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Commit 914f2bf

Browse files
authored
Release 0.2.0 (#430)
* new in_channels != 3 initialization * docs fixes * version resolving
1 parent 225823b commit 914f2bf

32 files changed

+233
-366
lines changed

‎.github/workflows/tests.yml

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@@ -29,7 +29,6 @@ jobs:
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python -m pip install codecov pytest mock
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pip3 install torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
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pip install .
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pip install -U git+https://github.com/rwightman/pytorch-image-models
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- name: Test
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run: |
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python -m pytest -s tests

‎README.md

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@@ -12,7 +12,7 @@ The main features of this library are:
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- High level API (just two lines to create a neural network)
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- 9 models architectures for binary and multi class segmentation (including legendary Unet)
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- 115 available encoders
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- 113 available encoders
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- All encoders have pre-trained weights for faster and better convergence
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### [📚 Project Documentation 📚](http://smp.readthedocs.io/)
@@ -297,8 +297,12 @@ The following is a list of supported encoders in the SMP. Select the appropriate
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|Encoder |Weights |Params, M |
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|--------------------------------|:------------------------------:|:------------------------------:|
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|mobilenet_v2 |imagenet |2M |
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|mobilenet_v3_large |imagenet |3M |
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|mobilenet_v3_small |imagenet |1M |
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|timm-mobilenetv3_large_075 |imagenet |1.78M |
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|timm-mobilenetv3_large_100 |imagenet |2.97M |
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|timm-mobilenetv3_large_minimal_100|imagenet |1.41M |
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|timm-mobilenetv3_small_075 |imagenet |0.57M |
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|timm-mobilenetv3_small_100 |imagenet |0.93M |
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|timm-mobilenetv3_small_minimal_100|imagenet |0.43M |
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</div>
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</details>
@@ -337,22 +341,6 @@ The following is a list of supported encoders in the SMP. Select the appropriate
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</div>
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</details>
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<details>
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<summary style="margin-left: 25px;">MobileNetV3</summary>
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<div style="margin-left: 25px;">
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|Encoder |Weights |Params, M |
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|--------------------------------|:------------------------------:|:------------------------------:|
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|timm-mobilenetv3_large_075 |imagenet |1.78M |
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|timm-mobilenetv3_large_100 |imagenet |2.97M |
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|timm-mobilenetv3_large_minimal_100|imagenet |1.41M |
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|timm-mobilenetv3_small_075 |imagenet |0.57M |
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|timm-mobilenetv3_small_100 |imagenet |0.93M |
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|timm-mobilenetv3_small_minimal_100|imagenet |0.43M |
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353-
</div>
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</details>
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\* `ssl`, `swsl` - semi-supervised and weakly-supervised learning on ImageNet ([repo](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models)).
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@@ -367,8 +355,9 @@ The following is a list of supported encoders in the SMP. Select the appropriate
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##### Input channels
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Input channels parameter allows you to create models, which process tensors with arbitrary number of channels.
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If you use pretrained weights from imagenet - weights of first convolution will be reused for
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1- or 2- channels inputs, for input channels > 4 weights of first convolution will be initialized randomly.
358+
If you use pretrained weights from imagenet - weights of first convolution will be reused. For
359+
1-channel case it would be a sum of weights of first convolution layer, otherwise channels would be
360+
populated with weights like `new_weight[:, i] = pretrained_weight[:, i % 3]` and than scaled with `new_weight * 3 / new_in_channels`.
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```python
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model = smp.FPN('resnet34', in_channels=1)
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mask = model(torch.ones([1, 1, 64, 64]))

‎docs/encoders.rst

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@@ -265,15 +265,23 @@ EfficientNet
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MobileNet
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~~~~~~~~~
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+---------------------+------------+-------------+
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| Encoder | Weights | Params, M |
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+=====================+============+=============+
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| mobilenet\_v2 | imagenet | 2M |
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+---------------------+------------+-------------+
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| mobilenet\_v3_large | imagenet | 3M |
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+---------------------+------------+-------------+
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| mobilenet\_v2_small | imagenet | 1M |
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+---------------------+------------+-------------+
268+
+---------------------------------------+------------+-------------+
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| Encoder | Weights | Params, M |
270+
+=======================================+============+=============+
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| mobilenet\_v2 | imagenet | 2M |
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+---------------------------------------+------------+-------------+
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| timm-mobilenetv3\_large\_075 | imagenet | 1.78M |
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+---------------------------------------+------------+-------------+
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| timm-mobilenetv3\_large\_100 | imagenet | 2.97M |
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+---------------------------------------+------------+-------------+
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| timm-mobilenetv3\_large\_minimal\_100 | imagenet | 1.41M |
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+---------------------------------------+------------+-------------+
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| timm-mobilenetv3\_small\_075 | imagenet | 0.57M |
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+---------------------------------------+------------+-------------+
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| timm-mobilenetv3\_small\_100 | imagenet | 0.93M |
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+---------------------------------------+------------+-------------+
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| timm-mobilenetv3\_small\_minimal\_100 | imagenet | 0.43M |
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+---------------------------------------+------------+-------------+
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DPN
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~~~
@@ -316,22 +324,3 @@ VGG
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+-------------+------------+-------------+
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| vgg19\_bn | imagenet | 20M |
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+-------------+------------+-------------+
319-
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MobileNetV3
321-
~~~~~~~~~
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+-----------------------------------+------------+-------------+
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| Encoder | Weights | Params, M |
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+===================================+============+=============+
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| timm-mobilenetv3_large_075 | imagenet | 1.78M |
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+-----------------------------------+------------+-------------+
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| timm-mobilenetv3_large_100 | imagenet | 2.97M |
329-
+-----------------------------------+------------+-------------+
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| timm-mobilenetv3_large_minimal_100| imagenet | 1.41M |
331-
+-----------------------------------+------------+-------------+
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| timm-mobilenetv3_small_075 | imagenet | 0.57M |
333-
+-----------------------------------+------------+-------------+
334-
| timm-mobilenetv3_small_100 | imagenet | 0.93M |
335-
+-----------------------------------+------------+-------------+
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| timm-mobilenetv3_small_minimal_100| imagenet | 0.43M |
337-
+-----------------------------------+------------+-------------+

‎docs/losses.rst

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@@ -17,6 +17,10 @@ DiceLoss
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~~~~~~~~
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.. autoclass:: segmentation_models_pytorch.losses.DiceLoss
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TverskyLoss
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~~~~~~~~
22+
.. autoclass:: segmentation_models_pytorch.losses.TverskyLoss
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FocalLoss
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~~~~~~~~~
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.. autoclass:: segmentation_models_pytorch.losses.FocalLoss

‎requirements.txt

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@@ -1,4 +1,4 @@
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torchvision>=0.9.0
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torchvision>=0.5.0
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pretrainedmodels==0.7.4
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efficientnet-pytorch==0.6.3
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timm==0.4.12
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@@ -1,3 +1,3 @@
1-
VERSION = (0, 1, 3)
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VERSION = (0, 2, 0)
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__version__ = '.'.join(map(str, VERSION))

‎segmentation_models_pytorch/encoders/__init__.py

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@@ -10,20 +10,14 @@
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from .inceptionv4 import inceptionv4_encoders
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from .efficientnet import efficient_net_encoders
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from .mobilenet import mobilenet_encoders
13-
from .mobilenet_v3 import mobilenet_v3_encoders
1413
from .xception import xception_encoders
1514
from .timm_efficientnet import timm_efficientnet_encoders
1615
from .timm_resnest import timm_resnest_encoders
1716
from .timm_res2net import timm_res2net_encoders
1817
from .timm_regnet import timm_regnet_encoders
1918
from .timm_sknet import timm_sknet_encoders
2019
from .timm_mobilenetv3 import timm_mobilenetv3_encoders
21-
try:
22-
from .timm_gernet import timm_gernet_encoders
23-
except ImportError as e:
24-
timm_gernet_encoders = {}
25-
print("Current timm version doesn't support GERNet."
26-
"If GERNet support is needed please update timm")
20+
from .timm_gernet import timm_gernet_encoders
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2822
from ._preprocessing import preprocess_input
2923

@@ -37,7 +31,6 @@
3731
encoders.update(inceptionv4_encoders)
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encoders.update(efficient_net_encoders)
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encoders.update(mobilenet_encoders)
40-
encoders.update(mobilenet_v3_encoders)
4134
encoders.update(xception_encoders)
4235
encoders.update(timm_efficientnet_encoders)
4336
encoders.update(timm_resnest_encoders)
@@ -68,7 +61,7 @@ def get_encoder(name, in_channels=3, depth=5, weights=None):
6861
))
6962
encoder.load_state_dict(model_zoo.load_url(settings["url"]))
7063

71-
encoder.set_in_channels(in_channels)
64+
encoder.set_in_channels(in_channels, pretrained=weightsisnotNone)
7265

7366
return encoder
7467

‎segmentation_models_pytorch/encoders/_base.py

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@@ -17,7 +17,7 @@ def out_channels(self):
1717
"""Return channels dimensions for each tensor of forward output of encoder"""
1818
return self._out_channels[: self._depth + 1]
1919

20-
def set_in_channels(self, in_channels):
20+
def set_in_channels(self, in_channels, pretrained=True):
2121
"""Change first convolution channels"""
2222
if in_channels == 3:
2323
return
@@ -26,7 +26,7 @@ def set_in_channels(self, in_channels):
2626
if self._out_channels[0] == 3:
2727
self._out_channels = tuple([in_channels] + list(self._out_channels)[1:])
2828

29-
utils.patch_first_conv(model=self, in_channels=in_channels)
29+
utils.patch_first_conv(model=self, new_in_channels=in_channels, pretrained=pretrained)
3030

3131
def get_stages(self):
3232
"""Method should be overridden in encoder"""

‎segmentation_models_pytorch/encoders/_utils.py

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@@ -2,7 +2,7 @@
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import torch.nn as nn
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44

5-
def patch_first_conv(model, in_channels):
5+
def patch_first_conv(model, new_in_channels, default_in_channels=3, pretrained=True):
66
"""Change first convolution layer input channels.
77
In case:
88
in_channels == 1 or in_channels == 2 -> reuse original weights
@@ -11,29 +11,38 @@ def patch_first_conv(model, in_channels):
1111

1212
# get first conv
1313
for module in model.modules():
14-
if isinstance(module, nn.Conv2d):
14+
if isinstance(module, nn.Conv2d)andmodule.in_channels==default_in_channels:
1515
break
16-
17-
# change input channels for first conv
18-
module.in_channels = in_channels
16+
1917
weight = module.weight.detach()
20-
reset = False
21-
22-
if in_channels == 1:
23-
weight = weight.sum(1, keepdim=True)
24-
elif in_channels == 2:
25-
weight = weight[:, :2] * (3.0 / 2.0)
18+
module.in_channels = new_in_channels
19+
20+
if not pretrained:
21+
module.weight = nn.parameter.Parameter(
22+
torch.Tensor(
23+
module.out_channels,
24+
new_in_channels // module.groups,
25+
*module.kernel_size
26+
)
27+
)
28+
module.reset_parameters()
29+
30+
elif new_in_channels == 1:
31+
new_weight = weight.sum(1, keepdim=True)
32+
module.weight = nn.parameter.Parameter(new_weight)
33+
2634
else:
27-
reset = True
28-
weight = torch.Tensor(
35+
new_weight = torch.Tensor(
2936
module.out_channels,
30-
module.in_channels // module.groups,
37+
new_in_channels // module.groups,
3138
*module.kernel_size
3239
)
3340

34-
module.weight = nn.parameter.Parameter(weight)
35-
if reset:
36-
module.reset_parameters()
41+
for i in range(new_in_channels):
42+
new_weight[:, i] = weight[:, i % default_in_channels]
43+
44+
new_weight = new_weight * (default_in_channels / new_in_channels)
45+
module.weight = nn.parameter.Parameter(new_weight)
3746

3847

3948
def replace_strides_with_dilation(module, dilation_rate):

‎segmentation_models_pytorch/encoders/densenet.py

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@@ -96,8 +96,8 @@ def load_state_dict(self, state_dict):
9696
del state_dict[key]
9797

9898
# remove linear
99-
state_dict.pop("classifier.bias")
100-
state_dict.pop("classifier.weight")
99+
state_dict.pop("classifier.bias", None)
100+
state_dict.pop("classifier.weight", None)
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102102
super().load_state_dict(state_dict)
103103

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