diff --git a/test_model.py b/test_model.py index f91bf13..e2f7121 100644 --- a/test_model.py +++ b/test_model.py @@ -17,131 +17,8 @@ device = 'cuda' wandb.init(project="crestereo", entity="cpt-captain") -# Ref: https://github.com/megvii-research/CREStereo/blob/master/test.py -def inference(left, right, model, n_iter=20): - print("Model Forwarding...") - imgL = left.transpose(2, 0, 1) - imgR = right.transpose(2, 0, 1) - imgL = np.ascontiguousarray(imgL[None, :, :, :]) - imgR = np.ascontiguousarray(imgR[None, :, :, :]) - - imgL = torch.tensor(imgL.astype("float32")).to(device) - imgR = torch.tensor(imgR.astype("float32")).to(device) - - imgL_dw2 = F.interpolate( - imgL, - size=(imgL.shape[2] // 2, imgL.shape[3] // 2), - mode="bilinear", - align_corners=True, - ) - imgR_dw2 = F.interpolate( - imgR, - size=(imgL.shape[2] // 2, imgL.shape[3] // 2), - mode="bilinear", - align_corners=True, - ) - # print(imgR_dw2.shape) - with torch.inference_mode(): - pred_flow_dw2 = model(imgL_dw2, imgR_dw2, iters=n_iter, flow_init=None) - - pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2) - pred_disp = torch.squeeze(pred_flow[:, 0, :, :]).cpu().detach().numpy() - - return pred_disp - - -def inference_ctd(left, right, gt_disp, mask, model, epoch, n_iter=20): - print("Model Forwarding...") - # print(left.shape) - left = left.cpu().detach().numpy() - imgL = left - imgR = right.cpu().detach().numpy() - imgL = np.ascontiguousarray(imgL[None, :, :, :]) - imgR = np.ascontiguousarray(imgR[None, :, :, :]) - - # chosen for convenience - device = torch.device('cuda:0') - - imgL = torch.tensor(imgL.astype("float32")).to(device) - imgR = torch.tensor(imgR.astype("float32")).to(device) - imgL = imgL.transpose(2, 3).transpose(1, 2) - - imgL_dw2 = F.interpolate( - imgL, - size=(imgL.shape[2] // 2, imgL.shape[3] // 2), - mode="bilinear", - align_corners=True, - ) - imgR_dw2 = F.interpolate( - imgR, - size=(imgL.shape[2] // 2, imgL.shape[3] // 2), - mode="bilinear", - align_corners=True, - ) - with torch.inference_mode(): - pred_flow_dw2 = model(image1=imgL_dw2, image2=imgR_dw2, iters=n_iter, flow_init=None) - pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2) - - log = {} - for i, (pf, pf_dw2) in enumerate(zip(pred_flow, pred_flow_dw2)): - pred_disp = torch.squeeze(pf[:, 0, :, :]).cpu().detach().numpy() - pred_disp_dw2 = torch.squeeze(pf_dw2[:, 0, :, :]).cpu().detach().numpy() - - pred_disp_norm = cv2.normalize(pred_disp, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U) - pred_disp_dw2_norm = cv2.normalize(pred_disp_dw2, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U) - - log[f'pred_{i}'] = wandb.Image( - np.array([pred_disp.reshape(480, 640)]), - caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}", - ) - log[f'pred_norm_{i}'] = wandb.Image( - np.array([pred_disp_norm.reshape(480, 640)]), - caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}", - ) - - log[f'pred_dw2_{i}'] = wandb.Image( - np.array([pred_disp_dw2.reshape(240, 320)]), - caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}", - ) - log[f'pred_dw2_norm_{i}'] = wandb.Image( - np.array([pred_disp_dw2_norm.reshape(240, 320)]), - caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}", - ) - - log['input_left'] = wandb.Image(left.astype('uint8'), caption="Input Left") - log['input_right'] = wandb.Image(right.cpu().detach().numpy().transpose(1, 2, 0).astype('uint8'), - caption="Input Right") - - log['gt_disp'] = wandb.Image(gt_disp, caption=f"GT Disparity\n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}") - - disp_error = gt_disp - disp - log['disp_error'] = wandb.Image( - normalize_and_colormap(disp_error), - caption=f"Disp. Error\n{disp_error.min():.{2}f}/{disp_error.max():.{2}f}\n{disp_error.mean():.{2}f}", - ) - - wandb.log(log) - - def do_infer(left_img, right_img, gt_disp, model): - in_h, in_w = left_img.shape[:2] - - # Resize image in case the GPU memory overflows - eval_h, eval_w = (in_h, in_w) - - # FIXME borked for some reason, hopefully not very important - - imgL = left_img.cpu().detach().numpy() if isinstance(left_img, torch.Tensor) else left_img - imgR = right_img.cpu().detach().numpy() if isinstance(right_img, torch.Tensor) else right_img - - imgL = cv2.resize(imgL, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR) - imgR = cv2.resize(imgR, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR) - - # pred = ctd_inference(imgL, imgR, gt_disp, None, model, None, n_iter=20) - pred = ctd_inference(left_img, right_img, gt_disp, None, model, None, n_iter=20, wandb_log=False) - - t = float(in_w) / float(eval_w) - disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t + disp = ctd_inference(left_img, right_img, gt_disp, None, model, None, n_iter=20, wandb_log=False) disp_vis = normalize_and_colormap(disp) gt_disp_vis = normalize_and_colormap(gt_disp) @@ -151,6 +28,10 @@ def do_infer(left_img, right_img, gt_disp, model): disp_err = normalize_and_colormap(disp_err.abs()) wandb.log({ + 'disp': wandb.Image( + disp, + caption=f"Pred. Disparity \n{disp.min():.{2}f}/{disp.max():.{2}f}", + ), 'disp_vis': wandb.Image( disp_vis, caption=f"Pred. Disparity \n{disp.min():.{2}f}/{disp.max():.{2}f}", @@ -193,7 +74,6 @@ if __name__ == '__main__': model = Model(max_disp=256, mixed_precision=False, test_mode=True) model = nn.DataParallel(model, device_ids=[device]) - # model.load_state_dict(torch.load(model_path), strict=False) state_dict = torch.load(model_path)['state_dict'] model.load_state_dict(state_dict, strict=True) model.to(device)