{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"#skip\n",
"! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#default_exp data.load"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"from fastai.torch_basics import *\n",
"from torch.utils.data.dataloader import _MultiProcessingDataLoaderIter,_SingleProcessDataLoaderIter,_DatasetKind\n",
"_loaders = (_MultiProcessingDataLoaderIter,_SingleProcessDataLoaderIter)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from nbdev.showdoc import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bs = 4\n",
"letters = list(string.ascii_lowercase)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataLoader helpers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fastai includes a replacement for Pytorch's *DataLoader* which is largely API-compatible, and adds a lot of useful functionality and flexibility. Before we look at the class, there are a couple of helpers we'll need to define."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def _wif(worker_id):\n",
" set_num_threads(1)\n",
" info = get_worker_info()\n",
" ds = info.dataset.d\n",
" ds.num_workers,ds.offs = info.num_workers,info.id\n",
" set_seed(info.seed)\n",
" ds.wif()\n",
"\n",
"class _FakeLoader:\n",
" def _fn_noops(self, x=None, *args, **kwargs): return x\n",
" \n",
" _IterableDataset_len_called,_auto_collation,collate_fn,drop_last = None,False,_fn_noops,False\n",
" _index_sampler,generator,prefetch_factor = Inf.count,None,2\n",
" dataset_kind = _dataset_kind = _DatasetKind.Iterable\n",
" \n",
" def __init__(self, d, pin_memory, num_workers, timeout, persistent_workers):\n",
" self.dataset,self.default,self.worker_init_fn = self,d,_wif\n",
" store_attr('d,pin_memory,num_workers,timeout,persistent_workers')\n",
"\n",
" def __iter__(self): return iter(self.d.create_batches(self.d.sample()))\n",
"\n",
" @property\n",
" def multiprocessing_context(self): return (None,multiprocessing)[self.num_workers>0]\n",
"\n",
" @contextmanager\n",
" def no_multiproc(self):\n",
" old_num_workers = self.num_workers\n",
" try:\n",
" self.num_workers = 0\n",
" yield self.d\n",
" finally: self.num_workers = old_num_workers\n",
"\n",
"_collate_types = (ndarray, Tensor, typing.Mapping, str)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def fa_collate(t):\n",
" \"A replacement for PyTorch `default_collate` which maintains types and handles `Sequence`s\"\n",
" b = t[0]\n",
" return (default_collate(t) if isinstance(b, _collate_types)\n",
" else type(t[0])([fa_collate(s) for s in zip(*t)]) if isinstance(b, Sequence)\n",
" else default_collate(t))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#e.g. x is int, y is tuple\n",
"t = [(1,(2,3)),(1,(2,3))]\n",
"test_eq(fa_collate(t), default_collate(t))\n",
"test_eq(L(fa_collate(t)).map(type), [Tensor,tuple])\n",
"\n",
"t = [(1,(2,(3,4))),(1,(2,(3,4)))]\n",
"test_eq(fa_collate(t), default_collate(t))\n",
"test_eq(L(fa_collate(t)).map(type), [Tensor,tuple])\n",
"test_eq(L(fa_collate(t)[1]).map(type), [Tensor,tuple])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"def fa_convert(t):\n",
" \"A replacement for PyTorch `default_convert` which maintains types and handles `Sequence`s\"\n",
" return (default_convert(t) if isinstance(t, _collate_types)\n",
" else type(t)([fa_convert(s) for s in t]) if isinstance(t, Sequence)\n",
" else default_convert(t))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t0 = array([1,2])\n",
"t = [t0,(t0,t0)]\n",
"\n",
"test_eq(fa_convert(t), default_convert(t))\n",
"test_eq(L(fa_convert(t)).map(type), [Tensor,tuple])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"class SkipItemException(Exception):\n",
" \"Raised to notify `DataLoader` to skip an item\"\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"\n",
"> SkipItemException() :: `Exception`\n",
"\n",
"Raised to notify [`DataLoader`](/data.load.html#DataLoader) to skip an item"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_doc(SkipItemException, title_level=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataLoader -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"@funcs_kwargs\n",
"class DataLoader(GetAttr):\n",
" _noop_methods = 'wif before_iter after_item before_batch after_batch after_iter'.split()\n",
" for o in _noop_methods: exec(f\"def {o}(self, x=None, *args, **kwargs): return x\")\n",
" _methods = _noop_methods + 'create_batches create_item create_batch retain \\\n",
" get_idxs sample shuffle_fn do_batch create_batch'.split()\n",
" _default = 'dataset'\n",
" def __init__(self, dataset=None, bs=None, num_workers=0, pin_memory=False, timeout=0, batch_size=None,\n",
" shuffle=False, drop_last=False, indexed=None, n=None, device=None, persistent_workers=False, **kwargs):\n",
" if batch_size is not None: bs = batch_size # PyTorch compatibility\n",
" assert not (bs is None and drop_last)\n",
" if indexed is None: indexed = (hasattr(dataset,'__getitem__')\n",
" and not isinstance(dataset, IterableDataset))\n",
" if not indexed and shuffle: raise ValueError(\"Can only shuffle an indexed dataset (not an iterable one).\")\n",
" if n is None:\n",
" try: n = len(dataset)\n",
" except TypeError: pass\n",
" store_attr('dataset,bs,shuffle,drop_last,indexed,n,pin_memory,timeout,device')\n",
" self.rng,self.num_workers,self.offs = random.Random(random.randint(0,2**32-1)),1,0\n",
" if sys.platform == \"win32\" and IN_NOTEBOOK and num_workers > 0:\n",
" print(\"Due to IPython and Windows limitation, python multiprocessing isn't available now.\")\n",
" print(\"So `number_workers` is changed to 0 to avoid getting stuck\")\n",
" num_workers = 0 \n",
" self.fake_l = _FakeLoader(self, pin_memory, num_workers, timeout, persistent_workers=persistent_workers)\n",
"\n",
" def __len__(self):\n",
" if self.n is None: raise TypeError\n",
" if self.bs is None: return self.n\n",
" return self.n//self.bs + (0 if self.drop_last or self.n%self.bs==0 else 1)\n",
"\n",
" def get_idxs(self):\n",
" idxs = Inf.count if self.indexed else Inf.nones\n",
" if self.n is not None: idxs = list(itertools.islice(idxs, self.n))\n",
" if self.shuffle: idxs = self.shuffle_fn(idxs)\n",
" return idxs\n",
" \n",
" def sample(self): \n",
" return (b for i,b in enumerate(self.__idxs) if i//(self.bs or 1)%self.num_workers==self.offs)\n",
"\n",
" def __iter__(self):\n",
" self.randomize()\n",
" self.before_iter()\n",
" self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)\n",
" for b in _loaders[self.fake_l.num_workers==0](self.fake_l):\n",
" if self.device is not None: b = to_device(b, self.device)\n",
" yield self.after_batch(b)\n",
" self.after_iter()\n",
" if hasattr(self, 'it'): del(self.it)\n",
"\n",
" def create_batches(self, samps):\n",
" if self.dataset is not None: self.it = iter(self.dataset)\n",
" res = filter(lambda o:o is not None, map(self.do_item, samps))\n",
" yield from map(self.do_batch, self.chunkify(res))\n",
"\n",
" def new(self, dataset=None, cls=None, **kwargs):\n",
" if dataset is None: dataset = self.dataset\n",
" if cls is None: cls = type(self)\n",
" cur_kwargs = dict(dataset=dataset, num_workers=self.fake_l.num_workers, pin_memory=self.pin_memory, timeout=self.timeout,\n",
" bs=self.bs, shuffle=self.shuffle, drop_last=self.drop_last, indexed=self.indexed, device=self.device)\n",
" for n in self._methods:\n",
" o = getattr(self, n)\n",
" if not isinstance(o, MethodType): cur_kwargs[n] = o\n",
" return cls(**merge(cur_kwargs, kwargs))\n",
"\n",
" @property\n",
" def prebatched(self): return self.bs is None\n",
" def do_item(self, s):\n",
" try: return self.after_item(self.create_item(s))\n",
" except SkipItemException: return None\n",
" def chunkify(self, b): return b if self.prebatched else chunked(b, self.bs, self.drop_last)\n",
" def shuffle_fn(self, idxs): return self.rng.sample(idxs, len(idxs))\n",
" def randomize(self): self.rng = random.Random(self.rng.randint(0,2**32-1))\n",
" def retain(self, res, b): return retain_types(res, b[0] if is_listy(b) else b)\n",
" def create_item(self, s):\n",
" if self.indexed: return self.dataset[s or 0]\n",
" elif s is None: return next(self.it)\n",
" else: raise IndexError(\"Cannot index an iterable dataset numerically - must use `None`.\")\n",
" def create_batch(self, b): return (fa_collate,fa_convert)[self.prebatched](b)\n",
" def do_batch(self, b): return self.retain(self.create_batch(self.before_batch(b)), b)\n",
" def to(self, device): self.device = device\n",
" def one_batch(self):\n",
" if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')\n",
" with self.fake_l.no_multiproc(): res = first(self)\n",
" if hasattr(self, 'it'): delattr(self, 'it')\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#export\n",
"add_docs(DataLoader, \"API compatible with PyTorch DataLoader, with a lot more callbacks and flexibility\",\n",
" get_idxs = \"Return a list of indices to reference the dataset. Calls `shuffle_fn` internally if `shuffle=True`.\",\n",
" sample = \"Same as `get_idxs` but returns a generator of indices to reference the dataset.\",\n",
" create_batches = \"Takes output of `sample` as input, and returns batches of data. Does not apply `after_batch`.\",\n",
" new = \"Create a new `DataLoader` with given arguments keeping remaining arguments same as original `DataLoader`.\",\n",
" prebatched = \"Check if `bs` is None.\",\n",
" do_item = \"Combines `after_item` and `create_item` to get an item from dataset by providing index as input.\",\n",
" chunkify = \"Used by `create_batches` to turn generator of items (`b`) into batches.\",\n",
" shuffle_fn = \"Returns a random permutation of `idxs`.\",\n",
" randomize = \"Set's `DataLoader` random number generator state.\",\n",
" retain = \"Cast each item of `res` to type of matching item in `b` if its a superclass.\",\n",
" create_item = \"Subset of the dataset containing the index values of sample if exists, else next iterator.\",\n",
" create_batch = \"Collate a list of items into a batch.\",\n",
" do_batch = \"Combines `create_batch` and `before_batch` to get a batch of items. Input is a list of items to collate.\",\n",
" to = \"Sets `self.device=device`.\",\n",
" one_batch = \"Return one batch from `DataLoader`.\",\n",
" wif = \"See pytorch `worker_init_fn` for details.\", \n",
" before_iter = \"Called before `DataLoader` starts to read/iterate over the dataset.\",\n",
" after_item = \"Takes output of `create_item` as input and applies this function on it.\",\n",
" before_batch = \"It is called before collating a list of items into a batch. Input is a list of items.\",\n",
" after_batch = \"After collating mini-batch of items, the mini-batch is passed through this function.\",\n",
" after_iter = \"Called after `DataLoader` has fully read/iterated over the dataset.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Arguments to `DataLoader`:\n",
"* `dataset`: dataset from which to load the data. Can be either map-style or iterable-style dataset.\n",
"* `bs` (int): how many samples per batch to load (if `batch_size` is provided then `batch_size` will override `bs`). If `bs=None`, then it is assumed that `dataset.__getitem__` returns a batch.\n",
"* `num_workers` (int): how many subprocesses to use for data loading. `0` means that the data will be loaded in the main process.\n",
"* `pin_memory` (bool): If `True`, the data loader will copy Tensors into CUDA pinned memory before returning them.\n",
"* `timeout` (float>0): the timeout value in seconds for collecting a batch from workers.\n",
"* `batch_size` (int): It is only provided for PyTorch compatibility. Use `bs`.\n",
"* `shuffle` (bool): If `True`, then data is shuffled every time dataloader is fully read/iterated.\n",
"* `drop_last` (bool): If `True`, then the last incomplete batch is dropped.\n",
"* `indexed` (bool): The `DataLoader` will make a guess as to whether the dataset can be indexed (or is iterable), but you can override it with this parameter. `True` by default.\n",
"* `n` (int): Defaults to `len(dataset)`. If you are using iterable-style dataset, you can specify the size with `n`.\n",
"* `device` (torch.device): Defaults to `default_device()` which is CUDA by default. You can specify device as `torch.device('cpu')`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Override `item` and use the default infinite sampler to get a stream of unknown length (`stop()` when you want to stop the stream)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#1) [0.8184584259771384]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class RandDL(DataLoader):\n",
" def create_item(self, s):\n",
" r = random.random()\n",
" return r if r<0.95 else stop()\n",
"\n",
"L(RandDL())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#9) [4,4,4,4,4,4,4,4,4]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L(RandDL(bs=4, drop_last=True)).map(len)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#16) [4,4,4,4,4,4,4,4,4,4...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dl = RandDL(bs=4, num_workers=4, drop_last=True)\n",
"L(dl).map(len)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_num_workers = 0 if sys.platform == \"win32\" else 4\n",
"test_eq(dl.fake_l.num_workers, test_num_workers)\n",
"with dl.fake_l.no_multiproc(): \n",
" test_eq(dl.fake_l.num_workers, 0)\n",
" L(dl).map(len)\n",
"test_eq(dl.fake_l.num_workers, test_num_workers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#15) [0.09997947451517075,0.9406352068577291,0.7365644613807534,0.03467093036515401,0.33835398224425894,0.8449277297768522,0.38410576483536174,0.6763438764319878,0.748649743338762,0.27015475248670817...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def _rand_item(s):\n",
" r = random.random()\n",
" return r if r<0.95 else stop()\n",
"\n",
"L(DataLoader(create_item=_rand_item))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you don't set `bs`, then `dataset` is assumed to provide an iterator or a `__getitem__` that returns a batch."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds1 = DataLoader(letters)\n",
"test_eq(L(ds1), letters)\n",
"test_eq(len(ds1), 26)\n",
"\n",
"test_shuffled(L(DataLoader(letters, shuffle=True)), letters)\n",
"\n",
"ds1 = DataLoader(letters, indexed=False)\n",
"test_eq(L(ds1), letters)\n",
"test_eq(len(ds1), 26)\n",
"\n",
"t2 = L(tensor([0,1,2]),tensor([3,4,5]))\n",
"ds2 = DataLoader(t2)\n",
"test_eq_type(L(ds2), t2)\n",
"\n",
"t3 = L(array([0,1,2], dtype=np.int64),array([3,4,5], dtype=np.int64))\n",
"ds3 = DataLoader(t3)\n",
"test_eq_type(L(ds3), t3.map(tensor))\n",
"\n",
"ds4 = DataLoader(t3, create_batch=noop, after_iter=lambda: setattr(t3, 'f', 1))\n",
"test_eq_type(L(ds4), t3)\n",
"test_eq(t3.f, 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you do set `bs`, then `dataset` is assumed to provide an iterator or a `__getitem__` that returns a single item of a batch."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def twoepochs(d): return ' '.join(''.join(list(o)) for _ in range(2) for o in d)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds1 = DataLoader(letters, bs=4, drop_last=True, num_workers=0)\n",
"test_eq(twoepochs(ds1), 'abcd efgh ijkl mnop qrst uvwx abcd efgh ijkl mnop qrst uvwx')\n",
"\n",
"ds1 = DataLoader(letters,4,num_workers=2)\n",
"test_eq(twoepochs(ds1), 'abcd efgh ijkl mnop qrst uvwx yz abcd efgh ijkl mnop qrst uvwx yz')\n",
"\n",
"ds1 = DataLoader(range(12), bs=4, num_workers=3)\n",
"test_eq_type(L(ds1), L(tensor([0,1,2,3]),tensor([4,5,6,7]),tensor([8,9,10,11])))\n",
"\n",
"ds1 = DataLoader([str(i) for i in range(11)], bs=4, after_iter=lambda: setattr(t3, 'f', 2))\n",
"test_eq_type(L(ds1), L(['0','1','2','3'],['4','5','6','7'],['8','9','10']))\n",
"test_eq(t3.f, 2)\n",
"\n",
"it = iter(DataLoader(map(noop,range(20)), bs=4, num_workers=1))\n",
"test_eq_type([next(it) for _ in range(3)], [tensor([0,1,2,3]),tensor([4,5,6,7]),tensor([8,9,10,11])])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Iterable dataloaders require specific tests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class DummyIterableDataset(IterableDataset):\n",
" def __iter__(self):\n",
" yield from range(11)\n",
"\n",
"ds1 = DataLoader(DummyIterableDataset(), bs=4)\n",
"# Check it yields fine, and check we can do multiple passes\n",
"for i in range(3):\n",
" test_eq_type(L(ds1), L(tensor([0,1,2,3]),tensor([4,5,6,7]),tensor([8,9,10])))\n",
"\n",
"# Check `drop_last` works fine (with multiple passes, since this will prematurely terminate the iterator)\n",
"ds1 = DataLoader(DummyIterableDataset(), bs=4, drop_last=True)\n",
"for i in range(3):\n",
" test_eq_type(L(ds1), L(tensor([0,1,2,3]),tensor([4,5,6,7])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4.27 ms, sys: 1.05 ms, total: 5.32 ms\n",
"Wall time: 316 ms\n",
"CPU times: user 12.7 ms, sys: 11.9 ms, total: 24.5 ms\n",
"Wall time: 197 ms\n",
"CPU times: user 14.5 ms, sys: 16.2 ms, total: 30.7 ms\n",
"Wall time: 127 ms\n"
]
},
{
"data": {
"text/plain": [
"(#26) ['r','c','q','n','j','s','l','p','b','y'...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class SleepyDL(list):\n",
" def __getitem__(self,i):\n",
" time.sleep(random.random()/50)\n",
" return super().__getitem__(i)\n",
"\n",
"t = SleepyDL(letters)\n",
"\n",
"%time test_eq(DataLoader(t, num_workers=0), letters)\n",
"%time test_eq(DataLoader(t, num_workers=2), letters)\n",
"%time test_eq(DataLoader(t, num_workers=4), letters)\n",
"\n",
"dl = DataLoader(t, shuffle=True, num_workers=1)\n",
"test_shuffled(L(dl), letters)\n",
"test_shuffled(L(dl), L(dl))\n",
"L(dl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 12 ms, sys: 22.3 ms, total: 34.3 ms\n",
"Wall time: 130 ms\n"
]
}
],
"source": [
"class SleepyQueue():\n",
" \"Simulate a queue with varying latency\"\n",
" def __init__(self, q): self.q=q\n",
" def __iter__(self):\n",
" while True:\n",
" time.sleep(random.random()/100)\n",
" try: yield self.q.get_nowait()\n",
" except queues.Empty: return\n",
"\n",
"q = Queue()\n",
"for o in range(30): q.put(o)\n",
"it = SleepyQueue(q)\n",
"\n",
"if not (sys.platform == \"win32\" and IN_NOTEBOOK):\n",
" %time test_shuffled(L(DataLoader(it, num_workers=4)), L(range(30)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class A(TensorBase): pass\n",
"\n",
"for nw in (0,2):\n",
" t = A(tensor([1,2]))\n",
" dl = DataLoader([t,t,t,t,t,t,t,t], bs=4, num_workers=nw)\n",
" b = first(dl)\n",
" test_eq(type(b), A)\n",
"\n",
" t = (A(tensor([1,2])),)\n",
" dl = DataLoader([t,t,t,t,t,t,t,t], bs=4, num_workers=nw)\n",
" b = first(dl)\n",
" test_eq(type(b[0]), A)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[tensor([16, 14, 5, 1, 39, 49, 10, 40, 7, 36, 28, 42, 32, 24, 43, 46, 4, 3,\n",
" 11, 48, 26, 35, 15, 25, 23, 8, 44, 47, 0, 34, 21, 17]),\n",
" tensor([45, 41, 6, 20, 38, 19, 29, 37, 13, 18, 2, 27, 30, 12, 33, 22, 9, 31])]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(DataLoader(list(range(50)),bs=32,shuffle=True,num_workers=3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class A(TensorBase): pass\n",
"t = A(tensor(1,2))\n",
"\n",
"tdl = DataLoader([t,t,t,t,t,t,t,t], bs=4, num_workers=2, after_batch=to_device)\n",
"b = first(tdl)\n",
"test_eq(type(b), A)\n",
"\n",
"# Unknown attributes are delegated to `dataset`\n",
"test_eq(tdl.pop(), tensor(1,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Override `get_idxs` to return the same index until consumption of the DL. This is intented to test consistent sampling behavior when `num_workers`>1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class AdamantDL(DataLoader):\n",
" def get_idxs(self):\n",
" r=random.randint(0,self.n-1)\n",
" return [r] * self.n\n",
"\n",
"test_eq(torch.cat(tuple(AdamantDL((list(range(50))),bs=16,num_workers=4))).unique().numel(),1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Converted 00_torch_core.ipynb.\n",
"Converted 01_layers.ipynb.\n",
"Converted 01a_losses.ipynb.\n",
"Converted 02_data.load.ipynb.\n",
"Converted 03_data.core.ipynb.\n",
"Converted 04_data.external.ipynb.\n",
"Converted 05_data.transforms.ipynb.\n",
"Converted 06_data.block.ipynb.\n",
"Converted 07_vision.core.ipynb.\n",
"Converted 08_vision.data.ipynb.\n",
"Converted 09_vision.augment.ipynb.\n",
"Converted 09b_vision.utils.ipynb.\n",
"Converted 09c_vision.widgets.ipynb.\n",
"Converted 10_tutorial.pets.ipynb.\n",
"Converted 10b_tutorial.albumentations.ipynb.\n",
"Converted 11_vision.models.xresnet.ipynb.\n",
"Converted 12_optimizer.ipynb.\n",
"Converted 13_callback.core.ipynb.\n",
"Converted 13a_learner.ipynb.\n",
"Converted 13b_metrics.ipynb.\n",
"Converted 14_callback.schedule.ipynb.\n",
"Converted 14a_callback.data.ipynb.\n",
"Converted 15_callback.hook.ipynb.\n",
"Converted 15a_vision.models.unet.ipynb.\n",
"Converted 16_callback.progress.ipynb.\n",
"Converted 17_callback.tracker.ipynb.\n",
"Converted 18_callback.fp16.ipynb.\n",
"Converted 18a_callback.training.ipynb.\n",
"Converted 18b_callback.preds.ipynb.\n",
"Converted 19_callback.mixup.ipynb.\n",
"Converted 20_interpret.ipynb.\n",
"Converted 20a_distributed.ipynb.\n",
"Converted 21_vision.learner.ipynb.\n",
"Converted 22_tutorial.imagenette.ipynb.\n",
"Converted 23_tutorial.vision.ipynb.\n",
"Converted 24_tutorial.siamese.ipynb.\n",
"Converted 24_vision.gan.ipynb.\n",
"Converted 30_text.core.ipynb.\n",
"Converted 31_text.data.ipynb.\n",
"Converted 32_text.models.awdlstm.ipynb.\n",
"Converted 33_text.models.core.ipynb.\n",
"Converted 34_callback.rnn.ipynb.\n",
"Converted 35_tutorial.wikitext.ipynb.\n",
"Converted 36_text.models.qrnn.ipynb.\n",
"Converted 37_text.learner.ipynb.\n",
"Converted 38_tutorial.text.ipynb.\n",
"Converted 39_tutorial.transformers.ipynb.\n",
"Converted 40_tabular.core.ipynb.\n",
"Converted 41_tabular.data.ipynb.\n",
"Converted 42_tabular.model.ipynb.\n",
"Converted 43_tabular.learner.ipynb.\n",
"Converted 44_tutorial.tabular.ipynb.\n",
"Converted 45_collab.ipynb.\n",
"Converted 46_tutorial.collab.ipynb.\n",
"Converted 50_tutorial.datablock.ipynb.\n",
"Converted 60_medical.imaging.ipynb.\n",
"Converted 61_tutorial.medical_imaging.ipynb.\n",
"Converted 65_medical.text.ipynb.\n",
"Converted 70_callback.wandb.ipynb.\n",
"Converted 71_callback.tensorboard.ipynb.\n",
"Converted 72_callback.neptune.ipynb.\n",
"Converted 73_callback.captum.ipynb.\n",
"Converted 74_callback.azureml.ipynb.\n",
"Converted 97_test_utils.ipynb.\n",
"Converted 99_pytorch_doc.ipynb.\n",
"Converted dev-setup.ipynb.\n",
"Converted app_examples.ipynb.\n",
"Converted camvid.ipynb.\n",
"Converted migrating_catalyst.ipynb.\n",
"Converted migrating_ignite.ipynb.\n",
"Converted migrating_lightning.ipynb.\n",
"Converted migrating_pytorch.ipynb.\n",
"Converted ulmfit.ipynb.\n",
"Converted index.ipynb.\n",
"Converted quick_start.ipynb.\n",
"Converted tutorial.ipynb.\n"
]
}
],
"source": [
"#hide\n",
"from nbdev.export import notebook2script\n",
"notebook2script()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# from subprocess import Popen, PIPE\n",
"# # test num_workers > 0 in scripts works when python process start method is spawn\n",
"# process = Popen([\"python\", \"dltest.py\"], stdout=PIPE)\n",
"# _, err = process.communicate(timeout=15)\n",
"# exit_code = process.wait()\n",
"# test_eq(exit_code, 0)"
]
}
],
"metadata": {
"jupytext": {
"split_at_heading": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}