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Text_Prompt.py
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import torch
import clip
label_text_map = []
with open('text/ntu120_label_map.txt') as infile:
lines = infile.readlines()
for ind, line in enumerate(lines):
label_text_map.append(line.rstrip().lstrip())
paste_text_map0 = []
with open('text/synonym_openai_t01.txt') as infile:
lines = infile.readlines()
for ind, line in enumerate(lines):
temp_list = line.rstrip().lstrip().split(',')
paste_text_map0.append(temp_list)
paste_text_map1 = []
with open('text/sentence_openai_t01.txt') as infile:
lines = infile.readlines()
for ind, line in enumerate(lines):
temp_list = line.rstrip().lstrip().split('.')
while len(temp_list) < 4:
temp_list.append(" ")
paste_text_map1.append(temp_list)
paste_text_map2 = []
with open('text/pasta_openai_t01.txt') as infile:
lines = infile.readlines()
for ind, line in enumerate(lines):
temp_list = line.rstrip().lstrip().split(';')
paste_text_map2.append(temp_list)
ucla_paste_text_map0 = []
with open('text/ucla_synonym_openai_t01.txt') as infile:
lines = infile.readlines()
for ind, line in enumerate(lines):
temp_list = line.rstrip().lstrip().split(',')
ucla_paste_text_map0.append(temp_list)
ucla_paste_text_map1 = []
with open('text/ucla_pasta_openai_t01.txt') as infile:
lines = infile.readlines()
for ind, line in enumerate(lines):
temp_list = line.rstrip().lstrip().split(';')
ucla_paste_text_map1.append(temp_list)
def text_prompt():
text_aug = [f"a photo of action {{}}", f"a picture of action {{}}", f"Human action of {{}}", f"{{}}, an action",
f"{{}} this is an action", f"{{}}, a video of action", f"Playing action of {{}}", f"{{}}",
f"Playing a kind of action, {{}}", f"Doing a kind of action, {{}}", f"Look, the human is {{}}",
f"Can you recognize the action of {{}}?", f"Video classification of {{}}", f"A video of {{}}",
f"The man is {{}}", f"The woman is {{}}"]
text_dict = {}
num_text_aug = len(text_aug)
for ii, txt in enumerate(text_aug):
text_dict[ii] = torch.cat([clip.tokenize(txt.format(c)) for c in label_text_map])
classes = torch.cat([v for k, v in text_dict.items()])
return classes, num_text_aug,text_dict
def text_prompt_openai_random():
print("Use text prompt openai synonym random")
total_list = []
for pasta_list in paste_text_map0:
temp_list = []
for item in pasta_list:
temp_list.append(clip.tokenize(item))
total_list.append(temp_list)
return total_list
def text_prompt_openai_random_bert():
print("Use text prompt openai synonym random bert")
total_list = []
for pasta_list in paste_text_map0:
temp_list = []
for item in pasta_list:
temp_list.append(item)
total_list.append(temp_list)
return total_list
def text_prompt_openai_pasta_pool_4part():
print("Use text prompt openai pasta pool")
text_dict = {}
num_text_aug = 5
for ii in range(num_text_aug):
if ii == 0:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[ii])) for pasta_list in paste_text_map2])
elif ii == 1:
text_dict[ii] = torch.cat([clip.tokenize((','.join(pasta_list[0:2]))) for pasta_list in paste_text_map2])
elif ii == 2:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0] +','.join(pasta_list[2:4]))) for pasta_list in paste_text_map2])
elif ii == 3:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0] +','+ pasta_list[4])) for pasta_list in paste_text_map2])
else:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0]+','+','.join(pasta_list[5:]))) for pasta_list in paste_text_map2])
classes = torch.cat([v for k, v in text_dict.items()])
return classes, num_text_aug, text_dict
def text_prompt_openai_pasta_pool_4part_bert():
print("Use text prompt openai pasta pool bert")
text_dict = {}
num_text_aug = 5
for ii in range(num_text_aug):
if ii == 0:
input_list = [pasta_list[ii] for pasta_list in paste_text_map2]
text_dict[ii] = input_list
elif ii == 1:
input_list = [','.join(pasta_list[0:2]) for pasta_list in paste_text_map2]
text_dict[ii] = input_list
elif ii == 2:
input_list = [pasta_list[0] +','.join(pasta_list[2:4]) for pasta_list in paste_text_map2]
text_dict[ii] = input_list
elif ii == 3:
input_list = [pasta_list[0] +','+ pasta_list[4] for pasta_list in paste_text_map2]
text_dict[ii] = input_list
else:
input_list = [pasta_list[0]+','+','.join(pasta_list[5:]) for pasta_list in paste_text_map2]
text_dict[ii] = input_list
return num_text_aug, text_dict
def text_prompt_openai_random_ucla():
print("Use text prompt openai synonym random UCLA")
total_list = []
for pasta_list in ucla_paste_text_map0:
temp_list = []
for item in pasta_list:
temp_list.append(clip.tokenize(item))
total_list.append(temp_list)
return total_list
def text_prompt_openai_pasta_pool_4part_ucla():
print("Use text prompt openai pasta pool ucla")
text_dict = {}
num_text_aug = 5
for ii in range(num_text_aug):
if ii == 0:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[ii])) for pasta_list in ucla_paste_text_map1])
elif ii == 1:
text_dict[ii] = torch.cat([clip.tokenize((','.join(pasta_list[0:2]))) for pasta_list in ucla_paste_text_map1])
elif ii == 2:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0] +','.join(pasta_list[2:4]))) for pasta_list in ucla_paste_text_map1])
elif ii == 3:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0] +','+ pasta_list[4])) for pasta_list in ucla_paste_text_map1])
else:
text_dict[ii] = torch.cat([clip.tokenize((pasta_list[0]+','+','.join(pasta_list[5:]))) for pasta_list in ucla_paste_text_map1])
classes = torch.cat([v for k, v in text_dict.items()])
return classes, num_text_aug, text_dict