c0nn3r

import torch
import itertools


def summed_permutations(matrix):
    columnwise_sum = torch.sum(matrix, dim=1)
    length = int(columnwise_sum.shape[0])

    result = torch.cat([
        torch.index_select(columnwise_sum, dim=0, index=torch.LongTensor(each))

c0nn3r

c0nn3r

def shape_list(x):
    """
    deal with dynamic shape in tensorflow cleanly
    """
    ps = x.get_shape().as_list()
    ts = tf.shape(x)
    return [ts[i] if ps[i] is None else ps[i] for i in range(len(ps))]

c0nn3r

(setv train_loader (torch.utils.data.DataLoader
  (datasets.MNIST "./data" :train True :download True
    :transform (transforms.Compose (list
      ((transforms.ToTensor)
      (transforms.Normalize :std 0.1307 :mean 0.3081))
  )))))

c0nn3r

        self.input_weight_offset = 0
        self.input_weight_slice = slice(self.input_weight_offset, self.activation_hidden_size)
        self.input_weight_view = (self.activation_hidden_size, 1)

        self.input_slice_offset = self.input_weight_offset + \
            self.slice_size(self.input_weight_slice)
        self.input_bias_slice = slice(self.input_slice_offset,
                                      self.input_slice_offset + self.activation_hidden_size)
        self.input_bias_view = (self.activation_hidden_size)

c0nn3r

import re
import os
import glob
import pandas as pd

import matplotlib.pyplot as plt

from collections import OrderedDict

fig_size = [12, 9]

c0nn3r

import torch.utils.model_zoo as model_zoo

from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',

c0nn3r

import cv2
import rospy

import numpy as np

from cv_bridge import CvBridge
from sensor_msgs.msg import CompressedImage, Image


class ImageLoader(object):

c0nn3r

ERROR: the following packages/stacks could not have their rosdep keys resolved
to system dependencies:
tf2_kdl: No definition of [eigen] for OS version []
message_filters: No definition of [boost] for OS version []
robot_state_publisher: No definition of [eigen] for OS version []
python_qt_binding: No definition of [python-qt5-bindings] for OS version []
rospack: No definition of [boost] for OS version []
opencv3: No definition of [protobuf] for OS version []
eigen_conversions: No definition of [eigen] for OS version []
rostest: No definition of [boost] for OS version []

c0nn3r

Traceback (most recent call last):
  File "main.py", line 173, in <module>
    train()
  File "main.py", line 140, in train
    output, hidden = model(data, hidden)
  File "/home/conner/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 225, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/conner/programming/learned_activations/experiments/word_language_model/model.py", line 57, in forward
    output, hidden = self.rnn(emb, hidden)
  File "/home/conner/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 225, in __call__