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NHWC->NCHW data = data.transpose(0, 3, 1, 2) - elif data.ndim == 5: + elif not nchw and data.ndim == 5: # NDHWC->NCDHW data = data.transpose(0, 4, 1, 2, 3) @@ -32,7 +32,7 @@ def writeBlob(data, name): np.save(name + '.npy', data) -def save(model, name, **kwargs): +def save(model, name, nchw = False, **kwargs): model.save(name) assert(len(kwargs) == 1) @@ -40,8 +40,8 @@ def save(model, name, **kwargs): inputData = gen_data(next(iter(kwargs.values()))) outputData = model(inputData) - writeBlob(inputData, name + '_in') - writeBlob(outputData, name + '_out') + writeBlob(inputData, name + '_in', nchw) + writeBlob(outputData, name + '_out', nchw) # Freeze model loaded = tf.saved_model.load(name) @@ -78,6 +78,49 @@ def save(model, name, **kwargs): ]) save(model, 'tf2_permute_nhwc_ncwh', average_pooling2d_input=tf.TensorSpec(shape=[None, 4, 6, 3], dtype=tf.float32)) ################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_first') +x = tf.keras.layers.Input(batch_shape = (1, 2, 3, 4), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.nn.conv2d(x, tf.constant(kernel, dtype=tf.float32), data_format = 'NCHW', padding = [[0, 0], [0, 0], [2, 1], [2, 1]], strides = [1, 1, 3, 2]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_asymmetric_pads_nchw', True, x=tf.TensorSpec(shape=[1, 2, 3, 4], dtype=tf.float32)) +################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_last') +x = tf.keras.layers.Input(batch_shape = (1, 3, 4, 2), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.nn.conv2d(x, tf.constant(kernel, dtype=tf.float32), data_format = 'NHWC', padding = [[0, 0], [2, 1], [2, 1], [0, 0]], strides = [1, 3, 2, 1]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_asymmetric_pads_nhwc', False, x=tf.TensorSpec(shape=[1, 3, 4, 2], dtype=tf.float32)) +################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_first') +x = tf.keras.layers.Input(batch_shape = (1, 1, 2, 3), name='x') +y = tf.nn.max_pool(x, ksize=2, data_format = "NCHW", padding = [[0, 0], [0, 0], [1, 0], [1, 1]], strides = [1, 1, 3, 2]) +model = tf.keras.Model(x, y) +save(model, 'max_pool2d_asymmetric_pads_nchw', True, x=tf.TensorSpec(shape=(1, 1, 2, 3), dtype=tf.float32)) +################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_last') +x = tf.keras.layers.Input(batch_shape = (1, 2, 3, 1), name='x') +y = tf.nn.max_pool(x, ksize=2, data_format = "NHWC", padding = [[0, 0], [1, 0], [1, 1], [0, 0]], strides = [1, 3, 2, 1]) +model = tf.keras.Model(x, y) +save(model, 'max_pool2d_asymmetric_pads_nhwc', False, x=tf.TensorSpec(shape=(1, 2, 3, 1), dtype=tf.float32)) +################dd################################################################ +tf.keras.backend.set_image_data_format('channels_first') +x = tf.keras.layers.Input(batch_shape = (1, 3, 2, 3), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.compat.v1.nn.conv2d_backprop_input(input_sizes=tf.constant([1, 2, 3, 4]), filter=kernel, out_backprop=x, data_format = "NCHW", padding = [[0, 0], [0, 0], [2, 1], [2, 1]], strides = [1, 1, 3, 2]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_backprop_input_asymmetric_pads_nchw', True, x=tf.TensorSpec(shape=(1, 3, 2, 3), dtype=tf.float32)) +################################################################################ +tf.keras.backend.set_image_data_format('channels_last') +x = tf.keras.layers.Input(batch_shape = (1, 2, 3, 3), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.compat.v1.nn.conv2d_backprop_input(input_sizes=tf.constant([1, 3, 4, 2]), filter=kernel, out_backprop=x, data_format = "NHWC", padding = [[0, 0], [2, 1], [2, 1], [0, 0]], strides = [1, 3, 2, 1]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_backprop_input_asymmetric_pads_nhwc', False, x=tf.TensorSpec(shape=(1, 2, 3, 3), dtype=tf.float32)) # 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