add mobilenet ssd pascal model

models/mobilenet_ssd_pascal/README.md

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+# Mobilenet SSD    
+
+We offer the mobilenet(1.0) ssd model trained on PASCAL VOC0712 dataset. This model can be deployed on embedded system
+and you can modify the network to adapt to your own application.
+
+## run the demo
+1. Install PaddlePaddle(see:  [PaddlePaddle installation instructions](http://paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html))
+
+2. Download the [parameters](https://pan.baidu.com/s/1o7S8yWq) trained on PASCAL VOC0712.
+
+3. `python infer.py`
+
+
+## train on your own dataset
+You can modify the network to adapt to your own application. PaddlePaddle provides a detailed document to show how to train your model with SSD, refer the document [here](https://github.com/PaddlePaddle/models/tree/develop/ssd).
+

models/mobilenet_ssd_pascal/config/label_list

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+background
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+pottedplant
+sheep
+sofa
+train
+tvmonitor

models/mobilenet_ssd_pascal/config/pascal_voc_conf.py

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+from easydict import EasyDict as edict
+import numpy as np
+
+__C = edict()
+cfg = __C
+
+__C.TRAIN = edict()
+
+__C.IMG_WIDTH = 300
+__C.IMG_HEIGHT = 300
+__C.IMG_CHANNEL = 3
+__C.CLASS_NUM = 21
+__C.BACKGROUND_ID = 0
+
+# training settings
+__C.TRAIN.MOMENTUM = 0.9
+__C.TRAIN.BATCH_SIZE = 32
+__C.TRAIN.LEARNING_RATE = 0.0005 / 4
+#__C.TRAIN.LEARNING_RATE = 0.001 / __C.TRAIN.BATCH_SIZE
+__C.TRAIN.NUM_PASS = 50000
+__C.TRAIN.L2REGULARIZATION = 0.0005 * 4
+#__C.TRAIN.L2REGULARIZATION = 0.0005 * __C.TRAIN.BATCH_SIZE
+__C.TRAIN.LEARNING_RATE_DECAY_A = 0.1
+__C.TRAIN.LEARNING_RATE_DECAY_B = 16551 * 80
+__C.TRAIN.LEARNING_RATE_SCHEDULE = 'discexp'
+#__C.TRAIN.LEARNING_RATE_SCHEDULE = 'constant'
+
+__C.NET = edict()
+
+# configuration for multibox_loss_layer
+__C.NET.MBLOSS = edict()
+__C.NET.MBLOSS.OVERLAP_THRESHOLD = 0.5
+__C.NET.MBLOSS.NEG_POS_RATIO = 3.0
+__C.NET.MBLOSS.NEG_OVERLAP = 0.5
+
+# configuration for detection_map
+__C.NET.DETMAP = edict()
+__C.NET.DETMAP.OVERLAP_THRESHOLD = 0.5
+__C.NET.DETMAP.EVAL_DIFFICULT = False
+__C.NET.DETMAP.AP_TYPE = "11point"
+
+# configuration for detection_output_layer 
+__C.NET.DETOUT = edict()
+__C.NET.DETOUT.CONFIDENCE_THRESHOLD = 0.01
+__C.NET.DETOUT.NMS_THRESHOLD = 0.45
+__C.NET.DETOUT.NMS_TOP_K = 400
+__C.NET.DETOUT.KEEP_TOP_K = 200
+
+################################################
+__C.NET.CONV11 = edict()
+__C.NET.CONV11.PB = edict()
+__C.NET.CONV11.PB.MIN_SIZE = [60]
+__C.NET.CONV11.PB.ASPECT_RATIO = [2.]
+#__C.NET.CONV11.PB.ASPECT_RATIO = [2.]
+__C.NET.CONV11.PB.VARIANCE = [0.1, 0.1, 0.2, 0.2]
+
+
+__C.NET.CONV13 = edict()
+__C.NET.CONV13.PB = edict()
+__C.NET.CONV13.PB.MIN_SIZE = [105]
+__C.NET.CONV13.PB.MAX_SIZE = [150]
+__C.NET.CONV13.PB.ASPECT_RATIO = [2., 3.]
+__C.NET.CONV13.PB.VARIANCE = [0.1, 0.1, 0.2, 0.2]
+
+
+__C.NET.CONV14_2 = edict()
+__C.NET.CONV14_2.PB = edict()
+__C.NET.CONV14_2.PB.MIN_SIZE = [150]
+__C.NET.CONV14_2.PB.MAX_SIZE = [195]
+__C.NET.CONV14_2.PB.ASPECT_RATIO = [2., 3.]
+__C.NET.CONV14_2.PB.VARIANCE = [0.1, 0.1, 0.2, 0.2]
+
+
+__C.NET.CONV15_2 = edict()
+__C.NET.CONV15_2.PB = edict()
+__C.NET.CONV15_2.PB.MIN_SIZE = [195]
+__C.NET.CONV15_2.PB.MAX_SIZE = [240]
+__C.NET.CONV15_2.PB.ASPECT_RATIO = [2., 3.]
+__C.NET.CONV15_2.PB.VARIANCE = [0.1, 0.1, 0.2, 0.2]
+
+__C.NET.CONV16_2 = edict()
+__C.NET.CONV16_2.PB = edict()
+__C.NET.CONV16_2.PB.MIN_SIZE = [240]
+__C.NET.CONV16_2.PB.MAX_SIZE = [285]
+__C.NET.CONV16_2.PB.ASPECT_RATIO = [2., 3.]
+__C.NET.CONV16_2.PB.VARIANCE = [0.1, 0.1, 0.2, 0.2]
+
+__C.NET.CONV17_2 = edict()
+__C.NET.CONV17_2.PB = edict()
+__C.NET.CONV17_2.PB.MIN_SIZE = [285]
+__C.NET.CONV17_2.PB.MAX_SIZE = [300]
+__C.NET.CONV17_2.PB.ASPECT_RATIO = [2., 3.]
+__C.NET.CONV17_2.PB.VARIANCE = [0.1, 0.1, 0.2, 0.2]
+

models/mobilenet_ssd_pascal/infer.py

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+import numpy as np
+import gzip
+import copy
+import cv2, os
+
+import paddle.v2 as paddle
+from mobilenet_ssd_pascal import net_conf
+from config.pascal_voc_conf import cfg
+
+label_lists = open('./config/label_list').readlines()
+
+def _infer(inferer, infer_data, threshold):
+    ret = []
+    infer_res = inferer.infer(input=infer_data)
+    keep_inds = np.where(infer_res[:, 2] >= threshold)[0]
+    for idx in keep_inds:
+        ret.append([
+            infer_res[idx][0], infer_res[idx][1] - 1, infer_res[idx][2],
+            infer_res[idx][3], infer_res[idx][4], infer_res[idx][5],
+            infer_res[idx][6]
+        ])
+    return ret
+
+def draw_result(frame, ret_res, h, w):
+    print ret_res
+    for det_res in ret_res:
+        img_idx = int(det_res[0])
+        label = int(det_res[1])
+        conf_score = det_res[2]
+        xmin = int(round(det_res[3] * w))
+        ymin = int(round(det_res[4] * h))
+        xmax = int(round(det_res[5] * w))
+        ymax = int(round(det_res[6] * h))
+        cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
+                  (0, (1 - xmin) * 255, xmin * 255), 2)
+        font=cv2.FONT_HERSHEY_SIMPLEX
+        cv2.putText(frame, label_lists[label + 1].strip(), (xmin + 10, ymin + 10), font, 1.0, (255, 0, 0), 2)
+
+
+def pre_process(img):
+    img = cv2.resize(img, (cfg.IMG_HEIGHT, cfg.IMG_WIDTH), interpolation=cv2.INTER_AREA)
+    # image should be RGB format
+    img = img[:, :, ::-1]
+    # image shoud be in CHW format 
+    img = np.swapaxes(img, 1, 2)
+    img = np.swapaxes(img, 1, 0)
+    img = img.astype('float32')
+
+    img_mean = np.array([104, 117, 124])[:, np.newaxis, np.newaxis].astype(
+            'float32')
+    img -= img_mean
+    img = img.flatten()
+    return img
+
+def infer(model_path,threshold):
+
+    net = net_conf(mode='infer')
+
+    assert os.path.isfile(model_path), 'Invalid model.'
+    parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_path))
+
+    #build the inference network
+    inferer = paddle.inference.Inference(
+        output_layer=net, parameters=parameters)
+
+    test_data = []
+
+    frame = cv2.imread('./images/example.jpg')
+
+    h, w, _ = frame.shape
+    img = copy.deepcopy(frame)
+
+    # preprocess the image
+    img = pre_process(img)
+    test_data.append([img])
+
+    #the forward process
+    ret_res = _infer(inferer, test_data, threshold)
+
+    draw_result(frame, ret_res, h, w)     
+    cv2.imwrite('./images/result.jpg', frame)
+
+if __name__ == "__main__":
+    # init paddle environment
+    paddle.init(use_gpu=False, trainer_count=1, gpu_id= 3)
+
+    infer(model_path='./mobilenet_ssd_pascal.tar.gz',
+        threshold=0.3)