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config.json

@@ -1,13 +1,13 @@
 {
-  "_name_or_path": "./model/IXC",
+  "_name_or_path": "AkashahS/GeoPixel-7B",
   "architectures": [
-    "GeoPixForCausalLM"
+    "GeoPixelForCausalLM"
   ],
   "attn_implementation": "flash_attention_2",
   "auto_map": {
     "AutoConfig": "configuration_internlm_xcomposer2.InternLMXcomposer2Config",
     "AutoModel": "modeling_internlm_xcomposer2.InternLMXComposer2ForCausalLM",
-    "AutoModelForCausalLM": "geopix.GeoPixForCausalLM"
+    "AutoModelForCausalLM": "geopixel.GeoPixelForCausalLM"
   },
   "bias": false,
   "bos_token_id": 1,

geopixel.py

@@ -0,0 +1,411 @@
+from typing import List, Optional, Tuple, Union
+
+import os
+import torch
+import numpy as np
+import torch.nn as nn
+import matplotlib.pyplot as plt
+from PIL import Image
+import torch.nn.functional as F
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from model.IXC.modeling_internlm_xcomposer2 import InternLMXComposer2ForCausalLM
+from model.IXC.modeling_internlm2 import InternLM2Model
+from model.sam2.build_sam import build_sam2_hf
+from model.sam2.utils.transforms import SAM2Transforms
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+
+def dice_loss(
+    inputs: torch.Tensor,
+    targets: torch.Tensor,
+    num_masks: float,
+    scale=1000,  # 100000.0,
+    eps=1e-6,
+):
+    """
+    Compute the DICE loss, similar to generalized IOU for masks
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    """
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1, 2)
+    targets = targets.flatten(1, 2)
+    numerator = 2 * (inputs / scale * targets).sum(-1)
+    denominator = (inputs / scale).sum(-1) + (targets / scale).sum(-1)
+    loss = 1 - (numerator + eps) / (denominator + eps)
+    loss = loss.sum() / (num_masks + 1e-8)
+    return loss
+
+
+def sigmoid_ce_loss(
+    inputs: torch.Tensor,
+    targets: torch.Tensor,
+    num_masks: float,
+):
+    """
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    Returns:
+        Loss tensor
+    """
+    loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    loss = loss.flatten(1, 2).mean(1).sum() / (num_masks + 1e-8)
+    return loss
+
+
+class GeoPixelMetaModel:
+    def __init__(
+        self,
+        config,
+        **kwargs,
+    ):
+        super(GeoPixelMetaModel, self).__init__(config)
+        self.config = config
+        self.config.train_mask_decoder = getattr(self.config, "train_mask_decoder", kwargs.get("train_mask_decoder", False))
+        self.config.out_dim = getattr(self.config, "out_dim", kwargs.get("out_dim", 256))
+        self.vision_pretrained = kwargs.get("vision_pretrained", None)
+        self.initialize_geopixel_modules(self.config)
+
+    def initialize_geopixel_modules(self, config):
+        # grounding vision model
+        self.visual_model = build_sam2_hf(self.vision_pretrained)
+
+        self._transform = SAM2Transforms(
+                    resolution=self.visual_model.image_size,
+                    mask_threshold=0.0,
+                    max_hole_area=0.0,
+                    max_sprinkle_area=0.0,
+                )
+        # Spatial dim for backbone feature maps
+        self._bb_feat_sizes = [
+            (256, 256),
+            (128, 128),
+            (64, 64),
+        ]
+        for param in self.visual_model.parameters():
+            param.requires_grad = False
+        if config.train_mask_decoder:
+            self.visual_model.sam_mask_decoder.train()
+            for param in self.visual_model.sam_mask_decoder.parameters():
+                param.requires_grad = True
+
+        # text projection layer
+        in_dim = config.hidden_size 
+        out_dim = config.out_dim    
+        text_projection_layers = [
+            nn.Linear(in_dim, in_dim),
+            nn.ReLU(inplace=True),
+            nn.Linear(in_dim, out_dim),
+            nn.Dropout(0.0),
+        ]
+        self.text_hidden_fcs = nn.ModuleList([nn.Sequential(*text_projection_layers)])
+        self.text_hidden_fcs.train()
+        for param in self.text_hidden_fcs.parameters():
+            param.requires_grad = True
+
+
+class GeoPixelModel(GeoPixelMetaModel, InternLM2Model):
+    def __init__(
+        self,
+        config,
+        **kwargs,
+    ):
+        super(GeoPixelModel, self).__init__(config, **kwargs)
+        self.config.use_cache = False
+
+
+class GeoPixelForCausalLM(InternLMXComposer2ForCausalLM):
+    def __init__(self,config,**kwargs,):
+        
+        self.ce_loss_weight = kwargs.pop("ce_loss_weight", None)
+        self.dice_loss_weight = kwargs.pop("dice_loss_weight", None)
+        self.bce_loss_weight = kwargs.pop("bce_loss_weight", None)
+        self.seg_token_idx = kwargs.pop("seg_token_idx")
+
+        super().__init__(config)
+        self.model = GeoPixelModel(config, **kwargs)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def encode_g_img(self, image):
+        """
+        Calculates the image embeddings for the provided image
+        Arguments:
+          image (np.ndarray or str)
+        """
+        if image is None:
+            return None
+        if isinstance(image, str):
+            _, ext = os.path.splitext(image)
+            if ext.lower() in {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp','.tif'}:
+                image = Image.open(image)
+                w, h = image.size
+                _orig_hw = [(h, w)] 
+            else:
+                print ('Unknow input format', image)
+                return None
+        else:
+            assert isinstance(image, torch.Tensor)
+            _orig_hw = [image.shape[:2]]
+        image = self.model._transform(image)
+        image = image[None, ...].to(self.device)
+        assert ( len(image.shape) == 4 and image.shape[1] == 3), f"image must be of size 1x3xHxW, got {image.shape}"
+        features = self.get_visual_embs(image)   
+        return features,_orig_hw
+
+    def get_visual_embs(self, img_batch: torch.FloatTensor):
+        with torch.no_grad():
+            torch.cuda.empty_cache()
+            img_batch = img_batch.to(self.device)
+            batch_size = img_batch.shape[0]
+            assert (
+                len(img_batch.shape) == 4 and img_batch.shape[1] == 3
+            ), f"grounding_img_batch must be of size Bx3xHxW, got {img_batch.shape}"
+            backbone_out = self.model.visual_model.forward_image(img_batch)
+            _, vision_feats, _, _ = self.model.visual_model._prepare_backbone_features(backbone_out)
+            if self.model.visual_model.directly_add_no_mem_embed:
+                vision_feats[-1] = vision_feats[-1] + self.model.visual_model.no_mem_embed
+            feats = [
+                feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
+                for feat, feat_size in zip(vision_feats[::-1], self.model._bb_feat_sizes[::-1])
+            ][::-1]
+            features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+        return features
+    
+    def forward(self, **kwargs):
+        return super().forward(**kwargs) if "past_key_values" in kwargs else self.model_forward(**kwargs)
+    
+    def model_forward(
+            self,
+            inference: bool = False,
+            **kwargs,
+    ):
+        samples = kwargs.get('samples', None)
+        if samples and samples['data_type'][0] == 'grounding': 
+            kwargs['output_hidden_states'] = True
+            torch.cuda.empty_cache()
+            outputs = super().forward(**kwargs)
+
+            if inference:
+                assert len(samples['text_input']) == 1 and len(samples['image'][0]) == 1 #single image and single query
+                output_hidden_states = [outputs.hidden_states]
+                outputs = None
+            else:
+                output_hidden_states = outputs.hidden_states
+
+            hidden_states = []
+            assert len(self.model.text_hidden_fcs) == 1
+            hidden_states.append(self.model.text_hidden_fcs[0](output_hidden_states[-1]))
+            last_hidden_state = torch.stack(hidden_states, dim=-1).sum(dim=-1)
+
+            seg_token_mask = outputs.seg_token_mask
+            pred_embeddings = [states[masks] for states, masks in zip(last_hidden_state, seg_token_mask)]
+            image_g_batch = torch.cat(samples['image_g'][0],dim = 0)
+            image_g_features = self.get_visual_embs(image_g_batch)
+            ori_hw = samples['ori_hw'][0]
+            all_pred_masks = []
+            for i in range(len(pred_embeddings)): #(bs,)
+                if (pred_embeddings[i].numel()== 0):
+                    pred_masks.append([])
+                    continue
+                (sparse_embeddings, dense_embeddings,) = self.model.visual_model.sam_prompt_encoder(
+                    points=None,
+                    boxes=None,
+                    masks=None,
+                    text_embeds=pred_embeddings[i].unsqueeze(1),
+                )
+                batch_mode = (pred_embeddings[i].shape[0]>1)
+                high_res_features = [
+                    feat_level[i].unsqueeze(0)
+                    for feat_level in image_g_features["high_res_feats"]
+                ]
+                sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype)
+                image_g_embeds = image_g_features['image_embed'][i].unsqueeze(0).to(torch.bfloat16)
+                low_res_masks, _, _ , _ = self.model.visual_model.sam_mask_decoder(
+                    image_embeddings=image_g_embeds,
+                    image_pe=self.model.visual_model.sam_prompt_encoder.get_dense_pe(),
+                    sparse_prompt_embeddings=sparse_embeddings,
+                    dense_prompt_embeddings=dense_embeddings,
+                    repeat_image=batch_mode,
+                    multimask_output=False,
+                    high_res_features=high_res_features,
+                )
+                pred_masks = self.model._transform.postprocess_masks(
+                    low_res_masks,
+                    ori_hw[i],
+                )
+
+                # pred_masks = pred_masks.squeeze(0) 
+                # all_pred_masks.append(pred_masks)
+                all_pred_masks.append(pred_masks[:, 0])
+                
+
+            model_output = outputs
+            gt_masks =  samples['masks'][0]
+            pred_masks = all_pred_masks 
+
+            if inference:
+                return {
+                    "pred_masks": pred_masks,
+                    "gt_masks": gt_masks,
+                }
+
+            ce_loss = model_output.loss
+            ce_loss = ce_loss * self.ce_loss_weight
+            mask_bce_loss = 0
+            mask_dice_loss = 0
+            num_masks = 0
+
+            for batch_idx in range(len(pred_masks)): # for every image
+                cur_gt_masks = torch.stack(
+                    [
+                        torch.from_numpy(gt_mask).to(dtype=pred_masks[batch_idx].dtype, device=pred_masks[batch_idx].device)
+                        for gt_mask in gt_masks[batch_idx]
+                    ],
+                    dim=0
+                ) # expected (bs,H,W)
+                cur_pred_masks = pred_masks[batch_idx]
+                assert (
+                    cur_gt_masks.shape[0] == cur_pred_masks.shape[0]
+                ), "gt_masks.shape: {}, pred_masks.shape: {}".format(
+                    cur_gt_masks.shape, cur_pred_masks.shape
+                )
+                mask_bce_loss += (
+                    sigmoid_ce_loss(cur_pred_masks, cur_gt_masks, num_masks=cur_gt_masks.shape[0])
+                    * cur_gt_masks.shape[0]
+                )
+                mask_dice_loss += (
+                    dice_loss(cur_pred_masks, cur_gt_masks, num_masks=cur_gt_masks.shape[0])
+                    * cur_gt_masks.shape[0]
+                )
+                num_masks += cur_gt_masks.shape[0] 
+
+            mask_bce_loss = self.bce_loss_weight * mask_bce_loss / (num_masks + 1e-8)
+            mask_dice_loss = self.dice_loss_weight * mask_dice_loss / (num_masks + 1e-8)
+            mask_loss = mask_bce_loss + mask_dice_loss
+
+            loss = ce_loss + mask_loss
+            outputs = CausalLMOutputWithPast(
+                loss=loss,
+                logits=model_output.logits,
+                past_key_values=model_output.past_key_values,
+                hidden_states=output_hidden_states,
+                attentions=model_output.attentions,
+            )
+            outputs.ce_loss = ce_loss
+            outputs.mask_bce_loss = mask_bce_loss
+            outputs.mask_dice_loss = mask_dice_loss
+            outputs.mask_loss = mask_loss
+        else: 
+            outputs =  super().forward(**kwargs)
+        return outputs
+
+    def evaluate(
+        self,
+        tokenizer,
+        query: str,
+        images: List[Tuple[str, str]] = [],
+        hd_num: int = 9,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        **kwargs,
+    ):
+        with torch.no_grad():
+            inputs, im_mask, _ = self.interleav_wrap_chat(query, images, history=history, hd_num=hd_num)        
+            print(im_mask.sum().item())
+            inputs = {
+                k: v.to(self.device)
+                for k, v in inputs.items() if torch.is_tensor(v)
+            }
+            # print(len(inputs['inputs_embeds'][0]))
+            eos_token_id = [
+                tokenizer.eos_token_id,
+                #tokenizer.convert_tokens_to_ids(['[UNUSED_TOKEN_145]'])[0]
+            ]
+            all_pred_masks = []
+            outputs = self.generate(
+                **inputs,
+                max_new_tokens=max_new_tokens,
+                im_mask=im_mask,
+                input_ids = None,
+                streamer= None,
+                num_beams=1,
+                do_sample=False,
+                temperature=1.0,
+                top_p= 1.0,
+                top_k = 0,
+                eos_token_id=eos_token_id,
+                repetition_penalty=1.0,
+                infer_mode = 'base',
+                output_hidden_states=True,
+                return_dict_in_generate=True,
+                **kwargs,
+            )
+            output_ids = outputs['sequences']
+            response = tokenizer.decode(output_ids[0].cpu().tolist(), skip_special_tokens=True)
+            response = response.replace("[UNUSED_TOKEN_145]","")
+            history = history + [(query, response)]
+            if len(images)==1 and isinstance(images[0], str): 
+                output_hidden_states = outputs.hidden_states[-1] 
+                seg_token_mask = output_ids[:, 1:-1] == self.seg_token_idx
+                inputs_embeds_len = inputs['inputs_embeds'].size(1)
+                seg_token_mask = torch.cat(
+                    [
+                        torch.zeros((seg_token_mask.shape[0], inputs_embeds_len)).bool().cuda(),
+                        seg_token_mask,
+                    ],
+                    dim=1,
+                )
+                hidden_states = []
+                assert len(self.model.text_hidden_fcs) == 1
+                hidden_states.append(self.model.text_hidden_fcs[0](output_hidden_states))
+                last_hidden_state = torch.stack(hidden_states, dim=-1).sum(dim=-1)
+                pred_embeddings = [states[masks] for states, masks in zip(last_hidden_state, seg_token_mask)]
+                image_g_features, ori_hw = self.encode_g_img(images[0]) 
+
+                for i in range(len(pred_embeddings)):
+                    if (pred_embeddings[i].numel()== 0):
+                        all_pred_masks.append([])
+                        continue
+                    (sparse_embeddings,dense_embeddings,) = self.model.visual_model.sam_prompt_encoder(
+                        points=None,
+                        boxes=None,
+                        masks=None,
+                        text_embeds=pred_embeddings[i].unsqueeze(1),
+                    )
+                    batch_mode = (pred_embeddings[i].shape[0]>1)
+                    high_res_features = [
+                        feat_level[i].unsqueeze(0)
+                        for feat_level in image_g_features["high_res_feats"]
+                    ]
+                    sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype)
+                    image_g_embeds = image_g_features['image_embed'][i].unsqueeze(0).to(torch.bfloat16)
+
+                    low_res_masks, _, _ , _  = self.model.visual_model.sam_mask_decoder(
+                        image_embeddings=image_g_embeds,
+                        image_pe=self.model.visual_model.sam_prompt_encoder.get_dense_pe(),
+                        sparse_prompt_embeddings=sparse_embeddings,
+                        dense_prompt_embeddings=dense_embeddings,
+                        repeat_image=batch_mode,
+                        multimask_output=False,
+                        high_res_features=high_res_features,
+                    )
+                    pred_masks = self.model._transform.postprocess_masks(
+                        low_res_masks,
+                        ori_hw[i],
+                    )
+                    all_pred_masks.append(pred_masks[:, 0])
+
+        return response, all_pred_masks

pytorch_model.bin.index.json

@@ -2218,7 +2218,7 @@
     "model.visual_model.memory_attention.norm.weight": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.0.dwconv.bias": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.0.dwconv.weight": "pytorch_model-00003-of-00003.bin",
-    "model.visual_model.memory_encoder.fuser.layers.0.gamma": "pytorch_model-00003-of-00003.bin",
+    "model.visual_model.memory_encoder.fuser.layers.0.weight": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.0.norm.bias": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.0.norm.weight": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.0.pwconv1.bias": "pytorch_model-00003-of-00003.bin",
@@ -2227,7 +2227,7 @@
     "model.visual_model.memory_encoder.fuser.layers.0.pwconv2.weight": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.1.dwconv.bias": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.1.dwconv.weight": "pytorch_model-00003-of-00003.bin",
-    "model.visual_model.memory_encoder.fuser.layers.1.gamma": "pytorch_model-00003-of-00003.bin",
+    "model.visual_model.memory_encoder.fuser.layers.1.weight": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.1.norm.bias": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.1.norm.weight": "pytorch_model-00003-of-00003.bin",
     "model.visual_model.memory_encoder.fuser.layers.1.pwconv1.bias": "pytorch_model-00003-of-00003.bin",