Anivrit3

.gitignore

@@ -10,3 +10,4 @@ MANIFEST
 /wandb
 /models
 /.ipynb_checkpoints
+*.*~

environment.yml

@@ -1,16 +1,183 @@
 name: fpt
+channels:
+  - anaconda
+  - pytorch
+  - conda-forge
+  - defaults
 dependencies:
-    - python=3.7
-    - pip:
-        - boto3==1.17.102
-        - einops==0.3.0
-        - matplotlib==3.2.1
-        - numpy==1.18.3
-        - tape-proteins==0.4
-        - tensorflow==2.3.0
-        - tensorflow-datasets==4.0.1
-        - torch==1.7.1
-        - torchvision==0.8.2
-        - transformers==4.1.1
-        - tqdm==4.46.0
-        - wandb==0.9.1
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=4.5=1_gnu
+  - _pytorch_select=0.1=cpu_0
+  - _tflow_select=2.3.0=mkl
+  - absl-py=0.13.0=py37h06a4308_0
+  - aiohttp=3.8.1=py37h7f8727e_0
+  - aiosignal=1.2.0=pyhd3eb1b0_0
+  - astor=0.8.1=py37h06a4308_0
+  - astunparse=1.6.3=py_0
+  - async-timeout=4.0.1=pyhd3eb1b0_0
+  - asynctest=0.13.0=py_0
+  - attrs=21.2.0=pyhd3eb1b0_0
+  - blas=1.0=mkl
+  - blinker=1.4=py37h06a4308_0
+  - boto3=1.20.16=pyhd8ed1ab_0
+  - botocore=1.23.16=pyhd8ed1ab_0
+  - bottleneck=1.3.2=py37heb32a55_1
+  - brotli=1.0.9=he6710b0_2
+  - brotlipy=0.7.0=py37h27cfd23_1003
+  - c-ares=1.17.1=h27cfd23_0
+  - ca-certificates=2021.10.26=h06a4308_2
+  - cachetools=4.2.2=pyhd3eb1b0_0
+  - certifi=2021.10.8=py37h06a4308_0
+  - cffi=1.14.6=py37h400218f_0
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - click=8.0.3=pyhd3eb1b0_0
+  - cryptography=3.4.8=py37hd23ed53_0
+  - cudatoolkit=10.2.89=hfd86e86_1
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dataclasses=0.8=pyh6d0b6a4_7
+  - dbus=1.13.18=hb2f20db_0
+  - dill=0.3.4=pyhd3eb1b0_0
+  - einops=0.3.2=pyhd8ed1ab_0
+  - expat=2.4.1=h2531618_2
+  - fontconfig=2.13.1=h6c09931_0
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freetype=2.11.0=h70c0345_0
+  - frozenlist=1.2.0=py37h7f8727e_0
+  - future=0.18.2=py37_1
+  - gast=0.4.0=pyhd3eb1b0_0
+  - giflib=5.2.1=h7b6447c_0
+  - glib=2.69.1=h5202010_0
+  - google-auth=1.33.0=pyhd3eb1b0_0
+  - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+  - google-pasta=0.2.0=pyhd3eb1b0_0
+  - googleapis-common-protos=1.53.0=py37h06a4308_0
+  - grpcio=1.42.0=py37hce63b2e_0
+  - gst-plugins-base=1.14.0=h8213a91_2
+  - gstreamer=1.14.0=h28cd5cc_2
+  - h5py=2.10.0=py37hd6299e0_1
+  - hdf5=1.10.6=hb1b8bf9_0
+  - icu=58.2=he6710b0_3
+  - idna=3.3=pyhd3eb1b0_0
+  - importlib-metadata=4.8.1=py37h06a4308_0
+  - intel-openmp=2021.4.0=h06a4308_3561
+  - jmespath=0.10.0=pyhd3eb1b0_0
+  - joblib=1.1.0=pyhd3eb1b0_0
+  - jpeg=9d=h7f8727e_0
+  - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+  - kiwisolver=1.3.1=py37h2531618_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.35.1=h7274673_9
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.3.0=h5101ec6_17
+  - libgfortran-ng=7.5.0=ha8ba4b0_17
+  - libgfortran4=7.5.0=ha8ba4b0_17
+  - libgomp=9.3.0=h5101ec6_17
+  - libpng=1.6.37=hbc83047_0
+  - libprotobuf=3.17.2=h4ff587b_1
+  - libstdcxx-ng=9.3.0=hd4cf53a_17
+  - libtiff=4.2.0=h85742a9_0
+  - libuuid=1.0.3=h7f8727e_2
+  - libuv=1.40.0=h7b6447c_0
+  - libwebp=1.2.0=h89dd481_0
+  - libwebp-base=1.2.0=h27cfd23_0
+  - libxcb=1.14=h7b6447c_0
+  - libxml2=2.9.12=h03d6c58_0
+  - lz4-c=1.9.3=h295c915_1
+  - markdown=3.3.4=py37h06a4308_0
+  - matplotlib=3.4.3=py37h06a4308_0
+  - matplotlib-base=3.4.3=py37hbbc1b5f_0
+  - mkl=2021.4.0=h06a4308_640
+  - mkl-service=2.4.0=py37h7f8727e_0
+  - mkl_fft=1.3.1=py37hd3c417c_0
+  - mkl_random=1.2.2=py37h51133e4_0
+  - multidict=5.1.0=py37h27cfd23_2
+  - munkres=1.1.4=py_0
+  - ncurses=6.3=h7f8727e_2
+  - ninja=1.10.2=py37hd09550d_3
+  - numexpr=2.7.3=py37h22e1b3c_1
+  - numpy=1.21.2=py37h20f2e39_0
+  - numpy-base=1.21.2=py37h79a1101_0
+  - oauthlib=3.1.1=pyhd3eb1b0_0
+  - olefile=0.46=py37_0
+  - openssl=1.1.1l=h7f8727e_0
+  - opt_einsum=3.3.0=pyhd3eb1b0_1
+  - pandas=1.3.4=py37h8c16a72_0
+  - pcre=8.45=h295c915_0
+  - pillow=8.4.0=py37h5aabda8_0
+  - pip=21.2.2=py37h06a4308_0
+  - promise=2.3=py37h06a4308_0
+  - psutil=5.8.0=py37h27cfd23_1
+  - pyasn1=0.4.8=pyhd3eb1b0_0
+  - pyasn1-modules=0.2.8=py_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pyjwt=2.1.0=py37h06a4308_0
+  - pyopenssl=21.0.0=pyhd3eb1b0_1
+  - pyparsing=3.0.4=pyhd3eb1b0_0
+  - pyqt=5.9.2=py37h05f1152_2
+  - pysocks=1.7.1=py37_1
+  - python=3.7.11=h12debd9_0
+  - python-dateutil=2.8.2=pyhd3eb1b0_0
+  - python-flatbuffers=2.0=pyhd3eb1b0_0
+  - pytorch=1.7.1=py3.7_cuda10.2.89_cudnn7.6.5_0
+  - pytz=2021.3=pyhd3eb1b0_0
+  - qt=5.9.7=h5867ecd_1
+  - readline=8.1=h27cfd23_0
+  - requests=2.26.0=pyhd3eb1b0_0
+  - requests-oauthlib=1.3.0=py_0
+  - rsa=4.7.2=pyhd3eb1b0_1
+  - s3transfer=0.5.0=pyhd3eb1b0_0
+  - scikit-learn=0.23.2=py37h0573a6f_0
+  - setuptools=58.0.4=py37h06a4308_0
+  - sip=4.19.8=py37hf484d3e_0
+  - six=1.16.0=pyhd3eb1b0_0
+  - sqlite=3.36.0=hc218d9a_0
+  - tensorboard=2.4.0=pyhc547734_0
+  - tensorboard-plugin-wit=1.6.0=py_0
+  - tensorflow=2.4.1=mkl_py37h2d14ff2_0
+  - tensorflow-base=2.4.1=mkl_py37h43e0292_0
+  - tensorflow-datasets=1.2.0=py37_0
+  - tensorflow-estimator=2.6.0=pyh7b7c402_0
+  - tensorflow-metadata=0.14.0=pyhe6710b0_1
+  - termcolor=1.1.0=py37h06a4308_1
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - tk=8.6.11=h1ccaba5_0
+  - torchvision=0.8.2=cpu_py37ha229d99_0
+  - tornado=6.1=py37h27cfd23_0
+  - tqdm=4.62.3=pyhd3eb1b0_1
+  - typing-extensions=3.10.0.2=hd3eb1b0_0
+  - typing_extensions=3.10.0.2=pyh06a4308_0
+  - urllib3=1.26.7=pyhd3eb1b0_0
+  - werkzeug=2.0.2=pyhd3eb1b0_0
+  - wheel=0.37.0=pyhd3eb1b0_1
+  - wrapt=1.13.3=py37h7f8727e_2
+  - xz=5.2.5=h7b6447c_0
+  - yarl=1.6.3=py37h27cfd23_0
+  - zipp=3.6.0=pyhd3eb1b0_0
+  - zlib=1.2.11=h7b6447c_3
+  - zstd=1.4.9=haebb681_0
+  - pip:
+    - biopython==1.79
+    - configparser==5.1.0
+    - docker-pycreds==0.4.0
+    - filelock==3.4.0
+    - gitdb==4.0.9
+    - gitpython==3.1.24
+    - lmdb==1.2.1
+    - packaging==21.3
+    - pathtools==0.1.2
+    - protobuf==3.19.1
+    - pyyaml==6.0
+    - regex==2021.11.10
+    - sacremoses==0.0.46
+    - scipy==1.7.3
+    - sentry-sdk==1.5.0
+    - shortuuid==1.0.8
+    - smmap==5.0.0
+    - subprocess32==3.5.4
+    - tape-proteins==0.5
+    - tensorboardx==2.4.1
+    - timm==0.4.12
+    - tokenizers==0.9.4
+    - transformers==4.1.1
+    - wandb==0.12.7
+    - yaspin==2.1.0

scripts/run.py

@@ -1,17 +1,24 @@
 from universal_computation.experiment import run_experiment
+from argparse import ArgumentParser
+import sys
 
 
 if __name__ == '__main__':
-
-    experiment_name = 'fpt'
+    parser = ArgumentParser(description='Pick the task to be run.')
+    parser.add_argument('name', help='the name of the experiment')
+    parser.add_argument('task', help='the name of the task to be run')
+    parser.add_argument('--model', default='gpt2', help='the model to use')
+    args = parser.parse_args()
+
+    experiment_name = args.name
 
     experiment_params = dict(
-        task='bit-memory',
+        task=args.task,
         n=1000,                # ignored if not a bit task
         num_patterns=5,        # ignored if not a bit task
-        patch_size=50,
+        patch_size=16,
 
-        model_name='gpt2',
+        model_name=args.model,
         pretrained=True,       # if vit this is forced to true, if lstm this is forced to false
 
         freeze_trans=True,     # if False, we don't check arguments other than in and out
@@ -31,4 +38,5 @@
         orth_gain=1.41,        # orthogonal initialization of input layer
     )
 
+    sys.argv = ['']  # clear args since run_experiment also has an argparser
     run_experiment(experiment_name, experiment_params)

universal_computation/datasets/eurosat.py

@@ -0,0 +1,104 @@
+import os
+import pathlib
+from pathlib import Path
+import pandas as pd
+from einops import rearrange
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+import torch
+from torch.utils.data import DataLoader
+from PIL import Image
+import torchvision.transforms as transforms
+
+from universal_computation.datasets.dataset import Dataset
+
+class EuroSatDatasetHelper(torch.utils.data.Dataset):
+    def __init__(self, img_dir, ann_file, transform=None, target_transform=None):
+        df = pd.read_csv(ann_file)
+        self.img_labels = df[['label','img_name', 'int_label']].reset_index(drop=True)
+        self.img_dir = img_dir
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __len__(self):
+        return len(self.img_labels)
+
+    def __getitem__(self, idx):
+        label = self.img_labels.iloc[idx, 0]
+        name = self.img_labels.iloc[idx, 1]
+        int_label = self.img_labels.iloc[idx, 2]
+        temp = os.path.join(self.img_dir, label)
+        img_path = os.path.join(temp,name)
+        img = Image.open(img_path)
+        if self.transform:
+            img = self.transform(img)
+        if self.target_transform:
+            int_label = self.target_transform(int_label)
+        return img, int_label
+
+
+class EuroSatDataset(Dataset):
+    def __init__(self, batch_size, patch_size=None, data_aug=True, *args, **kwargs):
+        super(EuroSatDataset, self).__init__(*args, **kwargs)
+
+        self.batch_size = batch_size
+        self.patch_size = patch_size
+
+        if data_aug:
+            transform = transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Resize((224,224), interpolation=3),
+                transforms.RandomApply([transforms.GaussianBlur(3)]),
+                transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+            ])
+        else:
+            transform = transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Resize((224,224), interpolation=3),
+                transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+            ])
+
+        val_transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Resize((224,224), interpolation=3),
+            transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
+        ])
+        train_test_dir = 'data/2750'
+        self.d_train = DataLoader(
+            EuroSatDatasetHelper(train_test_dir, os.path.join(train_test_dir, 'train.csv'), transform=transform),
+            batch_size=batch_size, drop_last=True, shuffle=True,
+        )
+        self.d_test = DataLoader(
+            EuroSatDatasetHelper(train_test_dir, os.path.join(train_test_dir, 'test.csv'), transform=val_transform), 
+            batch_size=batch_size, drop_last=True, shuffle=True,
+        )
+
+        self.train_enum = enumerate(self.d_train)
+        self.test_enum = enumerate(self.d_test)
+
+        self.train_size = len(self.d_train)
+        self.test_size = len(self.d_test)
+
+    def reset_test(self):
+        self.test_enum = enumerate(self.d_test)
+
+    def get_batch(self, batch_size=None, train=True):
+        if train:
+            _, (x, y) = next(self.train_enum, (None, (None, None)))
+            if x is None:
+                self.train_enum = enumerate(self.d_train)
+                _, (x, y) = next(self.train_enum)
+        else:
+            _, (x, y) = next(self.test_enum, (None, (None, None)))
+            if x is None:
+                self.test_enum = enumerate(self.d_test)
+                _, (x, y) = next(self.train_enum)
+
+        if self.patch_size is not None:
+            x = rearrange(x, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=self.patch_size, p2=self.patch_size)
+
+        x = x.to(device=self.device)
+        y = y.to(device=self.device)
+
+        self._ind += 1
+
+        return x, y

universal_computation/datasets/helpers/annotations.py

@@ -0,0 +1,23 @@
+import pandas as pd
+import os
+import pathlib
+from pathlib import Path
+
+data = 'data/2750'
+
+df = pd.DataFrame(columns=['label', 'int_label', 'img_name'])
+labels_dict = {}
+counter = 0
+for subdir in os.listdir(data):
+    labels_dict[subdir] = counter
+    filepath = os.path.join(data,subdir)
+    if os.path.isdir(filepath):
+        for file in os.listdir(filepath):
+            dict = {'label': subdir, 'int_label': labels_dict[subdir], 'img_name': file}
+            df = df.append(dict, ignore_index = True)
+    counter += 1
+train = df.sample(frac=0.75,random_state=200) #random state is a seed value
+test = df.drop(train.index)
+
+train.to_csv(f'{data}/train.csv')
+test.to_csv(f'{data}/test.csv')