Cleanup python

README.md

@@ -17,9 +17,11 @@ year={2019}
 
 ## Enviroment
  - Python3
- - [Pytorch](http://pytorch.org/) before 0.4 (for newer vesion, please see issue #3 )
+ - [Pytorch](http://pytorch.org/) >= 1.0
  - json
 
+To install the dependencies use `pip3 install -r requirements-cpu.txt -f https://download.pytorch.org/whl/torch_stable.html` or `pip3 install -r requirements-gpu.txt`.
+
 ## Getting started
 ### CUB
 * Change directory to `./filelists/CUB`
@@ -52,19 +54,19 @@ See test.json for reference
 
 ## Train
 Run
-```python ./train.py --dataset [DATASETNAME] --model [BACKBONENAME] --method [METHODNAME] [--OPTIONARG]```
+```python3 ./train.py --dataset [DATASETNAME] --model [BACKBONENAME] --method [METHODNAME] [--OPTIONARG]```
 
 For example, run `python ./train.py --dataset miniImagenet --model Conv4 --method baseline --train_aug`  
 Commands below follow this example, and please refer to io_utils.py for additional options.
 
 ## Save features
 Save the extracted feature before the classifaction layer to increase test speed. This is not applicable to MAML, but are required for other methods.
 Run
-```python ./save_features.py --dataset miniImagenet --model Conv4 --method baseline --train_aug```
+```python3 ./save_features.py --dataset miniImagenet --model Conv4 --method baseline --train_aug```
 
 ## Test
 Run
-```python ./test.py --dataset miniImagenet --model Conv4 --method baseline --train_aug```
+```python3 ./test.py --dataset miniImagenet --model Conv4 --method baseline --train_aug```
 
 ## Results
 * The test results will be recorded in `./record/results.txt`

filelists/CUB/download_CUB.sh

@@ -1,4 +1,4 @@
 #!/usr/bin/env bash
 wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz
 tar -zxvf CUB_200_2011.tgz
-python write_CUB_filelist.py
+python3 write_CUB_filelist.py

filelists/emnist/download_emnist.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
 wget https://github.com/NanqingD/DAOSL/raw/master/data/emnist.zip 
 unzip emnist.zip
-python invert_emnist.py
-python write_cross_char_valnovel_filelist.py
+python3 invert_emnist.py
+python3 write_cross_char_valnovel_filelist.py

filelists/miniImagenet/download_miniImagenet.sh

@@ -4,5 +4,5 @@ wget https://raw.githubusercontent.com/twitter/meta-learning-lstm/master/data/mi
 wget https://raw.githubusercontent.com/twitter/meta-learning-lstm/master/data/miniImagenet/test.csv
 wget http://image-net.org/image/ILSVRC2015/ILSVRC2015_CLS-LOC.tar.gz 
 tar -zxvf ILSVRC2015_CLS-LOC.tar.gz
-python write_miniImagenet_filelist.py
-python write_cross_filelist.py
+python3 write_miniImagenet_filelist.py
+python3 write_cross_filelist.py

filelists/omniglot/download_omniglot.sh

@@ -14,6 +14,6 @@ mv $DATADIR/images_evaluation/* $DATADIR/
 rmdir $DATADIR/images_background
 rmdir $DATADIR/images_evaluation
 
-python rot_omniglot.py
-python write_omniglot_filelist.py
-python write_cross_char_base_filelist.py
+python3 rot_omniglot.py
+python3 write_omniglot_filelist.py
+python3 write_cross_char_base_filelist.py

requirements-cpu.txt

@@ -0,0 +1,4 @@
+torch==1.4.0+cpu
+torchvision==0.5.0+cpu
+Pillow
+h5py

requirements-gpu.txt

@@ -0,0 +1,4 @@
+torch>=1.0.0
+torchvision
+Pillow
+h5py