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## waveloss-unittest.ipynb

      
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              May 6, 2019 14:49
            
          
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## rle-test.ipynb

      
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                / rle-test.ipynb
            
            
              Created
              April 9, 2019 22:14
            
              
                response to https://github.com/facebookresearch/maskrcnn-benchmark/pull/473#discussion_r273623157
              
          
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## test-abstract-cityscapes.ipynb

      
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              March 19, 2019 13:19
            
          
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## waveloss-unittest.ipynb

      
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              March 17, 2019 23:19
            
          
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## gta180k-test-on-cityscapes.ipynb

      
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              Created
              March 13, 2019 11:45
            
          
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## cityscapes-polygons-vs-binarymask.ipynb

      
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              Created
              March 6, 2019 23:33
            
          
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## test-abstract-cityscapes.ipynb

      
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              Created
              March 4, 2019 15:56
            
          
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## test-cityscapes-style-eval.py
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",

## cocowrapper.py
class COCOWrapper(object):
    """
    Mimics ONLY the basic utilities from pycocotools.coco.COCO class which are
    required for and used by pycocotools.coco.COCOeval

    The implementation focuses to cover the bare minimum to make the script
    running.
    """
    def __init__(self, dataset, every_prediction=None):
        # follow COCO notation: gt -> ground truth, dt -> detection

## debugdataset.py
import os
import glob
from PIL import Image


import numpy as np
import torch
import torchvision
import random
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import torch\n",
	"import numpy as np\n",
	class COCOWrapper(object):
	"""
	Mimics ONLY the basic utilities from pycocotools.coco.COCO class which are
	required for and used by pycocotools.coco.COCOeval

	The implementation focuses to cover the bare minimum to make the script
	running.
	"""
	def __init__(self, dataset, every_prediction=None):
	# follow COCO notation: gt -> ground truth, dt -> detection
	import os
	import glob
	from PIL import Image


	import numpy as np
	import torch
	import torchvision
	import random