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name: pdf_extractor_env
channels:
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - blas=1.0=openblas
  - bottleneck=1.3.5=py39h7deecbd_0
  - brotli=1.0.9=he6710b0_2

sherwoac

name: instant_ngp_env
channels:
  - nvidia
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=4.5=1_gnu
  - alsa-lib=1.2.3=h516909a_0
  - blas=1.0=mkl

sherwoac

sherwoac

    def _get_unit_square_intercepts(self, slopes, intercept):
        """
        returns unit square intercepts for given slope (a) and intercepts (b)
        y = ax + b
        solves:
        right: y = a + b
        x = 1
        y = slopes + intercept
        left: y = b
        x = 0

sherwoac

# cf from https://arxiv.org/abs/1812.07035
import tensorflow as tf

def rotation_matrix_to_label_6d_flat(rotation_matrices):
    """
    takes batch x 3x3 rotation matrix, returns a flat batch x 6 floats for loss
    :param rotation_matrices: batch x (3x3) rotation matrix
    :return: flat batch x 6 floats for loss
    """
    return tf.concat([rotation_matrices[:, :, 0], rotation_matrices[:, :, 1]], axis=1)

sherwoac

import numpy as np
import tensorflow as tf
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from matplotlib import cm


def explore_embedding_size(number_of_categories, embedding_sizes):
    fig = plt.figure(figsize=(15, 9))
    figure_title = f"Embedding size TSNEs for {number_of_categories} categories"