Python torch.utils.data.items() Examples
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code examples of torch.utils.data.items().
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Example #1
Source File: voc.py From pretorched-x with MIT License | 6 votes |
def read_object_labels(root, dataset, set): path_labels = os.path.join(root, 'VOCdevkit', dataset, 'ImageSets', 'Main') labeled_data = dict() num_classes = len(object_categories) for i in range(num_classes): file = os.path.join(path_labels, object_categories[i] + '_' + set + '.txt') data = read_image_label(file) if i == 0: for (name, label) in data.items(): labels = np.zeros(num_classes) labels[i] = label labeled_data[name] = labels else: for (name, label) in data.items(): labeled_data[name][i] = label return labeled_data
Example #2
Source File: voc.py From pretorched-x with MIT License | 6 votes |
def write_object_labels_csv(file, labeled_data): # write a csv file print('[dataset] write file %s' % file) with open(file, 'w') as csvfile: fieldnames = ['name'] fieldnames.extend(object_categories) writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for (name, labels) in labeled_data.items(): example = {'name': name} for i in range(20): example[fieldnames[i + 1]] = int(labels[i]) writer.writerow(example) csvfile.close()
Example #3
Source File: loaders.py From world-models with MIT License | 6 votes |
def load_next_buffer(self): """ Loads next buffer """ self._buffer_fnames = self._files[self._buffer_index:self._buffer_index + self._buffer_size] self._buffer_index += self._buffer_size self._buffer_index = self._buffer_index % len(self._files) self._buffer = [] self._cum_size = [0] # progress bar pbar = tqdm(total=len(self._buffer_fnames), bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} {postfix}') pbar.set_description("Loading file buffer ...") for f in self._buffer_fnames: with np.load(f) as data: self._buffer += [{k: np.copy(v) for k, v in data.items()}] self._cum_size += [self._cum_size[-1] + self._data_per_sequence(data['rewards'].shape[0])] pbar.update(1) pbar.close()
Example #4
Source File: voc.py From SPN.pytorch with MIT License | 6 votes |
def read_object_labels(root, dataset, set): path_labels = os.path.join(root, 'VOCdevkit', dataset, 'ImageSets', 'Main') labeled_data = dict() num_classes = len(object_categories) for i in range(num_classes): file = os.path.join(path_labels, object_categories[i] + '_' + set + '.txt') data = read_image_label(file) if i == 0: for (name, label) in data.items(): labels = np.zeros(num_classes) labels[i] = label labeled_data[name] = labels else: for (name, label) in data.items(): labeled_data[name][i] = label return labeled_data
Example #5
Source File: voc.py From SPN.pytorch with MIT License | 6 votes |
def write_object_labels_csv(file, labeled_data): # write a csv file print('[dataset] write file %s' % file) with open(file, 'w') as csvfile: fieldnames = ['name'] fieldnames.extend(object_categories) writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for (name, labels) in labeled_data.items(): example = {'name': name} for i in range(20): example[fieldnames[i + 1]] = int(labels[i]) writer.writerow(example) csvfile.close()
Example #6
Source File: voc.py From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License | 6 votes |
def read_object_labels(root, dataset, set): path_labels = os.path.join(root, 'VOCdevkit', dataset, 'ImageSets', 'Main') labeled_data = dict() num_classes = len(object_categories) for i in range(num_classes): file = os.path.join(path_labels, object_categories[i] + '_' + set + '.txt') data = read_image_label(file) if i == 0: for (name, label) in data.items(): labels = np.zeros(num_classes) labels[i] = label labeled_data[name] = labels else: for (name, label) in data.items(): labeled_data[name][i] = label return labeled_data
Example #7
Source File: voc.py From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License | 6 votes |
def write_object_labels_csv(file, labeled_data): # write a csv file print('[dataset] write file %s' % file) with open(file, 'w') as csvfile: fieldnames = ['name'] fieldnames.extend(object_categories) writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for (name, labels) in labeled_data.items(): example = {'name': name} for i in range(20): example[fieldnames[i + 1]] = int(labels[i]) writer.writerow(example) csvfile.close()
Example #8
Source File: dataset.py From skorch with BSD 3-Clause "New" or "Revised" License | 6 votes |
def _apply_to_data(data, func, unpack_dict=False): """Apply a function to data, trying to unpack different data types. """ apply_ = partial(_apply_to_data, func=func, unpack_dict=unpack_dict) if isinstance(data, dict): if unpack_dict: return [apply_(v) for v in data.values()] return {k: apply_(v) for k, v in data.items()} if isinstance(data, (list, tuple)): try: # e.g.list/tuple of arrays return [apply_(x) for x in data] except TypeError: return func(data) return func(data)
Example #9
Source File: dataset.py From skorch with BSD 3-Clause "New" or "Revised" License | 6 votes |
def unpack_data(data): """Unpack data returned by the net's iterator into a 2-tuple. If the wrong number of items is returned, raise a helpful error message. """ # Note: This function cannot detect it when a user only returns 1 # item that is exactly of length 2 (e.g. because the batch size is # 2). In that case, the item will be erroneously split into X and # y. try: X, y = data return X, y except ValueError: # if a 1-tuple/list or something else like a torch tensor if not isinstance(data, (tuple, list)) or len(data) < 2: raise ValueError(ERROR_MSG_1_ITEM) raise ValueError(ERROR_MSG_MORE_THAN_2_ITEMS.format(len(data)))
Example #10
Source File: voc.py From wildcat.pytorch with MIT License | 6 votes |
def read_object_labels(root, dataset, set): path_labels = os.path.join(root, 'VOCdevkit', dataset, 'ImageSets', 'Main') labeled_data = dict() num_classes = len(object_categories) for i in range(num_classes): file = os.path.join(path_labels, object_categories[i] + '_' + set + '.txt') data = read_image_label(file) if i == 0: for (name, label) in data.items(): labels = np.zeros(num_classes) labels[i] = label labeled_data[name] = labels else: for (name, label) in data.items(): labeled_data[name][i] = label return labeled_data
Example #11
Source File: voc.py From wildcat.pytorch with MIT License | 6 votes |
def write_object_labels_csv(file, labeled_data): # write a csv file print('[dataset] write file %s' % file) with open(file, 'w') as csvfile: fieldnames = ['name'] fieldnames.extend(object_categories) writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writeheader() for (name, labels) in labeled_data.items(): example = {'name': name} for i in range(20): example[fieldnames[i + 1]] = int(labels[i]) writer.writerow(example) csvfile.close()
Example #12
Source File: dataloader.py From self-critical.pytorch with MIT License | 5 votes |
def state_dict(self): def get_prefetch_num(split): if self.loaders[split].num_workers > 0: return (self.iters[split]._send_idx - self.iters[split]._rcvd_idx) * self.batch_size else: return 0 return {split: loader.sampler.state_dict(get_prefetch_num(split)) \ for split, loader in self.loaders.items()}
Example #13
Source File: dataloader.py From ImageCaptioning.pytorch with MIT License | 5 votes |
def state_dict(self): def get_prefetch_num(split): if self.loaders[split].num_workers > 0: return (self.iters[split]._send_idx - self.iters[split]._rcvd_idx) * self.batch_size else: return 0 return {split: loader.sampler.state_dict(get_prefetch_num(split)) \ for split, loader in self.loaders.items()}