0523-1621

diffusion.ipynb

@@ -283,8 +283,8 @@
     "    mixed_precision = \"fp16\"\n",
     "    gradient_accumulation_steps = 1\n",
     "\n",
-    "    date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
-    "    run_name = f'{date}' # the unique name of each experiment\n",
+    "    # date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
+    "    # run_name = f'{date}' # the unique name of each experiment\n",
     "\n",
     "# config = TrainConfig()\n",
     "# print(\"device =\", config.device)"
@@ -294,22 +294,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "resumed nn_model from model_state.pth\n",
-      "Number of parameters for nn_model: 111048705\n",
-      "resumed ema_model from model_state.pth\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# @dataclass\n",
     "class DDPM21CM:\n",
     "    def __init__(self):\n",
     "        config = TrainConfig()\n",
+    "        # date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
+    "        config.run_name = datetime.datetime.now().strftime(\"%m%d-%H%M\") # the unique name of each experiment\n",
     "        self.config = config\n",
     "        # dataset = Dataset4h5(config.dataset_name, num_image=config.num_image, HII_DIM=config.HII_DIM, num_redshift=config.num_redshift, drop_prob=config.drop_prob, dim=config.dim)\n",
     "        # # self.shape_loaded = dataset.images.shape\n",
@@ -380,7 +372,7 @@
     "                self.repo_id = create_repo(\n",
     "                    repo_id=self.config.hub_model_id or Path(self.config.output_dir).name, exist_ok=True\n",
     "                ).repo_id\n",
-    "            self.accelerator.init_trackers(f\"{self.config.date}\")\n",
+    "            self.accelerator.init_trackers(f\"{self.config.run_name}\")\n",
     "\n",
     "        self.nn_model, self.optimizer, self.dataloader, self.lr_scheduler = \\\n",
     "            self.accelerator.prepare(\n",
@@ -444,7 +436,7 @@
     "                        upload_folder(\n",
     "                            repo_id = self.repo_id,\n",
     "                            folder_path = \".\",#config.output_dir,\n",
-    "                            commit_message = f\"{self.config.date}\",\n",
+    "                            commit_message = f\"{self.config.run_name}\",\n",
     "                            ignore_patterns = [\"step_*\", \"epoch_*\", \"*.npy\", \"__pycache__\"],\n",
     "                            )\n",
     "                    if self.config.save_model:\n",
@@ -500,13 +492,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a5f2462b91824a309b66da5c9e46905c",
+       "model_id": "6dca1df1da3148f28c71fed756c7abc9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -524,6 +516,7 @@
       "resumed nn_model from model_state.pth\n",
       "Number of parameters for nn_model: 111048705\n",
       "resumed ema_model from model_state.pth\n",
+      "run_name = 0523-1621\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",
@@ -545,187 +538,14 @@
      "output_type": "stream",
      "text": [
       "params loaded: (240, 2)\n",
-      "images rescaled to [-1.0, 1.1086735725402832]\n",
-      "params rescaled to [0.0, 0.9959690281993576]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca18a0ce55d643dbaf33a148f579c7a7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1034bb856742454398d95d08087ad46b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "feb8f77c25fe4a478a4b04885109b934",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b517c548fe24cf493beac54b559027d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe21226d2ea14120b926ac1ea2b2f48a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2526c949e3484788b4818cb4f3590750",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fbc6f3ddea3343d498d19ca32b877f84",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec74582b77764d8387bfaec48a30ff6b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52099c31f7cb4190b773f1bfeefa4d75",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2ebf165487d4a06aa5c49afb7f27572",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "saved model at ./outputs/model_state_09.pth\n",
-      "resumed nn_model from model_state.pth\n",
-      "Number of parameters for nn_model: 111048705\n",
-      "resumed ema_model from model_state.pth\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 240 images randomly\n",
-      "images loaded: (240, 1, 64, 512)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Detected kernel version 3.10.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "params loaded: (240, 2)\n",
-      "images rescaled to [-1.0, 1.1578054428100586]\n",
-      "params rescaled to [0.0, 0.9981726090056542]\n"
+      "images rescaled to [-1.0, 1.1240839958190918]\n",
+      "params rescaled to [0.0, 0.9972546078293054]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e9240b35009f40f29dd25cf6f45e90f1",
+       "model_id": "15d75d83ca9f4f49be17a89f6ddd58e1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -739,7 +559,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "adf6eb441a3b40bd84aa7c274be78f3a",
+       "model_id": "66959c994f6b40649ab527212de8d3c2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -753,7 +573,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8153ead286dc4b07801b22897b38ff84",
+       "model_id": "564f6d85e359481f973a49f75b180440",
        "version_major": 2,
        "version_minor": 0
       },
@@ -767,7 +587,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "21f4e4b41dfb44928c23aa89d2c14b36",
+       "model_id": "079a2325ab83494282c83b76ffb8e52e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -781,7 +601,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "54bb7a5c70f84fc484336be7155c8215",
+       "model_id": "fefa0f8dbfeb474d90e0aaf55f8ca5e8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -795,7 +615,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e0332693a3fc4611907cd10d4f0ba467",
+       "model_id": "b216c0bb3bd4457f9230b32b8d2ede1f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -809,7 +629,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e22d84514b6499397fd5216542ea317",
+       "model_id": "78d4bdad3dc34ba18f3074802c67bf61",
        "version_major": 2,
        "version_minor": 0
       },
@@ -823,7 +643,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9193803414974584bd5ce5819022eddc",
+       "model_id": "e78d2d3247b442b78f06b38b65944887",
        "version_major": 2,
        "version_minor": 0
       },
@@ -837,7 +657,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "39aa6a86ec44471a95ffece6c40a2fb0",
+       "model_id": "5e1d909d5f3f4c26a11bd40978c57f4e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -851,7 +671,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a815218832c4c4c855d06737e6131b6",
+       "model_id": "d1f56418378049b59ba1f9de7c5676f1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -870,6 +690,7 @@
     "    repeat = 2\n",
     "    for i in range(repeat):\n",
     "        ddpm21cm = DDPM21CM()\n",
+    "        print(f\"run_name = {ddpm21cm.config.run_name}\")\n",
     "        notebook_launcher(ddpm21cm.train, num_processes=1)"
    ]
   },