update the operator.

Signed-off-by: wxywb <xy.wang@zilliz.com>

README.md

@@ -25,7 +25,7 @@ import towhee
 
 towhee.glob('./image.jpg') \
       .image_decode() \
-      .image_captioning.magic(model_name='expansionnet_rf') \
+      .image_captioning.magic(model_name='magic_mscoco') \
       .show()
 ```
 <img src="./cap.png" alt="result1" style="height:20px;"/>
@@ -37,11 +37,11 @@ import towhee
 
 towhee.glob['path']('./image.jpg') \
       .image_decode['path', 'img']() \
-      .image_captioning.magic['img', 'text'](model_name='expansionnet_rf') \
+      .image_captioning.magic['img', 'text'](model_name='magic_mscoco') \
       .select['img', 'text']() \
       .show()
 ```
-<img src="./tabular.png" alt="result2" style="height:60px;"/>
+<img src="./tab.png" alt="result2" style="height:60px;"/>
 
 
 <br />
@@ -51,7 +51,7 @@ towhee.glob['path']('./image.jpg') \
 
 Create the operator via the following factory method
 
-***expansionnet_v2(model_name)***
+***magic(model_name)***
 
 **Parameters:**
 
@@ -64,16 +64,14 @@ Create the operator via the following factory method
 
 ## Interface
 
-An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.
+An image captioning operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.
 
 
 **Parameters:**
 
 ​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* 
 
-​  The image to generate embedding.	
-
-
+​  The image to generate caption.	
 
 **Returns:** *str*
 

language_model/dataclass.py

@@ -2,7 +2,6 @@ import json
 import random
 import torch
 import numpy as np
-import progressbar
 from torch.nn.utils import rnn
 
 class Data:
@@ -67,13 +66,9 @@ class Data:
 
         res_token_list, res_token_id_list = [], []
         n = len(lines)
-        p = progressbar.ProgressBar(n)
-        p.start()
         for i in range(n):
-            p.update(i)
             text = lines[i].strip('\n')
             self.process_one_text(text, res_token_list, res_token_id_list)
-        p.finish()
         print ('{} processed!'.format(path))
         return res_token_list, res_token_id_list
 

language_model/simctg.py

@@ -1,7 +1,6 @@
 import os
 import sys
 import operator
-from tqdm import tqdm
 from operator import itemgetter
 import torch
 from torch import nn
@@ -13,9 +12,6 @@ from torch.nn import CrossEntropyLoss
 from loss_func import contrastive_loss
 from utlis import PlugAndPlayContrastiveDecodingOneStepFast
 
-import seaborn as sns
-import matplotlib.pyplot as plt
-import pandas as pd
 import datetime
 
 train_fct = CrossEntropyLoss()

language_model/train.py

@@ -8,7 +8,6 @@ import random
 import numpy as np
 import time
 import logging
-import progressbar
 
 import logging
 logging.getLogger('transformers.generation_utils').disabled = True

language_model/trainer.py

@@ -8,9 +8,7 @@ import random
 import numpy as np
 import time
 import logging
-import progressbar
 
-import logging
 logging.getLogger('transformers.generation_utils').disabled = True
 
 def eval_model(args, model, data, cuda_available, device):
@@ -19,10 +17,7 @@ def eval_model(args, model, data, cuda_available, device):
     val_loss, token_sum = 0., 0.
     model.eval()
     with torch.no_grad():
-        p = progressbar.ProgressBar(eval_step)
-        p.start()
         for idx in range(eval_step):
-            p.update(idx)
             batch_input_tensor, batch_labels, _ = \
             data.get_next_validation_batch(batch_size=dataset_batch_size, mode='test')
             if cuda_available:
@@ -33,7 +28,6 @@ def eval_model(args, model, data, cuda_available, device):
             one_val_token_sum = torch.sum(one_val_token_sum)
             val_loss += one_val_loss.item()
             token_sum += one_val_token_sum.item()
-        p.finish()
     model.train()
     val_loss = val_loss / token_sum
     return val_loss

requirements.txt

@@ -0,0 +1,4 @@
+torch
+torchvision
+numpy
+transformers