def test(self, valLoader, epoch):
print(
"*********************************test****************************"
)
self.model.eval()
avgLoss = 0
visImg = []
self.progbar = progbar(len(valLoader), width=self.opt.barwidth)
print("valLoader : ", len(valLoader))
for i, (inputData, line, imgids) in enumerate(valLoader):
if self.opt.debug and i > 10:
break
start = time.time()
with torch.no_grad():
inputData_var, line_var = Variable(inputData), Variable(line)
#print(inputData_var.shape, ' ', line_var.shape)
if self.opt.GPU:
inputData_var = inputData_var.cuda()
line_var = line_var.cuda()
dataTime = time.time() - start
loss, line_loss, line_result = self.model.forward(
inputData_var, line_var)
#print(line_result)
img_line = line[0][0].numpy()
''' img_origin imshow
img_origin = inputData[0].numpy()
print(line_result.shape)
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
img_origin = np.transpose(img_origin, (1, 2, 0))
img_origin = t.unNormalize(img_origin, mean, std)
'''
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
img_result = line_result[0].cpu().detach().numpy()
print(np.min(img_result), ' ', np.max(img_result))
img_result_plt = line_result[0][0].cpu().detach().numpy()
img_result = np.transpose(img_result, (1, 2, 0))
img_result = t.unNormalize(img_result, mean, std)
print(img_result)
print(np.min(img_result), ' ', np.max(img_result))
img_result = np.clip(img_result, 0, 255)
img_result = np.uint8(img_result)
#print(img_result)
img_result = cv2.resize(img_result,
dsize=(320, 320),
interpolation=cv2.INTER_LANCZOS4)
cv2.imshow("Gray", img_result)
cv2.waitKey(30000)
#print(img_result)
#print(img_result.shape)
#plt.figure(1)
#plt.axis("off")
# cv2.imshow("Gray",img_result)
#plt.imshow(img_result_plt,cmap="gray")
# plt.imshow(cv2.cvtColor(img_origin, cv2.COLOR_BGR2RGB))
# plt.imshow(img)
#plt.show()
#print("x_1 shape[0]", x_1.shape)
# plt.figure(figsize=(8, 8))
# arr_img1 = []
# if i == 1:
# idx = 1
# for j in range(36):
# img = x_1[1][j].cpu().detach().numpy()
# ax = plt.subplot(6, 6, idx)
# ax.set_xticks([])
# ax.set_yticks([])
# plt.imshow(img, cmap='gray')
# idx += 1
# cv2.imshow("img",img)
# cv2.waitKey(300)
# plt.show()
# for _ in range(8):
# for _ in range(8):
# ax = plt.subplot(8, 8, idx)
# ax.set_xticks([])
# ax.set_yticks([])
# plt.imshow(arr_img1[idx], cmap='gray')
# idx += 1
# print("x_1 shape[0]", x_1[0][0].shape)
# print("x_1[0][0] type : ", type(x_1[0][0]))
# img = x_1[0][0].cpu().detach().numpy()
# print(img)
# cv2.imshow("img",img)
# cv2.waitKey(300000)
return 0
def process(im):
mean = torch.Tensor([0.485, 0.456, 0.406])
std = torch.Tensor([0.229, 0.224, 0.225])
im = np.transpose(im, (1, 2, 0))
im = t.unNormalize(im, mean, std)
return im