app.py

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
    
pth_path = './res2net_pretrain_model_7_1.pt'
category_num = 7


# "cuda" only when GPUs are available.
device = "cuda" if torch.cuda.is_available() else "cpu"

# Initialize a model, and put it on the device specified.
# 导入res2net预训练模型     
pthfile = '/cbd_lixiaogang_lixianneng/morror_art/pre_train_model/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()

labels = ['怀旧','伤感','快乐','激励','清新','浪漫','思念']

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(debug=True)
#share=True