Open AI-ML-Enthusiast opened 4 years ago
kaanaykutkabakci
commented
4 years ago Did you find any way to run .h5 model with OpenCV @ibrahimLearning? I have tried so many way, but i could not find any solution. Then, i trained my dataset with TF OD API, but i could not get same accuracy.
taylan24
commented
4 years ago I also want to convert .h5 to .pb and .pbtxt, How did you do that? can you share any sources about this process @ibrahimLearning ? thank you in advance
ZouJiu1
commented
4 years ago convert-to pb file
#!encoding=utf-8
'''
#-----------------
Authors:邹九
Time:2019-11-21
#-----------------
'''
"""
Copyright (c) 2019, by the Authors: Amir H. Abdi
This script is freely available under the MIT Public License.
Please see the License file in the root for details.
The following code snippet will convert the keras model files
to the freezed .pb tensorflow weight file. The resultant TensorFlow model
holds both the model architecture and its associated weights.
"""
import tensorflow as tf
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
from pathlib import Path
from absl import app
from absl import flags
from absl import logging
from mrcnn import model as modellib
from mrcnn.config import Config
import keras
import os
from keras import backend as K
from keras.models import model_from_json, model_from_yaml
from keras.utils.vis_utils import plot_model
COCO_MODEL_PATH = r'../logs/shapes20191113T1540_mask_rcnn_shapes_0199.h5'
K.set_learning_phase(0)
FLAGS = flags.FLAGS
flags.DEFINE_string('input_model', default=r'', help='Path to the input model.')
flags.DEFINE_string('input_model_json', None, 'Path to the input model '
'architecture in json format.')
flags.DEFINE_string('input_model_yaml', None, 'Path to the input model architecture in yaml format.')
flags.DEFINE_string('output_model', default=r'./shapes20191113T1540_mask_rcnn_shapes_0199.pb', help='Path where the converted model will be stored.')
flags.DEFINE_boolean('save_graph_def', False,
'Whether to save the graphdef.pbtxt file which contains '
'the graph definition in ASCII format.')
flags.DEFINE_string('output_nodes_prefix', None,
'If set, the output nodes will be renamed to '
'`output_nodes_prefix`+i, where `i` will numerate the '
'number of of output nodes of the network.')
flags.DEFINE_boolean('quantize', False,
'If set, the resultant TensorFlow graph weights will be '
'converted from float into eight-bit equivalents. See '
'documentation here: '
'https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms')
flags.DEFINE_boolean('channels_first', False,
'Whether channels are the first dimension of a tensor. '
'The default is TensorFlow behaviour where channels are '
'the last dimension.')
flags.DEFINE_boolean('output_meta_ckpt', False,
'If set to True, exports the model as .meta, .index, and '
'.data files, with a checkpoint file. These can be later '
'loaded in TensorFlow to continue training.')
flags.mark_flag_as_required('input_model')
flags.mark_flag_as_required('output_model')
def load_model(input_model_path, input_json_path=None, input_yaml_path=None):
if not Path(input_model_path).exists():
raise FileNotFoundError(
'Model file `{}` does not exist.'.format(input_model_path))
try:
model = keras.models.load_model(input_model_path)
return model
except FileNotFoundError as err:
logging.error('Input mode file (%s) does not exist.', FLAGS.input_model)
raise err
except ValueError as wrong_file_err:
if input_json_path:
if not Path(input_json_path).exists():
raise FileNotFoundError(
'Model description json file `{}` does not exist.'.format(
input_json_path))
try:
model = model_from_json(open(str(input_json_path)).read())
model.load_weights(input_model_path)
return model
except Exception as err:
logging.error("Couldn't load model from json.")
raise err
elif input_yaml_path:
if not Path(input_yaml_path).exists():
raise FileNotFoundError(
'Model description yaml file `{}` does not exist.'.format(
input_yaml_path))
try:
model = model_from_yaml(open(str(input_yaml_path)).read())
model.load_weights(input_model_path)
return model
except Exception as err:
logging.error("Couldn't load model from yaml.")
raise err
else:
logging.error(
'Input file specified only holds the weights, and not '
'the model definition. Save the model using '
'model.save(filename.h5) which will contain the network '
'architecture as well as its weights. '
'If the model is saved using the '
'model.save_weights(filename) function, either '
'input_model_json or input_model_yaml flags should be set to '
'to import the network architecture prior to loading the '
'weights. \n'
'Check the keras documentation for more details '
'(https://keras.io/getting-started/faq/)')
raise wrong_file_err
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"
# Number of classes (including background)
NUM_CLASSES = 1 + 14 # background + 15 object
# Choose the number of GPU devices
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# Use small images for faster training. Set the limits of the small side
# the large side, and that determines the image shape.
IMAGE_RESIZE_MODE = "square"
IMAGE_MAX_DIM = 896
RPN_ANCHOR_SCALES = (8 * 6, 16 * 6, 32 * 6, 64 * 6, 128 * 6) # anchor side in pixels
# RPN_ANCHOR_SCALES = (8*5, 16*5, 32*5, 64*5, 128*5) # 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 = 100
# Use a small epoch since the data is simple
# STEPS_PER_EPOCH = 1000
STEPS_PER_EPOCH = 1000
# use small validation steps since the epoch is small
VALIDATION_STEPS = 25
def main(args):
# If output_model path is relative and in cwd, make it absolute from root
output_model = FLAGS.output_model
if str(Path(output_model).parent) == '.':
output_model = str((Path.cwd() / output_model))
output_fld = Path(output_model).parent
output_model_name = Path(output_model).name
output_model_stem = Path(output_model).stem
output_model_pbtxt_name = output_model_stem + '.pbtxt'
# Create output directory if it does not exist
Path(output_model).parent.mkdir(parents=True, exist_ok=True)
if FLAGS.channels_first:
K.set_image_data_format('channels_first')
else:
K.set_image_data_format('channels_last')
# model = load_model(FLAGS.input_model, FLAGS.input_model_json, FLAGS.input_model_yaml)
##--------------------------------------------------------------------------------------#
config = ShapesConfig()
config.display()
MODEL_DIR = r'E:\Desktop\Projects\Mask_RCNN-master\logs'
model = modellib.MaskRCNN(mode="inference", config=config,\
model_dir=MODEL_DIR)
model.load_weights(COCO_MODEL_PATH, by_name=True)#exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",\
# "mrcnn_bbox", "mrcnn_mask"])
# print(model.summary())
# plot_model(model, to_file='model1.png', show_shapes=True)
# model_json = model.to_json()
# with open(r'./modle.json', 'w') as file:
# file.write(model_json)
print('loaded model and saved json file')
##--------------------------------------------------------------------------------------#
# TODO(amirabdi): Support networks with multiple inputs
# orig_output_node_names = [node.op.name for node in model.outputs]
orig_output_node_names = ['mrcnn_detection/Reshape_1', 'mrcnn_class/Softmax', 'mrcnn_bbox/Reshape',\
'mrcnn_mask/Sigmoid', 'ROI/packed_2', 'rpn_class/concat', 'rpn_bbox/concat']
if FLAGS.output_nodes_prefix:
num_output = len(orig_output_node_names)
pred = [None] * num_output
converted_output_node_names = [None] * num_output
# Create dummy tf nodes to rename output
for i in range(num_output):
converted_output_node_names[i] = '{}{}'.format(
FLAGS.output_nodes_prefix, i)
pred[i] = tf.identity(model.outputs[i],
name=converted_output_node_names[i])
else:
converted_output_node_names = orig_output_node_names
logging.info('Converted output node names are: %s',
str(converted_output_node_names))
sess = K.get_session()
if FLAGS.output_meta_ckpt:
saver = tf.train.Saver()
saver.save(sess, str(output_fld / output_model_stem))
if FLAGS.save_graph_def:
tf.train.write_graph(sess.graph.as_graph_def(), str(output_fld),
output_model_pbtxt_name, as_text=True)
logging.info('Saved the graph definition in ascii format at %s',
str(Path(output_fld) / output_model_pbtxt_name))
if FLAGS.quantize:
from tensorflow.tools.graph_transforms import TransformGraph
transforms = ["quantize_weights", "quantize_nodes"]
transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [],
converted_output_node_names,
transforms)
constant_graph = graph_util.convert_variables_to_constants(
sess,
transformed_graph_def,
converted_output_node_names)
else:
constant_graph = graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(),
converted_output_node_names)
graph_io.write_graph(constant_graph, str(output_fld), output_model_name,
as_text=False)
logging.info('Saved the freezed graph at %s',
str(Path(output_fld) / output_model_name))
if __name__ == "__main__":
app.run(main)load pb model
def load_detection_model(model):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(model, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
input_image = tf.get_default_graph().get_tensor_by_name('input_image:0')
input_image_meta = tf.get_default_graph().get_tensor_by_name('input_image_meta:0')
input_anchors = tf.get_default_graph().get_tensor_by_name('input_anchors:0')
detections = tf.get_default_graph().get_tensor_by_name('mrcnn_detection/Reshape_1:0')
mrcnn_mask = tf.get_default_graph().get_tensor_by_name('mrcnn_mask/Sigmoid:0')
sessd=tf.Session(config=config,graph=detection_graph)
print('Loaded detection model from file "%s"' % model)
return sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask
sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask = load_detection_model(model_path)
results = model.detect_pb([image], sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask,verbose=1)use model, add to mrcnn/model.py
def detect_pb(self, images, sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask, verbose=1):
"""Runs the detection pipeline.
images: List of images, potentially of different sizes.
Returns a list of dicts, one dict per image. The dict contains:
rois: [N, (y1, x1, y2, x2)] detection bounding boxes
class_ids: [N] int class IDs
scores: [N] float probability scores for the class IDs
masks: [H, W, N] instance binary masks
"""
assert self.mode == "inference", "Create model in inference mode."
assert len(
images) == self.config.BATCH_SIZE, "len(images) must be equal to BATCH_SIZE"
# if verbose:
# log("Processing {} images".format(len(images)))
# for image in images:
# log("image", image)
# Mold inputs to format expected by the neural network
molded_images, image_metas, windows = self.mold_inputs(images)
# Validate image sizes
# All images in a batch MUST be of the same size
image_shape = molded_images[0].shape
# print(image_shape, molded_images.shape)
for g in molded_images[1:]:
assert g.shape == image_shape,\
"After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes."
# Anchors
anchors = self.get_anchors(image_shape)
# Duplicate across the batch dimension because Keras requires it
# TODO: can this be optimized to avoid duplicating the anchors?
anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape)
# if verbose:
# log("molded_images", molded_images)
# log("image_metas", image_metas)
# log("anchors", anchors)
# Run object detection
# detections, _, _, mrcnn_mask, _, _, _ =\
# self.keras_model.predict([molded_images, image_metas, anchors], verbose=0)
detectionsed, mrcnn_masked = sessd.run([detections, mrcnn_mask], feed_dict = {input_image: molded_images, \
input_image_meta: image_metas, \
input_anchors: anchors})
mrcnn_masked = np.expand_dims(mrcnn_masked, 0)
detections = np.array(detectionsed)
mrcnn_mask = np.array(mrcnn_masked)
# Process detections
results = []
for i, image in enumerate(images):
xi = detections[i]
yi = mrcnn_mask[i]
moldedi = molded_images[i]
windowsi = windows[i]
final_rois, final_class_ids, final_scores, final_masks =\
self.unmold_detections(detections[i], mrcnn_mask[i],
image.shape, molded_images[i].shape,
windows[i])
results.append({
"rois": final_rois,
"class_ids": final_class_ids,
"scores": final_scores,
"masks": final_masks,
})
return results
mak27arr
commented
4 years ago convert-to pb file
#!encoding=utf-8 ''' #----------------- Authors:邹九 Time:2019-11-21 #----------------- ''' """ Copyright (c) 2019, by the Authors: Amir H. Abdi This script is freely available under the MIT Public License. Please see the License file in the root for details. The following code snippet will convert the keras model files to the freezed .pb tensorflow weight file. The resultant TensorFlow model holds both the model architecture and its associated weights. """ import tensorflow as tf from tensorflow.python.framework import graph_util from tensorflow.python.framework import graph_io from pathlib import Path from absl import app from absl import flags from absl import logging from mrcnn import model as modellib from mrcnn.config import Config import keras import os from keras import backend as K from keras.models import model_from_json, model_from_yaml from keras.utils.vis_utils import plot_model COCO_MODEL_PATH = r'../logs/shapes20191113T1540_mask_rcnn_shapes_0199.h5' K.set_learning_phase(0) FLAGS = flags.FLAGS flags.DEFINE_string('input_model', default=r'', help='Path to the input model.') flags.DEFINE_string('input_model_json', None, 'Path to the input model ' 'architecture in json format.') flags.DEFINE_string('input_model_yaml', None, 'Path to the input model architecture in yaml format.') flags.DEFINE_string('output_model', default=r'./shapes20191113T1540_mask_rcnn_shapes_0199.pb', help='Path where the converted model will be stored.') flags.DEFINE_boolean('save_graph_def', False, 'Whether to save the graphdef.pbtxt file which contains ' 'the graph definition in ASCII format.') flags.DEFINE_string('output_nodes_prefix', None, 'If set, the output nodes will be renamed to ' '`output_nodes_prefix`+i, where `i` will numerate the ' 'number of of output nodes of the network.') flags.DEFINE_boolean('quantize', False, 'If set, the resultant TensorFlow graph weights will be ' 'converted from float into eight-bit equivalents. See ' 'documentation here: ' 'https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms') flags.DEFINE_boolean('channels_first', False, 'Whether channels are the first dimension of a tensor. ' 'The default is TensorFlow behaviour where channels are ' 'the last dimension.') flags.DEFINE_boolean('output_meta_ckpt', False, 'If set to True, exports the model as .meta, .index, and ' '.data files, with a checkpoint file. These can be later ' 'loaded in TensorFlow to continue training.') flags.mark_flag_as_required('input_model') flags.mark_flag_as_required('output_model') def load_model(input_model_path, input_json_path=None, input_yaml_path=None): if not Path(input_model_path).exists(): raise FileNotFoundError( 'Model file `{}` does not exist.'.format(input_model_path)) try: model = keras.models.load_model(input_model_path) return model except FileNotFoundError as err: logging.error('Input mode file (%s) does not exist.', FLAGS.input_model) raise err except ValueError as wrong_file_err: if input_json_path: if not Path(input_json_path).exists(): raise FileNotFoundError( 'Model description json file `{}` does not exist.'.format( input_json_path)) try: model = model_from_json(open(str(input_json_path)).read()) model.load_weights(input_model_path) return model except Exception as err: logging.error("Couldn't load model from json.") raise err elif input_yaml_path: if not Path(input_yaml_path).exists(): raise FileNotFoundError( 'Model description yaml file `{}` does not exist.'.format( input_yaml_path)) try: model = model_from_yaml(open(str(input_yaml_path)).read()) model.load_weights(input_model_path) return model except Exception as err: logging.error("Couldn't load model from yaml.") raise err else: logging.error( 'Input file specified only holds the weights, and not ' 'the model definition. Save the model using ' 'model.save(filename.h5) which will contain the network ' 'architecture as well as its weights. ' 'If the model is saved using the ' 'model.save_weights(filename) function, either ' 'input_model_json or input_model_yaml flags should be set to ' 'to import the network architecture prior to loading the ' 'weights. \n' 'Check the keras documentation for more details ' '(https://keras.io/getting-started/faq/)') raise wrong_file_err 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" # Number of classes (including background) NUM_CLASSES = 1 + 14 # background + 15 object # Choose the number of GPU devices # os.environ['CUDA_VISIBLE_DEVICES'] = '0' # Use small images for faster training. Set the limits of the small side # the large side, and that determines the image shape. IMAGE_RESIZE_MODE = "square" IMAGE_MAX_DIM = 896 RPN_ANCHOR_SCALES = (8 * 6, 16 * 6, 32 * 6, 64 * 6, 128 * 6) # anchor side in pixels # RPN_ANCHOR_SCALES = (8*5, 16*5, 32*5, 64*5, 128*5) # 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 = 100 # Use a small epoch since the data is simple # STEPS_PER_EPOCH = 1000 STEPS_PER_EPOCH = 1000 # use small validation steps since the epoch is small VALIDATION_STEPS = 25 def main(args): # If output_model path is relative and in cwd, make it absolute from root output_model = FLAGS.output_model if str(Path(output_model).parent) == '.': output_model = str((Path.cwd() / output_model)) output_fld = Path(output_model).parent output_model_name = Path(output_model).name output_model_stem = Path(output_model).stem output_model_pbtxt_name = output_model_stem + '.pbtxt' # Create output directory if it does not exist Path(output_model).parent.mkdir(parents=True, exist_ok=True) if FLAGS.channels_first: K.set_image_data_format('channels_first') else: K.set_image_data_format('channels_last') # model = load_model(FLAGS.input_model, FLAGS.input_model_json, FLAGS.input_model_yaml) ##--------------------------------------------------------------------------------------# config = ShapesConfig() config.display() MODEL_DIR = r'E:\Desktop\Projects\Mask_RCNN-master\logs' model = modellib.MaskRCNN(mode="inference", config=config,\ model_dir=MODEL_DIR) model.load_weights(COCO_MODEL_PATH, by_name=True)#exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",\ # "mrcnn_bbox", "mrcnn_mask"]) # print(model.summary()) # plot_model(model, to_file='model1.png', show_shapes=True) # model_json = model.to_json() # with open(r'./modle.json', 'w') as file: # file.write(model_json) print('loaded model and saved json file') ##--------------------------------------------------------------------------------------# # TODO(amirabdi): Support networks with multiple inputs # orig_output_node_names = [node.op.name for node in model.outputs] orig_output_node_names = ['mrcnn_detection/Reshape_1', 'mrcnn_class/Softmax', 'mrcnn_bbox/Reshape',\ 'mrcnn_mask/Sigmoid', 'ROI/packed_2', 'rpn_class/concat', 'rpn_bbox/concat'] if FLAGS.output_nodes_prefix: num_output = len(orig_output_node_names) pred = [None] * num_output converted_output_node_names = [None] * num_output # Create dummy tf nodes to rename output for i in range(num_output): converted_output_node_names[i] = '{}{}'.format( FLAGS.output_nodes_prefix, i) pred[i] = tf.identity(model.outputs[i], name=converted_output_node_names[i]) else: converted_output_node_names = orig_output_node_names logging.info('Converted output node names are: %s', str(converted_output_node_names)) sess = K.get_session() if FLAGS.output_meta_ckpt: saver = tf.train.Saver() saver.save(sess, str(output_fld / output_model_stem)) if FLAGS.save_graph_def: tf.train.write_graph(sess.graph.as_graph_def(), str(output_fld), output_model_pbtxt_name, as_text=True) logging.info('Saved the graph definition in ascii format at %s', str(Path(output_fld) / output_model_pbtxt_name)) if FLAGS.quantize: from tensorflow.tools.graph_transforms import TransformGraph transforms = ["quantize_weights", "quantize_nodes"] transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [], converted_output_node_names, transforms) constant_graph = graph_util.convert_variables_to_constants( sess, transformed_graph_def, converted_output_node_names) else: constant_graph = graph_util.convert_variables_to_constants( sess, sess.graph.as_graph_def(), converted_output_node_names) graph_io.write_graph(constant_graph, str(output_fld), output_model_name, as_text=False) logging.info('Saved the freezed graph at %s', str(Path(output_fld) / output_model_name)) if __name__ == "__main__": app.run(main)load pb model
def load_detection_model(model): config = tf.ConfigProto() config.gpu_options.allow_growth = True detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(model, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') input_image = tf.get_default_graph().get_tensor_by_name('input_image:0') input_image_meta = tf.get_default_graph().get_tensor_by_name('input_image_meta:0') input_anchors = tf.get_default_graph().get_tensor_by_name('input_anchors:0') detections = tf.get_default_graph().get_tensor_by_name('mrcnn_detection/Reshape_1:0') mrcnn_mask = tf.get_default_graph().get_tensor_by_name('mrcnn_mask/Sigmoid:0') sessd=tf.Session(config=config,graph=detection_graph) print('Loaded detection model from file "%s"' % model) return sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask = load_detection_model(model_path) results = model.detect_pb([image], sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask,verbose=1)use model, add to mrcnn/model.py
def detect_pb(self, images, sessd, input_image, input_image_meta, input_anchors, detections, mrcnn_mask, verbose=1): """Runs the detection pipeline. images: List of images, potentially of different sizes. Returns a list of dicts, one dict per image. The dict contains: rois: [N, (y1, x1, y2, x2)] detection bounding boxes class_ids: [N] int class IDs scores: [N] float probability scores for the class IDs masks: [H, W, N] instance binary masks """ assert self.mode == "inference", "Create model in inference mode." assert len( images) == self.config.BATCH_SIZE, "len(images) must be equal to BATCH_SIZE" # if verbose: # log("Processing {} images".format(len(images))) # for image in images: # log("image", image) # Mold inputs to format expected by the neural network molded_images, image_metas, windows = self.mold_inputs(images) # Validate image sizes # All images in a batch MUST be of the same size image_shape = molded_images[0].shape # print(image_shape, molded_images.shape) for g in molded_images[1:]: assert g.shape == image_shape,\ "After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes." # Anchors anchors = self.get_anchors(image_shape) # Duplicate across the batch dimension because Keras requires it # TODO: can this be optimized to avoid duplicating the anchors? anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) # if verbose: # log("molded_images", molded_images) # log("image_metas", image_metas) # log("anchors", anchors) # Run object detection # detections, _, _, mrcnn_mask, _, _, _ =\ # self.keras_model.predict([molded_images, image_metas, anchors], verbose=0) detectionsed, mrcnn_masked = sessd.run([detections, mrcnn_mask], feed_dict = {input_image: molded_images, \ input_image_meta: image_metas, \ input_anchors: anchors}) mrcnn_masked = np.expand_dims(mrcnn_masked, 0) detections = np.array(detectionsed) mrcnn_mask = np.array(mrcnn_masked) # Process detections results = [] for i, image in enumerate(images): xi = detections[i] yi = mrcnn_mask[i] moldedi = molded_images[i] windowsi = windows[i] final_rois, final_class_ids, final_scores, final_masks =\ self.unmold_detections(detections[i], mrcnn_mask[i], image.shape, molded_images[i].shape, windows[i]) results.append({ "rois": final_rois, "class_ids": final_class_ids, "scores": final_scores, "masks": final_masks, }) return results
Get error has shape (1024, 60), but the saved weight has shape (1024, 8)
@dkurt @moorage @waleedka I am trying to convert MaskRCNN model into tensorflow model (.pb and .pbtxt) for using in opencvDNN, like this:
net = cv2.dnn.readNetFromTensorflow()I converted the MaskRCNN model into .pb and .pbtxt. but it fails to load with opencvDNN.I think we need to open this issue, so I opened it. Is there any suggestion or idea please share here.
Thanks