add gen files

test/tf/gen_tf_pb.py

+import numpy as np
+import tensorflow as tf
+
+
+def add_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
+        tf.add(g1_input, g2_input, name = 'add1')
+
+    tf.train.write_graph(g1, '.', 'add_test.pb', as_text=False)
+
+def add_bcast_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(2,1), name = '1')
+        tf.math.add(g1_input, g2_input, name = 'add_bcast1')
+
+    tf.train.write_graph(g1, '.', 'add_bcast_test.pb', as_text=False)
+
+def assert_less_equal_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(2,3), name = '1')
+        with tf.control_dependencies([tf.assert_less_equal(g1_input, g2_input)]):
+            tf.add(g1_input, g2_input, name = 'add1')
+    
+    tf.train.write_graph(g1, '.', 'assert_less_equal_test.pb', as_text=False)
+
+def batchmatmul_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,2,8,4), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,2,4,8), name = '1')
+        tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='batchmatmul1')
+
+    tf.train.write_graph(g1, '.', 'batchmatmul_test.pb', as_text=False)
+
+def batchnorm_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1, 16, 16, 32), name = '0')
+        g1_scale = tf.constant(1.0, dtype=tf.float32, shape=[32], name = '1')
+        g1_offset = tf.placeholder(tf.float32, shape=(32), name = '2')
+        g1_mean = tf.placeholder(tf.float32, shape=(32), name = '3')
+        g1_variance = tf.placeholder(tf.float32, shape=(32), name = '4')
+        tf.nn.fused_batch_norm(
+                g1_input, g1_scale, g1_offset, g1_mean, g1_variance,
+                epsilon=0.00001, is_training=False, name='batchnorm1')
+
+    tf.train.write_graph(g1, '.', 'batchnorm_test.pb', as_text=False)
+
+def biasadd_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,500), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(500), name = '1')
+        tf.nn.bias_add(g1_input, g2_input, name = 'bias_add1')
+
+    tf.train.write_graph(g1, '.', 'biasadd_test.pb', as_text=False)
+
+def cast_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.cast(g1_input, dtype=tf.int32, name='cast1')
+
+    tf.train.write_graph(g1, '.', 'cast_test.pb', as_text=False)
+def concat_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(4,7,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(4,2,3), name = '1')
+        tf.concat([g1_input, g2_input], axis=1, name = 'concat1')
+
+    tf.train.write_graph(g1, '.', 'concat_test.pb', as_text=False)
+
+def const_test(g1=tf.Graph()):
+    with g1.as_default():
+        tf.constant(1.0, dtype=tf.float32 ,name='constant1')
+
+    tf.train.write_graph(g1, '.', 'constant_test.pb', as_text=False)
+
+def conv_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
+        g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,32), name = '1')
+        tf.nn.conv2d(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'conv1')
+
+    tf.train.write_graph(g1, '.', 'conv_test.pb', as_text=False)
+
+def depthwiseconv_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
+        g1_weights = tf.constant(value=1.0, dtype=tf.float32, shape=(3,3,3,1), name = '1')
+        tf.nn.depthwise_conv2d_native(g1_input, g1_weights, [1,1,1,1], "SAME", name = 'depthwiseconv1')
+
+    tf.train.write_graph(g1, '.', 'depthwise_conv_test.pb', as_text=False)
+
+def expanddims_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2,3,4), name = '0')
+        tf.expand_dims(g1_input, axis=-1, name='expanddims_neg')
+
+    tf.train.write_graph(g1, '.', 'expanddims_neg_test.pb', as_text=False)
+
+def gather_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2,4), name = '0')
+        tf.gather(g1_input, [1,1], axis=1, name='gather1')
+
+    tf.train.write_graph(g1, '.', 'gather_test.pb', as_text=False)
+
+def identity_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.identity(g1_input, 'identity')
+
+    tf.train.write_graph(g1, '.', 'identity_test.pb', as_text=False)
+
+def matmul_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(8,4), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(4,8), name = '1')
+        tf.matmul(g1_input, g2_input, transpose_a=True, transpose_b=True, name='matmul1')
+
+    tf.train.write_graph(g1, '.', 'matmul_test.pb', as_text=False)
+
+def mean_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.math.reduce_mean(
+                g1_input,
+                axis=(2,3),
+                keepdims=True,
+                name='mean1'
+            )
+        tf.math.reduce_mean(
+                g1_input,
+                axis=(2,3),
+                keepdims=False,
+                name='mean2'
+            )
+
+    tf.train.write_graph(g1, '.', 'mean_test.pb', as_text=False)
+
+def mean_test_nhwc(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
+        tf.math.reduce_mean(
+                g1_input,
+                axis=(1,2),
+                keepdims=True,
+                name='mean1'
+            )
+        tf.math.reduce_mean(
+                g1_input,
+                axis=(1,2),
+                keepdims=False,
+                name='mean2'
+            )
+
+    tf.train.write_graph(g1, '.', 'mean_test_nhwc.pb', as_text=False)
+
+def mul_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,1,1,16), name = '1')
+        tf.multiply(g1_input, g2_input, name='mul1')
+    tf.train.write_graph(g1, '.', 'mul_test.pb', as_text=False)
+
+def pack_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(2), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(2), name = '1')
+        g3_input = tf.placeholder(tf.float32, shape=(2), name = '2')
+        tf.stack([g1_input, g2_input, g3_input], axis=1, name = 'pack1')
+
+    tf.train.write_graph(g1, '.', 'pack_test.pb', as_text=False)
+
+def pack_test_nhwc(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '1')
+        g3_input = tf.placeholder(tf.float32, shape=(1,1,1,2), name = '2')
+        tf.stack([g1_input, g2_input, g3_input], axis=3, name = 'pack1')
+
+    tf.train.write_graph(g1, '.', 'pack_test_nhwc.pb', as_text=False)
+
+def pooling_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,16,16,3), name = '0')
+        tf.nn.avg_pool(
+                value=g1_input,
+                ksize=(1,2,2,1),
+                strides=(1,2,2,1),
+                padding='VALID',
+                data_format='NHWC',
+                name='avg_pooling'
+            )
+        tf.nn.max_pool(
+                value=g1_input,
+                ksize=(1,2,2,1),
+                strides=(1,2,2,1),
+                padding='VALID',
+                data_format='NHWC',
+                name='max_pooling'
+            )
+
+    tf.train.write_graph(g1, '.', 'pooling_test.pb', as_text=False)
+
+def pow_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
+        tf.pow(g1_input, g2_input, name = 'pow1')
+
+    tf.train.write_graph(g1, '.', 'pow_test.pb', as_text=False)
+
+def relu_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.nn.relu(g1_input, 'relu')
+
+    tf.train.write_graph(g1, '.', 'relu_test.pb', as_text=False)
+
+def relu6_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.nn.relu6(g1_input, 'relu6')
+
+    tf.train.write_graph(g1, '.', 'relu6_test.pb', as_text=False)
+
+def reshape_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(16), name = '0')
+        tf.reshape(g1_input, (1,1,1,16), 'reshape')
+
+    tf.train.write_graph(g1, '.', 'reshape_test.pb', as_text=False)
+
+def rsqrt_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.math.rsqrt(g1_input, 'rsqrt')
+
+    tf.train.write_graph(g1, '.', 'rsqrt_test.pb', as_text=False)
+
+def slice_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(5,10), name = '0')
+        tf.slice(g1_input, [1, 0], [2, -1], name = 'slice1')
+
+    tf.train.write_graph(g1, '.', 'slice_test.pb', as_text=False)
+
+def softmax_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3), name = '0')
+        tf.nn.softmax(g1_input, name='softmax')
+
+    tf.train.write_graph(g1, '.', 'softmax_test.pb', as_text=False)
+
+def sqdiff_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
+        tf.squared_difference(g1_input, g2_input, name = 'sqdiff')
+
+    tf.train.write_graph(g1, '.', 'sqdiff_test.pb', as_text=False)
+
+def squeeze_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,2,3,1), name = '0')
+        tf.squeeze(g1_input, name='squeeze')
+
+    tf.train.write_graph(g1, '.', 'squeeze_test.pb', as_text=False)
+
+def stopgradient_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.stop_gradient(g1_input, 'stopgradient')
+
+    tf.train.write_graph(g1, '.', 'stopgradient_test.pb', as_text=False)
+
+def stridedslice_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,1,1,10), name = '0')
+        tf.strided_slice(g1_input, [0, 0, 0, 0], [1, 1, 1, 5], [1,1,1,1], shrink_axis_mask=2, name = 'stridedslice1')
+
+    tf.train.write_graph(g1, '.', 'stridedslice_test.pb', as_text=False)
+
+def stridedslice_masks_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,3,10), name = '0')
+        tf.strided_slice(g1_input, [0, 1, 1, 0], [0, 0, 0, 0], [1,1,1,1], begin_mask=9, end_mask=15, name = 'stridedslice1')
+
+    tf.train.write_graph(g1, '.', 'stridedslice_masks_test.pb', as_text=False)
+
+def sub_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '0')
+        g2_input = tf.placeholder(tf.float32, shape=(1,2,2,3), name = '1')
+        tf.subtract(g1_input, g2_input, name = 'sub1')
+
+    tf.train.write_graph(g1, '.', 'sub_test.pb', as_text=False)
+
+def tanh_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.tanh(g1_input, 'tanh')
+
+    tf.train.write_graph(g1, '.', 'tanh_test.pb', as_text=False)
+
+def transpose_test(g1=tf.Graph()):
+    with g1.as_default():
+        g1_input = tf.placeholder(tf.float32, shape=(1,3,16,16), name = '0')
+        tf.transpose(g1_input, perm=[0,2,3,1], name = 'transpose')
+
+    tf.train.write_graph(g1, '.', 'transpose_test.pb', as_text=False)