQdP75D1Dgb8/cK+ViPkqo/1b8taDJf7zCsUCoU1CJ2KoFEoFIoroGkUYS1tV2SSAvzJDFnUqSoHvBCCMF9njlW3vFVRiTIx3c5KB7yMoNGwWFShW4XiOqEbkKBp2nFN04qB+cCYS9pMAz7WNO08gKZpqdd4jIq6YC+LrZa74I250hWvaHpEjoAWXRBF2bR2t+BoySfl3DkZMaQzwLB/yciVuuDsC/ctk9vvx0Pynrr1k3oEUvbDTRNrf6zeBm7/GiKGwfIZsPtb2PhfCOgI4YPqNp4yWt8C/V+UWfCf96v+9cWthG2fQLeHoPXIup8vpK90wm98D7JO172fpoxtqUnmklUAoT5OZOQXk31pFIwxD7JOlee/r4o5R1ZBCXd1CbyycehtLorcqhbvSLnNuOCCH9uxOXlGE2uPNvI/dwcWgMEB2txKak4RRpNZTlgNehVykmHnFw09QoVCcT2gkxE07iqCRqFQKOpEwxVhpRZFWMsE+FIHfGKGXLoa7F3ZAQ8yhkY54K3nQhFWKyNoSoX6kgYqMKZQKOqd5kBFlSipdF9FIoFIIcRmIcQ2IcTw6joTQjwkhNglhNiVlpZ2FYarsJoyAb4sB96YqxzwTZWWPWHqnzBtDQ6PrmNFj+/pd/5ldg/9CaatgZ6PXVn/rv4w+VdwcIe5t8noly0fVX7smCMz0qti/3wQeplDXRcMtnDndxA2EH59Es4nygz2urrfK3LzSzLz3pgLXwyGv/4ts+fLyEmBpdPBrx0MeePKzzf0DUCDVa9ceV9NEZsyAf6SQqzeMiryePol1+npsXLrIwX4BTtP09zdgd7h3ld1mOV4ldYHSI8r39Uj1AsfFzuW7k2+NmOoC6ZiOLwEWo8k22zP4PfW88KiA/K5kL7y/9LG/8r6HwqFQnEFaEJHYYkZd8c6rkpSKBSKG5wGE+DhQrZ7TVR2wOdjZ9Dh51L1csowH2dSc43kFFUutKSoTF0y4AGVA69Q3FgYgAhgADARmCOEcK+qoaZpszVN66JpWhcfnyZQvO56plyAzyrd5igH/HXCU4MiCHCz5/9+PlR/sXBuLS6I8Ktnwp//V/nx+3PwUWf4rK8U6c+flMdazLIQZPhgcL6C//c29nDXD7KfoJ7S+V9fhA+CR7dAu9th/VtSiE89isVUWsC2pBBu/6p+4jrcg6DP0zKXP3HTlffX1LCp3gEPVM6BTz0it75tSM4qZFNCOuM7t0Cvq4fJF2twD5IFqisI8HqdYFT7ANbFpl1w7K+a2bgc5QmroPA8tL+L77efJKfIxC/7zrDjRKZ8ftCrMlpn68cNO85GxP7TWZxIr6YOgUKhqBZLqXTkqiJoFAqFok40WAY81CUDXt5gJmYU0NLLEV01F+VlhViPpebRMcijHkZ6fVNXB7wS4BWK64ZkoOI6/xal+yqSBGzXNK0EOCGEiEMK8juvzRAVdaIqB7y9EuCvB5zsDMwc3ZaH5+7mm82JTOsXWj8dewTD47tl7YCqKDwPR5fDoSVSpF89U+Z2B3SE3DMw7J/Vdl1UYibpfAGnMws5fb6AUxkFFJSYeahvKMHeFWIFbR3h3sVgsYCunr0iDh4wbjbmyFswL3sK8WkfNpujGaDby4KAFxEnHeiuyyfI0xFxpc773k/B3h9gxYvw0PraF3RtytiW/jyLL/4cBXk6oteJygJo6hEw2INHMIvWHkfT4I7OLa7RYJGFS73CK63uGNsxgK82n+CPwyncFVoCmz8AlwDo/ED9fzbrwoEF4OhNUVA/vlqwkZ6hXpzMyOe1ZYf59Yk+6AM6QtQY2PoRdJsma0ncwFgsGlO/3YmHoy1/zOhXPxM8x9aCe0vwCrvyvhSKRoxZL2t7qCKsCoVCUTca9E6g1hnwJRcc8FXlv5cRVuquSVACvFUYa+uAL4ugMasMeIXiOmEnECGECEEK7xOAuy9psxTpfP9aCOGNjKQ5fk1Hqag9lQR4VYT1emJolB+DWvvy/uo4Rrb3J8DdoX461htAX81Ejb2rjLvp+ZiMiDn8MxxaDDtmy89bq4sd6+dyinj1l0PsPZVFaq7xoufsDDqEgJ/3JPPSyDbc2z3oYtG7CoFT0zQOn8nB19UO32pWQl6Oo2dzWLw7iaX7nNFy/81/7L9kgG4XO5xv5q2Uzpwvje/wc7WjW4gXXYM9aBvgRht/Fxxta3nZbOMgo2h+mgx7voGuD9Z6vE2WsiKsl0TQ2Oh1BHk6Vo6gST0C3pFY0PHT7tP0Dvci0LPqqMmrhnc4nD100a52zd0I8XZi6d4z3JXxi9yZewbO7IUWna/t+C6lMAti/4DO9/PTvnNk5Bfz1OAI0vOMPD5vL/N3nuKe7i3h5pfhyK+yJsHwfzXsmBuYmJQc0vOJPMYnAAAgAElEQVSKSc8r5reDKYy+KeDKOizOh3kT5IqdifPqZ5B1ITtJ1lGoTfFrhaKWlOjl31yVAa9QKBR1o2Ed8FYK8HalUTWFJWYsFo2TGQX0j6x+eXOQpyM2esGxS5e3KqqkpNTJbmulA95GRdAoFNcVmqaZhBCPAysBPfCVpmmHhRCvA7s0TVtW+txQIUQMYAae1zQto+FGrbCKigK8pqkM+OsMIQSvjW7LkPfXM+nL7bxyaxQDWvladaymaZgsmtWr36rEI1jGrPR5GtLj5WfM5sIkwMb4NGbM30dhiZlb2vkT5OlIkKcjgZ4OBHo44u1sx7ncIl5YdIBXlh7iz8Nneef29vi7VZ5IsFg0/ow5xyfrEjiQlI0Q0DXYk5Ht/BkR3Qxf16rF+Hyjif2ns9hz6jwrDp3l8JkcDDrBwNa+jO8cTa/IuyBlF938b2K33o5jaXlsP5HJjhOZbD+Rwa/7zwAyhj7E24m2AW5E+bsSFeBKpyB3XOxrECKixkBwX1j7JrQdV+e3uslRTQQNQKi3U+UImrSjENyHrcczSDpfyPPDWl2DQV6CdyQcWS5z1Q0y41gIweibAvhi7UEsGd+jCx8Mx/6Co782vAB/ZBmYjZii72D2/GN0CHSne4gnAHNDTvKflbHc2i4AN59I6HA37JwD7e+EgA5Xd1wHFkJBBvSYfnXPUwfWx8m6NEGejsxaHcfIdv5X5oI/9heYjTJmymKWKymuNZoGC+6VE6JP7b/wd1+hqGfMFmm+c1MOeIVCoagTTcIBX+aQKioxcy63CKPJclkHvEGvI9jLqd4LsWYVFDP12128MSaaqIDrZwl/cR0d8MX1lTmrUCgaHE3Tfgd+v2TfqxW+1oBnSh+KpkJFAb6kADSzyoC/zgj0dGT2pC688ssh7v96J/0jfXh5ZBsi/KqeaMkuLGHx7iS+336S05kFdA/xYmBrXwa18b3stVWNeEeUf2m2aHy4Jp4P18YT4evMJ/d0Ity36vH4uznw3QPd+H77Kf712xGGvb+B18dEM6ZDAEIITGYLyw+k8Mm6BOLO5RHk6cgbY9qSnlfM7wdTmLnsMK/9epiuLT25pV0zugR7Ens2lz2nzrPnVBaxZ3Mo1Qxo19yNmaOiGH1TAF7OdhcGEdQdkIWRIvxciPBz4d4eLdE0jTPZRcScySHmTA6Hz2Sz99T5clHexd7AA71DeKBPSPWChBAw4m34rA/8VX08z3VHeQRNZTNMiLcTmxLSOZGeT2J6PqdSUpick8y8E068c2gPrvYGhrVtdo0HjBTgNTOcPwE+FyYAxnQIIG3dZ+iMOdDvBVm898hyGPzatR9jRQ4sBM8wfs8M4HTmPl4eGVW+gmTmqLbc+r+NvL86jtdGt5Uu+OPr4dvRMt4psOvVGZOmwerXICcZ3AKhza1X5zx1ZENcGm38XXliYDiP/rCHX/efYWzHS2vO14K4FXJrzIaUfdC8ASZlTm6WKzJAZv3f/NK1H4PihqBMgHd3UEVYFQqFoi40CQe8EAJ7g56iEjOJ6TJLMriGm8RwX2eOns294jFWZH1cGrtPnmfJniSiAqLqte+GpLwIq7UZ8Hp5ca8c8AqFQtHIsXUCoZcCfFGO3Kcc8Ncd/SJ9WPV0f77bmsisNfEMn7WRu7sFMWNwRLnQHHMmh7nbTrJ0bzKFJWY6BbnTv0dLNsan8/ryGF5fHkOojxODWvsysLUfXYI96uSOT88zMmP+PjYlpDOuU3PeHBtdY3SLEIJJPVrSJ9ybZxfuY8aCfaw8fJZe4d7M2XCcU5kFRPo5M2tCB0a288dQOq6nh0QSfy6X3w6msOLgWV77Naa8Txc7Ax2C3BkyMIKOQe50DHTH3bF2ooEQgubuDjR3d2BIlF/5/uyCEg4mZzN3m3y/v9p0gim9g3mgT0jV5/BrK+NnGlPxzqtNeQRN5VoCYb7OGE0Wbv7POgA6iTgm28EJXSCDWvsxrlNzq0069YpXuNymx10kwId6O/Gw/WoSCCM8sBu0GSWLEafFXtSuthSbLMxaE8f5ghI6BrrTMciDUG+namtcXUTWaUjciDbgJT5df5wwHyeGtLnwGY0KcOXu7kHM3XaSid2CaNXMH6asgO9Gw9yxcPcCCO5T57FXy7lDUny3cYJfHoVm0XKlTCMgz2hiz6nzPNAnhOFtm9G6mQuz1sRza/sLv1NqhcUCcSvlCpfEjXKCoyEE+K0fg6MXtOgmv+720A2f9a+4OpQ74FUEjUKhUNSJBi7Cav3FjoOtnsISMyczpJOmpdflcyHDfZ35M+YcRpMZO0P9XMRvik8HYEN8Wr3011goL8KqHPAKhUJxfSGEdMEXZcv4GVDL069TbA06HuwbyrhOLZi1Oo7vt59i6b5k7unekl2Jmew6eR57Gx1jbmrOpJ4tiW5+4XNwMiOftUdTWXs0lW+3nGTOxhM42OjpEuxBj1AveoZ50a65W42C/PbjGTzx416yC0t4e3w77uwSWKtCpiHeTvz0SC8+33CM91fFseLQWW5q4cbLIzszuI1flcJkhJ8LM/xcmDE4koTUPA4lZ9PG35VwX+f6KbBYBW6ONvSJ8KZPhDcxZ3L439p4PlybwFebE7m/VzBT+4Tg4XSJED/g73BwEZB1VcbU6LApNcpUEUFzSzt/MvKM+LrYE+rjRJszZ+FP+L/7x4NHy2s80AqUreJIj794f+ImWppP8nzJQzyclk9465FSgD/ya50F+DyjiUfm7mZTQjoudgbmbT8FyGiHDoHudAxyp2uwJ73CvKr+P3RoEQA7XAdzJEVGN136/+PZIa34dX8Kry8/zPdTuyPcA0tF+DHw/XiY8IPMLq9PYv+Q2/uWwve3w09T4IGV5ZE+Dcm2YxmUmDX6R/ig0wmeHhLJw3N388u+M4yvS8Hf5N2QnwbD/i0jd06sh77XeJFgegLEroD+L0D0ePikB2x6/7LFsK97CrPAwf3qnyf1CJw/Ca2GX/1zNRLMFg2DkBPcCoVCoag9TSKCBqRbvqjEQmJGATZ6UWOhsTAfZ8ylefGR1SzDrg2aprE5IR2DThB3Lo+U7MIqM0qbIsW1zIC31cufW4kS4BUKhaLxc6kArxzw1zWeTrb8Y0w0k3oG86/fj/DZ+mMEezny8sg23NE5sErnWksvJ6b0DmFK7xDyjCY2xaez5Vg6245n8O7KWACcbPV0CfakU5AHQshs9fxiEwVGM3lGE3lGE9tPZBLk6cg3U7rVOapPrxM8OiCcEdH+ZOYbS89nnZAe7utMuK9znc5bV6ICXPn03s4cPZvD/9Yk8PG6BL7afIL2LdyILI2zifB1JtLPBc+BLwM3SCHWMgd8FRE0bg42PD7wQmQRR+KkYO8WeI0GVw12LuDiX1mA3/E5FnsPfjX2wn//GZ4ZEimdzkeXQ7/nan2atFwjU77ZwZGUXN69vT3jO7XgeHoee05msff0efaeymLWmng0DSZ2C+TNse0unkzSNNi/AAK78/6uYpq52jO2Q+UYFQ8nW54ZEsnMZYdZefgsw6P9waUZ3P+bdMH/OBHu+AZaj6z1a6iWuD/kexPYDcZ8BAsnweqZMPzf9XeOOrIhPg0HGz2dgz0AWcS6bYArH66NZ0yHgNq74ONWyBVmEYOlGL/7aygpApvaF4euM9s+Br0tdJ0Gzj5w00TYMQd6PApuVxCt01Q5sVF+tkfNgo73Xr3z5J6VcU75qdDnGRj4SpVFw683zBYNLwcb61bpKBQKhaISTUaAt7PRlTvgAz0da3Q1ld2AHUvNqxcB/kR6Pmeyi5jUoyVzt51kY1w6d3Zt4BuFeqLYLJeTWV+EVUXQKBQKRZPB3lXGzxiz5fdKgL8hCPd15qv7u5KaU4S3s53VN8zOdgaGRzdjeLTM4E7PM7L9eCZbj6ez7XhmeRFDO4MOJzsDTnZ6nGwNONkZmNA1kL+NaF1zYVIrCPF2IsT7CjLprzGtm7ny8T2diDuXy7dbEolJyWHJnmTyjKbyNj6ON5AgZuMAejvIO1dz29Qj0kneGAQs7wgZQVNG1mk4+hu6Xk/Q+aQ/v+xL5unBEYjWt8Kaf0B2ErhZ755OTM/nvq92kJZr5Iv7unBza1k0OdzXhXBfl/J7i9yiEr79cwdzt+zn5fwMXh/XARsbOym2psZA2hFO93yDbX9l8vLINtXWcbqnexDztp/izd+OMKCVr7z3cvKGyb9Kh/qCSTBuNrS7ve7vWRl5qVKILssgjxoN3R+BbZ9Ay94Nnge/MT6dHqGe5SujhRA8PTiSB7/bxZK9ydzZpZb3dbErIKgnOHhAaH/Y/ikk7YCQfldh9FWQnwH75sFNd/H9oQL2nNzHk52fIPjAQtjwjhShbzQ2vQ8WE6x8CcKHgItfzcfUFrMJFk2Vpoa242DTe/L3wJiPG8VKj6uJ2aLhrgqwKhQKRZ1pEhnwZW2NJWaSs4pqzH8HCPWRbeqrEOvmBBk/80CfEP6MOcv6+LTrR4A3qSKsCoVCcd1SyQGvirDeSPi6Xpkb09vZjpHt/RnZ3h+AwmIzBr2oUz78jUCknwv/vK0dIFdPpmQXEZ+aR/y5XOLO5bKrgcd3zRBCCpFxK2H4W/L76kg7KsWyxoB3JBz8SbrMhZCuZoAuUxnjruOFxQfYn5RNhzajpQB/9Dfo/rBVXR9IymLK1zuxaBrzpnWnY5BH5UZmE8T+hsu2z3j81BYetweOAe9e0k5n4P2Utrg5mJnQLajacxr0OmaOiuLuL7YzZ8NxnhhUuvLAwUPGxMy7CxY/COZi6HC3Va+jWuJXARpEDruwb8jrcHr7leXBz78HDHZw+1d1HtrpzAJOpOdzX8+LI44GtfGlfQs3/rc2nts6Nrf+99r5k3IiZGhp1EvL3tINf3z9tRPgd30JpiLSoqfyxlcxGE0Wlu6Duf6j6LVnLqLXk+AVVr/ntFhgX2l0kat//fZ9pZw9BMfWQId74eBC+ONFucKjvln3bzi5CcZ+KlccNIuGNa9Dbgrc9X29x98IIb4CbgVSNU2LvuS5Z4H/AD6apqULuVxsFnALUADcr2nanir67Ax8AzgAvwNPaZqm1TQWs0WrvuC4QqFQKGqkQe+eaiPA29voKSiWDvia8t8BHG0NNHd3ICGtfgT4TQnpNHd3INjLkb4RPmyKTy8vRNLUKS/CaqUAX3ZxqhzwCoVC0QQoE+BVEVZFPeBgq1fiu5UIISMT+0f68GDfUN65/aaGHtK1pfUtcP6EdLhXR0GmdMn7tr5247ocXhHy92V+mowT2f0NRI4Aj5YMi26GrV7HWyuOsKfAC82ntcyBt4INcWlMmL0NB1s9i6b3qiy+F2TCpg/gww6w8D7ISZKxFqNmsb3NS7xhupd5Lg9g7Pd36PcCZwf/j59ji7ivZ0uca8hj7hXuzYjoZny8LoHjFe+L7FzgnkXSvb3sCelevxLiVoBLADRrf2GfwQ5u/xo0ZB68qbh2fWadllE/hxZLgbWOlK3c6Rvhc9H+Mhf86cxCFu9Osr7DuNKs+1Yj5NbeFZp3kjnw14KSItgxG8KH8O4egabBL4/1ZnKvYJ4/N5Qii55D8/5GVkEt3++aWPcvWPY4fDNSrnhoTGz5n4yyGvYm9HseDv98oSZBfRG/Cjb+BzpOkhNWQkDfZ+G22XBqG3w1XH5m65dvgEpB80KIQGAocKrC7hFAROnjIeDTavr8FJhWoa1VQfYmi4ZbLYuZKxQKheICDSvA29bOAZ90vpCCYrNVDniAMF9njtWDAG+2aGw5lkGfcG+EEPSL9CG7sIQDSddHIa1ikwWdwOpiZXYGJcArFApFk0FlwCsUioag1S1yG/tb9W3KxHnfNld/PNZQXog1Tgp4BRnQbRogs+tfGN6Kg0nZjPtkCwtyb8KSuJmCrKpjdjLyjPx+MIWXlx7kgW920tLLiSXTexHmU1qnwFQsRe9fZ8B7UTIr3SMY7voBntwn8+U730/3u14kevzfeSVjCHcd6UtWj+d570xb7Aw67u8VbNXLevnWKJxsDdz/9U4y8owXnrB1lC5h52YyVqNsora2mIxw7C/pfr90tYNniMyDP7MHVr9Wu34P/iS3No4yXqSObIhLo7m7A2E+le8hB7TyoUOgO/9bm2D9vU3sCrlaoqLDPKQ/JO+p+3tYGw7+BPlpnG4zlUW7k5jUsyU3Bbozc1RbFjw7hi3edxCVvoqp73zDx38lUFBsqrnPmohZBhvele733BSYexsUnr/yfuuD7CRZmLjTfXJ1R+8Z4NMafnv2wrVPdVjMUrw/vFSufLncOZY8BH7RcMslS1JuugvuXQw5yfDlEEg5cOWvqRRN0zYAmVU89T7wAnJ6q4wxwHeaZBvgLoS4aKlC6feumqZtK3W9fweMtWYsKoJGoVAorowGFeDtrHRcA9jb6DiVWQBglQMeIMzHiWOp+Viu0Kl+MDmb3CITvSO8Aegb7o0QsCEu/Yr6bSyUmC1Wu9/hggNeFWFVKBSKJoC9u4qgUSgU1x6XZtC8i4xpqY7UGLn1aYQC/I7PpcgaOqD86Qf7hrL9/wbzxthoNhp6oMPCvz94n9eWHeZAUhYrDqYw85dDDH1/PZ3fXM2jP+zh5z3JjG7rxU+j7fCN+xF+fQo+7w//bg5zBsoc73bj4ZFNcP9ymZWuu9ikdFvHFnxyTydizuRw5+db+bk0s9zL2c6ql9Xc3YE5k7twLqeIB7/bRVGJ+cKTDh4w/gvIOgm/176oLAAnN0NxHkRWY6SNGg3dHpZFQxNWW9enpsGBhRDYXU6CHF4CGcdqPbQSs4WtxzLoF+ldZUFnIQRPD4kkOauQn3Zb4V4uyoHETZVfa2h/0MzyvbiaaBps/Rj82vGPg1442Rl4/Obw8qcDPR0ZNPVNNDsXXnH8mXdXxjLwP+tZsiep7vfEqUdh6XRZYPeuH2DCD/L/yA93gLF+VptbS1GJmVmr41lxMOXCzm2fyvel56Pye4MtjPpQCuJr/1l9ZyWF8NNk+PNluf3m1qpXWphL5AoOczHc8e2FItMVCe0PD6wEoYOvR1zZi6wBIcQYIFnTtP2XPNUcqPghTirdd2mbpBraVDzXQ0KIXUKIXSazWUXQKBQKxRXQYAK8EFR5EVQdFQu2WuuAD/d1prDETEpOUa3HV5Gy/PdeYV4AeDjZ0r65Gxvi066o38aC0WSp1XJyW+WAVygUiqaDvRuU5ENhpnQR6hu0/ItCobiRaD0SzuyF7OSqn087KicFXQOu7biqw7UFGBxg/wI57m4PVXJ0O9sZmNSjJR89cz9GpwDudN7PD9tPMvqjzUz/YQ8LdyXh52rPC8NbseTRXhwYGst7x0bi/O0QWD5DOuvt3WSB0tu/gmeOyAKOzdpddmjD2jbjy/u7cDqzEIsG0/qG1uqldQryYNaEDuw7ncXTC/ZdLMa27An9/wYHFsD++bXqF5BRHwb7y+efD30D3IJgo5VO9nOHIO0ItL8TejwGOhvYXPvCovtOZ5FrNNHvkviZivSL8KZTkDsfrU2oObbl2BqwlFyInymjRTf5Hhy/yjE0CWsg7QjHIu5n9dE0pg8Iw8PpklgQR0/0fZ6kQ8EWVoyzx8/VjmcW7mfsJ5vZlViVmfoyFGbB/LuxGByY7f86/T/YyksHfMi7dbZ0/M+fKCNxrgExZ3IY/dEm3l8dx2Pz9vDLvmRpMNj9LbS9Ddwr1EMI6g5dp8L2zyCpcrySJS8D49ej0Y4sZ3/UC2QOfEdOCH7eF357TsZClbH6NVlgd/SH4B1eqa9y/KLgwdXgEVJ/L/oShBCOwEvAq1ftJBXQNG22pmldNE3rYkHg7qgEeIVCoagrDSbA62ohvsMFAV6vEzT3qGLWuQrKlnheaSHWTfHpRPm74l3BZdIv0od9p7PILiy5or4bA8VmS61WI5QJ8MoBr1AoFE0Aeze5zU5S7neFQnFtaT1SbmN/r/r51KMyfqaW9wVXDZ1OCmynt4GtC9w0odqmQqfDLno07Qp3s/XZ7rx7e3sWT+/JgdeGMndqdx4dEE6nvI3oV78KYQNl1MuT++DFkzB5mRSko8eDk5fVw+sb4cOi6T35/N7OBHpatyK4IsOj/fm/W9qw4tBZ/r3ikmz+fs/JYqK/PVvuNDeazNRYm1HTZCZ66AAZaVMdBjvpZD+5ybqIjgMLQGeAtuPAxQ863gv7f4ScMzUfW4ENcWnoBPQK8662jRCC54a2IiW7iM5vrubOz7by6bpjxJ7Nrfz6Y/8AB08puFfExh6Celz9HPitH6G5+PO32HCaudozpVc1Ym/36eDoTZsjH/Dzo715/66bSM0xcvtnW3nshz2cLl1ZflksZooWPoA5M5FJuY/zr03Z+LrYsWDnaXovc2JT9OtwYgMsmiJd4lcJs0Xjs/XHGPPxJs4XlPDZvZ3pFuLJMwv3c3T5h1CcC72eqHzgoFflSpxfn2RPYiqv/nKI+7/ewcR3FpD4bh9I3stjxU8wZk8H+q8JZsPwP6Drg7LA7f86wc4vZfTO1o+g6zT5/7UmXANg2pr6fxMuEAaEAPuFEIlAC2CPEKIZkAwEVmjbonRfRZJL91+uTbUoB7xCoVDUnSYjwHuIfMbrNhDobme1W7uVnws6IS+86kphsZndJ8/TJ+Lii7Z+kT4yGz6h6cfQlJgs2NbCAV/2/huVA16hUCgaP2UCfNYplf+uUCiuLd6R4BVetQCvadJx6tNICrCW4VUaQ9Ph7pp/Z7a+FcxGvFM2ckeXQDq39Lxwn3L2IPz8sIzhufM76dD1DLniyYa2AW4MjvKr8/FT+4Rwf69g5mw8wXdbEy88odPDuDmgt6Hwx8k8v2An0TNXMuT9Dfy449TFsTUVSYuV8TWRwy57Xk3TONxsDJqNI2z//PKDtJjh4CIIH8LKE8XM2XCc2LApaBazjF+pBRvi0ugQ6I5bDc7dXuHeLHu8N9P7h5FnNPH2H0cZ9sEG+rz9F//380G2Hc8AswniV0LE0KpXk4X0l5/pq1Wg9OwhOP4XcS0nsvN0Pk8Piai+ppqds5xUObEBXexybuvYgrXP9WfG4AjWHk1l0HvreW3ZYRbuPM1fsanEnMkhPc9YvjLiWFoeaz59CvsTa3jNNBm/dgNY9XQ/fnqkFyue6kuUvyv37gzhY4fp8v/30ulgqf97w6TzBdw9ZxtvrTjKoNZ+rJzRj+HRzfhycle6tHDC/eCXZPr2gIAOlQ+2d8M0/F04d4hVX7zC4t1JuGbF8Jnxb/gbclnfYzYTpzzJL4/1JsjLkcnzE/jY4WG0hzfIrPffnoGFk8C/Awy7TJTNpRisi4aqC5qmHdQ0zVfTtGBN04KRETKdNE07CywD7hOSHkC2pmkplxyfAuQIIXoIGUdwH/CLtedXArxCoVDUnQZbh25lvc9yRp77hA62vzLL0Q8YZNUxHk62jOnQnHnbT/HogDCrcxIrsjMxk2Kzhd7hFwvwHQLdcbEzsD4ujRHt/Ks5umlQbLZgUwsHvF25A/7KsvUVCoVCcQ2o6ID3CG7QoSgUihsMIWQx1m2fyqiIst9HAPlpMhqrsRRgLcM3SuaNd32w5rZBPaUb+uhyaFuhjmF+Ovx4t3y9E36Q7uhGghCCV26NIul8Ia8tO0yAmwODo/wwWzTWJOnZb/8kz6f/g6i0DzB0fpEDSdn8fclB3l0Zy709WjKpR0t8XCrcU8WtkNuIqgV4o8nMsn1n+GLjCWLP5fKJ2wBGHPwJMeQf4FSNKz1xI+SmsNTvcWbMlfEh/wQ+su/F4G1f8Iv9HXRuE0aYj/NlI03P5xdzIDmbpwZFWPXetG/hTvsW7jw3rBVns4tYF5vK2qOp/Lw3mR+2n+LNjjncW3geWlWTdR/aH9YgXeHtbq+yyenMAr7bmkhmfgmTe7WkfQt3q8YGwNaP0WyceP5EZyJ8nRnfqcXl23eeAnu/h4WT4ZZ3cOz6IDMGRzKhaxDvrDzKt1sTK9UdNegEXs62dMzfyGc2c9ntdSsP3/MmLTwvRMBG+rkwb1p3fj94ljd/sye/JJMXDi4gx2zD8a4zScwyk5iRz8mMgvKtu6MNA1v5MrCNL12DPS9vqMtORju+jn2J53hgXyuKNT3v3t6e2zu3KP95O9kZ+KbrKRx+O8/UMwOYFJvKgFa+F3WTmJ7PjHWePGLuyjM2S3hqWFfs184EJ3e4dzFDfS9M/i16pBcvLj7AuytjOXymGe9O+BmnY8tlHNWIt66qqH45hBA/AgMAbyFEEjBT07Qvq2n+O3ALkAAUAFMq9LNP07SyWYpHgW8AB2BF6cMq3B1ta26kUCgUiippMAG+YqZ7jaTF0T5NFnC6I/9H0J622j3y2M3hLN2XzJebTvDC8No7bDYlpGOr19E12OOi/TZ6Hb3CvdgQl4amabXKs29slJjr5oBXGfAKhULRBCgTvAozwb99w45FoVDceLS+FbZ8CPGrLhYlywqwNjYBvvvDUkj1iay5rd4gJxiO/AqmYln80VQMC++D/FSY8ruMwGhk6HWCDyd2YMLsbTzx414e6hfK0n3JnMwooLl7B25pfhdTkhdAuylotw1i2/FMvtx0nA/XxPPZumOM7RjAfT2DifRzwTZuJTRrD24X13HMKijmh+2n+GZLImm5Rlo3c+HpwZF8umEQt+h+J2Xtp/iPeqXK8Zn2zadE58iLh5pzd/cgHr85nB0nMok9/CC3JmwkZdUsBq+4HT9XO969/Sb6RVad774pIR1Nk9E9taWZmz0TugUxoVsQRSVm3lgeQ97utzAZDOT698OjqoP8O8i/ucfXXfRZ1zSNXSfP8+XGE/wZcxadENjb6Fm8J4k+4d5MHxBGrzCv6u8nzSbZ58GfiA28gwOxgjn3tcZQ0/2bjT1MWQGLp8poodSjMPwtmrnZ896dHfjXbe1IyzWSmltEao6RczlFpOYaEalHeCrxc0r8OtN56ldVis9CCEa29+fm1j588lcLPttUxCMx8wldHNEAAB7wSURBVHA9tJalpkms1zoS4OZASy9HhrX1IzmriO+2nuSLTSdwsTPQL9KHm1v70jfCm/ysdHKO/oXNyfX4pG3Ht/gUAugIzLdtjfPd39E8JPDiAWgaDjs/wewTxVlTbx6eu5uv7+9Kr3BvNE3jp91JvLbsMAadwHDru9isHY3NH89KZ/s9i8D1YgOdg62eWRM6EN3clbdWHOV4Wj5z7htGYNvbytuYzBb2nMrir9hU1sWmcTa7EG9nO3xd7fB1scfXxQ4fFzt8Xetvwk3TtIk1PB9c4WsNeKyadh0qfL0LiK7LeFQGvEKhUNSdBhPgg2qTW7j2DUx6e/5bNIa/8yPE/1njMscywn2duaWdP99tPclD/UJrPWu7KT6dTi3dcbSt/Fb1i/Rh5eFzHEvLI9y36S7rL65lEVa9TqDXCYrN1SxFVSgUCkXjoaLjVEXQKBSKa02LLuDkA0d/u0SAPyq3Po1MgLd3hcBuNbcro82tsO97SNwAYYNgxfNwcjOM+wKad75647xCHG0NfDG5C7d9vIVZa+Lp3NKDF4e3ZmiUHwZLT5hzCJY+gnhkMz3D/OgZ5sXxtDy+2nyCRbuTWLgrCU+Ry0677fzsPIFtP+0n0MORQE8HDiRls2DnaQpLzPSN8Oa9O2+iT7g3QgiGRzdj++y5BO/6kmXNJzG6U/BF4zqflY3dwaUsL+nG0yPa83C/UIQQjO3YHDo2R5s/kidOrKVF/xeYvT2VR77fzYKHetKuhVul17ghLg1XewM3VfFcbbC30fPP29qRk3CYbblteHH2fj6f1Jno5pf0q9NDcN/yHPhik4XfDp7hq02JHEzOxs3Bhof7hzFdvxSn+F85atOGH1ICefqLMPxbBDN9QBhDo5qh0wkZw3Nyi1yNEbMMCtLRHLx4MbkPXYM9GNzGt4qRVjV4V5g4H1a9KrPMMxLgjq/BwQN7Gz2Bno4X6glkHoctn8OpeeDoBhO/r9H57Whr4LlhrTjZ5VNWbxlGz7h3+Sb3XczhQ9GPeAu8wsrb5htNbE5IZ+3RVI4cOURczGzC9DtpL46hFxr5mh37dFGscBnGed9edHFNp3fMG4gFQ2D0RxA1+sKJE1ZDagz6sZ8xN6IHE2dvY+q3u/hwYkeW7ElixaGz9Aj15L07OxDg7gAuH0PcShj+r4uviyoghOChfmG0aubKE/P2MOqjTbw1rj05RSWsj01jQ3wauUUm9DpB55YedAzyJyPPSGqukR0nMknLM173BjUVQaNQKBR1p8EEeKtJ3g1HlnEo9BG+jOnFDPeNOKx/R2bvWek6f/zmcH47kMI3WxKZMdgKN0spGXlGYlJyeG5o1cf0K3VTrI9Lb9ICvNFkKS+sai22ep2KoFEoFIqmwEUC/JWJEAqFQlFrdHpoNQIO/Qwm4wVBL+2IjG9xtlJIbKyE3gw2TnBkOWSegN3fQJ+nof0dDT2yGvF1sefnR3uRlmekbUCFvw96B7j9K5g9AObdAfctAwd3Qn2ceXNsO54d0oq1R1NxOLoYfbyFXbbd2BifxrkcIwA2esGomwKY1jeUNv4XF/9u1cyFnLEv4LrkblYvmkN8+n08PTgSnU5wKqOAr2f/j5laIS0G3M+d/cO4FNHnWfRHf+N2VtN36jTGfbKFKd/sYMl0meNdhqZpbIhPo0+Ed81OcWvIOIZr/glCer2GZa/G+E+38O9x7Rh3SQxMbkBvXI4u5+15f7DohIG0XCOhPk68OTaa8Z1a4JC4Gub9G7wiaJu1gn+Z8/iXPSRlNGPL/Fa87xRNf/c0orPWYm9MR7NxREQOg7bj+CQphP1rT7F4Uuvarb7W6WWGuU9rWP40fDEY7l54QRw/sw82fwAxv8jCtzdNgL7PyoKiVtLSy4mWo+4B0x2w/TP069+GT3pAz8dlFr2tE05FZxmavZShmUvAtBtsIM0liriA6RjCb8a3TW96OzvRu2LHfYfAogdkFnvXB2HoP6Wzf/MscAmA6PF4Gmz5/sHuTJi9lWnf7cJGL/j7iNZM6xsqJzNAivcVBfzL0D/Sh2WP9+Ghubt45HsZgeTrYseI6Gbc3MqX3hHeuNpXFqI1TSOn0ERqbhGRb1v91jUp3JUAr1AoFHWm8Qvwq/8Bjl4kRkzBFHOMvC6P47D+b3D8LwgbaFUXbfxdGRLlx1ebTjC1TwguVfzBrIotxzIAKuW/lxHo6UiotxMb4tKY2qeaCvRNgNpG0IC8sL7eZ/gVCoXiusCugvihHPAKhaIhaDUS9nwns73DB8t9qUdk/EwTjnEEpBgYMQQOLZavMXI4DKw6WqUx4utqX3Vkhm9ruGsu/DgRfrgDJv0sC3si62yN79wCju8DJ1/eenwy6HQUlZhJzirE1d7m4pz4S3CNHoG2LpznCtfSb20v4s7lMrlnME/8uJf/mtdS7OhHr4Fjqj64RWdZ7HTLR/h1nca3D3Rl/Kdbmfz1DhZP74Wnk1ztHJ+ax7kcY+X4GVMxFGRIUVroQacr3epBbwv6au4TY2VMdvPu4/i1TzMen7eHZxbuZ//pLAa08mVTQjqbE9IpOWdgjR0Uxa2hc9gE7uoWSP8IHykEZ52WhXn92sGDq0BnA2cPwMktND+5mbEnNmNrXI/xrA1/WTqw3DyRtcaO+CR6ElHgwtZjyQxr60fnlp6X/6FWR6dJ4Bkqxew5A2Hgy7J+wfF18lqh1xPQ49Eri00y2ELvJ6H9nbBqJmx6D/bPB7cWkLRDtvHvAIP/AW3H4uMRzGUDgjxD4IGVsOYf0sF/ahv0niF/lwx5Q54P8HGxY960Hny4Jp6J3YIqr06oJcHeTix5tDerYs4S6edClL9rjZMeQgjcHG1qLPjblHFVArxCoVDUmcYtwB/7Sy7hG/4WQzqE8T9HV3zaDoa9/4P171otwAM8OTCCUTHn+G7rSR67OdyqYzYnpONib6DdZf6A94v0Yf7OUxSVmGuXa9+IKDZZqozYuRy2Bj3FZiXAKxQKRaPH1hmEDjSLEuAVCkXDENofbBxlDE34YNA0GUFTTaHKJkebURCzVDqMx82RYu71QMQQ6YT/6X74cQLc8xPYOMjnzCWQsAaiRkkRGxnVEubjXHO/Oh2i+8MErXieD/uambHpHCsPn6Odh4n+xv2IDtMv/x72fQa+GwP75xHe5QG+nNyFe77YztRvdzLvwR442OrZEJcGcHE+/PmT8O0oyDpZTcdC3l92vBdaj7w4fiXuD/BtCx4t8Qa+n9qdt1Yc5YtNJ/h260lsDbJmWK/2N1Oyy5dXQtLR3VEhgshUDIumyDz3O7+98D427wTNOyF6PY6txQKZx9A5+hCWq2fEuTzCzuUSn5pL3Lk8HGwNdappdhHBvWHaWpg3AX5/Dpz9YPBr0OWBaqNZ6oRLMxj3uex31atQUgCDXoWosRfF0liFwVY6+EP6wc+PwJIHwdYFOk++qJmfqz3/vK1dvb0EZzsDt3WsodDtDYQQtazjp1AoFIqLaLwCvKbB6tfALRC6PICLwYZRN5Uug+v9FKx4ARI3QXAfq7pr18KNAa18+HLTCab0Dq5RcNY0jY3x6fQM9brsssV+kd58syWRnYmZdSrw0xgoMWt1iKBRDniFQqFoEuh00tlWlCWzYBUKheJaY+MA4YOki/iW/0LeWTBmN74CrHWl9Ujo97wUbq+337NRo+G2z2DJQ7BgEkz4QQrTp7bJn2Hk8Lr122EirH2D0YW/4DblLZbvP8PMZlsQa0zQ/q7LHxvSX+brb54FHe+jS7AnsyZ0ZPoPu3ly/l4+u7cz6+PSCPNxorl7qdCdnSTF96IsGP62FPg1i3xYzKCZpTP+0BIplDt4QLs75c/UPVDmsfeZUT4Eg17Hy7dGMSy6GcYSC12CPS6Ik1k3y8kJi6V8coI1/4CknXDHN9UL0DodeEdgA0Q4QoSfCyPxr7rtleARLB34iZtKI5Tqr2hoJYK6w9SV9dNX5DCYvhl+f16K8fU5YaCoEYOohygnhUKhuIFpvL9FY36BlH1w80uVi790ug+cfGH9O7Xq8omBEWTmFzNv+6ka257KLCA5q5A+EVXHz5TRI9QLW72u3GXRFJFFWGu3/NfWoFMCvEKhUDQVym5SlQNeoVA0FK1GQm4KnNkLqTFy3/UiwNs4yDgPj+CGHsnVof2dMGoWJKyCxVOlizvuDxnZEnpz3fq0c4GOkyBmKf2blfDuHTfhHPcz+EaBX/TljxVCZpSfT4TlM6C4gOHRzXhtVFtWxZzjpSUH2XGigjkqJ0WK74XnZZROj0eg2zTo/jD0mA69HpcGryGvw1MHZJuwgbD7a/i8L3zaWwr0rW6pNJSuwZ70ifC+2Bkc0h8K0i98zo/+JuNTuk6DtrfV7f2qb+xcZG2Gqym+Xw1cA+QkUPeHG3okNxx6XROPC1MoFIoGpnEK8GYTrH1DLuOsygFh4yCz5U6sh9M7rO62c0sPeod78fmG4xSVmC/bdlNCOlB9/nsZjrYGugR7sCEu3epxNDZKzBZsDbVbTmZr0FGiImgUCoWiaaAEeIXihkIIMVwIESuESBBC/K2K5+8XQqQJIfaVPh6s8NxkIUR86WPypcfWmchhMms79jcZPwPgc50I8DcCnSdL5/iRX2HpI3I1Q3Df8lz4OtFtmnSf7/xSFrA9vV2K/dbUBWh1i8wC3zsXZveHlP1M7hXMI/3DWLDrNEaThf6RPpCXKsX3vFS4d7F0zl8OnU6K77d/Bc/Gwoh3wdFTTgoEdLLudYX2l9sT6+UkwdLpMvd82D+tO16haIQoAV6hUCiujMYpwO/7ATISZAGj6vL/ujwAjl61dsE/fnMEablGFuw8fdl2mxPS8XezJ9TbqcY++0X6EHsul7PZRbUaS2PBWAcHvI1eOeAVCoWiyVAuwKvl2grF9Y4QQg98DIwAooCJQoioKpou0DStQ+nji9JjPYGZQHegGzBTCOFRLwNz9ISWvaQbOPWIXM3q5FUvXSuuET0egUEz4eBPkHms7vEzZXiGSCF999dSSAeItrIugBAw5B9w3y9gzIU5g2Dzh7wwNILxnVrg7WxLdz+LFN9zkmV+fWC32o3P0RO6PwSPbJLRJzorb53dWoBnGMSvkvn5GjJ65tJV3QpFE0IJ8AqFQnFlND4BvqQQ1r0FzbvIPMXqsHWCno/JpZDJe6zuvkeoJ12DPfhs/TGMpqpd8GaLxpZjGfQO966x2jlAv9LljRvim2YMTbHZgl1tM+ANOlWEVaFQKJoKygGvUNxIdAMSNE07rmlaMTAfGGPlscOAVZqmZWqadh5YBVyhylqB1iMh7SgkrAbfKywmqWgY+j4D/V8Ee3doXTmSpdb0eERmr2/6AFr2kXnrtSF0AEzfIldYrHoF3Q/j+O9wXzY90QHH+eNl4dW7F8rJn2tJaH84/peMXBr7sZxsUCiaMEqAVygUiivDKtXVimWsdkKIBaXPbxdCBNd5RDvmQO4ZWY29JvG76zR58bfhP1Z3L4TgiYERpGQXsXh3cpVtYs7kkFVQQt8a8t/LaOPvgo+LXZPNgS8xW7C9TKHZqlAOeIVCoWhC2LuXbq+z4oAKhaIqmgMVl3omle67lPFCiANCiEVCiDLV09pj60ZZhnbeWZn1rWia3PwSPH9MOr2vlOC+4NtWZqy3v7NufTh6wl3fy5z60zvg017Yfz8K0uNh4jwI6Xvl46wtZdn4PR6FNqOu/fkVinpGCfAKhUJxZRhqalBhGesQ5EX4TiHEMk3TYio0mwqc1zQtXAgxAXgbuHz5es0Mybsh45iMmyl7pB6BsEHWXSjZu8rCOev+DWcPQbMKBXssZjAZwWwEnY1c8qczgBD0jfDmpkB33lsVR0xKNi08HAn0cKSFhwMtPBzYmCCF9F5h1gnworTP5ftTmDB7K2E+zoT6OBPm40SYjzPN3R3QXYU/WBaLRm6RiZyiErILS8gpKCbfWIKTnQ2ujra4Odjgam+Ds73hsn8wZRHWUgFe08BUJFciFOeDuVi+dzaO8mGwAyGwM+jIM5rq/TUpao/ZolFitqATAhu9sGrVhkKhuMFQDniFQnExvwI/appmFEI8DHwLDLT2YCHEQ8BDAEFBQdaf1aMl+LWDcwdlrSdF00Vf422kdQgB/Z+HVTMhytqFGtX00/l+aNlbFopNPQIT5sk894ag9Ug5KXClMT0KRSNBCfAKhUJxZVhz5VS+jBVACFG2jLWiAD8GeK3060XAR0IIoWmaVm2vKQdgTukFkdCBexB4hUOXXtDrCetfQfeHYctH8NVwMNhK0d1UBJYqxGGhA4M9Qm/LIp0tmRYw79OwVBhmITAaGO+gw+cL66vCv22x8LKjCdNZCyVnNCq+9DOArh5FUYGGDgt6zYQNJrww44cJW3EhUseoGTChpwQDmegxY4NFVHa5C+BPoeGx3wwHSqCkALTLOdsF2DjysdmGArMgZaZAIKMNBQJNUPqV3HeBSz4Kpd9aEJVaCASl/8pfb8XxXo7qP3DWo9V4lguIWh9Rdo66oyHQNNAu00vF97Di2Go6r7qsUiiuU5QAr1DcSCQDFXM8WpTuK0fTtIwK334BlBVVSgYGXHLsuktPoGnabGA2QJcuXWp3WdP6FinA+6oCrIr/b+/uYyyr7zqOvz87O8NuWR52YUECKtQSKyR0G7FWqQlibJrYUExoJX0IGh/+qbGNGhWfGjGa+I/VRBNrLAEramstLWmaWASC9g9LaUullCa2pk0g2BW3UKDdnZ3Zr3/8zuy5s7vsLsvcuXvPeb+SmznnzJkzv/s993xm8j33nNu58qfbYyOcfzn8wn3wnX2w44KN2eap2LLgO981KFttwEvSS3IyDfhjXYr6wy+0TlWtJHkGOA94anKlyXfLvPKSnXDTB1rTfeelp/6hNNt3wpv+ot0Lfus2WDijbWvtsbDUmvErB/rm/Ooyiyv7uXD1IBCWVw/x7eUVvr28yvMHVnh+eZXvOvsMOHv7SQ9jEdjVTRfFgZVDPLt/hWf3H+TZ/SusHJpoatf6ycN/ygLHboHW4S9rP7plYZGFxSUWti6xuLTE4tI2lpaWWFxc4uDBZZaXD3Bw+QArB5dZXT7A6soydWil/Xyt2yoJ7LjofDj7HFh6GSxuh8Uz2/RCd1Lj4Hdac777evDpZ3hq37eoqu4BVYcoaPOHx56jptbORWxZaxB3X9eaym1bcKjbTtteJsqzvkbHqliow23xeoEaHkvWfqob5JFjrnW1q4n5yRMJx/7nZP2JhDpqvZP5lyYUW7a0d7lvSTuxs6WbLlrNDh3qalewWutPBq37PWsTR7weTuSUTh6se6G/8HOtI2dO5f+8qhPfvmojnOr4XkCqgK9u3AalSVe9pV01ts0PYZVG4DPA5UkuozXUbwLeOrlCkouq6slu9nrgsW76X4A/nvjg1dcDt2zo6H7oF9vf6ouv3tDNSoctbJ1t810aoDMWT7+PD5SkebJB1w6enKPeLXO8D1l9Ma68oT1O0VL3OHdjRkOAbd1j9wZt83RzXveQtIF+1XeWaEp2XdZu2SZp8Lo3w/wyrZm+ANxWVY8muRV4qKruBn4lyfXACrAP+NnuZ/cl+UNaEx/g1qrat6ED3LEbrvudDd2kJGm6zlza1NaRJA3OyaToCS9jnVjn8SRbgXOA/0OSJEnSpqqqTwCfOGLZ709M38ILvLO9qm4DbpvqACVJkqQROZnriA5fxppkiXYZ691HrHM3cHM3fSNw33Hv/y5JkiRJkiRJ0sCd8B3wJ3kZ6/uBDyT5Cu0y1pumOWhJkiRJkiRJkk53J3Ujr5O4jHU/8OaNHZokSZIkSZIkSfPLj7KWJEmSJEmSJGkKbMBLkiRJkiRJkjQFNuAlSZIkSZIkSZoCG/CSJEmSJEmSJE2BDXhJkiRJkiRJkqbABrwkSZIkSZIkSVNgA16SJEmSJEmSpClIVc3mFyf/C3x9Jr98Ns4Hnpr1IE4D1qFnLRrr0Pv+qjpr1oPYSCPLel/LPWvRWIeetWjM+fnna7mxDj1r0ViHnlk/33wt96xFYx161qJ50Tm/dVojOZGq2j2r3z0LSR6qqqtnPY5Zsw49a9FYh16Sh2Y9ho02pqz3tdyzFo116FmLxpyff76WG+vQsxaNdeiZ9fPN13LPWjTWoWctmlPJeW9BI0mSJEmSJEnSFNiAlyRJkiRJkiRpCmzAb56/nvUAThPWoWctGuvQsxbzzf3XsxaNdehZi8Y6zD/3YWMdetaisQ49azHf3H89a9FYh561aF50HWb2IaySJEmSJEmSJA2Z74CXJEmSJEmSJGkKbMBPQZLbkuxN8sWJZbuS3JPkv7qvO2c5xs2Q5LuT3J/kS0keTfKubvmoapFkW5IHk3yhq8MfdMsvS/LpJF9J8sEkS7Me62ZIspDk80k+3s2PtQ5fS/JIkofXPkF7bMfGPDPnG3O+Z9avZ9Y3Zv18M+sbs74x59cz5xtzfr6Z84053zPr1zPrm43Iehvw03E78IYjlv0WcG9VXQ7c280P3Qrwa1V1BfBa4J1JrmB8tTgAXFdVrwL2AG9I8lrgT4D3VtUrgG8CPz/DMW6mdwGPTcyPtQ4AP15Ve6rq6m5+bMfGPLsdcx7M+Ulm/Xpmfc+sn1+3Y9aDWb/GnF/PnO+Z8/Prdsx5MOcnmfXrmfW9l5T1NuCnoKr+Ddh3xOI3AXd003cAN2zqoGagqp6sqs9108/SDtqLGVktqnmum13sHgVcB3y4Wz74OgAkuQT4KeBvuvkwwjocx6iOjXlmzjfmfM+s75n1JzS642NemfWNWd+Y8z1z/oRGdWzMM3O+Med7Zn3PrD+hF3V82IDfPBdW1ZPd9P8AF85yMJstyaXAq4FPM8JadJftPAzsBe4Bvgo8XVUr3SqP0/7ADd2fAb8BHOrmz2OcdYD2R/yTST6b5Je6ZaM7NgZm1Ptv7DkPZv0Es75n1g/PqPff2LPenD/MnO+Z88Mz6v039pwHs36CWd97yVm/dZqj07FVVSWpWY9jsyTZAfwz8O6q+lY7adaMpRZVtQrsSXIucBfwyhkPadMleSOwt6o+m+TaWY/nNPC6qnoiyQXAPUm+PPnNsRwbQzW2/WfON2a9WX8MZv2AjW3/mfXmPJjzx2DOD9jY9p8535j1Zv0xvOSs9x3wm+cbSS4C6L7unfF4NkWSRVqA31lVH+kWj7IWAFX1NHA/8CPAuUnWToJdAjwxs4FtjmuA65N8DfhH2qVLf8746gBAVT3Rfd1L+6P+GkZ8bAzEKPefOX80s96sX2PWD9Io959Zv545b86vMecHaZT7z5w/mllv1q/ZiKy3Ab957gZu7qZvBj42w7Fsiu7+UO8HHquqP5341qhqkWR3d+aUJNuBn6TdU+1+4MZutcHXoapuqapLqupS4Cbgvqp6GyOrA0CSM5OctTYNvB74IiM7NgZodPvPnO+Z9Y1Z3zPrB2t0+8+sb8z5xpzvmfODNbr9Z873zPrGrO9tVNanahRXkGyqJP8AXAucD3wDeA/wUeBDwPcAXwfeUlVHftjHoCR5HfDvwCP094z6bdq9xEZTiyRX0T6QYYF20utDVXVrkpfTziTuAj4PvL2qDsxupJunu4Tp16vqjWOsQ/ec7+pmtwJ/X1V/lOQ8RnRszDNzvjHne2b90cx6s37emfWNWd+Y80cz5835eWfON+Z8z6w/mlm/MVlvA16SJEmSJEmSpCnwFjSSJEmSJEmSJE2BDXhJkiRJkiRJkqbABrwkSZIkSZIkSVNgA16SJEmSJEmSpCmwAS9JkiRJkiRJ0hTYgJdOIMm1ST4+63FIkqbHrJekYTPnJWn4zHqdrmzAS5IkSZIkSZI0BTbgNRhJ3p7kwSQPJ3lfkoUkzyV5b5JHk9ybZHe37p4k/5HkP5PclWRnt/wVSf41yReSfC7J93Wb35Hkw0m+nOTOJJnZE5WkETPrJWnYzHlJGj6zXmNjA16DkOQHgJ8BrqmqPcAq8DbgTOChqroSeAB4T/cjfwv8ZlVdBTwysfxO4C+r6lXAjwJPdstfDbwbuAJ4OXDN1J+UJGkds16Shs2cl6ThM+s1RltnPQBpg/wE8IPAZ7qTm9uBvcAh4IPdOn8HfCTJOcC5VfVAt/wO4J+SnAVcXFV3AVTVfoBuew9W1ePd/MPApcCnpv+0JEkTzHpJGjZzXpKGz6zX6NiA11AEuKOqblm3MPm9I9arU9z+gYnpVTx2JGkWzHpJGjZzXpKGz6zX6HgLGg3FvcCNSS4ASLIryffSXuM3duu8FfhUVT0DfDPJj3XL3wE8UFXPAo8nuaHbxhlJXrapz0KSdDxmvSQNmzkvScNn1mt0PAukQaiqLyX5XeCTSbYAB4F3As8Dr+m+t5d2nzGAm4G/6gL6v4Gf65a/A3hfklu7bbx5E5+GJOk4zHpJGjZzXpKGz6zXGKXqVK/okE5/SZ6rqh2zHockaXrMekkaNnNekobPrNeQeQsaSZIkSZIkSZKmwHfAS5IkSZIkSZI0Bb4DXpIkSZIkSZKkKbABL0mSJEmSJEnSFNiAlyRJkiRJkiRpCmzAS5IkSZIkSZI0BTbgJUmSJEmSJEmaAhvwkiRJkiRJkiRNwf8DRm1c7wHtA4UAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 50/50 [13:01<00:00, 14.93s/epoch, discriminator_loss=1.26e-5, discriminator_accu=0.583, generator_loss=0.562, generator_psnr=157, val_discriminator_loss=3.33e-7, val_discriminator_accu=0.586, val_generator_loss=0.598, val_generator_psnr=156]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "execution_count": 16, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], "source": [ - "epochs = 50\n", - "experiment.log_parameter(name=\"num_epochs\", value=epochs)\n", - "\n", - "metric_names = [\n", - " \"discriminator_loss\",\n", - " \"discriminator_accu\",\n", - " \"generator_loss\",\n", - " \"generator_psnr\",\n", - "]\n", - "columns = metric_names + [f\"val_{metric_name}\" for metric_name in metric_names]\n", - "dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns)\n", - "progressbar = tqdm.tqdm(unit=\"epoch\", total=epochs, position=0)\n", - "\n", - "train_iter.reset()\n", - "dev_iter.reset()\n", - "\n", - "for i in range(epochs):\n", + "def trainer(\n", + " i: int, # current epoch\n", + " columns: list, # dataframe column names, i.e. the metric names\n", + " train_iter: chainer.iterators.serial_iterator.SerialIterator,\n", + " dev_iter: chainer.iterators.serial_iterator.SerialIterator,\n", + " g_model, # generator_model\n", + " g_optimizer, # generator_optimizer\n", + " d_model, # discriminator_model\n", + " d_optimizer, # discriminator_optimizer\n", + ") -> pd.DataFrame:\n", + " \"\"\"\n", + " Trains the Super Resolution Generative Adversarial Networks (SRGAN)'s\n", + " Discriminator and Generator components one after another for one epoch.\n", + " Also does evaluation on a development dataset and reports metrics.\n", + " \"\"\"\n", + "\n", " metrics_dict = {mn: [] for mn in columns} # reset metrics dictionary\n", "\n", " ## Part 1 - Training on training dataset\n", @@ -1383,9 +1319,9 @@ " ## 1.1 - Train Discriminator\n", " d_train_loss, d_train_accu = train_eval_discriminator(\n", " input_arrays=train_arrays,\n", - " g_model=generator_model,\n", - " d_model=discriminator_model,\n", - " d_optimizer=discriminator_optimizer,\n", + " g_model=g_model,\n", + " d_model=d_model,\n", + " d_optimizer=d_optimizer,\n", " )\n", " metrics_dict[\"discriminator_loss\"].append(d_train_loss)\n", " metrics_dict[\"discriminator_accu\"].append(d_train_accu)\n", @@ -1393,9 +1329,9 @@ " ## 1.2 - Train Generator\n", " g_train_loss, g_train_psnr = train_eval_generator(\n", " input_arrays=train_arrays,\n", - " g_model=generator_model,\n", - " d_model=discriminator_model,\n", - " g_optimizer=generator_optimizer,\n", + " g_model=g_model,\n", + " d_model=d_model,\n", + " g_optimizer=g_optimizer,\n", " )\n", " metrics_dict[\"generator_loss\"].append(g_train_loss)\n", " metrics_dict[\"generator_psnr\"].append(g_train_psnr)\n", @@ -1406,77 +1342,64 @@ " dev_arrays = chainer.dataset.concat_examples(batch=dev_batch)\n", " ## 2.1 - Evaluate Discriminator\n", " d_train_loss, d_train_accu = train_eval_discriminator(\n", - " input_arrays=dev_arrays,\n", - " g_model=generator_model,\n", - " d_model=discriminator_model,\n", - " train=False,\n", + " input_arrays=dev_arrays, g_model=g_model, d_model=d_model, train=False\n", " )\n", " metrics_dict[\"val_discriminator_loss\"].append(d_train_loss)\n", " metrics_dict[\"val_discriminator_accu\"].append(d_train_accu)\n", "\n", " ## 2.2 - Evaluate Generator\n", " g_dev_loss, g_dev_psnr = train_eval_generator(\n", - " input_arrays=dev_arrays,\n", - " g_model=generator_model,\n", - " d_model=discriminator_model,\n", - " train=False,\n", + " input_arrays=dev_arrays, g_model=g_model, d_model=d_model, train=False\n", " )\n", " metrics_dict[\"val_generator_loss\"].append(g_dev_loss)\n", " metrics_dict[\"val_generator_psnr\"].append(g_dev_psnr)\n", "\n", - " ## Part 3 - Plot loss and metric information using livelossplot\n", - " dataframe.loc[i] = [np.mean(metrics_dict[metric]) for metric in dataframe.keys()]\n", - " livelossplot.draw_plot(\n", - " logs=dataframe.to_dict(orient=\"records\"),\n", - " metrics=metric_names,\n", - " max_cols=4,\n", - " figsize=(21, 9),\n", - " max_epoch=epochs,\n", - " )\n", - " progressbar.set_postfix(ordered_dict=dataframe.loc[i].to_dict())\n", - " experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i)\n", - " progressbar.update(n=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "model = generator_model" + " return metrics_dict" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "execution_count": 17, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ - "os.makedirs(name=\"model/weights\", exist_ok=True)\n", - "# Save generator model's parameter weights in Numpy Zipped format\n", - "chainer.serializers.save_npz(\n", - " file=\"model/weights/srgan_generator_model_weights.npz\", obj=model\n", - ")\n", - "# Save generator model's architecture in ONNX format\n", - "dummy_inputs = {\n", - " \"x\": np.random.rand(32, 1, 10, 10).astype(\"float32\"),\n", - " \"w1\": np.random.rand(32, 1, 100, 100).astype(\"float32\"),\n", - " \"w2\": np.random.rand(32, 1, 20, 20).astype(\"float32\"),\n", - "}\n", - "_ = onnx_chainer.export(\n", - " model=model,\n", - " args={\"inputs\": dummy_inputs},\n", - " filename=\"model/weights/srgan_generator_model_architecture.onnx\",\n", - " export_params=False,\n", - " save_text=True,\n", - ")\n", - "\n", - "# Upload model weights file to Comet.ML and finish Comet.ML experiment\n", - "experiment.log_asset(\n", - " file_path=\"model/weights/srgan_generator_model_weights.npz\",\n", - " file_name=\"srgan_generator_model_weights.npz\",\n", - ")" + "def save_model_weights_and_architecture(\n", + " trained_model,\n", + " model_basename: str = \"srgan_generator_model\",\n", + " save_path: str = \"model/weights\",\n", + ") -> (str, str):\n", + " \"\"\"\n", + " Save the trained neural network's parameter weights and architecture,\n", + " respectively to zipped Numpy (.npz) and ONNX (.onnx, .onnx.txt) format.\n", + " \"\"\"\n", + "\n", + " os.makedirs(name=save_path, exist_ok=True)\n", + "\n", + " # Save generator model's parameter weights in Numpy Zipped format\n", + " model_weights_path: str = os.path.join(save_path, f\"{model_basename}_weights.npz\")\n", + " chainer.serializers.save_npz(file=model_weights_path, obj=trained_model)\n", + "\n", + " # Save generator model's architecture in ONNX format\n", + " dummy_inputs = {\n", + " \"x\": np.random.rand(32, 1, 10, 10).astype(\"float32\"),\n", + " \"w1\": np.random.rand(32, 1, 100, 100).astype(\"float32\"),\n", + " \"w2\": np.random.rand(32, 1, 20, 20).astype(\"float32\"),\n", + " }\n", + " model_architecture_path: str = os.path.join(\n", + " save_path, f\"{model_basename}_architecture.onnx\"\n", + " )\n", + " _ = onnx_chainer.export(\n", + " model=trained_model,\n", + " args={\"inputs\": dummy_inputs},\n", + " filename=model_architecture_path,\n", + " export_params=False,\n", + " save_text=True,\n", + " )\n", + " assert os.path.exists(f\"{model_architecture_path}.txt\")\n", + "\n", + " return model_weights_path, model_architecture_path" ] }, { @@ -1495,13 +1418,19 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "def get_deepbedmap_test_result(test_filepath: str = \"highres/2007tx\"):\n", + "def get_deepbedmap_test_result(\n", + " test_filepath: str = \"highres/2007tx\",\n", + " model=None,\n", + " model_weights_path: str = \"model/weights/srgan_generator_model_weights.npz\",\n", + " outfilesuffix: str = \"\", # unique suffix (e.g. ID) for temporary files\n", + " redo_testtrack: bool = True,\n", + ") -> float:\n", " \"\"\"\n", " Gets Root Mean Squared Error of elevation difference between\n", " DeepBedMap topography and reference groundtruth xyz tracks\n", @@ -1516,37 +1445,264 @@ " )\n", "\n", " # Run input datasets through trained neural network model\n", - " model = deepbedmap.load_trained_model()\n", + " model = deepbedmap.load_trained_model(\n", + " model=model, model_weights_path=model_weights_path\n", + " )\n", " Y_hat = model.forward(inputs={\"x\": X_tile, \"w1\": W1_tile, \"w2\": W2_tile}).array\n", "\n", " # Save infered deepbedmap to grid file(s)\n", + " outfilepath: str = f\"model/deepbedmap3_{outfilesuffix}\"\n", " deepbedmap.save_array_to_grid(\n", - " window_bound=window_bound, array=Y_hat, outfilepath=\"model/deepbedmap3\"\n", + " window_bound=window_bound, array=Y_hat, outfilepath=outfilepath\n", " )\n", "\n", " # Load xyz table for test region\n", - " data_prep = _load_ipynb_modules(\"data_prep.ipynb\")\n", - " track_test = data_prep.ascii_to_xyz(pipeline_file=f\"{test_filepath}.json\")\n", - " track_test.to_csv(\"track_test.xyz\", sep=\"\\t\", index=False)\n", + " if redo_testtrack:\n", + " data_prep = _load_ipynb_modules(\"data_prep.ipynb\")\n", + " track_test = data_prep.ascii_to_xyz(pipeline_file=f\"{test_filepath}.json\")\n", + " track_test.to_csv(\"track_test.xyz\", sep=\"\\t\", index=False)\n", "\n", " # Get the elevation (z) value at specified x, y points along the groundtruth track\n", - " !gmt grdtrack track_test.xyz -Gmodel/deepbedmap3.nc -h1 -i0,1,2 > track_deepbedmap3.xyzi\n", - " df_deepbedmap3 = pd.read_table(\n", - " \"track_deepbedmap3.xyzi\", header=1, names=[\"x\", \"y\", \"z\", \"z_interpolated\"]\n", + " outtrackpath: str = f\"model/track_deepbedmap3_{outfilesuffix}\"\n", + " !gmt grdtrack track_test.xyz -G{outfilepath}.nc -h1 -i0,1,2 > {outtrackpath}.xyzi\n", + " df_deepbedmap3 = pd.read_csv(\n", + " f\"{outtrackpath}.xyzi\",\n", + " sep=\"\\t\",\n", + " header=1,\n", + " names=[\"x\", \"y\", \"z\", \"z_interpolated\"],\n", " )\n", "\n", " # Calculate elevation error between groundtruth xyz tracks and deepbedmap\n", " df_deepbedmap3[\"error\"] = df_deepbedmap3.z_interpolated - df_deepbedmap3.z\n", " rmse_deepbedmap3 = (df_deepbedmap3.error ** 2).mean() ** 0.5\n", "\n", - " return rmse_deepbedmap3" + " os.remove(path=f\"{outfilepath}.nc\")\n", + " # os.remove(path=f\"{outfilepath}.tif\")\n", + " os.remove(path=f\"{outtrackpath}.xyzi\")\n", + "\n", + " return float(rmse_deepbedmap3)" ] }, { - "cell_type": "code", - "execution_count": 25, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. Hyperparameter tuning" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "Tuning the various hyperparameters on our test area using [Optuna](https://github.com/pfnet/optuna).\n", + "Yes, not exactly proper I know, but we have lots of areas we can test on later.\n", + "\n", + "Also logging all the experiments using [Comet.ML](https://www.comet.ml) to https://www.comet.ml/weiji14/deepbedmap." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def objective(\n", + " trial: optuna.trial.Trial = optuna.trial.FixedTrial(\n", + " params={\n", + " \"batch_size_exponent\": 7,\n", + " \"num_residual_blocks\": 10,\n", + " \"residual_scaling\": 0.3,\n", + " \"learning_rate\": 5e-4,\n", + " \"num_epochs\": 100,\n", + " }\n", + " ),\n", + " enable_livelossplot: bool = False, # Default: False, no plots makes it go faster!\n", + " enable_comet_logging: bool = True, # Default: True, log experiment to Comet.ML\n", + ") -> float:\n", + " \"\"\"\n", + " Objective function for tuning the Hyperparameters of our DeepBedMap model.\n", + " Uses the Optuna (https://github.com/pfnet/optuna) library.\n", + "\n", + " List of hyperparameters tuned:\n", + " - Learning rate\n", + " - Number of residual blocks\n", + " - Batch Size\n", + " - Number of training epochs\n", + " \"\"\"\n", + "\n", + " # Start tracking experiment using Comet.ML\n", + " experiment = comet_ml.Experiment(\n", + " workspace=\"weiji14\",\n", + " project_name=\"deepbedmap\",\n", + " disabled=not enable_comet_logging,\n", + " )\n", + "\n", + " # Don't use cached stuff if it's a FixedTrial or the first trial\n", + " if not hasattr(trial, \"trial_id\") or trial.trial_id == 1:\n", + " refresh_cache = True\n", + " elif trial.trial_id > 1: # Use cache if trial.trial_id > 1\n", + " refresh_cache = False\n", + "\n", + " ## Load Dataset\n", + " dataset, quilt_hash = load_data_into_memory(\n", + " redownload=True if refresh_cache else False\n", + " )\n", + " experiment.log_parameter(name=\"dataset_hash\", value=quilt_hash)\n", + " experiment.log_parameter(name=\"use_gpu\", value=cupy.is_available())\n", + " batch_size: int = int(\n", + " 2 ** trial.suggest_int(name=\"batch_size_exponent\", low=6, high=7)\n", + " )\n", + " experiment.log_parameter(name=\"batch_size\", value=batch_size)\n", + " train_iter, train_len, dev_iter, dev_len = get_train_dev_iterators(\n", + " dataset=dataset, first_size=int(len(dataset) * 0.95), batch_size=batch_size\n", + " )\n", + " experiment.log_parameters(\n", + " dic={\"train_set_samples\": train_len, \"dev_set_samples\": dev_len}\n", + " )\n", + "\n", + " ## Compile Model\n", + " num_residual_blocks: int = trial.suggest_int(\n", + " name=\"num_residual_blocks\", low=8, high=12\n", + " )\n", + " residual_scaling: float = trial.suggest_discrete_uniform(\n", + " name=\"residual_scaling\", low=0.1, high=0.3, q=0.05\n", + " )\n", + " learning_rate: float = trial.suggest_discrete_uniform(\n", + " name=\"learning_rate\", high=8e-4, low=4e-4, q=5e-5\n", + " )\n", + " g_model, g_optimizer, d_model, d_optimizer = compile_srgan_model(\n", + " num_residual_blocks=num_residual_blocks,\n", + " residual_scaling=residual_scaling,\n", + " learning_rate=learning_rate,\n", + " )\n", + " experiment.log_parameters(\n", + " dic={\n", + " \"num_residual_blocks\": g_model.num_residual_blocks,\n", + " \"residual_scaling\": g_model.residual_scaling,\n", + " \"generator_optimizer\": \"adam\",\n", + " \"generator_lr\": g_optimizer.alpha, # learning rate\n", + " \"generator_epsilon\": g_optimizer.eps, # epsilon\n", + " \"discriminator_optimizer\": \"adam\",\n", + " \"discriminator_lr\": d_optimizer.alpha, # learning rate\n", + " \"discriminator_adam_epsilon\": d_optimizer.eps, # epsilon\n", + " }\n", + " )\n", + "\n", + " ## Run Trainer and save trained model\n", + " epochs: int = trial.suggest_int(name=\"num_epochs\", low=30, high=60)\n", + " experiment.log_parameter(name=\"num_epochs\", value=epochs)\n", + "\n", + " metric_names = [\n", + " \"discriminator_loss\",\n", + " \"discriminator_accu\",\n", + " \"generator_loss\",\n", + " \"generator_psnr\",\n", + " ]\n", + " columns = metric_names + [f\"val_{metric_name}\" for metric_name in metric_names]\n", + " dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns)\n", + " progressbar = tqdm.tqdm(unit=\"epoch\", total=epochs, position=0)\n", + "\n", + " train_iter.reset()\n", + " dev_iter.reset()\n", + "\n", + " for i in range(epochs):\n", + " metrics_dict = trainer(\n", + " i=i,\n", + " columns=columns,\n", + " train_iter=train_iter,\n", + " dev_iter=dev_iter,\n", + " g_model=g_model,\n", + " g_optimizer=g_optimizer,\n", + " d_model=d_model,\n", + " d_optimizer=d_optimizer,\n", + " )\n", + "\n", + " ## Record loss and metric information, and plot using livelossplot if enabled\n", + " dataframe.loc[i] = [\n", + " np.mean(metrics_dict[metric]) for metric in dataframe.keys()\n", + " ]\n", + " epoch_metrics = dataframe.loc[i].to_dict()\n", + " if enable_livelossplot == True:\n", + " livelossplot.draw_plot(\n", + " logs=dataframe.to_dict(orient=\"records\"),\n", + " metrics=metric_names,\n", + " max_cols=4,\n", + " figsize=(21, 9),\n", + " max_epoch=None,\n", + " )\n", + " progressbar.set_postfix(ordered_dict=epoch_metrics)\n", + " progressbar.update(n=1)\n", + " experiment.log_metrics(dic=epoch_metrics, step=i)\n", + "\n", + " ## Pruning unpromising trials with vanishing/exploding gradients\n", + " if (\n", + " epoch_metrics[\"generator_psnr\"] < 0\n", + " or np.isnan(epoch_metrics[\"generator_loss\"])\n", + " or np.isnan(epoch_metrics[\"discriminator_loss\"])\n", + " ):\n", + " experiment.end()\n", + " raise optuna.structs.TrialPruned()\n", + "\n", + " model_weights_path, model_architecture_path = save_model_weights_and_architecture(\n", + " trained_model=g_model\n", + " )\n", + " experiment.log_asset(\n", + " file_path=model_weights_path, file_name=os.path.basename(model_weights_path)\n", + " )\n", + " experiment.log_asset(\n", + " file_path=model_architecture_path,\n", + " file_name=os.path.basename(model_architecture_path),\n", + " )\n", + "\n", + " ## Evaluate model and return metrics\n", + " rmse_test = get_deepbedmap_test_result(\n", + " model=g_model,\n", + " model_weights_path=model_weights_path,\n", + " outfilesuffix=f\"{trial.trial_id if hasattr(trial, 'trial_id') else ''}\",\n", + " redo_testtrack=True if refresh_cache else False,\n", + " )\n", + " print(f\"Experiment yielded Root Mean Square Error of {rmse_test:.2f} on test set\")\n", + " experiment.log_metric(name=\"rmse_test\", value=rmse_test)\n", + " experiment.end()\n", + "\n", + " return rmse_test" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [13:26<00:00, 7.99s/epoch, discriminator_loss=0.418, discriminator_accu=0.501, generator_loss=0.442, generator_psnr=156, val_discriminator_loss=0.322, val_discriminator_accu=0.742, val_generator_loss=0.31, val_generator_psnr=156]" + ] + }, { "name": "stdout", "output_type": "stream", @@ -1554,33 +1710,67 @@ "Tiling: lowres/bedmap2_bed.tif\n", "Tiling: misc/REMA_100m_dem.tif\n", "Tiling: misc/MEaSUREs_IceFlowSpeed_450m.tif\n", - "Experiment yielded Root Mean Square Error of 88.70 on test set\n" + "Experiment yielded Root Mean Square Error of 49.26 on test set\n" ] - } - ], - "source": [ - "rmse_test = get_deepbedmap_test_result()\n", - "print(f\"Experiment yielded Root Mean Square Error of {rmse_test:.2f} on test set\")\n", - "experiment.log_metric(name=\"rmse_test\", value=rmse_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ + }, { "name": "stderr", "output_type": "stream", "text": [ + "COMET INFO: ----------------------------\n", + "COMET INFO: Comet.ml Experiment Summary:\n", + "COMET INFO: Metrics:\n", + "COMET INFO: discriminator_accu: 0.5008680555555556\n", + "COMET INFO: discriminator_loss: 0.4181348959294458\n", + "COMET INFO: generator_loss: 0.4416767838928435\n", + "COMET INFO: generator_psnr: 156.48906270209267\n", + "COMET INFO: rmse_test: 49.25984777384268\n", + "COMET INFO: val_discriminator_accu: 0.7421875\n", + "COMET INFO: val_discriminator_loss: 0.32224634289741516\n", + "COMET INFO: val_generator_loss: 0.30959925055503845\n", + "COMET INFO: val_generator_psnr: 155.94837079004643\n", + "COMET INFO: Uploads:\n", + "COMET INFO: assets: 2\n", + "COMET INFO: figures: 0\n", + "COMET INFO: images: 0\n", + "COMET INFO: ----------------------------\n", "COMET INFO: Uploading stats to Comet before program termination (may take several seconds)\n", - "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/weiji14/deepbedmap/d64dd9dd8dc54b3397a36d26337080c3\n", - "\n" + "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n", + "COMET INFO: Still uploading\n" ] } ], "source": [ - "experiment.end()" + "n_trials = 1\n", + "if n_trials == 1: # run training once only, i.e. just test the objective function\n", + " objective(enable_livelossplot=True, enable_comet_logging=True)\n", + "elif n_trials > 1: # perform hyperparameter tuning with multiple experimental trials\n", + " tpe_seed = int(\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"]\n", + " ) # different seed for different GPU\n", + " sampler = optuna.samplers.TPESampler(\n", + " seed=tpe_seed\n", + " ) # Tree-structured Parzen Estimator\n", + " study = optuna.create_study(\n", + " storage=\"sqlite:///model/logs/train.db\",\n", + " study_name=\"DeepBedMap_tuning\",\n", + " load_if_exists=True,\n", + " sampler=sampler,\n", + " )\n", + " study.optimize(func=objective, n_trials=100, n_jobs=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "if n_trials > 1:\n", + " study = optuna.Study(\n", + " study_name=\"DeepBedMap_tuning\", storage=\"sqlite:///model/logs/train.db\"\n", + " )\n", + " study.trials_dataframe().nsmallest(n=10, columns=\"value\")" ] } ], diff --git a/srgan_train.py b/srgan_train.py index cbbc717..fe6fafd 100644 --- a/srgan_train.py +++ b/srgan_train.py @@ -30,7 +30,10 @@ import sys import typing -os.environ["CUDA_VISIBLE_DEVICES"] = "0" +try: # check if CUDA_VISIBLE_DEVICES environment variable is set + os.environ["CUDA_VISIBLE_DEVICES"] +except KeyError: # if not set, then set it to the first GPU + os.environ["CUDA_VISIBLE_DEVICES"] = "0" import comet_ml import IPython.display @@ -47,6 +50,7 @@ import cupy import livelossplot import onnx_chainer +import optuna from features.environment import _load_ipynb_modules @@ -58,86 +62,95 @@ seed = 42 random.seed = seed np.random.seed(seed=seed) -# cupy.random.seed(seed=seed) +if cupy.is_available(): + for c in range(cupy.cuda.runtime.getDeviceCount()): + with cupy.cuda.Device(c): + cupy.random.seed(seed=42) -# Start tracking experiment using Comet.ML -experiment = comet_ml.Experiment( - workspace="weiji14", project_name="deepbedmap", disabled=False -) # %% [markdown] # # 1. Load data +# - Download pre-packaged data from [Quilt](https://github.com/quiltdata/quilt) +# - Convert arrays for Chainer, from Numpy (CPU) to CuPy (GPU) format (if available) # %% -hash = "1ccc9dc7f6344e1ec27b7aa972f2739d192d3e5adef8a64528b86bc799e2df60" -quilt.install(package="weiji14/deepbedmap/model/train", hash=hash, force=True) -pkg = quilt.load(pkginfo="weiji14/deepbedmap/model/train", hash=hash) -experiment.log_parameter(name="dataset_hash", value=hash) +def load_data_into_memory( + redownload: bool = True, + quilt_hash: str = "07346a5773aad87a71a57f83624289f5af507ad12f7008aec29eee209f98c399", +) -> (chainer.datasets.dict_dataset.DictDataset, str): + """ + Downloads the prepackaged tiled data from quilt based on a hash, + and loads it into CPU or GPU memory depending on what is available. + """ + + if redownload: + quilt.install( + package="weiji14/deepbedmap/model/train", hash=quilt_hash, force=True + ) + pkg = quilt.load(pkginfo="weiji14/deepbedmap/model/train", hash=quilt_hash) + + W1_data = pkg.W1_data() # miscellaneous data REMA + W2_data = pkg.W2_data() # miscellaneous data MEASURES Ice Flow + X_data = pkg.X_data() # low resolution BEDMAP2 + Y_data = pkg.Y_data() # high resolution groundtruth + # print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape) + + # Detect if there is a CUDA GPU first + if cupy.is_available(): + print("Using GPU") + W1_data = chainer.backend.cuda.to_gpu(array=W1_data, device=None) + W2_data = chainer.backend.cuda.to_gpu(array=W2_data, device=None) + X_data = chainer.backend.cuda.to_gpu(array=X_data, device=None) + Y_data = chainer.backend.cuda.to_gpu(array=Y_data, device=None) + else: + print("Using CPU only") + + return ( + chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data), + quilt_hash, + ) -# %% -W1_data = pkg.W1_data() # miscellaneous data REMA -W2_data = pkg.W2_data() # miscellaneous data MEASURES Ice Flow -X_data = pkg.X_data() # low resolution BEDMAP2 -Y_data = pkg.Y_data() # high resolution groundtruth -# W1_data = np.load(file="model/train/W1_data.npy") -# W2_data = np.load(file="model/train/W2_data.npy") -# X_data = np.load(file="model/train/X_data.npy") -# Y_data = np.load(file="model/train/Y_data.npy") -print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape) # %% [markdown] -# ## 1.1 Convert arrays for Chainer -# - From Numpy (CPU) to CuPy (GPU) format -# - From NHWC format to NCHW format, where N=number of tiles, H=height, W=width, C=channels +# ## 1.1 Split dataset into training (train) and development (dev) sets # %% -# Detect if there is a CUDA GPU first -try: - cupy.cuda.get_device_id() - xp = cupy - print("Using GPU") - experiment.log_parameter(name="use_gpu", value=True) - - W1_data = chainer.backend.cuda.to_gpu(array=W1_data) - W2_data = chainer.backend.cuda.to_gpu(array=W2_data) - X_data = chainer.backend.cuda.to_gpu(array=X_data) - Y_data = chainer.backend.cuda.to_gpu(array=Y_data) -except: # CUDARuntimeError - xp = np - print("Using CPU only") - experiment.log_parameter(name="use_gpu", value=False) +def get_train_dev_iterators( + dataset: chainer.datasets.dict_dataset.DictDataset, + first_size: int, # size of training set + batch_size: int = 64, + seed: int = 42, +) -> ( + chainer.iterators.serial_iterator.SerialIterator, + int, + chainer.iterators.serial_iterator.SerialIterator, + int, +): + """ + Create Chainer Dataset Iterators after splitting dataset into + training and development (validation) sets. + """ -# %% -W1_data = xp.rollaxis(a=W1_data, axis=3, start=1) -W2_data = xp.rollaxis(a=W2_data, axis=3, start=1) -X_data = xp.rollaxis(a=X_data, axis=3, start=1) -Y_data = xp.rollaxis(a=Y_data, axis=3, start=1) -print(W1_data.shape, W2_data.shape, X_data.shape, Y_data.shape) + # Train/Dev split of the dataset + train_set, dev_set = chainer.datasets.split_dataset_random( + dataset=dataset, first_size=first_size, seed=seed + ) -# %% [markdown] -# ## 1.2 Split dataset into training (train) and development (dev) sets + # Create Chainer Dataset Iterators out of the split datasets + train_iter = chainer.iterators.SerialIterator( + dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True + ) + dev_iter = chainer.iterators.SerialIterator( + dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False + ) -# %% -dataset = chainer.datasets.DictDataset(X=X_data, W1=W1_data, W2=W2_data, Y=Y_data) -train_set, dev_set = chainer.datasets.split_dataset_random( - dataset=dataset, first_size=int(len(X_data) * 0.95), seed=seed -) -experiment.log_parameters( - dic={"train_set_samples": len(train_set), "dev_set_samples": len(dev_set)} -) -print( - f"Training dataset: {len(train_set)} tiles, Development dataset: {len(dev_set)} tiles" -) + print( + f"Training dataset: {len(train_set)} tiles,", + f"Development dataset: {len(dev_set)} tiles", + ) + + return train_iter, len(train_set), dev_iter, len(dev_set) -# %% -batch_size = 32 -experiment.log_parameter(name="batch_size", value=batch_size) -train_iter = chainer.iterators.SerialIterator( - dataset=train_set, batch_size=batch_size, repeat=True, shuffle=True -) -dev_iter = chainer.iterators.SerialIterator( - dataset=dev_set, batch_size=batch_size, repeat=True, shuffle=False -) # %% [markdown] # # 2. Architect model @@ -239,8 +252,14 @@ class ResidualDenseBlock(chainer.Chain): Final output has a residual scaling factor. """ - def __init__(self, in_out_channels: int = 64, inter_channels: int = 32): + def __init__( + self, + in_out_channels: int = 64, + inter_channels: int = 32, + residual_scaling: float = 0.3, + ): super().__init__() + self.residual_scaling = residual_scaling init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option="fan_in") with self.init_scope(): @@ -285,7 +304,7 @@ def __init__(self, in_out_channels: int = 64, inter_channels: int = 32): initialW=init_weights, ) - def forward(self, x, residual_scaling: float = 0.2): + def forward(self, x): """ Forward computation, i.e. evaluate based on input x """ @@ -311,7 +330,7 @@ def forward(self, x, residual_scaling: float = 0.2): a5 = self.conv_layer5(a4_cat) # Final concatenation, with residual scaling of 0.2 - a6 = F.add(a5 * residual_scaling, a0) + a6 = F.add(a5 * self.residual_scaling, a0) return a6 @@ -329,15 +348,27 @@ class ResInResDenseBlock(chainer.Chain): """ - def __init__(self, denseblock_class=ResidualDenseBlock, out_channels: int = 64): + def __init__( + self, + denseblock_class=ResidualDenseBlock, + out_channels: int = 64, + residual_scaling: float = 0.3, + ): super().__init__() + self.residual_scaling = residual_scaling with self.init_scope(): - self.residual_dense_block1 = denseblock_class() - self.residual_dense_block2 = denseblock_class() - self.residual_dense_block3 = denseblock_class() + self.residual_dense_block1 = denseblock_class( + residual_scaling=residual_scaling + ) + self.residual_dense_block2 = denseblock_class( + residual_scaling=residual_scaling + ) + self.residual_dense_block3 = denseblock_class( + residual_scaling=residual_scaling + ) - def forward(self, x, residual_scaling: float = 0.2): + def forward(self, x): """ Forward computation, i.e. evaluate based on input x """ @@ -346,7 +377,7 @@ def forward(self, x, residual_scaling: float = 0.2): a3 = self.residual_dense_block3(a2) # Final concatenation, with residual scaling of 0.2 - a4 = F.add(a3 * residual_scaling, x) + a4 = F.add(a3 * self.residual_scaling, x) return a4 @@ -390,18 +421,20 @@ class GeneratorModel(chainer.Chain): >>> y_pred.shape (1, 1, 32, 32) >>> generator_model.count_params() - 3333249 + 7649793 """ def __init__( self, inblock_class=DeepbedmapInputBlock, resblock_class=ResInResDenseBlock, - num_residual_blocks: int = 4, + num_residual_blocks: int = 10, + residual_scaling: float = 0.3, out_channels: int = 1, ): super().__init__() self.num_residual_blocks = num_residual_blocks + self.residual_scaling = residual_scaling init_weights = chainer.initializers.HeNormal(scale=0.1, fan_option="fan_in") with self.init_scope(): @@ -416,9 +449,9 @@ def __init__( pad=1, # 'same' padding initialW=init_weights, ) - self.residual_network = resblock_class().repeat( - n_repeat=num_residual_blocks - ) + self.residual_network = resblock_class( + residual_scaling=residual_scaling + ).repeat(n_repeat=num_residual_blocks) self.post_residual_conv_layer = L.Convolution2D( in_channels=None, out_channels=64, @@ -846,39 +879,49 @@ def calculate_discriminator_loss( # %% # Build the models -generator_model = GeneratorModel() -discriminator_model = DiscriminatorModel() -experiment.log_parameter( - name="num_residual_blocks", value=generator_model.num_residual_blocks -) +def compile_srgan_model( + num_residual_blocks: int = 10, + residual_scaling: float = 0.3, + learning_rate: float = 6.5e-4, +): + """ + Instantiate our Super Resolution Generative Adversarial Network (SRGAN) model here. + The Generator and Discriminator neural networks are created, + and an Adam loss optimization function is linked to the models. + + Returns: + 1) generator_model + 2) generator_optimizer + 3) discriminator_model + 4) discriminator_optimizer + """ -# Transfer models to GPU if available -if xp == cupy: # Check if CuPy was loaded, i.e. GPU is available - generator_model.to_gpu(device=0) - discriminator_model.to_gpu(device=0) + # Instantiate our Generator and Discriminator Neural Network models + generator_model = GeneratorModel( + num_residual_blocks=num_residual_blocks, residual_scaling=residual_scaling + ) + discriminator_model = DiscriminatorModel() + + # Transfer models to GPU if available + if cupy.is_available(): # Check if CuPy was loaded, i.e. GPU is available + generator_model.to_gpu(device=None) + discriminator_model.to_gpu(device=None) + + # Setup optimizer, using Adam + generator_optimizer = chainer.optimizers.Adam(alpha=learning_rate, eps=1e-8).setup( + link=generator_model + ) + discriminator_optimizer = chainer.optimizers.Adam( + alpha=learning_rate, eps=1e-8 + ).setup(link=discriminator_model) + + return ( + generator_model, + generator_optimizer, + discriminator_model, + discriminator_optimizer, + ) -# %% -# Setup optimizer, using Adam -generator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup( - link=generator_model -) -experiment.log_parameters( - dic={ - "generator_optimizer": "adam", - "generator_lr": generator_optimizer.alpha, # learning rate - "generator_epsilon": generator_optimizer.eps, - } -) -discriminator_optimizer = chainer.optimizers.Adam(alpha=6e-4, eps=1e-8).setup( - link=discriminator_model -) -experiment.log_parameters( - dic={ - "discriminator_optimizer": "adam", - "discriminator_lr": discriminator_optimizer.alpha, # learning rate - "discriminator_adam_epsilon": discriminator_optimizer.eps, - } -) # %% [markdown] # # 3. Train model @@ -1076,23 +1119,22 @@ def train_eval_generator( # %% -epochs = 50 -experiment.log_parameter(name="num_epochs", value=epochs) - -metric_names = [ - "discriminator_loss", - "discriminator_accu", - "generator_loss", - "generator_psnr", -] -columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names] -dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns) -progressbar = tqdm.tqdm(unit="epoch", total=epochs, position=0) - -train_iter.reset() -dev_iter.reset() - -for i in range(epochs): +def trainer( + i: int, # current epoch + columns: list, # dataframe column names, i.e. the metric names + train_iter: chainer.iterators.serial_iterator.SerialIterator, + dev_iter: chainer.iterators.serial_iterator.SerialIterator, + g_model, # generator_model + g_optimizer, # generator_optimizer + d_model, # discriminator_model + d_optimizer, # discriminator_optimizer +) -> pd.DataFrame: + """ + Trains the Super Resolution Generative Adversarial Networks (SRGAN)'s + Discriminator and Generator components one after another for one epoch. + Also does evaluation on a development dataset and reports metrics. + """ + metrics_dict = {mn: [] for mn in columns} # reset metrics dictionary ## Part 1 - Training on training dataset @@ -1102,9 +1144,9 @@ def train_eval_generator( ## 1.1 - Train Discriminator d_train_loss, d_train_accu = train_eval_discriminator( input_arrays=train_arrays, - g_model=generator_model, - d_model=discriminator_model, - d_optimizer=discriminator_optimizer, + g_model=g_model, + d_model=d_model, + d_optimizer=d_optimizer, ) metrics_dict["discriminator_loss"].append(d_train_loss) metrics_dict["discriminator_accu"].append(d_train_accu) @@ -1112,9 +1154,9 @@ def train_eval_generator( ## 1.2 - Train Generator g_train_loss, g_train_psnr = train_eval_generator( input_arrays=train_arrays, - g_model=generator_model, - d_model=discriminator_model, - g_optimizer=generator_optimizer, + g_model=g_model, + d_model=d_model, + g_optimizer=g_optimizer, ) metrics_dict["generator_loss"].append(g_train_loss) metrics_dict["generator_psnr"].append(g_train_psnr) @@ -1125,65 +1167,58 @@ def train_eval_generator( dev_arrays = chainer.dataset.concat_examples(batch=dev_batch) ## 2.1 - Evaluate Discriminator d_train_loss, d_train_accu = train_eval_discriminator( - input_arrays=dev_arrays, - g_model=generator_model, - d_model=discriminator_model, - train=False, + input_arrays=dev_arrays, g_model=g_model, d_model=d_model, train=False ) metrics_dict["val_discriminator_loss"].append(d_train_loss) metrics_dict["val_discriminator_accu"].append(d_train_accu) ## 2.2 - Evaluate Generator g_dev_loss, g_dev_psnr = train_eval_generator( - input_arrays=dev_arrays, - g_model=generator_model, - d_model=discriminator_model, - train=False, + input_arrays=dev_arrays, g_model=g_model, d_model=d_model, train=False ) metrics_dict["val_generator_loss"].append(g_dev_loss) metrics_dict["val_generator_psnr"].append(g_dev_psnr) - ## Part 3 - Plot loss and metric information using livelossplot - dataframe.loc[i] = [np.mean(metrics_dict[metric]) for metric in dataframe.keys()] - livelossplot.draw_plot( - logs=dataframe.to_dict(orient="records"), - metrics=metric_names, - max_cols=4, - figsize=(21, 9), - max_epoch=epochs, - ) - progressbar.set_postfix(ordered_dict=dataframe.loc[i].to_dict()) - experiment.log_metrics(dic=dataframe.loc[i].to_dict(), step=i) - progressbar.update(n=1) + return metrics_dict -# %% -model = generator_model # %% -os.makedirs(name="model/weights", exist_ok=True) -# Save generator model's parameter weights in Numpy Zipped format -chainer.serializers.save_npz( - file="model/weights/srgan_generator_model_weights.npz", obj=model -) -# Save generator model's architecture in ONNX format -dummy_inputs = { - "x": np.random.rand(32, 1, 10, 10).astype("float32"), - "w1": np.random.rand(32, 1, 100, 100).astype("float32"), - "w2": np.random.rand(32, 1, 20, 20).astype("float32"), -} -_ = onnx_chainer.export( - model=model, - args={"inputs": dummy_inputs}, - filename="model/weights/srgan_generator_model_architecture.onnx", - export_params=False, - save_text=True, -) - -# Upload model weights file to Comet.ML and finish Comet.ML experiment -experiment.log_asset( - file_path="model/weights/srgan_generator_model_weights.npz", - file_name="srgan_generator_model_weights.npz", -) +def save_model_weights_and_architecture( + trained_model, + model_basename: str = "srgan_generator_model", + save_path: str = "model/weights", +) -> (str, str): + """ + Save the trained neural network's parameter weights and architecture, + respectively to zipped Numpy (.npz) and ONNX (.onnx, .onnx.txt) format. + """ + + os.makedirs(name=save_path, exist_ok=True) + + # Save generator model's parameter weights in Numpy Zipped format + model_weights_path: str = os.path.join(save_path, f"{model_basename}_weights.npz") + chainer.serializers.save_npz(file=model_weights_path, obj=trained_model) + + # Save generator model's architecture in ONNX format + dummy_inputs = { + "x": np.random.rand(32, 1, 10, 10).astype("float32"), + "w1": np.random.rand(32, 1, 100, 100).astype("float32"), + "w2": np.random.rand(32, 1, 20, 20).astype("float32"), + } + model_architecture_path: str = os.path.join( + save_path, f"{model_basename}_architecture.onnx" + ) + _ = onnx_chainer.export( + model=trained_model, + args={"inputs": dummy_inputs}, + filename=model_architecture_path, + export_params=False, + save_text=True, + ) + assert os.path.exists(f"{model_architecture_path}.txt") + + return model_weights_path, model_architecture_path + # %% [markdown] # # 4. Evaluate model @@ -1192,7 +1227,13 @@ def train_eval_generator( # ## Evaluation on independent test set # %% -def get_deepbedmap_test_result(test_filepath: str = "highres/2007tx"): +def get_deepbedmap_test_result( + test_filepath: str = "highres/2007tx", + model=None, + model_weights_path: str = "model/weights/srgan_generator_model_weights.npz", + outfilesuffix: str = "", # unique suffix (e.g. ID) for temporary files + redo_testtrack: bool = True, +) -> float: """ Gets Root Mean Squared Error of elevation difference between DeepBedMap topography and reference groundtruth xyz tracks @@ -1207,36 +1248,239 @@ def get_deepbedmap_test_result(test_filepath: str = "highres/2007tx"): ) # Run input datasets through trained neural network model - model = deepbedmap.load_trained_model() + model = deepbedmap.load_trained_model( + model=model, model_weights_path=model_weights_path + ) Y_hat = model.forward(inputs={"x": X_tile, "w1": W1_tile, "w2": W2_tile}).array # Save infered deepbedmap to grid file(s) + outfilepath: str = f"model/deepbedmap3_{outfilesuffix}" deepbedmap.save_array_to_grid( - window_bound=window_bound, array=Y_hat, outfilepath="model/deepbedmap3" + window_bound=window_bound, array=Y_hat, outfilepath=outfilepath ) # Load xyz table for test region - data_prep = _load_ipynb_modules("data_prep.ipynb") - track_test = data_prep.ascii_to_xyz(pipeline_file=f"{test_filepath}.json") - track_test.to_csv("track_test.xyz", sep="\t", index=False) + if redo_testtrack: + data_prep = _load_ipynb_modules("data_prep.ipynb") + track_test = data_prep.ascii_to_xyz(pipeline_file=f"{test_filepath}.json") + track_test.to_csv("track_test.xyz", sep="\t", index=False) # Get the elevation (z) value at specified x, y points along the groundtruth track - !gmt grdtrack track_test.xyz -Gmodel/deepbedmap3.nc -h1 -i0,1,2 > track_deepbedmap3.xyzi - df_deepbedmap3 = pd.read_table( - "track_deepbedmap3.xyzi", header=1, names=["x", "y", "z", "z_interpolated"] + outtrackpath: str = f"model/track_deepbedmap3_{outfilesuffix}" + !gmt grdtrack track_test.xyz -G{outfilepath}.nc -h1 -i0,1,2 > {outtrackpath}.xyzi + df_deepbedmap3 = pd.read_csv( + f"{outtrackpath}.xyzi", + sep="\t", + header=1, + names=["x", "y", "z", "z_interpolated"], ) # Calculate elevation error between groundtruth xyz tracks and deepbedmap df_deepbedmap3["error"] = df_deepbedmap3.z_interpolated - df_deepbedmap3.z rmse_deepbedmap3 = (df_deepbedmap3.error ** 2).mean() ** 0.5 - return rmse_deepbedmap3 + os.remove(path=f"{outfilepath}.nc") + # os.remove(path=f"{outfilepath}.tif") + os.remove(path=f"{outtrackpath}.xyzi") + + return float(rmse_deepbedmap3) + +# %% [markdown] +# # 5. Hyperparameter tuning + +# %% [markdown] +# Tuning the various hyperparameters on our test area using [Optuna](https://github.com/pfnet/optuna). +# Yes, not exactly proper I know, but we have lots of areas we can test on later. +# +# Also logging all the experiments using [Comet.ML](https://www.comet.ml) to https://www.comet.ml/weiji14/deepbedmap. # %% -rmse_test = get_deepbedmap_test_result() -print(f"Experiment yielded Root Mean Square Error of {rmse_test:.2f} on test set") -experiment.log_metric(name="rmse_test", value=rmse_test) +def objective( + trial: optuna.trial.Trial = optuna.trial.FixedTrial( + params={ + "batch_size_exponent": 7, + "num_residual_blocks": 10, + "residual_scaling": 0.3, + "learning_rate": 5e-4, + "num_epochs": 100, + } + ), + enable_livelossplot: bool = False, # Default: False, no plots makes it go faster! + enable_comet_logging: bool = True, # Default: True, log experiment to Comet.ML +) -> float: + """ + Objective function for tuning the Hyperparameters of our DeepBedMap model. + Uses the Optuna (https://github.com/pfnet/optuna) library. + + List of hyperparameters tuned: + - Learning rate + - Number of residual blocks + - Batch Size + - Number of training epochs + """ + + # Start tracking experiment using Comet.ML + experiment = comet_ml.Experiment( + workspace="weiji14", + project_name="deepbedmap", + disabled=not enable_comet_logging, + ) + + # Don't use cached stuff if it's a FixedTrial or the first trial + if not hasattr(trial, "trial_id") or trial.trial_id == 1: + refresh_cache = True + elif trial.trial_id > 1: # Use cache if trial.trial_id > 1 + refresh_cache = False + + ## Load Dataset + dataset, quilt_hash = load_data_into_memory( + redownload=True if refresh_cache else False + ) + experiment.log_parameter(name="dataset_hash", value=quilt_hash) + experiment.log_parameter(name="use_gpu", value=cupy.is_available()) + batch_size: int = int( + 2 ** trial.suggest_int(name="batch_size_exponent", low=6, high=7) + ) + experiment.log_parameter(name="batch_size", value=batch_size) + train_iter, train_len, dev_iter, dev_len = get_train_dev_iterators( + dataset=dataset, first_size=int(len(dataset) * 0.95), batch_size=batch_size + ) + experiment.log_parameters( + dic={"train_set_samples": train_len, "dev_set_samples": dev_len} + ) + + ## Compile Model + num_residual_blocks: int = trial.suggest_int( + name="num_residual_blocks", low=8, high=12 + ) + residual_scaling: float = trial.suggest_discrete_uniform( + name="residual_scaling", low=0.1, high=0.3, q=0.05 + ) + learning_rate: float = trial.suggest_discrete_uniform( + name="learning_rate", high=8e-4, low=4e-4, q=5e-5 + ) + g_model, g_optimizer, d_model, d_optimizer = compile_srgan_model( + num_residual_blocks=num_residual_blocks, + residual_scaling=residual_scaling, + learning_rate=learning_rate, + ) + experiment.log_parameters( + dic={ + "num_residual_blocks": g_model.num_residual_blocks, + "residual_scaling": g_model.residual_scaling, + "generator_optimizer": "adam", + "generator_lr": g_optimizer.alpha, # learning rate + "generator_epsilon": g_optimizer.eps, # epsilon + "discriminator_optimizer": "adam", + "discriminator_lr": d_optimizer.alpha, # learning rate + "discriminator_adam_epsilon": d_optimizer.eps, # epsilon + } + ) + + ## Run Trainer and save trained model + epochs: int = trial.suggest_int(name="num_epochs", low=30, high=60) + experiment.log_parameter(name="num_epochs", value=epochs) + + metric_names = [ + "discriminator_loss", + "discriminator_accu", + "generator_loss", + "generator_psnr", + ] + columns = metric_names + [f"val_{metric_name}" for metric_name in metric_names] + dataframe = pd.DataFrame(index=np.arange(epochs), columns=columns) + progressbar = tqdm.tqdm(unit="epoch", total=epochs, position=0) + + train_iter.reset() + dev_iter.reset() + + for i in range(epochs): + metrics_dict = trainer( + i=i, + columns=columns, + train_iter=train_iter, + dev_iter=dev_iter, + g_model=g_model, + g_optimizer=g_optimizer, + d_model=d_model, + d_optimizer=d_optimizer, + ) + + ## Record loss and metric information, and plot using livelossplot if enabled + dataframe.loc[i] = [ + np.mean(metrics_dict[metric]) for metric in dataframe.keys() + ] + epoch_metrics = dataframe.loc[i].to_dict() + if enable_livelossplot == True: + livelossplot.draw_plot( + logs=dataframe.to_dict(orient="records"), + metrics=metric_names, + max_cols=4, + figsize=(21, 9), + max_epoch=None, + ) + progressbar.set_postfix(ordered_dict=epoch_metrics) + progressbar.update(n=1) + experiment.log_metrics(dic=epoch_metrics, step=i) + + ## Pruning unpromising trials with vanishing/exploding gradients + if ( + epoch_metrics["generator_psnr"] < 0 + or np.isnan(epoch_metrics["generator_loss"]) + or np.isnan(epoch_metrics["discriminator_loss"]) + ): + experiment.end() + raise optuna.structs.TrialPruned() + + model_weights_path, model_architecture_path = save_model_weights_and_architecture( + trained_model=g_model + ) + experiment.log_asset( + file_path=model_weights_path, file_name=os.path.basename(model_weights_path) + ) + experiment.log_asset( + file_path=model_architecture_path, + file_name=os.path.basename(model_architecture_path), + ) + + ## Evaluate model and return metrics + rmse_test = get_deepbedmap_test_result( + model=g_model, + model_weights_path=model_weights_path, + outfilesuffix=f"{trial.trial_id if hasattr(trial, 'trial_id') else ''}", + redo_testtrack=True if refresh_cache else False, + ) + print(f"Experiment yielded Root Mean Square Error of {rmse_test:.2f} on test set") + experiment.log_metric(name="rmse_test", value=rmse_test) + experiment.end() + + return rmse_test + # %% -experiment.end() +n_trials = 1 +if n_trials == 1: # run training once only, i.e. just test the objective function + objective(enable_livelossplot=True, enable_comet_logging=True) +elif n_trials > 1: # perform hyperparameter tuning with multiple experimental trials + tpe_seed = int( + os.environ["CUDA_VISIBLE_DEVICES"] + ) # different seed for different GPU + sampler = optuna.samplers.TPESampler( + seed=tpe_seed + ) # Tree-structured Parzen Estimator + study = optuna.create_study( + storage="sqlite:///model/logs/train.db", + study_name="DeepBedMap_tuning", + load_if_exists=True, + sampler=sampler, + ) + study.optimize(func=objective, n_trials=100, n_jobs=1) + + +# %% +if n_trials > 1: + study = optuna.Study( + study_name="DeepBedMap_tuning", storage="sqlite:///model/logs/train.db" + ) + study.trials_dataframe().nsmallest(n=10, columns="value") diff --git a/test_ipynb.ipynb b/test_ipynb.ipynb index 04d25af..f754c15 100644 --- a/test_ipynb.ipynb +++ b/test_ipynb.ipynb @@ -294,7 +294,7 @@ "Trying:\n", " generator_model.count_params()\n", "Expecting:\n", - " 3333249\n", + " 7649793\n", "ok\n", "Trying:\n", " calculate_discriminator_loss(\n", @@ -416,7 +416,7 @@ "Expecting:\n", " True\n", "ok\n", - "15 items had no tests:\n", + "21 items had no tests:\n", " srgan_train\n", " srgan_train.DeepbedmapInputBlock\n", " srgan_train.DeepbedmapInputBlock.__init__\n", @@ -431,7 +431,13 @@ " srgan_train.ResidualDenseBlock\n", " srgan_train.ResidualDenseBlock.__init__\n", " srgan_train.ResidualDenseBlock.forward\n", + " srgan_train.compile_srgan_model\n", " srgan_train.get_deepbedmap_test_result\n", + " srgan_train.get_train_dev_iterators\n", + " srgan_train.load_data_into_memory\n", + " srgan_train.objective\n", + " srgan_train.save_model_weights_and_architecture\n", + " srgan_train.trainer\n", "7 items passed all tests:\n", " 4 tests in srgan_train.DiscriminatorModel\n", " 4 tests in srgan_train.GeneratorModel\n", @@ -440,7 +446,7 @@ " 1 tests in srgan_train.psnr\n", " 8 tests in srgan_train.train_eval_discriminator\n", " 8 tests in srgan_train.train_eval_generator\n", - "27 tests in 22 items.\n", + "27 tests in 28 items.\n", "27 passed and 0 failed.\n", "Test passed.\n" ] @@ -508,6 +514,36 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@fixture.srgan_train\n", + "Feature: Train Super Resolution Model # features/srgan_train.feature:3\n", + " In order to have a well performing super resolution model\n", + " As a machine learning engineer,\n", + " We want to craft and teach the model to do well on a test area\n", + " Background: Load the prepared data # features/srgan_train.feature:8\n", + "\n", + " Scenario Outline: Train Super Resolution Model with fixed hyperparameters -- @1.1 Fixed hyperparameters # features/srgan_train.feature:19\n", + " Given a prepared collection of tiled raster data # features/steps/test_srgan_train.py:6\n", + " Given some hyperparameter settings 1 0.3 5e-4 # features/steps/test_srgan_train.py:14\n", + " And a compiled neural network model # features/steps/test_srgan_train.py:25\n", + " When the model is trained for a while # features/steps/test_srgan_train.py:35\n", + " Then we know how well the model performs on our test area # features/steps/test_srgan_train.py:60\n", + "\n" + ] + } + ], + "source": [ + "_integration_test_ipynb(path=\"features/srgan_train.feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", diff --git a/test_ipynb.py b/test_ipynb.py index c1974c3..0bf291b 100644 --- a/test_ipynb.py +++ b/test_ipynb.py @@ -86,5 +86,8 @@ def _integration_test_ipynb(path: str, summary: bool = False): # %% _integration_test_ipynb(path="features/data_prep.feature") +# %% +_integration_test_ipynb(path="features/srgan_train.feature") + # %% _integration_test_ipynb(path="features/deepbedmap.feature")