--- a
+++ b/BioSeqNet/resnest/torch/ablation.py
@@ -0,0 +1,106 @@
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+## Created by: Hang Zhang
+## Email: zhanghang0704@gmail.com
+## Copyright (c) 2020
+##
+## LICENSE file in the root directory of this source tree 
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+"""ResNeSt ablation study models"""
+
+import torch
+from .resnet import ResNet, Bottleneck
+
+__all__ = ['resnest50_fast_1s1x64d', 'resnest50_fast_2s1x64d', 'resnest50_fast_4s1x64d',
+           'resnest50_fast_1s2x40d', 'resnest50_fast_2s2x40d', 'resnest50_fast_4s2x40d',
+           'resnest50_fast_1s4x24d']
+
+_url_format = 'https://hangzh.s3.amazonaws.com/encoding/models/{}-{}.pth'
+
+_model_sha256 = {name: checksum for checksum, name in [
+    ('d8fbf808', 'resnest50_fast_1s1x64d'),
+    ('44938639', 'resnest50_fast_2s1x64d'),
+    ('f74f3fc3', 'resnest50_fast_4s1x64d'),
+    ('32830b84', 'resnest50_fast_1s2x40d'),
+    ('9d126481', 'resnest50_fast_2s2x40d'),
+    ('41d14ed0', 'resnest50_fast_4s2x40d'),
+    ('d4a4f76f', 'resnest50_fast_1s4x24d'),
+    ]}
+
+def short_hash(name):
+    if name not in _model_sha256:
+        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
+    return _model_sha256[name][:8]
+
+resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for
+    name in _model_sha256.keys()
+}
+
+def resnest50_fast_1s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=1, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_1s1x64d'], progress=True, check_hash=True))
+    return model
+
+def resnest50_fast_2s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_2s1x64d'], progress=True, check_hash=True))
+    return model
+
+def resnest50_fast_4s1x64d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=4, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_4s1x64d'], progress=True, check_hash=True))
+    return model
+
+def resnest50_fast_1s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=1, groups=2, bottleneck_width=40,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_1s2x40d'], progress=True, check_hash=True))
+    return model
+
+def resnest50_fast_2s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=2, groups=2, bottleneck_width=40,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_2s2x40d'], progress=True, check_hash=True))
+    return model
+
+def resnest50_fast_4s2x40d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=4, groups=2, bottleneck_width=40,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_4s2x40d'], progress=True, check_hash=True))
+    return model
+
+def resnest50_fast_1s4x24d(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=1, groups=4, bottleneck_width=24,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=True, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50_fast_1s4x24d'], progress=True, check_hash=True))
+    return model