Resolve conflicts.

.gitignore

@@ -1,3 +1,6 @@
+# Custom 
+tmp
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -51,6 +51,28 @@ Mostly, we can save 4 times GPU memories by reducing the stride from the first b
 
 Below is the README from the original repo:
 
+_IMPORTANT NOTE_: In the latest update, I switched hosting providers for the pretrained models, as the previous models were becoming extremely expensive to host. This _will_ break old versions of the library. I apologize, but I cannot afford to keep serving the models on the old provider. Everything should work properly if you update the library: 
+```
+pip install --upgrade efficientnet-pytorch
+```
+
+### Update (January 23, 2020)
+
+This update adds a new category of pre-trained model based on adversarial training, called _advprop_. It is important to note that the preprocessing required for the advprop pretrained models is slightly different from normal ImageNet preprocessing. As a result, by default, advprop models are not used. To load a model with advprop, use:
+```
+model = EfficientNet.from_pretrained("efficientnet-b0", advprop=True)
+```
+There is also a new, large `efficientnet-b8` pretrained model that is only available in advprop form. When using these models, replace ImageNet preprocessing code as follows:
+```
+if advprop:  # for models using advprop pretrained weights
+    normalize = transforms.Lambda(lambda img: img * 2.0 - 1.0)
+else:
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                                     std=[0.229, 0.224, 0.225])
+
+```
+This update also addresses multiple other issues ([#115](https://github.com/lukemelas/EfficientNet-PyTorch/issues/115), [#128](https://github.com/lukemelas/EfficientNet-PyTorch/issues/128)). 
+
 ### Update (October 15, 2019)
 
 This update allows you to choose whether to use a memory-efficient Swish activation. The memory-efficient version is chosen by default, but it cannot be used when exporting using PyTorch JIT. For this purpose, we have also included a standard (export-friendly) swish activation function. To switch to the export-friendly version, simply call `model.set_swish(memory_efficient=False)` after loading your desired model. This update addresses issues [#88](https://github.com/lukemelas/EfficientNet-PyTorch/pull/88) and [#89](https://github.com/lukemelas/EfficientNet-PyTorch/pull/89).

efficientnet_pytorch_3d/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.5.1"
+__version__ = "0.6.3"
 from .model import EfficientNet3D
 from .utils import (
     GlobalParams,

efficientnet_pytorch_3d/model.py

@@ -9,7 +9,6 @@
     get_same_padding_conv3d,
     get_model_params,
     efficientnet_params,
-    load_pretrained_weights,
     Swish,
     MemoryEfficientSwish,
 )
@@ -211,10 +210,8 @@ def get_image_size(cls, model_name):
         return res
 
     @classmethod
-    def _check_model_name_is_valid(cls, model_name, also_need_pretrained_weights=False):
-        """ Validates model name. None that pretrained weights are only available for
-        the first four models (efficientnet-b{i} for i in 0,1,2,3) at the moment. """
-        num_models = 4 if also_need_pretrained_weights else 8
-        valid_models = ['efficientnet-b'+str(i) for i in range(num_models)]
+    def _check_model_name_is_valid(cls, model_name):
+        """ Validates model name. """ 
+        valid_models = ['efficientnet-b'+str(i) for i in range(9)]
         if model_name not in valid_models:
             raise ValueError('model_name should be one of: ' + ', '.join(valid_models))

efficientnet_pytorch_3d/utils.py

@@ -172,6 +172,8 @@ def efficientnet_params(model_name):
         'efficientnet-b5': (1.6, 2.2, 456, 0.4),
         'efficientnet-b6': (1.8, 2.6, 528, 0.5),
         'efficientnet-b7': (2.0, 3.1, 600, 0.5),
+        'efficientnet-b8': (2.2, 3.6, 672, 0.5),
+        'efficientnet-l2': (4.3, 5.3, 800, 0.5),
     }
     return params_dict[model_name]
 
@@ -293,28 +295,3 @@ def get_model_params(model_name, override_params):
         # ValueError will be raised here if override_params has fields not included in global_params.
         global_params = global_params._replace(**override_params)
     return blocks_args, global_params
-
-
-url_map = {
-    'efficientnet-b0': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b0-355c32eb.pth',
-    'efficientnet-b1': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b1-f1951068.pth',
-    'efficientnet-b2': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b2-8bb594d6.pth',
-    'efficientnet-b3': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b3-5fb5a3c3.pth',
-    'efficientnet-b4': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b4-6ed6700e.pth',
-    'efficientnet-b5': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b5-b6417697.pth',
-    'efficientnet-b6': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b6-c76e70fd.pth',
-    'efficientnet-b7': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b7-dcc49843.pth',
-}
-
-
-def load_pretrained_weights(model, model_name, load_fc=True):
-    """ Loads pretrained weights, and downloads if loading for the first time. """
-    state_dict = model_zoo.load_url(url_map[model_name])
-    if load_fc:
-        model.load_state_dict(state_dict)
-    else:
-        state_dict.pop('_fc.weight')
-        state_dict.pop('_fc.bias')
-        res = model.load_state_dict(state_dict, strict=False)
-        assert set(res.missing_keys) == set(['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'
-    print('Loaded pretrained weights for {}'.format(model_name))

example.py

@@ -2,13 +2,15 @@
 import torch
 from torchsummary import summary
 
+device = 'cpu'
+
 model = EfficientNet3D.from_name("efficientnet-b0", override_params={'num_classes': 2}, in_channels=1)
 
-summary(model, input_size=(1, 200, 1024, 200))
+summary(model, input_size=(1, 224, 224, 224))
 
-model = model.to("cuda:3")
-inputs = torch.randn((1, 1, 200, 1024, 200)).to("cuda:3")
-labels = torch.tensor([0]).to("cuda:3")
+model = model.to(device)
+inputs = torch.randn((1, 1, 224, 224, 224)).to(device)
+labels = torch.tensor([0]).to(device)
 # test forward
 num_classes = 2