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https://github.com/sbrl/research-rainfallradar
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spaces → spaces
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1 changed files with 86 additions and 86 deletions
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@ -60,68 +60,68 @@ logger.info("Validation Dataset:", dataset_validate)
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# ██ ██ ██████ ██████ ███████ ███████
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def convolution_block(
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block_input,
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num_filters=256,
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kernel_size=3,
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dilation_rate=1,
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padding="same",
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use_bias=False,
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block_input,
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num_filters=256,
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kernel_size=3,
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dilation_rate=1,
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padding="same",
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use_bias=False,
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):
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x = tf.keras.layers.Conv2D(
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num_filters,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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padding="same",
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use_bias=use_bias,
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kernel_initializer=tf.keras.initializers.HeNormal(),
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)(block_input)
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x = tf.keras.layers.BatchNormalization()(x)
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return tf.nn.relu(x)
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x = tf.keras.layers.Conv2D(
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num_filters,
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kernel_size=kernel_size,
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dilation_rate=dilation_rate,
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padding="same",
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use_bias=use_bias,
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kernel_initializer=tf.keras.initializers.HeNormal(),
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)(block_input)
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x = tf.keras.layers.BatchNormalization()(x)
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return tf.nn.relu(x)
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def DilatedSpatialPyramidPooling(dspp_input):
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dims = dspp_input.shape
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x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
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x = convolution_block(x, kernel_size=1, use_bias=True)
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out_pool = tf.keras.layers.UpSampling2D(
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size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
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)(x)
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dims = dspp_input.shape
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x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
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x = convolution_block(x, kernel_size=1, use_bias=True)
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out_pool = tf.keras.layers.UpSampling2D(
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size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
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)(x)
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out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
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out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
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out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
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out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
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out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
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out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
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out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
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out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
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x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
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output = convolution_block(x, kernel_size=1)
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return output
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x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
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output = convolution_block(x, kernel_size=1)
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return output
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def DeeplabV3Plus(image_size, num_classes, num_channels=3):
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model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
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resnet50 = tf.keras.applications.ResNet50(
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weights="imagenet" if num_channels == 3 else None,
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model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
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resnet50 = tf.keras.applications.ResNet50(
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weights="imagenet" if num_channels == 3 else None,
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include_top=False, input_tensor=model_input
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)
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x = resnet50.get_layer("conv4_block6_2_relu").output
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x = DilatedSpatialPyramidPooling(x)
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)
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x = resnet50.get_layer("conv4_block6_2_relu").output
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x = DilatedSpatialPyramidPooling(x)
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input_a = tf.keras.layers.UpSampling2D(
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size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
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interpolation="bilinear",
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)(x)
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input_b = resnet50.get_layer("conv2_block3_2_relu").output
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input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
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input_a = tf.keras.layers.UpSampling2D(
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size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
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interpolation="bilinear",
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)(x)
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input_b = resnet50.get_layer("conv2_block3_2_relu").output
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input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
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x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
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x = convolution_block(x)
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x = convolution_block(x)
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x = tf.keras.layers.UpSampling2D(
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size=(image_size // x.shape[1], image_size // x.shape[2]),
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interpolation="bilinear",
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)(x)
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model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
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return tf.keras.Model(inputs=model_input, outputs=model_output)
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x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
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x = convolution_block(x)
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x = convolution_block(x)
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x = tf.keras.layers.UpSampling2D(
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size=(image_size // x.shape[1], image_size // x.shape[2]),
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interpolation="bilinear",
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)(x)
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model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
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return tf.keras.Model(inputs=model_input, outputs=model_output)
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model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8)
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@ -138,9 +138,9 @@ summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(
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optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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loss=loss,
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metrics=["accuracy"],
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optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
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loss=loss,
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metrics=["accuracy"],
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)
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logger.info(">>> Beginning training")
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history = model.fit(dataset_train,
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@ -195,59 +195,59 @@ plt.close()
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# Loading the Colormap
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colormap = loadmat(
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PATH_COLOURMAP
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PATH_COLOURMAP
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)["colormap"]
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colormap = colormap * 100
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colormap = colormap.astype(np.uint8)
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def infer(model, image_tensor):
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predictions = model.predict(tf.expand_dims((image_tensor), axis=0))
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predictions = tf.squeeze(predictions)
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predictions = tf.argmax(predictions, axis=2)
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return predictions
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predictions = model.predict(tf.expand_dims((image_tensor), axis=0))
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predictions = tf.squeeze(predictions)
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predictions = tf.argmax(predictions, axis=2)
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return predictions
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def decode_segmentation_masks(mask, colormap, n_classes):
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r = np.zeros_like(mask).astype(np.uint8)
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g = np.zeros_like(mask).astype(np.uint8)
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b = np.zeros_like(mask).astype(np.uint8)
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for l in range(0, n_classes):
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idx = mask == l
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r[idx] = colormap[l, 0]
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g[idx] = colormap[l, 1]
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b[idx] = colormap[l, 2]
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rgb = np.stack([r, g, b], axis=2)
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return rgb
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r = np.zeros_like(mask).astype(np.uint8)
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g = np.zeros_like(mask).astype(np.uint8)
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b = np.zeros_like(mask).astype(np.uint8)
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for l in range(0, n_classes):
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idx = mask == l
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r[idx] = colormap[l, 0]
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g[idx] = colormap[l, 1]
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b[idx] = colormap[l, 2]
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rgb = np.stack([r, g, b], axis=2)
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return rgb
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def get_overlay(image, coloured_mask):
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image = tf.keras.preprocessing.image.array_to_img(image)
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image = np.array(image).astype(np.uint8)
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overlay = cv2.addWeighted(image, 0.35, coloured_mask, 0.65, 0)
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return overlay
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image = tf.keras.preprocessing.image.array_to_img(image)
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image = np.array(image).astype(np.uint8)
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overlay = cv2.addWeighted(image, 0.35, coloured_mask, 0.65, 0)
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return overlay
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def plot_samples_matplotlib(filepath, display_list, figsize=(5, 3)):
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_, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)
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for i in range(len(display_list)):
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if display_list[i].shape[-1] == 3:
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axes[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
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else:
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axes[i].imshow(display_list[i])
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plt.savefig(filepath)
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_, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)
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for i in range(len(display_list)):
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if display_list[i].shape[-1] == 3:
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axes[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
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else:
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axes[i].imshow(display_list[i])
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plt.savefig(filepath)
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def plot_predictions(filepath, input_items, colormap, model):
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for input_tensor in input_items:
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prediction_mask = infer(image_tensor=input_tensor, model=model)
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prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
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overlay = get_overlay(input_tensor, prediction_colormap)
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plot_samples_matplotlib(
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for input_tensor in input_items:
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prediction_mask = infer(image_tensor=input_tensor, model=model)
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prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
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overlay = get_overlay(input_tensor, prediction_colormap)
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plot_samples_matplotlib(
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filepath,
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[input_tensor, overlay, prediction_colormap],
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[input_tensor, overlay, prediction_colormap],
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figsize=(18, 14)
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)
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)
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def get_items_from_batched(dataset, count):
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result = []
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