0706-2100

diffusion.ipynb

@@ -259,7 +259,7 @@
     "    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#2#50#20#2#100 # 10\n",
+    "    batch_size = 1#2#50#20#2#100 # 10\n",
     "    n_epoch = 10#50#20#20#2#5#25 # 120\n",
     "    HII_DIM = 64\n",
     "    num_redshift = 128#64#512#256#256#64#512#128\n",
@@ -564,15 +564,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of parameters for nn_model: 111048705\n",
-      "----------------- num_image = 20 -----------------\n",
-      "run_name = 0706-2044\n",
+      "Number of parameters for nn_model: 306285057\n",
+      "----------------- num_image = 50 -----------------\n",
+      "run_name = 0706-2100\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"
+      "loading 50 images randomly\n",
+      "images loaded: (50, 1, 64, 64, 128)\n"
      ]
     },
     {
@@ -586,21 +587,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "images loaded: (20, 1, 64, 128)\n",
-      "params loaded: (20, 2)\n",
-      "images rescaled to [-1.0, 1.0278661251068115]\n",
-      "params rescaled to [0.005739769005289105, 0.972333144312969]\n"
+      "params loaded: (50, 2)\n",
+      "images rescaled to [-1.0, 1.121453046798706]\n",
+      "params rescaled to [0.02178423211262565, 0.9987535256930432]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "432face5514f46dbab6a0bd6752ee9dd",
+       "model_id": "aeff9b53f53c454b8d03f7b86f096a66",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -609,12 +609,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43bc633a08524f1ea75546311244f8aa",
+       "model_id": "c741dbbfd1d14e5d92a0e34acce9ab29",
        "version_major": 2,
        "version_minor": 0
       },
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@@ -623,12 +623,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "884b273aa5d9468ba1facd835f4de2df",
+       "model_id": "7878b2bc18bb4765bdd0278201499c1b",
        "version_major": 2,
        "version_minor": 0
       },
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@@ -637,12 +637,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7eabba7c6741415ea5e6fd097e33dbb2",
+       "model_id": "e4a4b9dc4a3a4d0fafabf0f61636e039",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -651,12 +651,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8cfc863995a74070b23bfcd73d314cca",
+       "model_id": "b13fb9930f4d475ea76bc4a70d790fb6",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -665,12 +665,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c53f6b374644535aa8e2bce7e961d80",
+       "model_id": "19337b95fd3f485fa8f88a3440416cd7",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -679,12 +679,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2494d9b658f7493088b64eab1006f2c4",
+       "model_id": "bdf02f0c587d4837b134027ecad6065f",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -693,12 +693,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "070d1da1b37043f8aac851d3bf5c22f3",
+       "model_id": "8066317a80394a3e9bce8ff7fe86e582",
        "version_major": 2,
        "version_minor": 0
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      "metadata": {},
@@ -707,12 +707,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "063fc075914d4e7bb5ffaf9f0ac14285",
+       "model_id": "ac4c9f0b5ad149e0a7e1bf8df834c0f3",
        "version_major": 2,
        "version_minor": 0
       },
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      "metadata": {},
@@ -721,12 +721,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01c9c1d56a394f3caf0c6fec1b072bcd",
+       "model_id": "8bcf474dcbe2414084f78d489e1102e6",
        "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 = [50]#[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": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -758,13 +758,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of parameters for nn_model: 111048705\n",
+      "sampling 6 images with normalized params = tensor([[0.2000, 0.5056]])\n",
+      "nn_model resumed from ./outputs/model_state-N20\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f7cc524aa6f4b25be2d44943494e36f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "if __name__ == \"__main__\":\n",
     "    # num_image_list = [1600,3200,6400,12800,25600]\n",
-    "    num_image_list = [200]\n",
+    "    num_image_list = [20]\n",
     "    # num_image_list = [3200,6400,12800,25600]\n",
     "    # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
     "    repeat = 6\n",
@@ -786,18 +810,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 7.7G\n",
+      "-rw-r--r--  1 bxia34 pace-jw254 193K Jul  6 20:46 Tvir4.400000095367432-zeta131.34100341796875-N20.npy\n",
+      "-rw-r--r--  1 bxia34 pace-jw254 848M Jul  6 20:45 model_state-N20\n",
+      "drwxr-xr-x 44 bxia34 pace-jw254 4.0K Jul  6 20:44 \u001b[0m\u001b[01;34mlogs\u001b[0m/\n",
+      "-rw-r--r--  1 bxia34 pace-jw254 6.1M Jul  5 14:44 Tvir4.400000095367432-zeta131.34100341796875-N200.npy\n",
+      "-rw-r--r--  1 bxia34 pace-jw254 2.3G Jul  5 12:20 model_state-N200\n",
+      "-rw-r--r--  1 bxia34 pace-jw254  13M Jul  3 17:05 Tvir4.800000190734863-zeta131.34100341796875-N25600.npy\n",
+      "-rw-r--r--  1 bxia34 pace-jw254  13M Jul  3 16:46 Tvir5.4770002365112305-zeta200.0-N25600.npy\n",
+      "-rw-r--r--  1 bxia34 pace-jw254  13M Jul  3 16:28 Tvir4.698999881744385-zeta30.0-N25600.npy\n",
+      "-rw-r--r--  1 bxia34 pace-jw254  13M Jul  3 16:09 Tvir5.599999904632568-zeta19.03700065612793-N25600.npy\n"
+     ]
+    }
+   ],
    "source": [
     "ls -lth outputs | head"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "samples.shape = (6, 1, 1, 64, 128)\n"
+     ]
+    }
+   ],
    "source": [
     "def plot_grid(samples, c=None, row=2, col=3):\n",
     "    print(\"samples.shape =\", samples.shape)\n",
@@ -817,9 +866,10 @@
     "        plt.close()\n",
     "        # plt.show()\n",
     "    \n",
-    "data = np.load(\"outputs/Tvir4.400000095367432-zeta131.34100341796875-N200.npy\")\n",
+    "data = np.load(\"outputs/Tvir4.400000095367432-zeta131.34100341796875-N20.npy\")\n",
     "# print(data.shape)\n",
-    "plot_grid(data)"
+    "plot_grid(data)\n",
+    "# plt.imshow(data)"
    ]
   },
   {