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| from functools import partial |
| from pathlib import Path |
| import urllib.request |
| import torch |
| from torch.nn import functional as F |
|
|
| from .modeling import ( |
| ImageEncoderViT, |
| MaskDecoder, |
| PromptEncoder, |
| Sam, |
| TwoWayTransformer, |
| ) |
|
|
|
|
| def build_sam_vit_h(image_size=1024, keep_resolution=False, checkpoint=None): |
| return _build_sam( |
| encoder_embed_dim=1280, |
| encoder_depth=32, |
| encoder_num_heads=16, |
| encoder_global_attn_indexes=[7, 15, 23, 31], |
| image_size=image_size, |
| keep_resolution=keep_resolution, |
| checkpoint=checkpoint, |
| ) |
|
|
|
|
| build_sam = build_sam_vit_h |
|
|
|
|
| def build_sam_vit_l(image_size=1024, keep_resolution=False, checkpoint=None): |
| return _build_sam( |
| encoder_embed_dim=1024, |
| encoder_depth=24, |
| encoder_num_heads=16, |
| encoder_global_attn_indexes=[5, 11, 17, 23], |
| image_size=image_size, |
| keep_resolution=keep_resolution, |
| checkpoint=checkpoint, |
| ) |
|
|
|
|
| def build_sam_vit_b(image_size=1024, keep_resolution=False, checkpoint=None): |
| return _build_sam( |
| encoder_embed_dim=768, |
| encoder_depth=12, |
| encoder_num_heads=12, |
| encoder_global_attn_indexes=[2, 5, 8, 11], |
| image_size=image_size, |
| keep_resolution=keep_resolution, |
| checkpoint=checkpoint, |
| ) |
|
|
|
|
| sam_model_registry = { |
| "default": build_sam_vit_h, |
| "vit_h": build_sam_vit_h, |
| "vit_l": build_sam_vit_l, |
| "vit_b": build_sam_vit_b, |
| } |
|
|
|
|
| def _build_sam( |
| encoder_embed_dim, |
| encoder_depth, |
| encoder_num_heads, |
| encoder_global_attn_indexes, |
| image_size=1024, |
| keep_resolution=False, |
| checkpoint=None, |
| ): |
| prompt_embed_dim = 256 |
| vit_patch_size = 16 |
| image_embedding_size = image_size // vit_patch_size |
| sam = Sam( |
| image_encoder=ImageEncoderViT( |
| depth=encoder_depth, |
| embed_dim=encoder_embed_dim, |
| img_size=image_size, |
| mlp_ratio=4, |
| norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), |
| num_heads=encoder_num_heads, |
| patch_size=vit_patch_size, |
| qkv_bias=True, |
| use_rel_pos=True, |
| global_attn_indexes=encoder_global_attn_indexes, |
| window_size=14, |
| out_chans=prompt_embed_dim, |
| ), |
| prompt_encoder=PromptEncoder( |
| embed_dim=prompt_embed_dim, |
| image_embedding_size=(image_embedding_size, image_embedding_size), |
| input_image_size=(image_size, image_size), |
| mask_in_chans=16, |
| ), |
| mask_decoder=MaskDecoder( |
| num_multimask_outputs=3, |
| transformer=TwoWayTransformer( |
| depth=2, |
| embedding_dim=prompt_embed_dim, |
| mlp_dim=2048, |
| num_heads=8, |
| ), |
| transformer_dim=prompt_embed_dim, |
| iou_head_depth=3, |
| iou_head_hidden_dim=256, |
| keep_resolution=keep_resolution, |
| ), |
| pixel_mean=[123.675, 116.28, 103.53], |
| pixel_std=[58.395, 57.12, 57.375], |
| ) |
| sam.eval() |
| checkpoint = Path(checkpoint) |
| if checkpoint.name == "sam_vit_b_01ec64.pth" and not checkpoint.exists(): |
| cmd = input("Download sam_vit_b_01ec64.pth from facebook AI? [y]/n: ") |
| if len(cmd) == 0 or cmd.lower() == 'y': |
| checkpoint.parent.mkdir(parents=True, exist_ok=True) |
| print("Downloading SAM ViT-B checkpoint...") |
| urllib.request.urlretrieve( |
| "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", |
| checkpoint, |
| ) |
| print(checkpoint.name, " is downloaded!") |
| elif checkpoint.name == "sam_vit_h_4b8939.pth" and not checkpoint.exists(): |
| cmd = input("Download sam_vit_h_4b8939.pth from facebook AI? [y]/n: ") |
| if len(cmd) == 0 or cmd.lower() == 'y': |
| checkpoint.parent.mkdir(parents=True, exist_ok=True) |
| print("Downloading SAM ViT-H checkpoint...") |
| urllib.request.urlretrieve( |
| "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", |
| checkpoint, |
| ) |
| print(checkpoint.name, " is downloaded!") |
| elif checkpoint.name == "sam_vit_l_0b3195.pth" and not checkpoint.exists(): |
| cmd = input("Download sam_vit_l_0b3195.pth from facebook AI? [y]/n: ") |
| if len(cmd) == 0 or cmd.lower() == 'y': |
| checkpoint.parent.mkdir(parents=True, exist_ok=True) |
| print("Downloading SAM ViT-L checkpoint...") |
| urllib.request.urlretrieve( |
| "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth", |
| checkpoint, |
| ) |
| print(checkpoint.name, " is downloaded!") |
|
|
| |
| if checkpoint is not None: |
| with open(checkpoint, "rb") as f: |
| state_dict = torch.load(f) |
| try: |
| sam.load_state_dict(state_dict) |
| except: |
| new_state_dict = load_from(sam, state_dict, image_size, vit_patch_size, encoder_global_attn_indexes) |
| sam.load_state_dict(new_state_dict) |
| return sam |
|
|
|
|
| def load_from(sam, state_dict, image_size, vit_patch_size, encoder_global_attn_indexes): |
| sam_dict = sam.state_dict() |
| except_keys = ['mask_tokens', 'output_hypernetworks_mlps', 'iou_prediction_head'] |
| new_state_dict = {k: v for k, v in state_dict.items() if |
| k in sam_dict.keys() and except_keys[0] not in k and except_keys[1] not in k and except_keys[2] not in k} |
| pos_embed = new_state_dict['image_encoder.pos_embed'] |
| token_size = int(image_size // vit_patch_size) |
| if pos_embed.shape[1] != token_size: |
| |
| pos_embed = pos_embed.permute(0, 3, 1, 2) |
| pos_embed = F.interpolate(pos_embed, (token_size, token_size), mode='bilinear', align_corners=False) |
| pos_embed = pos_embed.permute(0, 2, 3, 1) |
| new_state_dict['image_encoder.pos_embed'] = pos_embed |
| rel_pos_keys = [k for k in sam_dict.keys() if 'rel_pos' in k] |
| global_rel_pos_keys = [] |
| for rel_pos_key in rel_pos_keys: |
| num = int(rel_pos_key.split('.')[2]) |
| if num in encoder_global_attn_indexes: |
| global_rel_pos_keys.append(rel_pos_key) |
| |
| for k in global_rel_pos_keys: |
| rel_pos_params = new_state_dict[k] |
| h, w = rel_pos_params.shape |
| rel_pos_params = rel_pos_params.unsqueeze(0).unsqueeze(0) |
| rel_pos_params = F.interpolate(rel_pos_params, (token_size * 2 - 1, w), mode='bilinear', align_corners=False) |
| new_state_dict[k] = rel_pos_params[0, 0, ...] |
| sam_dict.update(new_state_dict) |
| return sam_dict |
|
|
