Update: I could make it work. My next post will describe how.
=================================================
I am trying to import models from the Cadene Pytorch model libraries.
I am trying for 2 days to make create_cnn working, but seems only learn = Learner(data, model) works. create_cnn is much better, but there is something I am missing. I understand that create_cnn expects a callable function and tried the following examples.
Here are working examples:
# this works
def get_model(pretrained=True, model_name = 'resnet50', **kwargs ): # pretrained key should be the first
arch = models.resnet50(pretrained, **kwargs )
return arch
learn = Learner(data, get_model(), metrics=[accuracy])
# this works
def get_model(pretrained=True, model_name = 'resnet50', **kwargs ): # pretrained key should be the first
arch = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
return arch
learn = Learner(data, get_model(), metrics=[accuracy])
Here are not working examples:
# this does not work
def get_model(pretrained=True, model_name = 'resnet50', **kwargs ): # pretrained key should be the first
arch = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
return arch
learn = Learner(data, get_model, metrics=[accuracy])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-45-814e6d5c8795> in <module>
4 return arch
5
----> 6 learn = Learner(data, get_model, metrics=[accuracy])
<string> in __init__(self, data, model, opt_func, loss_func, metrics, true_wd, bn_wd, wd, train_bn, path, model_dir, callback_fns, callbacks, layer_groups)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/basic_train.py in __post_init__(self)
145 self.path = Path(ifnone(self.path, self.data.path))
146 (self.path/self.model_dir).mkdir(parents=True, exist_ok=True)
--> 147 self.model = self.model.to(self.data.device)
148 self.loss_func = ifnone(self.loss_func, self.data.loss_func)
149 self.metrics=listify(self.metrics)
AttributeError: 'function' object has no attribute 'to'
# this does not work
def get_model(pretrained=True, model_name = 'resnet50', **kwargs ): # pretrained key should be the first
arch = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
return arch
learn = create_cnn(data, get_model(), metrics=[accuracy])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-74cb1e5cde9c> in <module>
4 return arch
5
----> 6 learn = create_cnn(data, get_model(), metrics=[accuracy])
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/learner.py in create_cnn(data, arch, cut, pretrained, lin_ftrs, ps, custom_head, split_on, bn_final, **kwargs)
53 "Build convnet style learners."
54 meta = cnn_config(arch)
---> 55 body = create_body(arch, pretrained, cut)
56 nf = num_features_model(body) * 2
57 head = custom_head or create_head(nf, data.c, lin_ftrs, ps=ps, bn_final=bn_final)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/learner.py in create_body(arch, pretrained, cut, body_fn)
30 def create_body(arch:Callable, pretrained:bool=True, cut:Optional[int]=None, body_fn:Callable[[nn.Module],nn.Module]=None):
31 "Cut off the body of a typically pretrained `model` at `cut` or as specified by `body_fn`."
---> 32 model = arch(pretrained)
33 if not cut and not body_fn: cut = cnn_config(arch)['cut']
34 return (nn.Sequential(*list(model.children())[:cut]) if cut
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/pretrainedmodels/models/torchvision_models.py in forward(self, input)
338
339 def forward(self, input):
--> 340 x = self.features(input)
341 x = self.logits(x)
342 return x
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/pretrainedmodels/models/torchvision_models.py in features(self, input)
320
321 def features(self, input):
--> 322 x = self.conv1(input)
323 x = self.bn1(x)
324 x = self.relu(x)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
318 def forward(self, input):
319 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 320 self.padding, self.dilation, self.groups)
321
322
TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not bool
# this does not work
def get_model(pretrained=True, model_name = 'resnet50', **kwargs ): # pretrained key should be the first
arch = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
return arch
learn = create_cnn(data, get_model, metrics=[accuracy])
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-44-86a48fcf2401> in <module>
4 return arch
5
----> 6 learn = create_cnn(data, get_model, metrics=[accuracy])
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/learner.py in create_cnn(data, arch, cut, pretrained, lin_ftrs, ps, custom_head, split_on, bn_final, **kwargs)
54 meta = cnn_config(arch)
55 body = create_body(arch, pretrained, cut)
---> 56 nf = num_features_model(body) * 2
57 head = custom_head or create_head(nf, data.c, lin_ftrs, ps=ps, bn_final=bn_final)
58 model = nn.Sequential(body, head)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/callbacks/hooks.py in num_features_model(m)
115 def num_features_model(m:nn.Module)->int:
116 "Return the number of output features for `model`."
--> 117 return model_sizes(m)[-1][1]
118
119 def total_params(m:nn.Module)->int:
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/callbacks/hooks.py in model_sizes(m, size)
110 "Pass a dummy input through the model `m` to get the various sizes of activations."
111 with hook_outputs(m) as hooks:
--> 112 x = dummy_eval(m, size)
113 return [o.stored.shape for o in hooks]
114
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/callbacks/hooks.py in dummy_eval(m, size)
105 def dummy_eval(m:nn.Module, size:tuple=(64,64)):
106 "Pass a `dummy_batch` in evaluation mode in `m` with `size`."
--> 107 return m.eval()(dummy_batch(m, size))
108
109 def model_sizes(m:nn.Module, size:tuple=(64,64))->Tuple[Sizes,Tensor,Hooks]:
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai-v1/lib/python3.7/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/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/torch/nn/modules/pooling.py in forward(self, input)
563 def forward(self, input):
564 return F.avg_pool2d(input, self.kernel_size, self.stride,
--> 565 self.padding, self.ceil_mode, self.count_include_pad)
566
567
RuntimeError: Given input size: (2048x2x2). Calculated output size: (2048x-4x-4). Output size is too small at /opt/conda/conda-bld/pytorch_1544202130060/work/aten/src/THNN/generic/SpatialAveragePooling.c:48
A working example shared here to import any Cadene model is very helpful. It will expand the usable models in fastai for everybody. I heard in Kaggle competitions that there are people could make it work with fastai v1, but could not do it myself. Here is an 8th Kaggle winner in Human Protein Atlas comp. that just did that!