Python nets.vgg.vgg_16() Examples
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Example #1
Source File: vgg_test.py From CVTron with Apache License 2.0 | 6 votes |
def testTrainEvalWithReuse(self): train_batch_size = 2 eval_batch_size = 1 train_height, train_width = 224, 224 eval_height, eval_width = 256, 256 num_classes = 1000 with self.test_session(): train_inputs = tf.random_uniform( (train_batch_size, train_height, train_width, 3)) logits, _ = vgg.vgg_16(train_inputs) self.assertListEqual(logits.get_shape().as_list(), [train_batch_size, num_classes]) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform( (eval_batch_size, eval_height, eval_width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False, spatial_squeeze=False) self.assertListEqual(logits.get_shape().as_list(), [eval_batch_size, 2, 2, num_classes]) logits = tf.reduce_mean(logits, [1, 2]) predictions = tf.argmax(logits, 1) self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
Example #2
Source File: vgg_test.py From edafa with MIT License | 6 votes |
def testTrainEvalWithReuse(self): train_batch_size = 2 eval_batch_size = 1 train_height, train_width = 224, 224 eval_height, eval_width = 256, 256 num_classes = 1000 with self.test_session(): train_inputs = tf.random_uniform( (train_batch_size, train_height, train_width, 3)) logits, _ = vgg.vgg_16(train_inputs) self.assertListEqual(logits.get_shape().as_list(), [train_batch_size, num_classes]) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform( (eval_batch_size, eval_height, eval_width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False, spatial_squeeze=False) self.assertListEqual(logits.get_shape().as_list(), [eval_batch_size, 2, 2, num_classes]) logits = tf.reduce_mean(logits, [1, 2]) predictions = tf.argmax(logits, 1) self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
Example #3
Source File: vgg_test.py From DeepLab_v3 with MIT License | 6 votes |
def testTrainEvalWithReuse(self): train_batch_size = 2 eval_batch_size = 1 train_height, train_width = 224, 224 eval_height, eval_width = 256, 256 num_classes = 1000 with self.test_session(): train_inputs = tf.random_uniform( (train_batch_size, train_height, train_width, 3)) logits, _ = vgg.vgg_16(train_inputs) self.assertListEqual(logits.get_shape().as_list(), [train_batch_size, num_classes]) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform( (eval_batch_size, eval_height, eval_width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False, spatial_squeeze=False) self.assertListEqual(logits.get_shape().as_list(), [eval_batch_size, 2, 2, num_classes]) logits = tf.reduce_mean(logits, [1, 2]) predictions = tf.argmax(logits, 1) self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
Example #4
Source File: model_train.py From ICDAR-2019-SROIE with MIT License | 6 votes |
def model(image): image = mean_image_subtraction(image) with slim.arg_scope(vgg.vgg_arg_scope()): conv5_3 = vgg.vgg_16(image) rpn_conv = slim.conv2d(conv5_3, 512, 3) lstm_output = Bilstm(rpn_conv, 512, 128, 512, scope_name='BiLSTM') bbox_pred = lstm_fc(lstm_output, 512, 10 * 4, scope_name="bbox_pred") cls_pred = lstm_fc(lstm_output, 512, 10 * 2, scope_name="cls_pred") # transpose: (1, H, W, A x d) -> (1, H, WxA, d) cls_pred_shape = tf.shape(cls_pred) cls_pred_reshape = tf.reshape(cls_pred, [cls_pred_shape[0], cls_pred_shape[1], -1, 2]) cls_pred_reshape_shape = tf.shape(cls_pred_reshape) cls_prob = tf.reshape(tf.nn.softmax(tf.reshape(cls_pred_reshape, [-1, cls_pred_reshape_shape[3]])), [-1, cls_pred_reshape_shape[1], cls_pred_reshape_shape[2], cls_pred_reshape_shape[3]], name="cls_prob") return bbox_pred, cls_pred, cls_prob
Example #5
Source File: vgg_test.py From tf-pose with Apache License 2.0 | 6 votes |
def testTrainEvalWithReuse(self): train_batch_size = 2 eval_batch_size = 1 train_height, train_width = 224, 224 eval_height, eval_width = 256, 256 num_classes = 1000 with self.test_session(): train_inputs = tf.random_uniform( (train_batch_size, train_height, train_width, 3)) logits, _ = vgg.vgg_16(train_inputs) self.assertListEqual(logits.get_shape().as_list(), [train_batch_size, num_classes]) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform( (eval_batch_size, eval_height, eval_width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False, spatial_squeeze=False) self.assertListEqual(logits.get_shape().as_list(), [eval_batch_size, 2, 2, num_classes]) logits = tf.reduce_mean(logits, [1, 2]) predictions = tf.argmax(logits, 1) self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
Example #6
Source File: vgg_test.py From edafa with MIT License | 5 votes |
def testFullyConvolutional(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 2, 2, num_classes])
Example #7
Source File: vgg_test.py From tensorflow-litterbox with Apache License 2.0 | 5 votes |
def testFullyConvolutional(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 2, 2, num_classes])
Example #8
Source File: vgg_test.py From edafa with MIT License | 5 votes |
def testGlobalPool(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False, global_pool=True) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 1, 1, num_classes])
Example #9
Source File: vgg_test.py From edafa with MIT License | 5 votes |
def testEndPoints(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', 'vgg_16/fc8' ] self.assertSetEqual(set(end_points.keys()), set(expected_names))
Example #10
Source File: vgg_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testFullyConvolutional(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 2, 2, num_classes])
Example #11
Source File: vgg_test.py From tensorflow-litterbox with Apache License 2.0 | 5 votes |
def testEndPoints(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', 'vgg_16/fc8' ] self.assertSetEqual(set(end_points.keys()), set(expected_names))
Example #12
Source File: vgg_test.py From tensorflow-litterbox with Apache License 2.0 | 5 votes |
def testEvaluation(self): batch_size = 2 height, width = 224, 224 num_classes = 1000 with self.test_session(): eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) predictions = tf.argmax(logits, 1) self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
Example #13
Source File: vgg_test.py From tensorflow-litterbox with Apache License 2.0 | 5 votes |
def testForward(self): batch_size = 1 height, width = 224, 224 with self.test_session() as sess: inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs) sess.run(tf.initialize_all_variables()) output = sess.run(logits) self.assertTrue(output.any())
Example #14
Source File: vgg_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testBuild(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes) self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed') self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
Example #15
Source File: vgg_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testEndPoints(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', 'vgg_16/fc8' ] self.assertSetEqual(set(end_points.keys()), set(expected_names))
Example #16
Source File: vgg_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testNoClasses(self): batch_size = 5 height, width = 224, 224 num_classes = None with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) net, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', ] self.assertSetEqual(set(end_points.keys()), set(expected_names)) self.assertTrue(net.op.name.startswith('vgg_16/fc7'))
Example #17
Source File: vgg_test.py From edafa with MIT License | 5 votes |
def testEvaluation(self): batch_size = 2 height, width = 224, 224 num_classes = 1000 with self.test_session(): eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) predictions = tf.argmax(logits, 1) self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
Example #18
Source File: vgg_test.py From edafa with MIT License | 5 votes |
def testBuild(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes) self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed') self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
Example #19
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testForward(self): batch_size = 1 height, width = 224, 224 with self.test_session() as sess: inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs) sess.run(tf.global_variables_initializer()) output = sess.run(logits) self.assertTrue(output.any())
Example #20
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testEvaluation(self): batch_size = 2 height, width = 224, 224 num_classes = 1000 with self.test_session(): eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) predictions = tf.argmax(logits, 1) self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
Example #21
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testNoClasses(self): batch_size = 5 height, width = 224, 224 num_classes = None with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) net, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', ] self.assertSetEqual(set(end_points.keys()), set(expected_names)) self.assertTrue(net.op.name.startswith('vgg_16/fc7'))
Example #22
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testEndPoints(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', 'vgg_16/fc8' ] self.assertSetEqual(set(end_points.keys()), set(expected_names))
Example #23
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testGlobalPool(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False, global_pool=True) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 1, 1, num_classes])
Example #24
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testFullyConvolutional(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 2, 2, num_classes])
Example #25
Source File: vgg_test.py From CVTron with Apache License 2.0 | 5 votes |
def testBuild(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes) self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed') self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
Example #26
Source File: vgg_test.py From R3Det_Tensorflow with MIT License | 5 votes |
def testForward(self): batch_size = 1 height, width = 224, 224 with self.test_session() as sess: inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs) sess.run(tf.global_variables_initializer()) output = sess.run(logits) self.assertTrue(output.any())
Example #27
Source File: vgg_test.py From R3Det_Tensorflow with MIT License | 5 votes |
def testEvaluation(self): batch_size = 2 height, width = 224, 224 num_classes = 1000 with self.test_session(): eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(eval_inputs, is_training=False) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) predictions = tf.argmax(logits, 1) self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
Example #28
Source File: vgg_test.py From R3Det_Tensorflow with MIT License | 5 votes |
def testEndPoints(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = vgg.vgg_16(inputs, num_classes) expected_names = ['vgg_16/conv1/conv1_1', 'vgg_16/conv1/conv1_2', 'vgg_16/pool1', 'vgg_16/conv2/conv2_1', 'vgg_16/conv2/conv2_2', 'vgg_16/pool2', 'vgg_16/conv3/conv3_1', 'vgg_16/conv3/conv3_2', 'vgg_16/conv3/conv3_3', 'vgg_16/pool3', 'vgg_16/conv4/conv4_1', 'vgg_16/conv4/conv4_2', 'vgg_16/conv4/conv4_3', 'vgg_16/pool4', 'vgg_16/conv5/conv5_1', 'vgg_16/conv5/conv5_2', 'vgg_16/conv5/conv5_3', 'vgg_16/pool5', 'vgg_16/fc6', 'vgg_16/fc7', 'vgg_16/fc8' ] self.assertSetEqual(set(end_points.keys()), set(expected_names))
Example #29
Source File: vgg_test.py From R3Det_Tensorflow with MIT License | 5 votes |
def testFullyConvolutional(self): batch_size = 1 height, width = 256, 256 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') self.assertListEqual(logits.get_shape().as_list(), [batch_size, 2, 2, num_classes])
Example #30
Source File: vgg_test.py From R3Det_Tensorflow with MIT License | 5 votes |
def testBuild(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = vgg.vgg_16(inputs, num_classes) self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed') self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])