Hey All!
I’m somewhat new to fastai, and so when this error popped up I was unable to really uncover why it’s occurring! I’m trying to run a GAN on some spectrograms of audio signals, so that I can grab the inner layers of the GAN to plot the embedded clustering of images. I’m following GAN tutorial in the documentation, with some adaptations for my own data.
My get_items function returns a list of strings with ‘WAV’ in the path name located in the provided path. My get_x function takes a path, and returns an image representation of the input WAV file.
from fastai.vision.all import *
dest_path = "/home/jupyter/data/"
path = Path("{}egyptian_fruit_bat_annotated_tiny".format(dest_path))
dls = DataBlock(blocks = ImageBlock,
get_items = get_items,
get_x = get_cqt).dataloaders(path, bs = 32)
from fastai.vision.gan import *
generator = basic_generator(15, n_channels = 3, n_extra_layers = 1)
critic = basic_critic(15, n_channels = 3, n_extra_layers = 1,
act_cls=partial(nn.LeakyReLU, negative_slope=0.2))
learner = GANLearner.wgan(dls, generator, critic, switch_eval=False,
opt_func = partial(Adam, betas = (0.,0.99)), wd=0.)
learner.recorder.train_metrics = True
learner.recorder.valid_metrics = True
learner.fit_one_cycle(1, 2e-4, wd = 0.)
Here’s the full traceback:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-25-145922edd5f0> in <module>
3
4 try:
----> 5 learner.fit_one_cycle(1, 2e-4, wd = 0.)
global learner.fit_one_cycle = <bound method Learner.fit_one_cycle of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
global wd = undefined
6 except Exception as exc:
7 tb = IPython.core.ultratb.VerboseTB()
/opt/conda/lib/python3.7/site-packages/fastcore/logargs.py in _f(*args=(<fastai.vision.gan.GANLearner object>, 1, 0.0002), **kwargs={'wd': 0.0})
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
inst = <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>
global to_return = undefined
global f = undefined
args = (<fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>, 1, 0.0002)
kwargs = {'wd': 0.0}
57 return _f
/opt/conda/lib/python3.7/site-packages/fastai/callback/schedule.py in fit_one_cycle(self=<fastai.vision.gan.GANLearner object>, n_epoch=1, lr_max=array([0.0002]), div=25.0, div_final=100000.0, pct_start=0.25, wd=0.0, moms=None, cbs=None, reset_opt=False)
111 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
112 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 113 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
self.fit = <bound method Learner.fit of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
n_epoch = 1
cbs = None
global ParamScheduler = <class 'fastai.callback.schedule.ParamScheduler'>
scheds = {'lr': <function combine_scheds.<locals>._inner at 0x7f079ffefd40>, 'mom': <function combine_scheds.<locals>._inner at 0x7f06fa38ff80>}
global L = <class 'fastcore.foundation.L'>
reset_opt = False
wd = 0.0
114
115 # Cell
/opt/conda/lib/python3.7/site-packages/fastcore/logargs.py in _f(*args=(<fastai.vision.gan.GANLearner object>, 1), **kwargs={'cbs': (#1) [ParamScheduler], 'reset_opt': False, 'wd': 0.0})
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
inst = <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>
global to_return = undefined
global f = undefined
args = (<fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>, 1)
kwargs = {'cbs': (#1) [ParamScheduler], 'reset_opt': False, 'wd': 0.0}
57 return _f
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in fit(self=<fastai.vision.gan.GANLearner object>, n_epoch=1, lr=None, wd=0.0, cbs=(#1) [ParamScheduler], reset_opt=False)
205 self.opt.set_hypers(lr=self.lr if lr is None else lr)
206 self.n_epoch = n_epoch
--> 207 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
self._with_events = <bound method Learner._with_events of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
self._do_fit = <bound method Learner._do_fit of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
global CancelFitException = <class 'fastcore.utils.CancelFitException'>
self._end_cleanup = <bound method Learner._end_cleanup of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
208
209 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self=<fastai.vision.gan.GANLearner object>, f=<bound method Learner._do_fit of <fastai.vision.gan.GANLearner object>>, event_type='fit', ex=<class 'fastcore.utils.CancelFitException'>, final=<bound method Learner._end_cleanup of <fastai.vision.gan.GANLearner object>>)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
self = <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>
f = <bound method Learner._do_fit of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_fit(self=<fastai.vision.gan.GANLearner object>)
195 for epoch in range(self.n_epoch):
196 self.epoch=epoch
--> 197 self._with_events(self._do_epoch, 'epoch', CancelEpochException)
self._with_events = <bound method Learner._with_events of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
self._do_epoch = <bound method Learner._do_epoch of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
global CancelEpochException = <class 'fastcore.utils.CancelEpochException'>
198
199 @log_args(but='cbs')
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self=<fastai.vision.gan.GANLearner object>, f=<bound method Learner._do_epoch of <fastai.vision.gan.GANLearner object>>, event_type='epoch', ex=<class 'fastcore.utils.CancelEpochException'>, final=<function noop>)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
self = <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>
f = <bound method Learner._do_epoch of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_epoch(self=<fastai.vision.gan.GANLearner object>)
189
190 def _do_epoch(self):
--> 191 self._do_epoch_train()
self._do_epoch_train = <bound method Learner._do_epoch_train of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
192 self._do_epoch_validate()
193
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_epoch_train(self=<fastai.vision.gan.GANLearner object>)
181 def _do_epoch_train(self):
182 self.dl = self.dls.train
--> 183 self._with_events(self.all_batches, 'train', CancelTrainException)
self._with_events = <bound method Learner._with_events of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
self.all_batches = <bound method Learner.all_batches of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
global CancelTrainException = <class 'fastcore.utils.CancelTrainException'>
184
185 def _do_epoch_validate(self, ds_idx=1, dl=None):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self=<fastai.vision.gan.GANLearner object>, f=<bound method Learner.all_batches of <fastai.vision.gan.GANLearner object>>, event_type='train', ex=<class 'fastcore.utils.CancelTrainException'>, final=<function noop>)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
self = <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>
f = <bound method Learner.all_batches of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in all_batches(self=<fastai.vision.gan.GANLearner object>)
159 def all_batches(self):
160 self.n_iter = len(self.dl)
--> 161 for o in enumerate(self.dl): self.one_batch(*o)
o = (0, (TensorImage([[[[0.1961, 0.2000, 0.1176, ..., 0.0000, 0.0000, 0.0000],
[0.2392, 0.2078, 0.1490, ..., 0.0000, 0.0000, 0.0000],
[0.2784, 0.2118, 0.1765, ..., 0.0000, 0.0000, 0.0000],
...,
[0.1686, 0.2157, 0.2667, ..., 0.0000, 0.0000, 0.0000],
[0.2431, 0.2235, 0.3059, ..., 0.0000, 0.0000, 0.0000],
[0.2863, 0.2745, 0.3255, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
...,
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.1686, 0.1725, 0.1647],
[0.0000, 0.0000, 0.0000, ..., 0.1294, 0.0863, 0.1608],
[0.0000, 0.0000, 0.0000, ..., 0.1020, 0.0431, 0.1804],
...,
[0.0000, 0.0000, 0.0000, ..., 0.3020, 0.2549, 0.2980],
[0.0000, 0.0000, 0.0000, ..., 0.2902, 0.2784, 0.3176],
[0.0000, 0.0000, 0.0000, ..., 0.2745, 0.3176, 0.3216]]],
[[[0.0157, 0.0039, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0078, 0.0000, 0.0039, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0118, ..., 0.0000, 0.0000, 0.0000],
...,
[0.1098, 0.0902, 0.0980, ..., 0.0000, 0.0000, 0.0000],
[0.1098, 0.0784, 0.0824, ..., 0.0000, 0.0000, 0.0000],
[0.1216, 0.0706, 0.0627, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
...,
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0039],
...,
[0.0000, 0.0000, 0.0000, ..., 0.1294, 0.0941, 0.1020],
[0.0000, 0.0000, 0.0000, ..., 0.1216, 0.0863, 0.1176],
[0.0000, 0.0000, 0.0000, ..., 0.1098, 0.0902, 0.1255]]],
[[[0.0196, 0.1098, 0.0471, ..., 0.0000, 0.0000, 0.0000],
[0.0196, 0.1098, 0.0706, ..., 0.0000, 0.0000, 0.0000],
[0.0157, 0.0863, 0.0902, ..., 0.0000, 0.0000, 0.0000],
...,
[0.2157, 0.2353, 0.2431, ..., 0.0000, 0.0000, 0.0000],
[0.1922, 0.2314, 0.2196, ..., 0.0000, 0.0000, 0.0000],
[0.1686, 0.2275, 0.1922, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
...,
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0745, 0.1176, 0.1137],
[0.0000, 0.0000, 0.0000, ..., 0.0627, 0.0980, 0.0824],
[0.0000, 0.0000, 0.0000, ..., 0.0549, 0.0784, 0.0588],
...,
[0.0000, 0.0000, 0.0000, ..., 0.2157, 0.2039, 0.1882],
[0.0000, 0.0000, 0.0000, ..., 0.2275, 0.2000, 0.2235],
[0.0000, 0.0000, 0.0000, ..., 0.2353, 0.2000, 0.2275]]],
...,
[[[0.0627, 0.0824, 0.1333, ..., 0.0000, 0.0000, 0.0000],
[0.0588, 0.0863, 0.1255, ..., 0.0000, 0.0000, 0.0000],
[0.0510, 0.0980, 0.1098, ..., 0.0000, 0.0000, 0.0000],
...,
[0.1843, 0.2157, 0.2157, ..., 0.0000, 0.0000, 0.0000],
[0.1882, 0.2157, 0.2078, ..., 0.0000, 0.0000, 0.0000],
[0.2000, 0.2157, 0.2000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
...,
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.1176, 0.1490, 0.0863],
[0.0000, 0.0000, 0.0000, ..., 0.1020, 0.1569, 0.0824],
[0.0000, 0.0000, 0.0000, ..., 0.0941, 0.1490, 0.0667],
...,
[0.0000, 0.0000, 0.0000, ..., 0.1843, 0.1686, 0.1922],
[0.0000, 0.0000, 0.0000, ..., 0.1843, 0.1647, 0.1843],
[0.0000, 0.0000, 0.0000, ..., 0.1882, 0.1608, 0.1725]]],
[[[0.0431, 0.0627, 0.0510, ..., 0.0000, 0.0000, 0.0000],
[0.0471, 0.0784, 0.0392, ..., 0.0000, 0.0000, 0.0000],
[0.0392, 0.0941, 0.0392, ..., 0.0000, 0.0000, 0.0000],
...,
[0.1451, 0.1176, 0.1333, ..., 0.0000, 0.0000, 0.0000],
[0.1569, 0.1216, 0.1451, ..., 0.0000, 0.0000, 0.0000],
[0.1647, 0.1137, 0.1373, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
...,
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.1216, 0.0431, 0.0039],
[0.0000, 0.0000, 0.0000, ..., 0.0902, 0.0392, 0.0078],
[0.0000, 0.0000, 0.0000, ..., 0.0549, 0.0392, 0.0157],
...,
[0.0000, 0.0000, 0.0000, ..., 0.1255, 0.1098, 0.0902],
[0.0000, 0.0000, 0.0000, ..., 0.1176, 0.1255, 0.0941],
[0.0000, 0.0000, 0.0000, ..., 0.0941, 0.1294, 0.0941]]],
[[[0.2706, 0.1843, 0.1922, ..., 0.0000, 0.0000, 0.0000],
[0.2549, 0.1922, 0.2353, ..., 0.0000, 0.0000, 0.0000],
[0.2275, 0.2000, 0.2588, ..., 0.0000, 0.0000, 0.0000],
...,
[0.2000, 0.2902, 0.3098, ..., 0.0000, 0.0000, 0.0000],
[0.2157, 0.2118, 0.3059, ..., 0.0000, 0.0000, 0.0000],
[0.2588, 0.1451, 0.2980, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
...,
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000]],
[[0.0000, 0.0000, 0.0000, ..., 0.0941, 0.2118, 0.1608],
[0.0000, 0.0000, 0.0000, ..., 0.0667, 0.2039, 0.1333],
[0.0000, 0.0000, 0.0000, ..., 0.0667, 0.1843, 0.1020],
...,
[0.0000, 0.0000, 0.0000, ..., 0.2667, 0.2549, 0.2902],
[0.0000, 0.0000, 0.0000, ..., 0.2902, 0.2431, 0.2784],
[0.0000, 0.0000, 0.0000, ..., 0.3020, 0.2000, 0.2588]]]]),))
global enumerate = undefined
self.dl = None
self.one_batch = <bound method Learner.one_batch of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
162
163 def _do_one_batch(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in one_batch(self=<fastai.vision.gan.GANLearner object>, i=0, b=(TensorImage([[[[0.1961, 0.2000, 0.1176, ..., 0....0.0000, 0.0000, ..., 0.3020, 0.2000, 0.2588]]]]),))
177 self.iter = i
178 self._split(b)
--> 179 self._with_events(self._do_one_batch, 'batch', CancelBatchException)
self._with_events = <bound method Learner._with_events of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
self._do_one_batch = <bound method Learner._do_one_batch of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
global CancelBatchException = <class 'fastcore.utils.CancelBatchException'>
180
181 def _do_epoch_train(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self=<fastai.vision.gan.GANLearner object>, f=<bound method Learner._do_one_batch of <fastai.vision.gan.GANLearner object>>, event_type='batch', ex=<class 'fastcore.utils.CancelBatchException'>, final=<function noop>)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
self = <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>
f = <bound method Learner._do_one_batch of <fastai.vision.gan.GANLearner object at 0x7f06e8a11b50>>
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_one_batch(self=<fastai.vision.gan.GANLearner object>)
162
163 def _do_one_batch(self):
--> 164 self.pred = self.model(*self.xb)
self.pred = None
self.model = GANModule(
(generator): Sequential(
(0): AddChannels()
(1): ConvLayer(
(0): ConvTranspose2d(100, 128, kernel_size=(4, 4), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): ConvLayer(
(0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): ConvLayer(
(0): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(5): Tanh()
)
(critic): Sequential(
(0): ConvLayer(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2)
)
(1): ConvLayer(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(2): ConvLayer(
(0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(3): Conv2d(128, 1, kernel_size=(4, 4), stride=(1, 1))
(4): Flatten(full=False)
)
)
self.xb = (None,)
165 self('after_pred')
166 if len(self.yb): self.loss = self.loss_func(self.pred, *self.yb)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self=GANModule(
(generator): Sequential(
(0): A...stride=(1, 1))
(4): Flatten(full=False)
)
), *input=(), **kwargs={})
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
result = undefined
self.forward = <bound method GANModule.forward of GANModule(
(generator): Sequential(
(0): AddChannels()
(1): ConvLayer(
(0): ConvTranspose2d(100, 128, kernel_size=(4, 4), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): ConvLayer(
(0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): ConvLayer(
(0): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(5): Tanh()
)
(critic): Sequential(
(0): ConvLayer(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2)
)
(1): ConvLayer(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(2): ConvLayer(
(0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(3): Conv2d(128, 1, kernel_size=(4, 4), stride=(1, 1))
(4): Flatten(full=False)
)
)>
input = ()
kwargs = {}
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/conda/lib/python3.7/site-packages/fastai/vision/gan.py in forward(self=GANModule(
(generator): Sequential(
(0): A...stride=(1, 1))
(4): Flatten(full=False)
)
), *args=())
19
20 def forward(self, *args):
---> 21 return self.generator(*args) if self.gen_mode else self.critic(*args)
self.generator = Sequential(
(0): AddChannels()
(1): ConvLayer(
(0): ConvTranspose2d(100, 128, kernel_size=(4, 4), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): ConvLayer(
(0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): ConvLayer(
(0): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(4): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(5): Tanh()
)
args = ()
self.gen_mode = False
self.critic = Sequential(
(0): ConvLayer(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2)
)
(1): ConvLayer(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(2): ConvLayer(
(0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(3): Conv2d(128, 1, kernel_size=(4, 4), stride=(1, 1))
(4): Flatten(full=False)
)
22
23 def switch(self, gen_mode=None):
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self=Sequential(
(0): ConvLayer(
(0): Conv2d(3,..., 4), stride=(1, 1))
(4): Flatten(full=False)
), *input=(), **kwargs={})
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
result = undefined
self.forward = <bound method Sequential.forward of Sequential(
(0): ConvLayer(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
(1): LeakyReLU(negative_slope=0.2)
)
(1): ConvLayer(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(2): ConvLayer(
(0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): LeakyReLU(negative_slope=0.2)
)
(3): Conv2d(128, 1, kernel_size=(4, 4), stride=(1, 1))
(4): Flatten(full=False)
)>
input = ()
kwargs = {}
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
TypeError: forward() missing 1 required positional argument: 'input'
I suspect that the error is stemming from my dataloaders object, but I just can’t figure out how to fix this!