import os
import sys
import numpy as np
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import keras

# 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 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)

# Path to Shapes trained weights
SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_shapes.h5")

Using TensorFlow backend.

# Run one of the code blocks

# Shapes toy dataset
# import shapes
# config = shapes.ShapesConfig()

# MS COCO Dataset
import coco

config = coco.CocoConfig()

# Device to load the neural network on.
# Useful if you're training a model on the same
# machine, in which case use CPU and leave the
# GPU for training.
DEVICE = "/cpu:0"  # /cpu:0 or /gpu:0

def get_ax(rows=1, cols=1, size=16):
    """Return a Matplotlib Axes array to be used in
    all visualizations in the notebook. Provide a
    central point to control graph sizes.

    Adjust the size attribute to control how big to render images
    """
    _, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows))
    return ax

# Create model in inference mode
with tf.device(DEVICE):
    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Set weights file path
if config.NAME == "shapes":
    weights_path = SHAPES_MODEL_PATH
elif config.NAME == "coco":
    weights_path = COCO_MODEL_PATH
# Or, uncomment to load the last model you trained
# weights_path = model.find_last()

# Load weights
print("Loading weights ", weights_path)
model.load_weights(weights_path, by_name=True)

# Show stats of all trainable weights
visualize.display_weight_stats(model)

# Pick layer types to display
LAYER_TYPES = ["Conv2D", "Dense", "Conv2DTranspose"]
# Get layers
layers = model.get_trainable_layers()
layers = list(filter(lambda l: l.__class__.__name__ in LAYER_TYPES, layers))
# Display Histograms
fig, ax = plt.subplots(
    len(layers), 2, figsize=(10, 3 * len(layers)), gridspec_kw={"hspace": 1}
)
for l, layer in enumerate(layers):
    weights = layer.get_weights()
    for w, weight in enumerate(weights):
        tensor = layer.weights[w]
        ax[l, w].set_title(tensor.name)
        _ = ax[l, w].hist(weight[w].flatten(), 50)