Fixed rpc tests.

Author: Miguel Alonso Jr

ml-agents-envs/mlagents_envs/rpc_utils.py

@@ -122,7 +122,7 @@ def process_pixels(
             image = Image.open(image_fp)
             # Normally Image loads lazily, load() forces it to do loading in the timer scope.
             image.load()
-        image_arrays.append(np.array(image, dtype=np.float32) / 255.0)
+        image_arrays.append(np.moveaxis(np.array(image, dtype=np.float32) / 255.0, -1, 0))
 
         # Look for the next header, starting from the current stream location
         try:
@@ -142,7 +142,7 @@ def _process_images_mapping(image_arrays, mappings):
     """
     Helper function for processing decompressed images with compressed channel mappings.
     """
-    image_arrays = np.concatenate(image_arrays, axis=2).transpose((2, 0, 1))
+    image_arrays = np.concatenate(image_arrays, axis=0).transpose((0, 1, 2))
 
     if len(mappings) != len(image_arrays):
         raise UnityObservationException(
@@ -178,15 +178,15 @@ def _process_images_num_channels(image_arrays, expected_channels):
     """
     if expected_channels == 1:
         # Convert to grayscale
-        img = np.mean(image_arrays[0], axis=2)
-        img = np.reshape(img, [img.shape[0], img.shape[1], 1])
+        img = np.mean(image_arrays[0], axis=0)
+        img = np.reshape(img, [1, img.shape[0], img.shape[1]])
     else:
-        img = np.concatenate(image_arrays, axis=2)
+        img = np.concatenate(image_arrays, axis=0)
         # We can drop additional channels since they may need to be added to include
         # numbers of observation channels not divisible by 3.
-        actual_channels = list(img.shape)[2]
+        actual_channels = list(img.shape)[0]
         if actual_channels > expected_channels:
-            img = img[..., 0:expected_channels]
+            img = img[0:expected_channels, ...]
     return img
 
 

ml-agents-envs/tests/test_rpc_utils.py

@@ -65,17 +65,18 @@ def generate_compressed_data(in_array: np.ndarray) -> bytes:
     image_arr = (in_array * 255).astype(np.uint8)
     bytes_out = bytes()
 
-    num_channels = in_array.shape[2]
+    num_channels = in_array.shape[0]
     num_images = (num_channels + 2) // 3
     # Split the input image into batches of 3 channels.
     for i in range(num_images):
-        sub_image = image_arr[..., 3 * i : 3 * i + 3]
+        sub_image = image_arr[3 * i : 3 * i + 3, ...]
         if (i == num_images - 1) and (num_channels % 3) != 0:
             # Pad zeros
             zero_shape = list(in_array.shape)
-            zero_shape[2] = 3 - (num_channels % 3)
+            zero_shape[0] = 3 - (num_channels % 3)
             z = np.zeros(zero_shape, dtype=np.uint8)
-            sub_image = np.concatenate([sub_image, z], axis=2)
+            sub_image = np.concatenate([sub_image, z], axis=0)
+        sub_image = np.moveaxis(sub_image, 0, -1)
         im = Image.fromarray(sub_image, "RGB")
         byteIO = io.BytesIO()
         im.save(byteIO, format="PNG")
@@ -92,7 +93,7 @@ def generate_compressed_proto_obs(
     obs_proto.compression_type = PNG
     if grayscale:
         # grayscale flag is only used for old API without mapping
-        expected_shape = [in_array.shape[0], in_array.shape[1], 1]
+        expected_shape = [1, in_array.shape[1], in_array.shape[2]]
         obs_proto.shape.extend(expected_shape)
     else:
         obs_proto.shape.extend(in_array.shape)
@@ -109,9 +110,9 @@ def generate_compressed_proto_obs_with_mapping(
     if mapping is not None:
         obs_proto.compressed_channel_mapping.extend(mapping)
         expected_shape = [
-            in_array.shape[0],
-            in_array.shape[1],
             len({m for m in mapping if m >= 0}),
+            in_array.shape[1],
+            in_array.shape[2],
         ]
         obs_proto.shape.extend(expected_shape)
     else:
@@ -233,10 +234,10 @@ def proto_from_steps_and_action(
 
 
 def test_process_pixels():
-    in_array = np.random.rand(128, 64, 3)
+    in_array = np.random.rand(3, 128, 64)
     byte_arr = generate_compressed_data(in_array)
     out_array = process_pixels(byte_arr, 3)
-    assert out_array.shape == (128, 64, 3)
+    assert out_array.shape == (3, 128, 64)
     assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
     assert np.allclose(in_array, out_array, atol=0.01)
 
@@ -245,21 +246,21 @@ def test_process_pixels_multi_png():
     height = 128
     width = 64
     num_channels = 7
-    in_array = np.random.rand(height, width, num_channels)
+    in_array = np.random.rand(num_channels, height, width)
     byte_arr = generate_compressed_data(in_array)
     out_array = process_pixels(byte_arr, num_channels)
-    assert out_array.shape == (height, width, num_channels)
+    assert out_array.shape == (num_channels, height, width)
     assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
     assert np.allclose(in_array, out_array, atol=0.01)
 
 
 def test_process_pixels_gray():
-    in_array = np.random.rand(128, 64, 3)
+    in_array = np.random.rand(3, 128, 64)
     byte_arr = generate_compressed_data(in_array)
     out_array = process_pixels(byte_arr, 1)
-    assert out_array.shape == (128, 64, 1)
-    assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01
-    assert np.allclose(in_array.mean(axis=2, keepdims=True), out_array, atol=0.01)
+    assert out_array.shape == (1, 128, 64)
+    assert np.mean(in_array.mean(axis=0, keepdims=True) - out_array) < 0.01
+    assert np.allclose(in_array.mean(axis=0, keepdims=True), out_array, atol=0.01)
 
 
 def test_vector_observation():
@@ -276,7 +277,7 @@ def test_vector_observation():
 
 
 def test_process_visual_observation():
-    shape = (128, 64, 3)
+    shape = (3, 128, 64)
     in_array_1 = np.random.rand(*shape)
     proto_obs_1 = generate_compressed_proto_obs(in_array_1)
     in_array_2 = np.random.rand(*shape)
@@ -292,51 +293,51 @@ def test_process_visual_observation():
     ap_list = [ap1, ap2]
     obs_spec = create_observation_specs_with_shapes([shape])[0]
     arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
-    assert list(arr.shape) == [2, 128, 64, 3]
+    assert list(arr.shape) == [2, 3, 128, 64]
     assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
 
 
 def test_process_visual_observation_grayscale():
-    in_array_1 = np.random.rand(128, 64, 3)
+    in_array_1 = np.random.rand(3, 128, 64)
     proto_obs_1 = generate_compressed_proto_obs(in_array_1, grayscale=True)
-    expected_out_array_1 = np.mean(in_array_1, axis=2, keepdims=True)
-    in_array_2 = np.random.rand(128, 64, 3)
+    expected_out_array_1 = np.mean(in_array_1, axis=0, keepdims=True)
+    in_array_2 = np.random.rand(3, 128, 64)
     in_array_2_mapping = [0, 0, 0]
     proto_obs_2 = generate_compressed_proto_obs_with_mapping(
         in_array_2, in_array_2_mapping
     )
-    expected_out_array_2 = np.mean(in_array_2, axis=2, keepdims=True)
+    expected_out_array_2 = np.mean(in_array_2, axis=0, keepdims=True)
 
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap2 = AgentInfoProto()
     ap2.observations.extend([proto_obs_2])
     ap_list = [ap1, ap2]
-    shape = (128, 64, 1)
+    shape = (1, 128, 64)
     obs_spec = create_observation_specs_with_shapes([shape])[0]
     arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
-    assert list(arr.shape) == [2, 128, 64, 1]
+    assert list(arr.shape) == [2, 1, 128, 64]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
 
 
 def test_process_visual_observation_padded_channels():
-    in_array_1 = np.random.rand(128, 64, 12)
+    in_array_1 = np.random.rand(12, 128, 64)
     in_array_1_mapping = [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
     proto_obs_1 = generate_compressed_proto_obs_with_mapping(
         in_array_1, in_array_1_mapping
     )
-    expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=2)
+    expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=0)
 
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap_list = [ap1]
-    shape = (128, 64, 8)
+    shape = (8, 128, 64)
     obs_spec = create_observation_specs_with_shapes([shape])[0]
 
     arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
-    assert list(arr.shape) == [1, 128, 64, 8]
+    assert list(arr.shape) == [1, 8, 128, 64]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
 
 

ml-agents/mlagents/trainers/tests/test_rpc_utils.py

@@ -65,17 +65,18 @@ def generate_compressed_data(in_array: np.ndarray) -> bytes:
     image_arr = (in_array * 255).astype(np.uint8)
     bytes_out = bytes()
 
-    num_channels = in_array.shape[2]
+    num_channels = in_array.shape[0]
     num_images = (num_channels + 2) // 3
     # Split the input image into batches of 3 channels.
     for i in range(num_images):
-        sub_image = image_arr[..., 3 * i : 3 * i + 3]
+        sub_image = image_arr[3 * i: 3 * i + 3, ...]
         if (i == num_images - 1) and (num_channels % 3) != 0:
             # Pad zeros
             zero_shape = list(in_array.shape)
-            zero_shape[2] = 3 - (num_channels % 3)
+            zero_shape[0] = 3 - (num_channels % 3)
             z = np.zeros(zero_shape, dtype=np.uint8)
-            sub_image = np.concatenate([sub_image, z], axis=2)
+            sub_image = np.concatenate([sub_image, z], axis=0)
+        sub_image = np.moveaxis(sub_image, 0, -1)
         im = Image.fromarray(sub_image, "RGB")
         byteIO = io.BytesIO()
         im.save(byteIO, format="PNG")
@@ -92,7 +93,7 @@ def generate_compressed_proto_obs(
     obs_proto.compression_type = PNG
     if grayscale:
         # grayscale flag is only used for old API without mapping
-        expected_shape = [in_array.shape[0], in_array.shape[1], 1]
+        expected_shape = [1, in_array.shape[1], in_array.shape[2]]
         obs_proto.shape.extend(expected_shape)
     else:
         obs_proto.shape.extend(in_array.shape)
@@ -109,9 +110,9 @@ def generate_compressed_proto_obs_with_mapping(
     if mapping is not None:
         obs_proto.compressed_channel_mapping.extend(mapping)
         expected_shape = [
-            in_array.shape[0],
-            in_array.shape[1],
             len({m for m in mapping if m >= 0}),
+            in_array.shape[1],
+            in_array.shape[2],
         ]
         obs_proto.shape.extend(expected_shape)
     else:
@@ -233,10 +234,10 @@ def proto_from_steps_and_action(
 
 
 def test_process_pixels():
-    in_array = np.random.rand(128, 64, 3)
+    in_array = np.random.rand(3, 128, 64)
     byte_arr = generate_compressed_data(in_array)
     out_array = process_pixels(byte_arr, 3)
-    assert out_array.shape == (128, 64, 3)
+    assert out_array.shape == (3, 128, 64)
     assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
     assert np.allclose(in_array, out_array, atol=0.01)
 
@@ -245,21 +246,21 @@ def test_process_pixels_multi_png():
     height = 128
     width = 64
     num_channels = 7
-    in_array = np.random.rand(height, width, num_channels)
+    in_array = np.random.rand(num_channels, height, width)
     byte_arr = generate_compressed_data(in_array)
     out_array = process_pixels(byte_arr, num_channels)
-    assert out_array.shape == (height, width, num_channels)
+    assert out_array.shape == (num_channels, height, width)
     assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01
     assert np.allclose(in_array, out_array, atol=0.01)
 
 
 def test_process_pixels_gray():
-    in_array = np.random.rand(128, 64, 3)
+    in_array = np.random.rand(3, 128, 64)
     byte_arr = generate_compressed_data(in_array)
     out_array = process_pixels(byte_arr, 1)
-    assert out_array.shape == (128, 64, 1)
-    assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01
-    assert np.allclose(in_array.mean(axis=2, keepdims=True), out_array, atol=0.01)
+    assert out_array.shape == (1, 128, 64)
+    assert np.mean(in_array.mean(axis=0, keepdims=True) - out_array) < 0.01
+    assert np.allclose(in_array.mean(axis=0, keepdims=True), out_array, atol=0.01)
 
 
 def test_vector_observation():
@@ -276,7 +277,7 @@ def test_vector_observation():
 
 
 def test_process_visual_observation():
-    shape = (128, 64, 3)
+    shape = (3, 128, 64)
     in_array_1 = np.random.rand(*shape)
     proto_obs_1 = generate_compressed_proto_obs(in_array_1)
     in_array_2 = np.random.rand(*shape)
@@ -292,51 +293,51 @@ def test_process_visual_observation():
     ap_list = [ap1, ap2]
     obs_spec = create_observation_specs_with_shapes([shape])[0]
     arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
-    assert list(arr.shape) == [2, 128, 64, 3]
+    assert list(arr.shape) == [2, 3, 128, 64]
     assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
 
 
 def test_process_visual_observation_grayscale():
-    in_array_1 = np.random.rand(128, 64, 3)
+    in_array_1 = np.random.rand(3, 128, 64)
     proto_obs_1 = generate_compressed_proto_obs(in_array_1, grayscale=True)
-    expected_out_array_1 = np.mean(in_array_1, axis=2, keepdims=True)
-    in_array_2 = np.random.rand(128, 64, 3)
+    expected_out_array_1 = np.mean(in_array_1, axis=0, keepdims=True)
+    in_array_2 = np.random.rand(3, 128, 64)
     in_array_2_mapping = [0, 0, 0]
     proto_obs_2 = generate_compressed_proto_obs_with_mapping(
         in_array_2, in_array_2_mapping
     )
-    expected_out_array_2 = np.mean(in_array_2, axis=2, keepdims=True)
+    expected_out_array_2 = np.mean(in_array_2, axis=0, keepdims=True)
 
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap2 = AgentInfoProto()
     ap2.observations.extend([proto_obs_2])
     ap_list = [ap1, ap2]
-    shape = (128, 64, 1)
+    shape = (1, 128, 64)
     obs_spec = create_observation_specs_with_shapes([shape])[0]
     arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
-    assert list(arr.shape) == [2, 128, 64, 1]
+    assert list(arr.shape) == [2, 1, 128, 64]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
     assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
 
 
 def test_process_visual_observation_padded_channels():
-    in_array_1 = np.random.rand(128, 64, 12)
+    in_array_1 = np.random.rand(12, 128, 64)
     in_array_1_mapping = [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
     proto_obs_1 = generate_compressed_proto_obs_with_mapping(
         in_array_1, in_array_1_mapping
     )
-    expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=2)
+    expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9], axis=0)
 
     ap1 = AgentInfoProto()
     ap1.observations.extend([proto_obs_1])
     ap_list = [ap1]
-    shape = (128, 64, 8)
+    shape = (8, 128, 64)
     obs_spec = create_observation_specs_with_shapes([shape])[0]
 
     arr = _process_maybe_compressed_observation(0, obs_spec, ap_list)
-    assert list(arr.shape) == [1, 128, 64, 8]
+    assert list(arr.shape) == [1, 8, 128, 64]
     assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)