From 2ba05399b6df5d0316ec1acde2a63c0a9b2fa1e4 Mon Sep 17 00:00:00 2001 From: Mostafa Azazi Date: Fri, 21 Feb 2020 21:23:16 +0200 Subject: [PATCH 1/5] first commit for chainerV7 --- scenes/chainer-caption/.gitignore | 2 + scenes/chainer-caption/README.md | 224 + .../chainer-caption/code/CaptionDataLoader.py | 64 + .../code/CaptionDataLoader2.py | 76 + .../chainer-caption/code/CaptionEvaluater.py | 129 + .../chainer-caption/code/CaptionGenerator.py | 276 + .../code/CaptionMultiDataLoader.py | 95 + .../code/Image2CaptionDecoder.py | 99 + scenes/chainer-caption/code/ResNet50.py | 117 + .../chainer-caption/code/ResNet50predict.py | 52 + .../code/ResNet_feature_extractor.py | 51 + scenes/chainer-caption/code/__init__.py | 0 .../CaptionGenerator.cpython-35.pyc | Bin 0 -> 8763 bytes .../Image2CaptionDecoder.cpython-35.pyc | Bin 0 -> 4254 bytes .../code/__pycache__/ResNet50.cpython-35.pyc | Bin 0 -> 4274 bytes .../__pycache__/image_loader.cpython-35.pyc | Bin 0 -> 2499 bytes 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create mode 100644 scenes/chainer-caption/webapi/uploads/.gitkeep diff --git a/scenes/chainer-caption/.gitignore b/scenes/chainer-caption/.gitignore new file mode 100644 index 0000000..8e341b9 --- /dev/null +++ b/scenes/chainer-caption/.gitignore @@ -0,0 +1,2 @@ +data/*.model +chainer_env/ \ No newline at end of file diff --git a/scenes/chainer-caption/README.md b/scenes/chainer-caption/README.md new file mode 100644 index 0000000..ff92541 --- /dev/null +++ b/scenes/chainer-caption/README.md @@ -0,0 +1,224 @@ +# image caption generation by chainer + +## Notes after two years. +I created this basically when I was almost undergrad and then things changed a lot! I feel a bit ashamed to show my dirty coding :) Well, the pretrained models are still effective and probably good if you just want to try generating captions in English/Chinese/Japanese. However, when it comes to training, probably not the best one, and many algorithmic improvements happened after I stop the maintenance of this code base… + +If you want to train image captioning, I highly recommend pytorch rather than chainer. I stopped using chainer because it always broke the compatibility when I upgrade it. For example, this code is written in chainer 1.x and never works in 2.x, 3.x, 4.x, … and 6.x (see how quickly they change the version! ). *1 + +If you still want to stay on this code, here’s some note that you should know + + +- I used python 2.7 at that time, and this code may not work with python3 or higher. +Make sure to use chainer 1.x. I tested on 1.24.0 with CUDA 8 (cuda 9 and chainer 1.x doesn’t work together). + +- I put more formal description of requirements in the later section so see it and please try to use miniconda (or anaconda) to reproduce the environment. + + +- I tried to change the code to make it possible to finetune the CNN part, then I kind of failed to document it, and now I don’t remember what I was doing :) Some files are leftovers that you don't need to use... e.g. `train_image_caption_model.py` is the leftover while `train_caption_model.py` is the good one. + + *1 This does NOT mean i do not like chainer anymore. It's still great tool to quickly prototype from scratch. I just want to say, if you want to minimize the maintenance effort for a long term, probably it is not the right tool, because of the frequent major version up that breaks the downward compatibility. + +## descrtiption + +This repository contains an implementation of typical image caption generation based on neural network (i.e. CNN + RNN). The model first extracts the image feature by CNN and then generates captions by RNN. CNN is ResNet50 and RNN is a standard LSTM . + +The training data is MSCOCO. I preprocessed MSCOCO images by extracting CNN features in advance. Then I trained the language model to generate captions. Not only English, I trained on Japanese and Chinese. + +I made pre-trained models available. For English captions, the model achieves CIDEr of 0.692 (Otheres are Bleu1: 0.657, Bleu2: 0.471, Bleu3: 0.327, Bleu4: 0.228, METEOR: 0.213, ROUGE_L: 0.47) for the MSCOCO validation dataset. The scores are increased a little bit when the beam search is used. For example, CIDEr is 0.716 with beam size of 5. If you want to achieve a better score, CNN has to be fine-tuned, but I haven’t tried because it’s computationally heavier. + + + +## requirements +- python 2.7 +- CUDA 8.0 +- chainer 1.24.0 http://chainer.org + +and some more packages. Make sure to use this version of chainer by `pip install chainer==1.24.0`. Different versions easily break the compatability. + +I use `conda` to manage the environment and here's what I did. +``` +conda create -y --prefix=conda python=2.7 +conda deactivate +conda activate ./conda +conda install --yes numpy scipy matplotlib pandas pillow #normal tools +CUDA_PATH=/usr/local/cuda-8.0/ pip install chainer==1.24.0 --no-cache-dir +CUDA_PATH=/usr/local/cuda-8.0/ pip install cupy==2.0 +pip install h5py +pip install nltk +``` +You can also see my [`env.yml`](./data/env.yml) that is basically the exact environment of mine. + + +## citation: +If you find this implementation useful, please consider to cite: +``` +@article{multilingual-caption-arxiv, +title={{Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation}}, +author={Satoshi Tsutsui, David Crandall}, +journal={arXiv:1706.06275}, +year={2017} +} + +@inproceedings{multilingual-caption, +author={Satoshi Tsutsui, David Crandall}, +booktitle = {CVPR Language and Vision Workshop}, +title = {{Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation}}, +year = {2017} +} +``` + +## I just want to generate caption! +OK, first, you need to download the models and other preprocessed files. +``` +bash download.sh +``` +Then you can generate caption. +``` +#English +python sample_code_beam.py \ +--rnn-model ./data/caption_en_model40.model \ +--cnn-model ./data/ResNet50.model \ +--vocab ./data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--gpu -1 \ +--img ./sample_imgs/COCO_val2014_000000185546.jpg \ + +#Japanese trained from machine translated Japanese (https://github.com/apple2373/mt-mscoco) +python sample_code_beam.py \ +--rnn-model ./data/caption_jp_mt_model40.model \ +--cnn-model ./data/ResNet50.model \ +--vocab ./data/MSCOCO/captions_train2014_jp_translation_processed_dic.json \ +--gpu -1 \ +--img ./sample_imgs/COCO_val2014_000000185546.jpg \ + + +#Japanese by YJCaptions (https://github.com/yahoojapan/YJCaptions) +python sample_code_beam.py \ +--rnn-model ./data/caption_jp_yj_model40.model \ +--cnn-model ./data/ResNet50.model \ +--vocab ./data/MSCOCO/yjcaptions26k_clean_processed_dic.json \ +--gpu -1 \ +--img ./sample_imgs/COCO_val2014_000000185546.jpg \ + +#Chinese trained from machine translated Chinese (https://github.com/apple2373/mt-mscoco) +python sample_code_beam.py \ +--rnn-model ./data/caption_cn_model40.model \ +--cnn-model ./data/ResNet50.model \ +--vocab ./data/MSCOCO/captions_train2014_cn_translation_processed_dic.json \ +--gpu -1 \ +--img ./sample_imgs/COCO_val2014_000000185546.jpg \ + +``` +See the help for other options. You can, for example, use beam search if you want. + +## I want to run caption generation module as a web API. +I have a simple script for that. +``` +cd webapi +python server.py --rnn-model ../data/caption_en_model40.model \ +--cnn-model ../data/ResNet50.model \ +--vocab ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--gpu -1 \ + +curl -X POST -F image=@./sample_imgs/COCO_val2014_000000185546.jpg http://localhost:8090/predict +#you should get json +``` + + +## I want to train the model by myself. +I made the preprocessed files (e.g., extracted ResNet features) available. You can download like this. +``` +bash download.sh train +``` +Then you can train like this. +``` +# Preprocessing + +cd ./code/ + +## English +## make sure to downalod captions_train2014.json from the orignal MSCOCO! +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014.json \ +--output ../data/MSCOCO/mscoco_caption_train2014_processed.json -\ +-outdic ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--outfreq ../data/MSCOCO/mscoco_caption_train2014_processed_freq.json + +## Japanese from Yahoo +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/yjcaptions26k_clean.json \ +--output ../data/MSCOCO/yjcaptions26k_clean_processed.json \ +--outdic ../data/MSCOCO/yjcaptions26k_clean_processed_dic.json \ +--outfreq ../data/MSCOCO/yjcaptions26k_clean_processed_freq.json \ +--cut 0 \ +--char True \ + +## Japanese from machine translation +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014_jp_translation.json \ +--output ../data/MSCOCO/captions_train2014_jp_translation_processed.json \ +--outdic ../data/MSCOCO/captions_train2014_jp_translation_processed_dic.json \ +--outfreq ../data/MSCOCO/captions_train2014_jp_translation_processed_freq.json \ +--cut 5 \ +--char True \ + +## Chinese from machine translation +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014_cn_translation.json \ +--output ../data/MSCOCO/captions_train2014_cn_translation_processed.json \ +--outdic ../data/MSCOCO/captions_train2014_cn_translation_processed_dic.json \ +--outfreq ../data/MSCOCO/captions_train2014_cn_translation_processed_freq.json \ +--cut 5 \ +--char True \ + +cd ../ + +# Train +## train English caption +python train_caption_model.py --savedir ./experiment1 --epoch 40 --batch 120 --gpu -1 \ +--vocab ./data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--captions ./data/MSCOCO/mscoco_caption_train2014_processed.json \ + +## train Chinese caption by machine translation +python train_caption_model.py --savedir ./experiment1cn --epoch 50 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/captions_train2014_cn_translation_processed_dic.json \ +--captions ./data/MSCOCO/captions_train2014_cn_translation_processed.json\ + +## train Japanese caption by machine translation +python train_caption_model.py --savedir ./experiment1jp_mt --epoch 40 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/captions_train2014_jp_translation_processed_dic.json \ +--captions ./data/MSCOCO/captions_train2014_jp_translation_processed.json\ +--preload True + +## train Japanese caption by Yahoo's +python train_caption_model.py --savedir ./experiment1jp_yj --epoch 40 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/yjcaptions26k_clean_processed_dic.json \ +--captions ./data/MSCOCO/yjcaptions26k_clean_processed.json \ +--preload True +``` + +## I want to train the model from my own data. +Alright, you need to do additional amount of work. +``` +cd code +#extract features using ResNet50 \ +python ResNet_feature_extractor.py --img-dir ../data/MSCOCO/train2014 \ + --out-dir ../data/MSCOCO/train2014_ResNet50_features \ + --gpu -1 +``` +`--gpu` is GPU id (-1 is CPU).`—img-dir` is the directory that you stores images. `—out-dir` is the directory that the ResNet features will be saved. The file name will be the same, but extension is “.npz”. +``` +#preprocess the json files. you need to have the same structure as MSCOCO json. +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014.json \ +--output ../data/MSCOCO/mscoco_caption_train2014_processed.json \ +--outdic ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--outfreq ../data/MSCOCO/mscoco_caption_train2014_processed_freq.json \ +—-cut 5 \ +—-char True +``` +`input` is the json file containing caption. `output` will be the main preprocessed output. `outdic` is the vocabulary file. `outfreq` is the internal file you don’t need it in the training. Just frequency count. `cut` is the cutoff frequency for minor words. Character based chunking will be used when `char` is True. You can use it for non-spaced languages like Japanese and Chinese. + +Then you can use my script above for training. + +## I want to fine-tune CNN part. +Officially, this doesn’t support the CNN tuning. That’s what I did in the original paeper. However, informally, I made it to train CNN part too... but I didn't document it, now, after two years, I don't remember very much. `train_image_caption_model.py` is the script to train the CNN part. I also remember that I tried to use another preprocessed json than I document here. Currently I have the `_dic` file and the main processed file separately but I combined them. The script to generate a preprocessed file in the new format (i.e that one compatible with `train_image_caption_model.py` ) should be `code/preprocess_captions.py`. That's what I vaguely remember. diff --git a/scenes/chainer-caption/code/CaptionDataLoader.py b/scenes/chainer-caption/code/CaptionDataLoader.py new file mode 100644 index 0000000..6077e07 --- /dev/null +++ b/scenes/chainer-caption/code/CaptionDataLoader.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +#class to get the data in a batch way +#loading on memory option (preload_all_features) took 6m10.400s (user time = 2m41.546s) to load if it is true + +import numpy as np + +class CaptionDataLoader(object): + def __init__(self, captions,image_feature_path,preload_all_features=False,filename_img_id=False): + self.captions = captions + self.image_feature_path=image_feature_path#path before image id. e.g. ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ + self.caption_ids = captions.keys() + self.random_indicies = np.random.permutation(len(self.captions)) + self.index_count=0 + self.epoch=1 + self.preload_all_features=preload_all_features + self.filename_img_id=filename_img_id + if self.preload_all_features: + if self.filename_img_id: + self.image_features=np.array([np.load("%s/%s.npz"%(self.image_feature_path,self.captions[caption_id]["image_id"]))['arr_0'] for caption_id in self.caption_ids]) + else: + self.image_features=np.array([np.load("%s%012d.npz"%(self.image_feature_path,self.captions[caption_id]["image_id"]))['arr_0'] for caption_id in self.caption_ids]) + + def get_batch(self,batch_size): + batch_data_indicies=self.random_indicies[self.index_count:self.index_count+batch_size] + self.index_count+=batch_size + if self.index_count > len(self.captions): + self.epoch+=1 + self.suffle_data() + self.index_count=0 + + #sorry the following lines are so complicated... + #this is just loading preprocessed images features and captions for this batch + if self.preload_all_features: + batch_image_features=self.image_features[batch_data_indicies] + else: + if self.filename_img_id: + batch_image_features=np.array([np.load("%s/%s.npz"%(self.image_feature_path,self.captions[self.caption_ids[i]]["image_id"]))['arr_0'] for i in batch_data_indicies]) + else: + batch_image_features=np.array([np.load("%s%012d.npz"%(self.image_feature_path,self.captions[self.caption_ids[i]]["image_id"]))['arr_0'] for i in batch_data_indicies]) + + batch_word_indices=[np.array(self.captions[self.caption_ids[i]]["token_ids"],dtype=np.int32) for i in batch_data_indicies] + + return batch_image_features,batch_word_indices + + def suffle_data(self): + self.random_indicies = np.random.permutation(len(self.captions)) + + +if __name__ == '__main__': + #test code + import json + with open("../data/MSCOCO/mscoco_caption_train2014_processed.json", 'r') as f: + captions = json.load(f) + dataset=CaptionDataLoader(captions,image_feature_path="../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_") + batch_image_features,batch_word_indices = dataset.get_batch(10) + print(batch_word_indices) + print(batch_image_features.shape) + + dataset=CaptionDataLoader(captions,image_feature_path="../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_",preload_all_features=True) + batch_image_features,batch_word_indices = dataset.get_batch(10) + print(batch_word_indices) + print(batch_image_features.shape) diff --git a/scenes/chainer-caption/code/CaptionDataLoader2.py b/scenes/chainer-caption/code/CaptionDataLoader2.py new file mode 100644 index 0000000..9b94689 --- /dev/null +++ b/scenes/chainer-caption/code/CaptionDataLoader2.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +#class to get the data in a batch way +#loading on memory option (preload_all_features) took 6m10.400s (user time = 2m41.546s) to load if it is true +#refactered version of CaptionDataLoader.py + +import numpy as np +import os +from image_loader import Image_loader +from ResNet50 import ResNet + +class CaptionDataLoader(object): + def __init__(self,dataset,image_feature_root,image_root="",preload_all_features=False,image_mean="imagenet",holding_raw_captions=False): + self.holding_raw_captions=holding_raw_captions + self.image_loader=Image_loader(mean=image_mean) + self.captions=dataset["captions"] + self.num_captions=len(self.captions) + self.images=dataset["images"] + self.caption2image={caption["idx"]:caption["image_idx"] for caption in dataset["captions"]} + self.image_feature_root=image_feature_root+"/"#path to preprocessed image features. It assume the feature are stored with the same name but only extension is changed to .npz + self.image_root=image_root+"/"#path to image directory + self.random_indicies = np.random.permutation(len(self.captions)) + self.index_count=0 + self.epoch=1 + self.preload_all_features=preload_all_features + if self.preload_all_features: + self.image_features=np.array([np.load("%s/%s.npz"%(self.image_feature_root, os.path.splitext(image["file_path"])[0] ))['arr_0'] for image in self.images]) + + def get_batch(self,batch_size,raw_image=False): + #if raw_image is true, it will give you Batchx3x224x224 otherwise it will be just features + batch_caption_indicies=self.random_indicies[self.index_count:self.index_count+batch_size] + self.index_count+=batch_size + if self.index_count > len(self.captions): + self.epoch+=1 + self.suffle_data() + self.index_count=0 + + #sorry the following lines are so complicated... + #this is just loading preprocessed images or image features and captions for this batch + if raw_image: + batch_images= np.array( [self.image_loader.load(self.image_root+self.images[self.caption2image[i]]["file_path"],expand_batch_dim=False) for i in batch_caption_indicies] ) + else: + if self.preload_all_features: + batch_images=self.image_features[[self.caption2image[i] for i in batch_caption_indicies]] + else: + batch_images=np.array([np.load("%s/%s.npz"%(self.image_feature_root, os.path.splitext(self.images[self.caption2image[i]]["file_path"])[0] ))['arr_0'] for i in batch_caption_indicies]) + if self.holding_raw_captions: + batch_word_indices=[self.captions[i]["caption"] for i in batch_caption_indicies] + else: + batch_word_indices=[np.array(self.captions[i]["caption"],dtype=np.int32) for i in batch_caption_indicies] + + return batch_images,batch_word_indices + + def suffle_data(self): + self.random_indicies = np.random.permutation(len(self.captions)) + + +if __name__ == '__main__': + #test code + import json + with open("../data/MSCOCO/mscoco_train2014_all_preprocessed.json", 'r') as f: + captions = json.load(f) + dataset=CaptionDataLoader(captions,image_feature_root="../data/MSCOCO/MSCOCO_ResNet50_features/",image_root="../data/MSCOCO/MSCOCO_raw_images/") + batch_images,batch_word_indices = dataset.get_batch(10,raw_image=True) + print(batch_word_indices) + print(batch_images) + + batch_image_features,batch_word_indices = dataset.get_batch(10) + print(batch_word_indices) + print(batch_image_features.shape) + + dataset=CaptionDataLoader(captions,image_feature_root="../data/MSCOCO/MSCOCO_ResNet50_features",preload_all_features=True) + batch_image_features,batch_word_indices = dataset.get_batch(10) + print(batch_word_indices) + print(batch_image_features.shape) diff --git a/scenes/chainer-caption/code/CaptionEvaluater.py b/scenes/chainer-caption/code/CaptionEvaluater.py new file mode 100644 index 0000000..71b2d93 --- /dev/null +++ b/scenes/chainer-caption/code/CaptionEvaluater.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +evaluate captions by Blue, Rouge, and Cider + +This can be used for any langauge assumning that the inputs are already segmented (i.e. tokenized). +The ground truth should be lowered before passing to here.. +''' + +import sys +sys.path.append('../coco-caption/') +sys.path.append('../coco-caption/pycocoevalcap/') +sys.path.append('./coco-caption/') +sys.path.append('./coco-caption/pycocoevalcap/') +from bleu.bleu import Bleu +from rouge.rouge import Rouge +from cider.cider import Cider +import string +import re + +class CaptionEvaluater(object): + def __init__(self,): + self.blue_scorer=Bleu(4) + self.rouge_scorer=Rouge() + self.cider_scorer=Cider() + self.truth=None + remove = string.punctuation+"、。，．" + self.remove_pattern = r"[{}]".format(remove) # create the pattern + + def remove_punctuation(self,line): + #I am not sure how unicode works in python, so just in case. + line=line.replace(u"","") + line=line.replace("","") + line=line.replace(u"。","") + line=line.replace('\u3002',"") + return re.sub(self.remove_pattern, "", line) + + def trnasform_utf8(self,line): + # return u' '.join(line).encode('utf-8').strip() + return line + + def set_ground_truth(self,ground_truth): + ''' + ground_truth should be a python dictonary whose shape is; + {"image_identifier": ["a caption", "a similar caption", ...], ...} + "image_identifier" can be either string or number. + ''' + for img in ground_truth: + # ground_truth[img]=map(self.trnasform_utf8,ground_truth[img]) + ground_truth[img]=map(self.remove_punctuation,ground_truth[img]) + self.truth=ground_truth + + def evaluate(self,predicetd_captions): + ''' + predicetd_captions should be a python dictonary whose shape is; + {"image_identifier": ["the prediced caption"], ...} + "image_identifier" need to be same as used in ground truth. + make sure the number of caption is only one, even though it uses python list. + ''' + for img in predicetd_captions: + # predicetd_captions[img]=map(self.trnasform_utf8,predicetd_captions[img]) + predicetd_captions[img]=map(self.remove_punctuation,predicetd_captions[img]) + + results={} + for i,score in enumerate(self.get_bleu(predicetd_captions)[0]): + results["bleu-%d"%i]=score + results["rouge"] = self.get_rouge(predicetd_captions)[0] + results["cider"] = self.get_cider(predicetd_captions)[0] + + return results + + def get_bleu(self,predicetd_captions): + score, scores = self.blue_scorer.compute_score(self.truth, predicetd_captions) + #output is a python list [bleu-1,bleu-2,bleu-3,bleu-4] + return score, scores + + def get_rouge(self,predicetd_captions): + score, scores = self.rouge_scorer.compute_score(self.truth, predicetd_captions) + return score, scores + + def get_cider(self,predicetd_captions): + score, scores = self.cider_scorer.compute_score(self.truth, predicetd_captions) + return score, scores + +if __name__ == '__main__': + #test + #spaceで区切ったのを入れればOK. + ground_truth={} + ground_truth['262148'] = ['オレンジ色 の シャツ を 着た 人 が います', + 'オレンジ色 の Tシャツ を 着ている 人 が 立って います', + '人 が オレンジ色 の シャツ を 着て 立って います', + ] + ground_truth[262148] = ['the skateboarder is putting on a show using the picnic table as his stage', + 'a skateboarder pulling tricks on top of a picnic table', + 'a man riding on a skateboard on top of a table', + 'a skate boarder doing a trick on a picnic table', + 'a person is riding a skateboard on a picnic table with a crowd watching'] + ground_truth[393225]= ['a bowl of soup that has some carrots shrimp and noodles in it', + 'the healthy food is in the bowl and ready to eat', + 'soup has carrots and shrimp in it as it sits next to chopsticks', + 'a tasty bowl of ramen is served for someone to enjoy', + 'bowl of asian noodle soup with shrimp and carrots'] + ground_truth[1] = ['the skateboarder is putting on a show using the picnic table as his stage', + 'a skateboarder pulling tricks on top of a picnic table', + 'a man riding on a skateboard on top of a table', + 'a skate boarder doing a trick on a picnic table', + 'a person is riding a skateboard on a picnic table with a crowd watching'] + ground_truth[2]= ['a bowl of soup that has some carrots shrimp and noodles in it', + 'the healthy food is in the bowl and ready to eat', + 'soup has carrots and shrimp in it as it sits next to chopsticks', + 'a tasty bowl of ramen is served for someone to enjoy', + 'bowl of asian noodle soup with shrimp and carrots'] + + + #prediceted は一つだけじゃないとダメ + predicted={} + predicted['262148']=['人 が オレンジ色 の シャツ を 着て 立って います。'] + predicted[262148]=['A man riding a skateboard down a ramp。'] + predicted[393225]=['A bowl of soup with carrots and a spoon.'] + predicted[1]=['a man riding a skateboard down a ramp。'] + predicted[2]=['a bowl of soup with carrots and a spoon、'] + #keyは数字でも文字列でもどっちでもいいけど、ground truth と predicedで対応が取れるように！ + + evaluater=CaptionEvaluater() + evaluater.set_ground_truth(ground_truth) + print(evaluater.evaluate(predicted)) + #https://github.com/tylin/coco-caption/issues/5 + #Yes, CIDEr can have values till 10 (technically). \ No newline at end of file diff --git a/scenes/chainer-caption/code/CaptionGenerator.py b/scenes/chainer-caption/code/CaptionGenerator.py new file mode 100644 index 0000000..9e27d37 --- /dev/null +++ b/scenes/chainer-caption/code/CaptionGenerator.py @@ -0,0 +1,276 @@ +# -*- coding: utf-8 -*- +#!/usr/bin/env python + +''' +caption generation module by beam search +currently you cannot use efficient generation by batch. Batch size is always one + +beam search might have a small bug or not the most efficient, but seems to be ok. +''' + +import os +import chainer +import numpy as np +import json +import math +from copy import deepcopy + +from chainer import cuda +import chainer.functions as F +from chainer import cuda, Function, gradient_check, Variable, optimizers +from chainer import serializers + +from image_loader import Image_loader +from ResNet50 import ResNet +from Image2CaptionDecoder import Image2CaptionDecoder + +#priority queue +#reference: http://www.bogotobogo.com/python/python_PriorityQueue_heapq_Data_Structure.php +try: + import Queue as Q # ver. < 3.0 +except ImportError: + import queue as Q + +import heapq + +class CaptionGenerator(object): + def __init__(self,rnn_model_place,cnn_model_place,dictonary_place,beamsize=3,depth_limit=50,gpu_id=-1,first_word="",hidden_dim=512,mean="imagenet"): + self.gpu_id=gpu_id + self.beamsize=beamsize + self.depth_limit=depth_limit + self.image_loader=Image_loader(mean) + self.index2token=self.parse_dic(dictonary_place) + + self.cnn_model=ResNet() + serializers.load_hdf5(cnn_model_place, self.cnn_model) + self.cnn_model.train = False + + self.rnn_model=Image2CaptionDecoder(len(self.token2index),hidden_dim=hidden_dim) + if len(rnn_model_place) > 0: + serializers.load_hdf5(rnn_model_place, self.rnn_model) + self.rnn_model.train = False + #added + chainer.global_config.train = False + + self.first_word=first_word + + #Gpu Setting + global xp + if self.gpu_id >= 0: + xp = cuda.cupy + cuda.get_device(gpu_id).use() + self.cnn_model.to_gpu() + self.rnn_model.to_gpu() + else: + xp=np + + def parse_dic(self,dictonary_place): + with open(dictonary_place, 'r') as f: + json_file = json.load(f) + if len(json_file) < 10:#this is ad-hock. I need to distinguish new format and old format... + self.token2index = { word['word']:word['idx'] for word in json_file["words"]} + else: + self.token2index = json_file + + return {v:k for k,v in self.token2index.items()} + + def successor(self,current_state): + ''' + Args: + current_state: a stete, python tuple (hx,cx,path,cost) + hidden: hidden states of LSTM + cell: cell states LSTM + path: word indicies so far as a python list e.g. initial is self.token2index[""] + cost: negative log likelihood + + Returns: + k_best_next_states: a python list whose length is the beam size. possible_sentences[i] = {"indicies": list of word indices,"cost":negative log likelihood so far} + + ''' + + word=[xp.array([current_state["path"][-1]],dtype=xp.int32)] + hx=current_state["hidden"] + cx=current_state["cell"] + hy, cy, next_words=self.rnn_model(hx,cx,word) + + word_dist=F.softmax(next_words[0]).data[0]#possible next word distributions + k_best_next_sentences=[] + for i in range(self.beamsize): + next_word_idx=int(xp.argmax(word_dist)) + k_best_next_sentences.append(\ + {\ + "hidden":hy,\ + "cell":cy,\ + "path":deepcopy(current_state["path"])+[next_word_idx],\ + "cost":current_state["cost"]-xp.log(word_dist[next_word_idx]) + }\ + ) + word_dist[next_word_idx]=0 + + return hy, cy, k_best_next_sentences + + def beam_search(self,initial_state): + ''' + Beam search is a graph search algorithm! So I use graph search abstraction + + Args: + initial state: an initial stete, python tuple (hx,cx,path,cost) + each state has + hx: hidden states + cx: cell states + path: word indicies so far as a python list e.g. initial is self.token2index[""] + cost: negative log likelihood + + Returns: + captions sorted by the cost (i.e. negative log llikelihood) + ''' + found_paths=[] + top_k_states=[initial_state] + while (len(found_paths) < self.beamsize): + #forward one step for all top k states, then only select top k after that + new_top_k_states=[] + for state in top_k_states: + #examine to next five possible states + hy, cy, k_best_next_states = self.successor(state) + for next_state in k_best_next_states: + new_top_k_states.append(next_state) + selected_top_k_states=heapq.nsmallest(self.beamsize, new_top_k_states, key=lambda x : x["cost"]) + + #within the selected states, let's check if it is terminal or not. + top_k_states=[] + for state in selected_top_k_states: + #is goal state? -> yes, then end the search + if state["path"][-1] == self.token2index[""] or len(state["path"])==self.depth_limit: + found_paths.append(state) + else: + top_k_states.append(state) + + return sorted(found_paths, key=lambda x: x["cost"]) + + def beam_search0(self,initial_state): + #original one. This takes much memory + ''' + Beam search is a graph search algorithm! So I use graph search abstraction + Args: + initial state: an initial stete, python tuple (hx,cx,path,cost) + each state has + hx: hidden states + cx: cell states + path: word indicies so far as a python list e.g. initial is self.token2index[""] + cost: negative log likelihood + Returns: + captions sorted by the cost (i.e. negative log llikelihood) + ''' + found_paths=[] + q = Q.PriorityQueue() + q.put((0,initial_state)) + while (len(found_paths) < self.beamsize): + i=0 + # this is just a one step ahead? + while not q.empty(): + if i == self.beamsize: + break + state=q.get()[1] + #is goal state? -> yes, then end the search + if state["path"][-1] == self.token2index[""] or len(state["path"])==self.depth_limit: + found_paths.append(state) + continue + #examine to next five possible states and add to priority queue + hy, cy, k_best_next_states = self.successor(state) + for next_state in k_best_next_states: + q.put((state["cost"],next_state)) + i+=1 + + return sorted(found_paths, key=lambda x: x["cost"]) + + def generate(self,image_file_path): + ''' + Args: + image_file_path: image_file_path + ''' + img=self.image_loader.load(image_file_path) + return self.generate_from_img(img) + + + def generate_from_img_feature(self,image_feature): + if self.gpu_id >= 0: + image_feature=cuda.to_gpu(image_feature) + + batch_size=1 + hx=xp.zeros((self.rnn_model.n_layers, batch_size, self.rnn_model.hidden_dim), dtype=xp.float32) + cx=xp.zeros((self.rnn_model.n_layers, batch_size, self.rnn_model.hidden_dim), dtype=xp.float32) + + hy,cy = self.rnn_model.input_cnn_feature(hx,cx,image_feature) + + + initial_state={\ + "hidden":hy,\ + "cell":cy,\ + "path":[self.token2index[self.first_word]],\ + "cost":0,\ + }\ + + captions=self.beam_search(initial_state) + + caption_candidates=[] + for caption in captions: + sentence= [self.index2token[word_idx] for word_idx in caption["path"]] + log_likelihood = -float(caption["cost"])#cost is the negative log likelihood + caption_candidates.append({"sentence":sentence,"log_likelihood":log_likelihood}) + + return caption_candidates + + def generate_from_img(self,image_array): + '''Generate Caption for an Numpy Image array + + Args: + image_array: numpy array of image + + Returns: + list of generated captions, sorted by the cost (i.e. negative log llikelihood) + + The structure is [caption,caption,caption,...] + Where caption = {"sentence": a generated sentence as a python list of word, "log_likelihood": The log llikelihood of the generated sentence} + + ''' + if self.gpu_id >= 0: + image_array=cuda.to_gpu(image_array) + image_feature=self.cnn_model(image_array, "feature").data.reshape(1,1,2048)#次元が一つ多いのは、NstepLSTMはsequaenceとみなすから。(sequence size, batch size, feature dim)ということ + return self.generate_from_img_feature(image_feature) + +if __name__ == '__main__': + xp=np + #test code on cpu + caption_generator=CaptionGenerator( + rnn_model_place="../experiment1/caption_model1.model",\ + cnn_model_place="../data/ResNet50.model",\ + dictonary_place="../data/MSCOCO/mscoco_caption_train2014_processed_dic.json",\ + beamsize=3,depth_limit=50,gpu_id=-1,) + + # batch_size=1 + # hx=xp.zeros((caption_generator.rnn_model.n_layers, batch_size, caption_generator.rnn_model.hidden_dim), dtype=xp.float32) + # cx=xp.zeros((caption_generator.rnn_model.n_layers, batch_size, caption_generator.rnn_model.hidden_dim), dtype=xp.float32) + # img=caption_generator.image_loader.load("../sample_imgs/COCO_val2014_000000185546.jpg") + # image_feature=caption_generator.cnn_model(img, "feature").data.reshape(1,1,2048)#次元が一つ多いのは、NstepLSTMはsequaenceとみなすから。(sequence size, batch size, feature dim)ということ + + # hy,cy = caption_generator.rnn_model.input_cnn_feature(hx,cx,image_feature) + # initial_state={\ + # "hidden":hy,\ + # "cell":cy,\ + # "path":[caption_generator.token2index[""]],\ + # "cost":0,\ + # }\ + + # #successor test + # next_states= caption_generator.successor(initial_state) + # print next_states + + # print "beam search test" + # captions= caption_generator.beam_search(initial_state) + # print captions + + captions = caption_generator.generate("../sample_imgs/COCO_val2014_000000185546.jpg") + for caption in captions: + print(caption["sentence"]) + print(caption["log_likelihood"]) + diff --git a/scenes/chainer-caption/code/CaptionMultiDataLoader.py b/scenes/chainer-caption/code/CaptionMultiDataLoader.py new file mode 100644 index 0000000..2101600 --- /dev/null +++ b/scenes/chainer-caption/code/CaptionMultiDataLoader.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +#class to keep several CaptionDataLoader +#Each caption loader is expcted to output raw captions instead of indicies +#because this loader holds the golbal dictonary + +import numpy as np +import os +import json +from CaptionDataLoader2 import CaptionDataLoader + + +class CaptionMultiDataLoader(object): + def __init__(self,datasets_paths,image_feature_root,image_root,preload): + self.num_captions=0 + all_captions_dic={}#dictonary to keep the info about several langs + all_words=set()#collet all words so that we can create new dic + #test code + for lang_and_json in datasets_paths.split(";")[0:-1]: + lang,json_file=lang_and_json.split(":") + all_captions_dic[lang]={} + all_captions_dic[lang]["json"]=json_file + with open(json_file, 'r') as f: + captions = json.load(f) + self.num_captions+=len(captions["captions"]) + #put the evaluation data into all_captions_dic[lang] + all_captions_dic[lang]["val"]={cap["file_path"]:cap["captions"] for cap in captions["val"]} + all_captions_dic[lang]["test"]={cap["file_path"]:cap["captions"] for cap in captions["test"]} + index2word={ word['idx']:word['word'] for word in captions["words"]} + for caption in captions["captions"]: + caption["caption"] = [index2word[word] for word in caption["caption"][1:]] + all_words=all_words.union(index2word.values()) + all_words.add(lang) + del captions["val"] + del captions["test"] + del captions["words"] + all_captions_dic[lang]["dataset"]=CaptionDataLoader(captions,image_feature_root=image_feature_root,image_root=image_root,preload_all_features=preload,holding_raw_captions=True) + + #first create a new dic + if "" in all_words: + all_words.remove("") + self.word2index={word:idx for idx,word in enumerate(list(all_words))} + + #all_captions_dic is defined in the train_image_caption_model + self.all_captions_dic=all_captions_dic + #initialize epoch changed flag + self.epoch_changed={} + for lang in self.all_captions_dic.keys(): + self.epoch_changed[lang]=False + self.epoch=1 + self.num_langs=len(self.all_captions_dic.keys()) + + def get_batch(self,batch_size,raw_image=False): + #just get several batches from all data_loaders + batch_size=int(batch_size/self.num_langs) + batch_word_indices=[] + imgs_list=[] + for lang,dataset_dic in self.all_captions_dic.items(): + dataset=dataset_dic["dataset"] + current_epoch=dataset.epoch + image_features,x_batch=dataset.get_batch(batch_size,raw_image) + imgs_list.append(image_features) + #this is just converting words into indicies and make it in a chainer training compatible way + batch_word_indices+=[np.array([self.word2index[word] for word in [lang]+sent],dtype=np.int32) for sent in x_batch] + #if this lang dataset entered the new epoch, mark it. + if dataset.epoch - current_epoch > 0: + self.epoch_changed[lang]=True + batch_images=np.vstack(imgs_list) + + if all(self.epoch_changed.values()): + self.epoch+=1 + for lang,dataset_dic in self.all_captions_dic.items(): + dataset=dataset_dic["dataset"] + dataset.suffle_data() + dataset.index_count=0 + + return batch_images,batch_word_indices + +if __name__ == '__main__': + datasets_paths=":../data/MSCOCO/mscoco_train2014_all_preprocessed.json;:../data/MSCOCO/mscoco_jp_all_preprocessed.json;" + image_feature_root="../data/MSCOCO/MSCOCO_ResNet50_features/" + image_root="../data/MSCOCO/MSCOCO_raw_images/" + dataset=CaptionMultiDataLoader(datasets_paths,image_feature_root,image_root) + batch_image_features,batch_word_indices = dataset.get_batch(10) + for batch in batch_word_indices: + print(batch) + print(dataset.word2index[""],dataset.word2index[""]) + print(batch_image_features.shape) + + batch_images,batch_word_indices = dataset.get_batch(10,raw_image=True) + for batch in batch_word_indices: + print(batch) + print(dataset.word2index[""],dataset.word2index[""]) + print(batch_images.shape) diff --git a/scenes/chainer-caption/code/Image2CaptionDecoder.py b/scenes/chainer-caption/code/Image2CaptionDecoder.py new file mode 100644 index 0000000..0f65629 --- /dev/null +++ b/scenes/chainer-caption/code/Image2CaptionDecoder.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +#implementation of LSTM net for caption generation +#inpired from https://github.com/dsanno/chainer-image-caption/blob/master/src/net.py +#consulted on http://www.monthly-hack.com/entry/2016/10/24/200000 +#consulted on http://qiita.com/chantera/items/d8104012c80e3ea96df7 + +import chainer +import chainer.functions as F +import chainer.links as L + +class Image2CaptionDecoder(chainer.Chain): + def __init__(self, vocaburary_size, img_feature_dim=2048, hidden_dim=512,dropout_ratio=0.5,train=True, n_layers=1): + super(Image2CaptionDecoder, self).__init__( + embed_word= L.EmbedID(vocaburary_size, hidden_dim), + embed_image= L.Linear(img_feature_dim, hidden_dim), + lstm = L.NStepLSTM(n_layers=n_layers,in_size=hidden_dim,out_size=hidden_dim,dropout=dropout_ratio), + decode_word = L.Linear(hidden_dim, vocaburary_size), + ) + self.dropout_ratio = dropout_ratio + self.train = train + #added + chainer.global_config.train = train + self.n_layers=n_layers + self.hidden_dim=hidden_dim + + def input_cnn_feature(self,hx,cx,image_feature): + h = self.embed_image(image_feature) + h = [F.reshape(img_embedding,(1,self.hidden_dim)) for img_embedding in h]#一回　python list/tuple にしないとerrorが出る + hy, cy, ys = self.lstm(hx, cx, h) + return hy,cy + + def __call__(self, hx, cx, caption_batch): + #hx (~chainer.Variable): Initial hidden states. + #cx (~chainer.Variable): Initial cell states. + #xs (list of ~chianer.Variable): List of input sequences.Each element ``xs[i]`` is a :class:`chainer.Variable` holding a sequence. + xs = [self.embed_word(caption) for caption in caption_batch] + hy, cy, ys = self.lstm(hx, cx, xs) + predicted_caption_batch = [self.decode_word(generated_caption) for generated_caption in ys] + if self.train: + loss=0 + for y, t in zip(predicted_caption_batch, caption_batch): + loss+=F.softmax_cross_entropy(y[0:-1], t[1:]) + return loss/len(predicted_caption_batch) + else: + return hy, cy, predicted_caption_batch + +class Image2CaptionDecoderOld(chainer.Chain): + def __init__(self, vocaburary_size, img_feature_dim=2048, hidden_dim=512,dropout_ratio=0.5,train=True): + self.dropout_ratio = dropout_ratio + super(Image2CaptionDecoderOld, self).__init__( + embed_word= L.EmbedID(vocaburary_size, hidden_dim), + embed_image= L.Linear(img_feature_dim, hidden_dim), + lstm = L.LSTM(hidden_dim, hidden_dim), + decode_word = L.Linear(hidden_dim, vocaburary_size), + ) + self.train = train + #added + chainer.global_config.train = train + + def input_cnn_feature(self, image_feature): + self.lstm.reset_state() + h = self.embed_image(image_feature) + self.lstm(F.dropout(h, ratio=self.dropout_ratio)) + + def __call__(self, cur_word, next_word=None): + h = self.embed_word(cur_word) + h = self.lstm(F.dropout(h, ratio=self.dropout_ratio)) + h = self.decode_word(F.dropout(h, ratio=self.dropout_ratio)) + if self.train: + self.loss = F.softmax_cross_entropy(h, next_word) + return self.loss + else: + return h + + +if __name__ == '__main__': + #test code on cpu + import numpy as np + image_feature=np.zeros([2,2048],dtype=np.float32) + x_batch=[[1,2,3,4,2,3,0,2],[1,2,3,3,1]] + x_batch = [np.array(x,dtype=np.int32) for x in x_batch] + model=Image2CaptionDecoder(5) + batch_size=len(x_batch) + hx=np.zeros((model.n_layers, batch_size, model.hidden_dim), dtype=np.float32) + cx=np.zeros((model.n_layers, batch_size, model.hidden_dim), dtype=np.float32) + hx,cx=model.input_cnn_feature(hx,cx,image_feature) + loss = model(hx, cx, x_batch) + + #test old one + image_feature=np.zeros([1,2048],dtype=np.float32) + x_list=np.array([[1,2,3,4,2,3,0,2]],dtype=np.int32).T + model=Image2CaptionDecoderOld(5) + model.input_cnn_feature(image_feature) + loss = 0 + for cur_word, next_word in zip(x_list, x_list[1:]): + loss += model(cur_word, next_word) + diff --git a/scenes/chainer-caption/code/ResNet50.py b/scenes/chainer-caption/code/ResNet50.py new file mode 100644 index 0000000..cd50d63 --- /dev/null +++ b/scenes/chainer-caption/code/ResNet50.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + + +#adapted from https://github.com/yasunorikudo/chainer-ResNet/blob/master/ResNet50.py + +import math +import chainer +import chainer.functions as F +import chainer.links as L + + +class BottleNeckA(chainer.Chain): + def __init__(self, in_size, ch, out_size, stride=2): + w = math.sqrt(2) + super(BottleNeckA, self).__init__( + conv1=L.Convolution2D(in_size, ch, 1, stride, 0, w), + bn1=L.BatchNormalization(ch), + conv2=L.Convolution2D(ch, ch, 3, 1, 1, w), + bn2=L.BatchNormalization(ch), + conv3=L.Convolution2D(ch, out_size, 1, 1, 0, w), + bn3=L.BatchNormalization(out_size), + + conv4=L.Convolution2D(in_size, out_size, 1, stride, 0, w), + bn4=L.BatchNormalization(out_size), + ) + + def __call__(self, x, train): + h1 = F.relu(self.bn1(self.conv1(x))) + h1 = F.relu(self.bn2(self.conv2(h1))) + h1 = self.bn3(self.conv3(h1)) + h2 = self.bn4(self.conv4(x)) + + return F.relu(h1 + h2) + + +class BottleNeckB(chainer.Chain): + def __init__(self, in_size, ch): + w = math.sqrt(2) + super(BottleNeckB, self).__init__( + conv1=L.Convolution2D(in_size, ch, 1, 1, 0, w), + bn1=L.BatchNormalization(ch), + conv2=L.Convolution2D(ch, ch, 3, 1, 1, w), + bn2=L.BatchNormalization(ch), + conv3=L.Convolution2D(ch, in_size, 1, 1, 0, w), + bn3=L.BatchNormalization(in_size), + ) + + def __call__(self, x, train): + h = F.relu(self.bn1(self.conv1(x))) + h = F.relu(self.bn2(self.conv2(h))) + h = self.bn3(self.conv3(h)) + + return F.relu(h + x) + + +class Block(chainer.Chain): + def __init__(self, layer, in_size, ch, out_size, stride=2): + super(Block, self).__init__() + links = [('a', BottleNeckA(in_size, ch, out_size, stride))] + for i in range(layer-1): + links += [('b{}'.format(i+1), BottleNeckB(out_size, ch))] + + for l in links: + self.add_link(*l) + self.forward = links + + def __call__(self, x, train): + for name, _ in self.forward: + f = getattr(self, name) + x = f(x, train) + + return x + + +class ResNet(chainer.Chain): + + insize = 224 + + def __init__(self): + w = math.sqrt(2) + super(ResNet, self).__init__( + conv1=L.Convolution2D(3, 64, 7, 2, 3, w), + bn1=L.BatchNormalization(64), + res2=Block(3, 64, 64, 256, 1), + res3=Block(4, 256, 128, 512), + res4=Block(6, 512, 256, 1024), + res5=Block(3, 1024, 512, 2048), + fc=L.Linear(2048, 1000), + ) + self.train = True + #added + chainer.global_config.train = True + + def clear(self): + self.loss = None + self.accuracy = None + + def __call__(self, x, t): + self.clear() + h = self.bn1(self.conv1(x)) + h = F.max_pooling_2d(F.relu(h), 3, stride=2) + h = self.res2(h, self.train) + h = self.res3(h, self.train) + h = self.res4(h, self.train) + h = self.res5(h, self.train) + h = F.average_pooling_2d(h, 7, stride=1) + if t=="feature": + return h + h = self.fc(h) + + if self.train: + self.loss = F.softmax_cross_entropy(h, t) + self.accuracy = F.accuracy(h, t) + return self.loss + else: + return h diff --git a/scenes/chainer-caption/code/ResNet50predict.py b/scenes/chainer-caption/code/ResNet50predict.py new file mode 100644 index 0000000..ca69ef1 --- /dev/null +++ b/scenes/chainer-caption/code/ResNet50predict.py @@ -0,0 +1,52 @@ +#!/usr/bin/python +# coding: UTF-8 + +#python ResNet50predict.py + +import os +import numpy as np +import chainer +from chainer import cuda +from chainer import serializers, Variable +import chainer.functions as F +from ResNet50 import ResNet +from image_loader import Image_loader +import argparse + +#parse arguments +parser = argparse.ArgumentParser() +parser.add_argument('--img', type=str, default="../sample_imgs/dog.jpg",help='place of a image that you want to predict') +parser.add_argument('--model', type=str, default='../data/ResNet50.model',help='place of the ResNet model') +parser.add_argument('--gpu', '-g', type=int, default=-1, help='GPU ID (negative value indicates CPU)') +args = parser.parse_args() + +#setup image loader +image_loader=Image_loader("imagenet") + +#set up and load the model +model = ResNet() +serializers.load_hdf5(args.model, model) +model.train = False + +#load image +img=image_loader.load(args.img) + +#GPU preparation +if args.gpu >= 0: + cuda.get_device(args.gpu).use() + model.to_gpu() + img = cuda.to_gpu(img, device=args.gpu) + +#predict! +#img = Variable(img) +pred = F.softmax(model(img, None)).data + +if args.gpu >= 0: + pred = cuda.to_cpu(pred) + +#print results +with open('../data/synset_words.txt') as f: + synsets = f.read().split('\n')[:-1] + +for i in np.argsort(pred)[0][-1::-1][:5]: + print(synsets[i], pred[0][i]) diff --git a/scenes/chainer-caption/code/ResNet_feature_extractor.py b/scenes/chainer-caption/code/ResNet_feature_extractor.py new file mode 100644 index 0000000..8ba24a0 --- /dev/null +++ b/scenes/chainer-caption/code/ResNet_feature_extractor.py @@ -0,0 +1,51 @@ +#!/usr/bin/python +# coding: UTF-8 + +#python ResNet_feature_extractor.py --img-dir ../data/MSCOCO/train2014 --out-dir ../data/MSCOCO/train2014_ResNet50_features +#python ResNet_feature_extractor.py --img-dir ../data/MSCOCO/val2014 --out-dir ../data/MSCOCO/val2014_ResNet50_features + +import os +import numpy as np +import chainer +from chainer import cuda +from chainer import serializers, Variable +import chainer.functions as F +from ResNet50 import ResNet +from image_loader import Image_loader +import argparse + +#parse arguments +parser = argparse.ArgumentParser() +parser.add_argument('--img-dir', type=str,help='The directory that the images are stored') +parser.add_argument('--out-dir', type=str,help='The directory that the features will be saved') +parser.add_argument('--model', type=str, default='../data/ResNet50.model',help='place of the ResNet model') +parser.add_argument('--gpu', '-g', type=int, default=-1, help='GPU ID (negative value indicates CPU)') +args = parser.parse_args() + +#setup image loader +image_loader=Image_loader(mean="imagenet") + +#set up and load the model +model = ResNet() +serializers.load_hdf5(args.model, model) +model.train = False + +#GPU preparation +if args.gpu >= 0: + cuda.get_device(args.gpu).use() + model.to_gpu() + +image_files = os.listdir(args.img_dir) +i=0 +for path in image_files: + name, ext = os.path.splitext(path) + print(i, 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cider.cider import Cider + + +#spaceで区切ったのを入れればOK. +ground_truth={} +ground_truth['262148'] = ['オレンジ色 の シャツ を 着た 人 が います', + 'オレンジ色 の Tシャツ を 着ている 人 が 立って います', +] +#prediceted は一つだけじゃないとダメ +predicted={} +predicted['262148']=['人 が オレンジ色 の シャツ を 着て 立って います'] + +#keyは数字でも文字列でもどっちでもいいけど、ground truth と predicedで対応が取れるように！ + +#compute blue +scorer=Bleu(4) +score, scores = scorer.compute_score(ground_truth, predicted) +print(scores) +for i,value in enumerate(scores): + print(i, np.mean(value)) # not same. Blue does not use standard mean.some weighted geometric mean? + +#meter requires other thesaurus to + +#compute Rouge +scorer=Rouge() +score, scores = scorer.compute_score(ground_truth, predicted) +print(score) +print(np.mean(scores)) + +#compute CIDEr +scorer=Cider() +score, scores = scorer.compute_score(ground_truth, predicted) +print(score) +print(np.mean(scores)) diff --git a/scenes/chainer-caption/code/create_MSCOCO_en_jp_dataset.py b/scenes/chainer-caption/code/create_MSCOCO_en_jp_dataset.py new file mode 100644 index 0000000..bc20d61 --- /dev/null +++ b/scenes/chainer-caption/code/create_MSCOCO_en_jp_dataset.py @@ -0,0 +1,104 @@ +#!/usr/bin/python +# coding: UTF-8 + +#output file is [{ "file_path": "path/img.jpg", "captions": ["a caption", "a similar caption" ...] }, ...] + + +import os +import argparse +import json + +import random +random.seed(0) + +''' +e.g. + +python3 create_MSCOCO_en_jp_dataset.py \ +--en ../data/MSCOCO/captions_train2014.json \ +--out-en ../data/MSCOCO/mscoco_en_only_26k.json \ +--jp ../data/MSCOCO/yjcaptions26k_clean.json \ +--out-jp ../data/MSCOCO/mscoco_jp.json +''' + +def read_MSCOCO_json(file_place,args): + + f = open(file_place, 'r') + jsonData = json.load(f) + f.close() + + imgs = jsonData['images'] + annotations=jsonData['annotations'] + img_id2captions={} + for caption_data in annotations: + caption_id=caption_data['id'] + image_id=caption_data['image_id'] + caption=caption_data['caption'] + if image_id not in img_id2captions: + img_id2captions[image_id]=[caption] + else: + img_id2captions[image_id].append(caption) + + # create the json blob + out = [] + for i,img in enumerate(imgs): + imgid = img['id'] + jimg = {} + jimg['file_path'] = os.path.join('train2014', img['file_name']) + jimg['captions'] = img_id2captions[imgid] + jimg['id'] = imgid + out.append(jimg) + + return out + +def save_raw_json(captions,outfile,args): + with open(args.en, 'r') as f: + jsonData=json.load(f) + captions={"annotations":captions,"images":jsonData['images'],"type":"captions","info":"dummy, not all image ids show up in captions","licenses":"dummy"} + with open(outfile, 'w') as f: + json.dump(captions, f, sort_keys=True, indent=4) + +if __name__ == '__main__': + #parse arguments + parser = argparse.ArgumentParser() + parser.add_argument('--en', type=str,help='json file of MSCOCO formatted captions for English') + parser.add_argument('--jp', type=str,help='json file of MSCOCO formatted captions for Japanese') + parser.add_argument('--out-jp', type=str, help='the output file for Japanese') + parser.add_argument('--out-en', type=str, help='the output file for English') + args = parser.parse_args() + + #load jsonfile + en_captions=read_MSCOCO_json(args.en,args) + jp_captions=read_MSCOCO_json(args.jp,args) + + en_img_ids=[caption["id"] for caption in en_captions] + jp_img_ids=[caption["id"] for caption in jp_captions] + + common_img_ids = set(en_img_ids) & set(jp_img_ids) + remain_img_ids = set(en_img_ids) - set(jp_img_ids) + + # common_img_ids=list(common_img_ids) + # random.shuffle(common_img_ids) + + print("common image ids",len(common_img_ids)) + + new_english_dataset=[] + for caption in en_captions: + img_id=caption["id"] + if img_id in common_img_ids: + new_english_dataset.append(caption) + + new_japanese_dataset=[] + for caption in jp_captions: + img_id=caption["id"] + if img_id in common_img_ids: + new_japanese_dataset.append(caption) + + assert(len(new_english_dataset) == len(new_japanese_dataset)) + print(args.out_jp) + print(args.out_en) + + with open(args.out_en,'w') as f: + json.dump(new_english_dataset,f, sort_keys=True, indent=4,ensure_ascii=False) + with open(args.out_jp,'w') as f: + json.dump(new_japanese_dataset,f, sort_keys=True, indent=4,ensure_ascii=False) \ No newline at end of file diff --git a/scenes/chainer-caption/code/evaluate_captions.py b/scenes/chainer-caption/code/evaluate_captions.py new file mode 100644 index 0000000..9133ebd --- /dev/null +++ b/scenes/chainer-caption/code/evaluate_captions.py @@ -0,0 +1,57 @@ +# -*- coding: utf-8 -*- +#!/usr/bin/env python + +import sys +sys.path.append('../coco-caption/') + +from pycocotools.coco import COCO +from pycocoevalcap.eval import COCOEvalCap +import argparse +import json + + +#parse arguments +parser = argparse.ArgumentParser() +parser.add_argument('--true', type=str,help='json file of true captions') +parser.add_argument('--predicted', type=str,help='json file of predicted captions') +parser.add_argument('--output', type=str, help='the filepath of output files') +args = parser.parse_args() + +# JSONファイル読み込み +with open(args.predicted,'r') as f: + results = json.load(f) + +#make the input to coco caption evaluator +results_list=[] +for key in results: + a_caption={} + image_id= int(key.split(".")[-2].split("_")[-1]) + caption = (" ".join(results[key][0]["sentence"][1:-1])).strip() + a_caption["image_id"]=image_id + a_caption["caption"]=caption+"." + results_list.append(a_caption) + +resFile="/tmp/coco_eval.json" +with open(resFile, 'w') as f: + json.dump(results_list, f, sort_keys=True, indent=4) + +# create coco object and cocoRes object +coco = COCO(args.true) +cocoRes = coco.loadRes(resFile) +# create cocoEval object by taking coco and cocoRes +cocoEval = COCOEvalCap(coco, cocoRes) + +# evaluate on a subset of images by setting +# please comment out this line when evaluating the full validation set +cocoEval.params['image_id'] = cocoRes.getImgIds() + +#evaluate results +cocoEval.evaluate() + +# print output evaluation scores +results={} +for metric, score in cocoEval.eval.items(): + results[metric]=score + +with open(args.output, 'w') as f: + json.dump(results, f, sort_keys=True, indent=4) diff --git a/scenes/chainer-caption/code/generate_caption.py b/scenes/chainer-caption/code/generate_caption.py new file mode 100644 index 0000000..60dcde3 --- /dev/null +++ b/scenes/chainer-caption/code/generate_caption.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +Sample code to generate caption using beam search +''' +import sys +import json +import os +# comment out the below if you want to do type check. Remeber this have to be done BEFORE import chainer +os.environ["CHAINER_TYPE_CHECK"] = "0" +import chainer + +import argparse +import numpy as np +import math +from chainer import cuda +import chainer.functions as F +from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers +from chainer import serializers + +from CaptionGenerator import CaptionGenerator + +#Parse arguments +parser = argparse.ArgumentParser() +parser.add_argument("-g", "--gpu",default=-1, type=int, help=u"GPU ID.CPU is -1") +parser.add_argument('--vocab',default='../data/MSCOCO/mscoco_caption_train2014_processed_dic.json', type=str,help='path to the vocaburary json') +parser.add_argument('--dir',default='../data/MSCOCO/val2014_ResNet50_features/', type=str,help='path to the image directory') +parser.add_argument('--true-json',default='', type=str,help='path to the true json file') +parser.add_argument('--cnn-model', type=str, default='../data/ResNet50.model',help='place of the ResNet model') +parser.add_argument('--rnn-model', type=str, default='../data/caption_model_en.model',help='place of the caption model') +parser.add_argument('--beam',default=3, type=int,help='beam size in beam search') +parser.add_argument('--depth',default=50, type=int,help='depth limit in beam search') +parser.add_argument('--output',default="../data/MSCOCO/val2014_predected_captions.json", type=str,help='output file name') +args = parser.parse_args() + +caption_generator=CaptionGenerator( + rnn_model_place=args.rnn_model,\ + cnn_model_place=args.cnn_model,\ + dictonary_place=args.vocab,\ + beamsize=args.beam,\ + depth_limit=args.depth,\ + gpu_id=args.gpu,) + +output_annotations={} + +def generate_caption(fname,caption_generator,args): + if fname[-4:] == '.npz': + image_feature=np.load(args.dir+"/"+fname)['arr_0'].reshape(1,2048) + captions=caption_generator.generate_from_img_feature(image_feature) + else: + captions=caption_generator.generate(args.dir+"/"+fname) + return captions + + +if len(args.true_json) == 0: + for i,fname in enumerate(os.listdir(args.dir)): + print(i, fname) + captions = generate_caption(fname,caption_generator,args) + output_annotations[fname] = captions + +else: + with open(args.true_json, 'r') as f: + annotations=json.load(f)["annotations"] + for i,annotation in enumerate(annotations): + fname=annotation["image_id"] + print(i, annotation["image_id"]) + captions = generate_caption(fname+".npz",caption_generator,args) + output_annotations[fname]={} + output_annotations[fname]["captions"] = captions + output_annotations[fname]["true"] = annotation["caption"] + +with open(args.output, 'w') as f: + json.dump(output_annotations, f, sort_keys=True, indent=4) diff --git a/scenes/chainer-caption/code/generate_caption_eval.py b/scenes/chainer-caption/code/generate_caption_eval.py new file mode 100644 index 0000000..1fd677b --- /dev/null +++ b/scenes/chainer-caption/code/generate_caption_eval.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +Sample code to generate caption using beam searcg using json file + +''' +import sys +import json +import os +# comment out the below if you want to do type check. Remeber this have to be done BEFORE import chainer +# os.environ["CHAINER_TYPE_CHECK"] = "0" +import chainer + +import argparse +import numpy as np +import math +from chainer import cuda +import chainer.functions as F +from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers +from chainer import serializers + +from CaptionGenerator import CaptionGenerator + +#Parse arguments +parser = argparse.ArgumentParser() +parser.add_argument("-g", "--gpu",default=-1, type=int, help=u"GPU ID.CPU is -1") +parser.add_argument('--eval_file',default='../data/MSCOCO/captions_val2014.json', type=str,help='evaluation file') +parser.add_argument('--image_feature_path',default='../data/MSCOCO/val2014_ResNet50_features/COCO_val2014_', type=str,help='path to the file of CNN features before image_id') +parser.add_argument('--filename_img_id',default=False,type=bool,help='image id is filename') +parser.add_argument('--cnn-model', type=str, default='../data/ResNet50.model',help='place of the ResNet model') +parser.add_argument('--rnn-model', type=str, default='../data/caption_en_model40.model',help='place of the caption model') +parser.add_argument('--vocab',default='../data/MSCOCO/mscoco_caption_train2014_processed_dic.json', type=str,help='path to the vocaburary json') +parser.add_argument('--beam',default=3, type=int,help='beam size in beam search') +parser.add_argument('--depth',default=50, type=int,help='depth limit in beam search') +parser.add_argument('--lang',default="", type=str,help='special word to indicate the langauge or just ') +parser.add_argument('--output',default="../data/MSCOCO/val2014_predected_captions.json", type=str,help='output file name') +parser.add_argument("--hidden",default=512, type=int, help=u"number of hidden units in LSTM") +args = parser.parse_args() + +caption_generator=CaptionGenerator( + rnn_model_place=args.rnn_model, + cnn_model_place=args.cnn_model, + dictonary_place=args.vocab, + beamsize=args.beam, + depth_limit=args.depth, + gpu_id=args.gpu, + first_word= args.lang, + hidden_dim=args.hidden, + ) + +with open(args.eval_file, 'r') as f: + captions = json.load(f) + +image_feature_path_set=set() +for caption in captions["annotations"]: + image_id = caption["image_id"] + if args.filename_img_id: + file_path="%s/%s.npz"%(args.image_feature_path,image_id) + else: + file_path="%s%012d.npz"%(args.image_feature_path,image_id) + image_feature_path_set.add(file_path) + +output_annotations={} +for i,fname in enumerate(image_feature_path_set): + print(i, fname) + image_feature=np.load(fname)['arr_0'].reshape(1,2048) + captions=caption_generator.generate_from_img_feature(image_feature) + output_annotations[fname] = captions + # if i==101: + # break + +with open(args.output, 'w') as f: + json.dump(output_annotations, f, sort_keys=True, indent=4) diff --git a/scenes/chainer-caption/code/generate_caption_table.py b/scenes/chainer-caption/code/generate_caption_table.py new file mode 100644 index 0000000..06491da --- /dev/null +++ b/scenes/chainer-caption/code/generate_caption_table.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +generate caption table +''' +import sys +import json +import os +import argparse +import numpy as np +import math +from image_loader import Image_loader +import shutil + +class HTMLPrinter(object): + def __init__(self,file_path): + self.file=open(file_path, 'w') + + def reset(self,file_path): + self.file=open(file_path, 'w') + + def write(self,line): + self.file.write(str(line.encode('utf_8') )+"\n") + + def close(self): + self.file.close() + self.file=None + +#Parse arguments +parser = argparse.ArgumentParser() +parser.add_argument('--dir',default='../data/AD/AD_viziometrics_figures/', type=str,help='path to the image directory') +parser.add_argument('--predicted',default='../experiment_ad_tl/AD_visiometrics_figure_titles_val_predicted_beam3.json', type=str,help='path to the predicted json file') +parser.add_argument('--output',default="../output1", type=str,help='output directory name') +args = parser.parse_args() + +if not os.path.isdir(args.output): + os.makedirs(args.output) + print("made the save directory", args.output) +image_dir=args.output+"/images/" +if not os.path.isdir(image_dir): + os.makedirs(image_dir) + +image_loader=Image_loader() + +with open(args.predicted, 'r') as f: + predictions=json.load(f) + +html=HTMLPrinter(file_path=args.output+"/captions.html") + +i=0 +html.write('') + +for image_filename,caption in predictions.items(): + # if image_filename[-4:]=='.npz': + # image_filename=image_filename[0:-4].split("/")[-1] + sys.stdout.write("\r%d" % i) + sys.stdout.flush() + i+=1 + image_file_path=args.dir+image_filename+".jpg" + image_np=image_loader.load(image_file_path,expand_batch_dim=False) + cropped_image_file=image_dir+image_filename+"_cropped.jpg" + image_loader.save(image_np,cropped_image_file) + shutil.copy2(image_file_path, image_dir) + + html.write("") + html.write(""%i) + html.write("") + html.write("") + html.write("") + +html.write("
%d") + data_uri = open(cropped_image_file, 'rb').read().encode('base64').replace('\n', '') + img_tag = ''.format(data_uri) + #html.write('%s'%(image_filename+"_cropped.jpg",image_filename)) + html.write(img_tag) + html.write("") + # html.write("true caption:") + # html.write(caption["true"]) + # html.write('

') + for predicted_cap in caption["captions"]: + # for predicted_cap in caption: + html.write(str(predicted_cap["log_likelihood"])+'
') + html.write( (" ".join(predicted_cap["sentence"]).replace("<","<").replace(">",">")) +'
') + html.write("
") +html.close() \ No newline at end of file diff --git a/scenes/chainer-caption/code/image_loader.py b/scenes/chainer-caption/code/image_loader.py new file mode 100644 index 0000000..275942f --- /dev/null +++ b/scenes/chainer-caption/code/image_loader.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +The class to read and crop image and output as numpy array. + +The implementatin is partly taken from the following +https://github.com/dsanno/chainer-recognize/blob/master/model.py#L32-L45 +http://qiita.com/dsanno/items/2c9ccfc53b5019475e0e#画像をnumpy配列に変換する +''' + +import numpy as np +from PIL import Image +import scipy.misc + + +class Image_loader(object): + def __init__(self,mean=None): + + #define mean + #mean is a channelwise mean + if mean == None: + self.mean_image = np.zeros((3,1,1)) + elif mean == "imagenet": + mean_image = np.ndarray((3, 224, 224), dtype=np.float32) + mean_image[0] = 103.939 + mean_image[1] = 116.779 + mean_image[2] = 123.68 + self.mean_image = mean_image + else: + self.mean_image = mean#user specified mean + + def load(self,image_path,image_w=224,image_h=224,expand_batch_dim=True): + ''' + given path, load and resize. + the output will be croped image whose size is image_w x image_h + ''' + image = Image.open(image_path).convert('RGB') + return self.resise(image,image_w,image_h,expand_batch_dim) + + def resise(self,image,image_w=224,image_h=224,expand_batch_dim=True): + ''' + given image numpy array, resize + the output will be croped image whose size is image_w x image_h + ''' + w, h = image.size + if w > h: + shape = (image_w * w // h, image_h) + else: + shape = (image_w, image_h * h // w) + x = (shape[0] - image_w) // 2 + y = (shape[1] - image_h) // 2 + pixels = np.asarray(image.resize(shape).crop((x, y, x + image_w, y + image_h))).astype(np.float32) + pixels = pixels[:,:,::-1].transpose(2,0,1) + pixels -= self.mean_image + if expand_batch_dim: + return pixels.reshape((1,) + pixels.shape) + else: + return pixels + + def save(self,np_image,file_path): + ''' + given numpy array (that is generated by self.resise() ), save it into file + + ''' + np_image=np_image.transpose(1,2,0)[:,:,::-1]#reverse rgb channel and tensor shape + scipy.misc.imsave(file_path, np_image) + diff --git a/scenes/chainer-caption/code/preprocess_Lifelog_captions.py b/scenes/chainer-caption/code/preprocess_Lifelog_captions.py new file mode 100644 index 0000000..7f739b1 --- /dev/null +++ b/scenes/chainer-caption/code/preprocess_Lifelog_captions.py @@ -0,0 +1,153 @@ +#!/usr/bin/python +# coding: UTF-8 + +import sys +import os +import argparse +import json +import nltk +from collections import Counter,OrderedDict +from itertools import chain +from copy import deepcopy +import numpy as np + +import tinysegmenter +segmenter = tinysegmenter.TinySegmenter() + +import random +random.seed(0) + + + +''' +e.g. + +python preprocess_Lifelog_captions.py \ +--input ../data/Lifelog/amt_captions.txt \ +--outdir ../data/Lifelog/ \ +--prefix Lifelog + + +''' +def create_new_caption_dataset(args,new_captions,new_cap_id=1): + captions={} + for caption in new_captions: + captions[new_cap_id]=caption + new_cap_id+=1 + + #count word frequencies + texts=[captions[caption_id]['tokens'] for caption_id in captions] + tokens=list(chain.from_iterable(texts)) + freq_count=Counter(tokens) + + print("total distinct words:", len(freq_count)) + + #remove words that appears less than 5 + id2word = [word for (word,freq) in freq_count.items() if freq >= args.cut] + id2word.append("") + word2id = {id2word[i]:i for i in xrange(len(id2word))} + + print("total distinct words after cutoff:", len(id2word)) + + for caption_id in captions: + caption_tokens=captions[caption_id]['tokens'] + #map each token into id + sentence=[] + for token in caption_tokens: + if token not in word2id: + token="" + sentence.append(word2id[token]) + del captions[caption_id]['tokens'] + captions[caption_id]['token_ids']=sentence + + return captions,word2id,new_cap_id + +def save_raw_json(captions,outfile,args): + captions={"annotations":captions,"images":"fake","type":"captions","info":"dummy, not all image ids show up in captions","licenses":"dummy"} + with open(outfile, 'w') as f: + json.dump(captions, f, sort_keys=True, indent=4) + +def save_training_json_and_dic(captions,word2id,outfile,outdic): + #save to json files + with open(outfile, 'w') as f: + json.dump(captions, f, sort_keys=True, indent=4) + + with open(outdic, 'w') as f: + json.dump(word2id, f, sort_keys=True, indent=4) + +if __name__ == '__main__': + #parse arguments + parser = argparse.ArgumentParser() + parser.add_argument('--input', type=str,help='text file of lifelog captions') + parser.add_argument('--outdir', type=str, help='the directory of output files') + parser.add_argument('--prefix', type=str, help='prefix of output files') + parser.add_argument('--cut', default = 0,type=int,help='cut off frequency') + args = parser.parse_args() + + + f = open(args.input) + line = f.readline() # 1行を文字列として読み込む(改行文字も含まれる) + + im2captions={} + while line: + line = f.readline().strip() + line_array=line.split(" ") + img_id=line_array[0][0:-4] + + if not os.path.exists("../data/Lifelog/amt_images_ResNet50Features/%s.npz"%img_id): + continue + + caption = [ word.lower().replace(".","") for word in line_array[1:] ] + if len(caption)==0: + continue + if "i" in caption: + caption=[""]+caption+[""] + else: + caption=[""]+caption+[""] + + caption={ + "image_id":img_id, + "caption":" ".join(caption), + "tokens":caption, + } + + if img_id not in im2captions: + im2captions[img_id]=[caption] + else: + im2captions[img_id].append(caption) + f.close() + img_ids=im2captions.keys() + + print(img_ids) + + captions_train=[] + captions_val=[] + + val_set=set(img_ids[0:10]) + train_set=set(img_ids[10:]) + + print(len(train_set)) + + for img_id, captions in im2captions.items(): + for caption in captions: + if img_id in val_set: + captions_val.append(caption) + elif img_id in train_set: + captions_train.append(caption) + + for img_id in val_set: + print(img_id) + for cap in im2captions[img_id]: + print(cap["caption"]) + + # sys.exit() + + print("new caption created") + + save_raw_json(captions_train,args.outdir+args.prefix+"_captions_train.json",args) + save_raw_json(captions_val,args.outdir+args.prefix+"_captions_val.json",args) + + captions,word2id,new_cap_id = create_new_caption_dataset(args,deepcopy(captions_train),new_cap_id=1) + outfile=args.outdir+args.prefix+"_train_preprocessed.json" + outdic=args.outdir+args.prefix+"_train_dic.json" + save_training_json_and_dic(captions,word2id,outfile,outdic) diff --git a/scenes/chainer-caption/code/preprocess_MSCOCO_captions.py b/scenes/chainer-caption/code/preprocess_MSCOCO_captions.py new file mode 100644 index 0000000..e7a98b6 --- /dev/null +++ b/scenes/chainer-caption/code/preprocess_MSCOCO_captions.py @@ -0,0 +1,106 @@ +#!/usr/bin/python +# coding: UTF-8 + +import os +import argparse +import json +import nltk +from collections import Counter,OrderedDict +from itertools import chain + +''' +e.g. +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014.json \ +--output ../data/MSCOCO/mscoco_caption_train2014_processed.json \ +--outdic ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--outfreq ../data/MSCOCO/mscoco_caption_train2014_processed_freq.json #this is just internal file +''' + +def read_MSCOCO_json(file_place,args): + + f = open(file_place, 'r') + jsonData = json.load(f) + f.close() + + captions={} + #key:caption_id + #image_id = captions[caption_id]['image_id'] + #caption = captions[caption_id]['tokens']#tokenized_caption + + for caption_data in jsonData['annotations']: + caption_id=caption_data['id'] + image_id=caption_data['image_id'] + caption=caption_data['caption'] + + caption=caption.replace('\n', '').strip().lower() + if caption[-1]=='.':#to delete the last period. + caption=caption[0:-1] + + caption_tokens=[''] + if args.char: + caption_tokens += list(caption) + else: + caption_tokens += nltk.word_tokenize(caption) + caption_tokens.append("") + + captions[caption_id]={} + captions[caption_id]['image_id']=image_id + captions[caption_id]['tokens']=caption_tokens + + return captions + +if __name__ == '__main__': + #parse arguments + parser = argparse.ArgumentParser() + parser.add_argument('--input', type=str,help='json file of MSCOCO formatted captions') + parser.add_argument('--output', type=str,help='the place of output file') + parser.add_argument('--outdic', type=str,help='the place of output dictonary file') + parser.add_argument('--outfreq', type=str,help='the place of output frequency map file') + parser.add_argument('--char', default = False,type=bool,help='character based tokenization. e.g. for Japanese') + parser.add_argument('--cut', default = 5,type=int,help='cut off frequency') + args = parser.parse_args() + + #load jsonfile + captions=read_MSCOCO_json(args.input,args) + + #count word frequencies + texts=[captions[caption_id]['tokens'] for caption_id in captions] + tokens=list(chain.from_iterable(texts)) + freq_count=Counter(tokens) + + # freq_count_ordered={} + # for word,freq in freq_count.most_common(len(freq_count)): + # freq_count_ordered[word]=freq + #頻度順にjsonを出したいけど、諦めたw　valueでorderするのはできない？ + + print("total distinct words:", len(freq_count)) + + #remove words that appears less than 5 + id2word = [word for (word,freq) in freq_count.items() if freq >= args.cut] + id2word.append("") + word2id = {id2word[i]:i for i in xrange(len(id2word))} + + print("total distinct words after cutoff:", len(id2word)) + + for caption_id in captions: + caption_tokens=captions[caption_id]['tokens'] + #map each token into id + sentence=[] + for token in caption_tokens: + if token not in word2id: + token="" + sentence.append(word2id[token]) + del captions[caption_id]['tokens'] + captions[caption_id]['token_ids']=sentence + + #save to json files + with open(args.output, 'w') as f: + json.dump(captions, f, sort_keys=True, indent=4) + + with open(args.outdic, 'w') as f: + json.dump(word2id, f, sort_keys=True, indent=4) + + if args.outfreq != None: + with open(args.outfreq, 'w') as f: + json.dump(freq_count, f, sort_keys=True, indent=4) \ No newline at end of file diff --git a/scenes/chainer-caption/code/preprocess_captions.py b/scenes/chainer-caption/code/preprocess_captions.py new file mode 100644 index 0000000..1efc3fe --- /dev/null +++ b/scenes/chainer-caption/code/preprocess_captions.py @@ -0,0 +1,151 @@ +#!/usr/bin/python +# coding: UTF-8 +#script for a new preprocess way. +#input file is [{ "file_path": "path/img.jpg", "captions": ["a caption", "a similar caption" ...] }, ...] + +import os +import argparse +import json +from collections import Counter,OrderedDict +from itertools import chain +import random + + +''' +e.g. +python preprocess_captions.py \ +--input ../data/MSCOCO/ms_coco_raw.json \ +--output ../data/MSCOCO/mscoco_train2014_all_preprocessed.json \ +''' + +if __name__ == '__main__': + #parse arguments + parser = argparse.ArgumentParser() + parser.add_argument('--input', type=str,help='json file of MSCOCO formatted captions') + parser.add_argument('--output', type=str,help='the place of output file') + parser.add_argument('--char', default = False,type=bool,help='character based tokenization. e.g. for Japanese') + parser.add_argument('--jp', default = False,type=bool,help='use Japanese segmenter. Install by "pip install tinysegmenter"') + parser.add_argument('--cn', default = False,type=bool,help='use Chinese segmenter. Install by "pip install jieba"') + parser.add_argument('--cut', default = 5,type=int,help='cut off frequency. this frequency will be the lowest to be kept.') + parser.add_argument('--lower', default = True,type=bool,help='make everything into lower case') + parser.add_argument('--remove-period', default = True,type=bool,help='remove the last period if a caption has a priod') + parser.add_argument('--val', default = 2000,type=int,help='number of validiation images') + parser.add_argument('--test', default = 2000,type=int,help='number of test images') + # parser.add_argument('--keep-info', default = False,type=bool,help='keep other image information other than "file_path" in the input json') + args = parser.parse_args() + + class Segmenter: + def __init__(self, args): + self.args=args + if self.args.jp: + import tinysegmenter + self.tinysegmenter = tinysegmenter.TinySegmenter() + self.segmenter= lambda sent: self.tinysegmenter.tokenize(sent) + elif self.args.cn: + import jieba + jieba.cut("我来到北京清华大") + self.segmenter= lambda sent: list(jieba.cut(sent)) + elif not args.char: + import nltk + self.nltk=nltk + self.segmenter = lambda sent: self.nltk.word_tokenize(sent) + else: + self.segmenter= lambda sent: list(sent) + + def segment(self,caption): + if self.args.lower: + caption=caption.replace('\n', '').strip().lower() + if self.args.remove_period and caption[-1]=='.': + caption=caption[0:-1]#to delete the last period. + return self.segmenter(caption) + + def word2idx_func(word,word2idx): + if word in word2idx: + return word2idx[word] + else: + return word2idx[""] + + segmenter=Segmenter(args) + + with open(args.input, 'r') as f: + jsonData = json.load(f) + + #split into train,val,test data: + train_data=[] + val_data=[] + test_data=[] + + random.seed(0)#in order to have the same split every time + random.shuffle(jsonData) + + for i,img in enumerate(jsonData): + if i < args.val: + new_captions=[] + for caption in img["captions"]: + new_cap=" ".join(segmenter.segment(caption.lower())) + new_captions.append(new_cap) + img["captions"]=new_captions + val_data.append(img) + elif i < args.test + args.val: + new_captions=[] + for caption in img["captions"]: + new_cap=" ".join(segmenter.segment(caption.lower())) + new_captions.append(new_cap) + img["captions"]=new_captions + test_data.append(img) + else: + train_data.append(img) + + #the info for the output json + image_idx=0 + caption_idx=0 + all_images=[] + all_captions=[] + all_words=[] + + for image in train_data: + for caption in image["captions"]: + caption_tokens=[''] + caption_tokens += segmenter.segment(caption) + caption_tokens.append("") + all_captions.append({"image_idx":image_idx,"caption":caption_tokens,"idx":caption_idx}) + caption_idx+=1 + del image["captions"] + all_images.append({"file_path":image["file_path"],"idx":image_idx}) + image_idx+=1 + + #count word frequencies + tokens=list(chain.from_iterable([caption["caption"] for caption in all_captions])) + freq_count=Counter(tokens) + + print("total distinct words:",len(freq_count)) + print("top 20 frequent words:") + for word,freq in freq_count.most_common(20): + print(u"(%s,%s)"%(word,freq)) + + #remove words that appears less than the cut + ukn_freq=0 + for word,freq in freq_count.most_common(): + if freq >= args.cut: + all_words.append({"word":word,"freq":freq,"idx":len(all_words)}) + else: + ukn_freq+=freq + all_words.append({"word":"","freq":ukn_freq,"idx":len(all_words)}) + + print("total distinct words after the cutoff:",len(all_words)) + + word2idx = {word["word"]:word["idx"] for word in all_words} + + for caption in all_captions: + caption["caption"] = [word2idx_func(word,word2idx) for word in caption["caption"]] + + #save to json files + preprocessed_file={} + preprocessed_file["images"]=all_images + preprocessed_file["captions"]=all_captions + preprocessed_file["words"]=all_words + preprocessed_file["val"]=val_data + preprocessed_file["test"]=test_data + with open(args.output, 'w') as f: + json.dump(preprocessed_file, f, sort_keys=True, indent=4) + print("The output file is saved to",args.output) \ No newline at end of file diff --git a/scenes/chainer-caption/code/preprocess_mscroro2originalformat.py b/scenes/chainer-caption/code/preprocess_mscroro2originalformat.py new file mode 100644 index 0000000..d0f4a7f --- /dev/null +++ b/scenes/chainer-caption/code/preprocess_mscroro2originalformat.py @@ -0,0 +1,34 @@ +import json +import os +train = json.load(open('../data/MSCOCO/captions_train2014.json', 'r')) + +imgs = train['images'] +annots = train['annotations'] + +# for efficiency lets group annotations by image +itoa = {} +for a in annots: + imgid = a['image_id'] + if not imgid in itoa: itoa[imgid] = [] + itoa[imgid].append(a) + +# create the json blob +out = [] +for i,img in enumerate(imgs): + imgid = img['id'] + + # coco specific here, they store train/val images separately + loc = 'train2014' if 'train' in img['file_name'] else 'val2014' + + jimg = {} + jimg['file_path'] = os.path.join(loc, img['file_name']) + jimg['id'] = imgid + + sents = [] + annotsi = itoa[imgid] + for a in annotsi: + sents.append(a['caption']) + jimg['captions'] = sents + out.append(jimg) + +json.dump(out, open('../data/MSCOCO/ms_coco_raw.json', 'w')) \ No newline at end of file diff --git a/scenes/chainer-caption/code/preprocess_multilingual_MSCOCO_captions.py b/scenes/chainer-caption/code/preprocess_multilingual_MSCOCO_captions.py new file mode 100644 index 0000000..6d50e7c --- /dev/null +++ b/scenes/chainer-caption/code/preprocess_multilingual_MSCOCO_captions.py @@ -0,0 +1,207 @@ +#!/usr/bin/python +# coding: UTF-8 + +import os +import argparse +import json +import nltk +from collections import Counter,OrderedDict +from itertools import chain +from copy import deepcopy + +import tinysegmenter +segmenter = tinysegmenter.TinySegmenter() + +import random +random.seed(0) + + + +''' +e.g. + +python preprocess_multilingual_MSCOCO_captions.py \ +--en ../data/MSCOCO/captions_train2014.json \ +--jp ../data/MSCOCO/yjcaptions26k_clean.json \ +--outdir ../data/MSCOCO/ \ +--prefix mscoco_caption_multi \ + +python preprocess_multilingual_MSCOCO_captions.py \ +--en ../data/MSCOCO/captions_train2014.json \ +--jp ../data/MSCOCO/yjcaptions26k_clean.json \ +--outdir ../data/MSCOCO/ \ +--prefix mscoco_caption_multi2 \ +''' + +def segment(caption,lang): + caption_tokens=[lang] + if lang=="": + caption_tokens += segmenter.tokenize(caption) + elif lang=="": + caption_tokens += nltk.word_tokenize(caption) + caption_tokens.append("") + + return caption_tokens + +def read_MSCOCO_json(file_place,args,lang): + + f = open(file_place, 'r') + jsonData = json.load(f) + f.close() + + captions={} + #key:caption_id + #image_id = captions[caption_id]['image_id'] + #caption = captions[caption_id]['tokens']#tokenized_caption + + for caption_data in jsonData['annotations']: + caption_id=caption_data['id'] + image_id=caption_data['image_id'] + caption=caption_data['caption'] + + caption=caption.replace('\n', '').strip().lower() + if caption[-1]=='.':#to delete the last period. + caption=caption[0:-1] + + caption_tokens=segment(caption,lang) + + captions[caption_id]={} + captions[caption_id]['image_id']=image_id + captions[caption_id]['tokens']=caption_tokens + captions[caption_id]['caption']=caption + captions[caption_id]['id']=caption_id + + return captions + + +def create_new_caption_dataset(args,new_captions,new_cap_id=1): + captions={} + for caption in new_captions: + captions[new_cap_id]=caption + new_cap_id+=1 + + #count word frequencies + texts=[captions[caption_id]['tokens'] for caption_id in captions] + tokens=list(chain.from_iterable(texts)) + freq_count=Counter(tokens) + + print("total distinct words:", len(freq_count)) + + #remove words that appears less than 5 + id2word = [word for (word,freq) in freq_count.items() if freq >= args.cut] + id2word.append("") + word2id = {id2word[i]:i for i in xrange(len(id2word))} + + print("total distinct words after cutoff:", len(id2word)) + + for caption_id in captions: + caption_tokens=captions[caption_id]['tokens'] + #map each token into id + sentence=[] + for token in caption_tokens: + if token not in word2id: + token="" + sentence.append(word2id[token]) + del captions[caption_id]['tokens'] + captions[caption_id]['token_ids']=sentence + + return captions,word2id,new_cap_id + +def save_raw_json(captions,outfile,args): + with open(args.en, 'r') as f: + jsonData=json.load(f) + captions={"annotations":captions,"images":jsonData['images'],"type":"captions","info":"dummy, not all image ids show up in captions","licenses":"dummy"} + with open(outfile, 'w') as f: + json.dump(captions, f, sort_keys=True, indent=4) + +def save_training_json_and_dic(captions,word2id,outfile,outdic): + #save to json files + with open(outfile, 'w') as f: + json.dump(captions, f, sort_keys=True, indent=4) + + with open(outdic, 'w') as f: + json.dump(word2id, f, sort_keys=True, indent=4) + +if __name__ == '__main__': + #parse arguments + parser = argparse.ArgumentParser() + parser.add_argument('--en', type=str,help='json file of MSCOCO formatted captions for English') + parser.add_argument('--jp', type=str,help='json file of MSCOCO formatted captions for Japanese') + parser.add_argument('--outdir', type=str, help='the directory of output files') + parser.add_argument('--prefix', type=str, help='prefix of output files') + parser.add_argument('--cut', default = 5,type=int,help='cut off frequency') + args = parser.parse_args() + + #load jsonfile + en_captions=read_MSCOCO_json(args.en,args,lang="") + jp_captions=read_MSCOCO_json(args.jp,args,lang="") + + en_img_ids=[caption["image_id"] for caption in en_captions.values()] + jp_img_ids=[caption["image_id"] for caption in jp_captions.values()] + + common_img_ids = set(en_img_ids) & set(jp_img_ids) + remain_img_ids = set(en_img_ids) - set(jp_img_ids) + + common_img_ids=list(common_img_ids) + random.shuffle(common_img_ids) + + print("common image ids", len(common_img_ids)) + + # en_remainings=[] + en_selected_captions_train=[] + en_selected_captions_val=[] + en_selected_captions_test=[] + jp_selected_captions_train=[] + jp_selected_captions_val=[] + jp_selected_captions_test=[] + + + val_set=set(common_img_ids[0:2000]) + test_set=set(common_img_ids[2000:4000]) + train_set=set(common_img_ids[4000:-1]) + + print(len(train_set)) + + for caption_id, caption in en_captions.items(): + if caption["image_id"] in val_set: + en_selected_captions_val.append(caption) + elif caption["image_id"] in test_set: + en_selected_captions_test.append(caption) + elif caption["image_id"] in train_set: + en_selected_captions_train.append(caption) + + for caption_id, caption in jp_captions.items(): + if caption["image_id"] in val_set: + jp_selected_captions_val.append(caption) + elif caption["image_id"] in test_set: + jp_selected_captions_test.append(caption) + elif caption["image_id"] in train_set: + jp_selected_captions_train.append(caption) + + print("new caption created") + + save_raw_json(en_selected_captions_train,args.outdir+args.prefix+"_en_captions_train.json",args) + save_raw_json(en_selected_captions_val,args.outdir+args.prefix+"_en_captions_val.json",args) + save_raw_json(en_selected_captions_test,args.outdir+args.prefix+"_en_captions_test.json",args) + save_raw_json(jp_selected_captions_train,args.outdir+args.prefix+"_jp_captions_train.json",args) + save_raw_json(jp_selected_captions_val,args.outdir+args.prefix+"_jp_captions_val.json",args) + save_raw_json(jp_selected_captions_test,args.outdir+args.prefix+"_jp_captions_test.json",args) + + new_captions=en_selected_captions_train+jp_selected_captions_train + captions,word2id,new_cap_id = create_new_caption_dataset(args,deepcopy(new_captions),new_cap_id=1) + outfile=args.outdir+args.prefix+"_en_jp_train_preprocessed.json" + outdic=args.outdir+args.prefix+"_en_jp_train_dic.json" + save_training_json_and_dic(captions,word2id,outfile,outdic) + + new_captions=en_selected_captions_train + captions,word2id,new_cap_id = create_new_caption_dataset(args,deepcopy(new_captions),new_cap_id) + outfile=args.outdir+args.prefix+"_en_train_preprocessed.json" + outdic=args.outdir+args.prefix+"_en_train_dic.json" + save_training_json_and_dic(captions,word2id,outfile,outdic) + + new_captions=jp_selected_captions_train + captions,word2id,new_cap_id = create_new_caption_dataset(args,deepcopy(new_captions),new_cap_id) + outfile=args.outdir+args.prefix+"_jp_train_preprocessed.json" + outdic=args.outdir+args.prefix+"_jp_train_dic.json" + save_training_json_and_dic(captions,word2id,outfile,outdic) + diff --git a/scenes/chainer-caption/data/.gitkeep b/scenes/chainer-caption/data/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/scenes/chainer-caption/data/MSCOCO/captions_train2014_cn_translation_processed_dic.json b/scenes/chainer-caption/data/MSCOCO/captions_train2014_cn_translation_processed_dic.json new file mode 100644 index 0000000..faebf7d --- /dev/null +++ b/scenes/chainer-caption/data/MSCOCO/captions_train2014_cn_translation_processed_dic.json @@ -0,0 +1,3094 @@ +{ + "$": 2118, + "'": 3080, + ",": 213, + "-": 358, + ".": 502, + "/": 641, + "0": 796, + "1": 934, + "2": 1082, + "3": 1213, + "4": 1357, + "5": 1493, + "6": 1641, + "7": 1798, + "8": 1951, + "9": 2082, + ":": 2218, + ";": 1788, + "": 1181, + "": 500, + "": 3091, + "=": 2672, + ">": 2816, + "[": 857, + "\\": 1001, + "`": 1550, + "a": 1709, + "b": 1866, + "c": 2013, + "d": 613, + "e": 2282, + "f": 2445, + "g": 2595, + "h": 2733, + "i": 2881, + "j": 3025, + "k": 88, + "l": 233, + "m": 386, + "n": 523, + "o": 923, + "p": 818, + "q": 956, + "r": 1105, + "s": 1234, + "t": 1381, + "u": 1512, + "v": 852, + "w": 1825, + "x": 1978, + "y": 2102, + "z": 2243, + "\u00af": 496, + "\u00b7": 1748, + "\u00eb": 46, + "\u00f0": 862, + "\u00f1": 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libedit=3.1.20181209=hc058e9b_0 + - libffi=3.2.1=hd88cf55_4 + - libgcc-ng=8.2.0=hdf63c60_1 + - libgfortran-ng=7.3.0=hdf63c60_0 + - libpng=1.6.36=hbc83047_0 + - libstdcxx-ng=8.2.0=hdf63c60_1 + - libtiff=4.0.10=h2733197_2 + - libuuid=1.0.3=h1bed415_2 + - libxcb=1.13=h1bed415_1 + - libxml2=2.9.9=he19cac6_0 + - matplotlib=2.2.3=py27hb69df0a_0 + - mkl=2019.3=199 + - mkl_fft=1.0.10=py27ha843d7b_0 + - mkl_random=1.0.2=py27hd81dba3_0 + - ncurses=6.1=he6710b0_1 + - numpy=1.16.2=py27h7e9f1db_0 + - numpy-base=1.16.2=py27hde5b4d6_0 + - olefile=0.46=py27_0 + - openssl=1.1.1b=h7b6447c_1 + - pandas=0.24.2=py27he6710b0_0 + - pcre=8.43=he6710b0_0 + - pillow=5.4.1=py27h34e0f95_0 + - pip=19.0.3=py27_0 + - pyparsing=2.3.1=py27_0 + - pyqt=5.9.2=py27h05f1152_2 + - python=2.7.16=h9bab390_0 + - python-dateutil=2.8.0=py27_0 + - pytz=2018.9=py27_0 + - qt=5.9.7=h5867ecd_1 + - readline=7.0=h7b6447c_5 + - scipy=1.2.1=py27h7c811a0_0 + - setuptools=40.8.0=py27_0 + - singledispatch=3.4.0.3=py27_0 + - sip=4.19.8=py27hf484d3e_0 + - six=1.12.0=py27_0 + - sqlite=3.27.2=h7b6447c_0 + - subprocess32=3.5.3=py27h7b6447c_0 + - tk=8.6.8=hbc83047_0 + - tornado=5.1.1=py27h7b6447c_0 + - wheel=0.33.1=py27_0 + - xz=5.2.4=h14c3975_4 + - zlib=1.2.11=h7b6447c_3 + - zstd=1.3.7=h0b5b093_0 + - pip: + - chainer==1.24.0 + - cupy==2.0.0 + - fastrlock==0.4 + - filelock==3.0.10 + - h5py==2.9.0 + - nltk==3.4 + - nose==1.3.7 + - protobuf==3.7.1 +prefix: /u/stsutsui/gpu/public-repos/chainer-caption-github/conda + diff --git a/scenes/chainer-caption/data/synset_words.txt b/scenes/chainer-caption/data/synset_words.txt new file mode 100644 index 0000000..a9e8c7f --- /dev/null +++ b/scenes/chainer-caption/data/synset_words.txt @@ -0,0 +1,1000 @@ +n01440764 tench, Tinca tinca +n01443537 goldfish, Carassius auratus +n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +n01491361 tiger shark, Galeocerdo cuvieri +n01494475 hammerhead, hammerhead shark +n01496331 electric ray, crampfish, numbfish, torpedo +n01498041 stingray +n01514668 cock +n01514859 hen +n01518878 ostrich, Struthio camelus +n01530575 brambling, Fringilla montifringilla +n01531178 goldfinch, Carduelis carduelis +n01532829 house finch, linnet, Carpodacus mexicanus +n01534433 junco, snowbird +n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea +n01558993 robin, American robin, Turdus migratorius +n01560419 bulbul +n01580077 jay +n01582220 magpie +n01592084 chickadee +n01601694 water ouzel, dipper +n01608432 kite +n01614925 bald eagle, American eagle, Haliaeetus leucocephalus +n01616318 vulture +n01622779 great grey owl, great gray owl, Strix nebulosa +n01629819 European fire salamander, Salamandra salamandra +n01630670 common newt, Triturus vulgaris +n01631663 eft +n01632458 spotted salamander, Ambystoma maculatum +n01632777 axolotl, mud puppy, Ambystoma mexicanum +n01641577 bullfrog, Rana catesbeiana +n01644373 tree frog, tree-frog +n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +n01664065 loggerhead, loggerhead turtle, Caretta caretta +n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +n01667114 mud turtle +n01667778 terrapin +n01669191 box turtle, box tortoise +n01675722 banded gecko +n01677366 common iguana, iguana, Iguana iguana +n01682714 American chameleon, anole, Anolis carolinensis +n01685808 whiptail, whiptail lizard +n01687978 agama +n01688243 frilled lizard, Chlamydosaurus kingi +n01689811 alligator lizard +n01692333 Gila monster, Heloderma suspectum +n01693334 green lizard, Lacerta viridis +n01694178 African chameleon, Chamaeleo chamaeleon +n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +n01697457 African crocodile, Nile crocodile, Crocodylus niloticus +n01698640 American alligator, Alligator mississipiensis +n01704323 triceratops +n01728572 thunder snake, worm snake, Carphophis amoenus +n01728920 ringneck snake, ring-necked snake, ring snake +n01729322 hognose snake, puff adder, sand viper +n01729977 green snake, grass snake +n01734418 king snake, kingsnake +n01735189 garter snake, grass snake +n01737021 water snake +n01739381 vine snake +n01740131 night snake, Hypsiglena torquata +n01742172 boa constrictor, Constrictor constrictor +n01744401 rock python, rock snake, Python sebae +n01748264 Indian cobra, Naja naja +n01749939 green mamba +n01751748 sea snake +n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus +n01756291 sidewinder, horned rattlesnake, Crotalus cerastes +n01768244 trilobite +n01770081 harvestman, daddy longlegs, Phalangium opilio +n01770393 scorpion +n01773157 black and gold garden spider, Argiope aurantia +n01773549 barn spider, Araneus cavaticus +n01773797 garden spider, Aranea diademata +n01774384 black widow, Latrodectus mactans +n01774750 tarantula +n01775062 wolf spider, hunting spider +n01776313 tick +n01784675 centipede +n01795545 black grouse +n01796340 ptarmigan +n01797886 ruffed grouse, partridge, Bonasa umbellus +n01798484 prairie chicken, prairie grouse, prairie fowl +n01806143 peacock +n01806567 quail +n01807496 partridge +n01817953 African grey, African gray, Psittacus erithacus +n01818515 macaw +n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +n01820546 lorikeet +n01824575 coucal +n01828970 bee eater +n01829413 hornbill +n01833805 hummingbird +n01843065 jacamar +n01843383 toucan +n01847000 drake +n01855032 red-breasted merganser, Mergus serrator +n01855672 goose +n01860187 black swan, Cygnus atratus +n01871265 tusker +n01872401 echidna, spiny anteater, anteater +n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +n01877812 wallaby, brush kangaroo +n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +n01883070 wombat +n01910747 jellyfish +n01914609 sea anemone, anemone +n01917289 brain coral +n01924916 flatworm, platyhelminth +n01930112 nematode, nematode worm, roundworm +n01943899 conch +n01944390 snail +n01945685 slug +n01950731 sea slug, nudibranch +n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore +n01968897 chambered nautilus, pearly nautilus, nautilus +n01978287 Dungeness crab, Cancer magister +n01978455 rock crab, Cancer irroratus +n01980166 fiddler crab +n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus +n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +n01985128 crayfish, crawfish, crawdad, crawdaddy +n01986214 hermit crab +n01990800 isopod +n02002556 white stork, Ciconia ciconia +n02002724 black stork, Ciconia nigra +n02006656 spoonbill +n02007558 flamingo +n02009229 little blue heron, Egretta caerulea +n02009912 American egret, great white heron, Egretta albus +n02011460 bittern +n02012849 crane +n02013706 limpkin, Aramus pictus +n02017213 European gallinule, Porphyrio porphyrio +n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana +n02018795 bustard +n02025239 ruddy turnstone, Arenaria interpres +n02027492 red-backed sandpiper, dunlin, Erolia alpina +n02028035 redshank, Tringa totanus +n02033041 dowitcher +n02037110 oystercatcher, oyster catcher +n02051845 pelican +n02056570 king penguin, Aptenodytes patagonica +n02058221 albatross, mollymawk +n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca +n02074367 dugong, Dugong dugon +n02077923 sea lion +n02085620 Chihuahua +n02085782 Japanese spaniel +n02085936 Maltese dog, Maltese terrier, Maltese +n02086079 Pekinese, Pekingese, Peke +n02086240 Shih-Tzu +n02086646 Blenheim spaniel +n02086910 papillon +n02087046 toy terrier +n02087394 Rhodesian ridgeback +n02088094 Afghan hound, Afghan +n02088238 basset, basset hound +n02088364 beagle +n02088466 bloodhound, sleuthhound +n02088632 bluetick +n02089078 black-and-tan coonhound +n02089867 Walker hound, Walker foxhound +n02089973 English foxhound +n02090379 redbone +n02090622 borzoi, Russian wolfhound +n02090721 Irish wolfhound +n02091032 Italian greyhound +n02091134 whippet +n02091244 Ibizan hound, Ibizan Podenco +n02091467 Norwegian elkhound, elkhound +n02091635 otterhound, otter hound +n02091831 Saluki, gazelle hound +n02092002 Scottish deerhound, deerhound +n02092339 Weimaraner +n02093256 Staffordshire bullterrier, Staffordshire bull terrier +n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +n02093647 Bedlington terrier +n02093754 Border terrier +n02093859 Kerry blue terrier +n02093991 Irish terrier +n02094114 Norfolk terrier +n02094258 Norwich terrier +n02094433 Yorkshire terrier +n02095314 wire-haired fox terrier +n02095570 Lakeland terrier +n02095889 Sealyham terrier, Sealyham +n02096051 Airedale, Airedale terrier +n02096177 cairn, cairn terrier +n02096294 Australian terrier +n02096437 Dandie Dinmont, Dandie Dinmont terrier +n02096585 Boston bull, Boston terrier +n02097047 miniature schnauzer +n02097130 giant schnauzer +n02097209 standard schnauzer +n02097298 Scotch terrier, Scottish terrier, Scottie +n02097474 Tibetan terrier, chrysanthemum dog +n02097658 silky terrier, Sydney silky +n02098105 soft-coated wheaten terrier +n02098286 West Highland white terrier +n02098413 Lhasa, Lhasa apso +n02099267 flat-coated retriever +n02099429 curly-coated retriever +n02099601 golden retriever +n02099712 Labrador retriever +n02099849 Chesapeake Bay retriever +n02100236 German short-haired pointer +n02100583 vizsla, Hungarian pointer +n02100735 English setter +n02100877 Irish setter, red setter +n02101006 Gordon setter +n02101388 Brittany spaniel +n02101556 clumber, clumber spaniel +n02102040 English springer, English springer spaniel +n02102177 Welsh springer spaniel +n02102318 cocker spaniel, English cocker spaniel, cocker +n02102480 Sussex spaniel +n02102973 Irish water spaniel +n02104029 kuvasz +n02104365 schipperke +n02105056 groenendael +n02105162 malinois +n02105251 briard +n02105412 kelpie +n02105505 komondor +n02105641 Old English sheepdog, bobtail +n02105855 Shetland sheepdog, Shetland sheep dog, Shetland +n02106030 collie +n02106166 Border collie +n02106382 Bouvier des Flandres, Bouviers des Flandres +n02106550 Rottweiler +n02106662 German shepherd, German shepherd dog, German police dog, alsatian +n02107142 Doberman, Doberman pinscher +n02107312 miniature pinscher +n02107574 Greater Swiss Mountain dog +n02107683 Bernese mountain dog +n02107908 Appenzeller +n02108000 EntleBucher +n02108089 boxer +n02108422 bull mastiff +n02108551 Tibetan mastiff +n02108915 French bulldog +n02109047 Great Dane +n02109525 Saint Bernard, St Bernard +n02109961 Eskimo dog, husky +n02110063 malamute, malemute, Alaskan malamute +n02110185 Siberian husky +n02110341 dalmatian, coach dog, carriage dog +n02110627 affenpinscher, monkey pinscher, monkey dog +n02110806 basenji +n02110958 pug, pug-dog +n02111129 Leonberg +n02111277 Newfoundland, Newfoundland dog +n02111500 Great Pyrenees +n02111889 Samoyed, Samoyede +n02112018 Pomeranian +n02112137 chow, chow chow +n02112350 keeshond +n02112706 Brabancon griffon +n02113023 Pembroke, Pembroke Welsh corgi +n02113186 Cardigan, Cardigan Welsh corgi +n02113624 toy poodle +n02113712 miniature poodle +n02113799 standard poodle +n02113978 Mexican hairless +n02114367 timber wolf, grey wolf, gray wolf, Canis lupus +n02114548 white wolf, Arctic wolf, Canis lupus tundrarum +n02114712 red wolf, maned wolf, Canis rufus, Canis niger +n02114855 coyote, prairie wolf, brush wolf, Canis latrans +n02115641 dingo, warrigal, warragal, Canis dingo +n02115913 dhole, Cuon alpinus +n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +n02117135 hyena, hyaena +n02119022 red fox, Vulpes vulpes +n02119789 kit fox, Vulpes macrotis +n02120079 Arctic fox, white fox, Alopex lagopus +n02120505 grey fox, gray fox, Urocyon cinereoargenteus +n02123045 tabby, tabby cat +n02123159 tiger cat +n02123394 Persian cat +n02123597 Siamese cat, Siamese +n02124075 Egyptian cat +n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +n02127052 lynx, catamount +n02128385 leopard, Panthera pardus +n02128757 snow leopard, ounce, Panthera uncia +n02128925 jaguar, panther, Panthera onca, Felis onca +n02129165 lion, king of beasts, Panthera leo +n02129604 tiger, Panthera tigris +n02130308 cheetah, chetah, Acinonyx jubatus +n02132136 brown bear, bruin, Ursus arctos +n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus +n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +n02134418 sloth bear, Melursus ursinus, Ursus ursinus +n02137549 mongoose +n02138441 meerkat, mierkat +n02165105 tiger beetle +n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +n02167151 ground beetle, carabid beetle +n02168699 long-horned beetle, longicorn, longicorn beetle +n02169497 leaf beetle, chrysomelid +n02172182 dung beetle +n02174001 rhinoceros beetle +n02177972 weevil +n02190166 fly +n02206856 bee +n02219486 ant, emmet, pismire +n02226429 grasshopper, hopper +n02229544 cricket +n02231487 walking stick, walkingstick, stick insect +n02233338 cockroach, roach +n02236044 mantis, mantid +n02256656 cicada, cicala +n02259212 leafhopper +n02264363 lacewing, lacewing fly +n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +n02268853 damselfly +n02276258 admiral +n02277742 ringlet, ringlet butterfly +n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +n02280649 cabbage butterfly +n02281406 sulphur butterfly, sulfur butterfly +n02281787 lycaenid, lycaenid butterfly +n02317335 starfish, sea star +n02319095 sea urchin +n02321529 sea cucumber, holothurian +n02325366 wood rabbit, cottontail, cottontail rabbit +n02326432 hare +n02328150 Angora, Angora rabbit +n02342885 hamster +n02346627 porcupine, hedgehog +n02356798 fox squirrel, eastern fox squirrel, Sciurus niger +n02361337 marmot +n02363005 beaver +n02364673 guinea pig, Cavia cobaya +n02389026 sorrel +n02391049 zebra +n02395406 hog, pig, grunter, squealer, Sus scrofa +n02396427 wild boar, boar, Sus scrofa +n02397096 warthog +n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius +n02403003 ox +n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +n02410509 bison +n02412080 ram, tup +n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +n02417914 ibex, Capra ibex +n02422106 hartebeest +n02422699 impala, Aepyceros melampus +n02423022 gazelle +n02437312 Arabian camel, dromedary, Camelus dromedarius +n02437616 llama +n02441942 weasel +n02442845 mink +n02443114 polecat, fitch, foulmart, foumart, Mustela putorius +n02443484 black-footed ferret, ferret, Mustela nigripes +n02444819 otter +n02445715 skunk, polecat, wood pussy +n02447366 badger +n02454379 armadillo +n02457408 three-toed sloth, ai, Bradypus tridactylus +n02480495 orangutan, orang, orangutang, Pongo pygmaeus +n02480855 gorilla, Gorilla gorilla +n02481823 chimpanzee, chimp, Pan troglodytes +n02483362 gibbon, Hylobates lar +n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus +n02484975 guenon, guenon monkey +n02486261 patas, hussar monkey, Erythrocebus patas +n02486410 baboon +n02487347 macaque +n02488291 langur +n02488702 colobus, colobus monkey +n02489166 proboscis monkey, Nasalis larvatus +n02490219 marmoset +n02492035 capuchin, ringtail, Cebus capucinus +n02492660 howler monkey, howler +n02493509 titi, titi monkey +n02493793 spider monkey, Ateles geoffroyi +n02494079 squirrel monkey, Saimiri sciureus +n02497673 Madagascar cat, ring-tailed lemur, Lemur catta +n02500267 indri, indris, Indri indri, Indri brevicaudatus +n02504013 Indian elephant, Elephas maximus +n02504458 African elephant, Loxodonta africana +n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +n02514041 barracouta, snoek +n02526121 eel +n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +n02606052 rock beauty, Holocanthus tricolor +n02607072 anemone fish +n02640242 sturgeon +n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus +n02643566 lionfish +n02655020 puffer, pufferfish, blowfish, globefish +n02666196 abacus +n02667093 abaya +n02669723 academic gown, academic robe, judge's robe +n02672831 accordion, piano accordion, squeeze box +n02676566 acoustic guitar +n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier +n02690373 airliner +n02692877 airship, dirigible +n02699494 altar +n02701002 ambulance +n02704792 amphibian, amphibious vehicle +n02708093 analog clock +n02727426 apiary, bee house +n02730930 apron +n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +n02749479 assault rifle, assault gun +n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack +n02776631 bakery, bakeshop, bakehouse +n02777292 balance beam, beam +n02782093 balloon +n02783161 ballpoint, ballpoint pen, ballpen, Biro +n02786058 Band Aid +n02787622 banjo +n02788148 bannister, banister, balustrade, balusters, handrail +n02790996 barbell +n02791124 barber chair +n02791270 barbershop +n02793495 barn +n02794156 barometer +n02795169 barrel, cask +n02797295 barrow, garden cart, lawn cart, wheelbarrow +n02799071 baseball +n02802426 basketball +n02804414 bassinet +n02804610 bassoon +n02807133 bathing cap, swimming cap +n02808304 bath towel +n02808440 bathtub, bathing tub, bath, tub +n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +n02814860 beacon, lighthouse, beacon light, pharos +n02815834 beaker +n02817516 bearskin, busby, shako +n02823428 beer bottle +n02823750 beer glass +n02825657 bell cote, bell cot +n02834397 bib +n02835271 bicycle-built-for-two, tandem bicycle, tandem +n02837789 bikini, two-piece +n02840245 binder, ring-binder +n02841315 binoculars, field glasses, opera glasses +n02843684 birdhouse +n02859443 boathouse +n02860847 bobsled, bobsleigh, bob +n02865351 bolo tie, bolo, bola tie, bola +n02869837 bonnet, poke bonnet +n02870880 bookcase +n02871525 bookshop, bookstore, bookstall +n02877765 bottlecap +n02879718 bow +n02883205 bow tie, bow-tie, bowtie +n02892201 brass, memorial tablet, plaque +n02892767 brassiere, bra, bandeau +n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty +n02895154 breastplate, aegis, egis +n02906734 broom +n02909870 bucket, pail +n02910353 buckle +n02916936 bulletproof vest +n02917067 bullet train, bullet +n02927161 butcher shop, meat market +n02930766 cab, hack, taxi, taxicab +n02939185 caldron, cauldron +n02948072 candle, taper, wax light +n02950826 cannon +n02951358 canoe +n02951585 can opener, tin opener +n02963159 cardigan +n02965783 car mirror +n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig +n02966687 carpenter's kit, tool kit +n02971356 carton +n02974003 car wheel +n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +n02978881 cassette +n02979186 cassette player +n02980441 castle +n02981792 catamaran +n02988304 CD player +n02992211 cello, violoncello +n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone +n02999410 chain +n03000134 chainlink fence +n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +n03000684 chain saw, chainsaw +n03014705 chest +n03016953 chiffonier, commode +n03017168 chime, bell, gong +n03018349 china cabinet, china closet +n03026506 Christmas stocking +n03028079 church, church building +n03032252 cinema, movie theater, movie theatre, movie house, picture palace +n03041632 cleaver, meat cleaver, chopper +n03042490 cliff dwelling +n03045698 cloak +n03047690 clog, geta, patten, sabot +n03062245 cocktail shaker +n03063599 coffee mug +n03063689 coffeepot +n03065424 coil, spiral, volute, whorl, helix +n03075370 combination lock +n03085013 computer keyboard, keypad +n03089624 confectionery, confectionary, candy store +n03095699 container ship, containership, container vessel +n03100240 convertible +n03109150 corkscrew, bottle screw +n03110669 cornet, horn, trumpet, trump +n03124043 cowboy boot +n03124170 cowboy hat, ten-gallon hat +n03125729 cradle +n03126707 crane +n03127747 crash helmet +n03127925 crate +n03131574 crib, cot +n03133878 Crock Pot +n03134739 croquet ball +n03141823 crutch +n03146219 cuirass +n03160309 dam, dike, dyke +n03179701 desk +n03180011 desktop computer +n03187595 dial telephone, dial phone +n03188531 diaper, nappy, napkin +n03196217 digital clock +n03197337 digital watch +n03201208 dining table, board +n03207743 dishrag, dishcloth +n03207941 dishwasher, dish washer, dishwashing machine +n03208938 disk brake, disc brake +n03216828 dock, dockage, docking facility +n03218198 dogsled, dog sled, dog sleigh +n03220513 dome +n03223299 doormat, welcome mat +n03240683 drilling platform, offshore rig +n03249569 drum, membranophone, tympan +n03250847 drumstick +n03255030 dumbbell +n03259280 Dutch oven +n03271574 electric fan, blower +n03272010 electric guitar +n03272562 electric locomotive +n03290653 entertainment center +n03291819 envelope +n03297495 espresso maker +n03314780 face powder +n03325584 feather boa, boa +n03337140 file, file cabinet, filing cabinet +n03344393 fireboat +n03345487 fire engine, fire truck +n03347037 fire screen, fireguard +n03355925 flagpole, flagstaff +n03372029 flute, transverse flute +n03376595 folding chair +n03379051 football helmet +n03384352 forklift +n03388043 fountain +n03388183 fountain pen +n03388549 four-poster +n03393912 freight car +n03394916 French horn, horn +n03400231 frying pan, frypan, skillet +n03404251 fur coat +n03417042 garbage truck, dustcart +n03424325 gasmask, respirator, gas helmet +n03425413 gas pump, gasoline pump, petrol pump, island dispenser +n03443371 goblet +n03444034 go-kart +n03445777 golf ball +n03445924 golfcart, golf cart +n03447447 gondola +n03447721 gong, tam-tam +n03450230 gown +n03452741 grand piano, grand +n03457902 greenhouse, nursery, glasshouse +n03459775 grille, radiator grille +n03461385 grocery store, grocery, food market, market +n03467068 guillotine +n03476684 hair slide +n03476991 hair spray +n03478589 half track +n03481172 hammer +n03482405 hamper +n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier +n03485407 hand-held computer, hand-held microcomputer +n03485794 handkerchief, hankie, hanky, hankey +n03492542 hard disc, hard disk, fixed disk +n03494278 harmonica, mouth organ, harp, mouth harp +n03495258 harp +n03496892 harvester, reaper +n03498962 hatchet +n03527444 holster +n03529860 home theater, home theatre +n03530642 honeycomb +n03532672 hook, claw +n03534580 hoopskirt, crinoline +n03535780 horizontal bar, high bar +n03538406 horse cart, horse-cart +n03544143 hourglass +n03584254 iPod +n03584829 iron, smoothing iron +n03590841 jack-o'-lantern +n03594734 jean, blue jean, denim +n03594945 jeep, landrover +n03595614 jersey, T-shirt, tee shirt +n03598930 jigsaw puzzle +n03599486 jinrikisha, ricksha, rickshaw +n03602883 joystick +n03617480 kimono +n03623198 knee pad +n03627232 knot +n03630383 lab coat, laboratory coat +n03633091 ladle +n03637318 lampshade, lamp shade +n03642806 laptop, laptop computer +n03649909 lawn mower, mower +n03657121 lens cap, lens cover +n03658185 letter opener, paper knife, paperknife +n03661043 library +n03662601 lifeboat +n03666591 lighter, light, igniter, ignitor +n03670208 limousine, limo +n03673027 liner, ocean liner +n03676483 lipstick, lip rouge +n03680355 Loafer +n03690938 lotion +n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +n03692522 loupe, jeweler's loupe +n03697007 lumbermill, sawmill +n03706229 magnetic compass +n03709823 mailbag, postbag +n03710193 mailbox, letter box +n03710637 maillot +n03710721 maillot, tank suit +n03717622 manhole cover +n03720891 maraca +n03721384 marimba, xylophone +n03724870 mask +n03729826 matchstick +n03733131 maypole +n03733281 maze, labyrinth +n03733805 measuring cup +n03742115 medicine chest, medicine cabinet +n03743016 megalith, megalithic structure +n03759954 microphone, mike +n03761084 microwave, microwave oven +n03763968 military uniform +n03764736 milk can +n03769881 minibus +n03770439 miniskirt, mini +n03770679 minivan +n03773504 missile +n03775071 mitten +n03775546 mixing bowl +n03776460 mobile home, manufactured home +n03777568 Model T +n03777754 modem +n03781244 monastery +n03782006 monitor +n03785016 moped +n03786901 mortar +n03787032 mortarboard +n03788195 mosque +n03788365 mosquito net +n03791053 motor scooter, scooter +n03792782 mountain bike, all-terrain bike, off-roader +n03792972 mountain tent +n03793489 mouse, computer mouse +n03794056 mousetrap +n03796401 moving van +n03803284 muzzle +n03804744 nail +n03814639 neck brace +n03814906 necklace +n03825788 nipple +n03832673 notebook, notebook computer +n03837869 obelisk +n03838899 oboe, hautboy, hautbois +n03840681 ocarina, sweet potato +n03841143 odometer, hodometer, mileometer, milometer +n03843555 oil filter +n03854065 organ, pipe organ +n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO +n03866082 overskirt +n03868242 oxcart +n03868863 oxygen mask +n03871628 packet +n03873416 paddle, boat paddle +n03874293 paddlewheel, paddle wheel +n03874599 padlock +n03876231 paintbrush +n03877472 pajama, pyjama, pj's, jammies +n03877845 palace +n03884397 panpipe, pandean pipe, syrinx +n03887697 paper towel +n03888257 parachute, chute +n03888605 parallel bars, bars +n03891251 park bench +n03891332 parking meter +n03895866 passenger car, coach, carriage +n03899768 patio, terrace +n03902125 pay-phone, pay-station +n03903868 pedestal, plinth, footstall +n03908618 pencil box, pencil case +n03908714 pencil sharpener +n03916031 perfume, essence +n03920288 Petri dish +n03924679 photocopier +n03929660 pick, plectrum, plectron +n03929855 pickelhaube +n03930313 picket fence, paling +n03930630 pickup, pickup truck +n03933933 pier +n03935335 piggy bank, penny bank +n03937543 pill bottle +n03938244 pillow +n03942813 ping-pong ball +n03944341 pinwheel +n03947888 pirate, pirate ship +n03950228 pitcher, ewer +n03954731 plane, carpenter's plane, woodworking plane +n03956157 planetarium +n03958227 plastic bag +n03961711 plate rack +n03967562 plow, plough +n03970156 plunger, plumber's helper +n03976467 Polaroid camera, Polaroid Land camera +n03976657 pole +n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +n03980874 poncho +n03982430 pool table, billiard table, snooker table +n03983396 pop bottle, soda bottle +n03991062 pot, flowerpot +n03992509 potter's wheel +n03995372 power drill +n03998194 prayer rug, prayer mat +n04004767 printer +n04005630 prison, prison house +n04008634 projectile, missile +n04009552 projector +n04019541 puck, hockey puck +n04023962 punching bag, punch bag, punching ball, punchball +n04026417 purse +n04033901 quill, quill pen +n04033995 quilt, comforter, comfort, puff +n04037443 racer, race car, racing car +n04039381 racket, racquet +n04040759 radiator +n04041544 radio, wireless +n04044716 radio telescope, radio reflector +n04049303 rain barrel +n04065272 recreational vehicle, RV, R.V. +n04067472 reel +n04069434 reflex camera +n04070727 refrigerator, icebox +n04074963 remote control, remote +n04081281 restaurant, eating house, eating place, eatery +n04086273 revolver, six-gun, six-shooter +n04090263 rifle +n04099969 rocking chair, rocker +n04111531 rotisserie +n04116512 rubber eraser, rubber, pencil eraser +n04118538 rugby ball +n04118776 rule, ruler +n04120489 running shoe +n04125021 safe +n04127249 safety pin +n04131690 saltshaker, salt shaker +n04133789 sandal +n04136333 sarong +n04141076 sax, saxophone +n04141327 scabbard +n04141975 scale, weighing machine +n04146614 school bus +n04147183 schooner +n04149813 scoreboard +n04152593 screen, CRT screen +n04153751 screw +n04154565 screwdriver +n04162706 seat belt, seatbelt +n04179913 sewing machine +n04192698 shield, buckler +n04200800 shoe shop, shoe-shop, shoe store +n04201297 shoji +n04204238 shopping basket +n04204347 shopping cart +n04208210 shovel +n04209133 shower cap +n04209239 shower curtain +n04228054 ski +n04229816 ski mask +n04235860 sleeping bag +n04238763 slide rule, slipstick +n04239074 sliding door +n04243546 slot, one-armed bandit +n04251144 snorkel +n04252077 snowmobile +n04252225 snowplow, snowplough +n04254120 soap dispenser +n04254680 soccer ball +n04254777 sock +n04258138 solar dish, solar collector, solar furnace +n04259630 sombrero +n04263257 soup bowl +n04264628 space bar +n04265275 space heater +n04266014 space shuttle +n04270147 spatula +n04273569 speedboat +n04275548 spider web, spider's web +n04277352 spindle +n04285008 sports car, sport car +n04286575 spotlight, spot +n04296562 stage +n04310018 steam locomotive +n04311004 steel arch bridge +n04311174 steel drum +n04317175 stethoscope +n04325704 stole +n04326547 stone wall +n04328186 stopwatch, stop watch +n04330267 stove +n04332243 strainer +n04335435 streetcar, tram, tramcar, trolley, trolley car +n04336792 stretcher +n04344873 studio couch, day bed +n04346328 stupa, tope +n04347754 submarine, pigboat, sub, U-boat +n04350905 suit, suit of clothes +n04355338 sundial +n04355933 sunglass +n04356056 sunglasses, dark glasses, shades +n04357314 sunscreen, sunblock, sun blocker +n04366367 suspension bridge +n04367480 swab, swob, mop +n04370456 sweatshirt +n04371430 swimming trunks, bathing trunks +n04371774 swing +n04372370 switch, electric switch, electrical switch +n04376876 syringe +n04380533 table lamp +n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle +n04392985 tape player +n04398044 teapot +n04399382 teddy, teddy bear +n04404412 television, television system +n04409515 tennis ball +n04417672 thatch, thatched roof +n04418357 theater curtain, theatre curtain +n04423845 thimble +n04428191 thresher, thrasher, threshing machine +n04429376 throne +n04435653 tile roof +n04442312 toaster +n04443257 tobacco shop, tobacconist shop, tobacconist +n04447861 toilet seat +n04456115 torch +n04458633 totem pole +n04461696 tow truck, tow car, wrecker +n04462240 toyshop +n04465501 tractor +n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +n04476259 tray +n04479046 trench coat +n04482393 tricycle, trike, velocipede +n04483307 trimaran +n04485082 tripod +n04486054 triumphal arch +n04487081 trolleybus, trolley coach, trackless trolley +n04487394 trombone +n04493381 tub, vat +n04501370 turnstile +n04505470 typewriter keyboard +n04507155 umbrella +n04509417 unicycle, monocycle +n04515003 upright, upright piano +n04517823 vacuum, vacuum cleaner +n04522168 vase +n04523525 vault +n04525038 velvet +n04525305 vending machine +n04532106 vestment +n04532670 viaduct +n04536866 violin, fiddle +n04540053 volleyball +n04542943 waffle iron +n04548280 wall clock +n04548362 wallet, billfold, notecase, pocketbook +n04550184 wardrobe, closet, press +n04552348 warplane, military plane +n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin +n04554684 washer, automatic washer, washing machine +n04557648 water bottle +n04560804 water jug +n04562935 water tower +n04579145 whiskey jug +n04579432 whistle +n04584207 wig +n04589890 window screen +n04590129 window shade +n04591157 Windsor tie +n04591713 wine bottle +n04592741 wing +n04596742 wok +n04597913 wooden spoon +n04599235 wool, woolen, woollen +n04604644 worm fence, snake fence, snake-rail fence, Virginia fence +n04606251 wreck +n04612504 yawl +n04613696 yurt +n06359193 web site, website, internet site, site +n06596364 comic book +n06785654 crossword puzzle, crossword +n06794110 street sign +n06874185 traffic light, traffic signal, stoplight +n07248320 book jacket, dust cover, dust jacket, dust wrapper +n07565083 menu +n07579787 plate +n07583066 guacamole +n07584110 consomme +n07590611 hot pot, hotpot +n07613480 trifle +n07614500 ice cream, icecream +n07615774 ice lolly, lolly, lollipop, popsicle +n07684084 French loaf +n07693725 bagel, beigel +n07695742 pretzel +n07697313 cheeseburger +n07697537 hotdog, hot dog, red hot +n07711569 mashed potato +n07714571 head cabbage +n07714990 broccoli +n07715103 cauliflower +n07716358 zucchini, courgette +n07716906 spaghetti squash +n07717410 acorn squash +n07717556 butternut squash +n07718472 cucumber, cuke +n07718747 artichoke, globe artichoke +n07720875 bell pepper +n07730033 cardoon +n07734744 mushroom +n07742313 Granny Smith +n07745940 strawberry +n07747607 orange +n07749582 lemon +n07753113 fig +n07753275 pineapple, ananas +n07753592 banana +n07754684 jackfruit, jak, jack +n07760859 custard apple +n07768694 pomegranate +n07802026 hay +n07831146 carbonara +n07836838 chocolate sauce, chocolate syrup +n07860988 dough +n07871810 meat loaf, meatloaf +n07873807 pizza, pizza pie +n07875152 potpie +n07880968 burrito +n07892512 red wine +n07920052 espresso +n07930864 cup +n07932039 eggnog +n09193705 alp +n09229709 bubble +n09246464 cliff, drop, drop-off +n09256479 coral reef +n09288635 geyser +n09332890 lakeside, lakeshore +n09399592 promontory, headland, head, foreland +n09421951 sandbar, sand bar +n09428293 seashore, coast, seacoast, sea-coast +n09468604 valley, vale +n09472597 volcano +n09835506 ballplayer, baseball player +n10148035 groom, bridegroom +n10565667 scuba diver +n11879895 rapeseed +n11939491 daisy +n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +n12144580 corn +n12267677 acorn +n12620546 hip, rose hip, rosehip +n12768682 buckeye, horse chestnut, conker +n12985857 coral fungus +n12998815 agaric +n13037406 gyromitra +n13040303 stinkhorn, carrion fungus +n13044778 earthstar +n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +n13054560 bolete +n13133613 ear, spike, capitulum +n15075141 toilet tissue, toilet paper, bathroom tissue diff --git a/scenes/chainer-caption/download.sh b/scenes/chainer-caption/download.sh new file mode 100644 index 0000000..bc890a2 --- /dev/null +++ b/scenes/chainer-caption/download.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +cd data +if [ ! -f ResNet50.model ]; then + wget https://www.dropbox.com/s/eqdmml7kj3545sv/ResNet50.model +fi +if [ ! -f caption_en_model40.model ]; then + wget https://www.dropbox.com/s/sy4ayrvush0bqle/caption_en_model40.model +fi + +cd MSCOCO +if [ "$1" == "train" ]; then + if [ ! -f train2014_ResNet50_features.zip ]; then + wget https://www.dropbox.com/s/v3tewruak581uf3/train2014_ResNet50_features.zip + unzip train2014_ResNet50_features.zip + fi + if [ ! -f val2014_ResNet50_features.zip ]; then + wget https://www.dropbox.com/s/qi3s55vzgmkiqkh/val2014_ResNet50_features.zip + unzip val2014_ResNet50_features.zip + fi + if [ ! -f captions_train2014_cn_translation.json ]; then + wget https://github.com/apple2373/mt-mscoco/raw/master/captions_train2014_cn_translation.json.zip + unzip captions_train2014_cn_translation.json.zip + rm captions_train2014_cn_translation.json.zip + fi + if [ ! -f captions_train2014_jp_translation.json.zip ]; then + wget https://github.com/apple2373/mt-mscoco/raw/master/captions_train2014_jp_translation.json.zip + unzip captions_train2014_jp_translation.json.zip + rm captions_train2014_jp_translation.json.zip + fi + if [ ! -f yjcaptions26k_clean.json ]; then + wget https://github.com/yahoojapan/YJCaptions/raw/master/yjcaptions26k.zip + unzip yjcaptions26k.zip + rm yjcaptions26k.zip + fi + if [ ! -f captions_train2014.json ]; then + wget http://msvocds.blob.core.windows.net/annotations-1-0-3/captions_train-val2014.zip + unzip captions_train-val2014.zip + rm captions_train-val2014.zip + fi +fi + diff --git a/scenes/chainer-caption/experiments/personal_notes.txt b/scenes/chainer-caption/experiments/personal_notes.txt new file mode 100644 index 0000000..16b8185 --- /dev/null +++ b/scenes/chainer-caption/experiments/personal_notes.txt @@ -0,0 +1,233 @@ +#train English caption +python train_caption_model.py --savedir ./experiment1 --epoch 10 --batch 256 --gpu 0 + +#train Chinese caption by machine translation +python train_caption_model.py --savedir ./experiment1cn --epoch 50 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/captions_train2014_cn_translation_processed_dic.json \ +--captions ./data/MSCOCO/captions_train2014_cn_translation_processed.json\ + +#train Japanese caption by machine translation +python train_caption_model.py --savedir ./experiment1jp_mt --epoch 40 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/captions_train2014_jp_translation_processed_dic.json \ +--captions ./data/MSCOCO/captions_train2014_jp_translation_processed.json\ +--preload True + +#train Japanese caption by Yahoo's +python train_caption_model.py --savedir ./experiment1jp_yj --epoch 40 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/yjcaptions26k_clean_processed_dic.json \ +--captions ./data/MSCOCO/yjcaptions26k_clean_processed.json \ +--preload True + +#preprocess captions +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014.json \ +--output ../data/MSCOCO/mscoco_caption_train2014_processed.json \ +--outdic ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--outfreq ../data/MSCOCO/mscoco_caption_train2014_processed_freq.json #this is just internal file + +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/yjcaptions26k_clean.json \ +--output ../data/MSCOCO/yjcaptions26k_clean_processed.json \ +--outdic ../data/MSCOCO/yjcaptions26k_clean_processed_dic.json \ +--outfreq ../data/MSCOCO/yjcaptions26k_clean_processed_freq.json \ +--cut 0 \ +--char True \ + +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014_cn_translation.json \ +--output ../data/MSCOCO/captions_train2014_cn_translation_processed.json \ +--outdic ../data/MSCOCO/captions_train2014_cn_translation_processed_dic.json \ +--outfreq ../data/MSCOCO/captions_train2014_cn_translation_processed_freq.json \ +--cut 5 \ +--char True \ + +python preprocess_MSCOCO_captions.py \ +--input ../data/MSCOCO/captions_train2014_jp_translation.json \ +--output ../data/MSCOCO/captions_train2014_jp_translation_processed.json \ +--outdic ../data/MSCOCO/captions_train2014_jp_translation_processed_dic.json \ +--outfreq ../data/MSCOCO/captions_train2014_jp_translation_processed_freq.json \ +--cut 5 \ +--char True \ + +#greedy +python sample_code.py --rnn-model ./experiment1/caption_model1.model --img ./sample_imgs/COCO_val2014_000000185546.jpg +python sample_code.py --rnn-model ./experiment1jp/caption_model1.model --vocab ./data/MSCOCO/yjcaptions26k_clean_processed_dic.json --img ./sample_imgs/COCO_val2014_000000241747.jpg +python sample_code.py --rnn-model ./experiment1cn/caption_model10.model --vocab ./data/MSCOCO/captions_train2014_cn_translation_processed_dic.json --img ./sample_imgs/COCO_val2014_000000185546.jpg + +#beam +python sample_code_beam.py \ +--rnn-model ./data/caption_en_model40.model \ +--cnn-model ./data/ResNet50.model \ +--vocab ./data/MSCOCO/mscoco_caption_train2014_processed_dic.json \ +--gpu -1 \ +--img ./sample_imgs/COCO_val2014_000000185546.jpg \ + +python sample_code_beam.py --rnn-model ./experiment1/caption_model10.model --img ./sample_imgs/COCO_val2014_000000185546.jpg +python sample_code_beam.py --rnn-model ./experiment1jp/caption_model10.model --vocab ./data/MSCOCO/yjcaptions26k_clean_processed_dic.json --img ./sample_imgs/COCO_val2014_000000241747.jpg +python sample_code_beam.py --rnn-model ./experiment1cn/caption_model10.model --vocab ./data/MSCOCO/captions_train2014_cn_translation_processed_dic.json --img ./sample_imgs/COCO_val2014_000000185546.jpg + +#webapp +python server.py --rnn-model ../experiment1/caption_model20.model --cnn-model ../data/ResNet50.model --vocab ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json + +#server +cd webapi +python server.py --rnn-model ../experiment1/caption_model20.model --cnn-model ../data/ResNet50.model --vocab ../data/MSCOCO/mscoco_caption_train2014_processed_dic.json --beam 3 +cd sample_imgs/ + curl -X POST -F image=@dog.jpg http://localhost:8090/predict + + #evalluate +python generate_caption.py --rnn-model ../experiment1/caption_model40.model -g 0 --beam 1 --output ../experiment1/caption_model40_val_predicted_beam1.json +python evaluate_captions.py \ +--true ../data/MSCOCO/captions_val2014-2.json \ +--predicted ../experiment1/caption_model40_val_predicted_beam1.json \ +--output ../experiment1/caption_model40_val_predicted_beam1_scores.json + +python generate_caption.py --rnn-model ../experiment1/caption_model40.model -g 0 --beam 5 --output ../experiment1/caption_model40_val_predicted_beam5.json +python evaluate_captions.py \ +--true ../data/MSCOCO/captions_val2014-2.json \ +--predicted ../experiment1/caption_model40_val_predicted_beam5.json \ +--output ../experiment1/caption_model40_val_predicted_beam5_scores.json + +#captions_val2014-2.json 何故かコレじゃないと動かない + +####################### +#### multilingual model #### +####################### + + #prepare dataset + see preprocess_multilingual_MSCOCO_captions.py + + #train multilingual en +python train_caption_model.py --savedir ./experiment1multi_en --epoch 40 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/mscoco_caption_multi_en_train_dic.json \ +--captions ./data/MSCOCO/mscoco_caption_multi_en_train_preprocessed.json \ +--preload True + +#train multilingual jp +python train_caption_model.py --savedir ./experiment1multi_jp --epoch 40 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/mscoco_caption_multi_jp_train_dic.json \ +--captions ./data/MSCOCO/mscoco_caption_multi_jp_train_preprocessed.json \ +--preload True + +#train multilingual muti +python train_caption_model.py --savedir ./experiment2multi --epoch 80 --batch 120 --gpu 0 \ +--vocab ./data/MSCOCO/mscoco_caption_multi_en_jp_train_dic.json \ +--captions ./data/MSCOCO/mscoco_caption_multi_en_jp_train_preprocessed.json \ +--preload True + +#generate multilingual +python sample_code_beam.py \ +--rnn-model ./experiment1multi/caption_model40.model \ +--cnn-model ./data/ResNet50.model \ +--vocab ./data/MSCOCO/mscoco_caption_en_jp_train_dic.json \ +--gpu -1 \ +--img ./sample_imgs/COCO_val2014_000000185546.jpg \ +--lang "" + + #generate english captions from milti model + python generate_caption_eval.py -g 0 --beam 1 \ +--rnn-model ../experiment1multi/caption_model40.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_jp_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment1multi/caption_model40_val_predicted.json \ +--lang "" + python generate_caption_eval.py -g 0 --beam 5 \ +--rnn-model ../experiment1multi/caption_model40.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_jp_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment1multi/caption_model40_val_predicted_beam5.json \ +--lang "" + + #generate english captions from milti model 2 + python generate_caption_eval.py -g 0 --beam 5 \ +--rnn-model ../experiment2multi/caption_model57.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_jp_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment2multi/caption_model57_val_predicted_beam4.json \ +--lang "" +python evaluate_captions.py \ +--true ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--predicted ../experiment2multi/caption_model57_val_predicted_beam4.json \ +--output ../experiment2multi/caption_model57_val_predicted_beam4_scores.json + + #generate english captions from only english model + python generate_caption_eval.py -g 0 --beam 1 \ +--rnn-model ../experiment1multi_en/caption_model40.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment1multi_en/caption_model40_val_predicted.json \ +--lang "" + python generate_caption_eval.py -g 0 --beam 5 \ +--rnn-model ../experiment1multi_en/caption_model40.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment1multi_en/caption_model40_val_predicted_beam5.json \ +--lang "" + +#generate caption based on 1024 hidden dim + python generate_caption_eval.py -g -1 --beam 5 \ +--rnn-model ../experiment3multi/caption_model45.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_jp_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment3multi/caption_model45_val_predicted_beam5.json \ +--lang "" \ +--hidden 1024 + python generate_caption_eval.py -g -1 --beam 5 \ +--rnn-model ../experiment3multi/caption_model45.model \ +--vocab ../data/MSCOCO/mscoco_caption_multi_en_jp_train_dic.json \ +--eval_file ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--image_feature_path ../data/MSCOCO/train2014_ResNet50_features/COCO_train2014_ \ +--output ../experiment3multi/caption_model45_val_predicted_beam5.json \ +--lang "" \ +--hidden 1024 + +python evaluate_captions.py \ +--true ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--predicted ../experiment3multi/caption_model45_val_predicted_beam5.json \ +--output ../experiment2multi/caption_model57_val_predicted_beam5_scores.json + +not yet tried +#generate japanese captions from milti model +#generate japanese captions from only english model + +python evaluate_captions.py \ +--true ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--predicted ../experiment1multi_en/caption_model40_val_predicted.json \ +--output ../experiment1multi_en/caption_model40_val_predicted_scores.json + +python evaluate_captions.py \ +--true ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--predicted ../experiment1multi/caption_model40_val_predicted.json \ +--output ../experiment1multi/caption_model40_val_predicted_scores.json + +python evaluate_captions.py \ +--true ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--predicted ../experiment1multi_en/caption_model40_val_predicted_beam5.json \ +--output ../experiment1multi_en/caption_model40_val_predicted_beam5_scores.json + +python evaluate_captions.py \ +--true ../data/MSCOCO/mscoco_caption_multi2_en_captions_val.json \ +--predicted ../experiment1multi/caption_model40_val_predicted_beam5.json \ +--output ../experiment1multi/caption_model40_val_predicted_scores.json + +#generate tables for david +python generate_caption_table.py \ +--dir ../data/MSCOCO/train2014/ \ +--predicted ../experiment1multi/caption_model40_val_predicted_beam5.json \ +--output ../output_multi_en + +python generate_caption_table.py \ +--dir ../data/MSCOCO/train2014/ \ +--predicted ../experiment1multi/caption_model40_val_predicted_jp_beam5.json \ +--output ../output_multi_jp + +python generate_caption_table.py \ +--dir ../data/MSCOCO/train2014/ \ +--predicted ../experiment1multi/caption_model40_val_predicted_jp_beam5.json \ +--output ../output_multi_jp \ No newline at end of file diff --git a/scenes/chainer-caption/experiments/personal_notes_multi.txt b/scenes/chainer-caption/experiments/personal_notes_multi.txt new file mode 100644 index 0000000..1921a60 --- /dev/null +++ b/scenes/chainer-caption/experiments/personal_notes_multi.txt @@ -0,0 +1,158 @@ +#Jp-En + +#First dataset preparation +python3 create_MSCOCO_en_jp_dataset.py \ +--en ../data/MSCOCO/captions_train2014.json \ +--out-en ../data/MSCOCO/mscoco_en_only_26k.json \ +--jp ../data/MSCOCO/yjcaptions26k_clean.json \ +--out-jp ../data/MSCOCO/mscoco_jp.json +common image ids 26500 + +#English only +python preprocess_captions.py \ +--input ../data/MSCOCO/mscoco_en_only_26k.json \ +--output ../data/MSCOCO/mscoco_en_only_26k_all_preprocessed.json \ +--val 2000 \ +--test 2000 \ +('total distinct words:', 14216) +top 20 frequent words: +(u'a', 185335) +('', 112551) +('', 112551) +(u'on', 40804) +(u'of', 38102) +(u'the', 37914) +(u'in', 34863) +(u'with', 29267) +(u'and', 26625) +(u'is', 18701) +(u'man', 14062) +(u'to', 13330) +(u'sitting', 9963) +(u'an', 9699) +(u'two', 9055) +(u',', 8518) +(u'at', 8377) +(u'people', 8227) +(u'standing', 7998) +(u'are', 7867) +('total distinct words after the cutoff:', 4948) +('The output file is saved to', '../data/MSCOCO/mscoco_en_only_26k_all_preprocessed.json') + +python train_image_caption_model.py \ +--gpu 0 \ +--savedir ./experiments/en_only_1 \ +--captions ./data/MSCOCO/mscoco_en_only_26k_all_preprocessed.json \ +--preload True \ +--epoch 40 \ +--batch 128 \ +--hidden 512 \ +--cnn-tune-after -1 \ + +#Japanese only +python preprocess_captions.py \ +--input ../data/MSCOCO/mscoco_jp.json \ +--output ../data/MSCOCO/mscoco_jp_all_preprocessed.json \ +--jp True \ +--val 2000 \ +--test 2000 \ + +('total distinct words:', 18823) +top 20 frequent words: +(の,114121) +(,111852) +(,111852) +(。,110211) +(が,104870) +(て,101423) +(ます,99709) +(い,75191) +(に,73340) +(を,59157) +(で,31035) +(た,26239) +(、,23518) +(し,18194) +(れ,16701) +(いる,16559) +(人,15548) +(上,15510) +(と,14558) +(あり,14184) +('total distinct words after the cutoff:', 6098) +('The output file is saved to', '../data/MSCOCO/mscoco_jp_all_preprocessed.json') + +python train_image_caption_model.py \ +--gpu 0 \ +--savedir ./experiments/jp_only_1 \ +--captions ./data/MSCOCO/mscoco_jp_all_preprocessed.json \ +--preload True \ +--epoch 40 \ +--batch 128 \ +--hidden 512 \ +--cnn-tune-after -1 \ + +#muti +python train_image_caption_model.py \ +--gpu 0 \ +--savedir ./experiments/en_jp_multi_1 \ +--captions ":./data/MSCOCO/mscoco_train2014_all_preprocessed.json;:./data/MSCOCO/mscoco_jp_all_preprocessed.json;" \ +--preload True \ +--epoch 40 \ +--batch 128 \ +--hidden 512 \ +--cnn-tune-after -1 \ + +#muti +python train_image_caption_model.py \ +--gpu 0 \ +--savedir ./experiments/en_jp_multi_2layer \ +--captions ":./data/MSCOCO/mscoco_train2014_all_preprocessed.json;:./data/MSCOCO/mscoco_jp_all_preprocessed.json;" \ +--preload True \ +--epoch 40 \ +--batch 128 \ +--hidden 512 \ +--layers 2 \ +--cnn-tune-after -1 \ + +#Ch-Jp +#First dataset preparation +python3 create_MSCOCO_cn_dataset_intersect_jp.py \ +--cn ../data/MSCOCO/captions_train2014_cn_translation.json \ +--out-cn ../data/MSCOCO/mscoco_mt_ch_only_26k.json \ +--jp ../data/MSCOCO/yjcaptions26k_clean.json + +common image ids 26500 +26500 +../data/MSCOCO/mscoco_mt_ch_only_26k.json + +#Chinese only +python preprocess_captions.py \ +--input ../data/MSCOCO/mscoco_mt_ch_only_26k.json \ +--output ../data/MSCOCO/mscoco_mt_ch_only_26k_all_preprocessed.json \ +--cn True \ +--val 2000 \ +--test 2000 \ + + +#Chinese only +python train_image_caption_model.py \ +--gpu 0 \ +--savedir ./experiments/cn_only_1 \ +--captions ./data/MSCOCO/mscoco_mt_ch_only_26k_all_preprocessed.json \ +--preload True \ +--epoch 40 \ +--batch 128 \ +--hidden 512 \ +--cnn-tune-after -1 \ + +#Chinese + Japanese +python train_image_caption_model.py \ +--gpu 0 \ +--savedir ./experiments/cn_jp_multi_1 \ +--captions ":./data/MSCOCO/mscoco_mt_ch_only_26k_all_preprocessed.json;:./data/MSCOCO/mscoco_jp_all_preprocessed.json;" \ +--preload False \ +--epoch 40 \ +--batch 128 \ +--hidden 512 \ +--cnn-tune-after -1 \ \ No newline at end of file diff --git a/scenes/chainer-caption/image_captioning_interface.py b/scenes/chainer-caption/image_captioning_interface.py new file mode 100644 index 0000000..39a9ec4 --- /dev/null +++ b/scenes/chainer-caption/image_captioning_interface.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +Sample code to generate caption using beam search +''' +import sys +import json +import os +# comment out the below if you want to do type check. Remeber this have to be done BEFORE import chainer +#os.environ["CHAINER_TYPE_CHECK"] = "0" +import chainer + +import argparse +import numpy as np +import math +from chainer import cuda +import chainer.functions as F +from chainer import cuda, Function, gradient_check, Variable, optimizers +from chainer import serializers + +sys.path.append('./code') +from CaptionGenerator import CaptionGenerator + + +class image_captioning (): + def __init__(self, + rnn_model_place='./data/caption_en_model40.model', + cnn_model_place='./data/ResNet50.model', + dictonary_place='./data/MSCOCO/mscoco_caption_train2014_processed_dic.json', + beamsize=3, + depth_limit=50, + gpu_id=-1, + first_word=""): + + self.caption_generator=CaptionGenerator( + rnn_model_place=rnn_model_place, + cnn_model_place=cnn_model_place, + dictonary_place=dictonary_place, + beamsize=beamsize, + depth_limit=depth_limit, + gpu_id=gpu_id, + first_word= first_word, + ) + + def generate_from_img_nparray(self,image_array): + image_feature=self.caption_generator.cnn_model(image_array, "feature").data.reshape(1,1,2048) + return self.caption_generator.generate_from_img_feature(image_feature) + + +if __name__ == '__main__': + image_captioning=image_captioning() + np_image = image_captioning.caption_generator.image_loader.load("test3.jpg") + captions = image_captioning.generate_from_img_nparray(np_image) + for caption in captions: + print (" ".join(caption["sentence"])) + print (caption["log_likelihood"]) + + diff --git a/scenes/chainer-caption/sample.png b/scenes/chainer-caption/sample.png new file mode 100644 index 0000000000000000000000000000000000000000..16296111619ab29872657f290a837322f06dbb56 GIT binary patch literal 628968 zcmZ^K1yml-vL^%r3Bg@I+}+*X^~2rW<-^@AxVt;S-QC?SNN^8s%m3c@cJJA@JLk-t zo~rtFS6A0`SM|(aI`bEur>h$GxtAFcT-T2Z$I>8k|BWOJj`^O;yjEBA|xLrkPwR_px~rLCXqlT z36nyGgpA7#3;RYS35$t{8qEE^@zUk#R^e*(Fs;Q0pFyMpD+z&Y1Qv@I|B5*;0kpOE;^ccWUSHWf~ki9l} 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Remeber this have to be done BEFORE import chainer +# os.environ["CHAINER_TYPE_CHECK"] = "0" +import chainer + +import argparse +import numpy as np +import math +from chainer import cuda +import chainer.functions as F +from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers +from chainer import serializers + + +sys.path.append('./code') +from image_loader import Image_loader +from ResNet50 import ResNet +from Image2CaptionDecoder import Image2CaptionDecoder + +#Parse arguments +parser = argparse.ArgumentParser() +parser.add_argument("-g", "--gpu",default=-1, type=int, help=u"GPU ID.CPU is -1") +parser.add_argument('--vocab',default='./data/MSCOCO/mscoco_caption_train2014_processed_dic.json', type=str,help='path to the vocaburary json') +parser.add_argument('--img',default='./sample_imgs/dog.jpg', type=str,help='path to the image') +parser.add_argument('--cnn-model', type=str, default='./data/ResNet50.model',help='place of the ResNet model') +parser.add_argument('--rnn-model', type=str, default='./data/caption_model.model',help='place of the caption model') +args = parser.parse_args() + +image_loader=Image_loader(mean='imagenet') +with open(args.vocab, 'r') as f: + token2index = json.load(f) +index2token={v:k for k,v in token2index.items()} + +cnn_model=ResNet() +serializers.load_hdf5(args.cnn_model, cnn_model) +cnn_model.train = False +rnn_model=Image2CaptionDecoder(len(token2index)) +serializers.load_hdf5(args.rnn_model, rnn_model) +rnn_model.train = False + +if args.gpu >= 0: + xp = cuda.cupy + cuda.get_device(args.gpu).use() + cnn_model.to_gpu() + rnn_model.to_gpu() +else: + xp=np + +batch_size=1 +hx=xp.zeros((rnn_model.n_layers, batch_size, rnn_model.hidden_dim), dtype=xp.float32) +cx=xp.zeros((rnn_model.n_layers, batch_size, rnn_model.hidden_dim), dtype=xp.float32) +img=image_loader.load(args.img) +if args.gpu >=0: + img=cuda.to_gpu(img) +image_feature=cnn_model(img, "feature").data.reshape(1,1,2048) + +hx,cx = rnn_model.input_cnn_feature(hx,cx,image_feature) +word=[xp.array([token2index[""]],dtype=xp.int32)] + +for i in xrange(50): + hx, cx, word = rnn_model(hx, cx, word) + word_idx=np.argmax(word[0].data) + print(index2token[int(word_idx)], end=' ') + word=[xp.array([word_idx],dtype=xp.int32)] + if token2index[""]==word_idx: + break + diff --git a/scenes/chainer-caption/sample_code_beam.py b/scenes/chainer-caption/sample_code_beam.py new file mode 100644 index 0000000..732d8fa --- /dev/null +++ b/scenes/chainer-caption/sample_code_beam.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +''' +Sample code to generate caption using beam search +''' +import sys +import json +import os +# comment out the below if you want to do type check. Remeber this have to be done BEFORE import chainer +#os.environ["CHAINER_TYPE_CHECK"] = "0" +import chainer +import time + +import argparse +import numpy as np +import math +from chainer import cuda +import chainer.functions as F +from chainer import cuda, Function, gradient_check, Variable, optimizers +from chainer import serializers + +sys.path.append('./code') +from CaptionGenerator import CaptionGenerator + +chainer.global_config.autotune = True +chainer.global_config.enable_backprop = False +chainer.global_config.type_check = False + +#Parse arguments +parser = argparse.ArgumentParser() +parser.add_argument("-g", "--gpu",default=-1, type=int, help=u"GPU ID.CPU is -1") +parser.add_argument('--vocab',default='./data/MSCOCO/mscoco_caption_train2014_processed_dic.json', type=str,help='path to the vocaburary json') +parser.add_argument('--img',default='./sample_imgs/dog.jpg', type=str,help='path to the image') +parser.add_argument('--cnn-model', type=str, default='./data/ResNet50.model',help='place of the ResNet model') +parser.add_argument('--rnn-model', type=str, default='./data/caption_model.model',help='place of the caption model') +parser.add_argument('--beam',default=3, type=int,help='beam size in beam search') +parser.add_argument('--depth',default=50, type=int,help='depth limit in beam search') +parser.add_argument('--lang',default="", type=str,help='special word to indicate the langauge or just ') +args = parser.parse_args() +tic = time.time() +caption_generator=CaptionGenerator( + rnn_model_place=args.rnn_model, + cnn_model_place=args.cnn_model, + dictonary_place=args.vocab, + beamsize=args.beam, + depth_limit=args.depth, + gpu_id=args.gpu, + first_word= args.lang, + ) +toc = time.time() +print ("loading model time: ",toc-tic) +image_path ="" +while True : + image_path = input("image path (to exit type exit): ") + if image_path == "exit": break + tic = time.time() + captions = caption_generator.generate(image_path) + toc = time.time() + print("caption_generator time: ",toc-tic) + for caption in captions: + print (" ".join(caption["sentence"])) + print (caption["log_likelihood"]) + print() diff --git a/scenes/chainer-caption/sample_imgs/COCO_val2014_000000185546.jpg b/scenes/chainer-caption/sample_imgs/COCO_val2014_000000185546.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e0f742c3e50670f6373339b40d8d32b02895d85c GIT binary patch literal 97146 zcmb4pbx<8$@a4nZ-QAtw?hXm=F2Nrz!QEX0!JXjl?h@ePJlx%b%jWylZtecNeWzyX z*4*wsb8Bi&*PQ-b`P>GeE6B*p03aYB04Dzi;1djx{BQPu4H6Ox@;`%yf`WvCfrf#B zfrf^LfrW#Gfq{dChK5Ceg@gakU|X$ z!V}^i3IYQFi2(tH0r5EmAOip(prQT?4*1^&_YVv_BoqQ71OVyZ0zGs9B*Z_kFwn4Y z(Ekl22uLVs3;+x!1uTa+92TXjDZFz~5;hfQLH#uXj)YqO+z##+vm0tjm*C{W0X!P6 z23mD<*N~JV{6Ww>H?Ne2n|o++%*`isGO+Yq-&Nl zhoc!u)}?X3btpob85ECWDYYEE#~?om#o&J;#=COc^Ju)w!2-!3hvrnD!gg6qr)Zor zBS_?WcNVmTXDQ^!8+QLKpSd#Q8{k;@;R&tFL>FJINeX!6tcPmEABgateS5bx_5uv= zTrZ4c!J0dv39d;pL)5u_yxV6VBt2Du<5Blr*i#6Ls#)gc#TA{*g~~$Y!7f;E9&o%w 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