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VadimLevinVadimLevin
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timm upgrade 0.6.12 (qubvel-org#696)
* config: timm version bump 0.4.12 -> 0.6.12 * fix: timm based EfficientNetBaseEncoder after timm upgrade to 0.6.12 EfficientNet act1 layer in timm is merged with bn1 layer after ab49d275de8a9c344aea086fd86d04c4cabb6098 commit * fix: use "public" API for timm models availability check * fix: timm SkNetEncoder after timm upgrade to 0.6.12 `zero_init_last_bn` was renamed to `zero_init_last` in 372ad5fa0dbeb74dcec81db06e9ff69b3d5a2eb6 commit * fix: timm RegNet encoder after timm upgrade to 0.6.12 Instead of plain dict config RegNet uses RegNetCfg dataclass. Dataclasses module were added in Python 3.7, thats why min required Python version for package is also increased. * feat: bump Python version used in Github Actions to 3.7
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+38
-37
lines changed

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+38
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‎.github/workflows/pypi.yml

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@@ -12,7 +12,7 @@ jobs:
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- name: Set up Python
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uses: actions/setup-python@v2
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with:
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python-version: '3.6'
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python-version: '3.7'
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip

‎.github/workflows/tests.yml

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@@ -18,7 +18,7 @@ jobs:
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: 3.6
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python-version: 3.7
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip

‎pyproject.toml

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@@ -1,6 +1,6 @@
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[tool.black]
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line-length = 119
3-
target-version = ['py36', 'py37', 'py38']
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target-version = ['py37', 'py38']
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include = '\.pyi?$'
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exclude = '''
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/(
@@ -16,4 +16,4 @@ exclude = '''
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| build
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| dist
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)/
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'''
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'''

‎requirements.txt

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@@ -1,7 +1,7 @@
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torchvision>=0.5.0
22
pretrainedmodels==0.7.4
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efficientnet-pytorch==0.7.1
4-
timm==0.4.12
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timm==0.6.12
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66
tqdm
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pillow

‎segmentation_models_pytorch/encoders/__init__.py

+2-2
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@@ -97,9 +97,9 @@ def get_preprocessing_params(encoder_name, pretrained="imagenet"):
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9898
if encoder_name.startswith("tu-"):
9999
encoder_name = encoder_name[3:]
100-
if encoder_name not in timm.models.registry._model_has_pretrained:
100+
if not timm.models.is_model_pretrained(encoder_name):
101101
raise ValueError(f"{encoder_name} does not have pretrained weights and preprocessing parameters")
102-
settings = timm.models.registry._model_default_cfgs[encoder_name]
102+
settings = timm.models.get_pretrained_cfg(encoder_name)
103103
else:
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all_settings = encoders[encoder_name]["pretrained_settings"]
105105
if pretrained not in all_settings.keys():

‎segmentation_models_pytorch/encoders/timm_efficientnet.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -105,7 +105,7 @@ def __init__(self, stage_idxs, out_channels, depth=5, **kwargs):
105105
def get_stages(self):
106106
return [
107107
nn.Identity(),
108-
nn.Sequential(self.conv_stem, self.bn1, self.act1),
108+
nn.Sequential(self.conv_stem, self.bn1),
109109
self.blocks[: self._stage_idxs[0]],
110110
self.blocks[self._stage_idxs[0] : self._stage_idxs[1]],
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self.blocks[self._stage_idxs[1] : self._stage_idxs[2]],

‎segmentation_models_pytorch/encoders/timm_regnet.py

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Original file line numberDiff line numberDiff line change
@@ -1,10 +1,11 @@
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from ._base import EncoderMixin
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from timm.models.regnet import RegNet
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from timm.models.regnet import RegNet, RegNetCfg
33
import torch.nn as nn
44

55

66
class RegNetEncoder(RegNet, EncoderMixin):
77
def __init__(self, out_channels, depth=5, **kwargs):
8+
kwargs["cfg"] = RegNetCfg(**kwargs["cfg"])
89
super().__init__(**kwargs)
910
self._depth = depth
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self._out_channels = out_channels
@@ -141,95 +142,95 @@ def _mcfg(**kwargs):
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"pretrained_settings": pretrained_settings["timm-regnetx_002"],
142143
"params": {
143144
"out_channels": (3, 32, 24, 56, 152, 368),
144-
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13),
145+
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13),
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},
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},
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"timm-regnetx_004": {
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"encoder": RegNetEncoder,
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"pretrained_settings": pretrained_settings["timm-regnetx_004"],
150151
"params": {
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"out_channels": (3, 32, 32, 64, 160, 384),
152-
"cfg": _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22),
153+
"cfg": _mcfg(w0=24, wa=24.48, wm=2.54, group_size=16, depth=22),
153154
},
154155
},
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"timm-regnetx_006": {
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"encoder": RegNetEncoder,
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"pretrained_settings": pretrained_settings["timm-regnetx_006"],
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"params": {
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"out_channels": (3, 32, 48, 96, 240, 528),
160-
"cfg": _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16),
161+
"cfg": _mcfg(w0=48, wa=36.97, wm=2.24, group_size=24, depth=16),
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},
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},
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"timm-regnetx_008": {
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"encoder": RegNetEncoder,
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"pretrained_settings": pretrained_settings["timm-regnetx_008"],
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"params": {
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"out_channels": (3, 32, 64, 128, 288, 672),
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"cfg": _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16),
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"cfg": _mcfg(w0=56, wa=35.73, wm=2.28, group_size=16, depth=16),
169170
},
170171
},
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"timm-regnetx_016": {
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"encoder": RegNetEncoder,
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"pretrained_settings": pretrained_settings["timm-regnetx_016"],
174175
"params": {
175176
"out_channels": (3, 32, 72, 168, 408, 912),
176-
"cfg": _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18),
177+
"cfg": _mcfg(w0=80, wa=34.01, wm=2.25, group_size=24, depth=18),
177178
},
178179
},
179180
"timm-regnetx_032": {
180181
"encoder": RegNetEncoder,
181182
"pretrained_settings": pretrained_settings["timm-regnetx_032"],
182183
"params": {
183184
"out_channels": (3, 32, 96, 192, 432, 1008),
184-
"cfg": _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25),
185+
"cfg": _mcfg(w0=88, wa=26.31, wm=2.25, group_size=48, depth=25),
185186
},
186187
},
187188
"timm-regnetx_040": {
188189
"encoder": RegNetEncoder,
189190
"pretrained_settings": pretrained_settings["timm-regnetx_040"],
190191
"params": {
191192
"out_channels": (3, 32, 80, 240, 560, 1360),
192-
"cfg": _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23),
193+
"cfg": _mcfg(w0=96, wa=38.65, wm=2.43, group_size=40, depth=23),
193194
},
194195
},
195196
"timm-regnetx_064": {
196197
"encoder": RegNetEncoder,
197198
"pretrained_settings": pretrained_settings["timm-regnetx_064"],
198199
"params": {
199200
"out_channels": (3, 32, 168, 392, 784, 1624),
200-
"cfg": _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17),
201+
"cfg": _mcfg(w0=184, wa=60.83, wm=2.07, group_size=56, depth=17),
201202
},
202203
},
203204
"timm-regnetx_080": {
204205
"encoder": RegNetEncoder,
205206
"pretrained_settings": pretrained_settings["timm-regnetx_080"],
206207
"params": {
207208
"out_channels": (3, 32, 80, 240, 720, 1920),
208-
"cfg": _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23),
209+
"cfg": _mcfg(w0=80, wa=49.56, wm=2.88, group_size=120, depth=23),
209210
},
210211
},
211212
"timm-regnetx_120": {
212213
"encoder": RegNetEncoder,
213214
"pretrained_settings": pretrained_settings["timm-regnetx_120"],
214215
"params": {
215216
"out_channels": (3, 32, 224, 448, 896, 2240),
216-
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19),
217+
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19),
217218
},
218219
},
219220
"timm-regnetx_160": {
220221
"encoder": RegNetEncoder,
221222
"pretrained_settings": pretrained_settings["timm-regnetx_160"],
222223
"params": {
223224
"out_channels": (3, 32, 256, 512, 896, 2048),
224-
"cfg": _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22),
225+
"cfg": _mcfg(w0=216, wa=55.59, wm=2.1, group_size=128, depth=22),
225226
},
226227
},
227228
"timm-regnetx_320": {
228229
"encoder": RegNetEncoder,
229230
"pretrained_settings": pretrained_settings["timm-regnetx_320"],
230231
"params": {
231232
"out_channels": (3, 32, 336, 672, 1344, 2520),
232-
"cfg": _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23),
233+
"cfg": _mcfg(w0=320, wa=69.86, wm=2.0, group_size=168, depth=23),
233234
},
234235
},
235236
# regnety
@@ -238,95 +239,95 @@ def _mcfg(**kwargs):
238239
"pretrained_settings": pretrained_settings["timm-regnety_002"],
239240
"params": {
240241
"out_channels": (3, 32, 24, 56, 152, 368),
241-
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25),
242+
"cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_size=8, depth=13, se_ratio=0.25),
242243
},
243244
},
244245
"timm-regnety_004": {
245246
"encoder": RegNetEncoder,
246247
"pretrained_settings": pretrained_settings["timm-regnety_004"],
247248
"params": {
248249
"out_channels": (3, 32, 48, 104, 208, 440),
249-
"cfg": _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25),
250+
"cfg": _mcfg(w0=48, wa=27.89, wm=2.09, group_size=8, depth=16, se_ratio=0.25),
250251
},
251252
},
252253
"timm-regnety_006": {
253254
"encoder": RegNetEncoder,
254255
"pretrained_settings": pretrained_settings["timm-regnety_006"],
255256
"params": {
256257
"out_channels": (3, 32, 48, 112, 256, 608),
257-
"cfg": _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25),
258+
"cfg": _mcfg(w0=48, wa=32.54, wm=2.32, group_size=16, depth=15, se_ratio=0.25),
258259
},
259260
},
260261
"timm-regnety_008": {
261262
"encoder": RegNetEncoder,
262263
"pretrained_settings": pretrained_settings["timm-regnety_008"],
263264
"params": {
264265
"out_channels": (3, 32, 64, 128, 320, 768),
265-
"cfg": _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25),
266+
"cfg": _mcfg(w0=56, wa=38.84, wm=2.4, group_size=16, depth=14, se_ratio=0.25),
266267
},
267268
},
268269
"timm-regnety_016": {
269270
"encoder": RegNetEncoder,
270271
"pretrained_settings": pretrained_settings["timm-regnety_016"],
271272
"params": {
272273
"out_channels": (3, 32, 48, 120, 336, 888),
273-
"cfg": _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25),
274+
"cfg": _mcfg(w0=48, wa=20.71, wm=2.65, group_size=24, depth=27, se_ratio=0.25),
274275
},
275276
},
276277
"timm-regnety_032": {
277278
"encoder": RegNetEncoder,
278279
"pretrained_settings": pretrained_settings["timm-regnety_032"],
279280
"params": {
280281
"out_channels": (3, 32, 72, 216, 576, 1512),
281-
"cfg": _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25),
282+
"cfg": _mcfg(w0=80, wa=42.63, wm=2.66, group_size=24, depth=21, se_ratio=0.25),
282283
},
283284
},
284285
"timm-regnety_040": {
285286
"encoder": RegNetEncoder,
286287
"pretrained_settings": pretrained_settings["timm-regnety_040"],
287288
"params": {
288289
"out_channels": (3, 32, 128, 192, 512, 1088),
289-
"cfg": _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25),
290+
"cfg": _mcfg(w0=96, wa=31.41, wm=2.24, group_size=64, depth=22, se_ratio=0.25),
290291
},
291292
},
292293
"timm-regnety_064": {
293294
"encoder": RegNetEncoder,
294295
"pretrained_settings": pretrained_settings["timm-regnety_064"],
295296
"params": {
296297
"out_channels": (3, 32, 144, 288, 576, 1296),
297-
"cfg": _mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25),
298+
"cfg": _mcfg(w0=112, wa=33.22, wm=2.27, group_size=72, depth=25, se_ratio=0.25),
298299
},
299300
},
300301
"timm-regnety_080": {
301302
"encoder": RegNetEncoder,
302303
"pretrained_settings": pretrained_settings["timm-regnety_080"],
303304
"params": {
304305
"out_channels": (3, 32, 168, 448, 896, 2016),
305-
"cfg": _mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25),
306+
"cfg": _mcfg(w0=192, wa=76.82, wm=2.19, group_size=56, depth=17, se_ratio=0.25),
306307
},
307308
},
308309
"timm-regnety_120": {
309310
"encoder": RegNetEncoder,
310311
"pretrained_settings": pretrained_settings["timm-regnety_120"],
311312
"params": {
312313
"out_channels": (3, 32, 224, 448, 896, 2240),
313-
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25),
314+
"cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_size=112, depth=19, se_ratio=0.25),
314315
},
315316
},
316317
"timm-regnety_160": {
317318
"encoder": RegNetEncoder,
318319
"pretrained_settings": pretrained_settings["timm-regnety_160"],
319320
"params": {
320321
"out_channels": (3, 32, 224, 448, 1232, 3024),
321-
"cfg": _mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25),
322+
"cfg": _mcfg(w0=200, wa=106.23, wm=2.48, group_size=112, depth=18, se_ratio=0.25),
322323
},
323324
},
324325
"timm-regnety_320": {
325326
"encoder": RegNetEncoder,
326327
"pretrained_settings": pretrained_settings["timm-regnety_320"],
327328
"params": {
328329
"out_channels": (3, 32, 232, 696, 1392, 3712),
329-
"cfg": _mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25),
330+
"cfg": _mcfg(w0=232, wa=115.89, wm=2.53, group_size=232, depth=20, se_ratio=0.25),
330331
},
331332
},
332333
}

‎segmentation_models_pytorch/encoders/timm_sknet.py

+3-3
Original file line numberDiff line numberDiff line change
@@ -73,7 +73,7 @@ def load_state_dict(self, state_dict, **kwargs):
7373
"out_channels": (3, 64, 64, 128, 256, 512),
7474
"block": SelectiveKernelBasic,
7575
"layers": [2, 2, 2, 2],
76-
"zero_init_last_bn": False,
76+
"zero_init_last": False,
7777
"block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}},
7878
},
7979
},
@@ -84,7 +84,7 @@ def load_state_dict(self, state_dict, **kwargs):
8484
"out_channels": (3, 64, 64, 128, 256, 512),
8585
"block": SelectiveKernelBasic,
8686
"layers": [3, 4, 6, 3],
87-
"zero_init_last_bn": False,
87+
"zero_init_last": False,
8888
"block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}},
8989
},
9090
},
@@ -95,7 +95,7 @@ def load_state_dict(self, state_dict, **kwargs):
9595
"out_channels": (3, 64, 256, 512, 1024, 2048),
9696
"block": SelectiveKernelBottleneck,
9797
"layers": [3, 4, 6, 3],
98-
"zero_init_last_bn": False,
98+
"zero_init_last": False,
9999
"cardinality": 32,
100100
"base_width": 4,
101101
},

‎setup.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -17,7 +17,7 @@
1717
URL = "https://github.com/qubvel/segmentation_models.pytorch"
1818
EMAIL = "[email protected]"
1919
AUTHOR = "Pavel Iakubovskii"
20-
REQUIRES_PYTHON = ">=3.6.0"
20+
REQUIRES_PYTHON = ">=3.7.0"
2121
VERSION = None
2222

2323
# The rest you shouldn't have to touch too much :)

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