priya-dwivedi

class CustomDataset(utils.Dataset):

    def load_custom(self, dataset_dir, subset):
        """Load a subset of the Balloon dataset.
        dataset_dir: Root directory of the dataset.
        subset: Subset to load: train or val
        """
        # Add classes. We have only one class to add.
        self.add_class("damage", 1, "damage")

priya-dwivedi

def distance_boxes (boxA, boxB):
    import math
    center_boxA = [(boxA[0] + boxA[2])/ 2.0, (boxA[1] + boxA[3])/2.0]
    center_boxB = [(boxB[0] + boxB[2])/ 2.0, (boxB[1] + boxB[3])/2.0]
    pixel_distance  = math.sqrt( ((center_boxA[0]-center_boxB[0])**2)+((center_boxA[1]-center_boxB[1])**2) )
    return pixel_distance

priya-dwivedi

def model_distancing(df,  proximity):

    print("Simulation Started")
    time1 = time.time()
    max_frame = df['frame_num'].max()
    for frame_num in range(3, max_frame):
        pairs_done = []
        if frame_num % 50 == 0:
            print("Completed: ", frame_num)
        ## Look at all tracks in this frame

priya-dwivedi

import docx2txt
 
def extract_programming_languages(file_name):
	# read in word file
	result = docx2txt.process(file_name)
	programming_languages_pattern = re.search(r'Programming Languages:[A-Za-z,\s0-9]*\.',result)
	print(programming_languages_pattern)
	programming_languages_line = programming_languages_pattern.group(0)

	languages = re.sub("Programming Languages: ","", programming_languages_line)

priya-dwivedi

import networkx as nx
edge_dict = {}
edge_dict['Mathew'] = languages_mathew
# create a directed-graph from a dataframe
G=nx.from_dict_of_lists(edge_dict,create_using=nx.MultiDiGraph())
plt.figure(figsize=(12,12))
pos = nx.spring_layout(G)
nx.draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.cm.Blues, pos = pos, node_size = 4500)
plt.show()

priya-dwivedi

edge_dict = {}

edge_dict[names[0]] = languages1
edge_dict[names[1]] = languages2
edge_dict[names[2]] = languages2

G=nx.from_dict_of_lists(edge_dict,create_using=nx.MultiDiGraph())
plt.figure(figsize=(12,12))

pos = nx.circular_layout(G)

priya-dwivedi

def get_max_degree_node(list_of_nodes_to_eliminate, G):
	max_degree=0
	all_remaining_nodes = [x for x in G.nodes() if x not in list_of_nodes_to_eliminate]
	max_node=all_remaining_nodes[0]
	for node in all_remaining_nodes:
    		degree = G.degree(node)
    		if degree>max_degree:
        			max_degree = degree
        			max_node = node
	return max_degree, max_node

priya-dwivedi

class DETRdemo(nn.Module):
    """
    Demo DETR implementation.

    Demo implementation of DETR in minimal number of lines, with the
    following differences wrt DETR in the paper:
    * learned positional encoding (instead of sine)
    * positional encoding is passed at input (instead of attention)
    * fc bbox predictor (instead of MLP)
    The model achieves ~40 AP on COCO val5k and runs at ~28 FPS on Tesla V100.

priya-dwivedi

## Load Data from NLP Library
from nlp import load_dataset
dataset = load_dataset('wikihow', 'all', data_dir='data/')
print(dataset.keys())
print("Size of train dataset: ", dataset['train'].shape)
print("Size of Validation dataset: ", dataset['validation'].shape)
## Look at Sample Examples
print(dataset['train'][0].keys())
print(" Example of text: ", dataset['train'][0]['text'])
print(" Example of Summary: ", dataset['train'][0]['headline'])

priya-dwivedi

class wikihow(Dataset):
    def __init__(self, tokenizer, type_path, num_samples, input_length, output_length, print_text=False):         
        self.dataset =  load_dataset('wikihow', 'all', data_dir='data/', split=type_path)
        if num_samples:
            self.dataset = self.dataset.select(list(range(0, num_samples)))
        self.input_length = input_length
        self.tokenizer = tokenizer
        self.output_length = output_length
        self.print_text = print_text