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from inspect import isfunction |
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import torch |
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from torch.nn.utils.rnn import pad_sequence |
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from scepter.modules.utils.distribute import we |
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def exists(x): |
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return x is not None |
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def default(val, d): |
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if exists(val): |
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return val |
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return d() if isfunction(d) else d |
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def disabled_train(self, mode=True): |
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"""Overwrite model.train with this function to make sure train/eval mode |
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does not change anymore.""" |
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return self |
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def transfer_size(para_num): |
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if para_num > 1000 * 1000 * 1000 * 1000: |
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bill = para_num / (1000 * 1000 * 1000 * 1000) |
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return '{:.2f}T'.format(bill) |
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elif para_num > 1000 * 1000 * 1000: |
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gyte = para_num / (1000 * 1000 * 1000) |
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return '{:.2f}B'.format(gyte) |
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elif para_num > (1000 * 1000): |
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meta = para_num / (1000 * 1000) |
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return '{:.2f}M'.format(meta) |
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elif para_num > 1000: |
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kelo = para_num / 1000 |
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return '{:.2f}K'.format(kelo) |
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else: |
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return para_num |
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def count_params(model): |
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total_params = sum(p.numel() for p in model.parameters()) |
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return transfer_size(total_params) |
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def expand_dims_like(x, y): |
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while x.dim() != y.dim(): |
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x = x.unsqueeze(-1) |
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return x |
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def unpack_tensor_into_imagelist(image_tensor, shapes): |
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image_list = [] |
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for img, shape in zip(image_tensor, shapes): |
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h, w = shape[0], shape[1] |
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image_list.append(img[:, :h * w].view(1, -1, h, w)) |
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return image_list |
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def find_example(tensor_list, image_list): |
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for i in tensor_list: |
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if isinstance(i, torch.Tensor): |
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return torch.zeros_like(i) |
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for i in image_list: |
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if isinstance(i, torch.Tensor): |
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_, c, h, w = i.size() |
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return torch.zeros_like(i.view(c, h * w).transpose(1, 0)) |
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return None |
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def pack_imagelist_into_tensor_v2(image_list): |
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example = None |
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image_tensor, shapes = [], [] |
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for img in image_list: |
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if img is None: |
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example = find_example(image_tensor, |
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image_list) if example is None else example |
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image_tensor.append(example) |
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shapes.append(None) |
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continue |
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_, c, h, w = img.size() |
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image_tensor.append(img.view(c, h * w).transpose(1, 0)) |
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shapes.append((h, w)) |
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image_tensor = pad_sequence(image_tensor, |
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batch_first=True).permute(0, 2, 1) |
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return image_tensor, shapes |
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def to_device(inputs, strict=True): |
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if inputs is None: |
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return None |
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if strict: |
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assert all(isinstance(i, torch.Tensor) for i in inputs) |
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return [i.to(we.device_id) if i is not None else None for i in inputs] |
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def check_list_of_list(ll): |
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return isinstance(ll, list) and all(isinstance(i, list) for i in ll) |
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