Python tensorflow.contrib.slim.python.slim.nets.resnet_utils.Block() Examples
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Example #1
Source File: resnet_v2.py From lambda-packs with MIT License | 6 votes |
def resnet_v2_block(scope, base_depth, num_units, stride): """Helper function for creating a resnet_v2 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck layer for each unit. num_units: The number of units in the block. stride: The stride of the block, implemented as a stride in the last unit. All other units have stride=1. Returns: A resnet_v2 bottleneck block. """ return resnet_utils.Block(scope, bottleneck, [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': 1 }] * (num_units - 1) + [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': stride }])
Example #2
Source File: slim_resnet_utils.py From X-Detector with Apache License 2.0 | 6 votes |
def resnet_v1_block(scope, base_depth, num_units, stride): """Helper function for creating a resnet_v1 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck layer for each unit. num_units: The number of units in the block. stride: The stride of the block, implemented as a stride in the last unit. All other units have stride=1. Returns: A resnet_v1 bottleneck block. """ return resnet_utils.Block(scope, bottleneck, [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': 1 }] * (num_units - 1) + [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': stride }])
Example #3
Source File: resnet_v1.py From keras-lambda with MIT License | 6 votes |
def resnet_v1_200(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_200'): """ResNet-200 model of [2]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #4
Source File: resnet_v1.py From keras-lambda with MIT License | 6 votes |
def resnet_v1_152(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_152'): """ResNet-152 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #5
Source File: resnet_v1.py From keras-lambda with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #6
Source File: resnet_v1.py From keras-lambda with MIT License | 6 votes |
def resnet_v1_50(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_50'): """ResNet-50 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #7
Source File: resnet_v2_test.py From keras-lambda with MIT License | 6 votes |
def _resnet_small(self, inputs, num_classes=None, global_pool=True, output_stride=None, include_root_block=True, reuse=None, scope='resnet_v2_small'): """A shallow and thin ResNet v2 for faster tests.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]), resnet_utils.Block('block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]), resnet_utils.Block('block4', bottleneck, [(32, 8, 1)] * 2) ] return resnet_v2.resnet_v2(inputs, blocks, num_classes, global_pool, output_stride, include_root_block, reuse, scope)
Example #8
Source File: resnet_v2.py From keras-lambda with MIT License | 6 votes |
def resnet_v2_200(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_200'): """ResNet-200 model of [2]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #9
Source File: resnet_v2.py From keras-lambda with MIT License | 6 votes |
def resnet_v2_152(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_152'): """ResNet-152 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #10
Source File: resnet_v2.py From keras-lambda with MIT License | 6 votes |
def resnet_v2_101(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_101'): """ResNet-101 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #11
Source File: resnet_v2.py From keras-lambda with MIT License | 6 votes |
def resnet_v2_50(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_50'): """ResNet-50 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #12
Source File: resnet_v2.py From conv-ensemble-str with Apache License 2.0 | 6 votes |
def resnet_v2_block(scope, base_depth, num_units, stride): """Helper function for creating a resnet_v2 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck layer for each unit. num_units: The number of units in the block. stride: The stride of the block, implemented as a stride in the last unit. All other units have stride=1. Returns: A resnet_v2 bottleneck block. """ return resnet_utils.Block(scope, bottleneck, [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': 1 }] * (num_units - 1) + [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': stride }])
Example #13
Source File: resnet_v1.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v1_200(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_200'): """ResNet-200 model of [2]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #14
Source File: resnet_v1.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v1_152(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_152'): """ResNet-152 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #15
Source File: resnet_v1.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v1_50(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_50'): """ResNet-50 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #16
Source File: resnet_v1.py From Chinese-Character-and-Calligraphic-Image-Processing with MIT License | 6 votes |
def resnet_v1_block(scope, base_depth, num_units, stride): """Helper function for creating a resnet_v1 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck layer for each unit. num_units: The number of units in the block. stride: The stride of the block, implemented as a stride in the last unit. All other units have stride=1. Returns: A resnet_v1 bottleneck block. """ return resnet_utils.Block(scope, bottleneck, [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': 1 }] * (num_units - 1) + [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': stride }])
Example #17
Source File: resnet_v1.py From lambda-packs with MIT License | 6 votes |
def resnet_v1_block(scope, base_depth, num_units, stride): """Helper function for creating a resnet_v1 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck layer for each unit. num_units: The number of units in the block. stride: The stride of the block, implemented as a stride in the last unit. All other units have stride=1. Returns: A resnet_v1 bottleneck block. """ return resnet_utils.Block(scope, bottleneck, [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': 1 }] * (num_units - 1) + [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': stride }])
Example #18
Source File: resnet_v2.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v2_50(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_50'): """ResNet-50 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 5 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #19
Source File: resnet_v2.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v2_101(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_101'): """ResNet-101 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #20
Source File: resnet_v2.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v2_152(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_152'): """ResNet-152 model of [1]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 7 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #21
Source File: resnet_v2.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v2_200(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v2_200'): """ResNet-200 model of [2]. See resnet_v2() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v2( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #22
Source File: resnet_v2_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def _resnet_small(self, inputs, num_classes=None, global_pool=True, output_stride=None, include_root_block=True, reuse=None, scope='resnet_v2_small'): """A shallow and thin ResNet v2 for faster tests.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]), resnet_utils.Block('block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]), resnet_utils.Block('block4', bottleneck, [(32, 8, 1)] * 2) ] return resnet_v2.resnet_v2(inputs, blocks, num_classes, global_pool, output_stride, include_root_block, reuse, scope)
Example #23
Source File: resnet_v1.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1( inputs, blocks, num_classes, global_pool, output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #24
Source File: resnet_v1_test.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def testEndPointsV1(self): """Test the end points of a tiny v1 bottleneck network.""" bottleneck = resnet_v1.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)]) ] inputs = create_test_input(2, 32, 16, 3) with arg_scope(resnet_utils.resnet_arg_scope()): _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') expected = [ 'tiny/block1/unit_1/bottleneck_v1/shortcut', 'tiny/block1/unit_1/bottleneck_v1/shortcut/BatchNorm', 'tiny/block1/unit_1/bottleneck_v1/conv1', 'tiny/block1/unit_1/bottleneck_v1/conv2', 'tiny/block1/unit_1/bottleneck_v1/conv3', 'tiny/block1/unit_1/bottleneck_v1/conv3/BatchNorm', 'tiny/block1/unit_2/bottleneck_v1/conv1', 'tiny/block1/unit_2/bottleneck_v1/conv2', 'tiny/block1/unit_2/bottleneck_v1/conv3', 'tiny/block1/unit_2/bottleneck_v1/conv3/BatchNorm', 'tiny/block2/unit_1/bottleneck_v1/shortcut', 'tiny/block2/unit_1/bottleneck_v1/shortcut/BatchNorm', 'tiny/block2/unit_1/bottleneck_v1/conv1', 'tiny/block2/unit_1/bottleneck_v1/conv2', 'tiny/block2/unit_1/bottleneck_v1/conv3', 'tiny/block2/unit_1/bottleneck_v1/conv3/BatchNorm', 'tiny/block2/unit_2/bottleneck_v1/conv1', 'tiny/block2/unit_2/bottleneck_v1/conv2', 'tiny/block2/unit_2/bottleneck_v1/conv3', 'tiny/block2/unit_2/bottleneck_v1/conv3/BatchNorm' ] self.assertItemsEqual(expected, end_points)
Example #25
Source File: iCAN_ResNet50_HICO.py From iCAN with MIT License | 5 votes |
def __init__(self): self.visualize = {} self.intermediate = {} self.predictions = {} self.score_summaries = {} self.event_summaries = {} self.train_summaries = [] self.losses = {} self.image = tf.placeholder(tf.float32, shape=[1, None, None, 3], name = 'image') self.spatial = tf.placeholder(tf.float32, shape=[None, 64, 64, 2], name = 'sp') self.H_boxes = tf.placeholder(tf.float32, shape=[None, 5], name = 'H_boxes') self.O_boxes = tf.placeholder(tf.float32, shape=[None, 5], name = 'O_boxes') self.gt_class_HO = tf.placeholder(tf.float32, shape=[None, 600], name = 'gt_class_HO') self.H_num = tf.placeholder(tf.int32) self.num_classes = 600 self.num_fc = 1024 self.scope = 'resnet_v1_50' self.stride = [16, ] self.lr = tf.placeholder(tf.float32) if tf.__version__ == '1.1.0': self.blocks = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', resnet_v1.bottleneck,[(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]), resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3), resnet_utils.Block('block5', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)] else: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block self.blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), resnet_v1_block('block3', base_depth=256, num_units=6, stride=1), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), resnet_v1_block('block5', base_depth=512, num_units=3, stride=1)]
Example #26
Source File: resnet_v2_test.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def testEndPointsV2(self): """Test the end points of a tiny v2 bottleneck network.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)]) ] inputs = create_test_input(2, 32, 16, 3) with arg_scope(resnet_utils.resnet_arg_scope()): _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') expected = [ 'tiny/block1/unit_1/bottleneck_v2/shortcut', 'tiny/block1/unit_1/bottleneck_v2/conv1', 'tiny/block1/unit_1/bottleneck_v2/conv2', 'tiny/block1/unit_1/bottleneck_v2/conv3', 'tiny/block1/unit_2/bottleneck_v2/conv1', 'tiny/block1/unit_2/bottleneck_v2/conv2', 'tiny/block1/unit_2/bottleneck_v2/conv3', 'tiny/block2/unit_1/bottleneck_v2/shortcut', 'tiny/block2/unit_1/bottleneck_v2/conv1', 'tiny/block2/unit_1/bottleneck_v2/conv2', 'tiny/block2/unit_1/bottleneck_v2/conv3', 'tiny/block2/unit_2/bottleneck_v2/conv1', 'tiny/block2/unit_2/bottleneck_v2/conv2', 'tiny/block2/unit_2/bottleneck_v2/conv3' ] self.assertItemsEqual(expected, end_points)
Example #27
Source File: resnet_v2_test.py From keras-lambda with MIT License | 5 votes |
def testEndPointsV2(self): """Test the end points of a tiny v2 bottleneck network.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)]) ] inputs = create_test_input(2, 32, 16, 3) with arg_scope(resnet_utils.resnet_arg_scope()): _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') expected = [ 'tiny/block1/unit_1/bottleneck_v2/shortcut', 'tiny/block1/unit_1/bottleneck_v2/conv1', 'tiny/block1/unit_1/bottleneck_v2/conv2', 'tiny/block1/unit_1/bottleneck_v2/conv3', 'tiny/block1/unit_2/bottleneck_v2/conv1', 'tiny/block1/unit_2/bottleneck_v2/conv2', 'tiny/block1/unit_2/bottleneck_v2/conv3', 'tiny/block2/unit_1/bottleneck_v2/shortcut', 'tiny/block2/unit_1/bottleneck_v2/conv1', 'tiny/block2/unit_1/bottleneck_v2/conv2', 'tiny/block2/unit_1/bottleneck_v2/conv3', 'tiny/block2/unit_2/bottleneck_v2/conv1', 'tiny/block2/unit_2/bottleneck_v2/conv2', 'tiny/block2/unit_2/bottleneck_v2/conv3' ] self.assertItemsEqual(expected, end_points)
Example #28
Source File: iCAN_ResNet50_VCOCO_Early.py From iCAN with MIT License | 5 votes |
def __init__(self): self.visualize = {} self.intermediate = {} self.predictions = {} self.score_summaries = {} self.event_summaries = {} self.train_summaries = [] self.losses = {} self.image = tf.placeholder(tf.float32, shape=[1, None, None, 3], name = 'image') self.spatial = tf.placeholder(tf.float32, shape=[None, 64, 64, 2], name = 'sp') self.H_boxes = tf.placeholder(tf.float32, shape=[None, 5], name = 'H_boxes') self.O_boxes = tf.placeholder(tf.float32, shape=[None, 5], name = 'O_boxes') self.gt_class_H = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_H') self.gt_class_HO = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_HO') self.Mask_HO = tf.placeholder(tf.float32, shape=[None, 29], name = 'HO_mask') self.Mask_H = tf.placeholder(tf.float32, shape=[None, 29], name = 'H_mask') self.H_num = tf.placeholder(tf.int32) self.num_classes = 29 self.num_fc = 1024 self.scope = 'resnet_v1_50' self.stride = [16, ] self.lr = tf.placeholder(tf.float32) if tf.__version__ == '1.1.0': self.blocks = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', resnet_v1.bottleneck,[(512, 128, 1)] * 3 + [(512, 128, 2)]), resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]), resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3), resnet_utils.Block('block5', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)] else: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block self.blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), resnet_v1_block('block3', base_depth=256, num_units=6, stride=1), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), resnet_v1_block('block5', base_depth=512, num_units=3, stride=1)]
Example #29
Source File: resnet_v1_test.py From keras-lambda with MIT License | 5 votes |
def testEndPointsV1(self): """Test the end points of a tiny v1 bottleneck network.""" bottleneck = resnet_v1.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)]) ] inputs = create_test_input(2, 32, 16, 3) with arg_scope(resnet_utils.resnet_arg_scope()): _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') expected = [ 'tiny/block1/unit_1/bottleneck_v1/shortcut', 'tiny/block1/unit_1/bottleneck_v1/shortcut/BatchNorm', 'tiny/block1/unit_1/bottleneck_v1/conv1', 'tiny/block1/unit_1/bottleneck_v1/conv2', 'tiny/block1/unit_1/bottleneck_v1/conv3', 'tiny/block1/unit_1/bottleneck_v1/conv3/BatchNorm', 'tiny/block1/unit_2/bottleneck_v1/conv1', 'tiny/block1/unit_2/bottleneck_v1/conv2', 'tiny/block1/unit_2/bottleneck_v1/conv3', 'tiny/block1/unit_2/bottleneck_v1/conv3/BatchNorm', 'tiny/block2/unit_1/bottleneck_v1/shortcut', 'tiny/block2/unit_1/bottleneck_v1/shortcut/BatchNorm', 'tiny/block2/unit_1/bottleneck_v1/conv1', 'tiny/block2/unit_1/bottleneck_v1/conv2', 'tiny/block2/unit_1/bottleneck_v1/conv3', 'tiny/block2/unit_1/bottleneck_v1/conv3/BatchNorm', 'tiny/block2/unit_2/bottleneck_v1/conv1', 'tiny/block2/unit_2/bottleneck_v1/conv2', 'tiny/block2/unit_2/bottleneck_v1/conv3', 'tiny/block2/unit_2/bottleneck_v1/conv3/BatchNorm' ] self.assertItemsEqual(expected, end_points)
Example #30
Source File: resnet_v1_test.py From keras-lambda with MIT License | 5 votes |
def _stack_blocks_nondense(self, net, blocks): """A simplified ResNet Block stacker without output stride control.""" for block in blocks: with variable_scope.variable_scope(block.scope, 'block', [net]): for i, unit in enumerate(block.args): depth, depth_bottleneck, stride = unit with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]): net = block.unit_fn( net, depth=depth, depth_bottleneck=depth_bottleneck, stride=stride, rate=1) return net