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Update app.py
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app.py
CHANGED
@@ -221,7 +221,7 @@ def res2net152_v1b_26w_4s(pretrained=False, **kwargs):
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class mutil_model(nn.Module):
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def __init__(self, category_num=
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super(mutil_model, self).__init__()
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self.model1 = res2net50_v1b_26w_4s(pretrained=False)
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self.model1.fc = nn.Sequential(
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@@ -241,39 +241,40 @@ class mutil_model(nn.Module):
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return x
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pth_path = './
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category_num =
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# "cuda" only when GPUs are available.
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device = "cuda" if torch.cuda.is_available() else "cpu"
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#
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# 导入res2net预训练模型
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# pthfile = '
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# 修改全连接层,输出维度为预测 分类
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# 增加人脸识别模型
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model = mutil_model(category_num=7)
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model_state = torch.load('./
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model.load_state_dict(model_state) # 加载模型参数
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model.eval()
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labels = ['
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import requests
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import torch
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@@ -327,4 +328,5 @@ gr.Interface(
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# inputs='image',
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# outputs='label',
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# examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
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).launch(
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class mutil_model(nn.Module):
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def __init__(self, category_num=10):
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super(mutil_model, self).__init__()
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self.model1 = res2net50_v1b_26w_4s(pretrained=False)
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self.model1.fc = nn.Sequential(
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return x
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pth_path = './res2net_pretrain_model_999.pt'
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category_num = 9
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# "cuda" only when GPUs are available.
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#device = "cuda" if torch.cuda.is_available() else "cpu"
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device = "cpu"
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#Initialize a model, and put it on the device specified.
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# 导入res2net预训练模型
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# pthfile = './res2net50_v1b.pth'
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model = res2net50_v1b_26w_4s(pretrained=False)
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# 修改全连接层,输出维度为预测 分类
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num_ftrs = model.fc.in_features
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model.fc = nn.Sequential(
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nn.Linear(in_features=2048, out_features=1000, bias=True),
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nn.Dropout(0.5),
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nn.Linear(1000, out_features=category_num)
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)
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model.fc = nn.Sequential(
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nn.Linear(in_features=2048, out_features=category_num, bias=True),
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)
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model = model.to(device)
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model.device = device
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model.load_state_dict(torch.load(pth_path,torch.device('cpu')))
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model.eval()
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# 增加人脸识别模型
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#model = mutil_model(category_num=7)
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#model_state = torch.load('./add_face_emotion_model_7.pt', map_location=torch.device('cpu')).state_dict()
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#model.load_state_dict(model_state) # 加载模型参数
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#model.eval()
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labels = ['中国风', '古典', '电子', '摇滚', '乡村', '说唱', '民谣', '动漫', '现代']
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import requests
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import torch
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# inputs='image',
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# outputs='label',
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# examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
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).launch(share=True)
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# share=True
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