srikara srikar2097

## average_embeddings.py
from collections import Counter, defaultdict
import itertools
import os
import random
import re

import numpy as np

EMBEDDING_FILE = "/u/nlp/data/depparser/nn/data/embeddings/en-cw.txt"
EMBEDDING_SERIALIZED = "embeddings.npz"

## plot_loss+sample.py
#https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
class PlotLosses(keras.callbacks.Callback):

    def __init__(self,imgs):
        super(PlotLosses, self).__init__()
        self.imgs=imgs

    def on_train_begin(self, logs={}):
        self.i = 0
        self.x = []

## Quickdraw_to_MNISTformat.ipynb

      
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                rajshah4
                / Quickdraw_to_MNISTformat.ipynb
            
            
              Created
              July 14, 2017 20:28
            
              
                Quickdraw npy files to MNIST test/train dataset with visualization
              
          
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## VideoFrameInterpolation.md

      
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                myungsub
                / VideoFrameInterpolation.md
            
            
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              December 5, 2020 11:47
            
          
    Papers from Super SloMo references


Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation [Paper]

Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
CVPR 2018 (splotlight)


Video frame synthesis using deep voxel flow [Paper] [Code]

Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala.
ICCV 2017


Video frame interpolation via adaptive separable convolution. [Paper] [Code]


## Spatial_Transformer_Example_Part1.ipynb

      
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                kvn219
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              December 31, 2020 05:04
            
              
                Spatial Transformer Networks with Tensorflow: Part I
              
          
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## click_heatmap.py
import cv2
import numpy as np
import argparse

'''
usage: python click_heatmap.py <image file>
    left-click: add point to heatmap
    s: save image (000.png, 001.png, ...)
    q: quit
    r: reset

## get_cars_stanford.py
# encoding:utf8

from scipy.io import loadmat
import pandas as pd
import numpy as np

mat_train = loadmat('devkit/cars_train_annos.mat')
mat_test = loadmat('devkit/cars_test_annos.mat')
meta = loadmat('devkit/cars_meta.mat')

## measure_img_similarity.py
import warnings
from skimage.measure import compare_ssim
from skimage.transform import resize
from scipy.stats import wasserstein_distance
from scipy.misc import imsave
from scipy.ndimage import imread
import numpy as np
import cv2

##

## hook_activations.py
import torch
import torch.nn as nn
import torch.nn.functional as F

import torchvision.models as tmodels
from functools import partial
import collections

# dummy data: 10 batches of images with batch size 16
dataset = [torch.rand(16,3,224,224).cuda() for _ in range(10)]

## calculate_trainset_mean_std.py
"""
in this script, we calculate the image per channel mean and standard
deviation in the training set, do not calculate the statistics on the
whole dataset, as per here http://cs231n.github.io/neural-networks-2/#datapre
"""

import numpy as np
from os import listdir
from os.path import join, isdir
from glob import glob
	from collections import Counter, defaultdict
	import itertools
	import os
	import random
	import re

	import numpy as np

	EMBEDDING_FILE = "/u/nlp/data/depparser/nn/data/embeddings/en-cw.txt"
	EMBEDDING_SERIALIZED = "embeddings.npz"
	#https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
	class PlotLosses(keras.callbacks.Callback):

	def __init__(self,imgs):
	super(PlotLosses, self).__init__()
	self.imgs=imgs

	def on_train_begin(self, logs={}):
	self.i = 0
	self.x = []
	import cv2
	import numpy as np
	import argparse

	'''
	usage: python click_heatmap.py <image file>
	left-click: add point to heatmap
	s: save image (000.png, 001.png, ...)
	q: quit
	r: reset
	# encoding:utf8

	from scipy.io import loadmat
	import pandas as pd
	import numpy as np

	mat_train = loadmat('devkit/cars_train_annos.mat')
	mat_test = loadmat('devkit/cars_test_annos.mat')
	meta = loadmat('devkit/cars_meta.mat')
	import warnings
	from skimage.measure import compare_ssim
	from skimage.transform import resize
	from scipy.stats import wasserstein_distance
	from scipy.misc import imsave
	from scipy.ndimage import imread
	import numpy as np
	import cv2

	##
	import torch
	import torch.nn as nn
	import torch.nn.functional as F

	import torchvision.models as tmodels
	from functools import partial
	import collections

	# dummy data: 10 batches of images with batch size 16
	dataset = [torch.rand(16,3,224,224).cuda() for _ in range(10)]
	"""
	in this script, we calculate the image per channel mean and standard
	deviation in the training set, do not calculate the statistics on the
	whole dataset, as per here http://cs231n.github.io/neural-networks-2/#datapre
	"""

	import numpy as np
	from os import listdir
	from os.path import join, isdir
	from glob import glob