added setup.py so everything is installed from src

Author: dansola

README.md

@@ -26,7 +26,7 @@ These proposals are incorporated into a Dataset class where a random index dicti
 Then PGA can be used on an image and its corresponding proposal dictionaries.
 
 ```python
-from models.basic_pga.basic_pga_parts import BlockPGA
+from src.models.basic_pga.basic_pga_parts import BlockPGA
 import torch
 
 img = torch.rand(1,3,500,500) # test image

blahblah.py

@@ -4,13 +4,13 @@ kiwisolver==1.3.1
 matplotlib==3.3.3
 olefile==0.46
 opencv-python==4.5.1.48
-Pillow @ file:///tmp/build/80754af9/pillow_1609786786540/work
+Pillow==8.1.0
 pyparsing==2.4.7
 python-dateutil==2.8.1
-six @ file:///tmp/build/80754af9/six_1605205327372/work
+six==1.15.0
 torch==1.7.1
 torchvision==0.8.2
-typing-extensions @ file:///tmp/build/80754af9/typing_extensions_1598376058250/work
+typing-extensions
 numpy~=1.19.2
 pillow~=8.1.0
 wandb~=0.10.15

requirements.txt

@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+import os
+import sys
+
+from setuptools import find_packages, setup
+from setuptools.command.install import install
+
+from src import VERSION
+
+
+class VerifyVersionCommand(install):
+    """Custom command to verify that the git tag matches our version"""
+
+    description = "verify that the package git tag matches our version"
+
+    def run(self):
+        tag = os.getenv("COMMIT_TAG")
+
+        if tag != VERSION:
+            info = "Git tag: {0} does not match the version of this app: {1}".format(
+                tag, VERSION
+            )
+            sys.exit(info)
+
+
+with open("requirements.txt") as f:
+    DEPENDENCIES = f.read().splitlines()
+
+setup(
+    name="src",
+    packages=find_packages(),
+    version=VERSION,
+    description="Light weight semantic segmentation.",
+    author="Daniel Sola",
+    license="MIT",
+    install_requires=DEPENDENCIES,
+    python_requires=">=3.8",
+    url="https://github.com/dansola/PGA-Net",
+    cmdclass={"verify": VerifyVersionCommand},
+)

setup.py

@@ -0,0 +1 @@
+VERSION = "0.0.1"

src/__init__.py

@@ -1,5 +1,5 @@
 from torch import nn
-from models.axial_unet.axial_unet_parts import Embed, AttentionDown, AttentionUp, conv1x1
+from src.models.axial_unet.axial_unet_parts import Embed, AttentionDown, AttentionUp, conv1x1
 
 
 class AxialUnet(nn.Module):

src/models/axial_unet/axial_unet.py

@@ -1,7 +1,7 @@
 import torch
 from torch import nn
-from models.axial_attention.positional import PositionalEncodingPermute2D, AxialPositionalEmbedding, elem_add
-from models.axial_attention.axial_attention import AxialAttention, AxialImageTransformer
+from src.models.axial_attention.positional import PositionalEncodingPermute2D, AxialPositionalEmbedding, elem_add
+from src.models.axial_attention.axial_attention import AxialAttention, AxialImageTransformer
 
 
 def conv1x1(in_planes, out_planes, stride=1):

src/models/axial_unet/axial_unet_parts.py

@@ -1,6 +1,6 @@
 import torch
 from torch import nn
-from models.axial_attention.axial_attention import AxialAttention
+from src.models.axial_attention.axial_attention import AxialAttention
 
 
 def conv1x1(in_planes, out_planes, stride=1):

src/models/basic_axial/basic_axial_parts.py

@@ -1,5 +1,5 @@
 from torch import nn
-from models.basic_axial.basic_axial_parts import BlockAxial, conv1x1
+from src.odels.basic_axial.basic_axial_parts import BlockAxial, conv1x1
 
 class BasicAxial(nn.Module):
     def __init__(self, channels, n_classes, embedding_dims, img_crop=320):

src/models/basic_axial/basic_axialnet.py

@@ -1,7 +1,7 @@
 import torch
 from torch import nn
-from models.basic_axial.basic_axial_parts import BlockAxial, conv1x1
-from models.basic_pga.basic_pga_parts import BlockPGA
+from src.models.basic_axial.basic_axial_parts import BlockAxial, conv1x1
+from src.models.basic_pga.basic_pga_parts import BlockPGA
 
 
 class BasicAxialPGA(nn.Module):

src/models/basic_pga/basic_pga_net.py

@@ -1,6 +1,6 @@
 import torch
 from torch import nn
-from models.basic_pga.utils import get_image_dicts, build_pos_tensors, build_rand_inds
+from src.models.basic_pga.utils import get_image_dicts, build_pos_tensors, build_rand_inds
 
 
 def conv1x1(in_planes, out_planes, stride=1):

src/models/basic_pga/basic_pga_parts.py

@@ -1,7 +1,7 @@
 import torch
 from torch import nn
-from models.axial_attention.axial_attention import AxialAttention
-from models.lbcnn.lbcnn_parts import ConvLBP, BlockLBP
+from src.models.axial_attention.axial_attention import AxialAttention
+from src.models.lbcnn.lbcnn_parts import ConvLBP, BlockLBP
 
 
 def conv1x1(in_planes, out_planes, stride=1):

src/models/lbcnn/axial_lbcnn.py

@@ -11,9 +11,9 @@
 import torch.nn.functional as F
 from torch import optim
 from tqdm import tqdm
-from eval.eval_axial import eval_net
-from models.basic_axial.basic_axialnet import BasicAxial
-from datasets.ice import Ice
+from src.eval.eval_axial import eval_net
+from src.models.basic_axial.basic_axialnet import BasicAxial
+from src.datasets.ice import Ice
 from torch.utils.data import DataLoader
 import wandb
 

src/train/train_axial.py

@@ -11,9 +11,9 @@
 import torch.nn.functional as F
 from torch import optim
 from tqdm import tqdm
-from eval.eval_pga import eval_net
-from models.basic_pga.basic_pga_net import BasicAxialPGA
-from datasets.ice import IceWithProposals
+from src.eval.eval_pga import eval_net
+from src.models.basic_pga.basic_pga_net import BasicAxialPGA
+from src.datasets.ice import IceWithProposals
 from torch.utils.data import DataLoader
 import wandb
 

src/train/train_pga.py

@@ -11,9 +11,9 @@
 import torch.nn.functional as F
 from torch import optim
 from tqdm import tqdm
-from eval.eval_unet import eval_net
-from models.unet.unet_model import UNet
-from datasets.ice import BasicDatasetIce
+from src.eval.eval_unet import eval_net
+from src.models.unet.unet_model import UNet
+from src.datasets.ice import BasicDatasetIce
 from torch.utils.data import DataLoader
 import wandb