Upload inference.py

inference.py

@@ -0,0 +1,105 @@
+import os, glob, sys, logging
+import argparse, datetime, time
+import numpy as np
+import cv2
+from PIL import Image
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from models import model, basic
+from utils import util
+
+
+def setup_model(checkpt_path, device="cuda"):
+    #print('--------------', torch.cuda.is_available())
+    """Load the model into memory to make running multiple predictions efficient"""
+    colorLabeler = basic.ColorLabel(device=device)
+    colorizer = model.AnchorColorProb(inChannel=1, outChannel=313, enhanced=True, colorLabeler=colorLabeler)
+    colorizer = colorizer.to(device)
+    #checkpt_path = "./checkpoints/disco-beta.pth.rar"
+    assert os.path.exists(checkpt_path), "No checkpoint found!"
+    data_dict = torch.load(checkpt_path, map_location=torch.device('cpu'))
+    colorizer.load_state_dict(data_dict['state_dict'])
+    colorizer.eval()
+    return colorizer, colorLabeler
+
+
+def resize_ab2l(gray_img, lab_imgs, vis=False):
+    H, W = gray_img.shape[:2]
+    reszied_ab = cv2.resize(lab_imgs[:,:,1:], (W,H), interpolation=cv2.INTER_LINEAR)
+    if vis:
+        gray_img = cv2.resize(lab_imgs[:,:,:1], (W,H), interpolation=cv2.INTER_LINEAR)
+        return np.concatenate((gray_img[:,:,np.newaxis], reszied_ab), axis=2)
+    else:
+        return np.concatenate((gray_img, reszied_ab), axis=2)
+
+def prepare_data(rgb_img, target_res):
+    rgb_img = np.array(rgb_img / 255., np.float32)
+    lab_img = cv2.cvtColor(rgb_img, cv2.COLOR_RGB2LAB)
+    org_grays = (lab_img[:,:,[0]]-50.) / 50.
+    lab_img = cv2.resize(lab_img, target_res, interpolation=cv2.INTER_LINEAR)
+        
+    lab_img = torch.from_numpy(lab_img.transpose((2, 0, 1)))
+    gray_img = (lab_img[0:1,:,:]-50.) / 50.
+    ab_chans = lab_img[1:3,:,:] / 110.
+    input_grays = gray_img.unsqueeze(0)
+    input_colors = ab_chans.unsqueeze(0)
+    return input_grays, input_colors, org_grays
+
+
+def colorize_grayscale(colorizer, color_class, rgb_img, hint_img, n_anchors, is_high_res, is_editable, device="cuda"):
+    n_anchors = int(n_anchors)
+    n_anchors = max(n_anchors, 3)
+    n_anchors = min(n_anchors, 14)
+    target_res = (512,512) if is_high_res else (256,256)
+    input_grays, input_colors, org_grays = prepare_data(rgb_img, target_res)
+    input_grays = input_grays.to(device)
+    input_colors = input_colors.to(device)
+    
+    if is_editable:
+        print('>>>:editable mode')
+        sampled_T = -1
+        _, input_colors, _ = prepare_data(hint_img, target_res)
+        input_colors = input_colors.to(device)
+        pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \
+                                                                input_colors, n_anchors, sampled_T)
+    else:
+        print('>>>:automatic mode')
+        sampled_T = 0
+        pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \
+                                                                input_colors, n_anchors, sampled_T)
+
+    pred_labs = torch.cat((input_grays,enhanced_ab), dim=1)
+    lab_imgs = basic.tensor2array(pred_labs).squeeze(axis=0)
+    lab_imgs = resize_ab2l(org_grays, lab_imgs)
+        
+    lab_imgs[:,:,0] = lab_imgs[:,:,0] * 50.0 + 50.0
+    lab_imgs[:,:,1:3] = lab_imgs[:,:,1:3] * 110.0
+    rgb_output = cv2.cvtColor(lab_imgs[:,:,:], cv2.COLOR_LAB2RGB)
+    return (rgb_output*255.0).astype(np.uint8)
+
+
+def predict_anchors(colorizer, color_class, rgb_img, n_anchors, is_high_res, is_editable, device="cuda"):
+    n_anchors = int(n_anchors)
+    n_anchors = max(n_anchors, 3)
+    n_anchors = min(n_anchors, 14)
+    target_res = (512,512) if is_high_res else (256,256)
+    input_grays, input_colors, org_grays = prepare_data(rgb_img, target_res)
+    input_grays = input_grays.to(device)
+    input_colors = input_colors.to(device)
+                
+    sampled_T, sp_size = 0, 16
+    pal_logit, ref_logit, enhanced_ab, affinity_map, spix_colors, hint_mask = colorizer(input_grays, \
+                                                            input_colors, n_anchors, sampled_T)
+    pred_probs = pal_logit
+    guided_colors = color_class.decode_ind2ab(ref_logit, T=0)
+    guided_colors = basic.upfeat(guided_colors, affinity_map, sp_size, sp_size)
+    anchor_masks = basic.upfeat(hint_mask, affinity_map, sp_size, sp_size)
+    marked_labs = basic.mark_color_hints(input_grays, guided_colors, anchor_masks, base_ABs=None)
+    lab_imgs = basic.tensor2array(marked_labs).squeeze(axis=0)
+    lab_imgs = resize_ab2l(org_grays, lab_imgs, vis=True)
+        
+    lab_imgs[:,:,0] = lab_imgs[:,:,0] * 50.0 + 50.0
+    lab_imgs[:,:,1:3] = lab_imgs[:,:,1:3] * 110.0
+    rgb_output = cv2.cvtColor(lab_imgs[:,:,:], cv2.COLOR_LAB2RGB)
+    return (rgb_output*255.0).astype(np.uint8)