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Merged
merged 6 commits into from
Dec 15, 2022
Merged

timm upgrade 0.6.12 #696

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1d3564b
config: timm version bump 0.4.12 -> 0.6.12
VadimLevin Dec 13, 2022
aca487d
fix: timm based EfficientNetBaseEncoder after timm upgrade to 0.6.12
VadimLevin Dec 13, 2022
9549ca9
fix: use "public" API for timm models availability check
VadimLevin Dec 13, 2022
c4f02e2
fix: timm SkNetEncoder after timm upgrade to 0.6.12
VadimLevin Dec 13, 2022
5e15774
fix: timm RegNet encoder after timm upgrade to 0.6.12
VadimLevin Dec 13, 2022
a545e6f
feat: bump Python version used in Github Actions to 3.7
VadimLevin Dec 14, 2022
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2 changes: 1 addition & 1 deletion .github/workflows/pypi.yml
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Expand Up @@ -12,7 +12,7 @@ jobs:
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: '3.6'
python-version: '3.7'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
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2 changes: 1 addition & 1 deletion .github/workflows/tests.yml
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Expand Up @@ -18,7 +18,7 @@ jobs:
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: 3.6
python-version: 3.7
- name: Install dependencies
run: |
python -m pip install --upgrade pip
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4 changes: 2 additions & 2 deletions pyproject.toml
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@@ -1,6 +1,6 @@
[tool.black]
line-length = 119
target-version = ['py36', 'py37', 'py38']
target-version = ['py37', 'py38']
include = '\.pyi?$'
exclude = '''
/(
Expand All @@ -16,4 +16,4 @@ exclude = '''
| build
| dist
)/
'''
'''
2 changes: 1 addition & 1 deletion requirements.txt
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@@ -1,7 +1,7 @@
torchvision>=0.5.0
pretrainedmodels==0.7.4
efficientnet-pytorch==0.7.1
timm==0.4.12
timm==0.6.12

tqdm
pillow
4 changes: 2 additions & 2 deletions segmentation_models_pytorch/encoders/__init__.py
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Expand Up @@ -97,9 +97,9 @@ def get_preprocessing_params(encoder_name, pretrained="imagenet"):

if encoder_name.startswith("tu-"):
encoder_name = encoder_name[3:]
if encoder_name not in timm.models.registry._model_has_pretrained:
if not timm.models.is_model_pretrained(encoder_name):
raise ValueError(f"{encoder_name} does not have pretrained weights and preprocessing parameters")
settings = timm.models.registry._model_default_cfgs[encoder_name]
settings = timm.models.get_pretrained_cfg(encoder_name)
else:
all_settings = encoders[encoder_name]["pretrained_settings"]
if pretrained not in all_settings.keys():
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2 changes: 1 addition & 1 deletion segmentation_models_pytorch/encoders/timm_efficientnet.py
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Expand Up @@ -105,7 +105,7 @@ def __init__(self, stage_idxs, out_channels, depth=5, **kwargs):
def get_stages(self):
return [
nn.Identity(),
nn.Sequential(self.conv_stem, self.bn1, self.act1),
nn.Sequential(self.conv_stem, self.bn1),
self.blocks[: self._stage_idxs[0]],
self.blocks[self._stage_idxs[0] : self._stage_idxs[1]],
self.blocks[self._stage_idxs[1] : self._stage_idxs[2]],
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51 changes: 26 additions & 25 deletions segmentation_models_pytorch/encoders/timm_regnet.py
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@@ -1,10 +1,11 @@
from ._base import EncoderMixin
from timm.models.regnet import RegNet
from timm.models.regnet import RegNet, RegNetCfg
import torch.nn as nn


class RegNetEncoder(RegNet, EncoderMixin):
def __init__(self, out_channels, depth=5, **kwargs):
kwargs["cfg"] = RegNetCfg(**kwargs["cfg"])
super().__init__(**kwargs)
self._depth = depth
self._out_channels = out_channels
Expand Down Expand Up @@ -141,95 +142,95 @@ def _mcfg(**kwargs):
"pretrained_settings": pretrained_settings["timm-regnetx_002"],
"params": {
"out_channels": (3, 32, 24, 56, 152, 368),
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13),
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13),
},
},
"timm-regnetx_004": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_004"],
"params": {
"out_channels": (3, 32, 32, 64, 160, 384),
"cfg": _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22),
"cfg": _mcfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22),
},
},
"timm-regnetx_006": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_006"],
"params": {
"out_channels": (3, 32, 48, 96, 240, 528),
"cfg": _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16),
"cfg": _mcfg(w0=48, wa=36.97, wm=2.24, group_size=24, depth=16),
},
},
"timm-regnetx_008": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_008"],
"params": {
"out_channels": (3, 32, 64, 128, 288, 672),
"cfg": _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16),
"cfg": _mcfg(w0=56, wa=35.73, wm=2.28, group_size=16, depth=16),
},
},
"timm-regnetx_016": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_016"],
"params": {
"out_channels": (3, 32, 72, 168, 408, 912),
"cfg": _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18),
"cfg": _mcfg(w0=80, wa=34.01, wm=2.25, group_size=24, depth=18),
},
},
"timm-regnetx_032": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_032"],
"params": {
"out_channels": (3, 32, 96, 192, 432, 1008),
"cfg": _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25),
"cfg": _mcfg(w0=88, wa=26.31, wm=2.25, group_size=48, depth=25),
},
},
"timm-regnetx_040": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_040"],
"params": {
"out_channels": (3, 32, 80, 240, 560, 1360),
"cfg": _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23),
"cfg": _mcfg(w0=96, wa=38.65, wm=2.43, group_size=40, depth=23),
},
},
"timm-regnetx_064": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_064"],
"params": {
"out_channels": (3, 32, 168, 392, 784, 1624),
"cfg": _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17),
"cfg": _mcfg(w0=184, wa=60.83, wm=2.07, group_size=56, depth=17),
},
},
"timm-regnetx_080": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_080"],
"params": {
"out_channels": (3, 32, 80, 240, 720, 1920),
"cfg": _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23),
"cfg": _mcfg(w0=80, wa=49.56, wm=2.88, group_size=120, depth=23),
},
},
"timm-regnetx_120": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_120"],
"params": {
"out_channels": (3, 32, 224, 448, 896, 2240),
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19),
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19),
},
},
"timm-regnetx_160": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_160"],
"params": {
"out_channels": (3, 32, 256, 512, 896, 2048),
"cfg": _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22),
"cfg": _mcfg(w0=216, wa=55.59, wm=2.1, group_size=128, depth=22),
},
},
"timm-regnetx_320": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnetx_320"],
"params": {
"out_channels": (3, 32, 336, 672, 1344, 2520),
"cfg": _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23),
"cfg": _mcfg(w0=320, wa=69.86, wm=2.0, group_size=168, depth=23),
},
},
# regnety
Expand All @@ -238,95 +239,95 @@ def _mcfg(**kwargs):
"pretrained_settings": pretrained_settings["timm-regnety_002"],
"params": {
"out_channels": (3, 32, 24, 56, 152, 368),
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25),
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13, se_ratio=0.25),
},
},
"timm-regnety_004": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_004"],
"params": {
"out_channels": (3, 32, 48, 104, 208, 440),
"cfg": _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25),
"cfg": _mcfg(w0=48, wa=27.89, wm=2.09, group_size=8, depth=16, se_ratio=0.25),
},
},
"timm-regnety_006": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_006"],
"params": {
"out_channels": (3, 32, 48, 112, 256, 608),
"cfg": _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25),
"cfg": _mcfg(w0=48, wa=32.54, wm=2.32, group_size=16, depth=15, se_ratio=0.25),
},
},
"timm-regnety_008": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_008"],
"params": {
"out_channels": (3, 32, 64, 128, 320, 768),
"cfg": _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25),
"cfg": _mcfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25),
},
},
"timm-regnety_016": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_016"],
"params": {
"out_channels": (3, 32, 48, 120, 336, 888),
"cfg": _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25),
"cfg": _mcfg(w0=48, wa=20.71, wm=2.65, group_size=24, depth=27, se_ratio=0.25),
},
},
"timm-regnety_032": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_032"],
"params": {
"out_channels": (3, 32, 72, 216, 576, 1512),
"cfg": _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25),
"cfg": _mcfg(w0=80, wa=42.63, wm=2.66, group_size=24, depth=21, se_ratio=0.25),
},
},
"timm-regnety_040": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_040"],
"params": {
"out_channels": (3, 32, 128, 192, 512, 1088),
"cfg": _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25),
"cfg": _mcfg(w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25),
},
},
"timm-regnety_064": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_064"],
"params": {
"out_channels": (3, 32, 144, 288, 576, 1296),
"cfg": _mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25),
"cfg": _mcfg(w0=112, wa=33.22, wm=2.27, group_size=72, depth=25, se_ratio=0.25),
},
},
"timm-regnety_080": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_080"],
"params": {
"out_channels": (3, 32, 168, 448, 896, 2016),
"cfg": _mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25),
"cfg": _mcfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25),
},
},
"timm-regnety_120": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_120"],
"params": {
"out_channels": (3, 32, 224, 448, 896, 2240),
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25),
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
},
},
"timm-regnety_160": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_160"],
"params": {
"out_channels": (3, 32, 224, 448, 1232, 3024),
"cfg": _mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25),
"cfg": _mcfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
},
},
"timm-regnety_320": {
"encoder": RegNetEncoder,
"pretrained_settings": pretrained_settings["timm-regnety_320"],
"params": {
"out_channels": (3, 32, 232, 696, 1392, 3712),
"cfg": _mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25),
"cfg": _mcfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
},
},
}
6 changes: 3 additions & 3 deletions segmentation_models_pytorch/encoders/timm_sknet.py
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Expand Up @@ -73,7 +73,7 @@ def load_state_dict(self, state_dict, **kwargs):
"out_channels": (3, 64, 64, 128, 256, 512),
"block": SelectiveKernelBasic,
"layers": [2, 2, 2, 2],
"zero_init_last_bn": False,
"zero_init_last": False,
"block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}},
},
},
Expand All @@ -84,7 +84,7 @@ def load_state_dict(self, state_dict, **kwargs):
"out_channels": (3, 64, 64, 128, 256, 512),
"block": SelectiveKernelBasic,
"layers": [3, 4, 6, 3],
"zero_init_last_bn": False,
"zero_init_last": False,
"block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}},
},
},
Expand All @@ -95,7 +95,7 @@ def load_state_dict(self, state_dict, **kwargs):
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": SelectiveKernelBottleneck,
"layers": [3, 4, 6, 3],
"zero_init_last_bn": False,
"zero_init_last": False,
"cardinality": 32,
"base_width": 4,
},
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -17,7 +17,7 @@
URL = "https://github.com/qubvel/segmentation_models.pytorch"
EMAIL = "qubvel@gmail.com"
AUTHOR = "Pavel Iakubovskii"
REQUIRES_PYTHON = ">=3.6.0"
REQUIRES_PYTHON = ">=3.7.0"
VERSION = None

# The rest you shouldn't have to touch too much :)
Expand Down