generation example

gen.py

@@ -0,0 +1,39 @@
+import open_clip
+import torch
+from PIL import Image
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, _, transform = open_clip.create_model_and_transforms(
+    model_name="coca_biogpt_vitb16",
+    pretrained="coca_biogpt_vitb16.pt",
+)
+model.to(device)
+model.eval()
+nb = 1
+path = "example.png"
+im = Image.open(path).convert("RGB")
+im = transform(im).unsqueeze(0)
+im = im.to(device)
+im = im.repeat(nb,1,1,1)
+print(im.shape)
+tokenizer = open_clip.get_tokenizer("coca_biogpt_vitb16")
+print(tokenizer.tokenizer)
+with torch.no_grad():
+    generated = model.generate(
+        im, 
+        pad_token_id=1, eos_token_id=2, sot_token_id=0, max_seq_len=256, seq_len=60,
+        # generation_type='top_p',
+        #generation_type='top_k',
+        generation_type='beam_search',
+        #repetition_penalty=1.,
+        #top_k=100,
+        #top_p=0.1,
+        # text=text,
+        #temperature=1.0,
+        #min_seq_len=40,
+    )
+    print(generated)
+    for i in range(nb):
+        if hasattr(tokenizer, "tokenizer"):
+            print(tokenizer.tokenizer.decode(generated[i]))
+        else:
+            print(open_clip.decode(generated[i]))