pre-release 0.10.0

rl_coach/debug_utils.py

@@ -0,0 +1,77 @@
+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import math
+
+import matplotlib.pyplot as plt
+import numpy as np
+from rl_coach.filters.observation.observation_stacking_filter import LazyStack
+
+
+def show_observation_stack(stack, channels_last=True, show=True, force_num_rows=None, row_to_update=0):
+    if isinstance(stack, LazyStack):
+        stack = np.array(stack)
+    if isinstance(stack, list):  # is list
+        stack_size = len(stack)
+    elif len(stack.shape) == 3:
+        stack_size = stack.shape[0]  # is numpy array
+    elif len(stack.shape) == 4:
+        stack_size = stack.shape[1]  # ignore batch dimension
+        stack = stack[0]
+    else:
+        raise ValueError("The observation stack must be a list, a numpy array or a LazyStack object")
+
+    if channels_last:
+        stack = np.transpose(stack, (2, 0, 1))
+        stack_size = stack.shape[0]
+
+    max_cols = 10
+    if force_num_rows:
+        rows = force_num_rows
+    else:
+        rows = math.ceil(stack_size / max_cols)
+    cols = max_cols if stack_size > max_cols else stack_size
+
+    for i in range(stack_size):
+        plt.subplot(rows, cols, row_to_update * cols + i + 1)
+        plt.imshow(stack[i], cmap='gray')
+
+    if show:
+        plt.show()
+
+
+def show_diff_between_two_observations(observation1, observation2):
+    plt.imshow(observation1 - observation2, cmap='gray')
+    plt.show()
+
+
+def plot_grayscale_observation(observation):
+    plt.imshow(observation, cmap='gray')
+    plt.show()
+
+
+def plot_episode_states(episode_transitions, state_variable: str='state', observation_index_in_stack: int=0):
+    observations = []
+    for transition in episode_transitions:
+        observations.append(np.array(getattr(transition, state_variable)['observation'])[..., observation_index_in_stack])
+    show_observation_stack(observations, False)
+
+
+def plot_list_of_observation_stacks(observation_stacks):
+    for idx, stack in enumerate(observation_stacks):
+        show_observation_stack(stack['observation'], True, False,
+                               force_num_rows=len(observation_stacks), row_to_update=idx)
+    plt.show()