Fix RTD Build (#887)

docs/source/conf.py

@@ -210,7 +210,7 @@ def _package_list_from_file(pfile):
     "pytorch-tabnet": "pytorch_tabnet",
     "pyDeprecate": "deprecate",
 }
-MOCK_PACKAGES = ["PyYAML", "tqdm"]
+MOCK_PACKAGES = ["numpy", "PyYAML", "tqdm"]
 if SPHINX_MOCK_REQUIREMENTS:
     # mock also base packages when we are on RTD since we don't install them there
     MOCK_PACKAGES += _package_list_from_file(os.path.join(_PATH_ROOT, "requirements.txt"))

flash/core/integrations/labelstudio/data_source.py

@@ -1,20 +1,21 @@
 import json
 import os
 from pathlib import Path
-from typing import Any, Mapping, Optional, Sequence, TypeVar, Union
+from typing import Any, Mapping, Optional, Sequence, Union
 
 import torch
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 
-from flash import DataSource
 from flash.core.data.auto_dataset import AutoDataset, IterableAutoDataset
-from flash.core.data.data_source import DefaultDataKeys, has_len
+from flash.core.data.data_source import DataSource, DefaultDataKeys, has_len
 from flash.core.utilities.imports import _PYTORCHVIDEO_AVAILABLE, _TEXT_AVAILABLE, _TORCHVISION_AVAILABLE
 from flash.core.utilities.stages import RunningStage
 
 if _TORCHVISION_AVAILABLE:
     from torchvision.datasets.folder import default_loader
-DATA_TYPE = TypeVar("DATA_TYPE")
+
+if _TEXT_AVAILABLE:
+    from transformers import AutoTokenizer
 
 
 class LabelStudioDataSource(DataSource):
@@ -80,7 +81,7 @@ def load_sample(self, sample: Mapping[str, Any] = None, dataset: Optional[Any] =
 
     def generate_dataset(
         self,
-        data: Optional[DATA_TYPE],
+        data: Optional[Any],
         running_stage: RunningStage,
     ) -> Optional[Union[AutoDataset, IterableAutoDataset]]:
         """Generate dataset from loaded data."""
@@ -201,8 +202,6 @@ class LabelStudioTextClassificationDataSource(LabelStudioDataSource):
     def __init__(self, backbone=None, max_length=128):
         super().__init__()
         if backbone:
-            if _TEXT_AVAILABLE:
-                from transformers import AutoTokenizer
             self.backbone = backbone
             self.tokenizer = AutoTokenizer.from_pretrained(backbone, use_fast=True)
             self.max_length = max_length

flash/core/model.py

@@ -46,12 +46,13 @@
     SerializerMapping,
 )
 from flash.core.data.properties import ProcessState
-from flash.core.optimizers import _OPTIMIZERS_REGISTRY, _SCHEDULERS_REGISTRY
+from flash.core.optimizers.optimizers import _OPTIMIZERS_REGISTRY
+from flash.core.optimizers.schedulers import _SCHEDULERS_REGISTRY
 from flash.core.registry import FlashRegistry
-from flash.core.serve import Composition
-from flash.core.utilities import providers
+from flash.core.serve.composition import Composition
 from flash.core.utilities.apply_func import get_callable_dict
 from flash.core.utilities.imports import requires
+from flash.core.utilities.providers import _HUGGINGFACE
 from flash.core.utilities.stages import RunningStage
 from flash.core.utilities.types import (
     DESERIALIZER_TYPE,
@@ -979,7 +980,7 @@ def _instantiate_lr_scheduler(self, optimizer: Optimizer) -> Dict[str, Any]:
 
         # Providers part
         if lr_scheduler_metadata is not None and "providers" in lr_scheduler_metadata.keys():
-            if lr_scheduler_metadata["providers"] == providers._HUGGINGFACE:
+            if lr_scheduler_metadata["providers"] == _HUGGINGFACE:
                 if lr_scheduler_data["name"] != "constant_schedule":
                     num_training_steps: int = self.get_num_training_steps()
                     num_warmup_steps: int = self._compute_warmup(

flash/core/optimizers/optimizers.py

@@ -4,19 +4,20 @@
 from torch import optim
 
 from flash.core.registry import FlashRegistry
-from flash.core.utilities.imports import _TORCH_OPTIMIZER_AVAILABLE
+from flash.core.utilities.imports import _TORCH_AVAILABLE, _TORCH_OPTIMIZER_AVAILABLE
 
 _OPTIMIZERS_REGISTRY = FlashRegistry("optimizer")
 
-_optimizers: List[Callable] = []
-for n in dir(optim):
-    _optimizer = getattr(optim, n)
+if _TORCH_AVAILABLE:
+    _optimizers: List[Callable] = []
+    for n in dir(optim):
+        _optimizer = getattr(optim, n)
 
-    if isclass(_optimizer) and _optimizer != optim.Optimizer and issubclass(_optimizer, optim.Optimizer):
-        _optimizers.append(_optimizer)
+        if isclass(_optimizer) and _optimizer != optim.Optimizer and issubclass(_optimizer, optim.Optimizer):
+            _optimizers.append(_optimizer)
 
-for fn in _optimizers:
-    _OPTIMIZERS_REGISTRY(fn, name=fn.__name__.lower())
+    for fn in _optimizers:
+        _OPTIMIZERS_REGISTRY(fn, name=fn.__name__.lower())
 
 
 if _TORCH_OPTIMIZER_AVAILABLE:

flash/core/optimizers/schedulers.py

@@ -13,26 +13,26 @@
 )
 
 from flash.core.registry import FlashRegistry
-from flash.core.utilities.imports import _TRANSFORMERS_AVAILABLE
+from flash.core.utilities.imports import _TORCH_AVAILABLE, _TRANSFORMERS_AVAILABLE
 from flash.core.utilities.providers import _HUGGINGFACE
 
 _SCHEDULERS_REGISTRY = FlashRegistry("scheduler")
 _STEP_SCHEDULERS = (StepLR, MultiStepLR, CosineAnnealingLR, CyclicLR, CosineAnnealingWarmRestarts)
 
-schedulers: List[_LRScheduler] = []
-for n in dir(lr_scheduler):
-    sched = getattr(lr_scheduler, n)
+if _TORCH_AVAILABLE:
+    schedulers: List[_LRScheduler] = []
+    for n in dir(lr_scheduler):
+        sched = getattr(lr_scheduler, n)
 
-    if inspect.isclass(sched) and sched != _LRScheduler and issubclass(sched, _LRScheduler):
-        schedulers.append(sched)
+        if inspect.isclass(sched) and sched != _LRScheduler and issubclass(sched, _LRScheduler):
+            schedulers.append(sched)
 
-# Adding `ReduceLROnPlateau` separately as it is subclassed from `object` and not `_LRScheduler`.
-schedulers.append(ReduceLROnPlateau)
+    # Adding `ReduceLROnPlateau` separately as it is subclassed from `object` and not `_LRScheduler`.
+    schedulers.append(ReduceLROnPlateau)
 
-
-for scheduler in schedulers:
-    interval = "step" if issubclass(scheduler, _STEP_SCHEDULERS) else "epoch"
-    _SCHEDULERS_REGISTRY(scheduler, name=scheduler.__name__.lower(), interval=interval)
+    for scheduler in schedulers:
+        interval = "step" if issubclass(scheduler, _STEP_SCHEDULERS) else "epoch"
+        _SCHEDULERS_REGISTRY(scheduler, name=scheduler.__name__.lower(), interval=interval)
 
 if _TRANSFORMERS_AVAILABLE:
     from transformers import optimization

flash/image/classification/integrations/baal/data.py

@@ -20,8 +20,8 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from torch.utils.data import DataLoader, Dataset, random_split
 
-from flash import DataModule
 from flash.core.data.auto_dataset import BaseAutoDataset
+from flash.core.data.data_module import DataModule
 from flash.core.data.data_pipeline import DataPipeline
 from flash.core.utilities.imports import _BAAL_AVAILABLE, requires
 

requirements/docs.txt

@@ -13,6 +13,3 @@ sphinx-paramlinks>=0.5.1
 sphinx-togglebutton>=0.2
 sphinx-copybutton>=0.3
 jinja2
-numpy>=1.21.2 # hotfix for docs, failing without numpy install
-pandas
-torch