From 9707d42d13f166939586afb96add8fd22a97aec4 Mon Sep 17 00:00:00 2001
From: gong1 <b.gong@fz-juelich.de>
Date: Tue, 17 Jan 2023 17:55:22 +0100
Subject: [PATCH] update the modular of models
---
.../model_modules/video_prediction/models/__init__.py | 2 +-
.../video_prediction/models/weatherBench3Dcnn.py | 7 +++----
2 files changed, 4 insertions(+), 5 deletions(-)
diff --git a/video_prediction_tools/model_modules/video_prediction/models/__init__.py b/video_prediction_tools/model_modules/video_prediction/models/__init__.py
index b2814ecd..c2470aea 100644
--- a/video_prediction_tools/model_modules/video_prediction/models/__init__.py
+++ b/video_prediction_tools/model_modules/video_prediction/models/__init__.py
@@ -7,7 +7,7 @@ from .vanilla_convLSTM_model import VanillaConvLstmVideoPredictionModel
from .test_model import TestModelVideoPredictionModel
from model_modules.model_architectures import known_models
from .convLSTM_GAN_model import ConvLstmGANVideoPredictionModel
-
+from .weatherBench3Dcnn import WeatherBenchModel
def get_model_class(model):
model_mappings = known_models()
diff --git a/video_prediction_tools/model_modules/video_prediction/models/weatherBench3Dcnn.py b/video_prediction_tools/model_modules/video_prediction/models/weatherBench3Dcnn.py
index ceba5355..033bd499 100644
--- a/video_prediction_tools/model_modules/video_prediction/models/weatherBench3Dcnn.py
+++ b/video_prediction_tools/model_modules/video_prediction/models/weatherBench3Dcnn.py
@@ -13,9 +13,6 @@ from .our_base_model import BaseModels
class WeatherBenchModel(BaseModels):
- filters = [64, 64, 64, 64, 2]
- kernels = [5, 5, 5, 5, 5]
-
def __init__(self, hparams_dict_config: dict=None, mode:str="train", **kwargs):
"""
This is class for building weatherBench architecture by using updated hparameters
@@ -58,8 +55,10 @@ class WeatherBenchModel(BaseModels):
def build_model(self, x):
"""Fully convolutional network"""
x = x[:, 0, :, :, :]
-
_idx = 0
+ filters = [64, 64, 64, 64, 2]
+ kernels = [5, 5, 5, 5, 5]
+
for f, k in zip(filters[:-1], kernels[:-1]):
with tf.variable_scope("conv_layer_"+str(_idx), reuse=tf.AUTO_REUSE):
x = ld.conv_layer(x, kernel_size=k, stride=1,
--
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