Fixed: convTOP should be constructed each time the forward() func…

…tion of `ConvTransposed2D` is called.

.gitignore

@@ -6,5 +6,4 @@ Dandelion.egg-info
 dandelion/__pycache__
 dandelion/model/__pycache__
 Cython/*.whl
-site
-Test
+site

Test/Test_BatchNorm.py

@@ -0,0 +1,92 @@
+# coding:utf-8
+# Test for BatchNorm class
+# Created   :   2, 27, 2018
+# Revised   :   2, 27, 2018
+# All rights reserved
+#------------------------------------------------------------------------------------------------
+__author__ = 'dawei.leng'
+import os, sys, psutil
+os.environ['THEANO_FLAGS'] = "floatX=float32, mode=FAST_RUN, warn_float64='raise'"
+
+import theano
+from theano import tensor
+from dandelion.module import *
+from dandelion.activation import *
+from lasagne.layers import InputLayer, BatchNormLayer_DV, get_output, get_all_updates
+import lasagne.nonlinearities as LACT
+import dandelion
+dandelion_path = os.path.split(dandelion.__file__)[0]
+print('dandelion path = %s\n' % dandelion_path)
+
+class build_model_D(Module):
+    def __init__(self, input_shape=None, axes='auto'):
+        super().__init__()
+        self.input_shape = input_shape
+        self.axes = axes
+        self.bn = BatchNorm(input_shape=self.input_shape, axes=self.axes)
+
+    def forward(self, x):
+        x = self.bn.forward(x)
+        return x
+
+    def predict(self, x):
+        return self.bn.predict(x)
+
+def build_model_L(input_shape=None, axes='auto'):
+    input_var = tensor.ftensor4('x')
+    input0 = InputLayer(shape=input_shape, input_var=input_var, name='input0')
+    result = BatchNormLayer_DV(input0, axes=axes, name='bn0')
+    return result
+
+def fix_update_bcasts(updates):
+    for param, update in updates.items():
+        if param.broadcastable != update.broadcastable:
+            updates[param] = tensor.patternbroadcast(update, param.broadcastable)
+    return updates
+
+
+if __name__ == '__main__':
+    import numpy as np
+    from lasagne_ext.utils import get_layer_by_name
+
+    B, C, H, W = 2, 1, 8, 8
+    input_shape = (None, C, H, W)
+    axes = 'auto'
+
+    model_D = build_model_D(input_shape=input_shape, axes=axes)
+    model_L = build_model_L(input_shape=input_shape, axes=axes)
+
+    X = get_layer_by_name(model_L, 'input0').input_var
+    #--- predict ---#
+    if 0:
+        y_D = model_D.predict(X)
+        y_L = get_output(model_L, deterministic=True)
+        fn_L = theano.function([X], y_L, no_default_updates=True)
+        fn_D = theano.function([X], y_D, no_default_updates=True)
+
+    #--- train ---#
+    if 1:
+        y_D = model_D.forward(X)
+        y_L = get_output(model_L, deterministic=False)
+
+    update_L = fix_update_bcasts(get_all_updates(model_L))
+    update_D = fix_update_bcasts(model_D.collect_self_updates())
+
+    fn_L = theano.function([X], y_L, updates=update_L, no_default_updates=True)
+    fn_D = theano.function([X], y_D, updates=update_D, no_default_updates=False)
+    # fn_L = theano.function([X], y_L, no_default_updates=True)
+    # fn_D = theano.function([X], y_D, no_default_updates=True)
+
+
+    for i in range(20):
+        x = np.random.rand(B, C, H, W).astype(np.float32)
+        y_D = fn_D(x)
+        y_L = fn_L(x)
+        diff = np.sum(np.abs(y_D - y_L))
+        print('i=%d, diff=%0.6f' % (i, diff))
+        if diff>1e-4:
+            print(y_D)
+            print(y_L)
+            raise ValueError('diff is too big')
+
+    print('Test passed')