0705-1109

diffusion.ipynb

@@ -75,7 +75,7 @@
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-       "model_id": "b7a51a73994a43178f1baa379210c892",
+       "model_id": "930fd496e3474e459def52921323253c",
        "version_major": 2,
        "version_minor": 0
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@@ -272,7 +272,7 @@
     "    stride = (2,2) if dim == 2 else (2,2,2)\n",
     "    num_image = 2000#32000#20000#15000#7000#25600#3000#10000#1000#10000#5000#2560#800#2560\n",
     "    batch_size = 2#50#20#2#100 # 10\n",
-    "    n_epoch = 50#20#20#2#5#25 # 120\n",
+    "    n_epoch = 10#50#20#20#2#5#25 # 120\n",
     "    HII_DIM = 64\n",
     "    num_redshift = 64#512#256#256#64#512#128\n",
     "    channel = 1\n",
@@ -555,16 +555,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_image_list = [4]#[1600,3200,6400,12800,25600]"
+    "num_image_list = [200]#[1600,3200,6400,12800,25600]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -572,14 +572,16 @@
      "output_type": "stream",
      "text": [
       "Number of parameters for nn_model: 306285057\n",
-      "----------------- num_image = 4 ------------------\n",
-      "run_name = 0705-1053\n",
+      "---------------- num_image = 200 -----------------\n",
+      "run_name = 0705-1109\n",
       "Launching training on one GPU.\n",
       "dataset content: <KeysViewHDF5 ['brightness_temp', 'density', 'kwargs', 'params', 'redshifts_distances', 'seeds', 'xH_box']>\n",
       "51200 images can be loaded\n",
       "field.shape = (64, 64, 514)\n",
       "params keys = [b'ION_Tvir_MIN', b'HII_EFF_FACTOR']\n",
-      "loading 4 images randomly\n"
+      "loading 200 images randomly\n",
+      "images loaded: (200, 1, 64, 64, 64)\n",
+      "params loaded: (200, 2)\n"
      ]
     },
     {
@@ -593,21 +595,19 @@
      "name": "stdout",
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      "text": [
-      "images loaded: (4, 1, 64, 64, 64)\n",
-      "params loaded: (4, 2)\n",
-      "images rescaled to [-1.0, 1.049072027206421]\n",
-      "params rescaled to [0.02179058530500466, 0.9278468466439764]\n"
+      "images rescaled to [-1.0, 1.2652204036712646]\n",
+      "params rescaled to [0.006617276082765411, 0.9997543714597507]\n"
      ]
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+       "model_id": "4890152cdd7f4a8d94d29b2fea5c2d59",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1106,12 +1106,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1a35ee7663e49fc9b2386059b53e8c8",
+       "model_id": "f004da39d4ff49f5a90098f0bc0568a7",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1120,12 +1120,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9bbdc5fe2e584026856fb9a2ce6eb1b7",
+       "model_id": "7c50fe95d26b48dab44797c66242f666",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1134,12 +1134,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cb8d4301ed446b1ad56554ff080560a",
+       "model_id": "280299f0f7b94483bb88cce5f659d6ad",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1148,12 +1148,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e0c4bee9568447b78db716554a3342b2",
+       "model_id": "18a2c92fe5954b48b17b286bb45ab845",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1162,12 +1162,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bdd4db66e7344f2f9ba41d1512e76458",
+       "model_id": "825c8957956f4f0a8c25b967015e113a",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1176,12 +1176,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55ceda2803084fc0bb46524e0d586602",
+       "model_id": "503a4bcabd714820bfbf705078db422a",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1190,12 +1190,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae7215706afa4ddfa3a674dd76ee8350",
+       "model_id": "ed04ab8b9bd54ff092ca89c478a7e681",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1204,12 +1204,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "512249484a9d45b69dea4ed238f3da30",
+       "model_id": "0fa61bc1ac594a5180a56fe21dd0d3a5",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1218,12 +1218,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ec68a068a604ef5b11c99993b8baace",
+       "model_id": "6d60e361bb274a09a0739ee6780c7dc7",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1232,12 +1232,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1d060aae57a47edbb55b9c19413a77c",
+       "model_id": "3172f8d978c54e68ac51e6eec23ae9cb",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1246,12 +1246,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f848c117255e48dea811b18a159a7175",
+       "model_id": "acf9dabf57df402088d659c85164155a",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1260,12 +1260,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b0c9aa8cca34000a81abf133d710ccf",
+       "model_id": "f1281bf575364662a9ee843a2336b94a",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1274,12 +1274,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "474eaaaa3ba340b0af823d796ddf7ea0",
+       "model_id": "5f3c79aad7644ae4916d8a2bc73c3b76",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1288,12 +1288,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c546e85d0c0a46da9a50001bc63c2f92",
+       "model_id": "bd4b87c28e024b58a2a8c8abdca92a75",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1387,7 +1387,7 @@
     }
    ],
    "source": [
-    "ll -lth outputs"
+    "# ll -lth outputs"
    ]
   },
   {
@@ -1842,10 +1842,11 @@
    ],
    "source": [
     "if __name__ == \"__main__\":\n",
-    "    num_image_list = [1600,3200,6400,12800,25600]\n",
+    "    # num_image_list = [1600,3200,6400,12800,25600]\n",
+    "    num_image_list = [200]\n",
     "    # num_image_list = [3200,6400,12800,25600]\n",
     "    # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
-    "    repeat = 800\n",
+    "    repeat = 2\n",
     "    config = TrainConfig()\n",
     "    for i, num_image in enumerate(num_image_list):\n",
     "        config.num_image = num_image\n",
@@ -1853,13 +1854,13 @@
     "\n",
     "        ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor([4.4, 131.341]), repeat=repeat)\n",
     "\n",
-    "        ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.6, 19.037)), repeat=repeat)\n",
+    "        # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.6, 19.037)), repeat=repeat)\n",
     "\n",
-    "        ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.699, 30)), repeat=repeat)\n",
+    "        # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.699, 30)), repeat=repeat)\n",
     "\n",
-    "        ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.477, 200)), repeat=repeat)\n",
+    "        # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.477, 200)), repeat=repeat)\n",
     "\n",
-    "        ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.8, 131.341)), repeat=repeat)"
+    "        # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.8, 131.341)), repeat=repeat)"
    ]
   },
   {