Ross Wightman
5 years ago
commit
7df83258c9
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name: Python tests
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on:
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push:
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branches: [ master ]
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pull_request:
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branches: [ master ]
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jobs:
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test:
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name: Run tests on ${{ matrix.os }} with Python ${{ matrix.python }}
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strategy:
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matrix:
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os: [ubuntu-latest, macOS-latest]
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python: ['3.8']
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torch: ['1.5.0']
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torchvision: ['0.6.0']
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python }}
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uses: actions/setup-python@v1
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with:
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python-version: ${{ matrix.python }}
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- name: Install testing dependencies
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run: |
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python -m pip install --upgrade pip
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pip install pytest pytest-timeout
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- name: Install torch on mac
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if: startsWith(matrix.os, 'macOS')
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run: pip install torch==${{ matrix.torch }} torchvision==${{ matrix.torchvision }}
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- name: Install torch on ubuntu
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if: startsWith(matrix.os, 'ubuntu')
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run: pip install torch==${{ matrix.torch }}+cpu torchvision==${{ matrix.torchvision }}+cpu -f https://download.pytorch.org/whl/torch_stable.html
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- name: Install requirements
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run: |
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.11
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- name: Run tests
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run: |
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pytest -vv --durations=0 ./tests
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import pytest
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import torch
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from timm import list_models, create_model
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@pytest.mark.timeout(300)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters='*efficientnet_l2*'))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_forward(model_name, batch_size):
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"""Run a single forward pass with each model"""
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model = create_model(model_name, pretrained=False)
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model.eval()
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inputs = torch.randn((batch_size, *model.default_cfg['input_size']))
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outputs = model(inputs)
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assert outputs.shape[0] == batch_size
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assert not torch.isnan(outputs).any(), 'Output included NaNs'
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@ -1,121 +1,561 @@
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from torchvision.models import Inception3
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from .helpers import load_pretrained
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from .helpers import load_pretrained
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from .registry import register_model
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from .registry import register_model
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from .layers import trunc_normal_, SelectAdaptivePool2d
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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**kwargs
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}
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__all__ = []
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default_cfgs = {
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default_cfgs = {
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# original PyTorch weights, ported from Tensorflow but modified
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# original PyTorch weights, ported from Tensorflow but modified
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'inception_v3': {
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'inception_v3': _cfg(
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'url': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
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url='https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
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'input_size': (3, 299, 299),
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has_aux=True), # checkpoint has aux logit layer weights
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, # also works well enough with resnet defaults
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'std': IMAGENET_INCEPTION_STD, # also works well enough with resnet defaults
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'num_classes': 1000,
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'first_conv': 'conv0',
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'classifier': 'fc'
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},
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# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
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# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
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'tf_inception_v3': {
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'tf_inception_v3': _cfg(
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
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'input_size': (3, 299, 299),
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num_classes=1001, has_aux=False),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN,
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'std': IMAGENET_INCEPTION_STD,
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'num_classes': 1001,
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'first_conv': 'conv0',
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'classifier': 'fc'
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},
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# my port of Tensorflow adversarially trained Inception V3 from
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# my port of Tensorflow adversarially trained Inception V3 from
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# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
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# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
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'adv_inception_v3': {
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'adv_inception_v3': _cfg(
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
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'input_size': (3, 299, 299),
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num_classes=1001, has_aux=False),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN,
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'std': IMAGENET_INCEPTION_STD,
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'num_classes': 1001,
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'first_conv': 'conv0',
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'classifier': 'fc'
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},
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# from gluon pretrained models, best performing in terms of accuracy/loss metrics
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# from gluon pretrained models, best performing in terms of accuracy/loss metrics
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# https://gluon-cv.mxnet.io/model_zoo/classification.html
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# https://gluon-cv.mxnet.io/model_zoo/classification.html
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'gluon_inception_v3': {
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'gluon_inception_v3': _cfg(
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'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
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'input_size': (3, 299, 299),
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mean=IMAGENET_DEFAULT_MEAN, # also works well with inception defaults
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'crop_pct': 0.875,
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std=IMAGENET_DEFAULT_STD, # also works well with inception defaults
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'interpolation': 'bicubic',
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has_aux=False,
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'mean': IMAGENET_DEFAULT_MEAN, # also works well with inception defaults
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)
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'std': IMAGENET_DEFAULT_STD, # also works well with inception defaults
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'num_classes': 1000,
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'first_conv': 'conv0',
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'classifier': 'fc'
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}
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}
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}
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def _assert_default_kwargs(kwargs):
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class InceptionV3Aux(nn.Module):
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# for imported models (ie torchvision) without capability to change these params,
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"""InceptionV3 with AuxLogits
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# make sure they aren't being set to non-defaults
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"""
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assert kwargs.pop('global_pool', 'avg') == 'avg'
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assert kwargs.pop('drop_rate', 0.) == 0.
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def __init__(self, inception_blocks=None, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
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super(InceptionV3Aux, self).__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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if inception_blocks is None:
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inception_blocks = [
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BasicConv2d, InceptionA, InceptionB, InceptionC,
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InceptionD, InceptionE, InceptionAux
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]
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assert len(inception_blocks) == 7
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conv_block = inception_blocks[0]
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inception_a = inception_blocks[1]
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inception_b = inception_blocks[2]
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inception_c = inception_blocks[3]
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inception_d = inception_blocks[4]
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inception_e = inception_blocks[5]
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inception_aux = inception_blocks[6]
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self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2)
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self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
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self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
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self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
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self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
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self.Mixed_5b = inception_a(192, pool_features=32)
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self.Mixed_5c = inception_a(256, pool_features=64)
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self.Mixed_5d = inception_a(288, pool_features=64)
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self.Mixed_6a = inception_b(288)
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self.Mixed_6b = inception_c(768, channels_7x7=128)
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self.Mixed_6c = inception_c(768, channels_7x7=160)
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self.Mixed_6d = inception_c(768, channels_7x7=160)
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self.Mixed_6e = inception_c(768, channels_7x7=192)
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self.AuxLogits = inception_aux(768, num_classes)
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self.Mixed_7a = inception_d(768)
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self.Mixed_7b = inception_e(1280)
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self.Mixed_7c = inception_e(2048)
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self.num_features = 2048
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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stddev = m.stddev if hasattr(m, 'stddev') else 0.1
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trunc_normal_(m.weight, std=stddev)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def forward_features(self, x):
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# N x 3 x 299 x 299
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x = self.Conv2d_1a_3x3(x)
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# N x 32 x 149 x 149
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x = self.Conv2d_2a_3x3(x)
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# N x 32 x 147 x 147
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x = self.Conv2d_2b_3x3(x)
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# N x 64 x 147 x 147
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x = F.max_pool2d(x, kernel_size=3, stride=2)
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# N x 64 x 73 x 73
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x = self.Conv2d_3b_1x1(x)
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# N x 80 x 73 x 73
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x = self.Conv2d_4a_3x3(x)
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# N x 192 x 71 x 71
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x = F.max_pool2d(x, kernel_size=3, stride=2)
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# N x 192 x 35 x 35
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x = self.Mixed_5b(x)
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# N x 256 x 35 x 35
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x = self.Mixed_5c(x)
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# N x 288 x 35 x 35
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x = self.Mixed_5d(x)
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# N x 288 x 35 x 35
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x = self.Mixed_6a(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6b(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6c(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6d(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6e(x)
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# N x 768 x 17 x 17
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aux = self.AuxLogits(x) if self.training else None
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# N x 768 x 17 x 17
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x = self.Mixed_7a(x)
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# N x 1280 x 8 x 8
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x = self.Mixed_7b(x)
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# N x 2048 x 8 x 8
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x = self.Mixed_7c(x)
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# N x 2048 x 8 x 8
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return x, aux
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def get_classifier(self):
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return self.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.num_classes = num_classes
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if self.num_classes > 0:
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self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
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else:
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self.fc = nn.Identity()
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def forward(self, x):
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x, aux = self.forward_features(x)
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x = self.global_pool(x).flatten(1)
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if self.drop_rate > 0:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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x = self.fc(x)
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return x, aux
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class InceptionV3(nn.Module):
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"""Inception-V3 with no AuxLogits
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FIXME two class defs are redundant, but less screwing around with torchsript fussyness and inconsistent returns
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"""
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def __init__(self, inception_blocks=None, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
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super(InceptionV3, self).__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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if inception_blocks is None:
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inception_blocks = [
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BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE]
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assert len(inception_blocks) >= 6
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conv_block = inception_blocks[0]
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inception_a = inception_blocks[1]
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inception_b = inception_blocks[2]
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inception_c = inception_blocks[3]
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inception_d = inception_blocks[4]
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inception_e = inception_blocks[5]
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self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2)
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self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
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self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
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self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
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self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
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self.Mixed_5b = inception_a(192, pool_features=32)
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self.Mixed_5c = inception_a(256, pool_features=64)
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self.Mixed_5d = inception_a(288, pool_features=64)
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self.Mixed_6a = inception_b(288)
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self.Mixed_6b = inception_c(768, channels_7x7=128)
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self.Mixed_6c = inception_c(768, channels_7x7=160)
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self.Mixed_6d = inception_c(768, channels_7x7=160)
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self.Mixed_6e = inception_c(768, channels_7x7=192)
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self.Mixed_7a = inception_d(768)
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self.Mixed_7b = inception_e(1280)
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self.Mixed_7c = inception_e(2048)
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self.num_features = 2048
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.fc = nn.Linear(2048, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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stddev = m.stddev if hasattr(m, 'stddev') else 0.1
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trunc_normal_(m.weight, std=stddev)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
|
||||||
|
|
||||||
|
def forward_features(self, x):
|
||||||
|
# N x 3 x 299 x 299
|
||||||
|
x = self.Conv2d_1a_3x3(x)
|
||||||
|
# N x 32 x 149 x 149
|
||||||
|
x = self.Conv2d_2a_3x3(x)
|
||||||
|
# N x 32 x 147 x 147
|
||||||
|
x = self.Conv2d_2b_3x3(x)
|
||||||
|
# N x 64 x 147 x 147
|
||||||
|
x = F.max_pool2d(x, kernel_size=3, stride=2)
|
||||||
|
# N x 64 x 73 x 73
|
||||||
|
x = self.Conv2d_3b_1x1(x)
|
||||||
|
# N x 80 x 73 x 73
|
||||||
|
x = self.Conv2d_4a_3x3(x)
|
||||||
|
# N x 192 x 71 x 71
|
||||||
|
x = F.max_pool2d(x, kernel_size=3, stride=2)
|
||||||
|
# N x 192 x 35 x 35
|
||||||
|
x = self.Mixed_5b(x)
|
||||||
|
# N x 256 x 35 x 35
|
||||||
|
x = self.Mixed_5c(x)
|
||||||
|
# N x 288 x 35 x 35
|
||||||
|
x = self.Mixed_5d(x)
|
||||||
|
# N x 288 x 35 x 35
|
||||||
|
x = self.Mixed_6a(x)
|
||||||
|
# N x 768 x 17 x 17
|
||||||
|
x = self.Mixed_6b(x)
|
||||||
|
# N x 768 x 17 x 17
|
||||||
|
x = self.Mixed_6c(x)
|
||||||
|
# N x 768 x 17 x 17
|
||||||
|
x = self.Mixed_6d(x)
|
||||||
|
# N x 768 x 17 x 17
|
||||||
|
x = self.Mixed_6e(x)
|
||||||
|
# N x 768 x 17 x 17
|
||||||
|
x = self.Mixed_7a(x)
|
||||||
|
# N x 1280 x 8 x 8
|
||||||
|
x = self.Mixed_7b(x)
|
||||||
|
# N x 2048 x 8 x 8
|
||||||
|
x = self.Mixed_7c(x)
|
||||||
|
# N x 2048 x 8 x 8
|
||||||
|
return x
|
||||||
|
|
||||||
|
def get_classifier(self):
|
||||||
|
return self.fc
|
||||||
|
|
||||||
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
||||||
|
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
|
||||||
|
self.num_classes = num_classes
|
||||||
|
if self.num_classes > 0:
|
||||||
|
self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
|
||||||
|
else:
|
||||||
|
self.fc = nn.Identity()
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.forward_features(x)
|
||||||
|
x = self.global_pool(x).flatten(1)
|
||||||
|
if self.drop_rate > 0:
|
||||||
|
x = F.dropout(x, p=self.drop_rate, training=self.training)
|
||||||
|
x = self.fc(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionA(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, pool_features, conv_block=None):
|
||||||
|
super(InceptionA, self).__init__()
|
||||||
|
if conv_block is None:
|
||||||
|
conv_block = BasicConv2d
|
||||||
|
self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)
|
||||||
|
|
||||||
|
self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
|
||||||
|
self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)
|
||||||
|
|
||||||
|
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
|
||||||
|
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
|
||||||
|
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)
|
||||||
|
|
||||||
|
self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)
|
||||||
|
|
||||||
|
def _forward(self, x):
|
||||||
|
branch1x1 = self.branch1x1(x)
|
||||||
|
|
||||||
|
branch5x5 = self.branch5x5_1(x)
|
||||||
|
branch5x5 = self.branch5x5_2(branch5x5)
|
||||||
|
|
||||||
|
branch3x3dbl = self.branch3x3dbl_1(x)
|
||||||
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
||||||
|
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
|
||||||
|
|
||||||
|
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
|
||||||
|
branch_pool = self.branch_pool(branch_pool)
|
||||||
|
|
||||||
|
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
outputs = self._forward(x)
|
||||||
|
return torch.cat(outputs, 1)
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionB(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, conv_block=None):
|
||||||
|
super(InceptionB, self).__init__()
|
||||||
|
if conv_block is None:
|
||||||
|
conv_block = BasicConv2d
|
||||||
|
self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)
|
||||||
|
|
||||||
|
self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
|
||||||
|
self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
|
||||||
|
self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)
|
||||||
|
|
||||||
|
def _forward(self, x):
|
||||||
|
branch3x3 = self.branch3x3(x)
|
||||||
|
|
||||||
|
branch3x3dbl = self.branch3x3dbl_1(x)
|
||||||
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
||||||
|
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
|
||||||
|
|
||||||
|
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
|
||||||
|
|
||||||
|
outputs = [branch3x3, branch3x3dbl, branch_pool]
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
outputs = self._forward(x)
|
||||||
|
return torch.cat(outputs, 1)
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionC(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, channels_7x7, conv_block=None):
|
||||||
|
super(InceptionC, self).__init__()
|
||||||
|
if conv_block is None:
|
||||||
|
conv_block = BasicConv2d
|
||||||
|
self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)
|
||||||
|
|
||||||
|
c7 = channels_7x7
|
||||||
|
self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
|
||||||
|
self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
|
||||||
|
self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))
|
||||||
|
|
||||||
|
self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
|
||||||
|
self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
|
||||||
|
self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
|
||||||
|
self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
|
||||||
|
self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))
|
||||||
|
|
||||||
|
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
|
||||||
|
|
||||||
|
def _forward(self, x):
|
||||||
|
branch1x1 = self.branch1x1(x)
|
||||||
|
|
||||||
|
branch7x7 = self.branch7x7_1(x)
|
||||||
|
branch7x7 = self.branch7x7_2(branch7x7)
|
||||||
|
branch7x7 = self.branch7x7_3(branch7x7)
|
||||||
|
|
||||||
|
branch7x7dbl = self.branch7x7dbl_1(x)
|
||||||
|
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
|
||||||
|
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
|
||||||
|
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
|
||||||
|
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
|
||||||
|
|
||||||
|
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
|
||||||
|
branch_pool = self.branch_pool(branch_pool)
|
||||||
|
|
||||||
|
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
outputs = self._forward(x)
|
||||||
|
return torch.cat(outputs, 1)
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionD(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, conv_block=None):
|
||||||
|
super(InceptionD, self).__init__()
|
||||||
|
if conv_block is None:
|
||||||
|
conv_block = BasicConv2d
|
||||||
|
self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
|
||||||
|
self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)
|
||||||
|
|
||||||
|
self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
|
||||||
|
self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
|
||||||
|
self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
|
||||||
|
self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)
|
||||||
|
|
||||||
|
def _forward(self, x):
|
||||||
|
branch3x3 = self.branch3x3_1(x)
|
||||||
|
branch3x3 = self.branch3x3_2(branch3x3)
|
||||||
|
|
||||||
|
branch7x7x3 = self.branch7x7x3_1(x)
|
||||||
|
branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
|
||||||
|
branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
|
||||||
|
branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
|
||||||
|
|
||||||
|
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
|
||||||
|
outputs = [branch3x3, branch7x7x3, branch_pool]
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
outputs = self._forward(x)
|
||||||
|
return torch.cat(outputs, 1)
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionE(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, conv_block=None):
|
||||||
|
super(InceptionE, self).__init__()
|
||||||
|
if conv_block is None:
|
||||||
|
conv_block = BasicConv2d
|
||||||
|
self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)
|
||||||
|
|
||||||
|
self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
|
||||||
|
self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
|
||||||
|
self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
|
||||||
|
|
||||||
|
self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
|
||||||
|
self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
|
||||||
|
self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
|
||||||
|
self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))
|
||||||
|
|
||||||
|
self.branch_pool = conv_block(in_channels, 192, kernel_size=1)
|
||||||
|
|
||||||
|
def _forward(self, x):
|
||||||
|
branch1x1 = self.branch1x1(x)
|
||||||
|
|
||||||
|
branch3x3 = self.branch3x3_1(x)
|
||||||
|
branch3x3 = [
|
||||||
|
self.branch3x3_2a(branch3x3),
|
||||||
|
self.branch3x3_2b(branch3x3),
|
||||||
|
]
|
||||||
|
branch3x3 = torch.cat(branch3x3, 1)
|
||||||
|
|
||||||
|
branch3x3dbl = self.branch3x3dbl_1(x)
|
||||||
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
||||||
|
branch3x3dbl = [
|
||||||
|
self.branch3x3dbl_3a(branch3x3dbl),
|
||||||
|
self.branch3x3dbl_3b(branch3x3dbl),
|
||||||
|
]
|
||||||
|
branch3x3dbl = torch.cat(branch3x3dbl, 1)
|
||||||
|
|
||||||
|
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
|
||||||
|
branch_pool = self.branch_pool(branch_pool)
|
||||||
|
|
||||||
|
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
outputs = self._forward(x)
|
||||||
|
return torch.cat(outputs, 1)
|
||||||
|
|
||||||
|
|
||||||
|
class InceptionAux(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, num_classes, conv_block=None):
|
||||||
|
super(InceptionAux, self).__init__()
|
||||||
|
if conv_block is None:
|
||||||
|
conv_block = BasicConv2d
|
||||||
|
self.conv0 = conv_block(in_channels, 128, kernel_size=1)
|
||||||
|
self.conv1 = conv_block(128, 768, kernel_size=5)
|
||||||
|
self.conv1.stddev = 0.01
|
||||||
|
self.fc = nn.Linear(768, num_classes)
|
||||||
|
self.fc.stddev = 0.001
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
# N x 768 x 17 x 17
|
||||||
|
x = F.avg_pool2d(x, kernel_size=5, stride=3)
|
||||||
|
# N x 768 x 5 x 5
|
||||||
|
x = self.conv0(x)
|
||||||
|
# N x 128 x 5 x 5
|
||||||
|
x = self.conv1(x)
|
||||||
|
# N x 768 x 1 x 1
|
||||||
|
# Adaptive average pooling
|
||||||
|
x = F.adaptive_avg_pool2d(x, (1, 1))
|
||||||
|
# N x 768 x 1 x 1
|
||||||
|
x = torch.flatten(x, 1)
|
||||||
|
# N x 768
|
||||||
|
x = self.fc(x)
|
||||||
|
# N x 1000
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class BasicConv2d(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, in_channels, out_channels, **kwargs):
|
||||||
|
super(BasicConv2d, self).__init__()
|
||||||
|
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
|
||||||
|
self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.conv(x)
|
||||||
|
x = self.bn(x)
|
||||||
|
return F.relu(x, inplace=True)
|
||||||
|
|
||||||
|
|
||||||
|
def _inception_v3(variant, pretrained=False, **kwargs):
|
||||||
|
default_cfg = default_cfgs[variant]
|
||||||
|
if kwargs.pop('features_only', False):
|
||||||
|
assert False, 'Not Implemented' # TODO
|
||||||
|
load_strict = False
|
||||||
|
model_kwargs.pop('num_classes', 0)
|
||||||
|
model_class = InceptionV3
|
||||||
|
else:
|
||||||
|
aux_logits = kwargs.pop('aux_logits', False)
|
||||||
|
if aux_logits:
|
||||||
|
model_class = InceptionV3Aux
|
||||||
|
load_strict = default_cfg['has_aux']
|
||||||
|
else:
|
||||||
|
model_class = InceptionV3
|
||||||
|
load_strict = not default_cfg['has_aux']
|
||||||
|
|
||||||
|
model = model_class(**kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(
|
||||||
|
model,
|
||||||
|
num_classes=kwargs.get('num_classes', 0),
|
||||||
|
in_chans=kwargs.get('in_chans', 3),
|
||||||
|
strict=load_strict)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
@register_model
|
@register_model
|
||||||
def inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
def inception_v3(pretrained=False, **kwargs):
|
||||||
# original PyTorch weights, ported from Tensorflow but modified
|
# original PyTorch weights, ported from Tensorflow but modified
|
||||||
default_cfg = default_cfgs['inception_v3']
|
model = _inception_v3('inception_v3', pretrained=pretrained, **kwargs)
|
||||||
assert in_chans == 3
|
|
||||||
_assert_default_kwargs(kwargs)
|
|
||||||
model = Inception3(num_classes=num_classes, aux_logits=True, transform_input=False)
|
|
||||||
if pretrained:
|
|
||||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
||||||
model.default_cfg = default_cfg
|
|
||||||
return model
|
return model
|
||||||
|
|
||||||
|
|
||||||
@register_model
|
@register_model
|
||||||
def tf_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
def tf_inception_v3(pretrained=False, **kwargs):
|
||||||
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
|
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
|
||||||
default_cfg = default_cfgs['tf_inception_v3']
|
model = _inception_v3('tf_inception_v3', pretrained=pretrained, **kwargs)
|
||||||
assert in_chans == 3
|
|
||||||
_assert_default_kwargs(kwargs)
|
|
||||||
model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False)
|
|
||||||
if pretrained:
|
|
||||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
||||||
model.default_cfg = default_cfg
|
|
||||||
return model
|
return model
|
||||||
|
|
||||||
|
|
||||||
@register_model
|
@register_model
|
||||||
def adv_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
def adv_inception_v3(pretrained=False, **kwargs):
|
||||||
# my port of Tensorflow adversarially trained Inception V3 from
|
# my port of Tensorflow adversarially trained Inception V3 from
|
||||||
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
|
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
|
||||||
default_cfg = default_cfgs['adv_inception_v3']
|
model = _inception_v3('adv_inception_v3', pretrained=pretrained, **kwargs)
|
||||||
assert in_chans == 3
|
|
||||||
_assert_default_kwargs(kwargs)
|
|
||||||
model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False)
|
|
||||||
if pretrained:
|
|
||||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
||||||
model.default_cfg = default_cfg
|
|
||||||
return model
|
return model
|
||||||
|
|
||||||
|
|
||||||
@register_model
|
@register_model
|
||||||
def gluon_inception_v3(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
def gluon_inception_v3(pretrained=False, **kwargs):
|
||||||
# from gluon pretrained models, best performing in terms of accuracy/loss metrics
|
# from gluon pretrained models, best performing in terms of accuracy/loss metrics
|
||||||
# https://gluon-cv.mxnet.io/model_zoo/classification.html
|
# https://gluon-cv.mxnet.io/model_zoo/classification.html
|
||||||
default_cfg = default_cfgs['gluon_inception_v3']
|
model = _inception_v3('gluon_inception_v3', pretrained=pretrained, **kwargs)
|
||||||
assert in_chans == 3
|
|
||||||
_assert_default_kwargs(kwargs)
|
|
||||||
model = Inception3(num_classes=num_classes, aux_logits=False, transform_input=False)
|
|
||||||
if pretrained:
|
|
||||||
load_pretrained(model, default_cfg, num_classes, in_chans)
|
|
||||||
model.default_cfg = default_cfg
|
|
||||||
return model
|
return model
|
||||||
|
|||||||
@ -0,0 +1,80 @@
|
|||||||
|
""" Split Attention Conv2d (for ResNeSt Models)
|
||||||
|
|
||||||
|
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
|
||||||
|
|
||||||
|
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
|
||||||
|
|
||||||
|
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
|
||||||
|
"""
|
||||||
|
import torch
|
||||||
|
import torch.nn.functional as F
|
||||||
|
from torch import nn
|
||||||
|
|
||||||
|
|
||||||
|
class RadixSoftmax(nn.Module):
|
||||||
|
def __init__(self, radix, cardinality):
|
||||||
|
super(RadixSoftmax, self).__init__()
|
||||||
|
self.radix = radix
|
||||||
|
self.cardinality = cardinality
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
batch = x.size(0)
|
||||||
|
if self.radix > 1:
|
||||||
|
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
|
||||||
|
x = F.softmax(x, dim=1)
|
||||||
|
x = x.reshape(batch, -1)
|
||||||
|
else:
|
||||||
|
x = torch.sigmoid(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class SplitAttnConv2d(nn.Module):
|
||||||
|
"""Split-Attention Conv2d
|
||||||
|
"""
|
||||||
|
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
|
||||||
|
dilation=1, groups=1, bias=False, radix=2, reduction_factor=4,
|
||||||
|
act_layer=nn.ReLU, norm_layer=None, drop_block=None, **kwargs):
|
||||||
|
super(SplitAttnConv2d, self).__init__()
|
||||||
|
self.radix = radix
|
||||||
|
self.drop_block = drop_block
|
||||||
|
mid_chs = out_channels * radix
|
||||||
|
attn_chs = max(in_channels * radix // reduction_factor, 32)
|
||||||
|
|
||||||
|
self.conv = nn.Conv2d(
|
||||||
|
in_channels, mid_chs, kernel_size, stride, padding, dilation,
|
||||||
|
groups=groups * radix, bias=bias, **kwargs)
|
||||||
|
self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None
|
||||||
|
self.act0 = act_layer(inplace=True)
|
||||||
|
self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups)
|
||||||
|
self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None
|
||||||
|
self.act1 = act_layer(inplace=True)
|
||||||
|
self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
|
||||||
|
self.rsoftmax = RadixSoftmax(radix, groups)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.conv(x)
|
||||||
|
if self.bn0 is not None:
|
||||||
|
x = self.bn0(x)
|
||||||
|
if self.drop_block is not None:
|
||||||
|
x = self.drop_block(x)
|
||||||
|
x = self.act0(x)
|
||||||
|
|
||||||
|
B, RC, H, W = x.shape
|
||||||
|
if self.radix > 1:
|
||||||
|
x = x.reshape((B, self.radix, RC // self.radix, H, W))
|
||||||
|
x_gap = x.sum(dim=1)
|
||||||
|
else:
|
||||||
|
x_gap = x
|
||||||
|
x_gap = F.adaptive_avg_pool2d(x_gap, 1)
|
||||||
|
x_gap = self.fc1(x_gap)
|
||||||
|
if self.bn1 is not None:
|
||||||
|
x_gap = self.bn1(x_gap)
|
||||||
|
x_gap = self.act1(x_gap)
|
||||||
|
x_attn = self.fc2(x_gap)
|
||||||
|
|
||||||
|
x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
|
||||||
|
if self.radix > 1:
|
||||||
|
out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))).sum(dim=1)
|
||||||
|
else:
|
||||||
|
out = x * x_attn
|
||||||
|
return out.contiguous()
|
||||||
@ -0,0 +1,60 @@
|
|||||||
|
import torch
|
||||||
|
import math
|
||||||
|
import warnings
|
||||||
|
|
||||||
|
|
||||||
|
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
||||||
|
# Cut & paste from PyTorch official master until it's in a few official releases - RW
|
||||||
|
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
||||||
|
def norm_cdf(x):
|
||||||
|
# Computes standard normal cumulative distribution function
|
||||||
|
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||||
|
|
||||||
|
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
||||||
|
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
||||||
|
"The distribution of values may be incorrect.",
|
||||||
|
stacklevel=2)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
# Values are generated by using a truncated uniform distribution and
|
||||||
|
# then using the inverse CDF for the normal distribution.
|
||||||
|
# Get upper and lower cdf values
|
||||||
|
l = norm_cdf((a - mean) / std)
|
||||||
|
u = norm_cdf((b - mean) / std)
|
||||||
|
|
||||||
|
# Uniformly fill tensor with values from [l, u], then translate to
|
||||||
|
# [2l-1, 2u-1].
|
||||||
|
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||||
|
|
||||||
|
# Use inverse cdf transform for normal distribution to get truncated
|
||||||
|
# standard normal
|
||||||
|
tensor.erfinv_()
|
||||||
|
|
||||||
|
# Transform to proper mean, std
|
||||||
|
tensor.mul_(std * math.sqrt(2.))
|
||||||
|
tensor.add_(mean)
|
||||||
|
|
||||||
|
# Clamp to ensure it's in the proper range
|
||||||
|
tensor.clamp_(min=a, max=b)
|
||||||
|
return tensor
|
||||||
|
|
||||||
|
|
||||||
|
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||||
|
# type: (Tensor, float, float, float, float) -> Tensor
|
||||||
|
r"""Fills the input Tensor with values drawn from a truncated
|
||||||
|
normal distribution. The values are effectively drawn from the
|
||||||
|
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
|
||||||
|
with values outside :math:`[a, b]` redrawn until they are within
|
||||||
|
the bounds. The method used for generating the random values works
|
||||||
|
best when :math:`a \leq \text{mean} \leq b`.
|
||||||
|
Args:
|
||||||
|
tensor: an n-dimensional `torch.Tensor`
|
||||||
|
mean: the mean of the normal distribution
|
||||||
|
std: the standard deviation of the normal distribution
|
||||||
|
a: the minimum cutoff value
|
||||||
|
b: the maximum cutoff value
|
||||||
|
Examples:
|
||||||
|
>>> w = torch.empty(3, 5)
|
||||||
|
>>> nn.init.trunc_normal_(w)
|
||||||
|
"""
|
||||||
|
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||||
@ -0,0 +1,264 @@
|
|||||||
|
""" ResNeSt Models
|
||||||
|
|
||||||
|
Paper: `ResNeSt: Split-Attention Networks` - https://arxiv.org/abs/2004.08955
|
||||||
|
|
||||||
|
Adapted from original PyTorch impl w/ weights at https://github.com/zhanghang1989/ResNeSt by Hang Zhang
|
||||||
|
|
||||||
|
Modified for torchscript compat, and consistency with timm by Ross Wightman
|
||||||
|
"""
|
||||||
|
import math
|
||||||
|
import torch
|
||||||
|
import torch.nn.functional as F
|
||||||
|
from torch import nn
|
||||||
|
|
||||||
|
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
||||||
|
from timm.models.layers import DropBlock2d
|
||||||
|
from .helpers import load_pretrained
|
||||||
|
from .layers import SelectiveKernelConv, ConvBnAct, create_attn
|
||||||
|
from .layers.split_attn import SplitAttnConv2d
|
||||||
|
from .registry import register_model
|
||||||
|
from .resnet import ResNet
|
||||||
|
|
||||||
|
|
||||||
|
def _cfg(url='', **kwargs):
|
||||||
|
return {
|
||||||
|
'url': url,
|
||||||
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
||||||
|
'crop_pct': 0.875, 'interpolation': 'bilinear',
|
||||||
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||||
|
'first_conv': 'conv1', 'classifier': 'fc',
|
||||||
|
**kwargs
|
||||||
|
}
|
||||||
|
|
||||||
|
default_cfgs = {
|
||||||
|
'resnest14d': _cfg(
|
||||||
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth'),
|
||||||
|
'resnest26d': _cfg(
|
||||||
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth'),
|
||||||
|
'resnest50d': _cfg(
|
||||||
|
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50-528c19ca.pth'),
|
||||||
|
'resnest101e': _cfg(
|
||||||
|
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest101-22405ba7.pth', input_size=(3, 256, 256)),
|
||||||
|
'resnest200e': _cfg(
|
||||||
|
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest200-75117900.pth', input_size=(3, 320, 320)),
|
||||||
|
'resnest269e': _cfg(
|
||||||
|
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest269-0cc87c48.pth', input_size=(3, 416, 416)),
|
||||||
|
'resnest50d_4s2x40d': _cfg(
|
||||||
|
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_4s2x40d-41d14ed0.pth',
|
||||||
|
interpolation='bicubic'),
|
||||||
|
'resnest50d_1s4x24d': _cfg(
|
||||||
|
url='https://hangzh.s3.amazonaws.com/encoding/models/resnest50_fast_1s4x24d-d4a4f76f.pth',
|
||||||
|
interpolation='bicubic')
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
class ResNestBottleneck(nn.Module):
|
||||||
|
"""ResNet Bottleneck
|
||||||
|
"""
|
||||||
|
# pylint: disable=unused-argument
|
||||||
|
expansion = 4
|
||||||
|
|
||||||
|
def __init__(self, inplanes, planes, stride=1, downsample=None,
|
||||||
|
radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False,
|
||||||
|
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d,
|
||||||
|
attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
|
||||||
|
super(ResNestBottleneck, self).__init__()
|
||||||
|
assert reduce_first == 1 # not supported
|
||||||
|
assert attn_layer is None # not supported
|
||||||
|
assert aa_layer is None # TODO not yet supported
|
||||||
|
assert drop_path is None # TODO not yet supported
|
||||||
|
|
||||||
|
group_width = int(planes * (base_width / 64.)) * cardinality
|
||||||
|
first_dilation = first_dilation or dilation
|
||||||
|
if avd and (stride > 1 or is_first):
|
||||||
|
avd_stride = stride
|
||||||
|
stride = 1
|
||||||
|
else:
|
||||||
|
avd_stride = 0
|
||||||
|
self.radix = radix
|
||||||
|
self.drop_block = drop_block
|
||||||
|
|
||||||
|
self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
|
||||||
|
self.bn1 = norm_layer(group_width)
|
||||||
|
self.act1 = act_layer(inplace=True)
|
||||||
|
self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None
|
||||||
|
|
||||||
|
if self.radix >= 1:
|
||||||
|
self.conv2 = SplitAttnConv2d(
|
||||||
|
group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
|
||||||
|
dilation=first_dilation, groups=cardinality, radix=radix, norm_layer=norm_layer, drop_block=drop_block)
|
||||||
|
self.bn2 = None # FIXME revisit, here to satisfy current torchscript fussyness
|
||||||
|
self.act2 = None
|
||||||
|
else:
|
||||||
|
self.conv2 = nn.Conv2d(
|
||||||
|
group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation,
|
||||||
|
dilation=first_dilation, groups=cardinality, bias=False)
|
||||||
|
self.bn2 = norm_layer(group_width)
|
||||||
|
self.act2 = act_layer(inplace=True)
|
||||||
|
self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None
|
||||||
|
|
||||||
|
self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False)
|
||||||
|
self.bn3 = norm_layer(planes*4)
|
||||||
|
self.act3 = act_layer(inplace=True)
|
||||||
|
self.downsample = downsample
|
||||||
|
|
||||||
|
def zero_init_last_bn(self):
|
||||||
|
nn.init.zeros_(self.bn3.weight)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
residual = x
|
||||||
|
|
||||||
|
out = self.conv1(x)
|
||||||
|
out = self.bn1(out)
|
||||||
|
if self.drop_block is not None:
|
||||||
|
out = self.drop_block(out)
|
||||||
|
out = self.act1(out)
|
||||||
|
|
||||||
|
if self.avd_first is not None:
|
||||||
|
out = self.avd_first(out)
|
||||||
|
|
||||||
|
out = self.conv2(out)
|
||||||
|
if self.bn2 is not None:
|
||||||
|
out = self.bn2(out)
|
||||||
|
if self.drop_block is not None:
|
||||||
|
out = self.drop_block(out)
|
||||||
|
out = self.act2(out)
|
||||||
|
|
||||||
|
if self.avd_last is not None:
|
||||||
|
out = self.avd_last(out)
|
||||||
|
|
||||||
|
out = self.conv3(out)
|
||||||
|
out = self.bn3(out)
|
||||||
|
if self.drop_block is not None:
|
||||||
|
out = self.drop_block(out)
|
||||||
|
|
||||||
|
if self.downsample is not None:
|
||||||
|
residual = self.downsample(x)
|
||||||
|
|
||||||
|
out += residual
|
||||||
|
out = self.act3(out)
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest14d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
""" ResNeSt-14d model. Weights ported from GluonCV.
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest14d']
|
||||||
|
model = ResNet(
|
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|
ResNestBottleneck, [1, 1, 1, 1], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
|
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|
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
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|
if pretrained:
|
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|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
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|
return model
|
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|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest26d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
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|
""" ResNeSt-26d model. Weights ported from GluonCV.
|
||||||
|
"""
|
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|
default_cfg = default_cfgs['resnest26d']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [2, 2, 2, 2], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
|
||||||
|
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest50d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
""" ResNeSt-50d model. Matches paper ResNeSt-50 model, https://arxiv.org/abs/2004.08955
|
||||||
|
Since this codebase supports all possible variations, 'd' for deep stem, stem_width 32, avg in downsample.
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest50d']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1,
|
||||||
|
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest101e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
""" ResNeSt-101e model. Matches paper ResNeSt-101 model, https://arxiv.org/abs/2004.08955
|
||||||
|
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest101e']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [3, 4, 23, 3], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
|
||||||
|
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest200e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
""" ResNeSt-200e model. Matches paper ResNeSt-200 model, https://arxiv.org/abs/2004.08955
|
||||||
|
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest200e']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [3, 24, 36, 3], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
|
||||||
|
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest269e(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
""" ResNeSt-269e model. Matches paper ResNeSt-269 model, https://arxiv.org/abs/2004.08955
|
||||||
|
Since this codebase supports all possible variations, 'e' for deep stem, stem_width 64, avg in downsample.
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest269e']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [3, 30, 48, 8], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1,
|
||||||
|
block_args=dict(radix=2, avd=True, avd_first=False), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest50d_4s2x40d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
"""ResNeSt-50 4s2x40d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest50d_4s2x40d']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=32, avg_down=True, base_width=40, cardinality=2,
|
||||||
|
block_args=dict(radix=4, avd=True, avd_first=True), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
@register_model
|
||||||
|
def resnest50d_1s4x24d(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
|
||||||
|
"""ResNeSt-50 1s4x24d from https://github.com/zhanghang1989/ResNeSt/blob/master/ablation.md
|
||||||
|
"""
|
||||||
|
default_cfg = default_cfgs['resnest50d_1s4x24d']
|
||||||
|
model = ResNet(
|
||||||
|
ResNestBottleneck, [3, 4, 6, 3], num_classes=num_classes, in_chans=in_chans,
|
||||||
|
stem_type='deep', stem_width=32, avg_down=True, base_width=24, cardinality=4,
|
||||||
|
block_args=dict(radix=1, avd=True, avd_first=True), **kwargs)
|
||||||
|
model.default_cfg = default_cfg
|
||||||
|
if pretrained:
|
||||||
|
load_pretrained(model, default_cfg, num_classes, in_chans)
|
||||||
|
return model
|
||||||
Loading…
Reference in new issue