Although FastAi is a great tool, everytime someone commits the code to github, it is getting broken easily.
I am not able to run the IMDB code nowadays and I am having the following NotImplementedError: 
Can you please help me?
Thank you
I would be happy if you let me know whether anyone able to run it nowadays or not?
Thank you
Could you post the method which isn’t implemented? On the plus side, imdb in dl2 does pretty much the same thing, so give that a go!
1 Like
Thank you so much for your valuable information.
I am so happy that someone replied me 
I am going to try IMDB in dl2,
On the other hand, I am receiving the below error for the lesson4:
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-24-357a8890c905> in <module>()
----> 1 learner.fit(3e-3, 4, wds=1e-6, cycle_len=1, cycle_mult=2)
~~fastai/courses/dl1/fastai/learner.py in fit(self, lrs, n_cycle, wds, **kwargs)
285 self.sched = None
286 layer_opt = self.get_layer_opt(lrs, wds)
--> 287 return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **kwargs)
288
289 def warm_up(self, lr, wds=None):
~~fastai/courses/dl1/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)
232 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
233 swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
--> 234 swa_eval_freq=swa_eval_freq, **kwargs)
235
236 def get_layer_groups(self): return self.models.get_layer_groups()
~~fastai/courses/dl1/fastai/model.py in fit(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)
127 batch_num += 1
128 for cb in callbacks: cb.on_batch_begin()
--> 129 loss = model_stepper.step(V(x),V(y), epoch)
130 avg_loss = avg_loss * avg_mom + loss * (1-avg_mom)
131 debias_loss = avg_loss / (1 - avg_mom**batch_num)
~~fastai/courses/dl1/fastai/model.py in step(self, xs, y, epoch)
46 def step(self, xs, y, epoch):
47 xtra = []
---> 48 output = self.m(*xs)
49 if isinstance(output,tuple): output,*xtra = output
50 if self.fp16: self.m.zero_grad()
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
89 def forward(self, input):
90 for module in self._modules.values():
---> 91 input = module(input)
92 return input
93
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~~fastai/courses/dl1/fastai/lm_rnn.py in forward(self, input)
84 self.reset()
85
---> 86 emb = self.encoder_with_dropout(input, dropout=self.dropoute if self.training else 0)
87 emb = self.dropouti(emb)
88
~~fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~~fastai/courses/dl1/fastai/rnn_reg.py in forward(self, words, dropout, scale)
174 if padding_idx is None: padding_idx = -1
175
--> 176 X = self.embed._backend.Embedding.apply(words,
177 masked_embed_weight, padding_idx, self.embed.max_norm,
178 self.embed.norm_type, self.embed.scale_grad_by_freq, self.embed.sparse)
~~fastai/venv/lib/python3.6/site-packages/torch/nn/backends/backend.py in __getattr__(self, name)
8 fn = self.function_classes.get(name)
9 if fn is None:
---> 10 raise NotImplementedError
11 return fn
12
NotImplementedError:This is me thinking aloud -
Try replacing this line with:
X = self.embed._backend.Embedding.apply(words,masked_embed_weight, padding_idx, self.embed.max_norm, self.embed.norm_type, self.embed.scale_grad_by_freq, self.embed.sparse)
with
v =nn.Embedding(self.embed.num_embeddings,
self.embed.embedding_dim, padding_idx, self.embed.max_norm,
self.embed.norm_type, self.embed.scale_grad_by_freq, self.embed.sparse)
v.weight = nn.Parameter(masked_embed_weight.data)
X = v(words)
EDIT: This should cover all eventualities
Thank you but this time I am having the following error:
Epoch
0% 0/1 [00:00<?, ?it/s]
0%| | 0/6873 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-65-b544778ca021> in <module>()
----> 1 learner.fit(lrs/2, 1, wds=wd, use_clr=(32,2), cycle_len=1)
~/fastai/courses/dl2/fastai/learner.py in fit(self, lrs, n_cycle, wds, **kwargs)
285 self.sched = None
286 layer_opt = self.get_layer_opt(lrs, wds)
--> 287 return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **kwargs)
288
289 def warm_up(self, lr, wds=None):
~/fastai/courses/dl2/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)
232 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16,
233 swa_model=self.swa_model if use_swa else None, swa_start=swa_start,
--> 234 swa_eval_freq=swa_eval_freq, **kwargs)
235
236 def get_layer_groups(self): return self.models.get_layer_groups()
~/fastai/courses/dl2/fastai/model.py in fit(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, **kwargs)
127 batch_num += 1
128 for cb in callbacks: cb.on_batch_begin()
--> 129 loss = model_stepper.step(V(x),V(y), epoch)
130 avg_loss = avg_loss * avg_mom + loss * (1-avg_mom)
131 debias_loss = avg_loss / (1 - avg_mom**batch_num)
~/fastai/courses/dl2/fastai/model.py in step(self, xs, y, epoch)
46 def step(self, xs, y, epoch):
47 xtra = []
---> 48 output = self.m(*xs)
49 if isinstance(output,tuple): output,*xtra = output
50 if self.fp16: self.m.zero_grad()
~/fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~/fastai/venv/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
89 def forward(self, input):
90 for module in self._modules.values():
---> 91 input = module(input)
92 return input
93
~/fastai/venv/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
489 result = self._slow_forward(*input, **kwargs)
490 else:
--> 491 result = self.forward(*input, **kwargs)
492 for hook in self._forward_hooks.values():
493 hook_result = hook(self, input, result)
~/fastai/courses/dl2/fastai/lm_rnn.py in forward(self, input)
99 outputs.append(raw_output)
100
--> 101 self.hidden = repackage_var(new_hidden)
102 return raw_outputs, outputs
103
~/fastai/courses/dl2/fastai/lm_rnn.py in repackage_var(h)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in <genexpr>(.0)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in repackage_var(h)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in <genexpr>(.0)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in repackage_var(h)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in <genexpr>(.0)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in repackage_var(h)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in <genexpr>(.0)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in repackage_var(h)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in <genexpr>(.0)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/courses/dl2/fastai/lm_rnn.py in repackage_var(h)
18 def repackage_var(h):
19 """Wraps h in new Variables, to detach them from their history."""
---> 20 return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
21
22
~/fastai/venv/lib/python3.6/site-packages/torch/tensor.py in __iter__(self)
358 # map will interleave them.)
359 if self.dim() == 0:
--> 360 raise TypeError('iteration over a 0-d tensor')
361 return iter(imap(lambda i: self[i], range(self.size(0))))
362
TypeError: iteration over a 0-d tensorI also tried this and got the same. This is from imdb in dl2:
TypeError: iteration over a 0-d tensor
Both the issues above are due to changes in Pytorch 0.4. You shouldn’t upgrade to 0.4 yet - it’s not supported by fastai. If you stick with the environment provided in the fastai github repo it will automatically ensure you have the correct version.
2 Likes
@jeremy I seem to have Pytorch 0.4 already in the env. I am facing dataloader errors when running IMDB notebook. Am I supposed to downgrade it?
Error:
--------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-40-99852b8a0098> in <module>()
1 m3.freeze_to(-1)
----> 2 m3.fit(lrs/2,1, metrics = [accuracy])
3 #m3.unfreeze()
4 #m3.fit(lrs, 1, metrics = [accuracy], cycle_len = 1)
~/fastai/courses/dl1/fastai/learner.py in fit(self, lrs, n_cycle, wds, **kwargs)
213 self.sched = None
214 layer_opt = self.get_layer_opt(lrs, wds)
--> 215 return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **kwargs)
216
217 def warm_up(self, lr, wds=None):
~/fastai/courses/dl1/fastai/learner.py in fit_gen(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, **kwargs)
160 n_epoch = sum_geom(cycle_len if cycle_len else 1, cycle_mult, n_cycle)
161 return fit(model, data, n_epoch, layer_opt.opt, self.crit,
--> 162 metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, **kwargs)
163
164 def get_layer_groups(self): return self.models.get_layer_groups()
~/fastai/courses/dl1/fastai/model.py in fit(model, data, epochs, opt, crit, metrics, callbacks, stepper, **kwargs)
91 t = tqdm(iter(data.trn_dl), leave=False, total=num_batch)
92 i = 0
---> 93 for (*x,y) in t:
94 batch_num += 1
95 for cb in callbacks: cb.on_batch_begin()
~/anaconda3/envs/fastai/lib/python3.6/site-packages/tqdm/_tqdm.py in __iter__(self)
953 """, fp_write=getattr(self.fp, 'write', sys.stderr.write))
954
--> 955 for obj in iterable:
956 yield obj
957 # Update and possibly print the progressbar.
~/fastai/courses/dl1/fastai/dataloader.py in __iter__(self)
77 else:
78 with ThreadPoolExecutor(max_workers=self.num_workers) as e:
---> 79 for batch in e.map(self.get_batch, iter(self.batch_sampler)):
80 yield get_tensor(batch, self.pin_memory)
81
~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
--> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.time())
~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
423 raise CancelledError()
424 elif self._state == FINISHED:
--> 425 return self.__get_result()
426
427 self._condition.wait(timeout)
~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/thread.py in run(self)
54
55 try:
---> 56 result = self.fn(*self.args, **self.kwargs)
57 except BaseException as exc:
58 self.future.set_exception(exc)
~/fastai/courses/dl1/fastai/dataloader.py in get_batch(self, indices)
66
67 def get_batch(self, indices):
---> 68 res = self.collate_fn([self.dataset[i] for i in indices], self.pad_idx)
69 if not self.transpose: return res
70 res[0] = res[0].T
~/fastai/courses/dl1/fastai/dataloader.py in <listcomp>(.0)
66
67 def get_batch(self, indices):
---> 68 res = self.collate_fn([self.dataset[i] for i in indices], self.pad_idx)
69 if not self.transpose: return res
70 res[0] = res[0].T
TypeError: 'TextDataLoader' object does not support indexing