From 4d7b04120ab3d812a3a304efb62a701bf2a0b318 Mon Sep 17 00:00:00 2001
From: rafaelvalle <rafaelvalle@nvidia.com>
Date: Wed, 5 Dec 2018 22:14:35 -0800
Subject: [PATCH] inference.ipynb: changing waverglow inference fo fp16

---
 inference.ipynb | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/inference.ipynb b/inference.ipynb
index dc5f8ce..3e25002 100644
--- a/inference.ipynb
+++ b/inference.ipynb
@@ -23,10 +23,7 @@
     {
      "name": "stderr",
      "output_type": "stream",
-     "text": [
-      "/home/dcg-adlr-rafaelvalle-source.cosmos597/repos/nvidia/tacotron2/plotting_utils.py:2: UserWarning: matplotlib.pyplot as already been imported, this call will have no effect.\n",
-      "  matplotlib.use(\"Agg\")\n"
-     ]
+     "text": []
     }
    ],
    "source": [
@@ -113,17 +110,15 @@
     {
      "name": "stderr",
      "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:425: SourceChangeWarning: source code of class 'glow_old.WaveGlow' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
-      "  warnings.warn(msg, SourceChangeWarning)\n",
-      "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:425: SourceChangeWarning: source code of class 'glow_old.WN' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
-      "  warnings.warn(msg, SourceChangeWarning)\n"
-     ]
+     "text": []
     }
    ],
    "source": [
     "waveglow_path = 'waveglow_old.pt'\n",
-    "waveglow = torch.load(waveglow_path)['model']"
+    "waveglow = torch.load(waveglow_path)['model']\n",
+    "waveglow.cuda().half()\n",
+    "for k in waveglow.convinv:\n",
+    "    k.float()"
    ]
   },
   {
@@ -210,7 +205,7 @@
    ],
    "source": [
     "with torch.no_grad():\n",
-    "    audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)\n",
+    "    audio = waveglow.infer(mel_outputs_postnet.half(), sigma=0.666)\n",
     "ipd.Audio(audio[0].data.cpu().numpy(), rate=hparams.sampling_rate)"
    ]
   }