End of summary

fastai2/data/block.py

@@ -117,6 +117,19 @@ def _apply_pipeline(p, x):
             raise e
     return x
 
+# Cell
+from .load import _collate_types
+
+def _find_fail_collate(s):
+    s = L(*s)
+    for x in s[0]:
+        if not isinstance(x, _collate_types): return f"{type(x).__name__} is not collatable"
+    for i in range_of(s[0]):
+        try: _ = default_collate(s.itemgot(i))
+        except:
+            shapes = [getattr(o[i], 'shape', None) for o in s]
+            return f"Could not collate the {i}-th members of your tuples because got the following shapes\n{','.join([str(s) for s in shapes])}"
+
 # Cell
 @patch
 def summary(self: DataBlock, source, bs=4, **kwargs):
@@ -147,7 +160,9 @@ def summary(self: DataBlock, source, bs=4, **kwargs):
         b = dls.train.create_batch(s)
         b = retain_types(b, s[0] if is_listy(s) else s)
     except Exception as e:
-        print("It's not possible to collate your items in a batch, make sure all parts of your samples are tensors of the same size")
+        print("Error! It's not possible to collate your items in a batch")
+        why = _find_fail_collate(s)
+        print("Make sure all parts of your samples are tensors of the same size" if why is None else why)
         raise e
 
     if len([f for f in dls.train.after_batch.fs if f.name != 'noop'])!=0:

fastai2/data/core.py

@@ -56,8 +56,10 @@ def _retain_dl(self,b):
     def new(self, dataset=None, cls=None, **kwargs):
         res = super().new(dataset, cls, do_setup=False, **kwargs)
         if not hasattr(self, '_n_inp') or not hasattr(self, '_types'):
-            self._one_pass()
-        res._n_inp,res._types = self._n_inp,self._types
+            try:
+                self._one_pass()
+                res._n_inp,res._types = self._n_inp,self._types
+            except: print("Could not do one pass in your dataloader, there is something wrong in it")
         return res
 
     def before_iter(self):

nbs/03_data.core.ipynb

@@ -152,8 +152,10 @@
     "    def new(self, dataset=None, cls=None, **kwargs):\n",
     "        res = super().new(dataset, cls, do_setup=False, **kwargs)\n",
     "        if not hasattr(self, '_n_inp') or not hasattr(self, '_types'):\n",
-    "            self._one_pass()\n",
-    "        res._n_inp,res._types = self._n_inp,self._types\n",
+    "            try: \n",
+    "                self._one_pass()\n",
+    "                res._n_inp,res._types = self._n_inp,self._types\n",
+    "            except: print(\"Could not do one pass in your dataloader, there is something wrong in it\")\n",
     "        return res\n",
     "\n",
     "    def before_iter(self):\n",

nbs/06_data.block.ipynb

@@ -404,6 +404,26 @@
     "    return x"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#export\n",
+    "from fastai2.data.load import _collate_types\n",
+    "\n",
+    "def _find_fail_collate(s):\n",
+    "    s = L(*s)\n",
+    "    for x in s[0]: \n",
+    "        if not isinstance(x, _collate_types): return f\"{type(x).__name__} is not collatable\"\n",
+    "    for i in range_of(s[0]):\n",
+    "        try: _ = default_collate(s.itemgot(i))\n",
+    "        except:\n",
+    "            shapes = [getattr(o[i], 'shape', None) for o in s]\n",
+    "            return f\"Could not collate the {i}-th members of your tuples because got the following shapes\\n{','.join([str(s) for s in shapes])}\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -440,7 +460,9 @@
     "        b = dls.train.create_batch(s)\n",
     "        b = retain_types(b, s[0] if is_listy(s) else s)\n",
     "    except Exception as e:\n",
-    "        print(\"It's not possible to collate your items in a batch, make sure all parts of your samples are tensors of the same size\")\n",
+    "        print(\"Error! It's not possible to collate your items in a batch\")\n",
+    "        why = _find_fail_collate(s)\n",
+    "        print(\"Make sure all parts of your samples are tensors of the same size\" if why is None else why)\n",
     "        raise e\n",
     "    \n",
     "    if len([f for f in dls.train.after_batch.fs if f.name != 'noop'])!=0:\n",