add tests; remove nightly from CI

.github/workflows/workflow.yml

@@ -36,7 +36,8 @@ jobs:
       max-parallel: 6
       matrix:
         python-version: [3.6]
-        TF-version: [1.3.0, 1.14.0, nightly]
+        # TF-version: [1.3.0, 1.14.0, nightly] # TODO make nightly work
+        TF-version: [1.3.0, 1.14.0]
     steps:
       - uses: actions/checkout@v1
       - name: Set up Python ${{ matrix.python-version }}

tensorpack/models/models_test.py

@@ -13,13 +13,13 @@ from .pool import FixedUnPooling
 
 class TestModel(unittest.TestCase):
 
-    def run_variable(self, var):
+    def eval(self, x, feed_dict=None):
         sess = tf.Session()
         sess.run(tf.global_variables_initializer())
-        if isinstance(var, list):
-            return sess.run(var)
+        if isinstance(x, list):
+            return sess.run(x, feed_dict=feed_dict)
         else:
-            return sess.run([var])[0]
+            return sess.run([x], feed_dict=feed_dict)[0]
 
     def make_variable(self, *args):
         if len(args) > 1:
@@ -36,7 +36,7 @@ class TestPool(TestModel):
         input = self.make_variable(mat)
         input = tf.reshape(input, [1, h, w, 3])
         output = FixedUnPooling('unpool', input, scale)
-        res = self.run_variable(output)
+        res = self.eval(output)
         self.assertEqual(res.shape, (1, scale * h, scale * w, 3))
 
         # mat is on corner
@@ -56,7 +56,7 @@ class TestPool(TestModel):
 #         inp = tf.reshape(inp, [1, h, w, 1])
 #
 #         output = BilinearUpSample(inp, scale)
-#         res = self.run_variable(output)[0, :, :, 0]
+#         res = self.eval(output)[0, :, :, 0]
 #
 #         from skimage.transform import rescale
 #         res2 = rescale(mat, scale, mode='edge')
@@ -83,9 +83,26 @@ class TestConv2DTranspose(TestModel):
                     input, 20, 3, strides=stride, padding=padding)
 
                 static_shape = output.shape
-                dynamic_shape = self.run_variable(output).shape
+                dynamic_shape = self.eval(output).shape
                 self.assertTrue(static_shape == dynamic_shape)
 
+    def test_unspecified_shape_match(self):
+        h, w = 12, 18
+        input = tf.placeholder(shape=(1, h, None, 3), dtype=tf.float32)
+        for padding in ["same", "valid"]:
+            for stride in [1, 2]:
+                output = Conv2DTranspose(
+                    'deconv_s{}_pad{}'.format(stride, padding),
+                    input, 20, 3, strides=stride, padding=padding)
+
+                static_shape = tuple(output.shape.as_list())
+                dynamic_shape = self.eval(
+                    output,
+                    feed_dict={input: np.random.rand(1, h, w, 3)}).shape
+                self.assertTrue(static_shape[2] is None)
+                self.assertTrue(static_shape[:2] == dynamic_shape[:2])
+                self.assertTrue(static_shape[3] == dynamic_shape[3])
+
 
 def run_test_case(case):
     suite = unittest.TestLoader().loadTestsFromTestCase(case)