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config.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "./",
3
+ "architectures": [
4
+ "InternVisionModel"
5
+ ],
6
+ "auto_map": {
7
+ "AutoConfig": "configuration_intern_vit.InternVisionConfig",
8
+ "AutoModel": "modeling_intern_vit.InternVisionModel"
9
+ },
10
+ "attention_dropout": 0.0,
11
+ "drop_path_rate": 0.0,
12
+ "dropout": 0.0,
13
+ "hidden_act": "gelu",
14
+ "hidden_size": 3200,
15
+ "image_size": 224,
16
+ "initializer_factor": 0.1,
17
+ "initializer_range": 1e-10,
18
+ "intermediate_size": 12800,
19
+ "layer_norm_eps": 1e-06,
20
+ "model_type": "intern_vit_6b",
21
+ "num_attention_heads": 25,
22
+ "num_channels": 3,
23
+ "num_hidden_layers": 48,
24
+ "patch_size": 14,
25
+ "qk_normalization": true,
26
+ "qkv_bias": false,
27
+ "torch_dtype": "bfloat16",
28
+ "transformers_version": "4.32.0",
29
+ "use_flash_attn": true
30
+ }
configuration_intern_vit.py ADDED
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1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2023 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+ import os
7
+ from typing import Union
8
+
9
+ from transformers.configuration_utils import PretrainedConfig
10
+ from transformers.utils import logging
11
+
12
+ logger = logging.get_logger(__name__)
13
+
14
+
15
+ class InternVisionConfig(PretrainedConfig):
16
+ r"""
17
+ This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
18
+ instantiate a vision encoder according to the specified arguments, defining the model architecture.
19
+
20
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
21
+ documentation from [`PretrainedConfig`] for more information.
22
+
23
+ Args:
24
+ num_channels (`int`, *optional*, defaults to 3):
25
+ Number of color channels in the input images (e.g., 3 for RGB).
26
+ patch_size (`int`, *optional*, defaults to 14):
27
+ The size (resolution) of each patch.
28
+ image_size (`int`, *optional*, defaults to 224):
29
+ The size (resolution) of each image.
30
+ qkv_bias (`bool`, *optional*, defaults to `False`):
31
+ Whether to add a bias to the queries and values in the self-attention layers.
32
+ hidden_size (`int`, *optional*, defaults to 3200):
33
+ Dimensionality of the encoder layers and the pooler layer.
34
+ num_attention_heads (`int`, *optional*, defaults to 25):
35
+ Number of attention heads for each attention layer in the Transformer encoder.
36
+ intermediate_size (`int`, *optional*, defaults to 12800):
37
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
38
+ qk_normalization (`bool`, *optional*, defaults to `True`):
39
+ Whether to normalize the queries and keys in the self-attention layers.
40
+ num_hidden_layers (`int`, *optional*, defaults to 48):
41
+ Number of hidden layers in the Transformer encoder.
42
+ use_flash_attn (`bool`, *optional*, defaults to `True`):
43
+ Whether to use flash attention mechanism.
44
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
45
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
46
+ `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
47
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
48
+ The epsilon used by the layer normalization layers.
49
+ dropout (`float`, *optional*, defaults to 0.0):
50
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
51
+ drop_path_rate (`float`, *optional*, defaults to 0.0):
52
+ Dropout rate for stochastic depth.
53
+ attention_dropout (`float`, *optional*, defaults to 0.0):
54
+ The dropout ratio for the attention probabilities.
55
+ initializer_range (`float`, *optional*, defaults to 0.02):
56
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
57
+ initializer_factor (`float`, *optional*, defaults to 0.1):
58
+ A factor for layer scale.
59
+ """
60
+
61
+ model_type = 'intern_vit_6b'
62
+
63
+ def __init__(
64
+ self,
65
+ num_channels=3,
66
+ patch_size=14,
67
+ image_size=224,
68
+ qkv_bias=False,
69
+ hidden_size=3200,
70
+ num_attention_heads=25,
71
+ intermediate_size=12800,
72
+ qk_normalization=True,
73
+ num_hidden_layers=48,
74
+ use_flash_attn=True,
75
+ hidden_act='gelu',
76
+ layer_norm_eps=1e-6,
77
+ dropout=0.0,
78
+ drop_path_rate=0.0,
79
+ attention_dropout=0.0,
80
+ initializer_range=0.02,
81
+ initializer_factor=0.1,
82
+ **kwargs,
83
+ ):
84
+ super().__init__(**kwargs)
85
+
86
+ self.hidden_size = hidden_size
87
+ self.intermediate_size = intermediate_size
88
+ self.dropout = dropout
89
+ self.drop_path_rate = drop_path_rate
90
+ self.num_hidden_layers = num_hidden_layers
91
+ self.num_attention_heads = num_attention_heads
92
+ self.num_channels = num_channels
93
+ self.patch_size = patch_size
94
+ self.image_size = image_size
95
+ self.initializer_range = initializer_range
96
+ self.initializer_factor = initializer_factor
97
+ self.attention_dropout = attention_dropout
98
+ self.layer_norm_eps = layer_norm_eps
99
+ self.hidden_act = hidden_act
100
+ self.qkv_bias = qkv_bias
101
+ self.qk_normalization = qk_normalization
102
+ self.use_flash_attn = use_flash_attn
103
+
104
+ @classmethod
105
+ def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
106
+ config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
107
+
108
+ if 'vision_config' in config_dict:
109
+ config_dict = config_dict['vision_config']
110
+
111
+ if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
112
+ logger.warning(
113
+ f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
114
+ f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
115
+ )
116
+
117
+ return cls.from_dict(config_dict, **kwargs)
flash_attention.py ADDED
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1
+ import torch
2
+ import torch.nn as nn
3
+ from einops import rearrange
4
+
5
+ try: # v1
6
+ from flash_attn.flash_attn_interface import \
7
+ flash_attn_unpadded_qkvpacked_func
8
+ except: # v2
9
+ from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
10
+
11
+ from flash_attn.bert_padding import pad_input, unpad_input
12
+
13
+
14
+ class FlashAttention(nn.Module):
15
+ """Implement the scaled dot product attention with softmax.
16
+ Arguments
17
+ ---------
18
+ softmax_scale: The temperature to use for the softmax attention.
19
+ (default: 1/sqrt(d_keys) where d_keys is computed at
20
+ runtime)
21
+ attention_dropout: The dropout rate to apply to the attention
22
+ (default: 0.0)
23
+ """
24
+
25
+ def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
26
+ super().__init__()
27
+ self.softmax_scale = softmax_scale
28
+ self.dropout_p = attention_dropout
29
+
30
+ def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
31
+ max_s=None, need_weights=False):
32
+ """Implements the multihead softmax attention.
33
+ Arguments
34
+ ---------
35
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
36
+ if unpadded: (nnz, 3, h, d)
37
+ key_padding_mask: a bool tensor of shape (B, S)
38
+ """
39
+ assert not need_weights
40
+ assert qkv.dtype in [torch.float16, torch.bfloat16]
41
+ assert qkv.is_cuda
42
+
43
+ if cu_seqlens is None:
44
+ batch_size = qkv.shape[0]
45
+ seqlen = qkv.shape[1]
46
+ if key_padding_mask is None:
47
+ qkv = rearrange(qkv, 'b s ... -> (b s) ...')
48
+ max_s = seqlen
49
+ cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
50
+ device=qkv.device)
51
+ output = flash_attn_unpadded_qkvpacked_func(
52
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
53
+ softmax_scale=self.softmax_scale, causal=causal
54
+ )
55
+ output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
56
+ else:
57
+ nheads = qkv.shape[-2]
58
+ x = rearrange(qkv, 'b s three h d -> b s (three h d)')
59
+ x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
60
+ x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
61
+ output_unpad = flash_attn_unpadded_qkvpacked_func(
62
+ x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
63
+ softmax_scale=self.softmax_scale, causal=causal
64
+ )
65
+ output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
66
+ indices, batch_size, seqlen),
67
+ 'b s (h d) -> b s h d', h=nheads)
68
+ else:
69
+ assert max_s is not None
70
+ output = flash_attn_unpadded_qkvpacked_func(
71
+ qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
72
+ softmax_scale=self.softmax_scale, causal=causal
73
+ )
74
+
75
+ return output, None
modeling_intern_vit.py ADDED
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1
+ # --------------------------------------------------------
2
+ # InternVL
3
+ # Copyright (c) 2023 OpenGVLab
4
+ # Licensed under The MIT License [see LICENSE for details]
5
+ # --------------------------------------------------------
6
+ from typing import Optional, Tuple, Union
7
+
8
+ import torch
9
+ import torch.nn.functional as F
10
+ import torch.utils.checkpoint
11
+ from einops import rearrange
12
+ from timm.models.layers import DropPath
13
+ from torch import nn
14
+ from transformers.activations import ACT2FN
15
+ from transformers.modeling_outputs import (BaseModelOutput,
16
+ BaseModelOutputWithPooling)
17
+ from transformers.modeling_utils import PreTrainedModel
18
+ from transformers.utils import logging
19
+
20
+ from .configuration_intern_vit import InternVisionConfig
21
+
22
+ try:
23
+ from .flash_attention import FlashAttention
24
+ has_flash_attn = True
25
+ except:
26
+ print('FlashAttention is not installed.')
27
+ has_flash_attn = False
28
+
29
+
30
+ logger = logging.get_logger(__name__)
31
+
32
+
33
+ class InternRMSNorm(nn.Module):
34
+ def __init__(self, hidden_size, eps=1e-6):
35
+ super().__init__()
36
+ self.weight = nn.Parameter(torch.ones(hidden_size))
37
+ self.variance_epsilon = eps
38
+
39
+ def forward(self, hidden_states):
40
+ input_dtype = hidden_states.dtype
41
+ hidden_states = hidden_states.to(torch.float32)
42
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
43
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
44
+ return self.weight * hidden_states.to(input_dtype)
45
+
46
+
47
+ try:
48
+ from apex.normalization import FusedRMSNorm
49
+
50
+ InternRMSNorm = FusedRMSNorm # noqa
51
+
52
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
53
+ except ImportError:
54
+ # using the normal InternRMSNorm
55
+ pass
56
+ except Exception:
57
+ logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
58
+ pass
59
+
60
+
61
+ class InternVisionEmbeddings(nn.Module):
62
+ def __init__(self, config: InternVisionConfig):
63
+ super().__init__()
64
+ self.config = config
65
+ self.embed_dim = config.hidden_size
66
+ self.image_size = config.image_size
67
+ self.patch_size = config.patch_size
68
+
69
+ self.class_embedding = nn.Parameter(
70
+ torch.randn(1, 1, self.embed_dim),
71
+ )
72
+
73
+ self.patch_embedding = nn.Conv2d(
74
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
75
+ )
76
+
77
+ self.num_patches = (self.image_size // self.patch_size) ** 2
78
+ self.num_positions = self.num_patches + 1
79
+
80
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
81
+
82
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
83
+ batch_size = pixel_values.shape[0]
84
+ target_dtype = self.patch_embedding.weight.dtype
85
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
86
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
87
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
88
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
89
+ embeddings = embeddings + self.position_embedding.to(target_dtype)
90
+ return embeddings
91
+
92
+
93
+ class InternAttention(nn.Module):
94
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
95
+
96
+ def __init__(self, config: InternVisionConfig):
97
+ super().__init__()
98
+ self.config = config
99
+ self.embed_dim = config.hidden_size
100
+ self.num_heads = config.num_attention_heads
101
+ self.use_flash_attn = config.use_flash_attn and has_flash_attn
102
+ if config.use_flash_attn and not has_flash_attn:
103
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
104
+ self.head_dim = self.embed_dim // self.num_heads
105
+ if self.head_dim * self.num_heads != self.embed_dim:
106
+ raise ValueError(
107
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
108
+ f' {self.num_heads}).'
109
+ )
110
+
111
+ self.scale = self.head_dim ** -0.5
112
+ self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
113
+ self.attn_drop = nn.Dropout(config.attention_dropout)
114
+ self.proj_drop = nn.Dropout(config.dropout)
115
+
116
+ self.qk_normalization = config.qk_normalization
117
+
118
+ if self.qk_normalization:
119
+ self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
120
+ self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
121
+
122
+ if self.use_flash_attn:
123
+ self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
124
+ self.proj = nn.Linear(self.embed_dim, self.embed_dim)
125
+
126
+ def _naive_attn(self, x):
127
+ B, N, C = x.shape
128
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
129
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
130
+
131
+ if self.qk_normalization:
132
+ B_, H_, N_, D_ = q.shape
133
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
134
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
135
+
136
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
137
+ attn = attn.softmax(dim=-1)
138
+ attn = self.attn_drop(attn)
139
+
140
+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
141
+ x = self.proj(x)
142
+ x = self.proj_drop(x)
143
+ return x
144
+
145
+ def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
146
+ qkv = self.qkv(x)
147
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
148
+
149
+ if self.qk_normalization:
150
+ q, k, v = qkv.unbind(2)
151
+ q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
152
+ k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
153
+ qkv = torch.stack([q, k, v], dim=2)
154
+
155
+ context, _ = self.inner_attn(
156
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
157
+ )
158
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
159
+ outs = self.proj_drop(outs)
160
+ return outs
161
+
162
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
163
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
164
+ return x
165
+
166
+
167
+ class InternMLP(nn.Module):
168
+ def __init__(self, config: InternVisionConfig):
169
+ super().__init__()
170
+ self.config = config
171
+ self.act = ACT2FN[config.hidden_act]
172
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
173
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
174
+
175
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
176
+ hidden_states = self.fc1(hidden_states)
177
+ hidden_states = self.act(hidden_states)
178
+ hidden_states = self.fc2(hidden_states)
179
+ return hidden_states
180
+
181
+
182
+ class InternVisionEncoderLayer(nn.Module):
183
+ def __init__(self, config: InternVisionConfig, drop_path_rate: float):
184
+ super().__init__()
185
+ self.embed_dim = config.hidden_size
186
+ self.intermediate_size = config.intermediate_size
187
+
188
+ self.attn = InternAttention(config)
189
+ self.mlp = InternMLP(config)
190
+ self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
191
+ self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
192
+
193
+ self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
194
+ self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
195
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
196
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
197
+
198
+ def forward(
199
+ self,
200
+ hidden_states: torch.Tensor,
201
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
202
+ """
203
+ Args:
204
+ hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
205
+ """
206
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
207
+
208
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
209
+
210
+ return hidden_states
211
+
212
+
213
+ class InternVisionEncoder(nn.Module):
214
+ """
215
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
216
+ [`InternEncoderLayer`].
217
+
218
+ Args:
219
+ config (`InternConfig`):
220
+ The corresponding vision configuration for the `InternEncoder`.
221
+ """
222
+
223
+ def __init__(self, config: InternVisionConfig):
224
+ super().__init__()
225
+ self.config = config
226
+ # stochastic depth decay rule
227
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
228
+ self.layers = nn.ModuleList([
229
+ InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
230
+ self.gradient_checkpointing = True
231
+
232
+ def forward(
233
+ self,
234
+ inputs_embeds,
235
+ output_hidden_states: Optional[bool] = None,
236
+ return_dict: Optional[bool] = None,
237
+ ) -> Union[Tuple, BaseModelOutput]:
238
+ r"""
239
+ Args:
240
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
241
+ Embedded representation of the inputs. Should be float, not int tokens.
242
+ output_hidden_states (`bool`, *optional*):
243
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
244
+ for more detail.
245
+ return_dict (`bool`, *optional*):
246
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
247
+ """
248
+ output_hidden_states = (
249
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
250
+ )
251
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
252
+
253
+ encoder_states = () if output_hidden_states else None
254
+ hidden_states = inputs_embeds
255
+
256
+ for idx, encoder_layer in enumerate(self.layers):
257
+ if output_hidden_states:
258
+ encoder_states = encoder_states + (hidden_states,)
259
+ if self.gradient_checkpointing and self.training:
260
+ layer_outputs = torch.utils.checkpoint.checkpoint(
261
+ encoder_layer,
262
+ hidden_states)
263
+ else:
264
+ layer_outputs = encoder_layer(
265
+ hidden_states,
266
+ )
267
+ hidden_states = layer_outputs
268
+
269
+ if output_hidden_states:
270
+ encoder_states = encoder_states + (hidden_states,)
271
+
272
+ if not return_dict:
273
+ return tuple(v for v in [hidden_states, encoder_states] if v is not None)
274
+ return BaseModelOutput(
275
+ last_hidden_state=hidden_states, hidden_states=encoder_states
276
+ )
277
+
278
+
279
+ class InternVisionModel(PreTrainedModel):
280
+ main_input_name = 'pixel_values'
281
+ config_class = InternVisionConfig
282
+
283
+ def __init__(self, config: InternVisionConfig):
284
+ super().__init__(config)
285
+ self.config = config
286
+
287
+ self.embeddings = InternVisionEmbeddings(config)
288
+ self.encoder = InternVisionEncoder(config)
289
+
290
+ def resize_pos_embeddings(self, old_size, new_size, patch_size):
291
+ pos_emb = self.embeddings.position_embedding
292
+ _, num_positions, embed_dim = pos_emb.shape
293
+ cls_emb = pos_emb[:, :1, :]
294
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
295
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
296
+ pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
297
+ pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
298
+ self.embeddings.position_embedding = nn.Parameter(pos_emb)
299
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
300
+
301
+ def get_input_embeddings(self):
302
+ return self.embeddings
303
+
304
+ def forward(
305
+ self,
306
+ pixel_values: Optional[torch.FloatTensor] = None,
307
+ output_hidden_states: Optional[bool] = None,
308
+ return_dict: Optional[bool] = None,
309
+ pixel_embeds: Optional[torch.FloatTensor] = None,
310
+ ) -> Union[Tuple, BaseModelOutputWithPooling]:
311
+ output_hidden_states = (
312
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
313
+ )
314
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
315
+
316
+ if pixel_values is None and pixel_embeds is None:
317
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
318
+
319
+ if pixel_embeds is not None:
320
+ hidden_states = pixel_embeds
321
+ else:
322
+ if len(pixel_values.shape) == 4:
323
+ hidden_states = self.embeddings(pixel_values)
324
+ else:
325
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
326
+ encoder_outputs = self.encoder(
327
+ inputs_embeds=hidden_states,
328
+ output_hidden_states=output_hidden_states,
329
+ return_dict=return_dict,
330
+ )
331
+ last_hidden_state = encoder_outputs.last_hidden_state
332
+ pooled_output = last_hidden_state[:, 0, :]
333
+
334
+ if not return_dict:
335
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
336
+
337
+ return BaseModelOutputWithPooling(
338
+ last_hidden_state=last_hidden_state,
339
+ pooler_output=pooled_output,
340
+ hidden_states=encoder_outputs.hidden_states,
341
+ attentions=encoder_outputs.attentions,
342
+ )
preprocessor_config.json ADDED
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+ {
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+ "crop_size": 224,
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+ "do_center_crop": true,
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+ "do_normalize": true,
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+ "do_resize": true,
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+ "feature_extractor_type": "CLIPFeatureExtractor",
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+ "image_mean": [
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+ 0.485,
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+ 0.456,
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+ 0.406
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+ ],
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+ "image_std": [
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+ 0.229,
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+ 0.224,
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+ 0.225
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+ ],
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+ "resample": 3,
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+ "size": 224
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+ }
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