ONNX_Runtime_G2.py

#Reference:https://zhuanlan.zhihu.com/p/479290520
#Pytorch 版本: 实现修改分辨率的功能
import torch 
from torch import nn 
from torch.nn.functional import interpolate 
import torch.onnx 
import cv2 
import numpy as np 
 
 
class SuperResolutionNet(nn.Module): 
 
    def __init__(self): 
        super().__init__() 
 
        self.conv1 = nn.Conv2d(3, 64, kernel_size=9, padding=4) 
        self.conv2 = nn.Conv2d(64, 32, kernel_size=1, padding=0) 
        self.conv3 = nn.Conv2d(32, 3, kernel_size=5, padding=2) 
 
        self.relu = nn.ReLU() 
 
    def forward(self, x, upscale_factor): 
        x = interpolate(x, 
                        scale_factor=upscale_factor.item(), 
                        mode='bicubic', 
                        align_corners=False) 
        out = self.relu(self.conv1(x)) 
        out = self.relu(self.conv2(out)) 
        out = self.conv3(out) 
        return out 
 
 
def init_torch_model(): 
    torch_model = SuperResolutionNet() 
 
    state_dict = torch.load('srcnn.pth')['state_dict'] 
 
    # Adapt the checkpoint 
    for old_key in list(state_dict.keys()): 
        new_key = '.'.join(old_key.split('.')[1:]) 
        state_dict[new_key] = state_dict.pop(old_key) 
 
    torch_model.load_state_dict(state_dict) 
    torch_model.eval() 
    return torch_model 
 
 
model = init_torch_model() 
 
input_img = cv2.imread('face.png').astype(np.float32) 
 
# HWC to NCHW 
input_img = np.transpose(input_img, [2, 0, 1]) 
input_img = np.expand_dims(input_img, 0) 
 
# Inference 
torch_output = model(torch.from_numpy(input_img), torch.tensor(6)).detach().numpy() 
 
# NCHW to HWC 
torch_output = np.squeeze(torch_output, 0) 
torch_output = np.clip(torch_output, 0, 255) 
torch_output = np.transpose(torch_output, [1, 2, 0]).astype(np.uint8) 
 
# Show image 
cv2.imwrite("face_torch_2.png", torch_output) 

# write
x = torch.randn(1, 3, 256, 256) 
 
with torch.no_grad(): 
    torch.onnx.export(model, (x, torch.tensor(4)), 
                      "srcnn2.onnx", 
                      opset_version=11, 
                      input_names=['input', 'factor'], 
                      output_names=['output'])