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Author: zrl4836

octConv_resnet.py

@@ -0,0 +1,126 @@
+import numpy as np
+import tensorflow as tf
+from tf_octConv import *
+from tf_cnn_basic import *
+from oct_Resnet_unit import *
+
+
+G = 1
+alpha = 0.25
+use_fp16 = True
+k_sec = {2: 3, 3: 4, 4: 6, 5: 3}
+
+
+def get_before_pool():
+    data = tf.Variable(name="data")
+    data = tf.cast(x=data, dtype=np.float16) if use_fp16 else data
+
+    # conv1
+    conv1 = Conv_BN_AC(data=data, num_filter=64, kernel=(7, 7), name='conv1', pad=(3, 3), stride=(2, 2))
+    pool1 = Pooling(data=conv1, pool_type="max", kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="pool1")
+
+    # conv2
+    num_in = 32
+    num_mid = 64
+    num_out = 256
+    i = 1
+    hf_conv1_x, lf_conv1_x = Residual_Unit_first(
+        data=pool1,
+        alpha=alpha,
+        num_in=(num_in if i == 1 else num_out),
+        num_mid=num_mid,
+        num_out=num_out,
+        name=('conv2_B%02d' % i),
+        first_block=(i == 1),
+        stride=((1, 1) if (i == 1) else (1, 1)))
+
+    for i in range(2, k_sec[2] + 1):
+        hf_conv2_x, lf_conv2_x = Residual_Unit(
+            hf_data=(hf_conv1_x if i == 1 else hf_conv2_x),
+            lf_data=(lf_conv1_x if i == 1 else lf_conv2_x),
+            alpha=alpha,
+            num_in=(num_in if i == 1 else num_out),
+            num_mid=num_mid,
+            num_out=num_out,
+            name=('conv2_B%02d' % i),
+            first_block=(i == 1),
+            stride=((1, 1) if (i == 1) else (1, 1)))
+
+    # conv3
+    num_in = num_out
+    num_mid = int(num_mid * 2)
+    num_out = int(num_out * 2)
+    for i in range(1, k_sec[3] + 1):
+        hf_conv3_x, lf_conv3_x = Residual_Unit(
+            hf_data=(hf_conv2_x if i == 1 else hf_conv3_x),
+            lf_data=(lf_conv2_x if i == 1 else lf_conv3_x),
+            alpha=alpha,
+            num_in=(num_in if i == 1 else num_out),
+            num_mid=num_mid,
+            num_out=num_out,
+            name=('conv3_B%02d' % i),
+            first_block=(i == 1),
+            stride=((2, 2) if (i == 1) else (1, 1)))
+
+
+    # conv4
+    num_in = num_out
+    num_mid = int(num_mid * 2)
+    num_out = int(num_out * 2)
+    for i in range(1, k_sec[4] + 1):
+        hf_conv4_x, lf_conv4_x = Residual_Unit(
+            hf_data=(hf_conv3_x if i == 1 else hf_conv4_x),
+            lf_data=(lf_conv3_x if i == 1 else lf_conv4_x),
+            alpha=alpha,
+            num_in=(num_in if i == 1 else num_out),
+            num_mid=num_mid,
+            num_out=num_out,
+            name=('conv4_B%02d' % i),
+            first_block=(i == 1),
+            stride=((2, 2) if (i == 1) else (1, 1)))
+
+
+    # conv5
+    num_in = num_out
+    num_mid = int(num_mid * 2)
+    num_out = int(num_out * 2)
+    i = 1
+    conv5_x = Residual_Unit_last(
+        hf_data=hf_conv4_x,
+        lf_data=lf_conv4_x,
+        alpha=alpha,
+        num_in=(num_in if i == 1 else num_out),
+        num_mid=num_mid,
+        num_out=num_out,
+        name=('conv5_B%02d' % i),
+        first_block=(i == 1),
+        stride=((2, 2) if (i == 1) else (1, 1)))
+
+    for i in range(2, k_sec[5] + 1):
+        conv5_x = Residual_Unit_norm(data=conv5_x,
+                                     num_in=num_out,
+                                     num_mid=num_mid,
+                                     num_out=num_out,
+                                     name=('conv5_B%02d' % i),
+                                     first_block=(i == 1),
+                                     stride=((2, 2) if (i == 1) else (1, 1)))
+
+    output = tf.cast(x=conv5_x, dtype=np.float32) if use_fp16 else conv5_x
+    # output
+    return output
+
+
+def get_linear(num_classes=1000):
+    before_pool = get_before_pool()
+    pool5 = Pooling(data=before_pool, pool_type="avg", kernel=(7, 7), stride=(1, 1), name="global-pool")
+    flat5 = tf.layers.Flatten(input=pool5, name='flatten')
+    fc6 = tf.layers.dense(inputs=flat5, units=num_classes, name='classifier')
+    return fc6
+
+
+def get_symbol(num_classes=1000):
+    fc6 = get_linear(num_classes)
+    softmax = tf.nn.softmax(logits=fc6, name='softmax')
+    sys_out = softmax
+    return sys_out
+