PushkarA07 commited on
Commit
4e92074
·
1 Parent(s): 8c51cde

Update app.py

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Files changed (1) hide show
  1. app.py +1 -7
app.py CHANGED
@@ -4,9 +4,6 @@ from fastai.vision.learner import create_body
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  import streamlit as st
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  from PIL import Image
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  import cv2 as cv
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-
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- # ---------Backend--------------------------------------------------------------
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-
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  import os
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  import glob
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  import time
@@ -35,7 +32,7 @@ class ColorizationDataset(Dataset):
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  if split == 'train':
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  self.transforms = transforms.Compose([
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  transforms.Resize((SIZE, SIZE), Image.BICUBIC),
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- transforms.RandomHorizontalFlip(), # A little data augmentation!
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  ])
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  elif split == 'val':
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  self.transforms = transforms.Resize((SIZE, SIZE), Image.BICUBIC)
@@ -59,7 +56,6 @@ class ColorizationDataset(Dataset):
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  return len(self.paths)
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- # A handy function to make our dataloaders
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  def make_dataloaders(batch_size=16, n_workers=4, pin_memory=True, **kwargs):
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  dataset = ColorizationDataset(**kwargs)
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  dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=n_workers,
@@ -388,8 +384,6 @@ class MyDataset(torch.utils.data.Dataset):
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  ab = img_lab[[1, 2], ...] / 110.
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  return {'L': L, 'ab': ab}
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-
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- # A handy function to make our dataloaders
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  def make_dataloaders2(batch_size=16, n_workers=4, pin_memory=True, **kwargs):
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  dataset = MyDataset(**kwargs)
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  dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=n_workers,
 
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  import streamlit as st
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  from PIL import Image
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  import cv2 as cv
 
 
 
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  import os
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  import glob
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  import time
 
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  if split == 'train':
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  self.transforms = transforms.Compose([
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  transforms.Resize((SIZE, SIZE), Image.BICUBIC),
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+ transforms.RandomHorizontalFlip(),
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  ])
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  elif split == 'val':
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  self.transforms = transforms.Resize((SIZE, SIZE), Image.BICUBIC)
 
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  return len(self.paths)
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  def make_dataloaders(batch_size=16, n_workers=4, pin_memory=True, **kwargs):
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  dataset = ColorizationDataset(**kwargs)
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  dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=n_workers,
 
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  ab = img_lab[[1, 2], ...] / 110.
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  return {'L': L, 'ab': ab}
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  def make_dataloaders2(batch_size=16, n_workers=4, pin_memory=True, **kwargs):
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  dataset = MyDataset(**kwargs)
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  dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=n_workers,