docs: add najeeb-kazmi as a contributor for question

.all-contributorsrc

@@ -67,6 +67,15 @@
       "contributions": [
         "question"
       ]
+    },
+    {
+      "login": "najeeb-kazmi",
+      "name": "Najeeb Kazmi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/14131235?v=4",
+      "profile": "https://github.com/najeeb-kazmi",
+      "contributions": [
+        "question"
+      ]
     }
   ],
   "contributorsPerLine": 7,

README.md

@@ -1,6 +1,6 @@
 # Skillful Nowcasting with Deep Generative Model of Radar (DGMR)
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-7-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-8-orange.svg?style=flat-square)](#contributors-)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 Implementation of DeepMind's Skillful Nowcasting GAN Deep Generative Model of Radar (DGMR) (https://arxiv.org/abs/2104.00954) in PyTorch Lightning.
 
@@ -117,6 +117,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/primeoc"><img src="https://avatars.githubusercontent.com/u/75205487?v=4?s=100" width="100px;" alt="cameron"/><br /><sub><b>cameron</b></sub></a><br /><a href="#question-primeoc" title="Answering Questions">💬</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/zhrli"><img src="https://avatars.githubusercontent.com/u/11074703?v=4?s=100" width="100px;" alt="zhrli"/><br /><sub><b>zhrli</b></sub></a><br /><a href="#question-zhrli" title="Answering Questions">💬</a></td>
     </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/najeeb-kazmi"><img src="https://avatars.githubusercontent.com/u/14131235?v=4?s=100" width="100px;" alt="Najeeb Kazmi"/><br /><sub><b>Najeeb Kazmi</b></sub></a><br /><a href="#question-najeeb-kazmi" title="Answering Questions">💬</a></td>
+    </tr>
   </tbody>
 </table>
 

dgmr/__init__.py

@@ -1,4 +1,4 @@
+from .common import ContextConditioningStack, LatentConditioningStack
 from .dgmr import DGMR
-from .generators import Sampler, Generator
-from .discriminators import SpatialDiscriminator, TemporalDiscriminator, Discriminator
-from .common import LatentConditioningStack, ContextConditioningStack
+from .discriminators import Discriminator, SpatialDiscriminator, TemporalDiscriminator
+from .generators import Generator, Sampler

dgmr/common.py

@@ -3,12 +3,13 @@
 import einops
 import torch
 import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin
 from torch.distributions import normal
-from torch.nn.utils.parametrizations import spectral_norm
 from torch.nn.modules.pixelshuffle import PixelUnshuffle
-from dgmr.layers.utils import get_conv_layer
+from torch.nn.utils.parametrizations import spectral_norm
+
 from dgmr.layers import AttentionLayer
-from huggingface_hub import PyTorchModelHubMixin
+from dgmr.layers.utils import get_conv_layer
 
 
 class GBlock(torch.nn.Module):

dgmr/dgmr.py

@@ -1,17 +1,18 @@
+import pytorch_lightning as pl
 import torch
+import torchvision
+
+from dgmr.common import ContextConditioningStack, LatentConditioningStack
+from dgmr.discriminators import Discriminator
+from dgmr.generators import Generator, Sampler
+from dgmr.hub import NowcastingModelHubMixin
 from dgmr.losses import (
-    NowcastingLoss,
     GridCellLoss,
+    NowcastingLoss,
+    grid_cell_regularizer,
     loss_hinge_disc,
     loss_hinge_gen,
-    grid_cell_regularizer,
 )
-import pytorch_lightning as pl
-import torchvision
-from dgmr.common import LatentConditioningStack, ContextConditioningStack
-from dgmr.generators import Sampler, Generator
-from dgmr.discriminators import Discriminator
-from dgmr.hub import NowcastingModelHubMixin
 
 
 class DGMR(pl.LightningModule, NowcastingModelHubMixin):

dgmr/discriminators.py

@@ -1,9 +1,10 @@
 import torch
+import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin
 from torch.nn.modules.pixelshuffle import PixelUnshuffle
 from torch.nn.utils.parametrizations import spectral_norm
-import torch.nn.functional as F
+
 from dgmr.common import DBlock
-from huggingface_hub import PyTorchModelHubMixin
 
 
 class Discriminator(torch.nn.Module, PyTorchModelHubMixin):

dgmr/generators.py

@@ -1,13 +1,15 @@
+import logging
+from typing import List
+
 import einops
 import torch
 import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin
 from torch.nn.modules.pixelshuffle import PixelShuffle
 from torch.nn.utils.parametrizations import spectral_norm
-from typing import List
+
 from dgmr.common import GBlock, UpsampleGBlock
 from dgmr.layers import ConvGRU
-from huggingface_hub import PyTorchModelHubMixin
-import logging
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.WARN)
@@ -27,6 +29,7 @@ def __init__(
 
         The sampler takes the output from the Latent and Context conditioning stacks and
         creates one stack of ConvGRU layers per future timestep.
+
         Args:
             forecast_steps: Number of forecast steps
             latent_channels: Number of input channels to the lowest ConvGRU layer

dgmr/hub.py

@@ -12,7 +12,6 @@
 
 import torch
 
-
 try:
     from huggingface_hub import cached_download, hf_hub_url
 

dgmr/layers/Attention.py

@@ -1,7 +1,7 @@
+import einops
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
-import einops
 
 
 def attention_einsum(q, k, v):

dgmr/layers/utils.py

@@ -1,4 +1,5 @@
 import torch
+
 from dgmr.layers import CoordConv
 
 

dgmr/losses.py

@@ -1,14 +1,15 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from pytorch_msssim import SSIM, MS_SSIM
+from pytorch_msssim import MS_SSIM, SSIM
 from torch.nn import functional as F
 
 
 class SSIMLoss(nn.Module):
     def __init__(self, convert_range: bool = False, **kwargs):
         """
         SSIM Loss, optionally converting input range from [-1,1] to [0,1]
+
         Args:
             convert_range:
             **kwargs:
@@ -28,6 +29,7 @@ class MS_SSIMLoss(nn.Module):
     def __init__(self, convert_range: bool = False, **kwargs):
         """
         Multi-Scale SSIM Loss, optionally converting input range from [-1,1] to [0,1]
+
         Args:
             convert_range:
             **kwargs:
@@ -78,6 +80,7 @@ def tv_loss(img, tv_weight):
     Inputs:
     - img: PyTorch Variable of shape (1, 3, H, W) holding an input image.
     - tv_weight: Scalar giving the weight w_t to use for the TV loss.
+
     Returns:
     - loss: PyTorch Variable holding a scalar giving the total variation loss
       for img weighted by tv_weight.
@@ -138,6 +141,7 @@ def forward(self, generated_images, targets):
         """
         Calculates the grid cell regularizer value, assumes generated images are the mean predictions from
         6 calls to the generater (Monte Carlo estimation of the expectations for the latent variable)
+
         Args:
             generated_images: Mean generated images from the generator
             targets: Ground truth future frames

setup.py

@@ -1,6 +1,7 @@
-from setuptools import setup, find_packages
 from pathlib import Path
 
+from setuptools import find_packages, setup
+
 this_directory = Path(__file__).parent
 install_requires = (this_directory / "requirements.txt").read_text().splitlines()
 long_description = (this_directory / "README.md").read_text()

train/run.py

@@ -1,19 +1,19 @@
 import torch.utils.data.dataset
-from dgmr import DGMR
+import wandb
 from datasets import load_dataset
-from torch.utils.data import DataLoader
 from pytorch_lightning import (
     LightningDataModule,
 )
 from pytorch_lightning.callbacks import ModelCheckpoint
-import wandb
+from torch.utils.data import DataLoader
+
+from dgmr import DGMR
 
 wandb.init(project="dgmr")
-from numpy.random import default_rng
-import os
-import numpy as np
 from pathlib import Path
-import tensorflow as tf
+
+import numpy as np
+from numpy.random import default_rng
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.loggers import LoggerCollection, WandbLogger
 from pytorch_lightning.utilities import rank_zero_only
@@ -136,7 +136,7 @@ def __len__(self):
     def __getitem__(self, item):
         try:
             row = next(self.iter_reader)
-        except Exception as e:
+        except Exception:
             rng = default_rng()
             self.iter_reader = iter(
                 self.reader.shuffle(seed=rng.integers(low=0, high=100000), buffer_size=1000)