From 6c3659313c0929bf804e075711e86bf25a0b873c Mon Sep 17 00:00:00 2001
From: hannandarryl <hannandarryl@gmail.com>
Date: Fri, 11 Feb 2022 20:29:33 +0000
Subject: [PATCH] fixed padding
---
keras/keras_model.py | 60 ++++++++++++++++++++++++++++++++++++++------
1 file changed, 53 insertions(+), 7 deletions(-)
diff --git a/keras/keras_model.py b/keras/keras_model.py
index 6327fe9..919dc7d 100644
--- a/keras/keras_model.py
+++ b/keras/keras_model.py
@@ -9,6 +9,12 @@ import torch.nn as nn
from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
from sparse_coding_torch.conv_sparse_model import ConvSparseLayer
+def load_pytorch_weights(file_path):
+ pytorch_checkpoint = torch.load(file_path, map_location='cpu')
+ weight_tensor = pytorch_checkpoint['model_state_dict']['filters'].swapaxes(1,3).swapaxes(2,4).swapaxes(0,4).numpy()
+
+ return weight_tensor
+
@tf.function
def do_recon(filters_1, filters_2, filters_3, filters_4, filters_5, activations, batch_size, stride):
out_1 = tf.nn.conv2d_transpose(activations, filters_1, output_shape=(batch_size, 100, 200, 1), strides=stride)
@@ -21,8 +27,9 @@ def do_recon(filters_1, filters_2, filters_3, filters_4, filters_5, activations,
return recon
-@tf.function
+# @tf.function
def do_recon_3d(filters, activations, batch_size, stride):
+ activations = tf.pad(activations, paddings=[[0,0], [2, 2], [0, 0], [0, 0], [0, 0]])
recon = tf.nn.conv3d_transpose(activations, filters, output_shape=(batch_size, 5, 100, 200, 1), strides=[1, stride, stride])
return recon
@@ -39,9 +46,10 @@ def conv_error(filters_1, filters_2, filters_3, filters_4, filters_5, e, stride)
return g
-@tf.function
+# @tf.function
def conv_error_3d(filters, e, stride):
- g = tf.nn.conv3d(e, filters, strides=[1, 1, stride, stride, 1], padding='SAME')
+ e = tf.pad(e, paddings=[[0,0], [0, 0], [7, 7], [7, 7], [0, 0]])
+ g = tf.nn.conv3d(e, filters, strides=[1, 1, stride, stride, 1], padding='VALID')
return g
@@ -54,7 +62,7 @@ def normalize_weights(filters, out_channels):
return adjusted
-@tf.function
+# @tf.function
def normalize_weights_3d(filters, out_channels):
norms = tf.norm(tf.reshape(filters[0], (out_channels, -1)), axis=1)
norms = tf.broadcast_to(tf.math.maximum(norms, 1e-12*tf.ones_like(norms)), filters[0].shape)
@@ -117,9 +125,9 @@ class SparseCode(keras.layers.Layer):
m = tf.zeros(shape=(self.batch_size, 100 // self.stride, 200 // self.stride, self.out_channels))
v = tf.zeros(shape=(self.batch_size, 100 // self.stride, 200 // self.stride, self.out_channels))
else:
- u = tf.zeros(shape=(self.batch_size, 5, 100 // self.stride, 200 // self.stride, self.out_channels))
- m = tf.zeros(shape=(self.batch_size, 5, 100 // self.stride, 200 // self.stride, self.out_channels))
- v = tf.zeros(shape=(self.batch_size, 5, 100 // self.stride, 200 // self.stride, self.out_channels))
+ u = tf.zeros(shape=(self.batch_size, 1, 100 // self.stride, 200 // self.stride, self.out_channels))
+ m = tf.zeros(shape=(self.batch_size, 1, 100 // self.stride, 200 // self.stride, self.out_channels))
+ v = tf.zeros(shape=(self.batch_size, 1, 100 // self.stride, 200 // self.stride, self.out_channels))
# tf.print('activations before:', tf.reduce_sum(u))
@@ -211,3 +219,41 @@ class Classifier(keras.layers.Layer):
x = self.ff_4(x)
return x
+
+class MobileModel(keras.Model):
+ def __init__(self, sparse_checkpoint, batch_size, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d):
+ super().__init__()
+ self.sparse_code = SparseCode(batch_size, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d)
+ self.classifier = Classifier()
+
+ self.out_channels = out_channels
+ self.in_channels = in_channels
+ self.stride = stride
+ self.lam = lam
+ self.activation_lr = activation_lr
+ self.max_activation_iter = max_activation_iter
+ self.batch_size = batch_size
+ self.run_2d = run_2d
+
+ pytorch_weights = load_pytorch_weights(sparse_checkpoint)
+
+ if run_2d:
+ weight_list = np.split(pytorch_weights, 5, axis=0)
+ self.filters_1 = tf.Variable(initial_value=weight_list[0].squeeze(0), dtype='float32', trainable=False)
+ self.filters_2 = tf.Variable(initial_value=weight_list[1].squeeze(0), dtype='float32', trainable=False)
+ self.filters_3 = tf.Variable(initial_value=weight_list[2].squeeze(0), dtype='float32', trainable=False)
+ self.filters_4 = tf.Variable(initial_value=weight_list[3].squeeze(0), dtype='float32', trainable=False)
+ self.filters_5 = tf.Variable(initial_value=weight_list[4].squeeze(0), dtype='float32', trainable=False)
+ else:
+ self.filters = tf.Variable(initial_value=pytorch_weights, dtype='float32', trainable=False)
+
+ @tf.function
+ def call(self, images):
+ if self.run_2d:
+ activations = self.sparse_code(images, [tf.stop_gradient(self.filters_1), tf.stop_gradient(self.filters_2), tf.stop_gradient(self.filters_3), tf.stop_gradient(self.filters_4), tf.stop_gradient(self.filters_5)])
+ else:
+ activations = self.sparse_code(images, tf.stop_gradient(self.filters))
+
+ pred = self.classifier(activations)
+
+ return pred
\ No newline at end of file
--
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