Create inference.py

added single image inference

inference.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep  3 13:14:47 2019
+
+@author: viswanatha
+"""
+
+import torch
+from net import *
+import cv2
+import numpy as np
+import argparse
+parser = argparse.ArgumentParser()
+
+#parser.add_argument('model_path',help='Path to the trained model')
+parser.add_argument('image_path',help='Path to test image')
+#parser.add_argument('use_gpu', help='True if gpu is available')
+args = parser.parse_args()
+
+def load_network(network):
+    #save_path = os.path.join(model_dir,'net_%s.pth'%args.which_epoch)
+    save_path = 'checkpoints/market/resnet50/net_last.pth'
+    network.load_state_dict(torch.load(save_path))
+    return network
+
+
+model_dict = torch.load('checkpoints/market/resnet50/net_last.pth')
+
+num_cls_dict = { 'market':30, 'duke':23 }
+num_ids_dict = { 'market':751, 'duke':702 }
+model_dict = {
+    'resnet18'  :  ResNet18_nFC,
+    'resnet34'  :  ResNet34_nFC,
+    'resnet50'  :  ResNet50_nFC,
+    'densenet'  :  DenseNet121_nFC,
+    'resnet50_softmax'  :  ResNet50_nFC_softmax,
+}
+
+num_cls = num_cls_dict['market']
+model = model_dict['resnet50'](num_cls)
+
+model = load_network(model)
+
+img = cv2.imread(args.image_path)
+im = np.moveaxis(img, -1, 0)
+im = np.expand_dims(im, axis=0)
+
+labels = ["young", "teenager", "adult", "old",
+          "backpack", "bag", "handbag",    
+          "clothes", "down", "up", "hair",    
+          "hat",   "gender",  
+          "upblack","upwhite", 
+          "upred", "uppurple", "upyellow", "upgray", 
+          "upblue", "upgreen",
+          "downblack", "downwhite",
+          "downpink", "downpurple", 
+          "downyellow", "downgray", 
+          "downblue", "downgreen", "downbrown"]
+
+img1 = torch.Tensor(im)
+model.eval()
+outs = model.forward(img1)
+for index in range(30):
+    if outs[:,index]==1:
+        print (labels[index])
+