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README.md

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----
-license: apache-2.0
----
+
+# hackergeek/RADIOCAP13
+
+**ROCO Radiology Image Captioning Model**
+
+This model is a medical image captioning system designed for radiology reports. It utilizes a frozen ViT encoder for image feature extraction and a custom decoder trained to generate captions. The model was trained on the full ROCO-radiology dataset.
+
+- **Encoder**: `google/vit-base-patch16-224-in21k` (frozen, features cached)
+- **Decoder**: Trained on **full ROCO dataset** (~81k samples) for **3 epochs**
+- **Trainable parameters**: Only decoder + ViT biases
+- **Vocab size**: 75460
+- **Sequence Length**: 32
+- **Generation**: Beam search (size=3)
+
+---
+
+## Usage
+
+```python
+from transformers import ViTModel
+import torch
+from PIL import Image
+from torchvision import transforms
+import json
+import os
+
+# Assuming SimpleTokenizer and BiasDecoder classes are available from your training script.
+# For a full runnable example, their definitions are included below.
+
+# Re-define necessary components and classes for a self-contained example
+IMG_SIZE = 224
+SEQ_LEN = 32
+VOCAB_SIZE = 75460
+
+transform = transforms.Compose([
+    transforms.Resize((IMG_SIZE, IMG_SIZE)),
+    transforms.ToTensor(),
+])
+
+def preprocess_image(img):
+    if img is None: raise ValueError("Image is None")
+    if not isinstance(img, Image.Image): img = Image.fromarray(img)
+    if img.mode != "RGB": img = img.convert("RGB")
+    return transform(img)
+
+# SimpleTokenizer class (copy-pasted from notebook for self-contained example)
+class SimpleTokenizer:
+    def __init__(self, word2idx=None):
+        if word2idx is None:
+            # Placeholder for actual vocab loading or creation if not loaded from file
+            self.word2idx = {} # Escaped
+        else:
+            self.word2idx = word2idx
+        self.idx2word = {v: k for k, v in self.word2idx.items()} # Escaped
+
+    def encode(self, text, max_len=SEQ_LEN):
+        tokens = [self.word2idx.get(w, self.word2idx["<PAD>"]) for w in text.lower().split()]
+        tokens = [self.word2idx["<SOS>"]] + tokens[:max_len-2] + [self.word2idx["<EOS>"]]
+        tokens += [self.word2idx["<PAD>"]] * (max_len - len(tokens))
+        return torch.tensor(tokens, dtype=torch.long)
+
+    def decode(self, tokens):
+        return " ".join(self.idx2word.get(t.item(), "<UNK>") for t in tokens if t not in [self.word2idx["<PAD>"], self.word2idx["<SOS>"], self.word2idx["<EOS>"]])
+
+    @classmethod
+    def load(cls, path):
+        with open(f"{path}/vocab.json", "r") as f: # Correctly escaped
+            word2idx = json.load(f)
+        tokenizer = cls(word2idx)
+        return tokenizer
+
+# BiasDecoder class (copy-pasted from notebook for self-contained example)
+class BiasDecoder(torch.nn.Module):
+    def __init__(self, feature_dim=768, vocab_size=VOCAB_SIZE):
+        super().__init__()
+        self.token_emb   = torch.nn.Embedding(vocab_size, feature_dim)
+        self.pos_emb     = torch.nn.Embedding(SEQ_LEN-1, feature_dim)
+        self.final_layer = torch.nn.Linear(feature_dim, vocab_size)
+
+    def forward(self, img_feat, target_seq):
+        x = self.token_emb(target_seq)
+        pos = torch.arange(x.size(1), device=x.device).clamp(max=self.pos_emb.num_embeddings-1)
+        x = x + self.pos_emb(pos)
+        x = x + img_feat.unsqueeze(1)
+        return self.final_layer(x)
+
+# Setup device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load ViT (frozen)
+vit = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")
+vit.eval()
+vit.to(device)
+
+# Load decoder
+decoder = BiasDecoder().to(device)
+# Assuming 'pytorch_model.bin' is in the current directory or specified path
+decoder.load_state_dict(torch.load("pytorch_model.bin", map_location=device))
+decoder.eval()
+
+# Load tokenizer
+# Assuming 'vocab.json' is in the current directory or specified path
+tokenizer = SimpleTokenizer.load("./")
+pad_idx = tokenizer.word2idx["<PAD>"]
+
+# Generation function
+@torch.no_grad()
+def generate_caption(model, img_feat, max_len=SEQ_LEN, beam_size=3):
+    model.eval()
+    img_feat = img_feat.to(device)
+    beams = [([tokenizer.word2idx["<SOS>"]], 0.0)]
+    for _ in range(max_len - 1):
+        candidates = []
+        for seq, score in beams:
+            inp = torch.tensor(seq + [pad_idx] * (SEQ_LEN - len(seq)), device=device).unsqueeze(0)
+            logits = model(img_feat, inp)
+            probs = torch.nn.functional.log_softmax(logits[0, len(seq)-1], dim=-1)
+            top_p, top_i = torch.topk(probs, beam_size)
+            for i in range(beam_size):
+                candidates.append((seq + [top_i[i].item()], score + top_p[i].item()))
+        beams = sorted(candidates, key=lambda x: x[1], reverse=True)[:beam_size]
+        if all(s[-1] == tokenizer.word2idx["<EOS>"] for s, _ in beams): break
+    words = [tokenizer.idx2word.get(i, "<UNK>") for i in beams[0][0][1:] if i != pad_idx]
+    return " ".join(words)
+
+# Example: Generate a caption for an image
+# For a real example, you would load an actual image and process it.
+# img_path = "path/to/your/image.jpg"
+# image = Image.open(img_path).convert("RGB")
+# img_tensor = preprocess_image(image).unsqueeze(0).to(device)
+# img_feat = vit(pixel_values=img_tensor).pooler_output
+# generated_caption = generate_caption(decoder, img_feat)
+# print(f"Generated caption: {generated_caption}")
+```
+
+---
+
+## Evaluation (on ROCO Test Set)
+
+- **BLEU-1**: N/A
+- **BLEU-2**: N/A
+- **BLEU-3**: N/A
+- **BLEU-4**: N/A
+- **Overall BLEU Score**: N/A
+
+*Note: BLEU scores were interrupted during computation. Please re-run the evaluation cell (`eXra19D_oqcs`) after pushing to get accurate scores.* 

config.json

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+{
+  "model_type": "vit-captioner-bias-decoder",
+  "feature_extractor": "google/vit-base-patch16-224-in21k",
+  "vocab_size": 75460,
+  "seq_len": 32,
+  "feature_dim": 768,
+  "training_epochs": 3,
+  "dataset": "ROCO-radiology (train + val + test)",
+  "trainable": "Decoder + ViT biases only",
+  "description": "ROCO radiology captioner trained for 3 epochs on full dataset using cached ViT features."
+}

generation_config.json

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+{"max_length": 32, "beam_size": 3}

pytorch_model.bin

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