Tensorflow学习(练习)—CPU训练模型

Mask R-CNN - Train on Shapes Dataset

This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be too slow to train on a CPU. On a GPU, you can start to get okay-ish results in a few minutes, and good results in less than an hour.

The code of the Shapes dataset is included below. It generates images on the fly, so it doesn't require downloading any data. And it can generate images of any size, so we pick a small image size to train faster.

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import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = os.path.abspath("../../")

# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn.config import Config
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log

%matplotlib inline

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)

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Configurations

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class ShapesConfig(Config):
"""Configuration for training on the toy shapes dataset.
Derives from the base Config class and overrides values specific
to the toy shapes dataset.
"""
# Give the configuration a recognizable name
NAME = "shapes"

# Train on 1 GPU and 8 images per GPU. We can put multiple images on each
# GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
GPU_COUNT = 1
IMAGES_PER_GPU = 8

# Number of classes (including background)
NUM_CLASSES = 1 + 3 # background + 3 shapes

# Use small images for faster training. Set the limits of the small side
# the large side, and that determines the image shape.
IMAGE_MIN_DIM = 128
IMAGE_MAX_DIM = 128

# Use smaller anchors because our image and objects are small
RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128) # anchor side in pixels

# Reduce training ROIs per image because the images are small and have
# few objects. Aim to allow ROI sampling to pick 33% positive ROIs.
TRAIN_ROIS_PER_IMAGE = 32

# Use a small epoch since the data is simple
STEPS_PER_EPOCH = 100

# use small validation steps since the epoch is small
VALIDATION_STEPS = 5

config = ShapesConfig()
config.display()

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运行结果:

Configurations:
BACKBONE                       resnet101
BACKBONE_STRIDES               [4, 8, 16, 32, 64]
BATCH_SIZE                     8
BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]
COMPUTE_BACKBONE_SHAPE         None
DETECTION_MAX_INSTANCES        100
DETECTION_MIN_CONFIDENCE       0.7
DETECTION_NMS_THRESHOLD        0.3
FPN_CLASSIF_FC_LAYERS_SIZE     1024
GPU_COUNT                      1
GRADIENT_CLIP_NORM             5.0
IMAGES_PER_GPU                 8
IMAGE_MAX_DIM                  128
IMAGE_META_SIZE                16
IMAGE_MIN_DIM                  128
IMAGE_MIN_SCALE                0
IMAGE_RESIZE_MODE              square
IMAGE_SHAPE                    [128 128   3]
LEARNING_MOMENTUM              0.9
LEARNING_RATE                  0.001
LOSS_WEIGHTS                   {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0}
MASK_POOL_SIZE                 14
MASK_SHAPE                     [28, 28]
MAX_GT_INSTANCES               100
MEAN_PIXEL                     [123.7 116.8 103.9]
MINI_MASK_SHAPE                (56, 56)
NAME                           shapes
NUM_CLASSES                    4
POOL_SIZE                      7
POST_NMS_ROIS_INFERENCE        1000
POST_NMS_ROIS_TRAINING         2000
ROI_POSITIVE_RATIO             0.33
RPN_ANCHOR_RATIOS              [0.5, 1, 2]
RPN_ANCHOR_SCALES              (8, 16, 32, 64, 128)
RPN_ANCHOR_STRIDE              1
RPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]
RPN_NMS_THRESHOLD              0.7
RPN_TRAIN_ANCHORS_PER_IMAGE    256
STEPS_PER_EPOCH                100
TOP_DOWN_PYRAMID_SIZE          256
TRAIN_BN                       False
TRAIN_ROIS_PER_IMAGE           32
USE_MINI_MASK                  True
USE_RPN_ROIS                   True
VALIDATION_STEPS               5
WEIGHT_DECAY                   0.0001
--------------------------------------------------------

Notebook Preferences

 

def get_ax(rows=1, cols=1, size=8):
"""Return a Matplotlib Axes array to be used in
all visualizations in the notebook. Provide a
central point to control graph sizes.

Change the default size attribute to control the size
of rendered images
"""
_, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
return ax

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Dataset

Create a synthetic dataset

Extend the Dataset class and add a method to load the shapes dataset, load_shapes(), and override the following methods:

  • load_image()
  • load_mask()
  • image_reference()

 

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class ShapesDataset(utils.Dataset):
"""Generates the shapes synthetic dataset. The dataset consists of simple
shapes (triangles, squares, circles) placed randomly on a blank surface.
The images are generated on the fly. No file access required.
"""

def load_shapes(self, count, height, width):
"""Generate the requested number of synthetic images.
count: number of images to generate.
height, width: the size of the generated images.
"""
# Add classes
self.add_class("shapes", 1, "square")
self.add_class("shapes", 2, "circle")
self.add_class("shapes", 3, "triangle")

# Add images
# Generate random specifications of images (i.e. color and
# list of shapes sizes and locations). This is more compact than
# actual images. Images are generated on the fly in load_image().
for i in range(count):
bg_color, shapes = self.random_image(height, width)
self.add_image("shapes", image_id=i, path=None,
width=width, height=height,
bg_color=bg_color, shapes=shapes)

def load_image(self, image_id):
"""Generate an image from the specs of the given image ID.
Typically this function loads the image from a file, but
in this case it generates the image on the fly from the
specs in image_info.
"""
info = self.image_info[image_id]
bg_color = np.array(info['bg_color']).reshape([1, 1, 3])
image = np.ones([info['height'], info['width'], 3], dtype=np.uint8)
image = image * bg_color.astype(np.uint8)
for shape, color, dims in info['shapes']:
image = self.draw_shape(image, shape, dims, color)
return image

def image_reference(self, image_id):
"""Return the shapes data of the image."""
info = self.image_info[image_id]
if info["source"] == "shapes":
return info["shapes"]
else:
super(self.__class__).image_reference(self, image_id)

def load_mask(self, image_id):
"""Generate instance masks for shapes of the given image ID.
"""
info = self.image_info[image_id]
shapes = info['shapes']
count = len(shapes)
mask = np.zeros([info['height'], info['width'], count], dtype=np.uint8)
for i, (shape, _, dims) in enumerate(info['shapes']):
mask[:, :, i:i+1] = self.draw_shape(mask[:, :, i:i+1].copy(),
shape, dims, 1)
# Handle occlusions
occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8)
for i in range(count-2, -1, -1):
mask[:, :, i] = mask[:, :, i] * occlusion
occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))
# Map class names to class IDs.
class_ids = np.array([self.class_names.index(s[0]) for s in shapes])
return mask.astype(np.bool), class_ids.astype(np.int32)

def draw_shape(self, image, shape, dims, color):
"""Draws a shape from the given specs."""
# Get the center x, y and the size s
x, y, s = dims
if shape == 'square':
cv2.rectangle(image, (x-s, y-s), (x+s, y+s), color, -1)
elif shape == "circle":
cv2.circle(image, (x, y), s, color, -1)
elif shape == "triangle":
points = np.array([[(x, y-s),
(x-s/math.sin(math.radians(60)), y+s),
(x+s/math.sin(math.radians(60)), y+s),
]], dtype=np.int32)
cv2.fillPoly(image, points, color)
return image

def random_shape(self, height, width):
"""Generates specifications of a random shape that lies within
the given height and width boundaries.
Returns a tuple of three valus:
* The shape name (square, circle, ...)
* Shape color: a tuple of 3 values, RGB.
* Shape dimensions: A tuple of values that define the shape size
and location. Differs per shape type.
"""
# Shape
shape = random.choice(["square", "circle", "triangle"])
# Color
color = tuple([random.randint(0, 255) for _ in range(3)])
# Center x, y
buffer = 20
y = random.randint(buffer, height - buffer - 1)
x = random.randint(buffer, width - buffer - 1)
# Size
s = random.randint(buffer, height//4)
return shape, color, (x, y, s)

def random_image(self, height, width):
"""Creates random specifications of an image with multiple shapes.
Returns the background color of the image and a list of shape
specifications that can be used to draw the image.
"""
# Pick random background color
bg_color = np.array([random.randint(0, 255) for _ in range(3)])
# Generate a few random shapes and record their
# bounding boxes
shapes = []
boxes = []
N = random.randint(1, 4)
for _ in range(N):
shape, color, dims = self.random_shape(height, width)
shapes.append((shape, color, dims))
x, y, s = dims
boxes.append([y-s, x-s, y+s, x+s])
# Apply non-max suppression wit 0.3 threshold to avoid
# shapes covering each other
keep_ixs = utils.non_max_suppression(np.array(boxes), np.arange(N), 0.3)
shapes = [s for i, s in enumerate(shapes) if i in keep_ixs]
return bg_color, shapes

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# Training dataset

dataset_train = ShapesDataset()

dataset_train.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])

dataset_train.prepare()

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# Load and display random samples

image_ids = np.random.choice(dataset_train.image_ids, 4)

for image_id in image_ids:

    image = dataset_train.load_image(image_id)

    mask, class_ids = dataset_train.load_mask(image_id)

    visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)

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Ceate Model

------------------------------------------------------------------------

# Create model in training mode

model = modellib.MaskRCNN(mode="training", config=config,model_dir=MODEL_DIR)

-----------------------------------------------------------------------

----------------------------------------------------------------------

# Which weights to start with?

init_with = "coco"  # imagenet, coco, or last

if init_with == "imagenet":

    model.load_weights(model.get_imagenet_weights(), by_name=True)

elif init_with == "coco":

    # Load weights trained on MS COCO, but skip layers that

    # are different due to the different number of classes

    # See README for instructions to download the COCO weights

    model.load_weights(COCO_MODEL_PATH, by_name=True,

                       exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", 

                                "mrcnn_bbox", "mrcnn_mask"])

elif init_with == "last":

    # Load the last model you trained and continue training

    model.load_weights(model.find_last(), by_name=True)

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Training

Train in two stages:

  1. Only the heads. Here we're freezing all the backbone layers and training only the randomly initialized layers (i.e. the ones that we didn't use pre-trained weights from MS COCO). To train only the head layers, pass layers='heads' to the train() function.

  2. Fine-tune all layers. For this simple example it's not necessary, but we're including it to show the process. Simply pass layers="all to train all layers.

 

--------------------------------------------------------------------------

# Train the head branches

# Passing layers="heads" freezes all layers except the head

# layers. You can also pass a regular expression to select

# which layers to train by name pattern.

model.train(dataset_train, dataset_val, 

            learning_rate=config.LEARNING_RATE, 

            epochs=1, 

            layers='heads')

-------------------------------------------------------------------------

Starting at epoch 0. LR=0.001

Checkpoint Path: C:\Users\luo\tensorflow\Mask_RCNN-master\logs\shapes20180817T1409\mask_rcnn_shapes_{epoch:04d}.h5
Selecting layers to train
fpn_c5p5               (Conv2D)
fpn_c4p4               (Conv2D)
fpn_c3p3               (Conv2D)
fpn_c2p2               (Conv2D)
fpn_p5                 (Conv2D)
fpn_p2                 (Conv2D)
fpn_p3                 (Conv2D)
fpn_p4                 (Conv2D)
In model:  rpn_model
    rpn_conv_shared        (Conv2D)
    rpn_class_raw          (Conv2D)
    rpn_bbox_pred          (Conv2D)
mrcnn_mask_conv1       (TimeDistributed)
mrcnn_mask_bn1         (TimeDistributed)
mrcnn_mask_conv2       (TimeDistributed)
mrcnn_mask_bn2         (TimeDistributed)
mrcnn_class_conv1      (TimeDistributed)
mrcnn_class_bn1        (TimeDistributed)
mrcnn_mask_conv3       (TimeDistributed)
mrcnn_mask_bn3         (TimeDistributed)
mrcnn_class_conv2      (TimeDistributed)
mrcnn_class_bn2        (TimeDistributed)
mrcnn_mask_conv4       (TimeDistributed)
mrcnn_mask_bn4         (TimeDistributed)
mrcnn_bbox_fc          (TimeDistributed)
mrcnn_mask_deconv      (TimeDistributed)
mrcnn_class_logits     (TimeDistributed)
mrcnn_mask             (TimeDistributed)
 
E:\Anaconda3\install1\lib\site-packages\tensorflow\python\ops\gradients_impl.py:97: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
 
Epoch 1/1
100/100 [==============================] - 2824s 28s/step - loss: 1.5765 - rpn_class_loss: 0.0302 - rpn_bbox_loss: 0.5675 - mrcnn_class_loss: 0.3577 - mrcnn_bbox_loss: 0.3586 - mrcnn_mask_loss: 0.2625 - val_loss: 0.9420 - val_rpn_class_loss: 0.0130 - val_rpn_bbox_loss: 0.4263 - val_mrcnn_class_loss: 0.1708 - val_mrcnn_bbox_loss: 0.1679 - val_mrcnn_mask_loss: 0.1640

-------------------------------------------------------------------

# Fine tune all layers

# Passing layers="all" trains all layers. You can also 

# pass a regular expression to select which layers to

# train by name pattern.

model.train(dataset_train, dataset_val, 

            learning_rate=config.LEARNING_RATE / 10,

            epochs=1, 

            layers="all")

-------------------------------------------------------------------

 

 

Starting at epoch 1. LR=0.0001

Checkpoint Path: C:\Users\luo\tensorflow\Mask_RCNN-master\logs\shapes20180817T1409\mask_rcnn_shapes_{epoch:04d}.h5
Selecting layers to train
conv1                  (Conv2D)
bn_conv1               (BatchNorm)
res2a_branch2a         (Conv2D)
bn2a_branch2a          (BatchNorm)
res2a_branch2b         (Conv2D)
bn2a_branch2b          (BatchNorm)
res2a_branch2c         (Conv2D)
res2a_branch1          (Conv2D)
bn2a_branch2c          (BatchNorm)
bn2a_branch1           (BatchNorm)
res2b_branch2a         (Conv2D)
bn2b_branch2a          (BatchNorm)
res2b_branch2b         (Conv2D)
bn2b_branch2b          (BatchNorm)
res2b_branch2c         (Conv2D)
bn2b_branch2c          (BatchNorm)
res2c_branch2a         (Conv2D)
bn2c_branch2a          (BatchNorm)
res2c_branch2b         (Conv2D)
bn2c_branch2b          (BatchNorm)
res2c_branch2c         (Conv2D)
bn2c_branch2c          (BatchNorm)
res3a_branch2a         (Conv2D)
bn3a_branch2a          (BatchNorm)
res3a_branch2b         (Conv2D)
bn3a_branch2b          (BatchNorm)
res3a_branch2c         (Conv2D)
res3a_branch1          (Conv2D)
bn3a_branch2c          (BatchNorm)
bn3a_branch1           (BatchNorm)
res3b_branch2a         (Conv2D)
bn3b_branch2a          (BatchNorm)
res3b_branch2b         (Conv2D)
bn3b_branch2b          (BatchNorm)
res3b_branch2c         (Conv2D)
bn3b_branch2c          (BatchNorm)
res3c_branch2a         (Conv2D)
bn3c_branch2a          (BatchNorm)
res3c_branch2b         (Conv2D)
bn3c_branch2b          (BatchNorm)
res3c_branch2c         (Conv2D)
bn3c_branch2c          (BatchNorm)
res3d_branch2a         (Conv2D)
bn3d_branch2a          (BatchNorm)
res3d_branch2b         (Conv2D)
bn3d_branch2b          (BatchNorm)
res3d_branch2c         (Conv2D)
bn3d_branch2c          (BatchNorm)
res4a_branch2a         (Conv2D)
bn4a_branch2a          (BatchNorm)
res4a_branch2b         (Conv2D)
bn4a_branch2b          (BatchNorm)
res4a_branch2c         (Conv2D)
res4a_branch1          (Conv2D)
bn4a_branch2c          (BatchNorm)
bn4a_branch1           (BatchNorm)
res4b_branch2a         (Conv2D)
bn4b_branch2a          (BatchNorm)
res4b_branch2b         (Conv2D)
bn4b_branch2b          (BatchNorm)
res4b_branch2c         (Conv2D)
bn4b_branch2c          (BatchNorm)
res4c_branch2a         (Conv2D)
bn4c_branch2a          (BatchNorm)
res4c_branch2b         (Conv2D)
bn4c_branch2b          (BatchNorm)
res4c_branch2c         (Conv2D)
bn4c_branch2c          (BatchNorm)
res4d_branch2a         (Conv2D)
bn4d_branch2a          (BatchNorm)
res4d_branch2b         (Conv2D)
bn4d_branch2b          (BatchNorm)
res4d_branch2c         (Conv2D)
bn4d_branch2c          (BatchNorm)
res4e_branch2a         (Conv2D)
bn4e_branch2a          (BatchNorm)
res4e_branch2b         (Conv2D)
bn4e_branch2b          (BatchNorm)
res4e_branch2c         (Conv2D)
bn4e_branch2c          (BatchNorm)
res4f_branch2a         (Conv2D)
bn4f_branch2a          (BatchNorm)
res4f_branch2b         (Conv2D)
bn4f_branch2b          (BatchNorm)
res4f_branch2c         (Conv2D)
bn4f_branch2c          (BatchNorm)
res4g_branch2a         (Conv2D)
bn4g_branch2a          (BatchNorm)
res4g_branch2b         (Conv2D)
bn4g_branch2b          (BatchNorm)
res4g_branch2c         (Conv2D)
bn4g_branch2c          (BatchNorm)
res4h_branch2a         (Conv2D)
bn4h_branch2a          (BatchNorm)
res4h_branch2b         (Conv2D)
bn4h_branch2b          (BatchNorm)
res4h_branch2c         (Conv2D)
bn4h_branch2c          (BatchNorm)
res4i_branch2a         (Conv2D)
bn4i_branch2a          (BatchNorm)
res4i_branch2b         (Conv2D)
bn4i_branch2b          (BatchNorm)
res4i_branch2c         (Conv2D)
bn4i_branch2c          (BatchNorm)
res4j_branch2a         (Conv2D)
bn4j_branch2a          (BatchNorm)
res4j_branch2b         (Conv2D)
bn4j_branch2b          (BatchNorm)
res4j_branch2c         (Conv2D)
bn4j_branch2c          (BatchNorm)
res4k_branch2a         (Conv2D)
bn4k_branch2a          (BatchNorm)
res4k_branch2b         (Conv2D)
bn4k_branch2b          (BatchNorm)
res4k_branch2c         (Conv2D)
bn4k_branch2c          (BatchNorm)
res4l_branch2a         (Conv2D)
bn4l_branch2a          (BatchNorm)
res4l_branch2b         (Conv2D)
bn4l_branch2b          (BatchNorm)
res4l_branch2c         (Conv2D)
bn4l_branch2c          (BatchNorm)
res4m_branch2a         (Conv2D)
bn4m_branch2a          (BatchNorm)
res4m_branch2b         (Conv2D)
bn4m_branch2b          (BatchNorm)
res4m_branch2c         (Conv2D)
bn4m_branch2c          (BatchNorm)
res4n_branch2a         (Conv2D)
bn4n_branch2a          (BatchNorm)
res4n_branch2b         (Conv2D)
bn4n_branch2b          (BatchNorm)
res4n_branch2c         (Conv2D)
bn4n_branch2c          (BatchNorm)
res4o_branch2a         (Conv2D)
bn4o_branch2a          (BatchNorm)
res4o_branch2b         (Conv2D)
bn4o_branch2b          (BatchNorm)
res4o_branch2c         (Conv2D)
bn4o_branch2c          (BatchNorm)
res4p_branch2a         (Conv2D)
bn4p_branch2a          (BatchNorm)
res4p_branch2b         (Conv2D)
bn4p_branch2b          (BatchNorm)
res4p_branch2c         (Conv2D)
bn4p_branch2c          (BatchNorm)
res4q_branch2a         (Conv2D)
bn4q_branch2a          (BatchNorm)
res4q_branch2b         (Conv2D)
bn4q_branch2b          (BatchNorm)
res4q_branch2c         (Conv2D)
bn4q_branch2c          (BatchNorm)
res4r_branch2a         (Conv2D)
bn4r_branch2a          (BatchNorm)
res4r_branch2b         (Conv2D)
bn4r_branch2b          (BatchNorm)
res4r_branch2c         (Conv2D)
bn4r_branch2c          (BatchNorm)
res4s_branch2a         (Conv2D)
bn4s_branch2a          (BatchNorm)
res4s_branch2b         (Conv2D)
bn4s_branch2b          (BatchNorm)
res4s_branch2c         (Conv2D)
bn4s_branch2c          (BatchNorm)
res4t_branch2a         (Conv2D)
bn4t_branch2a          (BatchNorm)
res4t_branch2b         (Conv2D)
bn4t_branch2b          (BatchNorm)
res4t_branch2c         (Conv2D)
bn4t_branch2c          (BatchNorm)
res4u_branch2a         (Conv2D)
bn4u_branch2a          (BatchNorm)
res4u_branch2b         (Conv2D)
bn4u_branch2b          (BatchNorm)
res4u_branch2c         (Conv2D)
bn4u_branch2c          (BatchNorm)
res4v_branch2a         (Conv2D)
bn4v_branch2a          (BatchNorm)
res4v_branch2b         (Conv2D)
bn4v_branch2b          (BatchNorm)
res4v_branch2c         (Conv2D)
bn4v_branch2c          (BatchNorm)
res4w_branch2a         (Conv2D)
bn4w_branch2a          (BatchNorm)
res4w_branch2b         (Conv2D)
bn4w_branch2b          (BatchNorm)
res4w_branch2c         (Conv2D)
bn4w_branch2c          (BatchNorm)
res5a_branch2a         (Conv2D)
bn5a_branch2a          (BatchNorm)
res5a_branch2b         (Conv2D)
bn5a_branch2b          (BatchNorm)
res5a_branch2c         (Conv2D)
res5a_branch1          (Conv2D)
bn5a_branch2c          (BatchNorm)
bn5a_branch1           (BatchNorm)
res5b_branch2a         (Conv2D)
bn5b_branch2a          (BatchNorm)
res5b_branch2b         (Conv2D)
bn5b_branch2b          (BatchNorm)
res5b_branch2c         (Conv2D)
bn5b_branch2c          (BatchNorm)
res5c_branch2a         (Conv2D)
bn5c_branch2a          (BatchNorm)
res5c_branch2b         (Conv2D)
bn5c_branch2b          (BatchNorm)
res5c_branch2c         (Conv2D)
bn5c_branch2c          (BatchNorm)
fpn_c5p5               (Conv2D)
fpn_c4p4               (Conv2D)
fpn_c3p3               (Conv2D)
fpn_c2p2               (Conv2D)
fpn_p5                 (Conv2D)
fpn_p2                 (Conv2D)
fpn_p3                 (Conv2D)
fpn_p4                 (Conv2D)
In model:  rpn_model
    rpn_conv_shared        (Conv2D)
    rpn_class_raw          (Conv2D)
    rpn_bbox_pred          (Conv2D)
mrcnn_mask_conv1       (TimeDistributed)
mrcnn_mask_bn1         (TimeDistributed)
mrcnn_mask_conv2       (TimeDistributed)
mrcnn_mask_bn2         (TimeDistributed)
mrcnn_class_conv1      (TimeDistributed)
mrcnn_class_bn1        (TimeDistributed)
mrcnn_mask_conv3       (TimeDistributed)
mrcnn_mask_bn3         (TimeDistributed)
mrcnn_class_conv2      (TimeDistributed)
mrcnn_class_bn2        (TimeDistributed)
mrcnn_mask_conv4       (TimeDistributed)
mrcnn_mask_bn4         (TimeDistributed)
mrcnn_bbox_fc          (TimeDistributed)
mrcnn_mask_deconv      (TimeDistributed)
mrcnn_class_logits     (TimeDistributed)
mrcnn_mask             (TimeDistributed)
 
E:\Anaconda3\install1\lib\site-packages\tensorflow\python\ops\gradients_impl.py:97: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "

 

# Save weights

# Typically not needed because callbacks save after every epoch

# Uncomment to save manually

# model_path = os.path.join(MODEL_DIR, "mask_rcnn_shapes.h5")

# model.keras_model.save_weights(model_path)

 

Detection

------------------------------------------------------------------------------

class InferenceConfig(ShapesConfig):

    GPU_COUNT = 1

    IMAGES_PER_GPU = 1

inference_config = InferenceConfig()

# Recreate the model in inference mode

model = modellib.MaskRCNN(mode="inference", 

                          config=inference_config,

                          model_dir=MODEL_DIR)

# Get path to saved weights

# Either set a specific path or find last trained weights

# model_path = os.path.join(ROOT_DIR, ".h5 file name here")

model_path = model.find_last()

# Load trained weights

print("Loading weights from ", model_path)

model.load_weights(model_path, by_name=True)

-----------------------------------------------------------------------------

运行结果:

Loading weights from  C:\Users\luo\tensorflow\Mask_RCNN-master\logs\shapes20180817T1459\mask_rcnn_shapes_0001.h5

----------------------------------------------------
# Test on a random image image_id = random.choice(dataset_val.image_ids) original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\ modellib.load_image_gt(dataset_val, inference_config, image_id, use_mini_mask=False) log("original_image", original_image) log("image_meta", image_meta) log("gt_class_id", gt_class_id) log("gt_bbox", gt_bbox) log("gt_mask", gt_mask) visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, dataset_train.class_names, figsize=(8, 8))

-----------------------------------------------------------------------------------

original_image           shape: (128, 128, 3)         min:   72.00000  max:  248.00000  uint8
image_meta               shape: (16,)                 min:    0.00000  max:  128.00000  int32
gt_class_id              shape: (3,)                  min:    2.00000  max:    3.00000  int32
gt_bbox                  shape: (3, 4)                min:    0.00000  max:  128.00000  int32
gt_mask                  shape: (128, 128, 3)         min:    0.00000  max:    1.00000  bool

Evaluation

-----------------------------------------------------

# Compute VOC-Style mAP @ IoU=0.5 # Running on 10 images. Increase for better accuracy. image_ids = np.random.choice(dataset_val.image_ids, 10) APs = [] for image_id in image_ids: # Load image and ground truth data image, image_meta, gt_class_id, gt_bbox, gt_mask =\ modellib.load_image_gt(dataset_val, inference_config, image_id, use_mini_mask=False) molded_images = np.expand_dims(modellib.mold_image(image, inference_config), 0) # Run object detection results = model.detect([image], verbose=0) r = results[0] # Compute AP AP, precisions, recalls, overlaps =\ utils.compute_ap(gt_bbox, gt_class_id, gt_mask, r["rois"], r["class_ids"], r["scores"], r['masks']) APs.append(AP) print("mAP: ", np.mean(APs))

----------------------------------------------------

运行结果:

mAP:  0.966666667163372
 

Mask R-CNN - Train on Shapes Dataset

This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be too slow to train on a CPU. On a GPU, you can start to get okay-ish results in a few minutes, and good results in less than an hour.

The code of the Shapes dataset is included below. It generates images on the fly, so it doesn't require downloading any data. And it can generate images of any size, so we pick a small image size to train faster.

In [13]:
 
 
1
import os
2
import sys
3
import random
4
import math
5
import re
6
import time
7
import numpy as np
8
import cv2
9
import matplotlib
10
import matplotlib.pyplot as plt
11
12
# Root directory of the project
13
ROOT_DIR = os.path.abspath("../../")
14
15
# Import Mask RCNN
16
sys.path.append(ROOT_DIR)  # To find local version of the library
17
from mrcnn.config import Config
18
from mrcnn import utils
19
import mrcnn.model as modellib
20
from mrcnn import visualize
21
from mrcnn.model import log
22
23
%matplotlib inline 
24
25
# Directory to save logs and trained model
26
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
27
28
# Local path to trained weights file
29
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
30
# Download COCO trained weights from Releases if needed
31
if not os.path.exists(COCO_MODEL_PATH):
32
    utils.download_trained_weights(COCO_MODEL_PATH)
 
 
 
 

Configurations

In [14]:
 
 
1
class ShapesConfig(Config):
2
    """Configuration for training on the toy shapes dataset.
3
    Derives from the base Config class and overrides values specific
4
    to the toy shapes dataset.
5
    """
6
    # Give the configuration a recognizable name
7
    NAME = "shapes"
8
9
    # Train on 1 GPU and 8 images per GPU. We can put multiple images on each
10
    # GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
11
    GPU_COUNT = 1
12
    IMAGES_PER_GPU = 8
13
14
    # Number of classes (including background)
15
    NUM_CLASSES = 1 + 3  # background + 3 shapes
16
17
    # Use small images for faster training. Set the limits of the small side
18
    # the large side, and that determines the image shape.
19
    IMAGE_MIN_DIM = 128
20
    IMAGE_MAX_DIM = 128
21
22
    # Use smaller anchors because our image and objects are small
23
    RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128)  # anchor side in pixels
24
25
    # Reduce training ROIs per image because the images are small and have
26
    # few objects. Aim to allow ROI sampling to pick 33% positive ROIs.
27
    TRAIN_ROIS_PER_IMAGE = 32
28
29
    # Use a small epoch since the data is simple
30
    STEPS_PER_EPOCH = 100
31
32
    # use small validation steps since the epoch is small
33
    VALIDATION_STEPS = 5
34

35
config = ShapesConfig()
36
config.display()
 
 
 
 
Configurations:
BACKBONE                       resnet101
BACKBONE_STRIDES               [4, 8, 16, 32, 64]
BATCH_SIZE                     8
BBOX_STD_DEV                   [0.1 0.1 0.2 0.2]
COMPUTE_BACKBONE_SHAPE         None
DETECTION_MAX_INSTANCES        100
DETECTION_MIN_CONFIDENCE       0.7
DETECTION_NMS_THRESHOLD        0.3
FPN_CLASSIF_FC_LAYERS_SIZE     1024
GPU_COUNT                      1
GRADIENT_CLIP_NORM             5.0
IMAGES_PER_GPU                 8
IMAGE_MAX_DIM                  128
IMAGE_META_SIZE                16
IMAGE_MIN_DIM                  128
IMAGE_MIN_SCALE                0
IMAGE_RESIZE_MODE              square
IMAGE_SHAPE                    [128 128   3]
LEARNING_MOMENTUM              0.9
LEARNING_RATE                  0.001
LOSS_WEIGHTS                   {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0}
MASK_POOL_SIZE                 14
MASK_SHAPE                     [28, 28]
MAX_GT_INSTANCES               100
MEAN_PIXEL                     [123.7 116.8 103.9]
MINI_MASK_SHAPE                (56, 56)
NAME                           shapes
NUM_CLASSES                    4
POOL_SIZE                      7
POST_NMS_ROIS_INFERENCE        1000
POST_NMS_ROIS_TRAINING         2000
ROI_POSITIVE_RATIO             0.33
RPN_ANCHOR_RATIOS              [0.5, 1, 2]
RPN_ANCHOR_SCALES              (8, 16, 32, 64, 128)
RPN_ANCHOR_STRIDE              1
RPN_BBOX_STD_DEV               [0.1 0.1 0.2 0.2]
RPN_NMS_THRESHOLD              0.7
RPN_TRAIN_ANCHORS_PER_IMAGE    256
STEPS_PER_EPOCH                100
TOP_DOWN_PYRAMID_SIZE          256
TRAIN_BN                       False
TRAIN_ROIS_PER_IMAGE           32
USE_MINI_MASK                  True
USE_RPN_ROIS                   True
VALIDATION_STEPS               5
WEIGHT_DECAY                   0.0001


 

Notebook Preferences

In [15]:
 
 
1
def get_ax(rows=1, cols=1, size=8):
2
    """Return a Matplotlib Axes array to be used in
3
    all visualizations in the notebook. Provide a
4
    central point to control graph sizes.
5

6
    Change the default size attribute to control the size
7
    of rendered images
8
    """
9
    _, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
10
    return ax
 
 
 
 

Dataset

Create a synthetic dataset

Extend the Dataset class and add a method to load the shapes dataset, load_shapes(), and override the following methods:

  • load_image()
  • load_mask()
  • image_reference()
In [16]:
 
 
1
class ShapesDataset(utils.Dataset):
2
    """Generates the shapes synthetic dataset. The dataset consists of simple
3
    shapes (triangles, squares, circles) placed randomly on a blank surface.
4
    The images are generated on the fly. No file access required.
5
    """
6
7
    def load_shapes(self, count, height, width):
8
        """Generate the requested number of synthetic images.
9
        count: number of images to generate.
10
        height, width: the size of the generated images.
11
        """
12
        # Add classes
13
        self.add_class("shapes", 1, "square")
14
        self.add_class("shapes", 2, "circle")
15
        self.add_class("shapes", 3, "triangle")
16
17
        # Add images
18
        # Generate random specifications of images (i.e. color and
19
        # list of shapes sizes and locations). This is more compact than
20
        # actual images. Images are generated on the fly in load_image().
21
        for i in range(count):
22
            bg_color, shapes = self.random_image(height, width)
23
            self.add_image("shapes", image_id=i, path=None,
24
                           width=width, height=height,
25
                           bg_color=bg_color, shapes=shapes)
26
27
    def load_image(self, image_id):
28
        """Generate an image from the specs of the given image ID.
29
        Typically this function loads the image from a file, but
30
        in this case it generates the image on the fly from the
31
        specs in image_info.
32
        """
33
        info = self.image_info[image_id]
34
        bg_color = np.array(info['bg_color']).reshape([1, 1, 3])
35
        image = np.ones([info['height'], info['width'], 3], dtype=np.uint8)
36
        image = image * bg_color.astype(np.uint8)
37
        for shape, color, dims in info['shapes']:
38
            image = self.draw_shape(image, shape, dims, color)
39
        return image
40
41
    def image_reference(self, image_id):
42
        """Return the shapes data of the image."""
43
        info = self.image_info[image_id]
44
        if info["source"] == "shapes":
45
            return info["shapes"]
46
        else:
47
            super(self.__class__).image_reference(self, image_id)
48
49
    def load_mask(self, image_id):
50
        """Generate instance masks for shapes of the given image ID.
51
        """
52
        info = self.image_info[image_id]
53
        shapes = info['shapes']
54
        count = len(shapes)
55
        mask = np.zeros([info['height'], info['width'], count], dtype=np.uint8)
56
        for i, (shape, _, dims) in enumerate(info['shapes']):
57
            mask[:, :, i:i+1] = self.draw_shape(mask[:, :, i:i+1].copy(),
58
                                                shape, dims, 1)
59
        # Handle occlusions
60
        occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8)
61
        for i in range(count-2, -1, -1):
62
            mask[:, :, i] = mask[:, :, i] * occlusion
63
            occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))
64
        # Map class names to class IDs.
65
        class_ids = np.array([self.class_names.index(s[0]) for s in shapes])
66
        return mask.astype(np.bool), class_ids.astype(np.int32)
67
68
    def draw_shape(self, image, shape, dims, color):
69
        """Draws a shape from the given specs."""
70
        # Get the center x, y and the size s
71
        x, y, s = dims
72
        if shape == 'square':
73
            cv2.rectangle(image, (x-s, y-s), (x+s, y+s), color, -1)
74
        elif shape == "circle":
75
            cv2.circle(image, (x, y), s, color, -1)
76
        elif shape == "triangle":
77
            points = np.array([[(x, y-s),
78
                                (x-s/math.sin(math.radians(60)), y+s),
79
                                (x+s/math.sin(math.radians(60)), y+s),
80
                                ]], dtype=np.int32)
81
            cv2.fillPoly(image, points, color)
82
        return image
83
84
    def random_shape(self, height, width):
85
        """Generates specifications of a random shape that lies within
86
        the given height and width boundaries.
87
        Returns a tuple of three valus:
88
        * The shape name (square, circle, ...)
89
        * Shape color: a tuple of 3 values, RGB.
90
        * Shape dimensions: A tuple of values that define the shape size
91
                            and location. Differs per shape type.
92
        """
93
        # Shape
94
        shape = random.choice(["square", "circle", "triangle"])
95
        # Color
96
        color = tuple([random.randint(0, 255) for _ in range(3)])
97
        # Center x, y
98
        buffer = 20
99
        y = random.randint(buffer, height - buffer - 1)
100
        x = random.randint(buffer, width - buffer - 1)
101
        # Size
102
        s = random.randint(buffer, height//4)
103
        return shape, color, (x, y, s)
104
105
    def random_image(self, height, width):
106
        """Creates random specifications of an image with multiple shapes.
107
        Returns the background color of the image and a list of shape
108
        specifications that can be used to draw the image.
109
        """
110
        # Pick random background color
111
        bg_color = np.array([random.randint(0, 255) for _ in range(3)])
112
        # Generate a few random shapes and record their
113
        # bounding boxes
114
        shapes = []
115
        boxes = []
116
        N = random.randint(1, 4)
117
        for _ in range(N):
118
            shape, color, dims = self.random_shape(height, width)
119
            shapes.append((shape, color, dims))
120
            x, y, s = dims
121
            boxes.append([y-s, x-s, y+s, x+s])
122
        # Apply non-max suppression wit 0.3 threshold to avoid
123
        # shapes covering each other
124
        keep_ixs = utils.non_max_suppression(np.array(boxes), np.arange(N), 0.3)
125
        shapes = [s for i, s in enumerate(shapes) if i in keep_ixs]
126
        return bg_color, shapes
 
 
 
In [17]:
 
 
1
# Training dataset
2
dataset_train = ShapesDataset()
3
dataset_train.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
4
dataset_train.prepare()
5
6
# Validation dataset
7
dataset_val = ShapesDataset()
8
dataset_val.load_shapes(50, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
9
dataset_val.prepare()
 
 
 
In [18]:
 
 
1
# Load and display random samples
2
image_ids = np.random.choice(dataset_train.image_ids, 4)
3
for image_id in image_ids:
4
    image = dataset_train.load_image(image_id)
5
    mask, class_ids = dataset_train.load_mask(image_id)
6
    visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)
 
 
 
 
 
 
 
 

Ceate Model

In [19]:
 
 
1
# Create model in training mode
2
model = modellib.MaskRCNN(mode="training", config=config,
3
                          model_dir=MODEL_DIR)
 
 
 
In [20]:
 
 
1
# Which weights to start with?
2
init_with = "coco"  # imagenet, coco, or last
3
4
if init_with == "imagenet":
5
    model.load_weights(model.get_imagenet_weights(), by_name=True)
6
elif init_with == "coco":
7
    # Load weights trained on MS COCO, but skip layers that
8
    # are different due to the different number of classes
9
    # See README for instructions to download the COCO weights
10
    model.load_weights(COCO_MODEL_PATH, by_name=True,
11
                       exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", 
12
                                "mrcnn_bbox", "mrcnn_mask"])
13
elif init_with == "last":
14
    # Load the last model you trained and continue training
15
    model.load_weights(model.find_last(), by_name=True)
 
 
 
 

Training

Train in two stages:

  1. Only the heads. Here we're freezing all the backbone layers and training only the randomly initialized layers (i.e. the ones that we didn't use pre-trained weights from MS COCO). To train only the head layers, pass layers='heads' to the train() function.

  2. Fine-tune all layers. For this simple example it's not necessary, but we're including it to show the process. Simply pass layers="all to train all layers.

In [21]:
 
 
1
# Train the head branches
2
# Passing layers="heads" freezes all layers except the head
3
# layers. You can also pass a regular expression to select
4
# which layers to train by name pattern.
5
model.train(dataset_train, dataset_val, 
6
            learning_rate=config.LEARNING_RATE, 
7
            epochs=1, 
8
            layers='heads')
 
 
 
 
Starting at epoch 0. LR=0.001

Checkpoint Path: C:\Users\luo\tensorflow\Mask_RCNN-master\logs\shapes20180817T1409\mask_rcnn_shapes_{epoch:04d}.h5
Selecting layers to train
fpn_c5p5               (Conv2D)
fpn_c4p4               (Conv2D)
fpn_c3p3               (Conv2D)
fpn_c2p2               (Conv2D)
fpn_p5                 (Conv2D)
fpn_p2                 (Conv2D)
fpn_p3                 (Conv2D)
fpn_p4                 (Conv2D)
In model:  rpn_model
    rpn_conv_shared        (Conv2D)
    rpn_class_raw          (Conv2D)
    rpn_bbox_pred          (Conv2D)
mrcnn_mask_conv1       (TimeDistributed)
mrcnn_mask_bn1         (TimeDistributed)
mrcnn_mask_conv2       (TimeDistributed)
mrcnn_mask_bn2         (TimeDistributed)
mrcnn_class_conv1      (TimeDistributed)
mrcnn_class_bn1        (TimeDistributed)
mrcnn_mask_conv3       (TimeDistributed)
mrcnn_mask_bn3         (TimeDistributed)
mrcnn_class_conv2      (TimeDistributed)
mrcnn_class_bn2        (TimeDistributed)
mrcnn_mask_conv4       (TimeDistributed)
mrcnn_mask_bn4         (TimeDistributed)
mrcnn_bbox_fc          (TimeDistributed)
mrcnn_mask_deconv      (TimeDistributed)
mrcnn_class_logits     (TimeDistributed)
mrcnn_mask             (TimeDistributed)
 
E:\Anaconda3\install1\lib\site-packages\tensorflow\python\ops\gradients_impl.py:97: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
 
Epoch 1/1
100/100 [==============================] - 2824s 28s/step - loss: 1.5765 - rpn_class_loss: 0.0302 - rpn_bbox_loss: 0.5675 - mrcnn_class_loss: 0.3577 - mrcnn_bbox_loss: 0.3586 - mrcnn_mask_loss: 0.2625 - val_loss: 0.9420 - val_rpn_class_loss: 0.0130 - val_rpn_bbox_loss: 0.4263 - val_mrcnn_class_loss: 0.1708 - val_mrcnn_bbox_loss: 0.1679 - val_mrcnn_mask_loss: 0.1640
In [22]:
 
 
1
# Fine tune all layers
2
# Passing layers="all" trains all layers. You can also 
3
# pass a regular expression to select which layers to
4
# train by name pattern.
5
model.train(dataset_train, dataset_val, 
6
            learning_rate=config.LEARNING_RATE / 10,
7
            epochs=1, 
8
            layers="all")
 
 
 
 
Starting at epoch 1. LR=0.0001

Checkpoint Path: C:\Users\luo\tensorflow\Mask_RCNN-master\logs\shapes20180817T1409\mask_rcnn_shapes_{epoch:04d}.h5
Selecting layers to train
conv1                  (Conv2D)
bn_conv1               (BatchNorm)
res2a_branch2a         (Conv2D)
bn2a_branch2a          (BatchNorm)
res2a_branch2b         (Conv2D)
bn2a_branch2b          (BatchNorm)
res2a_branch2c         (Conv2D)
res2a_branch1          (Conv2D)
bn2a_branch2c          (BatchNorm)
bn2a_branch1           (BatchNorm)
res2b_branch2a         (Conv2D)
bn2b_branch2a          (BatchNorm)
res2b_branch2b         (Conv2D)
bn2b_branch2b          (BatchNorm)
res2b_branch2c         (Conv2D)
bn2b_branch2c          (BatchNorm)
res2c_branch2a         (Conv2D)
bn2c_branch2a          (BatchNorm)
res2c_branch2b         (Conv2D)
bn2c_branch2b          (BatchNorm)
res2c_branch2c         (Conv2D)
bn2c_branch2c          (BatchNorm)
res3a_branch2a         (Conv2D)
bn3a_branch2a          (BatchNorm)
res3a_branch2b         (Conv2D)
bn3a_branch2b          (BatchNorm)
res3a_branch2c         (Conv2D)
res3a_branch1          (Conv2D)
bn3a_branch2c          (BatchNorm)
bn3a_branch1           (BatchNorm)
res3b_branch2a         (Conv2D)
bn3b_branch2a          (BatchNorm)
res3b_branch2b         (Conv2D)
bn3b_branch2b          (BatchNorm)
res3b_branch2c         (Conv2D)
bn3b_branch2c          (BatchNorm)
res3c_branch2a         (Conv2D)
bn3c_branch2a          (BatchNorm)
res3c_branch2b         (Conv2D)
bn3c_branch2b          (BatchNorm)
res3c_branch2c         (Conv2D)
bn3c_branch2c          (BatchNorm)
res3d_branch2a         (Conv2D)
bn3d_branch2a          (BatchNorm)
res3d_branch2b         (Conv2D)
bn3d_branch2b          (BatchNorm)
res3d_branch2c         (Conv2D)
bn3d_branch2c          (BatchNorm)
res4a_branch2a         (Conv2D)
bn4a_branch2a          (BatchNorm)
res4a_branch2b         (Conv2D)
bn4a_branch2b          (BatchNorm)
res4a_branch2c         (Conv2D)
res4a_branch1          (Conv2D)
bn4a_branch2c          (BatchNorm)
bn4a_branch1           (BatchNorm)
res4b_branch2a         (Conv2D)
bn4b_branch2a          (BatchNorm)
res4b_branch2b         (Conv2D)
bn4b_branch2b          (BatchNorm)
res4b_branch2c         (Conv2D)
bn4b_branch2c          (BatchNorm)
res4c_branch2a         (Conv2D)
bn4c_branch2a          (BatchNorm)
res4c_branch2b         (Conv2D)
bn4c_branch2b          (BatchNorm)
res4c_branch2c         (Conv2D)
bn4c_branch2c          (BatchNorm)
res4d_branch2a         (Conv2D)
bn4d_branch2a          (BatchNorm)
res4d_branch2b         (Conv2D)
bn4d_branch2b          (BatchNorm)
res4d_branch2c         (Conv2D)
bn4d_branch2c          (BatchNorm)
res4e_branch2a         (Conv2D)
bn4e_branch2a          (BatchNorm)
res4e_branch2b         (Conv2D)
bn4e_branch2b          (BatchNorm)
res4e_branch2c         (Conv2D)
bn4e_branch2c          (BatchNorm)
res4f_branch2a         (Conv2D)
bn4f_branch2a          (BatchNorm)
res4f_branch2b         (Conv2D)
bn4f_branch2b          (BatchNorm)
res4f_branch2c         (Conv2D)
bn4f_branch2c          (BatchNorm)
res4g_branch2a         (Conv2D)
bn4g_branch2a          (BatchNorm)
res4g_branch2b         (Conv2D)
bn4g_branch2b          (BatchNorm)
res4g_branch2c         (Conv2D)
bn4g_branch2c          (BatchNorm)
res4h_branch2a         (Conv2D)
bn4h_branch2a          (BatchNorm)
res4h_branch2b         (Conv2D)
bn4h_branch2b          (BatchNorm)
res4h_branch2c         (Conv2D)
bn4h_branch2c          (BatchNorm)
res4i_branch2a         (Conv2D)
bn4i_branch2a          (BatchNorm)
res4i_branch2b         (Conv2D)
bn4i_branch2b          (BatchNorm)
res4i_branch2c         (Conv2D)
bn4i_branch2c          (BatchNorm)
res4j_branch2a         (Conv2D)
bn4j_branch2a          (BatchNorm)
res4j_branch2b         (Conv2D)
bn4j_branch2b          (BatchNorm)
res4j_branch2c         (Conv2D)
bn4j_branch2c          (BatchNorm)
res4k_branch2a         (Conv2D)
bn4k_branch2a          (BatchNorm)
res4k_branch2b         (Conv2D)
bn4k_branch2b          (BatchNorm)
res4k_branch2c         (Conv2D)
bn4k_branch2c          (BatchNorm)
res4l_branch2a         (Conv2D)
bn4l_branch2a          (BatchNorm)
res4l_branch2b         (Conv2D)
bn4l_branch2b          (BatchNorm)
res4l_branch2c         (Conv2D)
bn4l_branch2c          (BatchNorm)
res4m_branch2a         (Conv2D)
bn4m_branch2a          (BatchNorm)
res4m_branch2b         (Conv2D)
bn4m_branch2b          (BatchNorm)
res4m_branch2c         (Conv2D)
bn4m_branch2c          (BatchNorm)
res4n_branch2a         (Conv2D)
bn4n_branch2a          (BatchNorm)
res4n_branch2b         (Conv2D)
bn4n_branch2b          (BatchNorm)
res4n_branch2c         (Conv2D)
bn4n_branch2c          (BatchNorm)
res4o_branch2a         (Conv2D)
bn4o_branch2a          (BatchNorm)
res4o_branch2b         (Conv2D)
bn4o_branch2b          (BatchNorm)
res4o_branch2c         (Conv2D)
bn4o_branch2c          (BatchNorm)
res4p_branch2a         (Conv2D)
bn4p_branch2a          (BatchNorm)
res4p_branch2b         (Conv2D)
bn4p_branch2b          (BatchNorm)
res4p_branch2c         (Conv2D)
bn4p_branch2c          (BatchNorm)
res4q_branch2a         (Conv2D)
bn4q_branch2a          (BatchNorm)
res4q_branch2b         (Conv2D)
bn4q_branch2b          (BatchNorm)
res4q_branch2c         (Conv2D)
bn4q_branch2c          (BatchNorm)
res4r_branch2a         (Conv2D)
bn4r_branch2a          (BatchNorm)
res4r_branch2b         (Conv2D)
bn4r_branch2b          (BatchNorm)
res4r_branch2c         (Conv2D)
bn4r_branch2c          (BatchNorm)
res4s_branch2a         (Conv2D)
bn4s_branch2a          (BatchNorm)
res4s_branch2b         (Conv2D)
bn4s_branch2b          (BatchNorm)
res4s_branch2c         (Conv2D)
bn4s_branch2c          (BatchNorm)
res4t_branch2a         (Conv2D)
bn4t_branch2a          (BatchNorm)
res4t_branch2b         (Conv2D)
bn4t_branch2b          (BatchNorm)
res4t_branch2c         (Conv2D)
bn4t_branch2c          (BatchNorm)
res4u_branch2a         (Conv2D)
bn4u_branch2a          (BatchNorm)
res4u_branch2b         (Conv2D)
bn4u_branch2b          (BatchNorm)
res4u_branch2c         (Conv2D)
bn4u_branch2c          (BatchNorm)
res4v_branch2a         (Conv2D)
bn4v_branch2a          (BatchNorm)
res4v_branch2b         (Conv2D)
bn4v_branch2b          (BatchNorm)
res4v_branch2c         (Conv2D)
bn4v_branch2c          (BatchNorm)
res4w_branch2a         (Conv2D)
bn4w_branch2a          (BatchNorm)
res4w_branch2b         (Conv2D)
bn4w_branch2b          (BatchNorm)
res4w_branch2c         (Conv2D)
bn4w_branch2c          (BatchNorm)
res5a_branch2a         (Conv2D)
bn5a_branch2a          (BatchNorm)
res5a_branch2b         (Conv2D)
bn5a_branch2b          (BatchNorm)
res5a_branch2c         (Conv2D)
res5a_branch1          (Conv2D)
bn5a_branch2c          (BatchNorm)
bn5a_branch1           (BatchNorm)
res5b_branch2a         (Conv2D)
bn5b_branch2a          (BatchNorm)
res5b_branch2b         (Conv2D)
bn5b_branch2b          (BatchNorm)
res5b_branch2c         (Conv2D)
bn5b_branch2c          (BatchNorm)
res5c_branch2a         (Conv2D)
bn5c_branch2a          (BatchNorm)
res5c_branch2b         (Conv2D)
bn5c_branch2b          (BatchNorm)
res5c_branch2c         (Conv2D)
bn5c_branch2c          (BatchNorm)
fpn_c5p5               (Conv2D)
fpn_c4p4               (Conv2D)
fpn_c3p3               (Conv2D)
fpn_c2p2               (Conv2D)
fpn_p5                 (Conv2D)
fpn_p2                 (Conv2D)
fpn_p3                 (Conv2D)
fpn_p4                 (Conv2D)
In model:  rpn_model
    rpn_conv_shared        (Conv2D)
    rpn_class_raw          (Conv2D)
    rpn_bbox_pred          (Conv2D)
mrcnn_mask_conv1       (TimeDistributed)
mrcnn_mask_bn1         (TimeDistributed)
mrcnn_mask_conv2       (TimeDistributed)
mrcnn_mask_bn2         (TimeDistributed)
mrcnn_class_conv1      (TimeDistributed)
mrcnn_class_bn1        (TimeDistributed)
mrcnn_mask_conv3       (TimeDistributed)
mrcnn_mask_bn3         (TimeDistributed)
mrcnn_class_conv2      (TimeDistributed)
mrcnn_class_bn2        (TimeDistributed)
mrcnn_mask_conv4       (TimeDistributed)
mrcnn_mask_bn4         (TimeDistributed)
mrcnn_bbox_fc          (TimeDistributed)
mrcnn_mask_deconv      (TimeDistributed)
mrcnn_class_logits     (TimeDistributed)
mrcnn_mask             (TimeDistributed)
 
E:\Anaconda3\install1\lib\site-packages\tensorflow\python\ops\gradients_impl.py:97: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
In [23]:
 
 
1
# Save weights
2
# Typically not needed because callbacks save after every epoch
3
# Uncomment to save manually
4
# model_path = os.path.join(MODEL_DIR, "mask_rcnn_shapes.h5")
5
# model.keras_model.save_weights(model_path)
 
 
 
 

Detection

In [26]:
 
 
1
class InferenceConfig(ShapesConfig):
2
    GPU_COUNT = 1
3
    IMAGES_PER_GPU = 1
4
5
inference_config = InferenceConfig()
6
7
# Recreate the model in inference mode
8
model = modellib.MaskRCNN(mode="inference", 
9
                          config=inference_config,
10
                          model_dir=MODEL_DIR)
11
12
# Get path to saved weights
13
# Either set a specific path or find last trained weights
14
# model_path = os.path.join(ROOT_DIR, ".h5 file name here")
15
model_path = model.find_last()
16
17
# Load trained weights
18
print("Loading weights from ", model_path)
19
model.load_weights(model_path, by_name=True)
 
 
 
 
Loading weights from  C:\Users\luo\tensorflow\Mask_RCNN-master\logs\shapes20180817T1459\mask_rcnn_shapes_0001.h5
In [27]:
 
 
1
# Test on a random image
2
image_id = random.choice(dataset_val.image_ids)
3
original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\
4
    modellib.load_image_gt(dataset_val, inference_config, 
5
                           image_id, use_mini_mask=False)
6
7
log("original_image", original_image)
8
log("image_meta", image_meta)
9
log("gt_class_id", gt_class_id)
10
log("gt_bbox", gt_bbox)
11
log("gt_mask", gt_mask)
12
13
visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, 
14
                            dataset_train.class_names, figsize=(8, 8))
 
 
 
 
original_image           shape: (128, 128, 3)         min:   72.00000  max:  248.00000  uint8
image_meta               shape: (16,)                 min:    0.00000  max:  128.00000  int32
gt_class_id              shape: (3,)                  min:    2.00000  max:    3.00000  int32
gt_bbox                  shape: (3, 4)                min:    0.00000  max:  128.00000  int32
gt_mask                  shape: (128, 128, 3)         min:    0.00000  max:    1.00000  bool
 
In [28]:
 
 
1
results = model.detect([original_image], verbose=1)
2
3
r = results[0]
4
visualize.display_instances(original_image, r['rois'], r['masks'], r['class_ids'], 
5
                            dataset_val.class_names, r['scores'], ax=get_ax())
 
 
 
 
Processing 1 images
image                    shape: (128, 128, 3)         min:   72.00000  max:  248.00000  uint8
molded_images            shape: (1, 128, 128, 3)      min:  -51.70000  max:  144.10000  float64
image_metas              shape: (1, 16)               min:    0.00000  max:  128.00000  int32
anchors                  shape: (1, 4092, 4)          min:   -0.71267  max:    1.20874  float32
 
 

Evaluation

In [29]:
 
 
1
# Compute VOC-Style mAP @ IoU=0.5
2
# Running on 10 images. Increase for better accuracy.
3
image_ids = np.random.choice(dataset_val.image_ids, 10)
4
APs = []
5
for image_id in image_ids:
6
    # Load image and ground truth data
7
    image, image_meta, gt_class_id, gt_bbox, gt_mask =\
8
        modellib.load_image_gt(dataset_val, inference_config,
9
                               image_id, use_mini_mask=False)
10
    molded_images = np.expand_dims(modellib.mold_image(image, inference_config), 0)
11
    # Run object detection
12
    results = model.detect([image], verbose=0)
13
    r = results[0]
14
    # Compute AP
15
    AP, precisions, recalls, overlaps =\
16
        utils.compute_ap(gt_bbox, gt_class_id, gt_mask,
17
                         r["rois"], r["class_ids"], r["scores"], r['masks'])
18
    APs.append(AP)
19

20
print("mAP: ", np.mean(APs))
 
 
 
 
mAP:  0.966666667163372
In [ ]:
 
 
1
 
 
 
posted @ 2018-08-17 15:37  西北逍遥  阅读(4605)  评论(0编辑  收藏  举报