batch_gen.py

import torch
import numpy as np
import random


class BatchGenerator(object):
    def __init__(self, num_classes, actions_dict, gt_path, features_path, sample_rate):
        self.list_of_examples = list()
        self.index = 0
        self.num_classes = num_classes
        self.actions_dict = actions_dict
        self.gt_path = gt_path
        self.features_path = features_path
        self.sample_rate = sample_rate

    def reset(self):
        self.index = 0
        random.shuffle(self.list_of_examples)

    def has_next(self):
        if self.index < len(self.list_of_examples):
            return True
        return False

    def read_data(self, vid_list_file):
        file_ptr = open(vid_list_file, "r")
        self.list_of_examples = file_ptr.read().split("\n")[:-1]
        file_ptr.close()
        random.shuffle(self.list_of_examples)

    def next_batch(self, batch_size):
        batch = self.list_of_examples[self.index : self.index + batch_size]
        self.index += batch_size

        batch_input = []
        batch_target = []
        for vid in batch:
            features = np.load(self.features_path + vid.split(".")[0] + ".npy")
            file_ptr = open(self.gt_path + vid, "r")
            content = file_ptr.read().split("\n")[:-1]

            classes = np.zeros(min(np.shape(features)[1], len(content)))
            for i in range(len(classes)):
                classes[i] = self.actions_dict[content[i]]

            batch_input.append(features[:, :: self.sample_rate])
            batch_target.append(classes[:: self.sample_rate])

        length_of_sequences = list(map(len, batch_target))
        batch_input_tensor = torch.zeros(
            len(batch_input),
            np.shape(batch_input[0])[0],
            max(length_of_sequences),
            dtype=torch.float,
        )
        batch_target_tensor = torch.ones(
            len(batch_input), max(length_of_sequences), dtype=torch.long
        ) * (-100)
        mask = torch.zeros(
            len(batch_input),
            self.num_classes,
            max(length_of_sequences),
            dtype=torch.float,
        )
        for i in range(len(batch_input)):
            batch_input_tensor[i, :, : np.shape(batch_input[i])[1]] = torch.from_numpy(
                batch_input[i]
            )
            batch_target_tensor[i, : np.shape(batch_target[i])[0]] = torch.from_numpy(
                batch_target[i]
            )
            mask[i, :, : np.shape(batch_target[i])[0]] = torch.ones(
                self.num_classes, np.shape(batch_target[i])[0]
            )

        return batch_input_tensor, batch_target_tensor, mask