fastai / tf-fit Goto Github PK

from fastai.vision import *
from fastai_tf_fit import *
import tensorflow as tf
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
import keras_applications
from keras_applications.resnet import ResNet152

path = Path(path_to_data_dir)

tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)

np.random.seed(42)
src = (ImageList.from_folder(path)
       .split_by_folder(train='train', valid='val')
       .label_from_folder())

data = (src.transform(tfms, size=112)
        .databunch()
        .normalize(imagenet_stats))

def categorical_accuracy(y_pred, y_true):
    return tf.keras.backend.mean(tf.keras.backend.equal(y_true, tf.keras.backend.argmax(y_pred, axis=-1)))

opt_fn = tf.train.AdamOptimizer
loss_fn = tf.losses.softmax_cross_entropy
metrics = [categorical_accuracy]

base_model = ResNet152(weights=path_to_weights,
                       include_top=False,
                       input_shape=(3,112,112),
                       backend=tf.keras.backend, 
                       layers=tf.keras.layers, 
                       models=tf.keras.models, 
                       utils=tf.keras.utils)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(number_of_classes, activation='sigmoid')(x)

model = Model(inputs=base_model.input, outputs=predictions)

learn = TfLearner(data, model, opt_fn, loss_fn, metrics=metrics, true_wd=True, bn_wd=True, wd=defaults.wd, train_bn=True)

---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-30-115701fe1db2> in <module>
----> 1 learn = TfLearner(data, model, opt_fn, loss_fn, metrics=metrics, true_wd=True, bn_wd=True, wd=defaults.wd, train_bn=True)

<string> in __init__(self, data, model, opt_func, loss_func, metrics, true_wd, bn_wd, wd, train_bn, path, model_dir, callback_fns, callbacks, layer_groups)

/opt/conda/lib/python3.6/site-packages/fastai_tf_fit/fastai_tf_fit.py in __post_init__(self)
    161         xb, yb = next(iter(self.data.train_dl))
    162         xb, yb = _pytorch_to_tf(xb), _pytorch_to_tf(yb)
--> 163         tf_loss_batch(self.model, xb, yb)
    164 
    165     def init(self, init): raise NotImplementedError

/opt/conda/lib/python3.6/site-packages/fastai_tf_fit/fastai_tf_fit.py in tf_loss_batch(model, xb, yb, loss_func, opt, cb_handler)
     42 
     43 
---> 44     if not loss_func: return forward(), yb[0]
     45 
     46     loss = None

/opt/conda/lib/python3.6/site-packages/fastai_tf_fit/fastai_tf_fit.py in forward()
     32 
     33     def forward():
---> 34         out = model(*xb)
     35         out = cb_handler.on_loss_begin(out)
     36         return out

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    677           with base_layer_utils.autocast_context_manager(
    678               input_list, self._mixed_precision_policy.should_cast_variables):
--> 679             outputs = self.call(inputs, *args, **kwargs)
    680           self._handle_activity_regularization(inputs, outputs)
    681           self._set_mask_metadata(inputs, outputs, previous_mask)

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in call(self, inputs, training, mask)
    749                                 ' implement a `call` method.')
    750 
--> 751     return self._run_internal_graph(inputs, training=training, mask=mask)
    752 
    753   def compute_output_shape(self, input_shape):

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in _run_internal_graph(self, inputs, training, mask)
    891 
    892           # Compute outputs.
--> 893           output_tensors = layer(computed_tensors, **kwargs)
    894 
    895           # Update tensor_dict.

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    677           with base_layer_utils.autocast_context_manager(
    678               input_list, self._mixed_precision_policy.should_cast_variables):
--> 679             outputs = self.call(inputs, *args, **kwargs)
    680           self._handle_activity_regularization(inputs, outputs)
    681           self._set_mask_metadata(inputs, outputs, previous_mask)

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py in call(self, inputs)
    194 
    195   def call(self, inputs):
--> 196     outputs = self._convolution_op(inputs, self.kernel)
    197 
    198     if self.use_bias:

/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py in __call__(self, inp, filter)
   1077 
   1078   def __call__(self, inp, filter):  # pylint: disable=redefined-builtin
-> 1079     return self.conv_op(inp, filter)
   1080 
   1081 

/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py in __call__(self, inp, filter)
    633 
    634   def __call__(self, inp, filter):  # pylint: disable=redefined-builtin
--> 635     return self.call(inp, filter)
    636 
    637 

/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py in __call__(self, inp, filter)
    232         padding=self.padding,
    233         data_format=self.data_format,
--> 234         name=self.name)
    235 
    236 

/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, data_format, dilations, name, filters)
   1951                            data_format=data_format,
   1952                            dilations=dilations,
-> 1953                            name=name)
   1954 
   1955 

/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)
   1029             input, filter, strides=strides, use_cudnn_on_gpu=use_cudnn_on_gpu,
   1030             padding=padding, explicit_paddings=explicit_paddings,
-> 1031             data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
   1032       except _core._SymbolicException:
   1033         pass  # Add nodes to the TensorFlow graph.

/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py in conv2d_eager_fallback(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name, ctx)
   1128   explicit_paddings, "data_format", data_format, "dilations", dilations)
   1129   _result = _execute.execute(b"Conv2D", 1, inputs=_inputs_flat, attrs=_attrs,
-> 1130                              ctx=_ctx, name=name)
   1131   _execute.record_gradient(
   1132       "Conv2D", _inputs_flat, _attrs, _result, name)

/opt/conda/lib/python3.6/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     if any(ops._is_keras_symbolic_tensor(x) for x in inputs):

/opt/conda/lib/python3.6/site-packages/six.py in raise_from(value, from_value)

UnimplementedError: Generic conv implementation only supports NHWC tensor format for now. [Op:Conv2D]

model = load_model('vggnet5.h5')
learn = TfLearner(data, model, opt_func=Adam, loss_func=keras.losses.categorical_crossentropy, metrics=accuracy, true_wd=True, bn_wd=True, wd=defaults.wd, train_bn=True)

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
   1618   try:
-> 1619     c_op = c_api.TF_FinishOperation(op_desc)
   1620   except errors.InvalidArgumentError as e:

InvalidArgumentError: Depth of output (32) is not a multiple of the number of groups (64) for 'VGG5/block1_conv1/convolution' (op: 'Conv2D') with input shapes: [64,3,64,64], [3,3,1,32].

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-22-18d78dd88662> in <module>
----> 1 learn = TfLearner(data, model, opt_func=Adam, loss_func=keras.losses.categorical_crossentropy, metrics=accuracy, true_wd=True, bn_wd=True, wd=defaults.wd, train_bn=True)

<string> in __init__(self, data, model, opt_func, loss_func, metrics, true_wd, bn_wd, wd, train_bn, path, model_dir, callback_fns, callbacks, layer_groups)

/kaggle/usr/lib/fastai_tf_fit/fastai_tf_fit.py in __post_init__(self)
    162         xb, yb = next(iter(self.data.train_dl))
    163         xb, yb = _pytorch_to_tf(xb), _pytorch_to_tf(yb)
--> 164         tf_loss_batch(self.model, xb, yb)
    165 
    166     def init(self, init): raise NotImplementedError

/kaggle/usr/lib/fastai_tf_fit/fastai_tf_fit.py in tf_loss_batch(model, xb, yb, loss_func, opt, cb_handler)
     43 
     44 
---> 45     if not loss_func: return forward(), yb[0]
     46 
     47     loss = None

/kaggle/usr/lib/fastai_tf_fit/fastai_tf_fit.py in forward()
     33 
     34     def forward():
---> 35         out = model(*xb)
     36         out = cb_handler.on_loss_begin(out)
     37         return out

/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
     73         if _SYMBOLIC_SCOPE.value:
     74             with get_graph().as_default():
---> 75                 return func(*args, **kwargs)
     76         else:
     77             return func(*args, **kwargs)

/opt/conda/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
    487             # Actually call the layer,
    488             # collecting output(s), mask(s), and shape(s).
--> 489             output = self.call(inputs, **kwargs)
    490             output_mask = self.compute_mask(inputs, previous_mask)
    491 

/opt/conda/lib/python3.6/site-packages/keras/engine/network.py in call(self, inputs, mask)
    581             return self._output_tensor_cache[cache_key]
    582         else:
--> 583             output_tensors, _, _ = self.run_internal_graph(inputs, masks)
    584             return output_tensors
    585 

/opt/conda/lib/python3.6/site-packages/keras/engine/network.py in run_internal_graph(self, inputs, masks)
    738                                     kwargs['mask'] = computed_mask
    739                             output_tensors = to_list(
--> 740                                 layer.call(computed_tensor, **kwargs))
    741                             output_masks = layer.compute_mask(computed_tensor,
    742                                                               computed_mask)

/opt/conda/lib/python3.6/site-packages/keras/layers/convolutional.py in call(self, inputs)
    169                 padding=self.padding,
    170                 data_format=self.data_format,
--> 171                 dilation_rate=self.dilation_rate)
    172         if self.rank == 3:
    173             outputs = K.conv3d(

/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in conv2d(x, kernel, strides, padding, data_format, dilation_rate)
   3715         padding=padding,
   3716         data_format=tf_data_format,
-> 3717         **kwargs)
   3718     if data_format == 'channels_first' and tf_data_format == 'NHWC':
   3719         x = tf.transpose(x, (0, 3, 1, 2))  # NHWC -> NCHW

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py in convolution_v2(input, filters, strides, padding, data_format, dilations, name)
    916       data_format=data_format,
    917       dilations=dilations,
--> 918       name=name)
    919 
    920 

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_ops.py in convolution_internal(input, filters, strides, padding, data_format, dilations, name, call_from_convolution)
   1008           data_format=data_format,
   1009           dilations=dilations,
-> 1010           name=name)
   1011     else:
   1012       if channel_index == 1:

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)
    967                   padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu,
    968                   explicit_paddings=explicit_paddings,
--> 969                   data_format=data_format, dilations=dilations, name=name)
    970   _result = _outputs[:]
    971   if _execute.must_record_gradient():

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
    740       op = g._create_op_internal(op_type_name, inputs, dtypes=None,
    741                                  name=scope, input_types=input_types,
--> 742                                  attrs=attr_protos, op_def=op_def)
    743 
    744     # `outputs` is returned as a separate return value so that the output

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
    593     return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access
    594         op_type, inputs, dtypes, input_types, name, attrs, op_def,
--> 595         compute_device)
    596 
    597   def capture(self, tensor, name=None, shape=None):

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
   3312           input_types=input_types,
   3313           original_op=self._default_original_op,
-> 3314           op_def=op_def)
   3315       self._create_op_helper(ret, compute_device=compute_device)
   3316     return ret

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
   1784           op_def, inputs, node_def.attr)
   1785       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1786                                 control_input_ops)
   1787       name = compat.as_str(node_def.name)
   1788     # pylint: enable=protected-access

/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
   1620   except errors.InvalidArgumentError as e:
   1621     # Convert to ValueError for backwards compatibility.
-> 1622     raise ValueError(str(e))
   1623 
   1624   return c_op

ValueError: Depth of output (32) is not a multiple of the number of groups (64) for 'VGG5/block1_conv1/convolution' (op: 'Conv2D') with input shapes: [64,3,64,64], [3,3,1,32].