code/studiogan/src/utils/style_misc.py

"""
this code is borrowed from https://github.com/NVlabs/stylegan2-ada-pytorch with few modifications

Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.

NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto.  Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
"""

import warnings
import torch
import numpy as np


#----------------------------------------------------------------------------
# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
# same constant is used multiple times.

_constant_cache = dict()

def constant(value, shape=None, dtype=None, device=None, memory_format=None):
    value = np.asarray(value)
    if shape is not None:
        shape = tuple(shape)
    if dtype is None:
        dtype = torch.get_default_dtype()
    if device is None:
        device = torch.device("cpu")
    if memory_format is None:
        memory_format = torch.contiguous_format

    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
    tensor = _constant_cache.get(key, None)
    if tensor is None:
        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
        if shape is not None:
            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
        tensor = tensor.contiguous(memory_format=memory_format)
        _constant_cache[key] = tensor
    return tensor

#----------------------------------------------------------------------------
# Replace NaN/Inf with specified numerical values.

try:
    nan_to_num = torch.nan_to_num # 1.8.0a0
except AttributeError:
    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
        assert isinstance(input, torch.Tensor)
        if posinf is None:
            posinf = torch.finfo(input.dtype).max
        if neginf is None:
            neginf = torch.finfo(input.dtype).min
        assert nan == 0
        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)

#----------------------------------------------------------------------------
# Symbolic assert.

try:
    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
except AttributeError:
    symbolic_assert = torch.Assert # 1.7.0

#----------------------------------------------------------------------------
# Context manager to suppress known warnings in torch.jit.trace().

class suppress_tracer_warnings(warnings.catch_warnings):
    def __enter__(self):
        super().__enter__()
        warnings.simplefilter("ignore", category=torch.jit.TracerWarning)
        return self

#----------------------------------------------------------------------------
# Assert that the shape of a tensor matches the given list of integers.
# None indicates that the size of a dimension is allowed to vary.
# Performs symbolic assertion when used in torch.jit.trace().

def assert_shape(tensor, ref_shape):
    if tensor.ndim != len(ref_shape):
        raise AssertionError(f"Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}")
    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
        if ref_size is None:
            pass
        elif isinstance(ref_size, torch.Tensor):
            with suppress_tracer_warnings(): # as_tensor results are registered as constants
                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f"Wrong size for dimension {idx}")
        elif isinstance(size, torch.Tensor):
            with suppress_tracer_warnings(): # as_tensor results are registered as constants
                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f"Wrong size for dimension {idx}: expected {ref_size}")
        elif size != ref_size:
            raise AssertionError(f"Wrong size for dimension {idx}: got {size}, expected {ref_size}")