move old models

Author: ayasyrev

src/model_constructor/base_constructor.py

@@ -1,10 +1,31 @@
-import torch.nn as nn
+"""First version of constructor.
+"""
+# Used in examples.
+# first implementation of xresnet - inspired by fastai version.
 from collections import OrderedDict
-from .layers import ConvLayer, Noop, Flatten
+from functools import partial
 
+import torch.nn as nn
 
-__all__ = ['act_fn', 'Stem', 'DownsampleBlock', 'BasicBlock', 'Bottleneck', 'BasicLayer', 'Body', 'Head', 'init_model',
-           'Net']
+from .layers import ConvLayer, Flatten, Noop
+
+__all__ = [
+    "act_fn",
+    "Stem",
+    "DownsampleBlock",
+    "BasicBlock",
+    "Bottleneck",
+    "BasicLayer",
+    "Body",
+    "Head",
+    "init_model",
+    "Net",
+    "DownsampleLayer",
+    "XResBlock",
+    "xresnet18",
+    "xresnet34",
+    "xresnet50",
+]
 
 
 act_fn = nn.ReLU(inplace=True)
@@ -162,3 +183,58 @@ def __init__(self, stem=Stem,
              ('head', head(body_out * expansion, num_classes, **kwargs))
              ]))
         self.init_model(self)
+
+
+# xresnet from fastai
+
+
+class DownsampleLayer(nn.Sequential):
+    """Downsample layer for Xresnet Resblock"""
+
+    def __init__(self, conv_layer, ni, nf, stride, act,
+                 pool=nn.AvgPool2d(2, ceil_mode=True), pool_1st=True,
+                 **kwargs):
+        layers = [] if stride == 1 else [('pool', pool)]
+        layers += [] if ni == nf else [('idconv', conv_layer(ni, nf, 1, act=act, **kwargs))]
+        if not pool_1st:
+            layers.reverse()
+        super().__init__(OrderedDict(layers))
+
+
+class XResBlock(nn.Module):
+    '''XResnet block'''
+
+    def __init__(self, ni, nh, expansion=1, stride=1, zero_bn=True,
+                 conv_layer=ConvLayer, act_fn=act_fn, **kwargs):
+        super().__init__()
+        nf, ni = nh * expansion, ni * expansion
+        layers = [('conv_0', conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, **kwargs)),
+                  ('conv_1', conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, act_fn=act_fn, **kwargs))
+                  ] if expansion == 1 else [
+                      ('conv_0', conv_layer(ni, nh, 1, act_fn=act_fn, **kwargs)),
+                      ('conv_1', conv_layer(nh, nh, 3, stride=stride, act_fn=act_fn, **kwargs)),
+                      ('conv_2', conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, act_fn=act_fn, **kwargs))
+        ]
+        self.convs = nn.Sequential(OrderedDict(layers))
+        self.identity = DownsampleLayer(conv_layer, ni, nf, stride,
+                                        act=False, act_fn=act_fn, **kwargs) if ni != nf or stride == 2 else Noop()
+        self.merge = Noop()
+        self.act_fn = act_fn
+
+    def forward(self, x):
+        return self.act_fn(self.merge(self.convs(x) + self.identity(x)))
+
+
+def xresnet18(**kwargs):
+    """Constructs xresnet18 model. """
+    return Net(stem_sizes=[32, 32], block=XResBlock, blocks=[2, 2, 2, 2], expansion=1, **kwargs)
+
+
+def xresnet34(**kwargs):
+    """Constructs xresnet34 model. """
+    return Net(stem_sizes=[32, 32], block=XResBlock, blocks=[3, 4, 6, 3], expansion=1, **kwargs)
+
+
+def xresnet50(**kwargs):
+    """Constructs xresnet50 model. """
+    return Net(stem_sizes=[32, 32], block=XResBlock, blocks=[3, 4, 6, 3], expansion=4, **kwargs)

src/model_constructor/mxresnet.py

@@ -1,16 +0,0 @@
-from functools import partial
-
-from .activations import Mish
-from .net import Net
-
-
-__all__ = ["mxresnet_parameters", "mxresnet34", "mxresnet50"]
-
-
-mxresnet_parameters = {"stem_sizes": [3, 32, 64, 64], "act_fn": Mish()}
-mxresnet34 = partial(
-    Net, name="MXResnet32", expansion=1, layers=[3, 4, 6, 3], **mxresnet_parameters
-)
-mxresnet50 = partial(
-    Net, name="MXResnet50", expansion=4, layers=[3, 4, 6, 3], **mxresnet_parameters
-)

src/model_constructor/net.py

@@ -6,7 +6,18 @@
 from .layers import ConvLayer, Flatten, SEBlock, SimpleSelfAttention, noop
 
 
-__all__ = ['init_cnn', 'act_fn', 'ResBlock', 'NewResBlock', 'Net', 'xresnet34', 'xresnet50']
+__all__ = [
+    "init_cnn",
+    "act_fn",
+    "ResBlock",
+    "NewResBlock",
+    "Net",
+    "xresnet34",
+    "xresnet50",
+    "mxresnet_parameters",
+    "mxresnet34",
+    "mxresnet50",
+]
 
 
 act_fn = nn.ReLU(inplace=True)
@@ -209,3 +220,11 @@ def __repr__(self):
 
 xresnet34 = partial(Net, name='xresnet34', expansion=1, layers=[3, 4, 6, 3])
 xresnet50 = partial(Net, name='xresnet34', expansion=4, layers=[3, 4, 6, 3])
+
+mxresnet_parameters = {"stem_sizes": [3, 32, 64, 64], "act_fn": nn.Mish()}
+mxresnet34 = partial(
+    Net, name="MXResnet32", expansion=1, layers=[3, 4, 6, 3], **mxresnet_parameters
+)
+mxresnet50 = partial(
+    Net, name="MXResnet50", expansion=4, layers=[3, 4, 6, 3], **mxresnet_parameters
+)

src/model_constructor/xresnet.py

@@ -1,60 +0,0 @@
-from collections import OrderedDict
-
-import torch.nn as nn
-
-from .base_constructor import Net
-from .layers import ConvLayer, Noop, act
-
-__all__ = ['DownsampleLayer', 'XResBlock', 'xresnet18', 'xresnet34', 'xresnet50']
-
-
-class DownsampleLayer(nn.Sequential):
-    """Downsample layer for Xresnet Resblock"""
-
-    def __init__(self, conv_layer, ni, nf, stride, act,
-                 pool=nn.AvgPool2d(2, ceil_mode=True), pool_1st=True,
-                 **kwargs):
-        layers = [] if stride == 1 else [('pool', pool)]
-        layers += [] if ni == nf else [('idconv', conv_layer(ni, nf, 1, act=act, **kwargs))]
-        if not pool_1st:
-            layers.reverse()
-        super().__init__(OrderedDict(layers))
-
-
-class XResBlock(nn.Module):
-    '''XResnet block'''
-
-    def __init__(self, ni, nh, expansion=1, stride=1, zero_bn=True,
-                 conv_layer=ConvLayer, act_fn=act, **kwargs):
-        super().__init__()
-        nf, ni = nh * expansion, ni * expansion
-        layers = [('conv_0', conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, **kwargs)),
-                  ('conv_1', conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, act_fn=act_fn, **kwargs))
-                  ] if expansion == 1 else [
-                      ('conv_0', conv_layer(ni, nh, 1, act_fn=act_fn, **kwargs)),
-                      ('conv_1', conv_layer(nh, nh, 3, stride=stride, act_fn=act_fn, **kwargs)),
-                      ('conv_2', conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, act_fn=act_fn, **kwargs))
-        ]
-        self.convs = nn.Sequential(OrderedDict(layers))
-        self.identity = DownsampleLayer(conv_layer, ni, nf, stride,
-                                        act=False, act_fn=act_fn, **kwargs) if ni != nf or stride == 2 else Noop()
-        self.merge = Noop()
-        self.act_fn = act_fn
-
-    def forward(self, x):
-        return self.act_fn(self.merge(self.convs(x) + self.identity(x)))
-
-
-def xresnet18(**kwargs):
-    """Constructs xresnet18 model. """
-    return Net(stem_sizes=[32, 32], block=XResBlock, blocks=[2, 2, 2, 2], expansion=1, **kwargs)
-
-
-def xresnet34(**kwargs):
-    """Constructs xresnet34 model. """
-    return Net(stem_sizes=[32, 32], block=XResBlock, blocks=[3, 4, 6, 3], expansion=1, **kwargs)
-
-
-def xresnet50(**kwargs):
-    """Constructs xresnet50 model. """
-    return Net(stem_sizes=[32, 32], block=XResBlock, blocks=[3, 4, 6, 3], expansion=4, **kwargs)

tests/test_models_old.py

@@ -2,8 +2,8 @@
 import torch
 from torch import nn
 
-from model_constructor.mxresnet import mxresnet34, mxresnet50
-from model_constructor.xresnet import xresnet18, xresnet34, xresnet50
+from model_constructor.base_constructor import xresnet18, xresnet34, xresnet50
+from model_constructor.net import mxresnet34, mxresnet50
 
 bs_test = 2
 img_size = 16