diff --git a/common_plot.py b/common_plot.py
index 48d17adf0264225d431071a7a9f5f574a7d30b25..28479e258f3170f47a6c5c2e1df59df768d623ec 100644 (file)
--- a/common_plot.py
+++ b/common_plot.py
loss = [float(i) for i in loss]
return iteration, loss
+def get_epochs(log):
+ gpus = re.findall(r' GPU (\d*):', log)
+ num_gpus = len(gpus)
+ #print num_gpus
+ max_iter = re.findall(r'max_iter: (\d*)', log)
+ iter_size = re.findall(r'iter_size: (\d*)', log)
+ batch_size = re.findall(r'batch_size: (\d*)',log)
+ max_iter = int(max_iter[0])
+ if len(iter_size) >0:
+ iter_size=int(iter_size[0])
+ else:
+ iter_size=1
+
+ batch_size = int(batch_size[0])
+ # print max_iter, iter_size, batch_size
+ num_epochs = int(round( (max_iter * iter_size * batch_size*num_gpus) / 1281167. +0.5))
+ return max_iter, num_epochs
def get_net_name(log):
return re.findall(r"Solving (.*)\n", log)[0]
data[net_name]["train_loss"]["loss"] = []
data[net_name]["train_loss"]["iteration"] = []
+ max_iter, epochs = get_epochs(log)
+ #print epochs
+ scale = float(epochs) / max_iter
+
iteration, accuracy = get_test_accuracy(log, top_k)
+ iteration = [k*scale for k in iteration]
data[net_name]["accuracy"]["iteration"].extend(iteration)
data[net_name]["accuracy"]["accuracy"].extend(accuracy)
iteration, loss = get_test_loss(log)
+ iteration = [k*scale for k in iteration]
data[net_name]["loss"]["iteration"].extend(iteration)
data[net_name]["loss"]["loss"].extend(loss)
+
iteration, loss = get_train_loss(log)
+ iteration = [k*scale for k in iteration]
data[net_name]["train_loss"]["iteration"].extend(iteration)
data[net_name]["train_loss"]["loss"].extend(loss)
plt.legend(nets, loc='lower right')
plt.title("Top {}".format(top_k))
- plt.xlabel("Iteration")
+ plt.xlabel("Epochs")
plt.ylabel("Accuracy [%]")
plt.ylim(0,100)
plt.grid()