Python tensorflow.contrib.slim.nets.resnet_v1.resnet_v1_101() Examples
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Example #1
Source File: resnet_v1.py From Translation-Invariant-Attacks with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #2
Source File: resnet_v1.py From hands-detection with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #3
Source File: resnet_v1.py From monopsr with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, store_non_strided_activations=False, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, store_non_strided_activations=store_non_strided_activations, reuse=reuse, scope=scope)
Example #4
Source File: resnet_v1.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, store_non_strided_activations=False, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, store_non_strided_activations=store_non_strided_activations, reuse=reuse, scope=scope)
Example #5
Source File: resnet_v1.py From nasnet-tensorflow with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #6
Source File: resnet_v1.py From HumanRecognition with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #7
Source File: resnet_v1.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, store_non_strided_activations=False, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, store_non_strided_activations=store_non_strided_activations, reuse=reuse, scope=scope)
Example #8
Source File: resnet_v1.py From Non-Targeted-Adversarial-Attacks with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #9
Source File: resnet_v1.py From motion-rcnn with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101', initial_conv_name='conv1'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope, initial_conv_name=initial_conv_name)
Example #10
Source File: resnet_v1.py From mtl-ssl with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, block_trainable=[], scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, block_trainable=block_trainable, scope=scope)
Example #11
Source File: resnet_v1.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, store_non_strided_activations=False, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, store_non_strided_activations=store_non_strided_activations, reuse=reuse, scope=scope)
Example #12
Source File: resnet_v1.py From SENet-tensorflow-slim with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, store_non_strided_activations=False, reuse=None, scope='resnet_v1_101', attention_module=None): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" blocks = [ resnet_v1_block('block1', base_depth=64, num_units=3, stride=2, attention_module=attention_module), resnet_v1_block('block2', base_depth=128, num_units=4, stride=2, attention_module=attention_module), resnet_v1_block('block3', base_depth=256, num_units=23, stride=2, attention_module=attention_module), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1, attention_module=attention_module), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, store_non_strided_activations=store_non_strided_activations, reuse=reuse, scope=scope)
Example #13
Source File: resnet_v1.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #14
Source File: resnet_v1.py From Action_Recognition_Zoo with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #15
Source File: resnet_v1.py From vehicle-triplet-reid with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #16
Source File: resnet_v1.py From Deep-Association-Learning with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #17
Source File: resnet_v1.py From Cross-Modal-Projection-Learning with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #18
Source File: resnet_v1.py From taskonomy with MIT License | 6 votes |
def resnet_v1_101_16x16(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)] * 23 ), 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 #19
Source File: resnet_v1.py From taskonomy with MIT License | 6 votes |
def resnet_v1_101_16x16(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)] * 23 ), 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 #20
Source File: resnet_v1.py From TwinGAN with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #21
Source File: resnet_v1.py From style_swap_tensorflow with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #22
Source File: resnet_v1.py From Targeted-Adversarial-Attack with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #23
Source File: resnet_v1.py From tumblr-emotions with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #24
Source File: resnet_v1.py From uai-sdk with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #25
Source File: resnet_v1.py From tensorflow_yolo2 with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
Example #26
Source File: resnet_v1.py From visil with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #27
Source File: resnet_v1.py From MAX-Image-Segmenter with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, store_non_strided_activations=False, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, store_non_strided_activations=store_non_strided_activations, reuse=reuse, scope=scope)
Example #28
Source File: resnet_v1.py From triplet-reid with MIT License | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #29
Source File: resnet_v1.py From DOTA_models with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_101'): """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" 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=23, stride=2), resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, spatial_squeeze=spatial_squeeze, reuse=reuse, scope=scope)
Example #30
Source File: resnet_v1.py From MobileNet with Apache License 2.0 | 6 votes |
def resnet_v1_101(inputs, num_classes=None, is_training=True, 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, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)