From b0994bf81f18c3bf35d1ef20ba50d36b467bbed6 Mon Sep 17 00:00:00 2001
From: vexilligera <vexilligera@gmail.com>
Date: Wed, 14 Nov 2018 20:53:25 +0800
Subject: [PATCH] Update utils.py

---
 utils.py | 86 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 83 insertions(+), 3 deletions(-)

diff --git a/utils.py b/utils.py
index 4400972..328e0dc 100644
--- a/utils.py
+++ b/utils.py
@@ -5,13 +5,21 @@
 import torch
 from PIL import Image
 from NodeServer import Draw
+from graphviz import Digraph
 
 def tensor2Image(tensor, path='', norm=True):
+    t = tensor
     if 'numpy' not in str(type(tensor)):
-        tensor = tensor.cpu().detach().numpy()
+        t = tensor.cpu().detach().numpy()
     if norm:
-        tensor *= 255
-    img = Image.fromarray(tensor).convert('RGB')
+        t = t * 255
+    if len(tensor.shape) != 3:
+        img = Image.fromarray(t).convert('RGB')
+    else:
+        r = Image.fromarray(t[0]).convert('L')
+        g = Image.fromarray(t[1]).convert('L')
+        b = Image.fromarray(t[2]).convert('L')
+        img = Image.merge("RGB", (r, g, b))
     if path == '':
         img.show()
     else:
@@ -114,3 +122,75 @@ def nextBatch(self):
         images = np.expand_dims(np.array(images), 1)
         self.iteration += 1
         return images, data, trajectories
+
+# neural network visualization and gradient debug
+# https://gist.github.com/apaszke/f93a377244be9bfcb96d3547b9bc424d
+def iter_graph(root, callback):
+    queue = [root]
+    seen = set()
+    while queue:
+        fn = queue.pop()
+        if fn in seen or type(fn) == type(None):
+            continue
+        seen.add(fn)
+        for next_fn, _ in fn.next_functions:
+            if next_fn is not None:
+                queue.append(next_fn)
+        callback(fn)
+
+def register_hooks(var):
+    fn_dict = {}
+    def hook_cb(fn):
+        def register_grad(grad_input, grad_output):
+            fn_dict[fn] = grad_input
+        fn.register_hook(register_grad)
+    iter_graph(var.grad_fn, hook_cb)
+
+    def is_bad_grad(grad_output):
+        if type(grad_output) != type(None):
+            grad_output = grad_output.data
+            return grad_output.ne(grad_output).any() or grad_output.gt(1e6).any()
+        return True
+
+    def make_dot():
+        node_attr = dict(style='filled',
+                        shape='box',
+                        align='left',
+                        fontsize='12',
+                        ranksep='0.1',
+                        height='0.2')
+        dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))
+
+        def size_to_str(size):
+            return '('+(', ').join(map(str, size))+')'
+
+        def build_graph(fn):
+            if hasattr(fn, 'variable'):  # if GradAccumulator
+                u = fn.variable
+                node_name = 'Variable\n ' + size_to_str(u.size())
+                dot.node(str(id(u)), node_name, fillcolor='lightblue')
+            else:
+                assert fn in fn_dict, fn
+                fillcolor = 'white'
+                if any(is_bad_grad(gi) for gi in fn_dict[fn]):
+                    fillcolor = 'red'
+                dot.node(str(id(fn)), str(type(fn).__name__), fillcolor=fillcolor)
+            for next_fn, _ in fn.next_functions:
+                if next_fn is not None:
+                    next_id = id(getattr(next_fn, 'variable', next_fn))
+                    dot.edge(str(next_id), str(id(fn)))
+        iter_graph(var.grad_fn, build_graph)
+
+        return dot
+
+    return make_dot
+
+def saveData(data, n_steps, path, idx=0):
+    step_data = []
+    for i in range(n_steps):
+        color_radius = data[i][0][idx].cpu().detach().numpy().tolist()
+        action = data[i][1][idx].cpu().detach().numpy().tolist()
+        step_data.append([color_radius] + action)
+    f = open(path, 'w')
+    f.write(str(step_data))
+    f.close()