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test #95

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1d7363b
Update train_frcnn.py
vijayml457 Sep 20, 2020
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Update train_frcnn.py
vijayml457 Sep 20, 2020
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Update losses.py
vijayml457 Sep 20, 2020
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Update losses.py
vijayml457 Sep 20, 2020
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Update train_frcnn.py
vijayml457 Sep 20, 2020
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Update resnet.py
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Update train_frcnn.py
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Update train_frcnn.py
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Update vgg.py
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Update RoiPoolingConv.py
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2 changes: 1 addition & 1 deletion keras_frcnn/RoiPoolingConv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ class RoiPoolingConv(Layer):
'''
def __init__(self, pool_size, num_rois, **kwargs):

self.dim_ordering = K.common.image_dim_ordering()
self.dim_ordering = K.image_data_format()
assert self.dim_ordering in {'tf', 'th'}, 'dim_ordering must be in {tf, th}'

self.pool_size = pool_size
Expand Down
6 changes: 3 additions & 3 deletions keras_frcnn/losses.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
from keras import backend as K
from keras.objectives import categorical_crossentropy

if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
import tensorflow as tf

lambda_rpn_regr = 1.0
Expand All @@ -15,7 +15,7 @@

def rpn_loss_regr(num_anchors):
def rpn_loss_regr_fixed_num(y_true, y_pred):
if K.common.image_dim_ordering() == 'th':
if K.image_data_format() == 'th':
x = y_true[:, 4 * num_anchors:, :, :] - y_pred
x_abs = K.abs(x)
x_bool = K.less_equal(x_abs, 1.0)
Expand All @@ -34,7 +34,7 @@ def rpn_loss_regr_fixed_num(y_true, y_pred):

def rpn_loss_cls(num_anchors):
def rpn_loss_cls_fixed_num(y_true, y_pred):
if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
return lambda_rpn_class * K.sum(y_true[:, :, :, :num_anchors] * K.binary_crossentropy(y_pred[:, :, :, :], y_true[:, :, :, num_anchors:])) / K.sum(epsilon + y_true[:, :, :, :num_anchors])
else:
return lambda_rpn_class * K.sum(y_true[:, :num_anchors, :, :] * K.binary_crossentropy(y_pred[:, :, :, :], y_true[:, num_anchors:, :, :])) / K.sum(epsilon + y_true[:, :num_anchors, :, :])
Expand Down
14 changes: 7 additions & 7 deletions keras_frcnn/resnet.py
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Expand Up @@ -17,7 +17,7 @@
from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization

def get_weight_path():
if K.common.image_dim_ordering() == 'th':
if K.image_data_format() == 'th':
return 'resnet50_weights_th_dim_ordering_th_kernels_notop.h5'
else:
return 'resnet50_weights_tf_dim_ordering_tf_kernels.h5'
Expand All @@ -39,7 +39,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=T

nb_filter1, nb_filter2, nb_filter3 = filters

if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
bn_axis = 3
else:
bn_axis = 1
Expand Down Expand Up @@ -68,7 +68,7 @@ def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainabl
# identity block time distributed

nb_filter1, nb_filter2, nb_filter3 = filters
if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
bn_axis = 3
else:
bn_axis = 1
Expand All @@ -95,7 +95,7 @@ def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainabl
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True):

nb_filter1, nb_filter2, nb_filter3 = filters
if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
bn_axis = 3
else:
bn_axis = 1
Expand Down Expand Up @@ -127,7 +127,7 @@ def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape,
# conv block time distributed

nb_filter1, nb_filter2, nb_filter3 = filters
if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
bn_axis = 3
else:
bn_axis = 1
Expand Down Expand Up @@ -156,7 +156,7 @@ def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape,
def nn_base(input_tensor=None, trainable=False):

# Determine proper input shape
if K.common.image_dim_ordering() == 'th':
if K.image_data_format() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
Expand All @@ -169,7 +169,7 @@ def nn_base(input_tensor=None, trainable=False):
else:
img_input = input_tensor

if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
bn_axis = 3
else:
bn_axis = 1
Expand Down
6 changes: 3 additions & 3 deletions keras_frcnn/vgg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@


def get_weight_path():
if K.common.image_dim_ordering() == 'th':
if K.image_data_format() == 'th':
print('pretrained weights not available for VGG with theano backend')
return
else:
Expand All @@ -37,7 +37,7 @@ def nn_base(input_tensor=None, trainable=False):


# Determine proper input shape
if K.common.image_dim_ordering() == 'th':
if K.image_data_format() == 'th':
input_shape = (3, None, None)
else:
input_shape = (None, None, 3)
Expand All @@ -50,7 +50,7 @@ def nn_base(input_tensor=None, trainable=False):
else:
img_input = input_tensor

if K.common.image_dim_ordering() == 'tf':
if K.image_data_format() == 'tf':
bn_axis = 3
else:
bn_axis = 1
Expand Down
15 changes: 8 additions & 7 deletions train_frcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,13 +81,14 @@
# set the path to weights based on backend and model
C.base_net_weights = nn.get_weight_path()

train_imgs, classes_count, class_mapping = get_data(options.train_path, 'trainval')
val_imgs, _, _ = get_data(options.train_path, 'test')
train_imgs, classes_count, class_mapping = get_data(options.train_path)
val_imgs, _, _ = get_data(options.train_path)

'''
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)

'''
C.class_mapping = class_mapping

inv_map = {v: k for k, v in class_mapping.items()}
Expand All @@ -109,14 +110,14 @@
#train_imgs = [s for s in all_imgs if s['imageset'] == 'trainval']
#val_imgs = [s for s in all_imgs if s['imageset'] == 'test']

print(f'Num train samples {len(train_imgs}')
print(f'Num train samples {len(train_imgs)}')
print(f'Num val samples {len(val_imgs)}')


data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.common.image_dim_ordering(), mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length,K.common.image_dim_ordering(), mode='val')
data_gen_train = data_generators.get_anchor_gt(train_imgs, classes_count, C, nn.get_img_output_length, K.image_data_format(), mode='train')
data_gen_val = data_generators.get_anchor_gt(val_imgs, classes_count, C, nn.get_img_output_length,K.image_data_format(), mode='val')

if K.common.image_dim_ordering() == 'th':
if K.image_data_format() == 'th':
input_shape_img = (3, None, None)
else:
input_shape_img = (None, None, 3)
Expand Down