Commit
•
ee42a63
1
Parent(s):
f98153c
Upload LamedLlamaForCausalLM
Browse files- config.json +54 -0
- configuration_m3d_lamed.py +5 -0
- generation_config.json +10 -0
- model-00001-of-00006.safetensors +3 -0
- model-00002-of-00006.safetensors +3 -0
- model-00003-of-00006.safetensors +3 -0
- model-00004-of-00006.safetensors +3 -0
- model-00005-of-00006.safetensors +3 -0
- model-00006-of-00006.safetensors +3 -0
- model.safetensors.index.json +720 -0
- modeling_m3d_lamed.py +2102 -0
config.json
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{
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"_name_or_path": "./LaMed/pretrained_model/llama-2-7b-chat",
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"architectures": [
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"LamedLlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_m3d_lamed.LamedConfig",
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"AutoModelForCausalLM": "modeling_m3d_lamed.LamedLlamaForCausalLM"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"image_channel": 1,
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"image_size": [
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32,
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256,
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256
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],
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"mm_hidden_size": 768,
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"mm_projector_type": "spp",
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"model_type": "lamed_llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"patch_size": [
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4,
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16,
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16
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],
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"pretraining_tp": 1,
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"proj_layer_num": 2,
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"proj_layer_type": "mlp",
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"proj_pooling_size": 2,
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"proj_pooling_type": "spatial",
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"seg_token_id": 32003,
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"segmentation_module": "segvol",
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.39.1",
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"use_cache": true,
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"vision_select_feature": "patch",
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"vision_select_layer": -1,
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"vision_tower": "vit3d",
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"vocab_size": 32004
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}
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configuration_m3d_lamed.py
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from transformers import LlamaConfig
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class LamedConfig(LlamaConfig):
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model_type = "lamed_llama"
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generation_config.json
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{
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"bos_token_id": 1,
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"do_sample": true,
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"eos_token_id": 2,
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"max_length": 4096,
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"pad_token_id": 0,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.39.1"
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}
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model-00001-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4840461952
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model-00002-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4857206856
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model-00003-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00004-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00005-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4857206904
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model-00006-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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size 3662911968
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model.safetensors.index.json
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|
modeling_m3d_lamed.py
ADDED
@@ -0,0 +1,2102 @@
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|
1 |
+
from __future__ import annotations
|
2 |
+
from typing import Union
|
3 |
+
from transformers import LlamaConfig, LlamaModel, LlamaForCausalLM
|
4 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
5 |
+
from transformers.generation.utils import GenerateOutput
|
6 |
+
from .configuration_m3d_lamed import LamedConfig
|
7 |
+
from abc import ABC, abstractmethod
|
8 |
+
from torch import Tensor
|
9 |
+
import math
|
10 |
+
from typing import Any, Dict, List
|
11 |
+
import torch
|
12 |
+
import torch.nn as nn
|
13 |
+
from typing import Optional, Tuple, Type
|
14 |
+
from monai.networks.blocks import PatchEmbed
|
15 |
+
import numpy as np
|
16 |
+
import torch.nn.functional as F
|
17 |
+
|
18 |
+
from einops import rearrange
|
19 |
+
from einops.layers.torch import Rearrange
|
20 |
+
from collections.abc import Sequence
|
21 |
+
from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
|
22 |
+
from monai.networks.blocks.transformerblock import TransformerBlock
|
23 |
+
from monai.networks.nets import ViT
|
24 |
+
|
25 |
+
|
26 |
+
class BinaryDiceLoss(nn.Module):
|
27 |
+
def __init__(self, smooth=1, p=2, reduction='mean'):
|
28 |
+
super(BinaryDiceLoss, self).__init__()
|
29 |
+
self.smooth = smooth
|
30 |
+
self.p = p
|
31 |
+
self.reduction = reduction
|
32 |
+
|
33 |
+
def forward(self, predict, target):
|
34 |
+
predict = torch.sigmoid(predict)
|
35 |
+
target_ = target.clone().float()
|
36 |
+
target_[target == -1] = 0
|
37 |
+
assert predict.shape[0] == target.shape[0], "predict & target batch size don't match\n" + str(predict.shape) + '\n' + str(target.shape[0])
|
38 |
+
predict = predict.contiguous().view(predict.shape[0], -1)
|
39 |
+
target_ = target_.contiguous().view(target_.shape[0], -1)
|
40 |
+
|
41 |
+
num = torch.sum(torch.mul(predict, target_), dim=1)
|
42 |
+
den = torch.sum(predict, dim=1) + torch.sum(target_, dim=1) + self.smooth
|
43 |
+
|
44 |
+
dice_score = 2*num / den
|
45 |
+
dice_loss = 1 - dice_score
|
46 |
+
|
47 |
+
# dice_loss_avg = dice_loss[target[:,0]!=-1].sum() / dice_loss[target[:,0]!=-1].shape[0]
|
48 |
+
dice_loss_avg = dice_loss.sum() / dice_loss.shape[0]
|
49 |
+
|
50 |
+
return dice_loss_avg
|
51 |
+
|
52 |
+
class BCELoss(nn.Module):
|
53 |
+
def __init__(self):
|
54 |
+
super(BCELoss, self).__init__()
|
55 |
+
self.criterion = nn.BCEWithLogitsLoss()
|
56 |
+
|
57 |
+
def forward(self, predict, target):
|
58 |
+
assert predict.shape == target.shape, 'predict & target shape do not match\n' + str(predict.shape) + '\n' + str(target.shape)
|
59 |
+
target_ = target.clone()
|
60 |
+
target_[target == -1] = 0
|
61 |
+
|
62 |
+
ce_loss = self.criterion(predict, target_.float())
|
63 |
+
|
64 |
+
return ce_loss
|
65 |
+
|
66 |
+
|
67 |
+
|
68 |
+
class LayerNorm2d(nn.Module):
|
69 |
+
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
|
70 |
+
super().__init__()
|
71 |
+
self.weight = nn.Parameter(torch.ones(num_channels))
|
72 |
+
self.bias = nn.Parameter(torch.zeros(num_channels))
|
73 |
+
self.eps = eps
|
74 |
+
|
75 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
76 |
+
u = x.mean(1, keepdim=True)
|
77 |
+
s = (x - u).pow(2).mean(1, keepdim=True)
|
78 |
+
x = (x - u) / torch.sqrt(s + self.eps)
|
79 |
+
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
80 |
+
return x
|
81 |
+
|
82 |
+
|
83 |
+
class MLPBlock(nn.Module):
|
84 |
+
def __init__(
|
85 |
+
self,
|
86 |
+
embedding_dim: int,
|
87 |
+
mlp_dim: int,
|
88 |
+
act: Type[nn.Module] = nn.GELU,
|
89 |
+
) -> None:
|
90 |
+
super().__init__()
|
91 |
+
self.lin1 = nn.Linear(embedding_dim, mlp_dim)
|
92 |
+
self.lin2 = nn.Linear(mlp_dim, embedding_dim)
|
93 |
+
self.act = act()
|
94 |
+
|
95 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
96 |
+
return self.lin2(self.act(self.lin1(x)))
|
97 |
+
|
98 |
+
|
99 |
+
class TwoWayTransformer(nn.Module):
|
100 |
+
def __init__(
|
101 |
+
self,
|
102 |
+
depth: int,
|
103 |
+
embedding_dim: int,
|
104 |
+
num_heads: int,
|
105 |
+
mlp_dim: int,
|
106 |
+
activation: Type[nn.Module] = nn.ReLU,
|
107 |
+
attention_downsample_rate: int = 2,
|
108 |
+
) -> None:
|
109 |
+
"""
|
110 |
+
A transformer decoder that attends to an input image using
|
111 |
+
queries whose positional embedding is supplied.
|
112 |
+
|
113 |
+
Args:
|
114 |
+
depth (int): number of layers in the transformer
|
115 |
+
embedding_dim (int): the channel dimension for the input embeddings
|
116 |
+
num_heads (int): the number of heads for multihead attention. Must
|
117 |
+
divide embedding_dim
|
118 |
+
mlp_dim (int): the channel dimension internal to the MLP block
|
119 |
+
activation (nn.Module): the activation to use in the MLP block
|
120 |
+
"""
|
121 |
+
super().__init__()
|
122 |
+
self.depth = depth
|
123 |
+
self.embedding_dim = embedding_dim
|
124 |
+
self.num_heads = num_heads
|
125 |
+
self.mlp_dim = mlp_dim
|
126 |
+
self.layers = nn.ModuleList()
|
127 |
+
|
128 |
+
for i in range(depth):
|
129 |
+
self.layers.append(
|
130 |
+
TwoWayAttentionBlock(
|
131 |
+
embedding_dim=embedding_dim,
|
132 |
+
num_heads=num_heads,
|
133 |
+
mlp_dim=mlp_dim,
|
134 |
+
activation=activation,
|
135 |
+
attention_downsample_rate=attention_downsample_rate,
|
136 |
+
skip_first_layer_pe=(i == 0),
|
137 |
+
)
|
138 |
+
)
|
139 |
+
|
140 |
+
self.final_attn_token_to_image = Attention(
|
141 |
+
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
|
142 |
+
)
|
143 |
+
self.norm_final_attn = nn.LayerNorm(embedding_dim)
|
144 |
+
|
145 |
+
def forward(
|
146 |
+
self,
|
147 |
+
image_embedding: Tensor,
|
148 |
+
image_pe: Tensor,
|
149 |
+
point_embedding: Tensor,
|
150 |
+
) -> Tuple[Tensor, Tensor]:
|
151 |
+
"""
|
152 |
+
Args:
|
153 |
+
image_embedding (torch.Tensor): image to attend to. Should be shape
|
154 |
+
B x embedding_dim x h x w for any h and w.
|
155 |
+
image_pe (torch.Tensor): the positional encoding to add to the image. Must
|
156 |
+
have the same shape as image_embedding.
|
157 |
+
point_embedding (torch.Tensor): the embedding to add to the query points.
|
158 |
+
Must have shape B x N_points x embedding_dim for any N_points.
|
159 |
+
|
160 |
+
Returns:
|
161 |
+
torch.Tensor: the processed point_embedding
|
162 |
+
torch.Tensor: the processed image_embedding
|
163 |
+
"""
|
164 |
+
# BxCxHxW -> BxHWxC == B x N_image_tokens x C
|
165 |
+
bs, c, h, w, d = image_embedding.shape
|
166 |
+
image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
|
167 |
+
image_pe = image_pe.flatten(2).permute(0, 2, 1)
|
168 |
+
|
169 |
+
# Prepare queries
|
170 |
+
queries = point_embedding
|
171 |
+
keys = image_embedding
|
172 |
+
|
173 |
+
# Apply transformer blocks and final layernorm
|
174 |
+
for layer in self.layers:
|
175 |
+
queries, keys = layer(
|
176 |
+
queries=queries,
|
177 |
+
keys=keys,
|
178 |
+
query_pe=point_embedding,
|
179 |
+
key_pe=image_pe,
|
180 |
+
)
|
181 |
+
|
182 |
+
# Apply the final attention layer from the points to the image
|
183 |
+
q = queries + point_embedding
|
184 |
+
k = keys + image_pe
|
185 |
+
attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
|
186 |
+
queries = queries + attn_out
|
187 |
+
queries = self.norm_final_attn(queries)
|
188 |
+
|
189 |
+
return queries, keys
|
190 |
+
|
191 |
+
|
192 |
+
class TwoWayAttentionBlock(nn.Module):
|
193 |
+
def __init__(
|
194 |
+
self,
|
195 |
+
embedding_dim: int,
|
196 |
+
num_heads: int,
|
197 |
+
mlp_dim: int = 2048,
|
198 |
+
activation: Type[nn.Module] = nn.ReLU,
|
199 |
+
attention_downsample_rate: int = 2,
|
200 |
+
skip_first_layer_pe: bool = False,
|
201 |
+
) -> None:
|
202 |
+
"""
|
203 |
+
A transformer block with four layers: (1) self-attention of sparse
|
204 |
+
inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
|
205 |
+
block on sparse inputs, and (4) cross attention of dense inputs to sparse
|
206 |
+
inputs.
|
207 |
+
|
208 |
+
Arguments:
|
209 |
+
embedding_dim (int): the channel dimension of the embeddings
|
210 |
+
num_heads (int): the number of heads in the attention layers
|
211 |
+
mlp_dim (int): the hidden dimension of the mlp block
|
212 |
+
activation (nn.Module): the activation of the mlp block
|
213 |
+
skip_first_layer_pe (bool): skip the PE on the first layer
|
214 |
+
"""
|
215 |
+
super().__init__()
|
216 |
+
self.self_attn = Attention(embedding_dim, num_heads)
|
217 |
+
self.norm1 = nn.LayerNorm(embedding_dim)
|
218 |
+
|
219 |
+
self.cross_attn_token_to_image = Attention(
|
220 |
+
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
|
221 |
+
)
|
222 |
+
self.norm2 = nn.LayerNorm(embedding_dim)
|
223 |
+
|
224 |
+
self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
|
225 |
+
self.norm3 = nn.LayerNorm(embedding_dim)
|
226 |
+
|
227 |
+
self.norm4 = nn.LayerNorm(embedding_dim)
|
228 |
+
self.cross_attn_image_to_token = Attention(
|
229 |
+
embedding_dim, num_heads, downsample_rate=attention_downsample_rate
|
230 |
+
)
|
231 |
+
|
232 |
+
self.skip_first_layer_pe = skip_first_layer_pe
|
233 |
+
|
234 |
+
def forward(
|
235 |
+
self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
|
236 |
+
) -> Tuple[Tensor, Tensor]:
|
237 |
+
# Self attention block
|
238 |
+
if self.skip_first_layer_pe:
|
239 |
+
queries = self.self_attn(q=queries, k=queries, v=queries)
|
240 |
+
else:
|
241 |
+
q = queries + query_pe
|
242 |
+
attn_out = self.self_attn(q=q, k=q, v=queries)
|
243 |
+
queries = queries + attn_out
|
244 |
+
queries = self.norm1(queries)
|
245 |
+
|
246 |
+
# Cross attention block, tokens attending to image embedding
|
247 |
+
q = queries + query_pe
|
248 |
+
k = keys + key_pe
|
249 |
+
attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
|
250 |
+
queries = queries + attn_out
|
251 |
+
queries = self.norm2(queries)
|
252 |
+
|
253 |
+
# MLP block
|
254 |
+
mlp_out = self.mlp(queries)
|
255 |
+
queries = queries + mlp_out
|
256 |
+
queries = self.norm3(queries)
|
257 |
+
|
258 |
+
# Cross attention block, image embedding attending to tokens
|
259 |
+
q = queries + query_pe
|
260 |
+
k = keys + key_pe
|
261 |
+
attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
|
262 |
+
keys = keys + attn_out
|
263 |
+
keys = self.norm4(keys)
|
264 |
+
|
265 |
+
return queries, keys
|
266 |
+
|
267 |
+
|
268 |
+
class Attention(nn.Module):
|
269 |
+
"""
|
270 |
+
An attention layer that allows for downscaling the size of the embedding
|
271 |
+
after projection to queries, keys, and values.
|
272 |
+
"""
|
273 |
+
|
274 |
+
def __init__(
|
275 |
+
self,
|
276 |
+
embedding_dim: int,
|
277 |
+
num_heads: int,
|
278 |
+
downsample_rate: int = 1,
|
279 |
+
) -> None:
|
280 |
+
super().__init__()
|
281 |
+
self.embedding_dim = embedding_dim
|
282 |
+
self.internal_dim = embedding_dim // downsample_rate
|
283 |
+
self.num_heads = num_heads
|
284 |
+
assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."
|
285 |
+
|
286 |
+
self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
|
287 |
+
self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
|
288 |
+
self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
|
289 |
+
self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
|
290 |
+
|
291 |
+
def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
|
292 |
+
b, n, c = x.shape
|
293 |
+
x = x.reshape(b, n, num_heads, c // num_heads)
|
294 |
+
return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
|
295 |
+
|
296 |
+
def _recombine_heads(self, x: Tensor) -> Tensor:
|
297 |
+
b, n_heads, n_tokens, c_per_head = x.shape
|
298 |
+
x = x.transpose(1, 2)
|
299 |
+
return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
|
300 |
+
|
301 |
+
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
|
302 |
+
# Input projections
|
303 |
+
q = self.q_proj(q)
|
304 |
+
k = self.k_proj(k)
|
305 |
+
v = self.v_proj(v)
|
306 |
+
|
307 |
+
# Separate into heads
|
308 |
+
q = self._separate_heads(q, self.num_heads)
|
309 |
+
k = self._separate_heads(k, self.num_heads)
|
310 |
+
v = self._separate_heads(v, self.num_heads)
|
311 |
+
|
312 |
+
# Attention
|
313 |
+
_, _, _, c_per_head = q.shape
|
314 |
+
attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
|
315 |
+
attn = attn / math.sqrt(c_per_head)
|
316 |
+
attn = torch.softmax(attn, dim=-1)
|
317 |
+
|
318 |
+
# Get output
|
319 |
+
out = attn @ v
|
320 |
+
out = self._recombine_heads(out)
|
321 |
+
out = self.out_proj(out)
|
322 |
+
|
323 |
+
return out
|
324 |
+
|
325 |
+
|
326 |
+
|
327 |
+
# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
|
328 |
+
class ImageEncoderViT(nn.Module):
|
329 |
+
def __init__(
|
330 |
+
self,
|
331 |
+
img_size: int = 1024,
|
332 |
+
patch_size: int = 16,
|
333 |
+
in_chans: int = 1,
|
334 |
+
embed_dim: int = 768,
|
335 |
+
depth: int = 12,
|
336 |
+
num_heads: int = 12,
|
337 |
+
mlp_ratio: float = 4.0,
|
338 |
+
out_chans: int = 256,
|
339 |
+
qkv_bias: bool = True,
|
340 |
+
norm_layer: Type[nn.Module] = nn.LayerNorm,
|
341 |
+
act_layer: Type[nn.Module] = nn.GELU,
|
342 |
+
use_abs_pos: bool = True,
|
343 |
+
use_rel_pos: bool = False,
|
344 |
+
rel_pos_zero_init: bool = True,
|
345 |
+
window_size: int = 0,
|
346 |
+
global_attn_indexes: Tuple[int, ...] = (),
|
347 |
+
) -> None:
|
348 |
+
"""
|
349 |
+
Args:
|
350 |
+
img_size (int): Input image size.
|
351 |
+
patch_size (int): Patch size.
|
352 |
+
in_chans (int): Number of input image channels.
|
353 |
+
embed_dim (int): Patch embedding dimension.
|
354 |
+
depth (int): Depth of ViT.
|
355 |
+
num_heads (int): Number of attention heads in each ViT block.
|
356 |
+
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
357 |
+
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
358 |
+
norm_layer (nn.Module): Normalization layer.
|
359 |
+
act_layer (nn.Module): Activation layer.
|
360 |
+
use_abs_pos (bool): If True, use absolute positional embeddings.
|
361 |
+
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
|
362 |
+
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
|
363 |
+
window_size (int): Window size for window attention blocks.
|
364 |
+
global_attn_indexes (list): Indexes for blocks using global attention.
|
365 |
+
"""
|
366 |
+
super().__init__()
|
367 |
+
self.img_size = img_size
|
368 |
+
|
369 |
+
# self.patch_embed = PatchEmbed(
|
370 |
+
# kernel_size=(patch_size, patch_size),
|
371 |
+
# stride=(patch_size, patch_size),
|
372 |
+
# in_chans=in_chans,
|
373 |
+
# embed_dim=embed_dim,
|
374 |
+
# )
|
375 |
+
|
376 |
+
self.patch_embed = PatchEmbed(
|
377 |
+
patch_size=patch_size,
|
378 |
+
in_chans=in_chans,
|
379 |
+
embed_dim=embed_dim,
|
380 |
+
spatial_dims=3,
|
381 |
+
)
|
382 |
+
|
383 |
+
self.pos_embed: Optional[nn.Parameter] = None
|
384 |
+
if use_abs_pos:
|
385 |
+
# Initialize absolute positional embedding with pretrain image size.
|
386 |
+
self.pos_embed = nn.Parameter(
|
387 |
+
torch.zeros(1, img_size // patch_size, img_size // patch_size, img_size // patch_size, embed_dim)
|
388 |
+
)
|
389 |
+
|
390 |
+
self.blocks = nn.ModuleList()
|
391 |
+
for i in range(depth):
|
392 |
+
block = Block(
|
393 |
+
dim=embed_dim,
|
394 |
+
num_heads=num_heads,
|
395 |
+
mlp_ratio=mlp_ratio,
|
396 |
+
qkv_bias=qkv_bias,
|
397 |
+
norm_layer=norm_layer,
|
398 |
+
act_layer=act_layer,
|
399 |
+
use_rel_pos=use_rel_pos,
|
400 |
+
rel_pos_zero_init=rel_pos_zero_init,
|
401 |
+
window_size=window_size if i not in global_attn_indexes else 0,
|
402 |
+
input_size=(img_size // patch_size, img_size // patch_size),
|
403 |
+
)
|
404 |
+
self.blocks.append(block)
|
405 |
+
|
406 |
+
self.neck = nn.Sequential(
|
407 |
+
nn.Conv2d(
|
408 |
+
embed_dim,
|
409 |
+
out_chans,
|
410 |
+
kernel_size=1,
|
411 |
+
bias=False,
|
412 |
+
),
|
413 |
+
LayerNorm2d(out_chans),
|
414 |
+
nn.Conv2d(
|
415 |
+
out_chans,
|
416 |
+
out_chans,
|
417 |
+
kernel_size=3,
|
418 |
+
padding=1,
|
419 |
+
bias=False,
|
420 |
+
),
|
421 |
+
LayerNorm2d(out_chans),
|
422 |
+
)
|
423 |
+
|
424 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
425 |
+
x = self.patch_embed(x)
|
426 |
+
print('patch embedded shape: ', x.shape) # embedded: [8, 768, 6, 6, 6]
|
427 |
+
if self.pos_embed is not None:
|
428 |
+
x = x + self.pos_embed
|
429 |
+
|
430 |
+
for blk in self.blocks:
|
431 |
+
x = blk(x)
|
432 |
+
|
433 |
+
x = self.neck(x.permute(0, 3, 1, 2))
|
434 |
+
|
435 |
+
return x
|
436 |
+
|
437 |
+
|
438 |
+
class Block(nn.Module):
|
439 |
+
"""Transformer blocks with support of window attention and residual propagation blocks"""
|
440 |
+
|
441 |
+
def __init__(
|
442 |
+
self,
|
443 |
+
dim: int,
|
444 |
+
num_heads: int,
|
445 |
+
mlp_ratio: float = 4.0,
|
446 |
+
qkv_bias: bool = True,
|
447 |
+
norm_layer: Type[nn.Module] = nn.LayerNorm,
|
448 |
+
act_layer: Type[nn.Module] = nn.GELU,
|
449 |
+
use_rel_pos: bool = False,
|
450 |
+
rel_pos_zero_init: bool = True,
|
451 |
+
window_size: int = 0,
|
452 |
+
input_size: Optional[Tuple[int, int]] = None,
|
453 |
+
) -> None:
|
454 |
+
"""
|
455 |
+
Args:
|
456 |
+
dim (int): Number of input channels.
|
457 |
+
num_heads (int): Number of attention heads in each ViT block.
|
458 |
+
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
459 |
+
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
460 |
+
norm_layer (nn.Module): Normalization layer.
|
461 |
+
act_layer (nn.Module): Activation layer.
|
462 |
+
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
|
463 |
+
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
|
464 |
+
window_size (int): Window size for window attention blocks. If it equals 0, then
|
465 |
+
use global attention.
|
466 |
+
input_size (tuple(int, int) or None): Input resolution for calculating the relative
|
467 |
+
positional parameter size.
|
468 |
+
"""
|
469 |
+
super().__init__()
|
470 |
+
self.norm1 = norm_layer(dim)
|
471 |
+
self.attn = Attention2(
|
472 |
+
dim,
|
473 |
+
num_heads=num_heads,
|
474 |
+
qkv_bias=qkv_bias,
|
475 |
+
use_rel_pos=use_rel_pos,
|
476 |
+
rel_pos_zero_init=rel_pos_zero_init,
|
477 |
+
input_size=input_size if window_size == 0 else (window_size, window_size),
|
478 |
+
)
|
479 |
+
|
480 |
+
self.norm2 = norm_layer(dim)
|
481 |
+
self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
|
482 |
+
|
483 |
+
self.window_size = window_size
|
484 |
+
|
485 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
486 |
+
shortcut = x
|
487 |
+
x = self.norm1(x)
|
488 |
+
# Window partition
|
489 |
+
if self.window_size > 0:
|
490 |
+
H, W = x.shape[1], x.shape[2]
|
491 |
+
x, pad_hw = window_partition(x, self.window_size)
|
492 |
+
|
493 |
+
x = self.attn(x)
|
494 |
+
# Reverse window partition
|
495 |
+
if self.window_size > 0:
|
496 |
+
x = window_unpartition(x, self.window_size, pad_hw, (H, W))
|
497 |
+
|
498 |
+
x = shortcut + x
|
499 |
+
x = x + self.mlp(self.norm2(x))
|
500 |
+
|
501 |
+
return x
|
502 |
+
|
503 |
+
|
504 |
+
class Attention2(nn.Module):
|
505 |
+
"""Multi-head Attention block with relative position embeddings."""
|
506 |
+
|
507 |
+
def __init__(
|
508 |
+
self,
|
509 |
+
dim: int,
|
510 |
+
num_heads: int = 8,
|
511 |
+
qkv_bias: bool = True,
|
512 |
+
use_rel_pos: bool = False,
|
513 |
+
rel_pos_zero_init: bool = True,
|
514 |
+
input_size: Optional[Tuple[int, int]] = None,
|
515 |
+
) -> None:
|
516 |
+
"""
|
517 |
+
Args:
|
518 |
+
dim (int): Number of input channels.
|
519 |
+
num_heads (int): Number of attention heads.
|
520 |
+
qkv_bias (bool): If True, add a learnable bias to query, key, value.
|
521 |
+
rel_pos (bool): If True, add relative positional embeddings to the attention map.
|
522 |
+
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
|
523 |
+
input_size (tuple(int, int) or None): Input resolution for calculating the relative
|
524 |
+
positional parameter size.
|
525 |
+
"""
|
526 |
+
super().__init__()
|
527 |
+
self.num_heads = num_heads
|
528 |
+
head_dim = dim // num_heads
|
529 |
+
self.scale = head_dim ** -0.5
|
530 |
+
|
531 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
532 |
+
self.proj = nn.Linear(dim, dim)
|
533 |
+
|
534 |
+
self.use_rel_pos = use_rel_pos
|
535 |
+
if self.use_rel_pos:
|
536 |
+
assert (
|
537 |
+
input_size is not None
|
538 |
+
), "Input size must be provided if using relative positional encoding."
|
539 |
+
# initialize relative positional embeddings
|
540 |
+
self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
|
541 |
+
self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
|
542 |
+
|
543 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
544 |
+
B, H, W, _ = x.shape
|
545 |
+
# qkv with shape (3, B, nHead, H * W, C)
|
546 |
+
qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
|
547 |
+
# q, k, v with shape (B * nHead, H * W, C)
|
548 |
+
q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
|
549 |
+
|
550 |
+
attn = (q * self.scale) @ k.transpose(-2, -1)
|
551 |
+
|
552 |
+
if self.use_rel_pos:
|
553 |
+
attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
|
554 |
+
|
555 |
+
attn = attn.softmax(dim=-1)
|
556 |
+
x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
|
557 |
+
x = self.proj(x)
|
558 |
+
|
559 |
+
return x
|
560 |
+
|
561 |
+
|
562 |
+
def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
|
563 |
+
"""
|
564 |
+
Partition into non-overlapping windows with padding if needed.
|
565 |
+
Args:
|
566 |
+
x (tensor): input tokens with [B, H, W, C].
|
567 |
+
window_size (int): window size.
|
568 |
+
|
569 |
+
Returns:
|
570 |
+
windows: windows after partition with [B * num_windows, window_size, window_size, C].
|
571 |
+
(Hp, Wp): padded height and width before partition
|
572 |
+
"""
|
573 |
+
B, H, W, C = x.shape
|
574 |
+
|
575 |
+
pad_h = (window_size - H % window_size) % window_size
|
576 |
+
pad_w = (window_size - W % window_size) % window_size
|
577 |
+
if pad_h > 0 or pad_w > 0:
|
578 |
+
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
|
579 |
+
Hp, Wp = H + pad_h, W + pad_w
|
580 |
+
|
581 |
+
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
|
582 |
+
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
583 |
+
return windows, (Hp, Wp)
|
584 |
+
|
585 |
+
|
586 |
+
def window_unpartition(
|
587 |
+
windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
|
588 |
+
) -> torch.Tensor:
|
589 |
+
"""
|
590 |
+
Window unpartition into original sequences and removing padding.
|
591 |
+
Args:
|
592 |
+
windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
|
593 |
+
window_size (int): window size.
|
594 |
+
pad_hw (Tuple): padded height and width (Hp, Wp).
|
595 |
+
hw (Tuple): original height and width (H, W) before padding.
|
596 |
+
|
597 |
+
Returns:
|
598 |
+
x: unpartitioned sequences with [B, H, W, C].
|
599 |
+
"""
|
600 |
+
Hp, Wp = pad_hw
|
601 |
+
H, W = hw
|
602 |
+
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
|
603 |
+
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
|
604 |
+
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
|
605 |
+
|
606 |
+
if Hp > H or Wp > W:
|
607 |
+
x = x[:, :H, :W, :].contiguous()
|
608 |
+
return x
|
609 |
+
|
610 |
+
|
611 |
+
def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
|
612 |
+
"""
|
613 |
+
Get relative positional embeddings according to the relative positions of
|
614 |
+
query and key sizes.
|
615 |
+
Args:
|
616 |
+
q_size (int): size of query q.
|
617 |
+
k_size (int): size of key k.
|
618 |
+
rel_pos (Tensor): relative position embeddings (L, C).
|
619 |
+
|
620 |
+
Returns:
|
621 |
+
Extracted positional embeddings according to relative positions.
|
622 |
+
"""
|
623 |
+
max_rel_dist = int(2 * max(q_size, k_size) - 1)
|
624 |
+
# Interpolate rel pos if needed.
|
625 |
+
if rel_pos.shape[0] != max_rel_dist:
|
626 |
+
# Interpolate rel pos.
|
627 |
+
rel_pos_resized = F.interpolate(
|
628 |
+
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
|
629 |
+
size=max_rel_dist,
|
630 |
+
mode="linear",
|
631 |
+
)
|
632 |
+
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
|
633 |
+
else:
|
634 |
+
rel_pos_resized = rel_pos
|
635 |
+
|
636 |
+
# Scale the coords with short length if shapes for q and k are different.
|
637 |
+
q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
|
638 |
+
k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
|
639 |
+
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
|
640 |
+
|
641 |
+
return rel_pos_resized[relative_coords.long()]
|
642 |
+
|
643 |
+
|
644 |
+
def add_decomposed_rel_pos(
|
645 |
+
attn: torch.Tensor,
|
646 |
+
q: torch.Tensor,
|
647 |
+
rel_pos_h: torch.Tensor,
|
648 |
+
rel_pos_w: torch.Tensor,
|
649 |
+
q_size: Tuple[int, int],
|
650 |
+
k_size: Tuple[int, int],
|
651 |
+
) -> torch.Tensor:
|
652 |
+
"""
|
653 |
+
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
|
654 |
+
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
|
655 |
+
Args:
|
656 |
+
attn (Tensor): attention map.
|
657 |
+
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
|
658 |
+
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
|
659 |
+
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
|
660 |
+
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
|
661 |
+
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
|
662 |
+
|
663 |
+
Returns:
|
664 |
+
attn (Tensor): attention map with added relative positional embeddings.
|
665 |
+
"""
|
666 |
+
q_h, q_w = q_size
|
667 |
+
k_h, k_w = k_size
|
668 |
+
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
|
669 |
+
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
|
670 |
+
|
671 |
+
B, _, dim = q.shape
|
672 |
+
r_q = q.reshape(B, q_h, q_w, dim)
|
673 |
+
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
|
674 |
+
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
|
675 |
+
|
676 |
+
attn = (
|
677 |
+
attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
|
678 |
+
).view(B, q_h * q_w, k_h * k_w)
|
679 |
+
|
680 |
+
return attn
|
681 |
+
|
682 |
+
|
683 |
+
class PromptEncoder(nn.Module):
|
684 |
+
def __init__(
|
685 |
+
self,
|
686 |
+
embed_dim: int,
|
687 |
+
image_embedding_size: Tuple[int, int, int],
|
688 |
+
input_image_size: Tuple[int, int, int],
|
689 |
+
mask_in_chans: int,
|
690 |
+
activation: Type[nn.Module] = nn.GELU,
|
691 |
+
) -> None:
|
692 |
+
"""
|
693 |
+
Encodes prompts for input to SAM's mask decoder.
|
694 |
+
|
695 |
+
Arguments:
|
696 |
+
embed_dim (int): The prompts' embedding dimension
|
697 |
+
image_embedding_size (tuple(int, int)): The spatial size of the
|
698 |
+
image embedding, as (H, W).
|
699 |
+
input_image_size (int): The padded size of the image as input
|
700 |
+
to the image encoder, as (H, W).
|
701 |
+
mask_in_chans (int): The number of hidden channels used for
|
702 |
+
encoding input masks.
|
703 |
+
activation (nn.Module): The activation to use when encoding
|
704 |
+
input masks.
|
705 |
+
"""
|
706 |
+
super().__init__()
|
707 |
+
self.embed_dim = embed_dim
|
708 |
+
self.input_image_size = input_image_size
|
709 |
+
self.image_embedding_size = image_embedding_size
|
710 |
+
self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
|
711 |
+
|
712 |
+
self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
|
713 |
+
point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
|
714 |
+
self.point_embeddings = nn.ModuleList(point_embeddings)
|
715 |
+
self.not_a_point_embed = nn.Embedding(1, embed_dim)
|
716 |
+
|
717 |
+
self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1], 4 * image_embedding_size[2])
|
718 |
+
self.mask_downscaling = nn.Sequential(
|
719 |
+
nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
|
720 |
+
LayerNorm2d(mask_in_chans // 4),
|
721 |
+
activation(),
|
722 |
+
nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
|
723 |
+
LayerNorm2d(mask_in_chans),
|
724 |
+
activation(),
|
725 |
+
nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
|
726 |
+
)
|
727 |
+
self.no_mask_embed = nn.Embedding(1, embed_dim)
|
728 |
+
|
729 |
+
def get_dense_pe(self) -> torch.Tensor:
|
730 |
+
"""
|
731 |
+
Returns the positional encoding used to encode point prompts,
|
732 |
+
applied to a dense set of points the shape of the image encoding.
|
733 |
+
|
734 |
+
Returns:
|
735 |
+
torch.Tensor: Positional encoding with shape
|
736 |
+
1x(embed_dim)x(embedding_h)x(embedding_w)
|
737 |
+
"""
|
738 |
+
return self.pe_layer(self.image_embedding_size).unsqueeze(0)
|
739 |
+
|
740 |
+
def _embed_points(
|
741 |
+
self,
|
742 |
+
points: torch.Tensor,
|
743 |
+
labels: torch.Tensor,
|
744 |
+
pad: bool,
|
745 |
+
) -> torch.Tensor:
|
746 |
+
"""Embeds point prompts."""
|
747 |
+
points = points + 0.5 # Shift to center of pixel
|
748 |
+
if pad:
|
749 |
+
padding_point = torch.zeros((points.shape[0], 1, 3), device=points.device)
|
750 |
+
padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
|
751 |
+
points = torch.cat([points, padding_point], dim=1)
|
752 |
+
labels = torch.cat([labels, padding_label], dim=1)
|
753 |
+
point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
|
754 |
+
point_embedding[labels == -1] = 0.0
|
755 |
+
point_embedding[labels == -1] += self.not_a_point_embed.weight
|
756 |
+
point_embedding[labels == 0] += self.point_embeddings[0].weight
|
757 |
+
point_embedding[labels == 1] += self.point_embeddings[1].weight
|
758 |
+
return point_embedding
|
759 |
+
|
760 |
+
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
|
761 |
+
"""Embeds box prompts."""
|
762 |
+
boxes = boxes + 0.5 # Shift to center of pixel
|
763 |
+
coords = boxes.reshape(-1, 2, 3)
|
764 |
+
corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
|
765 |
+
corner_embedding[:, 0, :] += self.point_embeddings[2].weight
|
766 |
+
corner_embedding[:, 1, :] += self.point_embeddings[3].weight
|
767 |
+
return corner_embedding
|
768 |
+
|
769 |
+
def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
|
770 |
+
"""Embeds mask inputs."""
|
771 |
+
mask_embedding = self.mask_downscaling(masks)
|
772 |
+
return mask_embedding
|
773 |
+
|
774 |
+
def _get_batch_size(
|
775 |
+
self,
|
776 |
+
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
777 |
+
boxes: Optional[torch.Tensor],
|
778 |
+
masks: Optional[torch.Tensor],
|
779 |
+
text_embedding: Optional[torch.Tensor],
|
780 |
+
) -> int:
|
781 |
+
"""
|
782 |
+
Gets the batch size of the output given the batch size of the input prompts.
|
783 |
+
"""
|
784 |
+
if points is not None:
|
785 |
+
return points[0].shape[0]
|
786 |
+
elif boxes is not None:
|
787 |
+
return boxes.shape[0]
|
788 |
+
elif masks is not None:
|
789 |
+
return masks.shape[0]
|
790 |
+
elif text_embedding is not None:
|
791 |
+
return text_embedding.shape[0]
|
792 |
+
else:
|
793 |
+
return 1
|
794 |
+
|
795 |
+
def _get_device(self) -> torch.device:
|
796 |
+
return self.point_embeddings[0].weight.device
|
797 |
+
|
798 |
+
def forward(
|
799 |
+
self,
|
800 |
+
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
801 |
+
boxes: Optional[torch.Tensor],
|
802 |
+
masks: Optional[torch.Tensor],
|
803 |
+
text_embedding: Optional[torch.Tensor],
|
804 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
805 |
+
"""
|
806 |
+
Embeds different types of prompts, returning both sparse and dense
|
807 |
+
embeddings.
|
808 |
+
|
809 |
+
Arguments:
|
810 |
+
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
|
811 |
+
and labels to embed.
|
812 |
+
boxes (torch.Tensor or none): boxes to embed
|
813 |
+
masks (torch.Tensor or none): masks to embed
|
814 |
+
text: test prompt (B, 768)
|
815 |
+
|
816 |
+
Returns:
|
817 |
+
torch.Tensor: sparse embeddings for the points and boxes, with shape
|
818 |
+
BxNx(embed_dim), where N is determined by the number of input points
|
819 |
+
and boxes.
|
820 |
+
torch.Tensor: dense embeddings for the masks, in the shape
|
821 |
+
Bx(embed_dim)x(embed_H)x(embed_W)
|
822 |
+
"""
|
823 |
+
# print('prompt encoder here...')
|
824 |
+
|
825 |
+
bs = self._get_batch_size(points, boxes, masks, text_embedding)
|
826 |
+
sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device(),
|
827 |
+
dtype=self.point_embeddings[0].weight.dtype)
|
828 |
+
# print('sparse_embeddings ', sparse_embeddings.shape)
|
829 |
+
if points is not None:
|
830 |
+
coords, labels = points
|
831 |
+
point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
|
832 |
+
sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
|
833 |
+
|
834 |
+
if boxes is not None:
|
835 |
+
box_embeddings = self._embed_boxes(boxes)
|
836 |
+
sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
|
837 |
+
|
838 |
+
if text_embedding is not None:
|
839 |
+
sparse_embeddings = torch.cat([sparse_embeddings, text_embedding.unsqueeze(dim=1)], dim=1)
|
840 |
+
|
841 |
+
# print('box_embeddings ', box_embeddings.shape)
|
842 |
+
# print('sparse_embeddings after box/point/text', sparse_embeddings.shape)
|
843 |
+
|
844 |
+
if masks is not None:
|
845 |
+
dense_embeddings = self._embed_masks(masks)
|
846 |
+
else:
|
847 |
+
dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1, 1).expand(
|
848 |
+
bs, -1, int(self.image_embedding_size[0]), int(self.image_embedding_size[1]),
|
849 |
+
int(self.image_embedding_size[2])
|
850 |
+
)
|
851 |
+
return sparse_embeddings, dense_embeddings
|
852 |
+
|
853 |
+
|
854 |
+
class PositionEmbeddingRandom(nn.Module):
|
855 |
+
"""
|
856 |
+
Positional encoding using random spatial frequencies.
|
857 |
+
"""
|
858 |
+
|
859 |
+
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
|
860 |
+
super().__init__()
|
861 |
+
if scale is None or scale <= 0.0:
|
862 |
+
scale = 1.0
|
863 |
+
self.register_buffer(
|
864 |
+
"positional_encoding_gaussian_matrix",
|
865 |
+
scale * torch.randn((3, num_pos_feats)),
|
866 |
+
)
|
867 |
+
|
868 |
+
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
|
869 |
+
"""Positionally encode points that are normalized to [0,1]."""
|
870 |
+
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
|
871 |
+
coords = 2 * coords - 1
|
872 |
+
coords = coords @ self.positional_encoding_gaussian_matrix
|
873 |
+
coords = 2 * np.pi * coords
|
874 |
+
# outputs d_1 x ... x d_n x C shape
|
875 |
+
return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
|
876 |
+
|
877 |
+
def forward(self, size: Tuple[int, int, int]) -> torch.Tensor:
|
878 |
+
"""Generate positional encoding for a grid of the specified size."""
|
879 |
+
h, w, d = size
|
880 |
+
device: Any = self.positional_encoding_gaussian_matrix.device
|
881 |
+
dtype = self.positional_encoding_gaussian_matrix.dtype
|
882 |
+
grid = torch.ones((h, w, d), device=device, dtype=dtype)
|
883 |
+
y_embed = grid.cumsum(dim=0) - 0.5
|
884 |
+
x_embed = grid.cumsum(dim=1) - 0.5
|
885 |
+
z_embed = grid.cumsum(dim=2) - 0.5
|
886 |
+
y_embed = y_embed / h
|
887 |
+
x_embed = x_embed / w
|
888 |
+
z_embed = z_embed / d
|
889 |
+
|
890 |
+
pe = self._pe_encoding(torch.stack([x_embed, y_embed, z_embed], dim=-1))
|
891 |
+
return pe.permute(3, 0, 1, 2) # C x H x W x D
|
892 |
+
|
893 |
+
def forward_with_coords(
|
894 |
+
self, coords_input: torch.Tensor, image_size: Tuple[int, int]
|
895 |
+
) -> torch.Tensor:
|
896 |
+
"""Positionally encode points that are not normalized to [0,1]."""
|
897 |
+
coords = coords_input.clone()
|
898 |
+
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
|
899 |
+
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
|
900 |
+
coords[:, :, 2] = coords[:, :, 2] / image_size[2]
|
901 |
+
return self._pe_encoding(coords.to(torch.float)) # B x N x C
|
902 |
+
|
903 |
+
|
904 |
+
class MaskDecoder(nn.Module):
|
905 |
+
def __init__(
|
906 |
+
self,
|
907 |
+
*,
|
908 |
+
image_encoder_type: str,
|
909 |
+
transformer_dim: int,
|
910 |
+
transformer: nn.Module,
|
911 |
+
num_multimask_outputs: int = 3,
|
912 |
+
activation: Type[nn.Module] = nn.GELU,
|
913 |
+
iou_head_depth: int = 3,
|
914 |
+
iou_head_hidden_dim: int = 256,
|
915 |
+
image_size,
|
916 |
+
patch_size,
|
917 |
+
) -> None:
|
918 |
+
"""
|
919 |
+
Predicts masks given an image and prompt embeddings, using a
|
920 |
+
transformer architecture.
|
921 |
+
|
922 |
+
Arguments:
|
923 |
+
transformer_dim (int): the channel dimension of the transformer
|
924 |
+
transformer (nn.Module): the transformer used to predict masks
|
925 |
+
num_multimask_outputs (int): the number of masks to predict
|
926 |
+
when disambiguating masks
|
927 |
+
activation (nn.Module): the type of activation to use when
|
928 |
+
upscaling masks
|
929 |
+
iou_head_depth (int): the depth of the MLP used to predict
|
930 |
+
mask quality
|
931 |
+
iou_head_hidden_dim (int): the hidden dimension of the MLP
|
932 |
+
used to predict mask quality
|
933 |
+
"""
|
934 |
+
super().__init__()
|
935 |
+
self.transformer_dim = transformer_dim
|
936 |
+
self.transformer = transformer
|
937 |
+
|
938 |
+
self.num_multimask_outputs = num_multimask_outputs
|
939 |
+
|
940 |
+
self.iou_token = nn.Embedding(1, transformer_dim)
|
941 |
+
self.num_mask_tokens = num_multimask_outputs + 1
|
942 |
+
self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
|
943 |
+
|
944 |
+
if image_encoder_type == 'swin_vit':
|
945 |
+
self.feat_shape = image_size / patch_size
|
946 |
+
self.output_upscaling = nn.Sequential(
|
947 |
+
nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
|
948 |
+
nn.LayerNorm(
|
949 |
+
(transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))),
|
950 |
+
# swin
|
951 |
+
activation(),
|
952 |
+
nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), # swin
|
953 |
+
# nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1), # vit
|
954 |
+
activation(),
|
955 |
+
)
|
956 |
+
else:
|
957 |
+
self.feat_shape = image_size / patch_size * 2
|
958 |
+
self.output_upscaling = nn.Sequential(
|
959 |
+
nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
|
960 |
+
nn.LayerNorm(
|
961 |
+
(transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))),
|
962 |
+
# vit
|
963 |
+
activation(),
|
964 |
+
nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
|
965 |
+
# nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1),
|
966 |
+
activation(),
|
967 |
+
)
|
968 |
+
self.output_hypernetworks_mlps = nn.ModuleList(
|
969 |
+
[
|
970 |
+
MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
|
971 |
+
for i in range(self.num_mask_tokens)
|
972 |
+
]
|
973 |
+
)
|
974 |
+
|
975 |
+
self.iou_prediction_head = MLP(
|
976 |
+
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
|
977 |
+
)
|
978 |
+
|
979 |
+
self.txt_align_upscaled_embedding = nn.Linear(768, 96)
|
980 |
+
|
981 |
+
def forward(
|
982 |
+
self,
|
983 |
+
image_embeddings: torch.Tensor,
|
984 |
+
text_embedding: Optional[torch.Tensor],
|
985 |
+
image_pe: torch.Tensor,
|
986 |
+
sparse_prompt_embeddings: torch.Tensor,
|
987 |
+
dense_prompt_embeddings: torch.Tensor,
|
988 |
+
multimask_output: bool,
|
989 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
990 |
+
"""
|
991 |
+
Predict masks given image and prompt embeddings.
|
992 |
+
|
993 |
+
Arguments:
|
994 |
+
image_embeddings (torch.Tensor): the embeddings from the image encoder
|
995 |
+
image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
|
996 |
+
sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
|
997 |
+
dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
|
998 |
+
multimask_output (bool): Whether to return multiple masks or a single
|
999 |
+
mask.
|
1000 |
+
|
1001 |
+
Returns:
|
1002 |
+
torch.Tensor: batched predicted masks
|
1003 |
+
torch.Tensor: batched predictions of mask quality
|
1004 |
+
"""
|
1005 |
+
# print('--------------decoder here--------------')
|
1006 |
+
masks, iou_pred = self.predict_masks(
|
1007 |
+
image_embeddings=image_embeddings,
|
1008 |
+
text_embedding=text_embedding,
|
1009 |
+
image_pe=image_pe,
|
1010 |
+
sparse_prompt_embeddings=sparse_prompt_embeddings,
|
1011 |
+
dense_prompt_embeddings=dense_prompt_embeddings,
|
1012 |
+
)
|
1013 |
+
|
1014 |
+
# Select the correct mask or masks for output
|
1015 |
+
if multimask_output:
|
1016 |
+
mask_slice = slice(1, None)
|
1017 |
+
else:
|
1018 |
+
mask_slice = slice(0, 1)
|
1019 |
+
masks = masks[:, mask_slice, :, :, :]
|
1020 |
+
iou_pred = iou_pred[:, mask_slice]
|
1021 |
+
|
1022 |
+
# Prepare output
|
1023 |
+
return masks, iou_pred
|
1024 |
+
|
1025 |
+
def predict_masks(
|
1026 |
+
self,
|
1027 |
+
image_embeddings: torch.Tensor,
|
1028 |
+
text_embedding: torch.Tensor,
|
1029 |
+
image_pe: torch.Tensor,
|
1030 |
+
sparse_prompt_embeddings: torch.Tensor,
|
1031 |
+
dense_prompt_embeddings: torch.Tensor,
|
1032 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
1033 |
+
"""Predicts masks. See 'forward' for more details."""
|
1034 |
+
# Concatenate output tokens
|
1035 |
+
output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
|
1036 |
+
output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
|
1037 |
+
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) # [2, 7=(5+2), 256]
|
1038 |
+
# Expand per-image data in batch direction to be per-mask
|
1039 |
+
if image_embeddings.shape[0] != tokens.shape[0]:
|
1040 |
+
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
|
1041 |
+
else:
|
1042 |
+
src = image_embeddings
|
1043 |
+
|
1044 |
+
src = src + dense_prompt_embeddings
|
1045 |
+
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
|
1046 |
+
b, c, h, w, d = src.shape
|
1047 |
+
|
1048 |
+
# Run the transformer
|
1049 |
+
hs, src = self.transformer(src, pos_src, tokens)
|
1050 |
+
iou_token_out = hs[:, 0, :]
|
1051 |
+
mask_tokens_out = hs[:, 1: (1 + self.num_mask_tokens), :]
|
1052 |
+
|
1053 |
+
# Upscale mask embeddings and predict masks using the mask tokens
|
1054 |
+
src = src.transpose(1, 2).view(b, c, h, w, d)
|
1055 |
+
# print('src ', src.shape) # vit:[B, 768, 12, 12, 6], swin: [B, 6, 6, 3]
|
1056 |
+
upscaled_embedding = self.output_upscaling(src)
|
1057 |
+
hyper_in_list: List[torch.Tensor] = []
|
1058 |
+
for i in range(self.num_mask_tokens):
|
1059 |
+
hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
|
1060 |
+
hyper_in = torch.stack(hyper_in_list, dim=1)
|
1061 |
+
b, c, h, w, d = upscaled_embedding.shape
|
1062 |
+
# print('hyper_in ', hyper_in.shape) # [2, 4, 96]
|
1063 |
+
# print('upscaled_embedding ', upscaled_embedding.shape) # [2, 96, 24, 24, 12]*
|
1064 |
+
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w * d)).view(b, -1, h, w, d)
|
1065 |
+
# print('masks here ', masks.shape) # [2, 4, 24, 24, 12]
|
1066 |
+
|
1067 |
+
if text_embedding is not None:
|
1068 |
+
# text_embedding: B x 768, upscaled_embedding: B x c x h x w x d => B x 1 x h x w x d
|
1069 |
+
text_embedding_down = self.txt_align_upscaled_embedding(text_embedding).unsqueeze(dim=1)
|
1070 |
+
upscaled_embedding = upscaled_embedding.view(b, c, h * w * d)
|
1071 |
+
# print('text_embedding_down ', text_embedding_down.shape) # [2, 1, 96]
|
1072 |
+
# text_embedding_norm = F.normalize(text_embedding_down, dim=-1)
|
1073 |
+
# upscaled_embedding_norm = F.normalize(upscaled_embedding, dim=1)
|
1074 |
+
# sim = (text_embedding_norm @ upscaled_embedding_norm).view(b, -1, h, w, d)
|
1075 |
+
# print(text_embedding_down.shape, upscaled_embedding.shape)
|
1076 |
+
sim = (text_embedding_down @ upscaled_embedding).view(b, -1, h, w, d)
|
1077 |
+
# print('sim ', sim.shape) # [B, 1, 24, 24, 12]
|
1078 |
+
sim = sim.repeat(1, masks.shape[1], 1, 1, 1)
|
1079 |
+
# print('sim after', sim.shape) # [B, 4, 24, 24, 12]
|
1080 |
+
masks = masks + sim
|
1081 |
+
# Generate mask quality predictions
|
1082 |
+
iou_pred = self.iou_prediction_head(iou_token_out)
|
1083 |
+
|
1084 |
+
return masks, iou_pred
|
1085 |
+
|
1086 |
+
|
1087 |
+
# Lightly adapted from
|
1088 |
+
# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
|
1089 |
+
class MLP(nn.Module):
|
1090 |
+
def __init__(
|
1091 |
+
self,
|
1092 |
+
input_dim: int,
|
1093 |
+
hidden_dim: int,
|
1094 |
+
output_dim: int,
|
1095 |
+
num_layers: int,
|
1096 |
+
sigmoid_output: bool = False,
|
1097 |
+
) -> None:
|
1098 |
+
super().__init__()
|
1099 |
+
self.num_layers = num_layers
|
1100 |
+
h = [hidden_dim] * (num_layers - 1)
|
1101 |
+
self.layers = nn.ModuleList(
|
1102 |
+
nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
|
1103 |
+
)
|
1104 |
+
self.sigmoid_output = sigmoid_output
|
1105 |
+
|
1106 |
+
def forward(self, x):
|
1107 |
+
for i, layer in enumerate(self.layers):
|
1108 |
+
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
1109 |
+
if self.sigmoid_output:
|
1110 |
+
x = F.sigmoid(x)
|
1111 |
+
return x
|
1112 |
+
|
1113 |
+
|
1114 |
+
class Sam(nn.Module):
|
1115 |
+
mask_threshold: float = 0.0
|
1116 |
+
image_format: str = "RGB"
|
1117 |
+
|
1118 |
+
def __init__(
|
1119 |
+
self,
|
1120 |
+
image_encoder: ImageEncoderViT,
|
1121 |
+
prompt_encoder: PromptEncoder,
|
1122 |
+
mask_decoder: MaskDecoder,
|
1123 |
+
pixel_mean: List[float] = [123.675, 116.28, 103.53],
|
1124 |
+
pixel_std: List[float] = [58.395, 57.12, 57.375],
|
1125 |
+
) -> None:
|
1126 |
+
"""
|
1127 |
+
SAM predicts object masks from an image and input prompts.
|
1128 |
+
|
1129 |
+
Arguments:
|
1130 |
+
image_encoder (ImageEncoderViT): The backbone used to encode the
|
1131 |
+
image into image embeddings that allow for efficient mask prediction.
|
1132 |
+
prompt_encoder (PromptEncoder): Encodes various types of input prompts.
|
1133 |
+
mask_decoder (MaskDecoder): Predicts masks from the image embeddings
|
1134 |
+
and encoded prompts.
|
1135 |
+
pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
|
1136 |
+
pixel_std (list(float)): Std values for normalizing pixels in the input image.
|
1137 |
+
"""
|
1138 |
+
super().__init__()
|
1139 |
+
self.image_encoder = image_encoder
|
1140 |
+
self.prompt_encoder = prompt_encoder
|
1141 |
+
self.mask_decoder = mask_decoder
|
1142 |
+
self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
|
1143 |
+
self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
|
1144 |
+
|
1145 |
+
@property
|
1146 |
+
def device(self) -> Any:
|
1147 |
+
return self.pixel_mean.device
|
1148 |
+
|
1149 |
+
@torch.no_grad()
|
1150 |
+
def forward(
|
1151 |
+
self,
|
1152 |
+
batched_input: List[Dict[str, Any]],
|
1153 |
+
multimask_output: bool,
|
1154 |
+
) -> List[Dict[str, torch.Tensor]]:
|
1155 |
+
"""
|
1156 |
+
Predicts masks end-to-end from provided images and prompts.
|
1157 |
+
If prompts are not known in advance, using SamPredictor is
|
1158 |
+
recommended over calling the model directly.
|
1159 |
+
|
1160 |
+
Arguments:
|
1161 |
+
batched_input (list(dict)): A list over input images, each a
|
1162 |
+
dictionary with the following keys. A prompt key can be
|
1163 |
+
excluded if it is not present.
|
1164 |
+
'image': The image as a torch tensor in 3xHxW format,
|
1165 |
+
already transformed for input to the model.
|
1166 |
+
'original_size': (tuple(int, int)) The original size of
|
1167 |
+
the image before transformation, as (H, W).
|
1168 |
+
'point_coords': (torch.Tensor) Batched point prompts for
|
1169 |
+
this image, with shape BxNx2. Already transformed to the
|
1170 |
+
input frame of the model.
|
1171 |
+
'point_labels': (torch.Tensor) Batched labels for point prompts,
|
1172 |
+
with shape BxN.
|
1173 |
+
'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
|
1174 |
+
Already transformed to the input frame of the model.
|
1175 |
+
'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
|
1176 |
+
in the form Bx1xHxW.
|
1177 |
+
multimask_output (bool): Whether the model should predict multiple
|
1178 |
+
disambiguating masks, or return a single mask.
|
1179 |
+
|
1180 |
+
Returns:
|
1181 |
+
(list(dict)): A list over input images, where each element is
|
1182 |
+
as dictionary with the following keys.
|
1183 |
+
'masks': (torch.Tensor) Batched binary mask predictions,
|
1184 |
+
with shape BxCxHxW, where B is the number of input prompts,
|
1185 |
+
C is determined by multimask_output, and (H, W) is the
|
1186 |
+
original size of the image.
|
1187 |
+
'iou_predictions': (torch.Tensor) The model's predictions
|
1188 |
+
of mask quality, in shape BxC.
|
1189 |
+
'low_res_logits': (torch.Tensor) Low resolution logits with
|
1190 |
+
shape BxCxHxW, where H=W=256. Can be passed as mask input
|
1191 |
+
to subsequent iterations of prediction.
|
1192 |
+
"""
|
1193 |
+
input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
|
1194 |
+
image_embeddings = self.image_encoder(input_images)
|
1195 |
+
|
1196 |
+
outputs = []
|
1197 |
+
for image_record, curr_embedding in zip(batched_input, image_embeddings):
|
1198 |
+
if "point_coords" in image_record:
|
1199 |
+
points = (image_record["point_coords"], image_record["point_labels"])
|
1200 |
+
else:
|
1201 |
+
points = None
|
1202 |
+
sparse_embeddings, dense_embeddings = self.prompt_encoder(
|
1203 |
+
points=points,
|
1204 |
+
boxes=image_record.get("boxes", None),
|
1205 |
+
masks=image_record.get("mask_inputs", None),
|
1206 |
+
)
|
1207 |
+
low_res_masks, iou_predictions = self.mask_decoder(
|
1208 |
+
image_embeddings=curr_embedding.unsqueeze(0),
|
1209 |
+
image_pe=self.prompt_encoder.get_dense_pe(),
|
1210 |
+
sparse_prompt_embeddings=sparse_embeddings,
|
1211 |
+
dense_prompt_embeddings=dense_embeddings,
|
1212 |
+
multimask_output=multimask_output,
|
1213 |
+
)
|
1214 |
+
masks = self.postprocess_masks(
|
1215 |
+
low_res_masks,
|
1216 |
+
input_size=image_record["image"].shape[-2:],
|
1217 |
+
original_size=image_record["original_size"],
|
1218 |
+
)
|
1219 |
+
masks = masks > self.mask_threshold
|
1220 |
+
outputs.append(
|
1221 |
+
{
|
1222 |
+
"masks": masks,
|
1223 |
+
"iou_predictions": iou_predictions,
|
1224 |
+
"low_res_logits": low_res_masks,
|
1225 |
+
}
|
1226 |
+
)
|
1227 |
+
return outputs
|
1228 |
+
|
1229 |
+
def postprocess_masks(
|
1230 |
+
self,
|
1231 |
+
masks: torch.Tensor,
|
1232 |
+
input_size: Tuple[int, ...],
|
1233 |
+
original_size: Tuple[int, ...],
|
1234 |
+
) -> torch.Tensor:
|
1235 |
+
"""
|
1236 |
+
Remove padding and upscale masks to the original image size.
|
1237 |
+
|
1238 |
+
Arguments:
|
1239 |
+
masks (torch.Tensor): Batched masks from the mask_decoder,
|
1240 |
+
in BxCxHxW format.
|
1241 |
+
input_size (tuple(int, int)): The size of the image input to the
|
1242 |
+
model, in (H, W) format. Used to remove padding.
|
1243 |
+
original_size (tuple(int, int)): The original size of the image
|
1244 |
+
before resizing for input to the model, in (H, W) format.
|
1245 |
+
|
1246 |
+
Returns:
|
1247 |
+
(torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
|
1248 |
+
is given by original_size.
|
1249 |
+
"""
|
1250 |
+
masks = F.interpolate(
|
1251 |
+
masks,
|
1252 |
+
(self.image_encoder.img_size, self.image_encoder.img_size),
|
1253 |
+
mode="bilinear",
|
1254 |
+
align_corners=False,
|
1255 |
+
)
|
1256 |
+
masks = masks[..., : input_size[0], : input_size[1]]
|
1257 |
+
masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
|
1258 |
+
return masks
|
1259 |
+
|
1260 |
+
def preprocess(self, x: torch.Tensor) -> torch.Tensor:
|
1261 |
+
"""Normalize pixel values and pad to a square input."""
|
1262 |
+
# Normalize colors
|
1263 |
+
# TODO
|
1264 |
+
x = (x - self.pixel_mean) / self.pixel_std
|
1265 |
+
|
1266 |
+
# Pad
|
1267 |
+
h, w = x.shape[-2:]
|
1268 |
+
padh = self.image_encoder.img_size - h
|
1269 |
+
padw = self.image_encoder.img_size - w
|
1270 |
+
x = F.pad(x, (0, padw, 0, padh))
|
1271 |
+
return x
|
1272 |
+
|
1273 |
+
|
1274 |
+
"""
|
1275 |
+
Examples::
|
1276 |
+
# for 3D single channel input with size (96,96,96), 4-channel output and feature size of 48.
|
1277 |
+
>>> net = SwinUNETR(img_size=(96,96,96), in_channels=1, out_channels=4, feature_size=48)
|
1278 |
+
# for 3D 4-channel input with size (128,128,128), 3-channel output and (2,4,2,2) layers in each stage.
|
1279 |
+
>>> net = SwinUNETR(img_size=(128,128,128), in_channels=4, out_channels=3, depths=(2,4,2,2))
|
1280 |
+
# for 2D single channel input with size (96,96), 2-channel output and gradient checkpointing.
|
1281 |
+
>>> net = SwinUNETR(img_size=(96,96), in_channels=3, out_channels=2, use_checkpoint=True, spatial_dims=2)
|
1282 |
+
"""
|
1283 |
+
|
1284 |
+
|
1285 |
+
def build_sam_vit_3d(args, checkpoint=None):
|
1286 |
+
print('build_sam_vit_3d...')
|
1287 |
+
return _build_sam(
|
1288 |
+
image_encoder_type='vit',
|
1289 |
+
embed_dim=768,
|
1290 |
+
patch_size=args.patch_size,
|
1291 |
+
checkpoint=checkpoint,
|
1292 |
+
image_size=args.image_size,
|
1293 |
+
)
|
1294 |
+
|
1295 |
+
|
1296 |
+
sam_model_registry = {
|
1297 |
+
"vit": build_sam_vit_3d,
|
1298 |
+
}
|
1299 |
+
|
1300 |
+
|
1301 |
+
def _build_sam(
|
1302 |
+
image_encoder_type,
|
1303 |
+
embed_dim,
|
1304 |
+
patch_size,
|
1305 |
+
checkpoint,
|
1306 |
+
image_size,
|
1307 |
+
):
|
1308 |
+
mlp_dim = 3072
|
1309 |
+
num_layers = 12
|
1310 |
+
num_heads = 12
|
1311 |
+
pos_embed = 'perceptron'
|
1312 |
+
dropout_rate = 0.0
|
1313 |
+
|
1314 |
+
image_encoder = ViT(
|
1315 |
+
in_channels=1,
|
1316 |
+
img_size=image_size,
|
1317 |
+
patch_size=patch_size,
|
1318 |
+
hidden_size=embed_dim,
|
1319 |
+
mlp_dim=mlp_dim,
|
1320 |
+
num_layers=num_layers,
|
1321 |
+
num_heads=num_heads,
|
1322 |
+
pos_embed=pos_embed,
|
1323 |
+
classification=False,
|
1324 |
+
dropout_rate=dropout_rate,
|
1325 |
+
)
|
1326 |
+
image_embedding_size = [int(item) for item in (np.array(image_size) / np.array(patch_size))]
|
1327 |
+
|
1328 |
+
if checkpoint is not None:
|
1329 |
+
with open(checkpoint, "rb") as f:
|
1330 |
+
state_dict = torch.load(f, map_location='cpu')['state_dict']
|
1331 |
+
encoder_dict = {k.replace('model.encoder.', ''): v for k, v in state_dict.items() if 'model.encoder.' in k}
|
1332 |
+
image_encoder.load_state_dict(encoder_dict)
|
1333 |
+
print(f'===> image_encoder.load_param: {checkpoint}')
|
1334 |
+
sam = Sam(
|
1335 |
+
image_encoder=image_encoder,
|
1336 |
+
prompt_encoder=PromptEncoder(
|
1337 |
+
embed_dim=embed_dim,
|
1338 |
+
image_embedding_size=image_embedding_size,
|
1339 |
+
input_image_size=image_size,
|
1340 |
+
mask_in_chans=16,
|
1341 |
+
),
|
1342 |
+
mask_decoder=MaskDecoder(
|
1343 |
+
image_encoder_type=image_encoder_type,
|
1344 |
+
num_multimask_outputs=3,
|
1345 |
+
transformer=TwoWayTransformer(
|
1346 |
+
depth=2,
|
1347 |
+
embedding_dim=embed_dim,
|
1348 |
+
mlp_dim=2048,
|
1349 |
+
num_heads=8,
|
1350 |
+
),
|
1351 |
+
transformer_dim=embed_dim,
|
1352 |
+
iou_head_depth=3,
|
1353 |
+
iou_head_hidden_dim=256,
|
1354 |
+
image_size=np.array(image_size),
|
1355 |
+
patch_size=np.array(patch_size),
|
1356 |
+
),
|
1357 |
+
pixel_mean=[123.675, 116.28, 103.53],
|
1358 |
+
pixel_std=[58.395, 57.12, 57.375],
|
1359 |
+
)
|
1360 |
+
sam.eval()
|
1361 |
+
return sam
|
1362 |
+
|
1363 |
+
class SegVol(nn.Module):
|
1364 |
+
def __init__(self,
|
1365 |
+
image_encoder,
|
1366 |
+
mask_decoder,
|
1367 |
+
prompt_encoder,
|
1368 |
+
roi_size,
|
1369 |
+
patch_size,
|
1370 |
+
):
|
1371 |
+
super().__init__()
|
1372 |
+
self.image_encoder = image_encoder
|
1373 |
+
self.mask_decoder = mask_decoder
|
1374 |
+
self.prompt_encoder = prompt_encoder
|
1375 |
+
self.feat_shape = np.array(roi_size)/np.array(patch_size)
|
1376 |
+
|
1377 |
+
def forward(self, image, text_emb=None, text=None, boxes=None, points=None):
|
1378 |
+
bs = image.shape[0]
|
1379 |
+
img_shape = (image.shape[2], image.shape[3], image.shape[4])
|
1380 |
+
image_embedding, _ = self.image_encoder(image)
|
1381 |
+
|
1382 |
+
image_embedding = image_embedding.transpose(1, 2).view(bs, -1,
|
1383 |
+
int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))
|
1384 |
+
|
1385 |
+
logits = self.forward_decoder(image_embedding, img_shape, text_emb=text_emb, text=text, boxes=boxes, points=points)
|
1386 |
+
|
1387 |
+
return logits
|
1388 |
+
|
1389 |
+
def forward_decoder(self, image_embedding, img_shape, text_emb=None, text=None, boxes=None, points=None):
|
1390 |
+
text_embedding = text_emb
|
1391 |
+
sparse_embeddings, dense_embeddings = self.prompt_encoder(
|
1392 |
+
points=None,
|
1393 |
+
boxes=None,
|
1394 |
+
masks=None,
|
1395 |
+
text_embedding=text_embedding,
|
1396 |
+
)
|
1397 |
+
|
1398 |
+
dense_pe = self.prompt_encoder.get_dense_pe()
|
1399 |
+
|
1400 |
+
low_res_masks, _ = self.mask_decoder(
|
1401 |
+
image_embeddings=image_embedding,
|
1402 |
+
text_embedding = text_embedding,
|
1403 |
+
image_pe=dense_pe,
|
1404 |
+
sparse_prompt_embeddings=sparse_embeddings,
|
1405 |
+
dense_prompt_embeddings=dense_embeddings,
|
1406 |
+
multimask_output=False,
|
1407 |
+
)
|
1408 |
+
logits = F.interpolate(low_res_masks, size=img_shape, mode='trilinear', align_corners=False)
|
1409 |
+
|
1410 |
+
return logits
|
1411 |
+
|
1412 |
+
|
1413 |
+
def build_segmentation_module(config, **kwargs):
|
1414 |
+
segmentation_module = getattr(config, 'segmentation_module')
|
1415 |
+
if 'segvol' in segmentation_module.lower():
|
1416 |
+
sam_model = sam_model_registry['vit'](args=config, checkpoint=None)
|
1417 |
+
seg_model = SegVol(
|
1418 |
+
image_encoder=sam_model.image_encoder,
|
1419 |
+
mask_decoder=sam_model.mask_decoder,
|
1420 |
+
prompt_encoder=sam_model.prompt_encoder,
|
1421 |
+
roi_size=config.image_size,
|
1422 |
+
patch_size=config.patch_size,
|
1423 |
+
)
|
1424 |
+
return seg_model
|
1425 |
+
else:
|
1426 |
+
raise ValueError(f'Unknown segmentation module: {segmentation_module}')
|
1427 |
+
|
1428 |
+
|
1429 |
+
class IdentityMap(nn.Module):
|
1430 |
+
def __init__(self):
|
1431 |
+
super().__init__()
|
1432 |
+
|
1433 |
+
def forward(self, x, *args, **kwargs):
|
1434 |
+
return x
|
1435 |
+
|
1436 |
+
@property
|
1437 |
+
def config(self):
|
1438 |
+
return {"mm_projector_type": 'identity'}
|
1439 |
+
|
1440 |
+
|
1441 |
+
|
1442 |
+
class SpatialPoolingProjector(nn.Module):
|
1443 |
+
def __init__(self, image_size, patch_size, in_dim, out_dim, layer_type, layer_num, pooling_type='spatial', pooling_size=2):
|
1444 |
+
super().__init__()
|
1445 |
+
self.in_dim = in_dim
|
1446 |
+
self.pooling_size = pooling_size
|
1447 |
+
|
1448 |
+
self.num_patches_pre = [img // pch for img, pch in zip(image_size, patch_size)]
|
1449 |
+
self.num_patches_post = [num // pooling_size for num in self.num_patches_pre]
|
1450 |
+
|
1451 |
+
if layer_type == 'linear':
|
1452 |
+
depth = int(layer_num)
|
1453 |
+
modules = [nn.Linear(in_dim, out_dim)]
|
1454 |
+
for _ in range(1, depth):
|
1455 |
+
modules.append(nn.Linear(out_dim, out_dim))
|
1456 |
+
self.projector = nn.Sequential(*modules)
|
1457 |
+
elif layer_type == 'mlp':
|
1458 |
+
depth = int(layer_num)
|
1459 |
+
modules = [nn.Linear(in_dim, out_dim)]
|
1460 |
+
for _ in range(1, depth):
|
1461 |
+
modules.append(nn.GELU())
|
1462 |
+
modules.append(nn.Linear(out_dim, out_dim))
|
1463 |
+
self.projector = nn.Sequential(*modules)
|
1464 |
+
else:
|
1465 |
+
print("Projector error!")
|
1466 |
+
|
1467 |
+
self.pooling_type = pooling_type
|
1468 |
+
|
1469 |
+
def forward(self, x):
|
1470 |
+
B = x.shape[0] # B*N*D
|
1471 |
+
|
1472 |
+
if self.pooling_type == 'spatial':
|
1473 |
+
to_3d = Rearrange("b (p1 p2 p3) d -> b d p1 p2 p3", b=B, d=self.in_dim, p1=self.num_patches_pre[0], p2=self.num_patches_pre[1], p3=self.num_patches_pre[2])
|
1474 |
+
x = to_3d(x)
|
1475 |
+
x = F.avg_pool3d(x, kernel_size=self.pooling_size, stride=self.pooling_size)
|
1476 |
+
to_seq = Rearrange("b d p1 p2 p3 -> b (p1 p2 p3) d", b=B, d=self.in_dim, p1=self.num_patches_post[0], p2=self.num_patches_post[1], p3=self.num_patches_post[2])
|
1477 |
+
x = to_seq(x)
|
1478 |
+
elif self.pooling_type == 'sequence':
|
1479 |
+
x = x.permute(0, 2, 1) #b d n
|
1480 |
+
x = F.avg_pool1d(x, kernel_size=self.pooling_size**3, stride=self.pooling_size**3)
|
1481 |
+
x = x.permute(0, 2, 1) #b n d
|
1482 |
+
|
1483 |
+
x = rearrange(x, "b n d -> (b n) d")
|
1484 |
+
x = self.projector(x)
|
1485 |
+
x = rearrange(x, "(b n) d -> b n d", b=B)
|
1486 |
+
|
1487 |
+
return x
|
1488 |
+
|
1489 |
+
@property
|
1490 |
+
def proj_out_num(self):
|
1491 |
+
num = 1
|
1492 |
+
for n in self.num_patches_post:
|
1493 |
+
num *= n
|
1494 |
+
return num
|
1495 |
+
|
1496 |
+
|
1497 |
+
class Minigpt(nn.Module):
|
1498 |
+
def __init__(self, config=None):
|
1499 |
+
super(Minigpt, self).__init__()
|
1500 |
+
# c*4 is the input size, and c is the output size for the linear layer
|
1501 |
+
inc, ouc = config.mm_hidden_size, config.hidden_size
|
1502 |
+
self.linear = nn.Linear(inc * 4, ouc)
|
1503 |
+
|
1504 |
+
def forward(self, x):
|
1505 |
+
# x is the input tensor with shape [b, num_tokens, c]
|
1506 |
+
b, num_tokens, c = x.shape
|
1507 |
+
|
1508 |
+
# Check if num_tokens is divisible by 4
|
1509 |
+
if num_tokens % 4 != 0:
|
1510 |
+
raise ValueError("num_tokens must be divisible by 4")
|
1511 |
+
|
1512 |
+
# Reshape x to [b, num_tokens/4, c*4]
|
1513 |
+
x = x.view(b, num_tokens // 4, c * 4)
|
1514 |
+
|
1515 |
+
# Apply the linear transformation
|
1516 |
+
x = self.linear(x)
|
1517 |
+
return x
|
1518 |
+
|
1519 |
+
|
1520 |
+
class Vanilla(nn.Module):
|
1521 |
+
def __init__(self, config=None):
|
1522 |
+
super(Vanilla, self).__init__()
|
1523 |
+
# c*4 is the input size, and c is the output size for the linear layer
|
1524 |
+
inc, ouc = config.mm_hidden_size, config.hidden_size
|
1525 |
+
self.linear = nn.Linear(inc * 4, ouc)
|
1526 |
+
|
1527 |
+
def forward(self, x):
|
1528 |
+
b, num_tokens, c = x.shape
|
1529 |
+
|
1530 |
+
# Check if num_tokens is divisible by 4
|
1531 |
+
if num_tokens % 4 != 0:
|
1532 |
+
raise ValueError("num_tokens must be divisible by 4")
|
1533 |
+
|
1534 |
+
# First, reshape to [b, num_tokens//4, 4, c]
|
1535 |
+
x = x.view(b, num_tokens // 4, 4, c)
|
1536 |
+
|
1537 |
+
# Then, permute to interleave the tokens
|
1538 |
+
x = x.permute(0, 1, 3, 2).contiguous()
|
1539 |
+
|
1540 |
+
# Finally, reshape to [b, num_tokens//4, c*4] to interleave features of 4 tokens
|
1541 |
+
x = x.view(b, num_tokens // 4, c * 4)
|
1542 |
+
|
1543 |
+
# Apply the linear transformation
|
1544 |
+
x = self.linear(x)
|
1545 |
+
return x
|
1546 |
+
|
1547 |
+
|
1548 |
+
def build_mm_projector(config, delay_load=False, **kwargs):
|
1549 |
+
projector_type = getattr(config, 'mm_projector_type')
|
1550 |
+
|
1551 |
+
if projector_type == 'linear':
|
1552 |
+
return nn.Linear(config.mm_hidden_size, config.hidden_size)
|
1553 |
+
|
1554 |
+
|
1555 |
+
elif projector_type == 'spp':
|
1556 |
+
return SpatialPoolingProjector(image_size=config.image_size,
|
1557 |
+
patch_size=config.patch_size,
|
1558 |
+
in_dim=config.mm_hidden_size,
|
1559 |
+
out_dim=config.hidden_size,
|
1560 |
+
layer_type=config.proj_layer_type,
|
1561 |
+
layer_num=config.proj_layer_num,
|
1562 |
+
pooling_type=config.proj_pooling_type,
|
1563 |
+
pooling_size=config.proj_pooling_size)
|
1564 |
+
|
1565 |
+
|
1566 |
+
elif projector_type == 'identity':
|
1567 |
+
return IdentityMap()
|
1568 |
+
else:
|
1569 |
+
raise ValueError(f'Unknown projector type: {projector_type}')
|
1570 |
+
|
1571 |
+
|
1572 |
+
|
1573 |
+
class myViT(nn.Module):
|
1574 |
+
"""
|
1575 |
+
Vision Transformer (ViT), based on: "Dosovitskiy et al.,
|
1576 |
+
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
|
1577 |
+
|
1578 |
+
ViT supports Torchscript but only works for Pytorch after 1.8.
|
1579 |
+
"""
|
1580 |
+
|
1581 |
+
def __init__(
|
1582 |
+
self,
|
1583 |
+
in_channels: int,
|
1584 |
+
img_size: Sequence[int] | int,
|
1585 |
+
patch_size: Sequence[int] | int,
|
1586 |
+
hidden_size: int = 768,
|
1587 |
+
mlp_dim: int = 3072,
|
1588 |
+
num_layers: int = 12,
|
1589 |
+
num_heads: int = 12,
|
1590 |
+
pos_embed: str = "conv",
|
1591 |
+
classification: bool = False,
|
1592 |
+
num_classes: int = 2,
|
1593 |
+
dropout_rate: float = 0.0,
|
1594 |
+
spatial_dims: int = 3,
|
1595 |
+
post_activation="Tanh",
|
1596 |
+
qkv_bias: bool = False,
|
1597 |
+
save_attn: bool = False,
|
1598 |
+
) -> None:
|
1599 |
+
"""
|
1600 |
+
Args:
|
1601 |
+
in_channels (int): dimension of input channels.
|
1602 |
+
img_size (Union[Sequence[int], int]): dimension of input image.
|
1603 |
+
patch_size (Union[Sequence[int], int]): dimension of patch size.
|
1604 |
+
hidden_size (int, optional): dimension of hidden layer. Defaults to 768.
|
1605 |
+
mlp_dim (int, optional): dimension of feedforward layer. Defaults to 3072.
|
1606 |
+
num_layers (int, optional): number of transformer blocks. Defaults to 12.
|
1607 |
+
num_heads (int, optional): number of attention heads. Defaults to 12.
|
1608 |
+
pos_embed (str, optional): position embedding layer type. Defaults to "conv".
|
1609 |
+
classification (bool, optional): bool argument to determine if classification is used. Defaults to False.
|
1610 |
+
num_classes (int, optional): number of classes if classification is used. Defaults to 2.
|
1611 |
+
dropout_rate (float, optional): faction of the input units to drop. Defaults to 0.0.
|
1612 |
+
spatial_dims (int, optional): number of spatial dimensions. Defaults to 3.
|
1613 |
+
post_activation (str, optional): add a final acivation function to the classification head
|
1614 |
+
when `classification` is True. Default to "Tanh" for `nn.Tanh()`.
|
1615 |
+
Set to other values to remove this function.
|
1616 |
+
qkv_bias (bool, optional): apply bias to the qkv linear layer in self attention block. Defaults to False.
|
1617 |
+
save_attn (bool, optional): to make accessible the attention in self attention block. Defaults to False.
|
1618 |
+
|
1619 |
+
Examples::
|
1620 |
+
|
1621 |
+
# for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone
|
1622 |
+
>>> net = ViT(in_channels=1, img_size=(96,96,96), pos_embed='conv')
|
1623 |
+
|
1624 |
+
# for 3-channel with image size of (128,128,128), 24 layers and classification backbone
|
1625 |
+
>>> net = ViT(in_channels=3, img_size=(128,128,128), pos_embed='conv', classification=True)
|
1626 |
+
|
1627 |
+
# for 3-channel with image size of (224,224), 12 layers and classification backbone
|
1628 |
+
>>> net = ViT(in_channels=3, img_size=(224,224), pos_embed='conv', classification=True, spatial_dims=2)
|
1629 |
+
|
1630 |
+
"""
|
1631 |
+
|
1632 |
+
super().__init__()
|
1633 |
+
|
1634 |
+
if not (0 <= dropout_rate <= 1):
|
1635 |
+
raise ValueError("dropout_rate should be between 0 and 1.")
|
1636 |
+
|
1637 |
+
if hidden_size % num_heads != 0:
|
1638 |
+
raise ValueError("hidden_size should be divisible by num_heads.")
|
1639 |
+
self.hidden_size = hidden_size
|
1640 |
+
self.classification = classification
|
1641 |
+
self.patch_embedding = PatchEmbeddingBlock(
|
1642 |
+
in_channels=in_channels,
|
1643 |
+
img_size=img_size,
|
1644 |
+
patch_size=patch_size,
|
1645 |
+
hidden_size=hidden_size,
|
1646 |
+
num_heads=num_heads,
|
1647 |
+
pos_embed=pos_embed,
|
1648 |
+
dropout_rate=dropout_rate,
|
1649 |
+
spatial_dims=spatial_dims,
|
1650 |
+
)
|
1651 |
+
self.blocks = nn.ModuleList(
|
1652 |
+
[
|
1653 |
+
TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate, qkv_bias, save_attn)
|
1654 |
+
for i in range(num_layers)
|
1655 |
+
]
|
1656 |
+
)
|
1657 |
+
self.norm = nn.LayerNorm(hidden_size)
|
1658 |
+
if self.classification:
|
1659 |
+
self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))
|
1660 |
+
# if post_activation == "Tanh":
|
1661 |
+
# self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())
|
1662 |
+
# else:
|
1663 |
+
# self.classification_head = nn.Linear(hidden_size, num_classes) # type: ignore
|
1664 |
+
|
1665 |
+
def forward(self, x):
|
1666 |
+
x = self.patch_embedding(x)
|
1667 |
+
if hasattr(self, "cls_token"):
|
1668 |
+
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
|
1669 |
+
x = torch.cat((cls_token, x), dim=1)
|
1670 |
+
hidden_states_out = []
|
1671 |
+
for blk in self.blocks:
|
1672 |
+
x = blk(x)
|
1673 |
+
hidden_states_out.append(x)
|
1674 |
+
x = self.norm(x)
|
1675 |
+
# if hasattr(self, "classification_head"):
|
1676 |
+
# x = self.classification_head(x[:, 0])
|
1677 |
+
return x, hidden_states_out
|
1678 |
+
|
1679 |
+
|
1680 |
+
class ViT3DTower(nn.Module):
|
1681 |
+
def __init__(self, config):
|
1682 |
+
super().__init__()
|
1683 |
+
self.config = config
|
1684 |
+
self.select_layer = config.vision_select_layer
|
1685 |
+
self.select_feature = config.vision_select_feature
|
1686 |
+
|
1687 |
+
self.vision_tower = myViT(
|
1688 |
+
in_channels=self.config.image_channel,
|
1689 |
+
img_size=self.config.image_size,
|
1690 |
+
patch_size=self.config.patch_size,
|
1691 |
+
pos_embed="perceptron",
|
1692 |
+
spatial_dims=len(self.config.patch_size),
|
1693 |
+
classification=True,
|
1694 |
+
)
|
1695 |
+
|
1696 |
+
def forward(self, images):
|
1697 |
+
last_feature, hidden_states = self.vision_tower(images)
|
1698 |
+
if self.select_layer == -1:
|
1699 |
+
image_features = last_feature
|
1700 |
+
elif self.select_layer < -1:
|
1701 |
+
image_features = hidden_states[self.select_feature]
|
1702 |
+
else:
|
1703 |
+
raise ValueError(f'Unexpected select layer: {self.select_layer}')
|
1704 |
+
|
1705 |
+
if self.select_feature == 'patch':
|
1706 |
+
image_features = image_features[:, 1:]
|
1707 |
+
elif self.select_feature == 'cls_patch':
|
1708 |
+
image_features = image_features
|
1709 |
+
else:
|
1710 |
+
raise ValueError(f'Unexpected select feature: {self.select_feature}')
|
1711 |
+
|
1712 |
+
return image_features
|
1713 |
+
|
1714 |
+
@property
|
1715 |
+
def dtype(self):
|
1716 |
+
return self.vision_tower.dtype
|
1717 |
+
|
1718 |
+
@property
|
1719 |
+
def device(self):
|
1720 |
+
return self.vision_tower.device
|
1721 |
+
|
1722 |
+
@property
|
1723 |
+
def hidden_size(self):
|
1724 |
+
return self.vision_tower.hidden_size
|
1725 |
+
|
1726 |
+
|
1727 |
+
def build_vision_tower(config, **kwargs):
|
1728 |
+
vision_tower = getattr(config, 'vision_tower', None)
|
1729 |
+
if 'vit3d' in vision_tower.lower():
|
1730 |
+
return ViT3DTower(config, **kwargs)
|
1731 |
+
else:
|
1732 |
+
raise ValueError(f'Unknown vision tower: {vision_tower}')
|
1733 |
+
|
1734 |
+
class LamedMetaModel:
|
1735 |
+
def __init__(self, config):
|
1736 |
+
super(LamedMetaModel, self).__init__(config)
|
1737 |
+
|
1738 |
+
self.config = config
|
1739 |
+
self.seg_enable = False
|
1740 |
+
|
1741 |
+
if hasattr(config, "vision_tower"):
|
1742 |
+
self.vision_tower = build_vision_tower(config)
|
1743 |
+
self.mm_projector = build_mm_projector(config)
|
1744 |
+
|
1745 |
+
if hasattr(config, "segmentation_module") and config.segmentation_module is not None:
|
1746 |
+
self.seg_enable = True
|
1747 |
+
self.seg_module = build_segmentation_module(config)
|
1748 |
+
|
1749 |
+
self.seg_projector = nn.Sequential(
|
1750 |
+
nn.Linear(config.hidden_size, config.hidden_size),
|
1751 |
+
nn.ReLU(inplace=True),
|
1752 |
+
nn.Linear(config.hidden_size, config.mm_hidden_size),
|
1753 |
+
nn.Dropout(0.1),
|
1754 |
+
)
|
1755 |
+
|
1756 |
+
self.dice_loss = BinaryDiceLoss()
|
1757 |
+
self.bce_loss = BCELoss()
|
1758 |
+
|
1759 |
+
def get_vision_tower(self):
|
1760 |
+
vision_tower = getattr(self, 'vision_tower', None)
|
1761 |
+
return vision_tower
|
1762 |
+
|
1763 |
+
def initialize_vision_modules(self, model_args):
|
1764 |
+
self.config.image_channel = model_args.image_channel
|
1765 |
+
self.config.image_size = model_args.image_size
|
1766 |
+
self.config.patch_size = model_args.patch_size
|
1767 |
+
|
1768 |
+
self.config.vision_tower = model_args.vision_tower
|
1769 |
+
self.config.vision_select_layer = model_args.vision_select_layer
|
1770 |
+
self.config.vision_select_feature = model_args.vision_select_feature
|
1771 |
+
|
1772 |
+
self.config.mm_projector_type = model_args.mm_projector_type
|
1773 |
+
self.config.proj_layer_type = model_args.proj_layer_type
|
1774 |
+
self.config.proj_layer_num = model_args.proj_layer_num
|
1775 |
+
self.config.proj_pooling_type = model_args.proj_pooling_type
|
1776 |
+
self.config.proj_pooling_size = model_args.proj_pooling_size
|
1777 |
+
|
1778 |
+
# vision tower
|
1779 |
+
if self.get_vision_tower() is None:
|
1780 |
+
self.vision_tower = build_vision_tower(self.config)
|
1781 |
+
# If you have a more robust vision encoder, try freezing the vision tower by requires_grad_(False)
|
1782 |
+
|
1783 |
+
|
1784 |
+
if model_args.pretrain_vision_model is not None:
|
1785 |
+
vision_model_weights = torch.load(model_args.pretrain_vision_model, map_location='cpu')
|
1786 |
+
self.vision_tower.vision_tower.load_state_dict(vision_model_weights, strict=True)
|
1787 |
+
|
1788 |
+
self.config.mm_hidden_size = self.vision_tower.hidden_size
|
1789 |
+
|
1790 |
+
# mm_projector
|
1791 |
+
if getattr(self, 'mm_projector', None) is None:
|
1792 |
+
self.mm_projector = build_mm_projector(self.config)
|
1793 |
+
|
1794 |
+
if model_args.pretrain_mm_mlp_adapter is not None:
|
1795 |
+
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
|
1796 |
+
def get_w(weights, keyword):
|
1797 |
+
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
|
1798 |
+
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=True)
|
1799 |
+
|
1800 |
+
def initialize_seg_modules(self, model_args):
|
1801 |
+
self.config.segmentation_module = model_args.segmentation_module
|
1802 |
+
|
1803 |
+
# segmentation_module
|
1804 |
+
if getattr(self, 'segmentation_module', None) is None:
|
1805 |
+
self.seg_module = build_segmentation_module(self.config)
|
1806 |
+
self.seg_projector = nn.Sequential(
|
1807 |
+
nn.Linear(self.config.hidden_size, self.config.hidden_size),
|
1808 |
+
nn.ReLU(inplace=True),
|
1809 |
+
nn.Linear(self.config.hidden_size, self.config.mm_hidden_size),
|
1810 |
+
nn.Dropout(0.1),
|
1811 |
+
)
|
1812 |
+
self.seg_enable = True
|
1813 |
+
|
1814 |
+
if model_args.pretrain_seg_module is not None:
|
1815 |
+
seg_module_weights = torch.load(model_args.pretrain_seg_module, map_location='cpu')
|
1816 |
+
self.seg_module.load_state_dict(seg_module_weights, strict=True)
|
1817 |
+
|
1818 |
+
self.dice_loss = BinaryDiceLoss()
|
1819 |
+
self.bce_loss = BCELoss()
|
1820 |
+
|
1821 |
+
class LamedMetaForCausalLM(ABC):
|
1822 |
+
@abstractmethod
|
1823 |
+
def get_model(self):
|
1824 |
+
pass
|
1825 |
+
|
1826 |
+
def get_vision_tower(self):
|
1827 |
+
return self.get_model().get_vision_tower()
|
1828 |
+
|
1829 |
+
def encode_images(self, images):
|
1830 |
+
image_features = self.get_model().get_vision_tower()(images)
|
1831 |
+
image_features = self.get_model().mm_projector(image_features)
|
1832 |
+
return image_features
|
1833 |
+
|
1834 |
+
def prepare_inputs_for_multimodal(
|
1835 |
+
self, input_ids, position_ids, attention_mask, past_key_values, labels,
|
1836 |
+
images,
|
1837 |
+
):
|
1838 |
+
vision_tower = self.get_vision_tower()
|
1839 |
+
if vision_tower is None or images is None or input_ids.shape[1] == 1:
|
1840 |
+
return input_ids, position_ids, attention_mask, past_key_values, None, labels
|
1841 |
+
else:
|
1842 |
+
image_features = self.encode_images(images)
|
1843 |
+
inputs_embeds = self.get_model().embed_tokens(input_ids)
|
1844 |
+
inputs_embeds = torch.cat(
|
1845 |
+
(inputs_embeds[:, :1, :], image_features, inputs_embeds[:, (image_features.shape[1] + 1):, :]), dim=1)
|
1846 |
+
return None, position_ids, attention_mask, past_key_values, inputs_embeds, labels
|
1847 |
+
|
1848 |
+
def initialize_vision_tokenizer(self, model_args, tokenizer):
|
1849 |
+
num_new_tokens = model_args.num_new_tokens
|
1850 |
+
|
1851 |
+
self.resize_token_embeddings(len(tokenizer))
|
1852 |
+
|
1853 |
+
if num_new_tokens > 0:
|
1854 |
+
input_embeddings = self.get_input_embeddings().weight.data
|
1855 |
+
output_embeddings = self.get_output_embeddings().weight.data
|
1856 |
+
|
1857 |
+
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
|
1858 |
+
dim=0, keepdim=True)
|
1859 |
+
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
|
1860 |
+
dim=0, keepdim=True)
|
1861 |
+
|
1862 |
+
input_embeddings[-num_new_tokens:] = input_embeddings_avg
|
1863 |
+
output_embeddings[-num_new_tokens:] = output_embeddings_avg
|
1864 |
+
|
1865 |
+
if model_args.tune_mm_mlp_adapter:
|
1866 |
+
for p in self.get_input_embeddings().parameters():
|
1867 |
+
p.requires_grad = True
|
1868 |
+
for p in self.get_output_embeddings().parameters():
|
1869 |
+
p.requires_grad = False
|
1870 |
+
else:
|
1871 |
+
# we add 4 new tokens
|
1872 |
+
# if new tokens need input, please train input_embeddings
|
1873 |
+
for p in self.get_input_embeddings().parameters():
|
1874 |
+
p.requires_grad = True
|
1875 |
+
# if new tokens need predict, please train output_embeddings
|
1876 |
+
for p in self.get_output_embeddings().parameters():
|
1877 |
+
p.requires_grad = True
|
1878 |
+
|
1879 |
+
if model_args.pretrain_mm_mlp_adapter:
|
1880 |
+
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
|
1881 |
+
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
|
1882 |
+
|
1883 |
+
if input_embeddings.shape == embed_tokens_weight.shape:
|
1884 |
+
input_embeddings = embed_tokens_weight
|
1885 |
+
elif embed_tokens_weight.shape[0] == num_new_tokens:
|
1886 |
+
input_embeddings[-num_new_tokens:] = embed_tokens_weight
|
1887 |
+
else:
|
1888 |
+
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
|
1889 |
+
|
1890 |
+
|
1891 |
+
|
1892 |
+
class LamedLlamaModel(LamedMetaModel, LlamaModel):
|
1893 |
+
config_class = LamedConfig
|
1894 |
+
def __init__(self, config: LlamaConfig):
|
1895 |
+
super(LamedLlamaModel, self).__init__(config)
|
1896 |
+
|
1897 |
+
|
1898 |
+
class LamedLlamaForCausalLM(LamedMetaForCausalLM, LlamaForCausalLM):
|
1899 |
+
config_class = LamedConfig
|
1900 |
+
|
1901 |
+
def __init__(self, config):
|
1902 |
+
super(LlamaForCausalLM, self).__init__(config)
|
1903 |
+
self.model = LamedLlamaModel(config)
|
1904 |
+
self.pretraining_tp = config.pretraining_tp
|
1905 |
+
self.vocab_size = config.vocab_size
|
1906 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
1907 |
+
|
1908 |
+
# Initialize weights and apply final processing
|
1909 |
+
self.post_init()
|
1910 |
+
|
1911 |
+
def get_model(self):
|
1912 |
+
return self.model
|
1913 |
+
|
1914 |
+
def forward(
|
1915 |
+
self,
|
1916 |
+
images: Optional[torch.FloatTensor] = None,
|
1917 |
+
input_ids: torch.LongTensor = None,
|
1918 |
+
labels: Optional[torch.LongTensor] = None,
|
1919 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1920 |
+
segs: Optional[torch.FloatTensor] = None,
|
1921 |
+
|
1922 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1923 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1924 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1925 |
+
use_cache: Optional[bool] = None,
|
1926 |
+
output_attentions: Optional[bool] = None,
|
1927 |
+
output_hidden_states: Optional[bool] = None,
|
1928 |
+
return_dict: Optional[bool] = None,
|
1929 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1930 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1931 |
+
|
1932 |
+
input_ids_pre = input_ids
|
1933 |
+
|
1934 |
+
if inputs_embeds is None:
|
1935 |
+
(
|
1936 |
+
input_ids,
|
1937 |
+
position_ids,
|
1938 |
+
attention_mask,
|
1939 |
+
past_key_values,
|
1940 |
+
inputs_embeds,
|
1941 |
+
labels
|
1942 |
+
) = self.prepare_inputs_for_multimodal(
|
1943 |
+
input_ids,
|
1944 |
+
position_ids,
|
1945 |
+
attention_mask,
|
1946 |
+
past_key_values,
|
1947 |
+
labels,
|
1948 |
+
images,
|
1949 |
+
)
|
1950 |
+
|
1951 |
+
try:
|
1952 |
+
seg_ids = torch.nonzero(torch.sum(segs, dim=(1, 2, 3, 4))).flatten().tolist()
|
1953 |
+
except:
|
1954 |
+
seg_ids = []
|
1955 |
+
|
1956 |
+
if self.get_model().seg_enable and seg_ids:
|
1957 |
+
outputs = super().forward(
|
1958 |
+
input_ids=input_ids,
|
1959 |
+
inputs_embeds=inputs_embeds,
|
1960 |
+
attention_mask=attention_mask,
|
1961 |
+
labels=labels,
|
1962 |
+
output_hidden_states=True,
|
1963 |
+
|
1964 |
+
position_ids=position_ids,
|
1965 |
+
past_key_values=past_key_values,
|
1966 |
+
use_cache=use_cache,
|
1967 |
+
output_attentions=output_attentions,
|
1968 |
+
return_dict=return_dict
|
1969 |
+
)
|
1970 |
+
|
1971 |
+
output_hidden_states = outputs.hidden_states
|
1972 |
+
|
1973 |
+
last_hidden_state = output_hidden_states[-1]
|
1974 |
+
|
1975 |
+
seg_token_mask = input_ids_pre[:, 1:] == self.config.seg_token_id
|
1976 |
+
seg_token_mask = torch.cat(
|
1977 |
+
[
|
1978 |
+
seg_token_mask,
|
1979 |
+
torch.zeros((seg_token_mask.shape[0], 1), dtype=seg_token_mask.dtype).cuda(),
|
1980 |
+
],
|
1981 |
+
dim=1,
|
1982 |
+
)
|
1983 |
+
|
1984 |
+
seg_prompts = []
|
1985 |
+
for i in seg_ids:
|
1986 |
+
if torch.sum(seg_token_mask[i]) == 1:
|
1987 |
+
seg_token = last_hidden_state[i][seg_token_mask[i]]
|
1988 |
+
seg_prompt = self.get_model().seg_projector(seg_token)
|
1989 |
+
elif torch.sum(seg_token_mask[i]) > 1:
|
1990 |
+
seg_tokens = last_hidden_state[i][seg_token_mask[i]]
|
1991 |
+
seg_token = torch.mean(seg_tokens, dim=0, keepdim=True)
|
1992 |
+
seg_prompt = self.get_model().seg_projector(seg_token)
|
1993 |
+
else:
|
1994 |
+
seg_prompt = torch.zeros([1, self.config.mm_hidden_size], dtype=last_hidden_state.dtype,
|
1995 |
+
device=last_hidden_state.device)
|
1996 |
+
seg_prompts.append(seg_prompt)
|
1997 |
+
|
1998 |
+
seg_prompts = torch.cat(seg_prompts, dim=0)
|
1999 |
+
logits = self.get_model().seg_module(images[seg_ids], text_emb=seg_prompts)
|
2000 |
+
loss_dice = self.get_model().dice_loss(logits, segs[seg_ids])
|
2001 |
+
loss_bce = self.get_model().bce_loss(logits, segs[seg_ids])
|
2002 |
+
seg_loss = loss_dice + loss_bce
|
2003 |
+
outputs.loss = outputs.loss + seg_loss
|
2004 |
+
return outputs
|
2005 |
+
else:
|
2006 |
+
return super().forward(
|
2007 |
+
input_ids=input_ids,
|
2008 |
+
attention_mask=attention_mask,
|
2009 |
+
position_ids=position_ids,
|
2010 |
+
past_key_values=past_key_values,
|
2011 |
+
inputs_embeds=inputs_embeds,
|
2012 |
+
labels=labels,
|
2013 |
+
use_cache=use_cache,
|
2014 |
+
output_attentions=output_attentions,
|
2015 |
+
output_hidden_states=output_hidden_states,
|
2016 |
+
return_dict=return_dict
|
2017 |
+
)
|
2018 |
+
|
2019 |
+
|
2020 |
+
@torch.no_grad()
|
2021 |
+
def generate(
|
2022 |
+
self,
|
2023 |
+
images: Optional[torch.Tensor] = None,
|
2024 |
+
inputs: Optional[torch.Tensor] = None,
|
2025 |
+
seg_enable: bool = False,
|
2026 |
+
**kwargs,
|
2027 |
+
) -> Union[GenerateOutput, torch.LongTensor, Any]:
|
2028 |
+
position_ids = kwargs.pop("position_ids", None)
|
2029 |
+
attention_mask = kwargs.pop("attention_mask", None)
|
2030 |
+
if "inputs_embeds" in kwargs:
|
2031 |
+
raise NotImplementedError("`inputs_embeds` is not supported")
|
2032 |
+
|
2033 |
+
if images is not None:
|
2034 |
+
(
|
2035 |
+
inputs,
|
2036 |
+
position_ids,
|
2037 |
+
attention_mask,
|
2038 |
+
_,
|
2039 |
+
inputs_embeds,
|
2040 |
+
_
|
2041 |
+
) = self.prepare_inputs_for_multimodal(
|
2042 |
+
inputs,
|
2043 |
+
position_ids,
|
2044 |
+
attention_mask,
|
2045 |
+
None,
|
2046 |
+
None,
|
2047 |
+
images,
|
2048 |
+
)
|
2049 |
+
else:
|
2050 |
+
inputs_embeds = self.get_model().embed_tokens(inputs)
|
2051 |
+
|
2052 |
+
if seg_enable:
|
2053 |
+
outputs = super().generate(
|
2054 |
+
inputs_embeds=inputs_embeds,
|
2055 |
+
output_hidden_states=True,
|
2056 |
+
return_dict_in_generate=True,
|
2057 |
+
**kwargs
|
2058 |
+
)
|
2059 |
+
|
2060 |
+
output_hidden_states = outputs.hidden_states
|
2061 |
+
output_ids = outputs.sequences
|
2062 |
+
|
2063 |
+
seg_token_mask = output_ids[:, 1:] == self.config.seg_token_id
|
2064 |
+
|
2065 |
+
last_tensors = [tuple[-1] for tuple in output_hidden_states]
|
2066 |
+
last_hidden_state = torch.cat(last_tensors[1:], dim=1)
|
2067 |
+
|
2068 |
+
seg_prompts = []
|
2069 |
+
for i in range(len(seg_token_mask)):
|
2070 |
+
if torch.sum(seg_token_mask[i]) == 1:
|
2071 |
+
seg_token = last_hidden_state[i][seg_token_mask[i]]
|
2072 |
+
seg_prompt = self.get_model().seg_projector(seg_token)
|
2073 |
+
elif torch.sum(seg_token_mask[i]) > 1:
|
2074 |
+
seg_tokens = last_hidden_state[i][seg_token_mask[i]]
|
2075 |
+
seg_token = torch.mean(seg_tokens, dim=0, keepdim=True)
|
2076 |
+
seg_prompt = self.get_model().seg_projector(seg_token)
|
2077 |
+
else:
|
2078 |
+
seg_prompt = torch.zeros([1, self.config.mm_hidden_size], dtype=last_hidden_state.dtype,
|
2079 |
+
device=last_hidden_state.device)
|
2080 |
+
seg_prompts.append(seg_prompt)
|
2081 |
+
|
2082 |
+
seg_prompts = torch.cat(seg_prompts, dim=0)
|
2083 |
+
logits = self.get_model().seg_module(images, seg_prompts)
|
2084 |
+
|
2085 |
+
return output_ids, logits
|
2086 |
+
else:
|
2087 |
+
output_ids = super().generate(
|
2088 |
+
inputs_embeds=inputs_embeds,
|
2089 |
+
**kwargs
|
2090 |
+
)
|
2091 |
+
return output_ids
|
2092 |
+
|
2093 |
+
|
2094 |
+
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
|
2095 |
+
inputs_embeds=None, **kwargs):
|
2096 |
+
images = kwargs.pop("images", None)
|
2097 |
+
inputs = super().prepare_inputs_for_generation(
|
2098 |
+
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
|
2099 |
+
)
|
2100 |
+
if images is not None:
|
2101 |
+
inputs['images'] = images
|
2102 |
+
return inputs
|