Merge pull request #40 from NHZlX/mobilenet_ssd_pascal_model

add mobilenet ssd pascal model

Author: NHZlX

models/mobilenet_ssd_pascal/README.md

@@ -0,0 +1,16 @@
+# 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/__init__.py

@@ -0,0 +1,21 @@
+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/label_list

@@ -0,0 +1,94 @@
+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/config/pascal_voc_conf.py

@@ -0,0 +1,88 @@
+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)