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  1. config.json +132 -8
  2. configuration_cxrmate_ed.py +8 -7
config.json CHANGED
@@ -9,14 +9,10 @@
9
  },
10
  "hidden_size": 768,
11
  "history": 0,
12
- "ignore_index": -100,
13
- "image_seq_length": 576,
14
- "image_token_index": 32000,
15
  "include_time_delta": true,
16
  "index_value_encoder_intermediate_size": 2048,
17
  "model_type": "cxrmate-ed",
18
  "pad_token_id": 4,
19
- "projector_hidden_act": "gelu",
20
  "prompt_report_sections_filter": [
21
  "indication",
22
  "history"
@@ -27,20 +23,94 @@
27
  "medrecon"
28
  ],
29
  "text_config": {
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
30
  "head_dim": 64,
 
31
  "hidden_size": 768,
 
 
 
 
 
32
  "intermediate_size": 3072,
 
 
 
 
 
 
 
 
 
 
 
33
  "model_type": "llama",
 
34
  "num_attention_heads": 12,
 
 
35
  "num_hidden_layers": 6,
36
  "num_key_value_heads": 12,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
37
  "vocab_size": 30000
38
  },
39
  "time_delta_monotonic_inversion": true,
40
  "torch_dtype": "float32",
41
  "transformers_version": "4.47.0",
42
  "vision_config": {
 
43
  "_name_or_path": "aehrc/uniformer_base_tl_384",
 
44
  "architectures": [
45
  "UniFormerModel"
46
  ],
@@ -49,42 +119,96 @@
49
  "AutoConfig": "aehrc/uniformer_base_tl_384--configuration_uniformer.UniFormerWithProjectionHeadConfig",
50
  "AutoModel": "aehrc/uniformer_base_tl_384--modelling_uniformer.UniFormerModel"
51
  },
 
 
 
 
52
  "conv_stem": false,
 
 
53
  "depth": [
54
  5,
55
  8,
56
  20,
57
  7
58
  ],
 
 
59
  "drop_path_rate": 0.3,
60
  "drop_rate": 0.0,
 
61
  "embed_dim": [
62
  64,
63
  128,
64
  320,
65
  512
66
  ],
 
 
 
 
 
 
67
  "head_dim": 64,
 
 
 
 
68
  "image_size": 384,
69
  "in_chans": 3,
70
  "init_value": 1e-06,
 
 
 
 
 
 
71
  "layer_norm_eps": 1e-06,
72
  "layer_scale": false,
 
 
 
73
  "mlp_ratio": 4,
74
  "model_type": "uniformer",
 
 
 
75
  "num_classes": 1000,
 
 
 
 
 
76
  "patch_size": [
77
  4,
78
  2,
79
  2,
80
  2
81
  ],
 
 
82
  "projection_size": null,
 
83
  "qk_scale": null,
84
  "qkv_bias": true,
 
 
85
  "representation_size": null,
86
- "torch_dtype": "float32"
87
- },
88
- "vision_feature_layer": -2,
89
- "vision_feature_select_strategy": "default"
 
 
 
 
 
 
 
 
 
 
 
 
 
90
  }
 
9
  },
10
  "hidden_size": 768,
11
  "history": 0,
 
 
 
12
  "include_time_delta": true,
13
  "index_value_encoder_intermediate_size": 2048,
14
  "model_type": "cxrmate-ed",
15
  "pad_token_id": 4,
 
16
  "prompt_report_sections_filter": [
17
  "indication",
18
  "history"
 
23
  "medrecon"
24
  ],
25
  "text_config": {
26
+ "_attn_implementation_autoset": false,
27
+ "_name_or_path": "",
28
+ "add_cross_attention": false,
29
+ "architectures": null,
30
+ "attention_bias": false,
31
+ "attention_dropout": 0.0,
32
+ "bad_words_ids": null,
33
+ "begin_suppress_tokens": null,
34
+ "bos_token_id": 1,
35
+ "chunk_size_feed_forward": 0,
36
+ "cross_attention_hidden_size": null,
37
+ "decoder_start_token_id": null,
38
+ "diversity_penalty": 0.0,
39
+ "do_sample": false,
40
+ "early_stopping": false,
41
+ "encoder_no_repeat_ngram_size": 0,
42
+ "eos_token_id": 2,
43
+ "exponential_decay_length_penalty": null,
44
+ "finetuning_task": null,
45
+ "forced_bos_token_id": null,
46
+ "forced_eos_token_id": null,
47
  "head_dim": 64,
48
+ "hidden_act": "silu",
49
  "hidden_size": 768,
50
+ "id2label": {
51
+ "0": "LABEL_0",
52
+ "1": "LABEL_1"
53
+ },
54
+ "initializer_range": 0.02,
55
  "intermediate_size": 3072,
56
+ "is_decoder": false,
57
+ "is_encoder_decoder": false,
58
+ "label2id": {
59
+ "LABEL_0": 0,
60
+ "LABEL_1": 1
61
+ },
62
+ "length_penalty": 1.0,
63
+ "max_length": 20,
64
+ "max_position_embeddings": 2048,
65
+ "min_length": 0,
66
+ "mlp_bias": false,
67
  "model_type": "llama",
68
+ "no_repeat_ngram_size": 0,
69
  "num_attention_heads": 12,
70
+ "num_beam_groups": 1,
71
+ "num_beams": 1,
72
  "num_hidden_layers": 6,
73
  "num_key_value_heads": 12,
74
+ "num_return_sequences": 1,
75
+ "output_attentions": false,
76
+ "output_hidden_states": false,
77
+ "output_scores": false,
78
+ "pad_token_id": null,
79
+ "prefix": null,
80
+ "pretraining_tp": 1,
81
+ "problem_type": null,
82
+ "pruned_heads": {},
83
+ "remove_invalid_values": false,
84
+ "repetition_penalty": 1.0,
85
+ "return_dict": true,
86
+ "return_dict_in_generate": false,
87
+ "rms_norm_eps": 1e-06,
88
+ "rope_scaling": null,
89
+ "rope_theta": 10000.0,
90
+ "sep_token_id": null,
91
+ "suppress_tokens": null,
92
+ "task_specific_params": null,
93
+ "temperature": 1.0,
94
+ "tf_legacy_loss": false,
95
+ "tie_encoder_decoder": false,
96
+ "tie_word_embeddings": false,
97
+ "tokenizer_class": null,
98
+ "top_k": 50,
99
+ "top_p": 1.0,
100
+ "torch_dtype": null,
101
+ "torchscript": false,
102
+ "typical_p": 1.0,
103
+ "use_bfloat16": false,
104
+ "use_cache": true,
105
  "vocab_size": 30000
106
  },
107
  "time_delta_monotonic_inversion": true,
108
  "torch_dtype": "float32",
109
  "transformers_version": "4.47.0",
110
  "vision_config": {
111
+ "_attn_implementation_autoset": false,
112
  "_name_or_path": "aehrc/uniformer_base_tl_384",
113
+ "add_cross_attention": false,
114
  "architectures": [
115
  "UniFormerModel"
116
  ],
 
119
  "AutoConfig": "aehrc/uniformer_base_tl_384--configuration_uniformer.UniFormerWithProjectionHeadConfig",
120
  "AutoModel": "aehrc/uniformer_base_tl_384--modelling_uniformer.UniFormerModel"
121
  },
122
+ "bad_words_ids": null,
123
+ "begin_suppress_tokens": null,
124
+ "bos_token_id": null,
125
+ "chunk_size_feed_forward": 0,
126
  "conv_stem": false,
127
+ "cross_attention_hidden_size": null,
128
+ "decoder_start_token_id": null,
129
  "depth": [
130
  5,
131
  8,
132
  20,
133
  7
134
  ],
135
+ "diversity_penalty": 0.0,
136
+ "do_sample": false,
137
  "drop_path_rate": 0.3,
138
  "drop_rate": 0.0,
139
+ "early_stopping": false,
140
  "embed_dim": [
141
  64,
142
  128,
143
  320,
144
  512
145
  ],
146
+ "encoder_no_repeat_ngram_size": 0,
147
+ "eos_token_id": null,
148
+ "exponential_decay_length_penalty": null,
149
+ "finetuning_task": null,
150
+ "forced_bos_token_id": null,
151
+ "forced_eos_token_id": null,
152
  "head_dim": 64,
153
+ "id2label": {
154
+ "0": "LABEL_0",
155
+ "1": "LABEL_1"
156
+ },
157
  "image_size": 384,
158
  "in_chans": 3,
159
  "init_value": 1e-06,
160
+ "is_decoder": false,
161
+ "is_encoder_decoder": false,
162
+ "label2id": {
163
+ "LABEL_0": 0,
164
+ "LABEL_1": 1
165
+ },
166
  "layer_norm_eps": 1e-06,
167
  "layer_scale": false,
168
+ "length_penalty": 1.0,
169
+ "max_length": 20,
170
+ "min_length": 0,
171
  "mlp_ratio": 4,
172
  "model_type": "uniformer",
173
+ "no_repeat_ngram_size": 0,
174
+ "num_beam_groups": 1,
175
+ "num_beams": 1,
176
  "num_classes": 1000,
177
+ "num_return_sequences": 1,
178
+ "output_attentions": false,
179
+ "output_hidden_states": false,
180
+ "output_scores": false,
181
+ "pad_token_id": null,
182
  "patch_size": [
183
  4,
184
  2,
185
  2,
186
  2
187
  ],
188
+ "prefix": null,
189
+ "problem_type": null,
190
  "projection_size": null,
191
+ "pruned_heads": {},
192
  "qk_scale": null,
193
  "qkv_bias": true,
194
+ "remove_invalid_values": false,
195
+ "repetition_penalty": 1.0,
196
  "representation_size": null,
197
+ "return_dict": true,
198
+ "return_dict_in_generate": false,
199
+ "sep_token_id": null,
200
+ "suppress_tokens": null,
201
+ "task_specific_params": null,
202
+ "temperature": 1.0,
203
+ "tf_legacy_loss": false,
204
+ "tie_encoder_decoder": false,
205
+ "tie_word_embeddings": true,
206
+ "tokenizer_class": null,
207
+ "top_k": 50,
208
+ "top_p": 1.0,
209
+ "torch_dtype": "float32",
210
+ "torchscript": false,
211
+ "typical_p": 1.0,
212
+ "use_bfloat16": false
213
+ }
214
  }
configuration_cxrmate_ed.py CHANGED
@@ -1,9 +1,9 @@
1
  from typing import Any
2
 
3
- from transformers import LlavaConfig
4
 
5
 
6
- class CXRMateEDConfig(LlavaConfig):
7
 
8
  model_type = 'cxrmate-ed'
9
 
@@ -51,7 +51,10 @@ class CXRMateEDConfig(LlavaConfig):
51
  pad_token_id: int = 4,
52
  **kwargs,
53
  ):
 
54
 
 
 
55
  self.index_value_encoder_intermediate_size = index_value_encoder_intermediate_size
56
  self.include_time_delta = include_time_delta
57
  self.time_delta_monotonic_inversion = time_delta_monotonic_inversion
@@ -61,8 +64,9 @@ class CXRMateEDConfig(LlavaConfig):
61
  self.prompt_report_sections_filter = prompt_report_sections_filter
62
  self.pad_token_id = pad_token_id
63
 
64
- self.hidden_size = self.text_config.hidden_size
65
 
 
66
  # self.ignore_index = ignore_index
67
  # self.image_token_index = image_token_index
68
  # self.projector_hidden_act = projector_hidden_act
@@ -94,7 +98,6 @@ class CXRMateEDConfig(LlavaConfig):
94
  # projection_dim=768,
95
  # )
96
 
97
- self.vision_config = vision_config
98
 
99
  # if isinstance(text_config, dict):
100
  # text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
@@ -102,9 +105,7 @@ class CXRMateEDConfig(LlavaConfig):
102
  # elif text_config is None:
103
  # text_config = CONFIG_MAPPING["llama"]()
104
 
105
- self.text_config = text_config
106
-
107
- super().__init__(**kwargs)
108
 
109
 
110
  # import transformers
 
1
  from typing import Any
2
 
3
+ import transformers
4
 
5
 
6
+ class CXRMateEDConfig(transformers.PretrainedConfig):
7
 
8
  model_type = 'cxrmate-ed'
9
 
 
51
  pad_token_id: int = 4,
52
  **kwargs,
53
  ):
54
+ transformers.PretrainedConfig.__init__(self, **kwargs)
55
 
56
+ self.vision_config = vision_config
57
+ self.text_config = text_config
58
  self.index_value_encoder_intermediate_size = index_value_encoder_intermediate_size
59
  self.include_time_delta = include_time_delta
60
  self.time_delta_monotonic_inversion = time_delta_monotonic_inversion
 
64
  self.prompt_report_sections_filter = prompt_report_sections_filter
65
  self.pad_token_id = pad_token_id
66
 
67
+ self.hidden_size = self.text_config.hidden_size if self.text_config is not None else None
68
 
69
+
70
  # self.ignore_index = ignore_index
71
  # self.image_token_index = image_token_index
72
  # self.projector_hidden_act = projector_hidden_act
 
98
  # projection_dim=768,
99
  # )
100
 
 
101
 
102
  # if isinstance(text_config, dict):
103
  # text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
 
105
  # elif text_config is None:
106
  # text_config = CONFIG_MAPPING["llama"]()
107
 
108
+ # super().__init__(**kwargs)
 
 
109
 
110
 
111
  # import transformers