From 8fb9c57ed62dcef721d50c1eeb9c20f65c509215 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 11 Sep 2022 23:24:24 +0300
Subject: add half() supporrt for CLIP interrogation

---
 modules/interrogate.py | 41 ++++++++++++++++++++++++-----------------
 1 file changed, 24 insertions(+), 17 deletions(-)

(limited to 'modules/interrogate.py')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index ed97a58b..7ebb79fc 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -1,3 +1,4 @@
+import contextlib
 import os
 import sys
 import traceback
@@ -6,7 +7,6 @@ import re
 
 import torch
 
-from PIL import Image
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
@@ -26,6 +26,7 @@ class InterrogateModels:
     clip_model = None
     clip_preprocess = None
     categories = None
+    dtype = None
 
     def __init__(self, content_dir):
         self.categories = []
@@ -60,14 +61,20 @@ class InterrogateModels:
     def load(self):
         if self.blip_model is None:
             self.blip_model = self.load_blip_model()
+            if not shared.cmd_opts.no_half:
+                self.blip_model = self.blip_model.half()
 
         self.blip_model = self.blip_model.to(shared.device)
 
         if self.clip_model is None:
             self.clip_model, self.clip_preprocess = self.load_clip_model()
+            if not shared.cmd_opts.no_half:
+                self.clip_model = self.clip_model.half()
 
         self.clip_model = self.clip_model.to(shared.device)
 
+        self.dtype = next(self.clip_model.parameters()).dtype
+
     def unload(self):
         if not shared.opts.interrogate_keep_models_in_memory:
             if self.clip_model is not None:
@@ -76,14 +83,14 @@ class InterrogateModels:
             if self.blip_model is not None:
                 self.blip_model = self.blip_model.to(devices.cpu)
 
+            devices.torch_gc()
 
     def rank(self, image_features, text_array, top_count=1):
         import clip
 
         top_count = min(top_count, len(text_array))
-        text_tokens = clip.tokenize([text for text in text_array]).cuda()
-        with torch.no_grad():
-            text_features = self.clip_model.encode_text(text_tokens).float()
+        text_tokens = clip.tokenize([text for text in text_array]).to(shared.device)
+        text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
         text_features /= text_features.norm(dim=-1, keepdim=True)
 
         similarity = torch.zeros((1, len(text_array))).to(shared.device)
@@ -94,13 +101,12 @@ class InterrogateModels:
         top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
         return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
 
-
     def generate_caption(self, pil_image):
         gpu_image = transforms.Compose([
             transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
             transforms.ToTensor(),
             transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-        ])(pil_image).unsqueeze(0).to(shared.device)
+        ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
 
         with torch.no_grad():
             caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@@ -116,22 +122,23 @@ class InterrogateModels:
             caption = self.generate_caption(pil_image)
             res = caption
 
-            images = self.clip_preprocess(pil_image).unsqueeze(0).to(shared.device)
+            images = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
 
-            with torch.no_grad():
-                image_features = self.clip_model.encode_image(images).float()
+            precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
+            with torch.no_grad(), precision_scope("cuda"):
+                image_features = self.clip_model.encode_image(images).type(self.dtype)
 
-            image_features /= image_features.norm(dim=-1, keepdim=True)
+                image_features /= image_features.norm(dim=-1, keepdim=True)
 
-            if shared.opts.interrogate_use_builtin_artists:
-                artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
+                if shared.opts.interrogate_use_builtin_artists:
+                    artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
 
-                res += ", " + artist[0]
+                    res += ", " + artist[0]
 
-            for name, topn, items in self.categories:
-                matches = self.rank(image_features, items, top_count=topn)
-                for match, score in matches:
-                    res += ", " + match
+                for name, topn, items in self.categories:
+                    matches = self.rank(image_features, items, top_count=topn)
+                    for match, score in matches:
+                        res += ", " + match
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)
-- 
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