From 7ccd4896f6616b0adc952a12ccc86446197f2f7d Mon Sep 17 00:00:00 2001
From: lyuwenyu <wenyu.lyu@gmail.com>
Date: Thu, 25 Mar 2021 14:38:31 +0800
Subject: [PATCH] add alexnet vgg resnet series

---
 hubconf.py | 119 +++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 116 insertions(+), 3 deletions(-)

diff --git a/hubconf.py b/hubconf.py
index 398fffe7..d348bccc 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -3,9 +3,9 @@ dependencies = ['paddle', 'numpy']
 
 import paddle
 
+from ppcls.modeling.architectures import alexnet as _alexnet
+from ppcls.modeling.architectures import vgg as _vgg 
 from ppcls.modeling.architectures import resnet as _resnet 
-
-
 # _checkpoints = {
 #     'ResNet18': 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams',
 #     'ResNet34': 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams',
@@ -37,6 +37,80 @@ def _load_pretrained_urls():
 _checkpoints = _load_pretrained_urls()
 
 
+
+def AlexNet(**kwargs):
+    '''AlexNet
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _alexnet.AlexNet(**kwargs)
+    if pretrained:
+        assert 'AlexNet' in _checkpoints, 'Not provide `AlexNet` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['AlexNet'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+
+def VGG11(**kwargs):
+    '''VGG11
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _vgg.VGG11(**kwargs)
+    if pretrained:
+        assert 'VGG11' in _checkpoints, 'Not provide `VGG11` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['VGG11'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+def VGG13(**kwargs):
+    '''VGG13
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _vgg.VGG13(**kwargs)
+    if pretrained:
+        assert 'VGG13' in _checkpoints, 'Not provide `VGG13` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['VGG13'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+def VGG16(**kwargs):
+    '''VGG16
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _vgg.VGG16(**kwargs)
+    if pretrained:
+        assert 'VGG16' in _checkpoints, 'Not provide `VGG16` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['VGG16'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+def VGG19(**kwargs):
+    '''VGG19
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _vgg.VGG19(**kwargs)
+    if pretrained:
+        assert 'VGG19' in _checkpoints, 'Not provide `VGG19` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['VGG19'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+
+
 def ResNet18(**kwargs):
     '''ResNet18
     '''
@@ -51,7 +125,6 @@ def ResNet18(**kwargs):
     return model
 
 
-
 def ResNet34(**kwargs):
     '''ResNet34
     '''
@@ -66,3 +139,43 @@ def ResNet34(**kwargs):
     return model
 
 
+def ResNet50(**kwargs):
+    '''ResNet50
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _resnet.ResNet50(**kwargs)
+    if pretrained:
+        assert 'ResNet50' in _checkpoints, 'Not provide `ResNet50` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['ResNet50'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+def ResNet101(**kwargs):
+    '''ResNet101
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _resnet.ResNet101(**kwargs)
+    if pretrained:
+        assert 'ResNet101' in _checkpoints, 'Not provide `ResNet101` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['ResNet101'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
+
+
+def ResNet152(**kwargs):
+    '''ResNet152
+    '''
+    pretrained = kwargs.pop('pretrained', False)
+
+    model = _resnet.ResNet152(**kwargs)
+    if pretrained:
+        assert 'ResNet152' in _checkpoints, 'Not provide `ResNet152` pretrained model.'
+        path = paddle.utils.download.get_weights_path_from_url(_checkpoints['ResNet152'])
+        model.set_state_dict(paddle.load(path))
+
+    return model
-- 
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