(image)
| 45 | |
| 46 | |
| 47 | def CPM(image): |
| 48 | image = image / 256.0 - 0.5 |
| 49 | |
| 50 | gmap = tf.constant(get_gaussian_map()) |
| 51 | gmap = tf.pad(gmap, [[0, 0], [0, 1], [0, 1], [0, 0]]) |
| 52 | pool_center = AvgPooling('mappool', gmap, 9, strides=8, padding='VALID') |
| 53 | with argscope(Conv2D, kernel_size=3, activation=tf.nn.relu): |
| 54 | shared = (LinearWrap(image) |
| 55 | .Conv2D('conv1_1', 64) |
| 56 | .Conv2D('conv1_2', 64) |
| 57 | .MaxPooling('pool1', 2) |
| 58 | # 184 |
| 59 | .Conv2D('conv2_1', 128) |
| 60 | .Conv2D('conv2_2', 128) |
| 61 | .MaxPooling('pool2', 2) |
| 62 | # 92 |
| 63 | .Conv2D('conv3_1', 256) |
| 64 | .Conv2D('conv3_2', 256) |
| 65 | .Conv2D('conv3_3', 256) |
| 66 | .Conv2D('conv3_4', 256) |
| 67 | .MaxPooling('pool3', 2) |
| 68 | # 46 |
| 69 | .Conv2D('conv4_1', 512) |
| 70 | .Conv2D('conv4_2', 512) |
| 71 | .Conv2D('conv4_3_CPM', 256) |
| 72 | .Conv2D('conv4_4_CPM', 256) |
| 73 | .Conv2D('conv4_5_CPM', 256) |
| 74 | .Conv2D('conv4_6_CPM', 256) |
| 75 | .Conv2D('conv4_7_CPM', 128)()) |
| 76 | |
| 77 | def add_stage(stage, l): |
| 78 | l = tf.concat([l, shared, pool_center], 3, |
| 79 | name='concat_stage{}'.format(stage)) |
| 80 | for i in range(1, 6): |
| 81 | l = Conv2D('Mconv{}_stage{}'.format(i, stage), l, 128, 7, activation=tf.nn.relu) |
| 82 | l = Conv2D('Mconv6_stage{}'.format(stage), l, 128, 1, activation=tf.nn.relu) |
| 83 | l = Conv2D('Mconv7_stage{}'.format(stage), l, 15, 1, activation=tf.identity) |
| 84 | return l |
| 85 | |
| 86 | out1 = (LinearWrap(shared) |
| 87 | .Conv2D('conv5_1_CPM', 512, 1, activation=tf.nn.relu) |
| 88 | .Conv2D('conv5_2_CPM', 15, 1, activation=tf.identity)()) |
| 89 | out2 = add_stage(2, out1) |
| 90 | out3 = add_stage(3, out2) |
| 91 | out4 = add_stage(4, out3) |
| 92 | out5 = add_stage(5, out4) |
| 93 | out6 = add_stage(6, out5) |
| 94 | tf.image.resize_bilinear(out6, [368, 368], name='resized_map') |
| 95 | |
| 96 | |
| 97 | def run_test(model_path, img_file): |
nothing calls this directly
no test coverage detected