0706-2033

diffusion.ipynb

@@ -255,11 +255,11 @@
     "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
     "    # repeat = 2\n",
     "\n",
-    "    # dim = 2\n",
-    "    dim = 3\n",
+    "    dim = 2\n",
+    "    # dim = 3\n",
     "    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",
+    "    batch_size = 2*64#2#50#20#2#100 # 10\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",
@@ -564,17 +564,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of parameters for nn_model: 306285057\n",
-      "----------------- num_image = 20 -----------------\n",
-      "run_name = 0706-2027\n",
+      "Number of parameters for nn_model: 111048705\n",
+      "---------------- num_image = 512 -----------------\n",
+      "run_name = 0706-2033\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 20 images randomly\n",
-      "images loaded: (20, 1, 64, 64, 64)\n",
-      "params loaded: (20, 2)\n"
+      "loading 512 images randomly\n",
+      "images loaded: (512, 1, 64, 64)\n"
      ]
     },
     {
@@ -588,19 +587,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "images rescaled to [-1.0, 1.1409437656402588]\n",
-      "params rescaled to [0.0034415905208975417, 0.992589217704908]\n"
+      "params loaded: (512, 2)\n",
+      "images rescaled to [-1.0, 1.116640567779541]\n",
+      "params rescaled to [0.00024580534048324054, 0.9967556649676916]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4cb3b6c929c142b190881125ad76b4e6",
+       "model_id": "0528cc7f8f7b4013a38dc1f5f5ecc99b",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -609,12 +609,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc0c9f8b67804154bdec49e8d4944340",
+       "model_id": "03c91703f35749bdb710e54196d0cd8e",
        "version_major": 2,
        "version_minor": 0
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@@ -623,12 +623,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98278f2e2e8448ec9ce239cb1c6f01a0",
+       "model_id": "f55a5c7a47074aa49326453b7b3d9a2b",
        "version_major": 2,
        "version_minor": 0
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@@ -637,12 +637,12 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "20a925d1f3314346ab09cc96026b7f6e",
+       "model_id": "4f41ba4f484b4548abc4a8694fd51fcf",
        "version_major": 2,
        "version_minor": 0
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@@ -651,12 +651,12 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a0d7df1189f7476296dce37c540136be",
+       "model_id": "5148cb8ca78b4787a533c420adffa654",
        "version_major": 2,
        "version_minor": 0
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@@ -665,12 +665,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "81b4422ea6de4378b98aae9df64c2d66",
+       "model_id": "ee1acc3b3dff479c973a66747f5a674d",
        "version_major": 2,
        "version_minor": 0
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@@ -679,12 +679,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bda2e8ce9612448eb617a5817d081958",
+       "model_id": "beea681d6de64561855a3ae95318fe7a",
        "version_major": 2,
        "version_minor": 0
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@@ -693,12 +693,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7fb1357f02f54bb882d5d35afc112258",
+       "model_id": "b627c0db8ba142eb8a11db3ba1d2cccd",
        "version_major": 2,
        "version_minor": 0
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@@ -707,12 +707,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e13735fba2f4f6887c33793608344a3",
+       "model_id": "ec52b0207eb049f6aff31f5847283d5b",
        "version_major": 2,
        "version_minor": 0
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@@ -721,12 +721,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ea1891b720f41ca80721bbd3f61431d",
+       "model_id": "e959387839db4d3b8a601723e19f7f35",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -734,7 +734,7 @@
     }
    ],
    "source": [
-    "num_image_list = [20]#[200]#[1600,3200,6400,12800,25600]\n",
+    "num_image_list = [512]#[20]#[200]#[1600,3200,6400,12800,25600]\n",
     "if __name__ == \"__main__\":\n",
     "    # torch.multiprocessing.set_start_method(\"spawn\")\n",
     "    # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
@@ -749,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [