arunmallya

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: [email protected]
## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected]
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""

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class ElementWiseConv2d(nn.Module):
    """Modified conv. Do we need mask for biases too?"""

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True,
                 mask_init='1s', mask_scale=1e-2,
                 threshold_fn='binarizer', threshold=None):
        super(ElementWiseConv2d, self).__init__()
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)

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DEFAULT_THRESHOLD = 5e-3

class Binarizer(torch.autograd.Function):
    """Binarizes {0, 1} a real valued tensor."""

    def __init__(self, threshold=DEFAULT_THRESHOLD):
        super(Binarizer, self).__init__()
        self.threshold = threshold

    def forward(self, inputs):

arunmallya

American_Goldfinch_0062_31921.jpg -> n01531178_12730.JPEG
Indigo_Bunting_0063_11820.jpg -> n01537544_9540.JPEG
Blue_Jay_0053_62744.jpg -> n01580077_4622.JPEG
American_Goldfinch_0131_32911.jpg -> n01531178_17834.JPEG
Dark_Eyed_Junco_0057_68650.jpg -> n01534433_12777.JPEG
Indigo_Bunting_0051_12837.jpg -> n01537544_2126.JPEG
Dark_Eyed_Junco_0102_67402.jpg -> n01534433_9482.JPEG
American_Goldfinch_0012_32338.jpg -> n01531178_14394.JPEG
Laysan_Albatross_0033_658.jpg -> n02058221_16284.JPEG
Black_Footed_Albatross_0024_796089.jpg -> n02058221_6390.JPEG

arunmallya

from __future__ import print_function
import torch

A = torch.rand(4, 4)
An = A.numpy()

idx = torch.ByteTensor([1, 0, 0, 1])
idxn = [True, False, False, True]

# Numpy indexing.

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from __future__ import print_function
import torch

A = torch.rand(4)
idx = torch.LongTensor([0, 3])

An = A.numpy()
idxn = idx.numpy()

# Numpy indexing.

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import torch.nn as nn

def myModule(nn.Module):
  def __init__(self):
    # Init stuff here
    self.X = nn.Sequential(
               nn.Linear(num_input_genes, num_tfs),
               nn.ReLU(),
               nn.BatchNorm1d(num_tfs)
             )

arunmallya

import torch
import torch.nn as nn
from torch.autograd import Variable

class SimpleModel(nn.Module):
	def __init__(self):
		super(SimpleModel, self).__init__()
		self.net = nn.Linear(10, 2)

	def forward(self, inputs):