File size: 5,045 Bytes
ee6345a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import torch
@torch.no_grad()
def generate(
vlm,
samples,
use_nucleus_sampling=False,
num_beams=5,
max_length=256,
min_length=1,
top_p=0.9,
repetition_penalty=1.5,
length_penalty=1.0,
num_captions=1,
temperature=1,
):
if "prompt" in samples.keys():
prompt = samples["prompt"]
else:
prompt = vlm.prompt
image = samples["image"]
bs = image.size(0)
if isinstance(prompt, str):
prompt = [prompt] * bs
else:
assert len(prompt) == bs, "The number of prompts must be equal to the batch size."
# For TextCaps
if "ocr_tokens" in samples.keys() and "{}" in prompt[0]:
prompt = [p.format(', '.join(samples['ocr_tokens'][i][:30])) for i, p in enumerate(prompt)]
query_tokens = vlm.query_tokens.expand(bs, -1, -1)
if vlm.qformer_text_input:
# remove ocr tokens in q_former (for eval textvqa)
# qformer_prompt = prompt
# qformer_prompt = ['Question: ' + qp.split(' Question: ')[1] for qp in qformer_prompt]
text_Qformer = vlm.tokenizer(
prompt,
padding='longest',
truncation=True,
max_length=vlm.max_txt_len,
return_tensors="pt",
).to(image.device)
query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
Qformer_atts = torch.cat([query_atts,text_Qformer.attention_mask],dim=1)
# For video data
if image.dim() == 5:
inputs_t5, atts_t5 = [], []
for j in range(image.size(2)):
this_frame = image[:,:,j,:,:]
with vlm.maybe_autocast():
frame_embeds = vlm.ln_vision(vlm.visual_encoder(this_frame))
frame_atts = torch.ones(frame_embeds.size()[:-1], dtype=torch.long).to(image.device)
if vlm.qformer_text_input:
frame_query_output = vlm.Qformer.bert(
text_Qformer.input_ids,
attention_mask = Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
else:
frame_query_output = vlm.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
frame_inputs_t5 = vlm.t5_proj(frame_query_output.last_hidden_state[:,:query_tokens.size(1),:])
frame_atts_t5 = torch.ones(frame_inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
inputs_t5.append(frame_inputs_t5)
atts_t5.append(frame_atts_t5)
inputs_t5 = torch.cat(inputs_t5, dim=1)
atts_t5 = torch.cat(atts_t5, dim=1)
else:
with vlm.maybe_autocast():
image_embeds = vlm.ln_vision(vlm.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
if vlm.qformer_text_input:
query_output = vlm.Qformer.bert(
text_Qformer.input_ids,
attention_mask=Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
else:
query_output = vlm.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_t5 = vlm.t5_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
input_tokens = vlm.t5_tokenizer(
prompt,
padding="longest",
return_tensors="pt"
).to(image.device)
encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
with vlm.maybe_autocast(dtype=torch.bfloat16):
inputs_embeds = vlm.t5_model.encoder.embed_tokens(input_tokens.input_ids)
inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)
outputs = vlm.t5_model.generate(
return_dict_in_generate=True,
output_scores=True,
inputs_embeds=inputs_embeds,
attention_mask=encoder_atts,
do_sample=use_nucleus_sampling,
top_p=top_p,
temperature=temperature,
num_beams=num_beams,
max_new_tokens=max_length,
min_length=min_length,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
num_return_sequences=num_captions,
)
output_text = vlm.t5_tokenizer.batch_decode(
outputs.sequences, skip_special_tokens=True
)
return output_text, outputs.sequences_scores
|