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Thanks for your repo, I tried to use your implementation but I keep getting this error when trying to train the model using learn.fit_one_cycle(20, max_lr=1e-2, callbacks=stage1_callbacks).
this is the trace of the error:
RuntimeError Traceback (most recent call last)
in
1 learn.fit_one_cycle(20, max_lr=1e-2,
----> 2 callbacks=stage1_callbacks
3 )
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
197 callbacks = [cb(self) for cb in self.callback_fns + listify(defaults.extra_callback_fns)] + listify(callbacks)
198 if defaults.extra_callbacks is not None: callbacks += defaults.extra_callbacks
--> 199 fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks)
200
201 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/basic_train.py in fit(epochs, learn, callbacks, metrics)
99 for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
100 xb, yb = cb_handler.on_batch_begin(xb, yb)
--> 101 loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler)
102 if cb_handler.on_batch_end(loss): break
103
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
24 if not is_listy(xb): xb = [xb]
25 if not is_listy(yb): yb = [yb]
---> 26 out = model(*xb)
27 out = cb_handler.on_loss_begin(out)
28
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
//res2fg_n.py in forward(self, x)
287
288 #res2 block layers
--> 289 x = self.layer1(x)
290 x = self.layer2(x)
291 x = self.layer3(x)
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
90 def forward(self, input):
91 for module in self._modules.values():
---> 92 input = module(input)
93 return input
94
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
/res2fg_n.py in forward(self, x)
145 if self.scale > 1:
146 if self.first_block:
--> 147 out = torch.cat((out, self.pool(xs[len(self.convs)])), 1)
148 else:
149 out = torch.cat((out, xs[len(self.convs)]), 1)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 32 and 30 in dimension 2 at /pytorch/aten/src/THC/generic/THCTensorMath.cu:71
Thanks
The text was updated successfully, but these errors were encountered:
Thanks for your repo, I tried to use your implementation but I keep getting this error when trying to train the model using learn.fit_one_cycle(20, max_lr=1e-2, callbacks=stage1_callbacks).
this is the trace of the error:
RuntimeError Traceback (most recent call last)
in
1 learn.fit_one_cycle(20, max_lr=1e-2,
----> 2 callbacks=stage1_callbacks
3 )
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/train.py in fit_one_cycle(learn, cyc_len, max_lr, moms, div_factor, pct_start, final_div, wd, callbacks, tot_epochs, start_epoch)
20 callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor, pct_start=pct_start,
21 final_div=final_div, tot_epochs=tot_epochs, start_epoch=start_epoch))
---> 22 learn.fit(cyc_len, max_lr, wd=wd, callbacks=callbacks)
23
24 def lr_find(learn:Learner, start_lr:Floats=1e-7, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, wd:float=None):
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
197 callbacks = [cb(self) for cb in self.callback_fns + listify(defaults.extra_callback_fns)] + listify(callbacks)
198 if defaults.extra_callbacks is not None: callbacks += defaults.extra_callbacks
--> 199 fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks)
200
201 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/basic_train.py in fit(epochs, learn, callbacks, metrics)
99 for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
100 xb, yb = cb_handler.on_batch_begin(xb, yb)
--> 101 loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler)
102 if cb_handler.on_batch_end(loss): break
103
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
24 if not is_listy(xb): xb = [xb]
25 if not is_listy(yb): yb = [yb]
---> 26 out = model(*xb)
27 out = cb_handler.on_loss_begin(out)
28
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
//res2fg_n.py in forward(self, x)
287
288 #res2 block layers
--> 289 x = self.layer1(x)
290 x = self.layer2(x)
291 x = self.layer3(x)
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
90 def forward(self, input):
91 for module in self._modules.values():
---> 92 input = module(input)
93 return input
94
~/anaconda3/envs/fastai_v1/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491 result = self._slow_forward(*input, **kwargs)
492 else:
--> 493 result = self.forward(*input, **kwargs)
494 for hook in self._forward_hooks.values():
495 hook_result = hook(self, input, result)
/res2fg_n.py in forward(self, x)
145 if self.scale > 1:
146 if self.first_block:
--> 147 out = torch.cat((out, self.pool(xs[len(self.convs)])), 1)
148 else:
149 out = torch.cat((out, xs[len(self.convs)]), 1)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 32 and 30 in dimension 2 at /pytorch/aten/src/THC/generic/THCTensorMath.cu:71
Thanks
The text was updated successfully, but these errors were encountered: