back/preprocessing/torch_utils/misc.py

# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/

import re
import contextlib
import functools
import numpy as np
import torch
import warnings
import dnnlib

#----------------------------------------------------------------------------
# Re-seed torch & numpy random generators based on the given arguments.

def set_random_seed(*args):
    seed = hash(args) % (1 << 31)
    torch.manual_seed(seed)
    np.random.seed(seed)

#----------------------------------------------------------------------------
# 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

#----------------------------------------------------------------------------
# Variant of constant() that inherits dtype and device from the given
# reference tensor by default.

def const_like(ref, value, shape=None, dtype=None, device=None, memory_format=None):
    if dtype is None:
        dtype = ref.dtype
    if device is None:
        device = ref.device
    return constant(value, shape=shape, dtype=dtype, device=device, memory_format=memory_format)

#----------------------------------------------------------------------------
# Cached construction of temporary tensors in pinned CPU memory.

@functools.lru_cache(None)
def pinned_buf(shape, dtype):
    return torch.empty(shape, dtype=dtype).pin_memory()

#----------------------------------------------------------------------------
# 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 temporarily suppress known warnings in torch.jit.trace().
# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672

@contextlib.contextmanager
def suppress_tracer_warnings():
    flt = ('ignore', None, torch.jit.TracerWarning, None, 0)
    warnings.filters.insert(0, flt)
    yield
    warnings.filters.remove(flt)

#----------------------------------------------------------------------------
# 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}')

#----------------------------------------------------------------------------
# Function decorator that calls torch.autograd.profiler.record_function().

def profiled_function(fn):
    def decorator(*args, **kwargs):
        with torch.autograd.profiler.record_function(fn.__name__):
            return fn(*args, **kwargs)
    decorator.__name__ = fn.__name__
    return decorator

#----------------------------------------------------------------------------
# Sampler for torch.utils.data.DataLoader that loops over the dataset
# indefinitely, shuffling items as it goes.

class InfiniteSampler(torch.utils.data.Sampler):
    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, start_idx=0):
        assert len(dataset) > 0
        assert num_replicas > 0
        assert 0 <= rank < num_replicas
        warnings.filterwarnings('ignore', '`data_source` argument is not used and will be removed')
        super().__init__(dataset)
        self.dataset_size = len(dataset)
        self.start_idx = start_idx + rank
        self.stride = num_replicas
        self.shuffle = shuffle
        self.seed = seed

    def __iter__(self):
        idx = self.start_idx
        epoch = None
        while True:
            if epoch != idx // self.dataset_size:
                epoch = idx // self.dataset_size
                order = np.arange(self.dataset_size)
                if self.shuffle:
                    np.random.RandomState(hash((self.seed, epoch)) % (1 << 31)).shuffle(order)
            yield int(order[idx % self.dataset_size])
            idx += self.stride

#----------------------------------------------------------------------------
# Utilities for operating with torch.nn.Module parameters and buffers.

def params_and_buffers(module):
    assert isinstance(module, torch.nn.Module)
    return list(module.parameters()) + list(module.buffers())

def named_params_and_buffers(module):
    assert isinstance(module, torch.nn.Module)
    return list(module.named_parameters()) + list(module.named_buffers())

@torch.no_grad()
def copy_params_and_buffers(src_module, dst_module, require_all=False):
    assert isinstance(src_module, torch.nn.Module)
    assert isinstance(dst_module, torch.nn.Module)
    src_tensors = dict(named_params_and_buffers(src_module))
    for name, tensor in named_params_and_buffers(dst_module):
        assert (name in src_tensors) or (not require_all)
        if name in src_tensors:
            tensor.copy_(src_tensors[name])

#----------------------------------------------------------------------------
# Context manager for easily enabling/disabling DistributedDataParallel
# synchronization.

@contextlib.contextmanager
def ddp_sync(module, sync):
    assert isinstance(module, torch.nn.Module)
    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
        yield
    else:
        with module.no_sync():
            yield

#----------------------------------------------------------------------------
# Check DistributedDataParallel consistency across processes.

def check_ddp_consistency(module, ignore_regex=None):
    assert isinstance(module, torch.nn.Module)
    for name, tensor in named_params_and_buffers(module):
        fullname = type(module).__name__ + '.' + name
        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
            continue
        tensor = tensor.detach()
        if tensor.is_floating_point():
            tensor = torch.nan_to_num(tensor)
        other = tensor.clone()
        torch.distributed.broadcast(tensor=other, src=0)
        assert (tensor == other).all(), fullname

#----------------------------------------------------------------------------
# Print summary table of module hierarchy.

@torch.no_grad()
def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
    assert isinstance(module, torch.nn.Module)
    assert not isinstance(module, torch.jit.ScriptModule)
    assert isinstance(inputs, (tuple, list))

    # Register hooks.
    entries = []
    nesting = [0]
    def pre_hook(_mod, _inputs):
        nesting[0] += 1
    def post_hook(mod, _inputs, outputs):
        nesting[0] -= 1
        if nesting[0] <= max_nesting:
            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]

    # Run module.
    outputs = module(*inputs)
    for hook in hooks:
        hook.remove()

    # Identify unique outputs, parameters, and buffers.
    tensors_seen = set()
    for e in entries:
        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}

    # Filter out redundant entries.
    if skip_redundant:
        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]

    # Construct table.
    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
    rows += [['---'] * len(rows[0])]
    param_total = 0
    buffer_total = 0
    submodule_names = {mod: name for name, mod in module.named_modules()}
    for e in entries:
        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
        param_size = sum(t.numel() for t in e.unique_params)
        buffer_size = sum(t.numel() for t in e.unique_buffers)
        output_shapes = [str(list(t.shape)) for t in e.outputs]
        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
        rows += [[
            name + (':0' if len(e.outputs) >= 2 else ''),
            str(param_size) if param_size else '-',
            str(buffer_size) if buffer_size else '-',
            (output_shapes + ['-'])[0],
            (output_dtypes + ['-'])[0],
        ]]
        for idx in range(1, len(e.outputs)):
            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
        param_total += param_size
        buffer_total += buffer_size
    rows += [['---'] * len(rows[0])]
    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]

    # Print table.
    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
    print()
    for row in rows:
        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
    print()

#----------------------------------------------------------------------------
# Tile a batch of images into a 2D grid.

def tile_images(x, w, h):
    assert x.ndim == 4 # NCHW => CHW
    return x.reshape(h, w, *x.shape[1:]).permute(2, 0, 3, 1, 4).reshape(x.shape[1], h * x.shape[2], w * x.shape[3])

#----------------------------------------------------------------------------