fixes #3762

README.md

@@ -69,10 +69,10 @@ segmentation model, a text sentiment model, a recommendation system, and
 a tabular model. For each of the applications, the code is much the
 same.
 
-Read through the [Tutorials](https://docs.fast.ai/tutorial) to learn how
-to train your own models on your own datasets. Use the navigation
-sidebar to look through the fastai documentation. Every class, function,
-and method is documented here.
+Read through the [Tutorials](https://docs.fast.ai/tutorial.html) to
+learn how to train your own models on your own datasets. Use the
+navigation sidebar to look through the fastai documentation. Every
+class, function, and method is documented here.
 
 To learn about the design and motivation of the library, read the [peer
 reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm).

fastai/_modidx.py

@@ -42,8 +42,7 @@
             'fastai.callback.captum': { 'fastai.callback.captum.CaptumInterpretation': 'https://docs.fast.ai/callback.captum.html#captuminterpretation',
                                         'fastai.callback.captum.CaptumInterpretation.get_baseline_img': 'https://docs.fast.ai/callback.captum.html#captuminterpretation.get_baseline_img',
                                         'fastai.callback.captum.CaptumInterpretation.insights': 'https://docs.fast.ai/callback.captum.html#captuminterpretation.insights',
-                                        'fastai.callback.captum.CaptumInterpretation.visualize': 'https://docs.fast.ai/callback.captum.html#captuminterpretation.visualize',
-                                        'fastai.callback.captum.json_clean': 'https://docs.fast.ai/callback.captum.html#json_clean'},
+                                        'fastai.callback.captum.CaptumInterpretation.visualize': 'https://docs.fast.ai/callback.captum.html#captuminterpretation.visualize'},
             'fastai.callback.comet': { 'fastai.callback.comet.CometCallback': 'https://docs.fast.ai/callback.comet.html#cometcallback',
                                        'fastai.callback.comet.CometCallback.after_batch': 'https://docs.fast.ai/callback.comet.html#cometcallback.after_batch',
                                        'fastai.callback.comet.CometCallback.after_epoch': 'https://docs.fast.ai/callback.comet.html#cometcallback.after_epoch',
@@ -1286,6 +1285,7 @@
                                     'fastai.vision.core.BBoxLabeler.setups': 'https://docs.fast.ai/vision.core.html#bboxlabeler.setups',
                                     'fastai.vision.core.BILINEAR': 'https://docs.fast.ai/vision.core.html#bilinear',
                                     'fastai.vision.core.Image': 'https://docs.fast.ai/vision.core.html#image',
+                                    'fastai.vision.core.Image.Image.__repr__': 'https://docs.fast.ai/vision.core.html#image.image.__repr__',
                                     'fastai.vision.core.Image.Image.aspect': 'https://docs.fast.ai/vision.core.html#image.image.aspect',
                                     'fastai.vision.core.Image.Image.n_px': 'https://docs.fast.ai/vision.core.html#image.image.n_px',
                                     'fastai.vision.core.Image.Image.reshape': 'https://docs.fast.ai/vision.core.html#image.image.reshape',

fastai/callback/captum.py

@@ -6,7 +6,7 @@
 from ..basics import *
 
 # %% auto 0
-__all__ = ['json_clean', 'CaptumInterpretation']
+__all__ = ['CaptumInterpretation']
 
 # %% ../nbs/70c_callback.captum.ipynb 6
 from ipykernel import jsonutil
@@ -92,7 +92,7 @@ def _get_attributions(self,enc_data,metric,n_steps,nt_type,baseline_type,strides
                                        sliding_window_shapes=sliding_window_shapes,
                                        baselines=baseline)
 
-# %% ../nbs/70c_callback.captum.ipynb 25
+# %% ../nbs/70c_callback.captum.ipynb 26
 @patch
 def insights(x: CaptumInterpretation,inp_data,debug=True):
     _baseline_func= lambda o: o*0

fastai/callback/tensorboard.py

@@ -8,13 +8,13 @@
 __all__ = ['TensorBoardBaseCallback', 'TensorBoardCallback', 'TensorBoardProjectorCallback', 'projector_word_embeddings',
            'tensorboard_log']
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 18
+# %% ../nbs/70a_callback.tensorboard.ipynb 19
 import tensorboard
 from torch.utils.tensorboard import SummaryWriter
 from .fp16 import ModelToHalf
 from .hook import hook_output
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 19
+# %% ../nbs/70a_callback.tensorboard.ipynb 20
 class TensorBoardBaseCallback(Callback):
     order = Recorder.order+1
     "Base class for tensorboard callbacks"
@@ -42,7 +42,7 @@ def __del__(self): self._remove()
     def _remove(self):
         if getattr(self, 'h', None): self.h.remove()
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 20
+# %% ../nbs/70a_callback.tensorboard.ipynb 22
 class TensorBoardCallback(TensorBoardBaseCallback):
     "Saves model topology, losses & metrics for tensorboard and tensorboard projector during training"
     def __init__(self, log_dir=None, trace_model=True, log_preds=True, n_preds=9, projector=False, layer=None):
@@ -79,7 +79,7 @@ def after_epoch(self):
     def before_validate(self):
         if self.projector: self._setup_projector()
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 21
+# %% ../nbs/70a_callback.tensorboard.ipynb 24
 class TensorBoardProjectorCallback(TensorBoardBaseCallback):
     "Extracts and exports image featuers for tensorboard projector during inference"
     def __init__(self, log_dir=None, layer=None):
@@ -94,13 +94,13 @@ def before_fit(self):
     def before_validate(self):
         self._setup_projector()
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 22
+# %% ../nbs/70a_callback.tensorboard.ipynb 26
 def _write_projector_embedding(learn, writer, feat):
     lbls = [learn.dl.vocab[l] for l in feat['lbl']] if getattr(learn.dl, 'vocab', None) else None     
     vecs = feat['vec'].squeeze()
     writer.add_embedding(vecs, metadata=lbls, label_img=feat['img'], global_step=learn.train_iter)
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 23
+# %% ../nbs/70a_callback.tensorboard.ipynb 27
 def _add_projector_features(learn, hook, feat):
     img = _normalize_for_projector(learn.x)
     first_epoch = True if learn.iter == 0 else False
@@ -110,12 +110,12 @@ def _add_projector_features(learn, hook, feat):
         feat['lbl'] = learn.y if first_epoch else torch.cat((feat['lbl'], learn.y),0)
     return feat
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 24
+# %% ../nbs/70a_callback.tensorboard.ipynb 28
 def _get_embeddings(model, layer):
     layer = model[0].encoder if layer == None else layer
     return layer.weight
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 25
+# %% ../nbs/70a_callback.tensorboard.ipynb 29
 @typedispatch
 def _normalize_for_projector(x:TensorImage):
     # normalize tensor to be between 0-1
@@ -127,10 +127,10 @@ def _normalize_for_projector(x:TensorImage):
     img = img.view(*sz)
     return img
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 26
+# %% ../nbs/70a_callback.tensorboard.ipynb 30
 from ..text.all import LMLearner, TextLearner
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 27
+# %% ../nbs/70a_callback.tensorboard.ipynb 31
 def projector_word_embeddings(learn=None, layer=None, vocab=None, limit=-1, start=0, log_dir=None):
     "Extracts and exports word embeddings from language models embedding layers"
     if not layer:
@@ -145,10 +145,10 @@ def projector_word_embeddings(learn=None, layer=None, vocab=None, limit=-1, star
     writer.add_embedding(emb[start:end], metadata=vocab[start:end], label_img=img[start:end])
     writer.close()
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 28
+# %% ../nbs/70a_callback.tensorboard.ipynb 33
 from ..vision.data import *
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 29
+# %% ../nbs/70a_callback.tensorboard.ipynb 34
 @typedispatch
 def tensorboard_log(x:TensorImage, y: TensorCategory, samples, outs, writer, step):
     fig,axs = get_grid(len(samples), return_fig=True)
@@ -158,10 +158,10 @@ def tensorboard_log(x:TensorImage, y: TensorCategory, samples, outs, writer, ste
             for b,r,c in zip(samples.itemgot(1),outs.itemgot(0),axs)]
     writer.add_figure('Sample results', fig, step)
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 30
+# %% ../nbs/70a_callback.tensorboard.ipynb 35
 from ..vision.core import TensorPoint,TensorBBox
 
-# %% ../nbs/70a_callback.tensorboard.ipynb 31
+# %% ../nbs/70a_callback.tensorboard.ipynb 36
 @typedispatch
 def tensorboard_log(x:TensorImage, y: TensorImageBase|TensorPoint|TensorBBox, samples, outs, writer, step):
     fig,axs = get_grid(len(samples), return_fig=True, double=True)

fastai/callback/wandb.py

@@ -157,7 +157,7 @@ def after_fit(self):
         self._wandb_step += 1
 
 
-# %% ../nbs/70_callback.wandb.ipynb 11
+# %% ../nbs/70_callback.wandb.ipynb 12
 @patch
 def gather_args(self:Learner):
     "Gather config parameters accessible to the learner"
@@ -185,7 +185,7 @@ def gather_args(self:Learner):
         args['dls.after_batch'] = f'{self.dls.after_batch}'
     return args
 
-# %% ../nbs/70_callback.wandb.ipynb 12
+# %% ../nbs/70_callback.wandb.ipynb 14
 def _make_plt(img):
     "Make plot to image resolution"
     # from https://stackoverflow.com/a/13714915
@@ -198,15 +198,15 @@ def _make_plt(img):
     fig.add_axes(ax)
     return fig, ax
 
-# %% ../nbs/70_callback.wandb.ipynb 13
+# %% ../nbs/70_callback.wandb.ipynb 15
 def _format_config_value(v):
     if isinstance(v, list):
         return [_format_config_value(item) for item in v]
     elif hasattr(v, '__stored_args__'):
         return {**_format_config(v.__stored_args__), '_name': v}
     return v
 
-# %% ../nbs/70_callback.wandb.ipynb 14
+# %% ../nbs/70_callback.wandb.ipynb 16
 def _format_config(config):
     "Format config parameters before logging them"
     for k,v in config.items():
@@ -216,12 +216,12 @@ def _format_config(config):
             config[k] = _format_config_value(v)
     return config
 
-# %% ../nbs/70_callback.wandb.ipynb 15
+# %% ../nbs/70_callback.wandb.ipynb 17
 def _format_metadata(metadata):
     "Format metadata associated to artifacts"
     for k,v in metadata.items(): metadata[k] = str(v)
 
-# %% ../nbs/70_callback.wandb.ipynb 16
+# %% ../nbs/70_callback.wandb.ipynb 18
 def log_dataset(path, name=None, metadata={}, description='raw dataset'):
     "Log dataset folder"
     # Check if wandb.init has been called in case datasets are logged manually
@@ -240,7 +240,7 @@ def log_dataset(path, name=None, metadata={}, description='raw dataset'):
         else: artifact_dataset.add_file(str(p.resolve()))
     wandb.run.use_artifact(artifact_dataset)
 
-# %% ../nbs/70_callback.wandb.ipynb 17
+# %% ../nbs/70_callback.wandb.ipynb 20
 def log_model(path, name=None, metadata={}, description='trained model'):
     "Log model file"
     if wandb.run is None:
@@ -255,7 +255,7 @@ def log_model(path, name=None, metadata={}, description='trained model'):
         fa.write(path.read_bytes())
     wandb.run.log_artifact(artifact_model)
 
-# %% ../nbs/70_callback.wandb.ipynb 18
+# %% ../nbs/70_callback.wandb.ipynb 22
 @typedispatch
 def wandb_process(x:TensorImage, y, samples, outs, preds):
     "Process `sample` and `out` depending on the type of `x/y`"
@@ -272,22 +272,22 @@ def wandb_process(x:TensorImage, y, samples, outs, preds):
             plt.close(fig)
     return {"Inputs":res_input, "Predictions":res_pred, "Ground_Truth":res_label}
 
-# %% ../nbs/70_callback.wandb.ipynb 19
+# %% ../nbs/70_callback.wandb.ipynb 23
 def _unlist(l):
     "get element of lists of lenght 1"
     if isinstance(l, (list, tuple)):
         if len(l) == 1: return l[0]
     else: return l
 
-# %% ../nbs/70_callback.wandb.ipynb 20
+# %% ../nbs/70_callback.wandb.ipynb 24
 @typedispatch
 def wandb_process(x:TensorImage, y:TensorCategory|TensorMultiCategory, samples, outs, preds):
     table = wandb.Table(columns=["Input image", "Ground_Truth", "Predictions"])
     for (image, label), pred_label in zip(samples,outs):
         table.add_data(wandb.Image(image.permute(1,2,0)), label, _unlist(pred_label))
     return {"Prediction_Samples": table}
 
-# %% ../nbs/70_callback.wandb.ipynb 21
+# %% ../nbs/70_callback.wandb.ipynb 25
 @typedispatch
 def wandb_process(x:TensorImage, y:TensorMask, samples, outs, preds):
     res = []
@@ -305,18 +305,18 @@ def wandb_process(x:TensorImage, y:TensorMask, samples, outs, preds):
                       )
     return {"Prediction_Samples": table}
 
-# %% ../nbs/70_callback.wandb.ipynb 22
+# %% ../nbs/70_callback.wandb.ipynb 26
 @typedispatch
 def wandb_process(x:TensorText, y:TensorCategory|TensorMultiCategory, samples, outs, preds):
     data = [[s[0], s[1], o[0]] for s,o in zip(samples,outs)]
     return {"Prediction_Samples": wandb.Table(data=data, columns=["Text", "Target", "Prediction"])}
 
-# %% ../nbs/70_callback.wandb.ipynb 23
+# %% ../nbs/70_callback.wandb.ipynb 27
 @typedispatch
 def wandb_process(x:Tabular, y:Tabular, samples, outs, preds):
     df = x.all_cols
     for n in x.y_names: df[n+'_pred'] = y[n].values
     return {"Prediction_Samples": wandb.Table(dataframe=df)}
 
-# %% ../nbs/70_callback.wandb.ipynb 27
+# %% ../nbs/70_callback.wandb.ipynb 32
 _all_ = ['wandb_process']

fastai/vision/core.py

@@ -6,6 +6,7 @@
 from ..data.all import *
 
 from PIL import Image
+
 try: BILINEAR,NEAREST = Image.Resampling.BILINEAR,Image.Resampling.NEAREST
 except AttributeError: from PIL.Image import BILINEAR,NEAREST
 
@@ -21,6 +22,11 @@
 # %% ../nbs/07_vision.core.ipynb 7
 _all_ = ['Image','ToTensor']
 
+# %% ../nbs/07_vision.core.ipynb 8
+@patch
+def __repr__(x:Image.Image):
+    return "<%s.%s image mode=%s size=%dx%d at 0x%X>" % (x.__class__.__module__, x.__class__.__name__, x.mode, x.size[0], x.size[1])
+
 # %% ../nbs/07_vision.core.ipynb 11
 imagenet_stats = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 cifar_stats    = ([0.491, 0.482, 0.447], [0.247, 0.243, 0.261])

fastai/vision/utils.py

@@ -23,7 +23,7 @@ def _get_downloaded_image_filename(dest, name, suffix):
 
     return candidate_name
 
-# %% ../nbs/09b_vision.utils.ipynb 8
+# %% ../nbs/09b_vision.utils.ipynb 7
 def _download_image_inner(dest, inp, timeout=4, preserve_filename=False):
     i,url = inp
     url = url.split("?")[0]
@@ -33,7 +33,7 @@ def _download_image_inner(dest, inp, timeout=4, preserve_filename=False):
     try: download_url(url, dest/f"{name}{suffix}", show_progress=False, timeout=timeout)
     except Exception as e: f"Couldn't download {url}."
 
-# %% ../nbs/09b_vision.utils.ipynb 10
+# %% ../nbs/09b_vision.utils.ipynb 9
 def download_images(dest, url_file=None, urls=None, max_pics=1000, n_workers=8, timeout=4, preserve_filename=False):
     "Download images listed in text file `url_file` to path `dest`, at most `max_pics`"
     if urls is None: urls = url_file.read_text().strip().split("\n")[:max_pics]
@@ -42,15 +42,15 @@ def download_images(dest, url_file=None, urls=None, max_pics=1000, n_workers=8,
     parallel(partial(_download_image_inner, dest, timeout=timeout, preserve_filename=preserve_filename),
              list(enumerate(urls)), n_workers=n_workers, threadpool=True)
 
-# %% ../nbs/09b_vision.utils.ipynb 12
+# %% ../nbs/09b_vision.utils.ipynb 11
 def resize_to(img, targ_sz, use_min=False):
     "Size to resize to, to hit `targ_sz` at same aspect ratio, in PIL coords (i.e w*h)"
     w,h = img.size
     min_sz = (min if use_min else max)(w,h)
     ratio = targ_sz/min_sz
     return int(w*ratio),int(h*ratio)
 
-# %% ../nbs/09b_vision.utils.ipynb 14
+# %% ../nbs/09b_vision.utils.ipynb 13
 def verify_image(fn):
     "Confirm that `fn` can be opened"
     try:
@@ -60,12 +60,12 @@ def verify_image(fn):
         return True
     except: return False
 
-# %% ../nbs/09b_vision.utils.ipynb 15
+# %% ../nbs/09b_vision.utils.ipynb 14
 def verify_images(fns):
     "Find images in `fns` that can't be opened"
     return L(fns[i] for i,o in enumerate(parallel(verify_image, fns)) if not o)
 
-# %% ../nbs/09b_vision.utils.ipynb 16
+# %% ../nbs/09b_vision.utils.ipynb 15
 def resize_image(file, dest, src='.', max_size=None, n_channels=3, ext=None,
                  img_format=None, resample=BILINEAR, resume=False, **kwargs ):
     "Resize file to dest to max_size"
@@ -89,7 +89,7 @@ def resize_image(file, dest, src='.', max_size=None, n_channels=3, ext=None,
         img.save(dest_fname, img_format, **kwargs)
     elif file != dest_fname : shutil.copy2(file, dest_fname)
 
-# %% ../nbs/09b_vision.utils.ipynb 19
+# %% ../nbs/09b_vision.utils.ipynb 18
 def resize_images(path, max_workers=defaults.cpus, max_size=None, recurse=False,
                   dest=Path('.'), n_channels=3, ext=None, img_format=None, resample=BILINEAR,
                   resume=None, **kwargs):

nbs/07_vision.core.ipynb

@@ -33,6 +33,7 @@
     "from fastai.data.all import *\n",
     "\n",
     "from PIL import Image\n",
+    "\n",
     "try: BILINEAR,NEAREST = Image.Resampling.BILINEAR,Image.Resampling.NEAREST\n",
     "except AttributeError: from PIL.Image import BILINEAR,NEAREST"
    ]
@@ -82,15 +83,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#It didn't use to be necessary to add ToTensor in all but we don't have the encodes methods defined here otherwise.\n",
-    "#TODO: investigate"
+    "#|exporti\n",
+    "@patch\n",
+    "def __repr__(x:Image.Image):\n",
+    "    return \"<%s.%s image mode=%s size=%dx%d at 0x%X>\" % (x.__class__.__module__, x.__class__.__name__, x.mode, x.size[0], x.size[1])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Core vision\n",
+    "\n",
     "> Basic image opening/processing functionality"
    ]
   },
@@ -1079,6 +1083,8 @@
       "ToTensor:\n",
       "encodes: (PILMask,object) -> encodes\n",
       "(PILBase,object) -> encodes\n",
+      "(PILMask,object) -> encodes\n",
+      "(PILBase,object) -> encodes\n",
       "decodes: \n",
       "<class '__main__.PILImageBW'>\n",
       "<class 'fastai.torch_core.TensorImageBW'>\n"