File size: 6,950 Bytes
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# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
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) # [b, c, h, w]
pos_embed = F.interpolate(pos_embed, (token_size, token_size), mode='bilinear', align_corners=False)
pos_embed = pos_embed.permute(0, 2, 3, 1) # [b, h, w, c]
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
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