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Repository: chaoyi-wu/RadFM
Branch: main
Commit: 8d798c554ca0
Files: 60
Total size: 1.4 MB
Directory structure:
gitextract_uqthi9mp/
├── LICENSE
├── Quick_demo/
│ ├── Language_files/
│ │ ├── config.json
│ │ ├── special_tokens_map.json
│ │ ├── tokenizer.model
│ │ └── tokenizer_config.json
│ ├── MedKEBERT/
│ │ ├── config.json
│ │ ├── special_tokens_map.json
│ │ ├── tokenizer.json
│ │ ├── tokenizer_config.json
│ │ └── vocab.txt
│ ├── Model/
│ │ └── RadFM/
│ │ ├── __init__.py
│ │ ├── blocks.py
│ │ ├── helpers.py
│ │ ├── multimodality_model.py
│ │ ├── my_embedding_layer.py
│ │ ├── position_encoding.py
│ │ ├── transformer_decoder.py
│ │ ├── utils.py
│ │ └── vit_3d.py
│ └── test.py
├── README.md
├── requirements.txt
└── src/
├── Dataset/
│ ├── dataset/
│ │ ├── MedPix_dataset.py
│ │ ├── __init__.py
│ │ ├── binary.py
│ │ ├── caption_prompt.json
│ │ ├── case_report.py
│ │ ├── chestxray.py
│ │ ├── cls_prompt.json
│ │ ├── data_csv/
│ │ │ └── README.md
│ │ ├── dicom_to_png_for_VinDR_sampled_using_mammo.py
│ │ ├── jpg2nii_data_convert.py
│ │ ├── mammo_prompt.json
│ │ ├── modality_prompt.json
│ │ ├── nii2npy_for_radiopaedio.py
│ │ ├── paper_inline.py
│ │ ├── pmcoa.py
│ │ ├── radiology_feature_prompt.json
│ │ ├── radiopaedia.py
│ │ ├── report_prompt.json
│ │ ├── spinexr_prompt.json
│ │ ├── vqa.py
│ │ └── yes_no_prompt.json
│ ├── multi_dataset.py
│ ├── multi_dataset_test.py
│ └── multi_dataset_test_for_close.py
├── Model/
│ └── RadFM/
│ ├── __init__.py
│ ├── blocks.py
│ ├── helpers.py
│ ├── multimodality_model.py
│ ├── my_embedding_layer.py
│ ├── position_encoding.py
│ ├── transformer_decoder.py
│ ├── utils.py
│ └── vit_3d.py
├── My_Trainer/
│ └── trainer.py
├── datasampler.py
├── output_csv_example/
│ └── caption_example.csv
├── test.py
└── train.py
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2025 chaoyi-wu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: Quick_demo/Language_files/config.json
================================================
{
"_name_or_path": "/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/llama-13b-hf",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 0,
"eos_token_id": 1,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"max_sequence_length": 2048,
"model_type": "llama",
"num_attention_heads": 40,
"num_hidden_layers": 40,
"pad_token_id": -1,
"rms_norm_eps": 1e-06,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"use_cache": true,
"vocab_size": 32000
}
================================================
FILE: Quick_demo/Language_files/special_tokens_map.json
================================================
{}
================================================
FILE: Quick_demo/Language_files/tokenizer_config.json
================================================
{"bos_token": "", "eos_token": "", "model_max_length": 1000000000000000019884624838656, "tokenizer_class": "LlamaTokenizer", "unk_token": ""}
================================================
FILE: Quick_demo/MedKEBERT/config.json
================================================
{
"_name_or_path": "xmcmic/Med-KEBERT",
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_hidden_states": true,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.24.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
================================================
FILE: Quick_demo/MedKEBERT/special_tokens_map.json
================================================
{
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
================================================
FILE: Quick_demo/MedKEBERT/tokenizer.json
================================================
{
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gitextract_uqthi9mp/
├── LICENSE
├── Quick_demo/
│ ├── Language_files/
│ │ ├── config.json
│ │ ├── special_tokens_map.json
│ │ ├── tokenizer.model
│ │ └── tokenizer_config.json
│ ├── MedKEBERT/
│ │ ├── config.json
│ │ ├── special_tokens_map.json
│ │ ├── tokenizer.json
│ │ ├── tokenizer_config.json
│ │ └── vocab.txt
│ ├── Model/
│ │ └── RadFM/
│ │ ├── __init__.py
│ │ ├── blocks.py
│ │ ├── helpers.py
│ │ ├── multimodality_model.py
│ │ ├── my_embedding_layer.py
│ │ ├── position_encoding.py
│ │ ├── transformer_decoder.py
│ │ ├── utils.py
│ │ └── vit_3d.py
│ └── test.py
├── README.md
├── requirements.txt
└── src/
├── Dataset/
│ ├── dataset/
│ │ ├── MedPix_dataset.py
│ │ ├── __init__.py
│ │ ├── binary.py
│ │ ├── caption_prompt.json
│ │ ├── case_report.py
│ │ ├── chestxray.py
│ │ ├── cls_prompt.json
│ │ ├── data_csv/
│ │ │ └── README.md
│ │ ├── dicom_to_png_for_VinDR_sampled_using_mammo.py
│ │ ├── jpg2nii_data_convert.py
│ │ ├── mammo_prompt.json
│ │ ├── modality_prompt.json
│ │ ├── nii2npy_for_radiopaedio.py
│ │ ├── paper_inline.py
│ │ ├── pmcoa.py
│ │ ├── radiology_feature_prompt.json
│ │ ├── radiopaedia.py
│ │ ├── report_prompt.json
│ │ ├── spinexr_prompt.json
│ │ ├── vqa.py
│ │ └── yes_no_prompt.json
│ ├── multi_dataset.py
│ ├── multi_dataset_test.py
│ └── multi_dataset_test_for_close.py
├── Model/
│ └── RadFM/
│ ├── __init__.py
│ ├── blocks.py
│ ├── helpers.py
│ ├── multimodality_model.py
│ ├── my_embedding_layer.py
│ ├── position_encoding.py
│ ├── transformer_decoder.py
│ ├── utils.py
│ └── vit_3d.py
├── My_Trainer/
│ └── trainer.py
├── datasampler.py
├── output_csv_example/
│ └── caption_example.csv
├── test.py
└── train.py
SYMBOL INDEX (381 symbols across 35 files)
FILE: Quick_demo/Model/RadFM/blocks.py
class PMC_CLIP_cfg (line 9) | class PMC_CLIP_cfg:
class Bottleneck (line 25) | class Bottleneck(nn.Module):
method __init__ (line 28) | def __init__(self, inplanes, planes, stride=1):
method forward (line 57) | def forward(self, x: torch.Tensor):
class AttentionPool2d (line 73) | class AttentionPool2d(nn.Module):
method __init__ (line 74) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
method forward (line 83) | def forward(self, x):
class ResNet (line 110) | class ResNet(nn.Module):
method __init__ (line 115) | def __init__(
method _make_layer (line 144) | def _make_layer(
method init_parameters (line 157) | def init_parameters(self):
method lock (line 163) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
method set_grad_checkpointing (line 171) | def set_grad_checkpointing(self, enable=True):
method stem (line 175) | def stem(self, x):
method forward (line 180) | def forward(self, x):
class ModifiedResNet (line 200) | class ModifiedResNet(nn.Module):
method __init__ (line 208) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
method _make_layer (line 237) | def _make_layer(self, planes, blocks, stride=1):
method init_parameters (line 246) | def init_parameters(self):
method lock (line 259) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
method set_grad_checkpointing (line 267) | def set_grad_checkpointing(self, enable=True):
method stem (line 271) | def stem(self, x):
method forward (line 278) | def forward(self, x):
class LayerNorm (line 294) | class LayerNorm(nn.LayerNorm):
method forward (line 297) | def forward(self, x: torch.Tensor):
class QuickGELU (line 303) | class QuickGELU(nn.Module):
method forward (line 305) | def forward(self, x: torch.Tensor):
class ResidualAttentionBlock (line 309) | class ResidualAttentionBlock(nn.Module):
method __init__ (line 310) | def __init__(
method attention (line 330) | def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor]...
method forward (line 333) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
class PatchDropout (line 339) | class PatchDropout(nn.Module):
method __init__ (line 344) | def __init__(self, prob, exclude_first_token=True):
method forward (line 350) | def forward(self, x):
class Transformer (line 379) | class Transformer(nn.Module):
method __init__ (line 380) | def __init__(
method forward (line 394) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
FILE: Quick_demo/Model/RadFM/helpers.py
function exists (line 11) | def exists(val):
function FeedForward (line 15) | def FeedForward(dim, mult=4):
class PerceiverAttention (line 25) | class PerceiverAttention(nn.Module):
method __init__ (line 26) | def __init__(self, *, dim, dim_head=64, heads=8):
method forward (line 39) | def forward(self, x, latents):
class PerceiverResampler (line 68) | class PerceiverResampler(nn.Module):
method __init__ (line 69) | def __init__(
method forward (line 107) | def forward(self, x):
class MaskedCrossAttention (line 138) | class MaskedCrossAttention(nn.Module):
method __init__ (line 139) | def __init__(
method forward (line 162) | def forward(self, x, media, media_locations=None, attend_previous=True):
class GatedCrossAttentionBlock (line 232) | class GatedCrossAttentionBlock(nn.Module):
method __init__ (line 233) | def __init__(
method forward (line 256) | def forward(
FILE: Quick_demo/Model/RadFM/multimodality_model.py
class MultiLLaMAForCausalLM (line 13) | class MultiLLaMAForCausalLM(nn.Module):
method __init__ (line 18) | def __init__(self, lang_model_path):
method forward (line 44) | def forward(self, lang_x, vision_x, attention_mask, labels, loss_rewei...
method generate (line 119) | def generate(self, lang_x, vision_x):
FILE: Quick_demo/Model/RadFM/my_embedding_layer.py
class MyEmbedding (line 18) | class MyEmbedding(nn.Module):
method __init__ (line 22) | def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver...
method forward (line 100) | def forward(self, text_input, vision_x, key_words_query=None):
FILE: Quick_demo/Model/RadFM/position_encoding.py
class PositionEmbeddingSine (line 11) | class PositionEmbeddingSine(nn.Module):
method __init__ (line 16) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
method forward (line 27) | def forward(self, tensor_list):
class PositionEmbeddingLearned (line 50) | class PositionEmbeddingLearned(nn.Module):
method __init__ (line 54) | def __init__(self, num_pos_feats=256):
method reset_parameters (line 60) | def reset_parameters(self):
method forward (line 64) | def forward(self, tensor_list):
class PositionEmbeddingLearned3d (line 77) | class PositionEmbeddingLearned3d(nn.Module):
method __init__ (line 81) | def __init__(self, num_pos_feats=256,h_patch_num = 16, w_patch_num = 1...
method reset_parameters (line 91) | def reset_parameters(self):
method forward (line 96) | def forward(self, B, h, w, d,x):
function build_position_encoding (line 107) | def build_position_encoding(args):
FILE: Quick_demo/Model/RadFM/transformer_decoder.py
class TransformerDecoder (line 16) | class TransformerDecoder(nn.Module):
method __init__ (line 18) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
method forward (line 25) | def forward(self, tgt, memory,
class TransformerDecoderLayer (line 59) | class TransformerDecoderLayer(nn.Module):
method __init__ (line 61) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
method with_pos_embed (line 80) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
method forward_post (line 83) | def forward_post(self, tgt, memory,
method forward_pre (line 109) | def forward_pre(self, tgt, memory,
method forward (line 132) | def forward(self, tgt, memory,
function _get_clones (line 147) | def _get_clones(module, N):
function _get_activation_fn (line 152) | def _get_activation_fn(activation):
FILE: Quick_demo/Model/RadFM/utils.py
function extend_instance (line 6) | def extend_instance(obj, mixin):
function getattr_recursive (line 15) | def getattr_recursive(obj, att):
function setattr_recursive (line 29) | def setattr_recursive(obj, att, val):
function get_visual_encoder (line 40) | def get_visual_encoder(model_str):
function vision_load_pretrain (line 69) | def vision_load_pretrain(resnet,model_path):
FILE: Quick_demo/Model/RadFM/vit_3d.py
function pair (line 10) | def pair(t):
class PreNorm (line 15) | class PreNorm(nn.Module):
method __init__ (line 16) | def __init__(self, dim, fn):
method forward (line 20) | def forward(self, x, **kwargs):
class FeedForward (line 23) | class FeedForward(nn.Module):
method __init__ (line 24) | def __init__(self, dim, hidden_dim, dropout = 0.):
method forward (line 33) | def forward(self, x):
class Attention (line 36) | class Attention(nn.Module):
method __init__ (line 37) | def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
method forward (line 55) | def forward(self, x):
class Transformer (line 68) | class Transformer(nn.Module):
method __init__ (line 69) | def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
method forward (line 77) | def forward(self, x):
class ViT (line 83) | class ViT(nn.Module):
method __init__ (line 84) | def __init__(self, *, image_size, image_patch_size, frames, frame_patc...
method forward (line 113) | def forward(self, video):
FILE: Quick_demo/test.py
function get_tokenizer (line 14) | def get_tokenizer(tokenizer_path, max_img_size=100, image_num=32):
function combine_and_preprocess (line 59) | def combine_and_preprocess(question, image_list, image_padding_tokens):
function main (line 110) | def main():
FILE: src/Dataset/dataset/MedPix_dataset.py
class MedPix_Single_Dataset (line 21) | class MedPix_Single_Dataset(Dataset):
method __init__ (line 28) | def __init__(self, csv_path, img_root="/gpfs/home/cs/leijiayu/data/Med...
method __len__ (line 146) | def __len__(self):
method get_image (line 150) | def get_image(self, img_path):
method __getitem__ (line 166) | def __getitem__(self, idx):
class MedPix_Multi_Dataset (line 246) | class MedPix_Multi_Dataset(Dataset):
method __init__ (line 253) | def __init__(self, csv_path, img_root="/gpfs/home/cs/leijiayu/data/Med...
method __len__ (line 376) | def __len__(self):
method get_image (line 380) | def get_image(self, img_path):
method __getitem__ (line 396) | def __getitem__(self, idx):
class MedPix_QA_Dataset (line 475) | class MedPix_QA_Dataset(Dataset):
method __init__ (line 481) | def __init__(self, csv_path, img_root="/gpfs/home/cs/leijiayu/data/Med...
method __len__ (line 498) | def __len__(self):
method get_image (line 502) | def get_image(self, img_path):
method __getitem__ (line 518) | def __getitem__(self, idx):
FILE: src/Dataset/dataset/binary.py
class Binary_Dataset (line 24) | class Binary_Dataset(Dataset):
method __init__ (line 37) | def __init__(self,csv_path,prompt_json_file):
method __len__ (line 50) | def __len__(self):
method __getitem__ (line 53) | def __getitem__(self, index):
FILE: src/Dataset/dataset/case_report.py
class CaseReport_dataset (line 19) | class CaseReport_dataset(Dataset):
method __init__ (line 26) | def __init__(self, csv_path, img_path):
method __len__ (line 48) | def __len__(self):
method __getitem__ (line 52) | def __getitem__(self, idx):
FILE: src/Dataset/dataset/chestxray.py
class ChestXray_Dataset (line 24) | class ChestXray_Dataset(Dataset):
method __init__ (line 38) | def __init__(self,csv_path,prompt_json_file):
method __len__ (line 49) | def __len__(self):
method __getitem__ (line 52) | def __getitem__(self, index):
FILE: src/Dataset/dataset/dicom_to_png_for_VinDR_sampled_using_mammo.py
function dcm_to_png (line 14) | def dcm_to_png(dcm_path,save_png_path):
function preprocess_csv (line 23) | def preprocess_csv(csv_path,data_dir,save_data_dir):
FILE: src/Dataset/dataset/jpg2nii_data_convert.py
function get_image (line 13) | def get_image(single_image_dir,single_image_filenames):
function convert_case (line 36) | def convert_case(case_id,image_root_dir,json_root_dir,save_case_dict,sav...
FILE: src/Dataset/dataset/nii2npy_for_radiopaedio.py
function resize_array (line 13) | def resize_array(array_list, shape_list):
function process_image_list (line 37) | def process_image_list(image_path_list):
function process_json_file (line 76) | def process_json_file(json_file,save_json_file,save_root_dir):
FILE: src/Dataset/dataset/paper_inline.py
class Paper_Inline_dataset (line 18) | class Paper_Inline_dataset(Dataset):
method __init__ (line 25) | def __init__(self, csv_path, img_path, sample_sentence_length=50, max_...
method __len__ (line 52) | def __len__(self):
method __getitem__ (line 56) | def __getitem__(self, idx):
method random_sample_sentence (line 83) | def random_sample_sentence(self, sentences_list, PMC_name):
FILE: src/Dataset/dataset/pmcoa.py
class PMCOA_Dataset (line 25) | class PMCOA_Dataset(Dataset):
method __init__ (line 47) | def __init__(self, csv_path, img_root_dir, prompt_json_file):
method __len__ (line 78) | def __len__(self):
method __getitem__ (line 82) | def __getitem__(self, index):
FILE: src/Dataset/dataset/radiopaedia.py
class Radio_Modality_Dataset (line 28) | class Radio_Modality_Dataset(Dataset):
method __init__ (line 41) | def __init__(self,csv_path,prompt_json_file,modality_json_file,down_sa...
method resize_image (line 53) | def resize_image(self, image):
method __len__ (line 69) | def __len__(self):
method __getitem__ (line 72) | def __getitem__(self, index):
class RadioVQA_Dataset (line 125) | class RadioVQA_Dataset(Dataset):
method __init__ (line 139) | def __init__(self,csv_path):
method __len__ (line 146) | def __len__(self):
method __getitem__ (line 149) | def __getitem__(self, index):
class RadioCaption_Dataset (line 186) | class RadioCaption_Dataset(Dataset):
method __init__ (line 187) | def __init__(self,json_path,prompt_json_file):
method __len__ (line 193) | def __len__(self):
method __getitem__ (line 196) | def __getitem__(self, index):
class Radiofeatures_Dataset (line 237) | class Radiofeatures_Dataset(Dataset):
method __init__ (line 238) | def __init__(self,json_path,prompt_json_file,disease_prompt_json_file,...
method __len__ (line 248) | def __len__(self):
method __getitem__ (line 251) | def __getitem__(self, index):
FILE: src/Dataset/dataset/vqa.py
class VQA_Dataset (line 24) | class VQA_Dataset(Dataset):
method __init__ (line 36) | def __init__(self,csv_path):
method __len__ (line 49) | def __len__(self):
method __getitem__ (line 52) | def __getitem__(self, index):
FILE: src/Dataset/multi_dataset.py
class umls_extractor (line 23) | class umls_extractor:
method __init__ (line 27) | def __init__(self):
method extract (line 34) | def extract(self, text):
function find_position (line 48) | def find_position(label, key_embeddings):
function stack_images (line 69) | def stack_images(images):
class multi_dataset (line 115) | class multi_dataset(Dataset):
method __init__ (line 120) | def __init__(self, text_tokenizer, max_seq=2048, max_img_size=100, ima...
method __len__ (line 338) | def __len__(self):
method __getitem__ (line 342) | def __getitem__(self, idx):
method text_add_image (line 434) | def text_add_image(self, images, question, answer):
FILE: src/Dataset/multi_dataset_test.py
function stack_images (line 20) | def stack_images(images):
class multi_dataset (line 71) | class multi_dataset(Dataset):
method __init__ (line 75) | def __init__(self, text_tokenizer, test_split='close', max_seq=2048, m...
method __len__ (line 277) | def __len__(self):
method __getitem__ (line 281) | def __getitem__(self, idx):
method text_add_image (line 333) | def text_add_image(self, images, question, answer):
FILE: src/Dataset/multi_dataset_test_for_close.py
function find_position (line 19) | def find_position(label, key_embeddings):
function stack_images (line 30) | def stack_images(images):
class multi_dataset_close (line 61) | class multi_dataset_close(Dataset):
method __init__ (line 62) | def __init__(self, text_tokenizer, test_split = 'close', max_seq = 204...
method __len__ (line 176) | def __len__(self):
method __getitem__ (line 179) | def __getitem__(self, idx):
method text_add_image (line 247) | def text_add_image(self,images,question,answer):
FILE: src/Model/RadFM/blocks.py
class PMC_CLIP_cfg (line 9) | class PMC_CLIP_cfg:
class Bottleneck (line 25) | class Bottleneck(nn.Module):
method __init__ (line 28) | def __init__(self, inplanes, planes, stride=1):
method forward (line 57) | def forward(self, x: torch.Tensor):
class AttentionPool2d (line 73) | class AttentionPool2d(nn.Module):
method __init__ (line 74) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
method forward (line 83) | def forward(self, x):
class ResNet (line 110) | class ResNet(nn.Module):
method __init__ (line 115) | def __init__(
method _make_layer (line 144) | def _make_layer(
method init_parameters (line 157) | def init_parameters(self):
method lock (line 163) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
method set_grad_checkpointing (line 171) | def set_grad_checkpointing(self, enable=True):
method stem (line 175) | def stem(self, x):
method forward (line 180) | def forward(self, x):
class ModifiedResNet (line 200) | class ModifiedResNet(nn.Module):
method __init__ (line 208) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
method _make_layer (line 237) | def _make_layer(self, planes, blocks, stride=1):
method init_parameters (line 246) | def init_parameters(self):
method lock (line 259) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
method set_grad_checkpointing (line 267) | def set_grad_checkpointing(self, enable=True):
method stem (line 271) | def stem(self, x):
method forward (line 278) | def forward(self, x):
class LayerNorm (line 294) | class LayerNorm(nn.LayerNorm):
method forward (line 297) | def forward(self, x: torch.Tensor):
class QuickGELU (line 303) | class QuickGELU(nn.Module):
method forward (line 305) | def forward(self, x: torch.Tensor):
class ResidualAttentionBlock (line 309) | class ResidualAttentionBlock(nn.Module):
method __init__ (line 310) | def __init__(
method attention (line 330) | def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor]...
method forward (line 333) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
class PatchDropout (line 339) | class PatchDropout(nn.Module):
method __init__ (line 344) | def __init__(self, prob, exclude_first_token=True):
method forward (line 350) | def forward(self, x):
class Transformer (line 379) | class Transformer(nn.Module):
method __init__ (line 380) | def __init__(
method forward (line 394) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
FILE: src/Model/RadFM/helpers.py
function exists (line 11) | def exists(val):
function FeedForward (line 15) | def FeedForward(dim, mult=4):
class PerceiverAttention (line 25) | class PerceiverAttention(nn.Module):
method __init__ (line 26) | def __init__(self, *, dim, dim_head=64, heads=8):
method forward (line 39) | def forward(self, x, latents):
class PerceiverResampler (line 68) | class PerceiverResampler(nn.Module):
method __init__ (line 69) | def __init__(
method forward (line 107) | def forward(self, x):
class MaskedCrossAttention (line 138) | class MaskedCrossAttention(nn.Module):
method __init__ (line 139) | def __init__(
method forward (line 162) | def forward(self, x, media, media_locations=None, attend_previous=True):
class GatedCrossAttentionBlock (line 232) | class GatedCrossAttentionBlock(nn.Module):
method __init__ (line 233) | def __init__(
method forward (line 256) | def forward(
FILE: src/Model/RadFM/multimodality_model.py
class MultiLLaMAForCausalLM (line 13) | class MultiLLaMAForCausalLM(nn.Module):
method __init__ (line 18) | def __init__(self, lang_model_path):
method forward (line 44) | def forward(self, lang_x, vision_x, attention_mask, labels, loss_rewei...
method generate (line 119) | def generate(self, lang_x, vision_x):
FILE: src/Model/RadFM/my_embedding_layer.py
class MyEmbedding (line 18) | class MyEmbedding(nn.Module):
method __init__ (line 22) | def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver...
method forward (line 100) | def forward(self, text_input, vision_x, key_words_query=None):
FILE: src/Model/RadFM/position_encoding.py
class PositionEmbeddingSine (line 11) | class PositionEmbeddingSine(nn.Module):
method __init__ (line 16) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
method forward (line 27) | def forward(self, tensor_list):
class PositionEmbeddingLearned (line 50) | class PositionEmbeddingLearned(nn.Module):
method __init__ (line 54) | def __init__(self, num_pos_feats=256):
method reset_parameters (line 60) | def reset_parameters(self):
method forward (line 64) | def forward(self, tensor_list):
class PositionEmbeddingLearned3d (line 77) | class PositionEmbeddingLearned3d(nn.Module):
method __init__ (line 81) | def __init__(self, num_pos_feats=256,h_patch_num = 16, w_patch_num = 1...
method reset_parameters (line 91) | def reset_parameters(self):
method forward (line 96) | def forward(self, B, h, w, d,x):
function build_position_encoding (line 107) | def build_position_encoding(args):
FILE: src/Model/RadFM/transformer_decoder.py
class TransformerDecoder (line 16) | class TransformerDecoder(nn.Module):
method __init__ (line 18) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
method forward (line 25) | def forward(self, tgt, memory,
class TransformerDecoderLayer (line 59) | class TransformerDecoderLayer(nn.Module):
method __init__ (line 61) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
method with_pos_embed (line 80) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
method forward_post (line 83) | def forward_post(self, tgt, memory,
method forward_pre (line 109) | def forward_pre(self, tgt, memory,
method forward (line 132) | def forward(self, tgt, memory,
function _get_clones (line 147) | def _get_clones(module, N):
function _get_activation_fn (line 152) | def _get_activation_fn(activation):
FILE: src/Model/RadFM/utils.py
function extend_instance (line 6) | def extend_instance(obj, mixin):
function getattr_recursive (line 15) | def getattr_recursive(obj, att):
function setattr_recursive (line 29) | def setattr_recursive(obj, att, val):
function get_visual_encoder (line 40) | def get_visual_encoder(model_str):
function vision_load_pretrain (line 69) | def vision_load_pretrain(resnet,model_path):
FILE: src/Model/RadFM/vit_3d.py
function pair (line 10) | def pair(t):
class PreNorm (line 15) | class PreNorm(nn.Module):
method __init__ (line 16) | def __init__(self, dim, fn):
method forward (line 20) | def forward(self, x, **kwargs):
class FeedForward (line 23) | class FeedForward(nn.Module):
method __init__ (line 24) | def __init__(self, dim, hidden_dim, dropout = 0.):
method forward (line 33) | def forward(self, x):
class Attention (line 36) | class Attention(nn.Module):
method __init__ (line 37) | def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
method forward (line 55) | def forward(self, x):
class Transformer (line 68) | class Transformer(nn.Module):
method __init__ (line 69) | def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
method forward (line 77) | def forward(self, x):
class ViT (line 83) | class ViT(nn.Module):
method __init__ (line 84) | def __init__(self, *, image_size, image_patch_size, frames, frame_patc...
method forward (line 113) | def forward(self, video):
FILE: src/My_Trainer/trainer.py
class Trainer (line 230) | class Trainer:
method __init__ (line 315) | def __init__(
method add_callback (line 701) | def add_callback(self, callback):
method pop_callback (line 712) | def pop_callback(self, callback):
method remove_callback (line 728) | def remove_callback(self, callback):
method _move_model_to_device (line 739) | def _move_model_to_device(self, model, device):
method _set_signature_columns_if_needed (line 745) | def _set_signature_columns_if_needed(self):
method _remove_unused_columns (line 753) | def _remove_unused_columns(self, dataset: "datasets.Dataset", descript...
method _get_collator_with_removed_columns (line 779) | def _get_collator_with_removed_columns(
method _get_train_sampler (line 797) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
method get_train_dataloader (line 868) | def get_train_dataloader(self) -> DataLoader:
method _get_eval_sampler (line 936) | def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.u...
method get_eval_dataloader (line 965) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
method get_test_dataloader (line 1015) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
method create_optimizer_and_scheduler (line 1063) | def create_optimizer_and_scheduler(self, num_training_steps: int):
method create_optimizer (line 1079) | def create_optimizer(self):
method get_optimizer_cls_and_kwargs (line 1136) | def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any...
method create_scheduler (line 1226) | def create_scheduler(self, num_training_steps: int, optimizer: torch.o...
method num_examples (line 1243) | def num_examples(self, dataloader: DataLoader) -> int:
method _hp_search_setup (line 1257) | def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
method _report_to_hp_search (line 1298) | def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, ...
method _tune_save_checkpoint (line 1316) | def _tune_save_checkpoint(self):
method call_model_init (line 1329) | def call_model_init(self, trial=None):
method torch_jit_model_eval (line 1343) | def torch_jit_model_eval(self, model, dataloader, training=False):
method ipex_optimize_model (line 1381) | def ipex_optimize_model(self, model, training=False, dtype=torch.float...
method _wrap_model (line 1404) | def _wrap_model(self, model, training=True, dataloader=None):
method train (line 1601) | def train(
method _inner_training_loop (line 1687) | def _inner_training_loop(
method _get_output_dir (line 2099) | def _get_output_dir(self, trial):
method _load_from_checkpoint (line 2119) | def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
method _load_best_model (line 2190) | def _load_best_model(self):
method _issue_warnings_after_load (line 2259) | def _issue_warnings_after_load(self, load_result):
method _maybe_log_save_evaluate (line 2272) | def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignor...
method _load_rng_state (line 2313) | def _load_rng_state(self, checkpoint):
method _save_checkpoint (line 2354) | def _save_checkpoint(self, model, trial, metrics=None):
method _load_optimizer_and_scheduler (line 2460) | def _load_optimizer_and_scheduler(self, checkpoint):
method hyperparameter_search (line 2520) | def hyperparameter_search(
method log (line 2616) | def log(self, logs: Dict[str, float]) -> None:
method _prepare_input (line 2633) | def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torc...
method _prepare_inputs (line 2651) | def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]])...
method compute_loss_context_manager (line 2667) | def compute_loss_context_manager(self):
method autocast_smart_context_manager (line 2673) | def autocast_smart_context_manager(self, cache_enabled: Optional[bool]...
method training_step (line 2692) | def training_step(self, model: nn.Module, inputs: Dict[str, Union[torc...
method compute_loss (line 2740) | def compute_loss(self, model, inputs, return_outputs=False):
method is_local_process_zero (line 2772) | def is_local_process_zero(self) -> bool:
method is_world_process_zero (line 2779) | def is_world_process_zero(self) -> bool:
method save_model (line 2791) | def save_model(self, output_dir: Optional[str] = None, _internal_call:...
method _save_tpu (line 2855) | def _save_tpu(self, output_dir: Optional[str] = None):
method _save (line 2883) | def _save(self, output_dir: Optional[str] = None, state_dict=None):
method store_flos (line 2915) | def store_flos(self):
method _sorted_checkpoints (line 2926) | def _sorted_checkpoints(
method _rotate_checkpoints (line 2950) | def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
method evaluate (line 2975) | def evaluate(
method predict (line 3046) | def predict(
method evaluation_loop (line 3108) | def evaluation_loop(
method _nested_gather (line 3319) | def _nested_gather(self, tensors, name=None):
method _pad_across_processes (line 3337) | def _pad_across_processes(self, tensor, pad_index=-100):
method prediction_step (line 3371) | def prediction_step(
method floating_point_ops (line 3476) | def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any...
method init_git_repo (line 3494) | def init_git_repo(self, at_init: bool = False):
method create_model_card (line 3541) | def create_model_card(
method _push_from_checkpoint (line 3597) | def _push_from_checkpoint(self, checkpoint_folder):
method push_to_hub (line 3639) | def push_to_hub(self, commit_message: Optional[str] = "End of training...
method prediction_loop (line 3699) | def prediction_loop(
method _gather_and_numpify (line 3845) | def _gather_and_numpify(self, tensors, name):
method _add_sm_patterns_to_gitignore (line 3861) | def _add_sm_patterns_to_gitignore(self) -> None:
FILE: src/datasampler.py
function make_batch (line 10) | def make_batch(index_list, batch_size, drop_last):
function batch_generation (line 23) | def batch_generation(dataset,batch_size_2D, batch_size_3D,drop_last=Fals...
class My_DistributedBatchSampler (line 44) | class My_DistributedBatchSampler(Sampler):
method __init__ (line 59) | def __init__(self, dataset, num_replicas=None, rank=None, batch_size_2...
method __iter__ (line 93) | def __iter__(self):
method __len__ (line 120) | def __len__(self):
method set_epoch (line 123) | def set_epoch(self, epoch: int) -> None:
FILE: src/test.py
function setup_seed (line 18) | def setup_seed(seed):
class ModelArguments (line 36) | class ModelArguments:
class DataArguments (line 46) | class DataArguments:
class TrainingArguments (line 54) | class TrainingArguments(transformers.TrainingArguments):
class DataCollator (line 68) | class DataCollator(object):
method __call__ (line 73) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
function main (line 134) | def main():
FILE: src/train.py
function compute_metrics (line 18) | def compute_metrics(eval_preds):
class ModelArguments (line 35) | class ModelArguments:
class DataArguments (line 46) | class DataArguments:
class TrainingArguments (line 53) | class TrainingArguments(transformers.TrainingArguments):
class DataCollator (line 67) | class DataCollator(object):
method __call__ (line 73) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
function main (line 137) | def main():
Condensed preview — 60 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,586K chars).
[
{
"path": "LICENSE",
"chars": 1066,
"preview": "MIT License\n\nCopyright (c) 2025 chaoyi-wu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\n"
},
{
"path": "Quick_demo/Language_files/config.json",
"chars": 593,
"preview": "{\n \"_name_or_path\": \"/home/cs/leijiayu/wuchaoyi/Finetune_LLAMA/LLAMA_Model/llama-13b-hf\",\n \"architectures\": [\n \"Lla"
},
{
"path": "Quick_demo/Language_files/special_tokens_map.json",
"chars": 2,
"preview": "{}"
},
{
"path": "Quick_demo/Language_files/tokenizer_config.json",
"chars": 141,
"preview": "{\"bos_token\": \"\", \"eos_token\": \"\", \"model_max_length\": 1000000000000000019884624838656, \"tokenizer_class\": \"LlamaTokeniz"
},
{
"path": "Quick_demo/MedKEBERT/config.json",
"chars": 690,
"preview": "{\n \"_name_or_path\": \"xmcmic/Med-KEBERT\",\n \"architectures\": [\n \"BertModel\"\n ],\n \"attention_probs_dropout_prob\": 0."
},
{
"path": "Quick_demo/MedKEBERT/special_tokens_map.json",
"chars": 125,
"preview": "{\n \"cls_token\": \"[CLS]\",\n \"mask_token\": \"[MASK]\",\n \"pad_token\": \"[PAD]\",\n \"sep_token\": \"[SEP]\",\n \"unk_token\": \"[UNK"
},
{
"path": "Quick_demo/MedKEBERT/tokenizer.json",
"chars": 676150,
"preview": "{\n \"version\": \"1.0\",\n \"truncation\": null,\n \"padding\": null,\n \"added_tokens\": [\n {\n \"id\": 0,\n \"content\":"
},
{
"path": "Quick_demo/MedKEBERT/tokenizer_config.json",
"chars": 373,
"preview": "{\n \"cls_token\": \"[CLS]\",\n \"do_basic_tokenize\": true,\n \"do_lower_case\": true,\n \"mask_token\": \"[MASK]\",\n \"name_or_pat"
},
{
"path": "Quick_demo/MedKEBERT/vocab.txt",
"chars": 222309,
"preview": "[PAD]\n[UNK]\n[CLS]\n[SEP]\n[MASK]\n!\n#\n$\n%\n&\n'\n(\n)\n*\n+\n,\n-\n.\n/\n0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n:\n;\n<\n=\n>\n?\n@\n[\n\\\n]\n^\n_\n`\na\nb\nc\nd\ne\nf\ng\nh"
},
{
"path": "Quick_demo/Model/RadFM/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "Quick_demo/Model/RadFM/blocks.py",
"chars": 14712,
"preview": "from collections import OrderedDict\nfrom typing import Tuple, Union, Callable, Optional\n\nimport torch\nimport torch.nn.fu"
},
{
"path": "Quick_demo/Model/RadFM/helpers.py",
"chars": 8535,
"preview": "\"\"\"\nTaken from https://github.com/lucidrains/flamingo-pytorch\n\"\"\"\n\nimport torch\nfrom einops import rearrange, repeat\nfro"
},
{
"path": "Quick_demo/Model/RadFM/multimodality_model.py",
"chars": 5754,
"preview": "# Import necessary libraries\nfrom torch import nn\nfrom transformers.models.llama import LlamaForCausalLM\nfrom .my_embedd"
},
{
"path": "Quick_demo/Model/RadFM/my_embedding_layer.py",
"chars": 12216,
"preview": "# Import necessary libraries\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch \nfrom .helpers import Pe"
},
{
"path": "Quick_demo/Model/RadFM/position_encoding.py",
"chars": 4861,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nVarious positional encodings for the transfor"
},
{
"path": "Quick_demo/Model/RadFM/transformer_decoder.py",
"chars": 6692,
"preview": "\"\"\"\nCode modified from DETR tranformer:\nhttps://github.com/facebookresearch/detr\nCopyright (c) Facebook, Inc. and its af"
},
{
"path": "Quick_demo/Model/RadFM/utils.py",
"chars": 2549,
"preview": "from .blocks import ModifiedResNet,PMC_CLIP_cfg\nimport torch\nfrom torchvision import transforms\nfrom PIL import Image\nim"
},
{
"path": "Quick_demo/Model/RadFM/vit_3d.py",
"chars": 4433,
"preview": "import torch\nfrom torch import nn\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom ."
},
{
"path": "Quick_demo/test.py",
"chars": 6791,
"preview": "# Import necessary libraries for data processing, model loading, and inference\nimport tqdm.auto as tqdm\nimport torch.nn."
},
{
"path": "README.md",
"chars": 18143,
"preview": "# RadFM\nThe official code for the paper \"Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D"
},
{
"path": "requirements.txt",
"chars": 456,
"preview": "einops==0.6.1\neinops-exts==0.0.4\nhuggingface-hub==0.16.4\nnibabel==5.1.0\nnmslib==2.1.1\nopencv-python==4.8.0.76\npandas==2."
},
{
"path": "src/Dataset/dataset/MedPix_dataset.py",
"chars": 26429,
"preview": "# Import necessary libraries for data processing, image handling, and model integration\nfrom torch.utils.data import Dat"
},
{
"path": "src/Dataset/dataset/__init__.py",
"chars": 426,
"preview": "from .radiopaedia import RadioVQA_Dataset,Radio_Modality_Dataset,Radiofeatures_Dataset,RadioCaption_Dataset\nfrom .binary"
},
{
"path": "src/Dataset/dataset/binary.py",
"chars": 2614,
"preview": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc "
},
{
"path": "src/Dataset/dataset/caption_prompt.json",
"chars": 1320,
"preview": "{\n \"caption_prompt\": [\n \"Can you provide a caption consists of finding and impression for this medical ima"
},
{
"path": "src/Dataset/dataset/case_report.py",
"chars": 4598,
"preview": "# Import necessary libraries for data processing, image handling, and model integration\nfrom torch.utils.data import Dat"
},
{
"path": "src/Dataset/dataset/chestxray.py",
"chars": 2937,
"preview": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc "
},
{
"path": "src/Dataset/dataset/cls_prompt.json",
"chars": 803,
"preview": "{\n \"caption_prompt\": [\n \"What is the diagnosis for this chest X-ray?\",\n \"Based on this X-ray, w"
},
{
"path": "src/Dataset/dataset/data_csv/README.md",
"chars": 192,
"preview": "Please check the [data_csv](https://huggingface.co/datasets/chaoyi-wu/RadFM_data_csv) to download the used train/test sp"
},
{
"path": "src/Dataset/dataset/dicom_to_png_for_VinDR_sampled_using_mammo.py",
"chars": 1423,
"preview": "import os\nimport cv2\nimport csv\nimport json\nimport imageio\n\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm"
},
{
"path": "src/Dataset/dataset/jpg2nii_data_convert.py",
"chars": 5242,
"preview": "#processed cases accoring to case_id_list, and save a csv file, with image path and image caption\nimport os\nimport cv2\ni"
},
{
"path": "src/Dataset/dataset/mammo_prompt.json",
"chars": 1482,
"preview": "{\n \"caption_prompt\": [\n \"What is the diagnosis for this mammogram?\",\n \"Based on this X-ray, wha"
},
{
"path": "src/Dataset/dataset/modality_prompt.json",
"chars": 470,
"preview": "{\n \"caption_prompt\": [\n \"What modality is used to take this image?\",\n \"What type of imaging modality is"
},
{
"path": "src/Dataset/dataset/nii2npy_for_radiopaedio.py",
"chars": 5031,
"preview": "import os\nimport cv2\nimport csv\nimport json\nimport subprocess\nimport pandas as pd\nimport numpy as np\nimport SimpleITK as"
},
{
"path": "src/Dataset/dataset/paper_inline.py",
"chars": 7428,
"preview": "# Import necessary libraries for data processing, image handling, and model integration\nfrom torch.utils.data import Dat"
},
{
"path": "src/Dataset/dataset/pmcoa.py",
"chars": 5402,
"preview": "# Import necessary libraries for data processing, image handling, and model interaction\nimport csv\nimport json\nimport lo"
},
{
"path": "src/Dataset/dataset/radiology_feature_prompt.json",
"chars": 1562,
"preview": "{\n \"caption_prompt\": [\n \"What disease can be diagnosed from these radiological images and what specific featur"
},
{
"path": "src/Dataset/dataset/radiopaedia.py",
"chars": 16312,
"preview": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport cv2\nimport torch\nimport rando"
},
{
"path": "src/Dataset/dataset/report_prompt.json",
"chars": 1248,
"preview": "{\n \"caption_prompt\": [\n \"Can you provide a radiology report for this medical image?\",\n \"Describ"
},
{
"path": "src/Dataset/dataset/spinexr_prompt.json",
"chars": 804,
"preview": "{\n \"caption_prompt\": [\n \"What is the diagnosis for this spine X-ray?\",\n \"Based on this X-ray, w"
},
{
"path": "src/Dataset/dataset/vqa.py",
"chars": 2871,
"preview": "import csv\nimport json\nimport logging\nimport os\nimport re\nimport difflib\nimport sys\nimport torch\nimport random\nfrom abc "
},
{
"path": "src/Dataset/dataset/yes_no_prompt.json",
"chars": 917,
"preview": "{\n \"caption_prompt\": [\n \"Is the disease visible in the image?\",\n \"Does the image show s"
},
{
"path": "src/Dataset/multi_dataset.py",
"chars": 24115,
"preview": "# Import necessary libraries for data processing and model interaction\nfrom torch.utils.data import Dataset\nimport numpy"
},
{
"path": "src/Dataset/multi_dataset_test.py",
"chars": 19438,
"preview": "# Import necessary libraries for data processing, model interaction, and image handling\nfrom torch.utils.data import Dat"
},
{
"path": "src/Dataset/multi_dataset_test_for_close.py",
"chars": 15282,
"preview": "from torch.utils.data import Dataset\nimport numpy as np\nimport transformers\nimport pandas as pd\nimport copy \nimport rand"
},
{
"path": "src/Model/RadFM/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/Model/RadFM/blocks.py",
"chars": 14712,
"preview": "from collections import OrderedDict\nfrom typing import Tuple, Union, Callable, Optional\n\nimport torch\nimport torch.nn.fu"
},
{
"path": "src/Model/RadFM/helpers.py",
"chars": 8535,
"preview": "\"\"\"\nTaken from https://github.com/lucidrains/flamingo-pytorch\n\"\"\"\n\nimport torch\nfrom einops import rearrange, repeat\nfro"
},
{
"path": "src/Model/RadFM/multimodality_model.py",
"chars": 5754,
"preview": "# Import necessary libraries\nfrom torch import nn\nfrom transformers.models.llama import LlamaForCausalLM\nfrom .my_embedd"
},
{
"path": "src/Model/RadFM/my_embedding_layer.py",
"chars": 12216,
"preview": "# Import necessary libraries\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch \nfrom .helpers import Pe"
},
{
"path": "src/Model/RadFM/position_encoding.py",
"chars": 4861,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\"\"\"\nVarious positional encodings for the transfor"
},
{
"path": "src/Model/RadFM/transformer_decoder.py",
"chars": 6692,
"preview": "\"\"\"\nCode modified from DETR tranformer:\nhttps://github.com/facebookresearch/detr\nCopyright (c) Facebook, Inc. and its af"
},
{
"path": "src/Model/RadFM/utils.py",
"chars": 2549,
"preview": "from .blocks import ModifiedResNet,PMC_CLIP_cfg\nimport torch\nfrom torchvision import transforms\nfrom PIL import Image\nim"
},
{
"path": "src/Model/RadFM/vit_3d.py",
"chars": 4433,
"preview": "import torch\nfrom torch import nn\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\nfrom ."
},
{
"path": "src/My_Trainer/trainer.py",
"chars": 188959,
"preview": "# coding=utf-8\n# Copyright 2020-present the HuggingFace Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (t"
},
{
"path": "src/datasampler.py",
"chars": 5874,
"preview": "import torch.distributed as dist\nimport math\nfrom torch.utils.data.sampler import Sampler\nfrom torch.utils.data.sampler "
},
{
"path": "src/output_csv_example/caption_example.csv",
"chars": 41877,
"preview": "Question,Ground Truth,Pred,belong_to\n['Could you provide an impression for this chest scan<image><image0><image1><image2"
},
{
"path": "src/test.py",
"chars": 7622,
"preview": "# Import necessary libraries for data processing, modeling, and utilities\nimport tqdm.auto as tqdm\nimport torch.nn.funct"
},
{
"path": "src/train.py",
"chars": 6344,
"preview": "# Import necessary libraries\nimport tqdm.auto as tqdm\nimport torch.nn.functional as F\nfrom typing import Optional, Dict,"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the chaoyi-wu/RadFM GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 60 files (1.4 MB), approximately 477.7k tokens, and a symbol index with 381 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.