Upload app.py

app.py

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+import gradio as gr
+import torch.nn as nn
+import math
+import torch.utils.model_zoo as model_zoo
+import torch
+import torch.nn.functional as F
+
+__all__ = ['Res2Net', 'res2net50_v1b', 'res2net101_v1b']
+
+model_urls = {
+    'res2net50_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth',
+    'res2net101_v1b_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth',
+}
+
+
+class Bottle2neck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale=4, stype='normal'):
+        """ Constructor
+        Args:
+            inplanes: input channel dimensionality
+            planes: output channel dimensionality
+            stride: conv stride. Replaces pooling layer.
+            downsample: None when stride = 1
+            baseWidth: basic width of conv3x3
+            scale: number of scale.
+            type: 'normal': normal set. 'stage': first block of a new stage.
+        """
+        super(Bottle2neck, self).__init__()
+
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+
+        if scale == 1:
+            self.nums = 1
+        else:
+            self.nums = scale - 1
+        if stype == 'stage':
+            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, bias=False))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+
+        self.conv3 = nn.Conv2d(width * scale, planes * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stype = stype
+        self.scale = scale
+        self.width = width
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0 or self.stype == 'stage':
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+        if self.scale != 1 and self.stype == 'normal':
+            out = torch.cat((out, spx[self.nums]), 1)
+        elif self.scale != 1 and self.stype == 'stage':
+            out = torch.cat((out, self.pool(spx[self.nums])), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Res2Net(nn.Module):
+
+    def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
+        self.inplanes = 64
+        super(Res2Net, self).__init__()
+        self.baseWidth = baseWidth
+        self.scale = scale
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(3, 32, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 32, 3, 1, 1, bias=False),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 64, 3, 1, 1, bias=False)
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.AvgPool2d(kernel_size=stride, stride=stride,
+                             ceil_mode=True, count_include_pad=False),
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=1, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample=downsample,
+                            stype='stage', baseWidth=self.baseWidth, scale=self.scale))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, baseWidth=self.baseWidth, scale=self.scale))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+
+
+def res2net50_v1b(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b model.
+    Res2Net-50 refers to the Res2Net-50_v1b_26w_4s.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net50_v1b_26w_4s']))
+    return model
+
+
+def res2net101_v1b(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
+    return model
+
+
+def res2net50_v1b_26w_4s(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.load(pthfile, map_location='cpu'))  # load model
+    return model
+
+
+def res2net101_v1b_26w_4s(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net101_v1b_26w_4s']))
+    return model
+
+
+def res2net152_v1b_26w_4s(pretrained=False, **kwargs):
+    """Constructs a Res2Net-50_v1b_26w_4s model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = Res2Net(Bottle2neck, [3, 8, 36, 3], baseWidth=26, scale=4, **kwargs)
+    if pretrained:
+        model.load_state_dict(model_zoo.load_url(model_urls['res2net152_v1b_26w_4s']))
+    return model
+
+
+class mutil_model(nn.Module):
+
+    def __init__(self, category_num=10):
+        super(mutil_model, self).__init__()
+        self.model1 = res2net50_v1b_26w_4s(pretrained=False)
+        self.model1.fc = nn.Sequential(
+            nn.Linear(in_features=2048, out_features=category_num, bias=True),
+        )
+        self.model2 = torch.load('./enet_b2_8' + '.pt', map_location=torch.device('cpu'))
+        self.model2.classifier = nn.Sequential(
+            nn.Linear(in_features=1408, out_features=category_num, bias=True),
+        )
+        self.fc = nn.Linear(in_features=category_num * 2, out_features=category_num, bias=True)
+
+    def forward(self, x):
+        x1 = self.model1(x)
+        x2 = self.model2(x)
+        x = torch.cat((x1, x2), 1)
+        x = self.fc(x)
+        return x
+
+
+pth_path = './image_loader10new_model.pt'
+category_num = 10
+
+# "cuda" only when GPUs are available.
+#device = "cuda" if torch.cuda.is_available() else "cpu"
+device = "cpu"
+#Initialize a model, and put it on the device specified.
+# 导入res2net预训练模型
+pthfile = './res2net50_v1b.pth'
+model = res2net50_v1b_26w_4s(pretrained=False)
+# 修改全连接层，输出维度为预测 分类
+num_ftrs = model.fc.in_features
+model.fc = nn.Sequential(
+        nn.Linear(in_features=2048, out_features=1000, bias=True),
+        nn.Dropout(0.5),
+        nn.Linear(1000, out_features=category_num)
+    )
+model.fc = nn.Sequential(
+   nn.Linear(in_features=2048, out_features=category_num, bias=True),
+)
+
+model = model.to(device)
+model.device = device
+model.load_state_dict(torch.load(pth_path,torch.device('cpu')))
+model.eval()
+
+
+# 增加人脸识别模型
+#model = mutil_model(category_num=7)
+#model_state = torch.load('./add_face_emotion_model_7.pt', map_location=torch.device('cpu')).state_dict()
+#model.load_state_dict(model_state)  # 加载模型参数
+#model.eval()
+
+labels = ['中国风', '古典', '电子', '摇滚', '乡村', '说唱', '民谣', '二次元', '轻音乐', '儿歌']
+
+import requests
+import torch
+
+import gradio as gr
+import torchvision.transforms as transforms
+
+# import cv2
+# from PIL import Image
+# PIL
+# from PIL import Image
+# inception_net = tf.keras.applications.MobileNetV2()  # load the model
+
+# Download human-readable labels for ImageNet.
+# response = requests.get("https://git.io/JJkYN")
+# labels = response.text.split("\n")
+print(len(labels))
+
+
+def classify_image(inp):
+    # inp = inp.convert('RGB')
+    # inp = Image.fromarray(inp.astype('uint8'), 'RGB')
+    transform_test = transforms.Compose([
+        # transforms.ToPILImage(),
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406),
+                             (0.229, 0.224, 0.225)),
+    ])
+    inp = transform_test(inp)
+    print(inp)
+    with torch.no_grad():
+        prediction = model(torch.unsqueeze(inp, 0)).flatten()
+    print(prediction)
+    prediction = torch.nn.Softmax(dim=0)(prediction)
+    print(prediction)
+    return {labels[i]: float(prediction[i].item()) for i in range(len(labels))}
+
+
+# print(classify_image("/jj.jpg"))
+# image = gr.inputs.Image(shape=(256, 256))
+# image = gr.inputs.Image()
+# print(image)
+# label = gr.outputs.Label(num_top_classes=6)
+
+gr.Interface(
+    classify_image,
+    # gr.inputs.Image(),
+    gr.inputs.Image(type='pil'),
+    outputs='label'
+    # inputs='image',
+    # outputs='label',
+    # examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
+).launch(share=True)
+# share=True