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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 = './res2net_model_6_new.pt' | |
category_num = 6 | |
# "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 = ['伤感', '开心', '励志', '宣泄', '平静', '感人'] | |
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 |