How should I test the trained model?

[LiaoPerry]

I have successfully trained the model, but I don't know how to test the model, although there is a test program on this website (#19), but there is a "KeyError: 'conv1.0.weight'" error. Error message

[LiaoPerry]

I have successfully trained the model, but I don't know how to test the model, although there is a test program on this website (#19), but there is a "KeyError: 'conv1.0.weight'" error. Error message

I later used this URL (#19) to provide the code to change the success!
Here is my modified code

import numpy as np
import os
import torch
import torch.nn as nn
import torchvision
import torch.utils.data as Data
import torchvision.datasets as dates
from torch.autograd import Variable
from torch.nn import functional as F
import utils.transforms as trans
import utils.utils as util
import layer.loss as ls
import utils.metric as mc
import shutil
import cv2
from PIL import Image
import matplotlib.pyplot as plt
import model.siameseNet.deeplab_v2 as models

### options = ['TSUNAMI','GSV','CMU','CD2014']
datasets = 'CD2014'
if datasets == 'TSUNAMI':
    import cfgs.TSUNAMIconfig as cfg
if datasets == 'GSV':
    import cfgs.GSVconfig as cfg
if datasets == 'CMU':
    import cfgs.CMUconfig as cfg
if datasets == 'CD2014':
    import cfgs.CD2014config as cfg


def data_transform(img1,img2,lbl):
    img1 = img1[:, :, ::-1]  # RGB -> BGR
    img1 = img1.astype(np.float64)
    img1 -= cfg.T0_MEAN_VALUE
    img1 = img1.transpose(2, 0, 1)
    img1 = torch.from_numpy(img1).float()
    img2 = img2[:, :, ::-1]  # RGB -> BGR
    img2 = img2.astype(np.float64)
    img2 -= cfg.T1_MEAN_VALUE
    img2 = img2.transpose(2, 0, 1)
    img2 = torch.from_numpy(img2).float()
    lbl = torch.from_numpy(lbl).long()
    return img1,img2,lbl

def various_distance(out_vec_t0, out_vec_t1,dist_flag):
    if dist_flag == 'l2':
        distance = F.pairwise_distance(out_vec_t0, out_vec_t1, p=2)
    if dist_flag == 'l1':
        distance = F.pairwise_distance(out_vec_t0, out_vec_t1, p=1)
    if dist_flag == 'cos':
        distance = 1 - F.cosine_similarity(out_vec_t0, out_vec_t1)
    return distance


testCase1_01 = '../cd2014/dataset/PTZ/twoPositionPTZCam/t0/in000925.jpg'
testCase1_02 = '../cd2014/dataset/PTZ/twoPositionPTZCam/t1/in001460.jpg'


img1 = Image.open(testCase1_01)
img2 = Image.open(testCase1_02)
height,width,_ = np.array(img1,dtype= np.uint8).shape
img1 = np.array(img1,dtype= np.uint8)
img2 = np.array(img2,dtype= np.uint8)
label = np.zeros((height,width,3),dtype=np.uint8)
img1, img2, label = data_transform(img1, img2, label)
inputs1,input2, targets = img1, img2, label
model = models.SiameseNet(norm_flag='l2')

pretrain_deeplab_path = "/tf/notebooks/cd2014/pretrain/CMU.pth"#or your trained model
# deeplab_pretrain_model = torch.load(pretrain_deeplab_path)
# model.init_parameters_from_deeplab(deeplab_pretrain_model)
checkpoint = torch.load(cfg.TRAINED_BEST_PERFORMANCE_CKPT)#The main different parts with https://github.com/gmayday1997/SceneChangeDet/issues/19
model.load_state_dict(checkpoint['state_dict'])#The main different parts with https://github.com/gmayday1997/SceneChangeDet/issues/19

model = model.cuda()
model.eval()

cont_conv5_total,cont_fc_total,cont_embedding_total,num = 0.0,0.0,0.0,0.0
metric_for_conditions = util.init_metric_for_class_for_cmu(1)

inputs1,input2,targets = inputs1.cuda(),input2.cuda(), targets.cuda()
inputs1,inputs2,targets = Variable(inputs1.unsqueeze(0), volatile=True),Variable(input2.unsqueeze(0),volatile=True) ,Variable(targets)
out_conv5,out_fc,out_embedding = model(inputs1,inputs2)
out_conv5_t0, out_conv5_t1 = out_conv5

output_t0,output_t1,dist_flag = out_conv5_t0,out_conv5_t1,'l2'
interp = nn.Upsample(size=[cfg.TRANSFROM_SCALES[1],cfg.TRANSFROM_SCALES[0]], mode='bilinear')
n, c, h, w = output_t0.data.shape
out_t0_rz = torch.transpose(output_t0.view(c, h * w), 1, 0)
out_t1_rz = torch.transpose(output_t1.view(c, h * w), 1, 0)
distance = various_distance(out_t0_rz,out_t1_rz,dist_flag=dist_flag)
similar_distance_map = distance.view(h,w).data.cpu().numpy()
similar_distance_map_rz = interp(Variable(torch.from_numpy(similar_distance_map[np.newaxis, np.newaxis, :])))
similar_dis_map_colorize = cv2.applyColorMap(np.uint8(255 * similar_distance_map_rz.data.cpu().numpy()[0][0]), cv2.COLORMAP_JET)
plt.imshow(similar_dis_map_colorize)
plt.show()

[deeprine]

모델을 성공적으로 훈련했지만 이 웹 사이트( #19 ) 에 테스트 프로그램이 있지만 모델을 테스트하는 방법을 모르지만 "KeyError: 'conv1.0.weight'" 오류가 있습니다. 에러 메시지

나중에 이 URL( #19 )을 사용하여 성공을 변경하는 코드를 제공했습니다!
수정된 코드는 다음과 같습니다.

import numpy as np
import os
import torch
import torch.nn as nn
import torchvision
import torch.utils.data as Data
import torchvision.datasets as dates
from torch.autograd import Variable
from torch.nn import functional as F
import utils.transforms as trans
import utils.utils as util
import layer.loss as ls
import utils.metric as mc
import shutil
import cv2
from PIL import Image
import matplotlib.pyplot as plt
import model.siameseNet.deeplab_v2 as models

### options = ['TSUNAMI','GSV','CMU','CD2014']
datasets = 'CD2014'
if datasets == 'TSUNAMI':
    import cfgs.TSUNAMIconfig as cfg
if datasets == 'GSV':
    import cfgs.GSVconfig as cfg
if datasets == 'CMU':
    import cfgs.CMUconfig as cfg
if datasets == 'CD2014':
    import cfgs.CD2014config as cfg


def data_transform(img1,img2,lbl):
    img1 = img1[:, :, ::-1]  # RGB -> BGR
    img1 = img1.astype(np.float64)
    img1 -= cfg.T0_MEAN_VALUE
    img1 = img1.transpose(2, 0, 1)
    img1 = torch.from_numpy(img1).float()
    img2 = img2[:, :, ::-1]  # RGB -> BGR
    img2 = img2.astype(np.float64)
    img2 -= cfg.T1_MEAN_VALUE
    img2 = img2.transpose(2, 0, 1)
    img2 = torch.from_numpy(img2).float()
    lbl = torch.from_numpy(lbl).long()
    return img1,img2,lbl

def various_distance(out_vec_t0, out_vec_t1,dist_flag):
    if dist_flag == 'l2':
        distance = F.pairwise_distance(out_vec_t0, out_vec_t1, p=2)
    if dist_flag == 'l1':
        distance = F.pairwise_distance(out_vec_t0, out_vec_t1, p=1)
    if dist_flag == 'cos':
        distance = 1 - F.cosine_similarity(out_vec_t0, out_vec_t1)
    return distance


testCase1_01 = '../cd2014/dataset/PTZ/twoPositionPTZCam/t0/in000925.jpg'
testCase1_02 = '../cd2014/dataset/PTZ/twoPositionPTZCam/t1/in001460.jpg'


img1 = Image.open(testCase1_01)
img2 = Image.open(testCase1_02)
height,width,_ = np.array(img1,dtype= np.uint8).shape
img1 = np.array(img1,dtype= np.uint8)
img2 = np.array(img2,dtype= np.uint8)
label = np.zeros((height,width,3),dtype=np.uint8)
img1, img2, label = data_transform(img1, img2, label)
inputs1,input2, targets = img1, img2, label
model = models.SiameseNet(norm_flag='l2')

pretrain_deeplab_path = "/tf/notebooks/cd2014/pretrain/CMU.pth"#or your trained model
# deeplab_pretrain_model = torch.load(pretrain_deeplab_path)
# model.init_parameters_from_deeplab(deeplab_pretrain_model)
checkpoint = torch.load(cfg.TRAINED_BEST_PERFORMANCE_CKPT)#The main different parts with https://github.com/gmayday1997/SceneChangeDet/issues/19
model.load_state_dict(checkpoint['state_dict'])#The main different parts with https://github.com/gmayday1997/SceneChangeDet/issues/19

model = model.cuda()
model.eval()

cont_conv5_total,cont_fc_total,cont_embedding_total,num = 0.0,0.0,0.0,0.0
metric_for_conditions = util.init_metric_for_class_for_cmu(1)

inputs1,input2,targets = inputs1.cuda(),input2.cuda(), targets.cuda()
inputs1,inputs2,targets = Variable(inputs1.unsqueeze(0), volatile=True),Variable(input2.unsqueeze(0),volatile=True) ,Variable(targets)
out_conv5,out_fc,out_embedding = model(inputs1,inputs2)
out_conv5_t0, out_conv5_t1 = out_conv5

output_t0,output_t1,dist_flag = out_conv5_t0,out_conv5_t1,'l2'
interp = nn.Upsample(size=[cfg.TRANSFROM_SCALES[1],cfg.TRANSFROM_SCALES[0]], mode='bilinear')
n, c, h, w = output_t0.data.shape
out_t0_rz = torch.transpose(output_t0.view(c, h * w), 1, 0)
out_t1_rz = torch.transpose(output_t1.view(c, h * w), 1, 0)
distance = various_distance(out_t0_rz,out_t1_rz,dist_flag=dist_flag)
similar_distance_map = distance.view(h,w).data.cpu().numpy()
similar_distance_map_rz = interp(Variable(torch.from_numpy(similar_distance_map[np.newaxis, np.newaxis, :])))
similar_dis_map_colorize = cv2.applyColorMap(np.uint8(255 * similar_distance_map_rz.data.cpu().numpy()[0][0]), cv2.COLORMAP_JET)
plt.imshow(similar_dis_map_colorize)
plt.show()

I proceeded according to this code, but there was an error.
"KeyError: 'conv1.0.weight'" error. Error message
Is there a way?